Employment and entrepreneurship support system based on artificial intelligence

Abstract

The invention discloses an employment and entrepreneurship support system based on artificial intelligence, relates to the technical field of occupational planning, and aims to solve the problem that a traditional support mode is insufficient in individuation and accuracy. The system comprises a multi-modal data acquisition module used for acquiring facial expressions, whole body dynamics and voice data of a user in at least 30 minutes of video dialogue in real time; a dialogue text is processed through a large language model based on a Transform architecture, continuous time sequence modeling is carried out on multi-modal dynamic behaviors by applying a liquid time constant network, and deep fusion reasoning is carried out in combination with multiple groups of multi-head Transform attention mechanisms. The core analyzes the real thought, psychological state, behavior pattern and core ability of the user through consistency verification, generates a structured dynamic user insight abstract, and customizes personalized vocational development or entrepreneurship planning according to the structured dynamic user insight abstract. According to the method, the potential of the user can be deeply informed, high-precision personalized planning is provided, the decision-making quality and success rate are improved, and the method has dynamic adaptation and continuous learning capabilities.

Description

Technical Field

[0001] The present invention relates to the technical field of career planning, and in particular to an employment and entrepreneurship support system based on artificial intelligence. Background Art

[0002] With economic restructuring and rapid technological development, society's demand for talent is becoming increasingly diverse, and the challenges individuals face in career development and entrepreneurship are becoming increasingly complex. Traditional employment and entrepreneurship services often rely on manual experience, resulting in information asymmetry, low service efficiency, and insufficient personalized guidance. In recent years, the application of artificial intelligence technology in the human resources field has gradually deepened, providing new solutions to these problems.

[0003] In the prior art, for example, Chinese invention patent CN118469392B discloses a method for creating a digital talent model based on artificial intelligence to measure the capabilities of employed individuals. This method collects information on target talents multiple times to obtain professional capability data and assessment scores, and then considers natural growth value and event-added value to build and modify the talent's digital model. This method addresses, to a certain extent, the issues of high subjectivity in assessments and inaccurate tracking of capability changes in traditional talent management systems, and attempts to enhance the ability to predict personalized development. This approach primarily establishes a dynamic assessment model to track the evolution of individual capabilities, aiming to provide a more accurate talent profile.

[0004] Another Chinese invention patent, CN114722959B, discloses a method and system for matching employment information based on a recommendation algorithm. This method analyzes data such as company ID, employee ID, and number of months on the job, using a collaborative recommendation algorithm to generate a similarity model. This model then uses the recommended individual's historical employment data to rank and recommend candidates. The core of this method is to use the objective metric of "number of months on the job" to measure the correlation between companies and employees, aiming to improve the accuracy, effectiveness, and reliability of employment information matching.

[0005] The above designs, by introducing a dynamic competency assessment model and a recommendation algorithm based on objective tenure, have made certain improvements in the objectivity and dynamism of talent competency assessment, as well as the accuracy of job matching. However, they still have certain limitations, such as insufficient insight into users' underlying needs and true intentions. While CN118469392B focuses on competency changes, it is limited in its ability to delve into users' true career expectations, intrinsic motivations, psychological states, and potential cognitive biases. Its assessment relies more on explicit input of "professional competency data." CN114722959B primarily matches based on historical employment data and tenure, lacking consideration of users' current underlying job-seeking motivations and future development potential. Existing employment support systems mostly rely on user-initiated textual information or structured data, failing to capture the rich connotations conveyed by users' non-verbal communication, such as tone of voice, facial expressions, and body language. This makes it difficult for the system to fully and comprehensively understand users. While both pursue personalization, CN118469392B's predictions are primarily based on historical ability evolution, and CN114722959B's recommendations are primarily based on historical behavior matching. Existing technologies still have room for improvement in how to combine users' underlying traits, potential abilities, and true thoughts revealed through non-verbal information to generate truly "tailored," forward-looking career development or entrepreneurship plans. Existing systems lack effective time-series modeling for processing user feedback after providing evaluations or recommendations, and for integrating feedback into the system for dynamic optimization and iterative adjustments. This makes it difficult to form a closed loop for continuous learning and service quality improvement. Existing technologies lack effective time-series modeling for the continuous changes and subtle dynamics of a user's emotions, behaviors, and cognitive states over a period of time (such as during a consultation), making it difficult to capture the key clues contained in this dynamic information.

[0006] Therefore, there is an urgent need for an AI-based employment and entrepreneurship support system that can deeply integrate multimodal information perception, use advanced time series modeling technology to understand users' dynamic behavior, gain insight into users' real needs and potential through deep reasoning, and combine user feedback for closed-loop optimization, thereby providing highly personalized, precise, and dynamic employment and entrepreneurship planning and guidance. Summary of the Invention

[0007] The purpose of this invention is to overcome the deficiencies of the prior art and to propose an employment and entrepreneurship support system based on artificial intelligence to solve the above-mentioned problems.

[0008] The purpose of the present invention is achieved through the following technical solutions: an employment and entrepreneurship support system based on artificial intelligence, comprising: A multimodal data acquisition module is used to collect real-time video data from video conversations with users lasting at least 30 minutes. The video data includes the user's facial expression information and body dynamic information, and simultaneously collects the user's voice data; An integrated large model processing core configured to implement the following functions: (a) Real-time multimodal perception and interaction capabilities: Natural language processing is performed through a large language model based on the Transformer architecture. The large language model uses its self-attention mechanism to understand the conversation context and its text generation capabilities to conduct long and fluent real-time video conversations with users and obtain the text content of the conversations. Liquid time constant networks are used to perform continuous time series modeling on the user's facial expression information flow, whole-body dynamic information flow in video data, and acoustic feature flow in speech data. Liquid time constant networks are used to process continuous time dynamic information by solving ordinary differential equations and have variable time constants coupled with hidden states to extract the user's real-time multimodal dynamic behavior time series features; Combining the first set of multi-head Transformer attention mechanisms, the system deeply integrates and performs preliminary inference on the conversation text content obtained by the large language model and the multimodal dynamic behavior temporal features extracted using the liquid time constant network. This allows the system to understand the user's immediate status, explicit needs, and preliminary implicit intentions, and generate a structured, dynamic user insight summary containing multimodal features. (b) Deep insight analysis and personalized planning functions: Receive and process dynamic user insight summaries; Combined with a second set of multi-head Transformer attention mechanisms, it performs global contextual understanding and deep logical reasoning on dynamic user insight summaries. This reasoning process includes performing consistency checks between verbal information and non-verbal multimodal behavioral features. Based on these results, it analyzes the user's psychological state, behavioral patterns, and true thoughts, and captures the user's long-range dependencies and core capabilities throughout the entire interaction process. Based on in-depth analysis results and leveraging the generation capabilities of large models, we can generate personalized career development plans or entrepreneurial plans for users. The planning output and feedback iteration module is used to present personalized career development plans or entrepreneurial plans to users and receive user feedback. This feedback will drive the integrated large model processing core to perform closed-loop optimization, including updating user insight summaries and iterative adjustments to personalized planning plans.

[0009] When using liquid time constant networks for continuous time series modeling, they are specifically configured to capture and characterize the dynamic patterns and key transition points of the time-varying evolution of users' emotions, gestures, and voice features over a conversation of at least 30 minutes.

[0010] When performing deep fusion and preliminary reasoning, the first group of multi-head Transformer attention mechanisms focuses on text semantic understanding, emotional temporal feature integration, and behavioral posture sequence pattern recognition through different attention heads to achieve multi-dimensional representation of the user's real-time status.

[0011] A structured, dynamic user insight summary with multimodal features, including data on at least the following dimensions: a time-series curve of the user's emotional valence and arousal, an index of key non-verbal behavior segments associated with the conversation topic, and preliminary assessments of cognitive load and engagement indicators.

[0012] The second set of multi-head Transformer attention mechanisms performs consistency checks, focusing on analyzing the degree of match or conflict between the certainty and emotional tendencies of users' verbal statements when discussing key decision points, past experiences, or future expectations, and their inner states reflected by facial micro-expressions and body language extracted using the liquid time constant network.

[0013] When analyzing a user's core competencies and strengths, the deep insight analysis and personalized planning functions utilize a second multi-head Transformer attention mechanism to integrate the user's potential strengths throughout the conversation, as indicated by language logic, problem-solving strategy demonstrations, and multimodal behavioral characteristics (such as confident posture and smooth gestures).

[0014] The generation of personalized plans is based on the core capabilities of the integrated large-scale model processing core, which is based on the user's real thoughts and in-depth analysis after consistency verification, combined with the built-in professional knowledge graph and entrepreneurial success factor model, and customized output through the generation and reasoning capabilities of multi-head Transformers.

[0015] The feedback closed-loop optimization mechanism includes: when users raise objections to the planning plan or provide additional information, the feedback will be input into the integrated large model processing core as new contextual information, triggering the weight adjustment or content correction of relevant items in the dynamic user insight summary, and re-running the deep insight analysis and personalized planning functions.

[0016] The features output by the liquid time constant network after continuous time series modeling are processed by an adaptation layer to match the input dimension and representation requirements of the first set of multi-head Transformer attention mechanisms.

[0017] After a video conversation with the user lasting at least 30 minutes, the integrated large-model processing core will first generate a preliminary dynamic user insight summary, and in subsequent interactions or user feedback, it will continue to incrementally update and enrich this summary through its real-time multimodal perception and interaction capabilities.

[0018] The beneficial effects of the present invention are: 1. Through real-time collection and analysis of at least 30 minutes of video conversations and multimodal data (facial expressions, body movements, and voice), the system is able to go beyond traditional questionnaires or single text exchanges to capture more nuanced and realistic user emotional dynamics and behavioral patterns. Combined with the continuous modeling of multimodal temporal features using Liquid Time Constant Networks (LTCs), it is possible to understand the user's state evolution and key transition points in different contexts, which is crucial for uncovering the user's true reactions and potential traits in specific scenarios. Leveraging the fusion reasoning capabilities of multi-headed Transformers, it deeply integrates linguistic and non-verbal information, particularly through consistency verification, to more accurately identify users' true thoughts, intrinsic motivations, and core values, and even uncover potential strengths and passions that the user themselves may not be aware of.

[0019] 2. Based on deep insights (including genuine thoughts after consistency verification and core competencies verified through multimodal validation), the career development or entrepreneurship planning solutions generated by the system are no longer generic templates but are truly "tailor-made," significantly improving the relevance and feasibility of the plans. Through the generation and reasoning capabilities of multi-headed Transformers, combined with the built-in professional knowledge graph and entrepreneurial success factor model, planning solutions can fully consider the user's uniqueness and the fit with the market environment, providing more scientific and forward-looking advice. For example, when analyzing a user's core competencies, the system integrates the strengths pointed out by the user in the conversation through language logic, problem-solving strategy demonstration, and multimodal behavioral characteristics, resulting in a more comprehensive and objective assessment.

[0020] 3. The system not only provides planning solutions but also explains the analytical basis and logic behind them (especially the results of consistency checks), enhancing transparency and credibility of the plans and helping users make more informed decisions. Through a closed-loop feedback optimization mechanism, users can raise objections to the plans or provide additional information. The system then updates user insights and iteratively adjusts the plans accordingly. This interactive optimization process empowers users to have a stronger sense of ownership and control over the final plan, thereby increasing their enthusiasm and success rate for subsequent actions.

[0021] 4. The incremental update and enrichment mechanism of dynamic user insight summaries, along with feedback loop optimization, enables the system to adapt to potential changes in user situations and cognitive growth over time, providing continuous and effective support. User feedback data also contributes to the continuous learning and optimization of system models (LTCs, Transformer, etc.), improving the intelligence and accuracy of the entire system service.

[0022] 5. Based on a precise understanding of users' true needs and core competencies, the system will be able to more effectively match users with appropriate training resources, mentors, incubation projects, and even investment opportunities in the future, improving the efficiency of resource utilization. The system's ability to deeply analyze multimodal information (especially nonverbal information and dynamic behavior), as well as its ability to tap into individual experience and potential, makes it particularly effective in assisting career transitions for those with unique experiences (such as military service) who may not be adept at expressing their strengths through traditional channels. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a timing diagram of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0025] It is to be noted that the directions of "left", "right", "up", "down", "front", "back", "inside" and "outside" in the following schemes are all relative directions and are not listed here one by one.

[0026] Example 1: like Figure 1 and Figure 2 As shown, this embodiment discloses an AI-based employment and entrepreneurship support system, specifically its core architecture and a specific implementation method for multimodal perception and generating preliminary user insights during user interaction. This system aims to lay a solid foundation for subsequent personalized career planning or entrepreneurial guidance by deeply understanding users.

[0027] The core architecture of the artificial intelligence-based employment and entrepreneurship support system described in this embodiment mainly includes a multimodal data acquisition module, an integrated large model processing core, and a planning output and feedback iteration module (this embodiment mainly focuses on the collaborative work of the first two to achieve preliminary insights).

[0028] The multimodal data acquisition module must include at least one high-definition camera (e.g., with a resolution of at least 1080p and a frame rate of at least 30 fps) for video capture and one or more high-quality microphones (e.g., a directional microphone array with noise reduction) for clear voice capture. The camera's position and angle should fully capture the user's facial expressions and full-body movements, primarily the upper body. Ideally, multiple cameras can be configured to capture from different angles to obtain richer posture information.

[0029] Continuously collect user's facial expression image sequences and dynamic information such as body posture, gestures, etc. Continuously collect user's voice signals.

[0030] A precise timestamp mechanism (e.g., based on the NTP network time protocol or a local high-precision clock source) is used to ensure that every frame in the video stream and every voice segment in the audio stream has a uniform and accurate time stamp, enabling strict alignment and synchronous analysis of multimodal information. The acquisition duration is set to at least 30 minutes of video conversation between the user and the system to ensure sufficient data samples for in-depth analysis.

[0031] The integrated large model processing core real-time multimodal perception and interaction functions are achieved through the following steps and technical means: A. Transformer-based Large Language Model (LLM) for natural language processing and conversational interaction: A pre-trained Transformer-based Large Language Model (LLM) with strong contextual understanding and text generation capabilities (e.g., with billions to hundreds of billions of parameters, such as the GPT series, BERT series, or other similar models) is used. This LLM is fine-tuned for the employment and entrepreneurship consulting field, making its conversational style more professional, guiding, and empathetic.

[0032] Receives real-time speech streams from the multimodal data acquisition module and converts them into text sequences through a high-precision automatic speech recognition engine (which can be integrated or used as a pre-module of LLM).

[0033] LLM uses its self-attention mechanism to analyze the text of the current conversation turn and the historical conversation context to understand the user's questions, statements, emotional tendencies, and potential intentions.

[0034] Based on the understanding of user intentions and preset dialogue strategies (for example, guiding users to discuss their experiences, skills, interests, confusions, etc. at different stages), LLM generates response strategies.

[0035] LLM leverages its powerful text generation capabilities to generate natural, fluent, and relevant responses. This text is then converted back into speech by a speech synthesis engine (which can be integrated or used as a post-module of LLM) and played back to the user through a speaker, forming a closed-loop voice conversation.

[0036] The user conversation text and system response text generated during the LLM processing are recorded and associated with the corresponding timestamps as important inputs for subsequent multimodal fusion analysis.

[0037] B. Using Liquid Time Constant Networks (LTCs) to Extract Multimodal Dynamic Behavior Timing Features: The liquid time constant networks (LTCs) used in this system are a special class of continuous-time recurrent neural networks. Their core concept is that the states of neurons in the network are described by a set of ordinary differential equations (ODEs), and the "time constants" of these neurons are dynamically changing and can be adjusted based on the input and the internal state of the network. This property makes LTCs well-suited for modeling continuous signals with complex temporal dynamics. Specifically, the neuron dynamics of LTCs can be expressed as: dx(t) / dt = -[1 / τ base + f gate (x(t), I(t), θ gate )] * x(t) + f input (x(t), I(t),θ input ) * A Where x(t) is the neuron state, I(t) is the input, and τ base is the fundamental time constant, f gate is a gating function implemented by a neural network (e.g. a small multilayer perceptron) that adjusts the actual time constant, f input is a function of another neural network processing the input and state, A is a learnable parameter or target state, and θ represents the parameters of the associated network. The output is computed using a numerical differential equation solver.

[0038] Facial landmarks and head pose parameters are extracted in real time from video frames. A pre-trained facial expression recognition model is used to extract expression category probabilities (such as joy, anger, sadness, surprise, fear, and disgust) and emotional dimension values ​​(such as valence and arousal). These time-varying feature sequences form the input to LTCs.

[0039] Independent or shared parameter LTCs models receive these facial feature sequences and, through their dynamic time constants and internal state evolution, capture the subtle dynamics of expression changes, their duration, and the transition patterns between different expressions. Specifically, the model is configured to capture and characterize the dynamic patterns and key transition points of a user's emotional evolution over time, such as accumulation, outburst, or subtle suppression of emotion, over a conversation lasting at least 30 minutes.

[0040] The pose estimation algorithm (such as OpenPose and MediaPipe Pose) extracts the coordinate sequence of key points of the human body from the video frames, and then calculates the body's orientation, degree of openness / closure (such as whether the arms are crossed), gesture type, frequency, amplitude, head posture changes (nodding, shaking, tilting), etc.

[0041] The LTCs model processes these sequences of gesture and movement features and learns their dynamic patterns, such as the user's typical gestures when expressing different content, body language characteristics of tense and relaxed states, and movement patterns synchronized with the rhythm of the conversation. The model is also configured to capture and characterize the dynamic patterns and key transition points of the user's gestures over time during a conversation of at least 30 minutes.

[0042] Extract Mel-frequency cepstral coefficients (MFCCs), pitch, energy, speaking rate, pause pattern, and acoustic emotion features (such as jitter, shimmer, formants, etc.) from the original speech signal.

[0043] The LTCs model uses temporal modeling of these acoustic feature sequences to capture dynamic information such as prosodic changes, emotional fluctuations, and fluency. The model is configured to capture and characterize the dynamic patterns and key transition points of the user's speech features over time during a conversation lasting at least 30 minutes.

[0044] After processing the time series data of each modality, LTCs output a sequence of feature vectors encoding its dynamic information. These sequences are processed through an adaptation layer (for example, one or more fully connected layers or a convolutional layer) before being input into the subsequent Transformer attention mechanism. This adaptation layer adjusts the feature vectors output by the LTCs to match the input dimensions and representation space expected by the multi-head Transformer attention mechanism, ensuring that features from different modalities can be integrated within a unified framework.

[0045] C. Combining the first multi-head Transformer attention mechanism for multimodal deep fusion and preliminary reasoning: A multimodal Transformer encoder architecture is used. Its input is a sequence of word embeddings (or sentence embeddings) of the conversation text obtained by the LLM and a sequence of multimodal dynamic behavior temporal features output by the LTCs after processing in the adaptation layer. These sequences are temporally aligned.

[0046] Within the feature sequence of each modality, a multi-head self-attention mechanism is first applied to capture the dependencies and contextual information between the internal elements of the modality (such as words in text and continuous expression states in expression sequences).

[0047] A variety of cross-modal attention mechanisms are used (for example, using text as the query and other modal features as the key and value, or vice versa; or concatenating all modalities and performing joint self-attention) to learn the correlation and influence between features of different modalities. The first set of multi-head Transformer attention mechanisms, during deep fusion and preliminary reasoning, focuses on text semantic understanding, integration of emotional temporal features (from facial and voice LTCs), and pattern recognition of behavioral posture sequences (from full-body dynamic LTCs) through different attention heads. For example, one attention head might focus on the association between words describing "challenge" in the text and the co-occurrence of nervous expressions and closed postures; another head might focus on the synchronization of positive words with high voice tone and open postures.

[0048] LTCs-based continuous multimodal temporal feature extraction and deep integration of Transformer generate dynamic user insight summaries. The formula is as follows: S insight (U,T dialogue )=AttnFuseθ Attn1 (LLM Txt (D audio (0…T dialogue )),Φ adapt (LTC MM (F V (0…Tdialogue),F A (0…T dialogue ),F B (0…T dialogue );θ LTC ))) in: S insight (U,T dialogue ): For user U, when the conversation duration reaches T dialogue(at least 30 minutes) to generate structured, dynamic user insight summaries containing multimodal features.

[0049] LLM Txt (D audio (0…T dialogue )):Indicates that the large language model (LLM) based on the Transformer architecture processes the conversation from the beginning (time 0) to the end (time T dialogue ) voice data stream D audio The conversation text sequence obtained later.

[0050] LTC MM (F V (0…T dialogue ),F A (0…T dialogue ),F B (0…T dialogue );θ LTC ): represents the modeling results of the liquid time constant network (LTCs) core processing unit for continuous time series data.

[0051] F V (0…T dialogue ): Feature sequence of facial expression information flow.

[0052] F A (0…T dialogue ): Acoustic feature flow sequence.

[0053] F B (0…T dialogue ): Feature sequence of whole-body dynamic information flow.

[0054] θ LTC : Parameters of the LTC network.

[0055] This function represents how LTCs processes the input multimodal continuous time stream by solving its internal ordinary differential equations and outputs temporal features that encode its dynamic information.

[0056] Φ adapt (⋅): represents an adaptation layer, which is used to transform the multimodal dynamic behavior temporal features output by LTCs in terms of dimension and representation.

[0057] AttnFuseθ Attn1 (⋅,⋅): represents the first set of multi-head Transformer attention mechanisms, With its parameter θ Attn1 Run to deeply fuse the conversation text content and the multimodal temporal features extracted and adapted by LTCs, and perform preliminary reasoning to generate insight summaries.

[0058] Through fusion analysis, the system can more accurately understand the user's current comprehensive state. For example, the user may express positively verbally, but the micro-expressions and gestures captured by LTCs may show that they may be hesitant or stressed.

[0059] Confirm explicit needs and conduct preliminary exploration of implicit intentions, confirm the needs explicitly expressed by users, and through analyzing multimodal behavioral clues (such as emotional reactions and changes in level of involvement when discussing specific topics), preliminarily infer the user's interests, concerns, or potential concerns that may not be directly stated.

[0060] A structured, dynamic user insight summary containing multimodal features is generated, and the results of the fusion reasoning are organized into a structured data object. This summary is dynamic, and a preliminary version is first generated after the user has engaged in a video conversation for at least 30 minutes. This summary is continuously updated and enriched through the real-time multimodal perception and interaction capabilities of this embodiment during subsequent interactions or user feedback. The summary contains data from at least the following dimensions: The user's emotional valence and arousal time series change curve is based on the output of facial expression LTCs and voice LTCs, combined with text sentiment analysis, to draw a diagram of the user's emotional fluctuations during the conversation.

[0061] An index of key non-verbal behavior segments associated with the conversation topic marks typical or abnormal facial expressions, postures, gestures, and voice features of users when discussing specific topics, and creates an index to facilitate subsequent review and analysis.

[0062] Preliminary assessment of cognitive load and engagement indicators, such as reaction time, conversation fluency, eye contact (if available), and gesture openness, to preliminarily assess the user's level of cognitive engagement at different stages of the conversation.

[0063] Working process: The user initiates a video conversation with the system (lasting at least 30 minutes). The multimodal data acquisition module captures the user's audio and video data in real time. The integrated large model processing core works in parallel: LLM is responsible for processing speech input, performing natural language understanding and generation, maintaining the fluency of the conversation, and outputting the conversation text.

[0064] The LTCs module group performs continuous temporal modeling on the facial expression stream, whole body dynamic stream and acoustic feature stream synchronized with the dialogue, captures their dynamic characteristics, and outputs temporal features through the adaptation layer.

[0065] The first group of multi-head Transformer attention mechanisms receives the multimodal temporal features of the conversation text embedding and LTCs output, performs deep fusion and preliminary reasoning, understands the user's immediate status and needs, and generates and continuously updates structured dynamic user insight summaries.

[0066] By combining the semantic understanding capabilities of LLM, the fine-grained temporal modeling capabilities of LTCs for continuous multimodal behaviors, and the fusion reasoning capabilities of multi-head Transformers, the system can go beyond simple text communication and more deeply and comprehensively perceive the user's true state, emotional dynamics, and potential intentions during the conversation.

[0067] The generated dynamic user insight summary presents multi-dimensional information in a structured manner, including temporal changes and key behavior indexes, providing rich and high-quality input for subsequent in-depth analysis and personalized planning. The summary can also be dynamically updated to reflect the user's latest status.

[0068] The application of LTCs enables the system to effectively process and understand complex behavioral patterns such as facial expressions, body postures, and voice intonation that change continuously over time, as well as their inherent connections, which is crucial for accurately grasping the user's status. The multi-headed Transformer attention mechanism improves the accuracy and robustness of judging the user's immediate status, explicit needs, and preliminary implicit intentions through weighted fusion and contextual association of information from different modalities. The detailed, multimodal, and structured user insights generated by this embodiment are the premise and guarantee for conducting in-depth analysis (such as consistency verification and core capability mining) and generating highly personalized and accurate employment and entrepreneurship plans in subsequent embodiments. The conversation duration of at least 30 minutes and the incremental update mechanism of dynamic summaries enable the system to observe and understand users over a longer period of time, capturing more stable characteristics and demand changes.

[0069] Example 2: like Figure 1 and Figure 2 As shown, this example builds on the "structured, dynamic user insight summary with multimodal features" generated in Example 1 and further describes how the integrated large-scale model processing core, through its "deep insight analysis and personalized planning capabilities," achieves a deeper understanding of users and ultimately generates highly personalized career development or entrepreneurship plans. This example aims to reveal the system's intelligent decision-making process, from initial perception to deep understanding and then to specific solution output.

[0070] The deep insight analysis and personalized planning function described in this embodiment is a key component of the integrated large model processing core, which receives and processes the dynamic user insight summary generated by the "real-time multimodal perception and interaction function" in Example 1.

[0071] Dynamic user insight summary. This summary includes the user's text content from a video conversation lasting at least 30 minutes, as well as multimodal dynamic behavioral temporal features extracted by LTCs and initially fused by the first set of multi-head Transformers (such as temporal change curves of emotional valence and arousal, indexes of key non-verbal behavior segments, and preliminary assessments of cognitive load and engagement indicators).

[0072] The integrated large model processing core first performs necessary preprocessing on the input summary, which may include data normalization, feature alignment (ensuring that all features strictly correspond in the time dimension), and feature selection or dimensionality reduction as needed, so that the second group of multi-head Transformer attention mechanisms can be efficiently processed.

[0073] Combined with the second set of multi-head Transformer attention mechanisms for in-depth insight analysis: This stage is the core of this embodiment. It uses the second set of multi-head Transformer attention mechanisms to perform global context understanding and deep logical reasoning on the preprocessed dynamic user insight summaries.

[0074] A. Verification of consistency between verbal information and non-verbal multimodal behavioral features: The second multi-head Transformer attention mechanism is specially configured to perform consistency checking. It simultaneously focuses on the text content (users' verbal statements) and the synchronized multimodal behavioral features (facial expressions, micro-expressions, body language, voice intonation, etc.) in the dynamic user insight summary.

[0075] When users discuss key decision points (e.g., career choices, whether to start a business, industry judgment), important past experiences (e.g., success stories, lessons learned from failure, challenges faced), or future expectations (e.g., career goals, ideal life state), the mechanism focuses on analyzing the degree of certainty in the user's verbal statements, the emotional tendencies expressed, and the degree of match or conflict between their internal physiological and psychological states as reflected by facial micro-expressions extracted and recorded in the summary through LTCs (e.g., whether there is a hint of hesitation or fear when expressing "very confident"), body language (e.g., when describing "passionate", is the body posture open or closed, and whether the gestures are coordinated and powerful), and voice prosody (e.g., speaking speed, pitch stability, and emotional color).

[0076] When significant inconsistencies or conflicts are detected (for example, positive verbal expressions but negative nonverbal signals, or vice versa), the system flags these points and, based on the context, preliminarily infers possible causes. For example, the user may be hiding their true feelings, experiencing unresolved internal conflicts, or attempting to express a lack of confidence in certain issues. These flags and inferences serve as crucial evidence for analyzing the user's true thoughts.

[0077] B. Analysis of user psychological state, behavior patterns, and true thoughts: Based on the results of consistency verification, as well as the overall trend of the user's emotional changes throughout the entire conversation (at least 30 minutes) (such as the pattern of emotional valence and arousal curve), the intensity of emotional reactions to specific stimuli (such as sensitive topics and stressful questions) and the speed of recovery, the system evaluates the user's core psychological state in employment and entrepreneurship, such as their main sources of anxiety, confidence level, internal driving force, and attitude towards risk and uncertainty.

[0078] By analyzing the multimodal behavior sequences of users when solving problems, expressing opinions, and responding to challenges, we can identify their typical behavior patterns, such as whether they tend to think carefully or act quickly, whether they prefer to make independent decisions or seek cooperation, and their communication style (such as direct, tactful, dominant, listening, etc.).

[0079] By integrating verbal expressions, non-verbal signals, consistency check results and behavioral patterns, the system attempts to infer the user's true inner thoughts on key issues (such as career preferences, entrepreneurial motivations, and perceptions of their own abilities), even if these thoughts may differ from the user's direct expressions.

[0080] C. Capturing long-distance dependencies and analyzing core competencies: The self-attention mechanism of the second multi-head Transformer group can span the entire user insight summary (i.e., at least 30 minutes of interaction records), capturing potential connections between user content and behaviors expressed at different time points and across different topics. For example, a user's casual mention of a hobby early on may reveal great enthusiasm through non-verbal signals when discussing skills or interests later. The system can connect these scattered points of information and identify potential deeper interests or overlooked skills.

[0081] When analyzing a user's core competencies, the system doesn't rely solely on the user's self-description or resume information during the conversation. Instead, it leverages a second multi-headed Transformer attention mechanism to integrate the user's multimodally verified potential strengths and core competencies, demonstrated throughout the conversation through clear language logic, effective problem-solving strategy demonstrations (for example, in AI-guided simulated situational questioning), and confident multimodal behavioral characteristics (for example, when describing areas of expertise, posture becomes more upright, gestures become more fluent and powerful, and eyes become more determined).

[0082] Based on the in-depth analysis results, we use the generation capabilities of large models to generate personalized plans: The generation of personalized plans is the result of an integrated large-scale model processing core based on the user's real thoughts inferred after the aforementioned consistency check, the core capabilities and strengths analyzed in depth, and the user's explicit needs and goals, combined with the system's built-in and continuously updated professional knowledge map (including industry information, job descriptions, salary ranges, development paths, required skills, etc.) and the entrepreneurial success factor model (including entrepreneurial models, success cases, risk factors, financing channels, policy support, etc. in different industries).

[0083] If the analysis results indicate a job search, the system leverages the generative and reasoning capabilities of a second set of multi-head Transformers to comprehensively consider the user's skills, experience, personality, interests, genuine beliefs, and core competencies, filtering and recommending several highly compatible career paths and specific positions from the career knowledge graph. The reasons for the recommendation will detail why these positions are suitable for the user, for example, "Given your exceptional analytical ability in solving complex logical problems (verified by problem-solving demonstrations and clear logical expression in conversations), your high level of focus and positive emotions when discussing technical details (analyzed through facial expression and voice LTCs models), combined with your preference for job stability (determined through consistency verification analysis), we believe that the 'Senior Data Analyst' position at Company A or the 'Algorithm Engineer' position in Industry B will well leverage your strengths and meet your career expectations." If the analysis indicates a startup, the system will generate a business plan that includes a specific business direction, preliminary business model recommendations, potential market analysis, key resource requirements, and risk warnings. For example, "Based on your strong desire to address social environmental issues (as evidenced by repeated mentions and high emotions during conversations), your extensive project management experience (verified through descriptions of past experiences and a confident demeanor), and your demonstrated talent for cross-team communication and coordination (as analyzed through multimodal interaction behavior), we recommend considering starting a business in the field of 'community-based shared environmental solutions.' Your initial direction could be to build a P2P platform connecting idle environmental resources with local needs. The market potential for this direction lies in..., key resources to focus on include..., and potential risks include...." The generated planning scheme is presented in a clear and structured manner, and provides explanations and basis for key recommended points, making it easy for users to understand and evaluate.

[0084] Working process: After the initial dynamic user insight summary is generated in Example 1, the deep insight analysis and personalized planning functions of the integrated large model processing core are activated: Receive and pre-process dynamic user insight summaries.

[0085] The second group of multi-head Transformer attention mechanisms conducts in-depth analysis of the summary. The core links include: performing consistency verification between verbal information and non-verbal multimodal behavioral characteristics; analyzing the user's psychological state, behavioral patterns, and true thoughts; capturing long-distance dependencies and integrating and analyzing core capabilities and specialties.

[0086] Based on the above in-depth analysis results, and combined with the built-in knowledge graph and model, and utilizing the generation capabilities of large models, we can customize and output personalized career development plans or entrepreneurial planning plans for users.

[0087] Based on the multimodal insight summary, Transformer is used for consistency verification, deep reasoning, and generation of highly personalized plans. The formula is as follows: P plan (U) = Gen Plan ( Interpret Deep ( S insight (U), C score (S insight (U) ; θ consistency ); θ interpret ) , K domain ; θ Attn 2 gen ) in: P plan(U): Personalized career development or entrepreneurship plan generated for user U.

[0088] S insight (U): dynamic user insight summary for user U obtained from the first embodiment.

[0089] ConsistencyScoreθ consistency (S insight (U)):denotes the consistency check function, which uses part of the second set of multi-head Transformer attention mechanism (given by the parameter θ consistency Characterization) to analyze S insight The degree of match or conflict between the verbal information and the non-verbal multimodal behavioral features in (U) is used to output one or a set of consistency scores / indicators.

[0090] InterpretDeepθ interpret (⋅,⋅): represents the deep interpretation function, which uses the other part of the second set of multi-head Transformer attention mechanism (determined by the parameter θ interpret representation), combined with the original insight summary S insight (U) and the consistency check result ConsistencyScore, perform global context understanding and deep logical reasoning, and ultimately form a deep user profile or a set of refined insight conclusions.

[0091] K domain : Represents the system's built-in domain knowledge base (such as professional knowledge graph, entrepreneurial success factor model, etc.).

[0092] GenPlanθ Attn2_gen (⋅,⋅): represents the planning generation function, which uses the generation and reasoning capabilities of the second set of multi-head Transformer attention mechanisms (determined by the parameters θ Attn2_gen representation), insights gained through deep interpretation and domain knowledge base K domain , customize career development or entrepreneurship planning for users plan (U).

[0093] By verifying the consistency of multimodal information, we can effectively identify discrepancies between users' verbal expressions and their internal state, more accurately grasping their true thoughts, concerns, and underlying needs that remain unexpressed, avoiding misjudgments based solely on superficial statements. Analysis of users' core competencies and strengths is no longer limited to self-assessment. Instead, it integrates their proven strengths demonstrated through language logic, problem-solving demonstrations, and multimodal behaviors during extended conversations, resulting in a more objective and reliable assessment.

[0094] Based on a deep understanding of users' true thoughts, core capabilities, psychological states, behavioral patterns, and long-distance dependencies, combined with professional knowledge graphs, the system can generate truly "tailor-made" career development or entrepreneurship plans, significantly improving the planning's pertinence and feasibility.

[0095] The proposed planning solutions are not only specific but also explain the basis for their recommendations (for example, the analysis results and model judgments used). This enhances the transparency and credibility of the plans, helps users make more informed decisions, and ultimately improves user satisfaction. By capturing long-range dependencies and analyzing multimodal information, the system may discover potential interests or skill combinations that users themselves may not have been aware of, thereby opening up new and unexpected career or entrepreneurial possibilities.

[0096] Example 3: like Figure 1 and Figure 2 As shown, based on Example 1 (core architecture, multimodal perception interaction and preliminary insights) and Example 2 (deep insight analysis and personalized planning), this embodiment further describes how the system receives user feedback on the generated planning scheme through its "planning output and feedback iteration module", and effectively integrates this feedback into the integrated large model processing core to achieve continuous updating of user insights and iterative optimization of planning schemes, thereby forming a dynamic closed-loop system that continuously improves service quality.

[0097] The feedback closed-loop optimization mechanism described in this embodiment mainly relies on the "planning output and feedback iteration module" and the collaborative work between this module and the "integrated large model processing core".

[0098] In Example 2, the "deep insight analysis and personalized planning function" within the integrated large-scale model processing core generates personalized career development or entrepreneurial plans for users. These plans are presented to users through a user-friendly interface. This interface supports multiple formats, including text, charts, and even simulation scenarios that combine preliminary plans, ensuring that users can clearly and comprehensively understand the plan content and the underlying rationale.

[0099] The system provides clear feedback channels, allowing users to express their opinions on various aspects of the plan (e.g., recommended career paths, feasibility of entrepreneurial projects, degree of agreement with their own ability assessments, rationality of the planned path, etc.). Users can provide feedback by entering text, selecting preset options (such as "Agree with this suggestion," "Questions," "Need more information"), rating, or annotating specific planning points.

[0100] While users are reviewing and evaluating plans, if they are still in a video conversation interaction with the system, the multimodal data acquisition module can continue to work (with explicit authorization from the user) to capture non-verbal signals such as facial expressions and emotional reactions when reading the plan, as a supplement to explicit feedback.

[0101] Users can proactively provide additional information, new personal changes, or specific modification requests to the planning plan that were not mentioned in the initial conversation (at least 30 minutes) or not fully understood by the system.

[0102] The user feedback collected by the planning output and feedback iteration module (whether explicit text, options, preliminary analysis results of optional implicit behavioral signals, or user supplementary information) is structured. For example, text feedback undergoes keyword extraction and sentiment analysis; option feedback is statistically analyzed; and supplementary information is labeled to determine its relevance to the original plan.

[0103] When users raise objections to the planning scheme or provide additional information, the structured feedback is re-input into the integrated big model processing core as new contextual information, along with the original dynamic user insight summary and the generated planning scheme.

[0104] The closed-loop optimization process of the integrated large model processing core, after receiving new context information containing user feedback, the integrated large model processing core will start the closed-loop optimization process, which mainly involves updating the dynamic user insight summary in the "real-time multimodal perception and interaction function" and re-running and adjusting the "deep insight analysis and personalized planning function".

[0105] A. Weight adjustment or content revision of dynamic user insight summaries: The integrated large-model processing core (particularly its components responsible for initial perception, such as the first set of multi-headed Transformers) analyzes the relevance of user feedback to the items in the existing dynamic user insight summary. For example, if a user indicates, "The system overestimates my skill A; I'm actually better at skill B," the system will locate the evaluation items for skills A and B in the insight summary.

[0106] For insights that users explicitly disagree with or have objections to (for example, an inferred true idea or an ability assessment), the system will reduce the confidence or weight of these items in subsequent analysis.

[0107] Based on supplementary information or explicit revisions provided by users, the system directly updates or supplements the relevant content of the dynamic user insight summary. For example, it may add a user's mention of skill B and record their self-assessment and supporting evidence. If user feedback includes new emotions or behavioral expressions (such as expressing great enthusiasm when discussing a previously overlooked point of interest), this new multimodal information will also be integrated to enrich and correct the original sentiment curve or behavioral pattern judgment.

[0108] Verify through continuous interaction (if applicable). If the system allows, a short clarification conversation can be conducted after receiving feedback, led by the LLM, to further confirm and refine the user feedback and ensure that the revisions to the insight summary are accurate.

[0109] B. Re-run the deep insight analysis and personalized planning function: Using the latest dynamic user insight summary, with adjusted weights and revised content, the integrated large-scale model's core "deep insight analysis and personalized planning capabilities" (specifically the second set of multi-headed Transformers) re-executes its analysis process. This includes re-checking consistency (because the user's true thoughts may become clearer due to feedback), re-evaluating psychological states and behavioral patterns, re-capturing long-range dependencies, and more accurately analyzing core capabilities and strengths.

[0110] Based on this more accurate and in-depth analysis of user feedback, the system will regenerate or adjust the original personalized career development plan or entrepreneurial plan. The new plan will strive to better reflect the user's latest preferences, revised ability assessment, and additional information. For example, if a user reports concerns about a recommended industry, the new plan may deprioritize that industry or provide strategies to overcome this concern. If the user adds new skills or resources, the new plan will attempt to incorporate them.

[0111] When outputting the adjusted planning scheme, the system will explain to the user the feedback on which the adjustment was made and the logic of the adjustment, enhancing transparency and user participation.

[0112] Working process: The system presents personalized planning solutions to users through planning output and feedback iteration modules.

[0113] This module allows users to provide feedback on planning solutions (explicit or optional implicit feedback, as well as supplementary information). This feedback is structured and fed into the integrated big model processing core as new context. The integrated big model processing core first updates the dynamic user insight summary, adjusting the weights of relevant items or revising / supplementing content. Then, based on the updated user insights, it reruns the deep insight analysis and personalized planning functions to generate an iterative planning solution.

[0114] The iterated plan is presented to the user again, who can continue to provide feedback, thus forming a closed loop of continuous optimization. This process can be repeated multiple times until the user reaches a high level of satisfaction with the plan or the system determines that the optimal solution has been achieved based on the current information.

[0115] User feedback drives dynamic iteration and system optimization of user insight summaries and planning solutions. The formula is as follows: in: (U) and (U): represent the insight summary and planning scheme of user U at the kth iteration respectively.

[0116] : Indicates the user's current planning scheme (U) Feedback provided.

[0117] : Indicates the insight summary update function (by parameter Representation), adjust or modify the insight summary of round k according to user feedback to obtain the updated (U).

[0118] RegeneratePlan (⋅,⋅): represents the plan regeneration function (consisting of parameters characterization), based on the updated insight summary (U) and domain knowledge base K domain , generate the planning scheme for the k+1th round (U).

[0119] : Represents the overall parameters or state of the integrated large model processing core at the kth iteration.

[0120] AdjustCoreParams(⋅,⋅,⋅,⋅;λ) (optional): Indicates an optional system core parameter adjustment process, where λ is the learning rate or adjustment factor, indicating that the system can fine-tune its own parameters based on feedback and the current iteration results to achieve long-term performance optimization.

[0121] By allowing users to participate in the plan revision process and fully respecting their opinions and feelings, the final plan is more likely to be accepted and recognized by users, significantly improving user satisfaction. The feedback loop mechanism enables the system to dynamically adjust its understanding and planning recommendations based on immediate user feedback and changing circumstances, truly adapting to each individual and changing situation. User feedback, especially information that identifies biases in the system's analysis or provides new perspectives, serves as valuable training data for continuously optimizing the performance of the various AI models (LLM, LTCs, Transformer, etc.) within the integrated large-scale model processing core, thereby enhancing the overall intelligence of the system. Transparent feedback processing mechanisms and explainable plan adjustments can enhance user trust in the system. Seeing that their opinions are effectively incorporated and reflected in the plan increases user motivation to participate in subsequent support processes. While the system strives to be comprehensive during the initial interaction (at least 30 minutes), the complexity and dynamic nature of users means that initial insights may be incomplete or require revision. The feedback loop mechanism provides an effective way to address these shortcomings, ensuring that user profiles and planning solutions continuously align with users' real situations and optimal choices. This closed-loop optimization capability enables the system to not only provide one-time planning suggestions, but also accompany the user's career development or entrepreneurial journey, and continuously provide dynamically adjusted and valuable support based on the new problems encountered, new experiences gained, and ideas generated by the user at different stages.

[0122] Application Cases: Li Ming, 35, retired from the army as a deputy battalion commander after 12 years of service. In the military, he was primarily responsible for the maintenance and management of technical equipment, personnel training, and some operational staff work. He possessed strong discipline, a strong sense of responsibility, strong teamwork, and the ability to withstand pressure. He also possessed certain skills in electronic equipment repair and management. After his retirement, he felt uncertain about his career prospects in the local government. On the one hand, he longed for a job that would allow him to realize his potential and offer growth prospects. On the other hand, he also harbored hopes of leveraging his experience and interests to start a business, but lacked a specific direction or business experience.

[0123] The process of using this system: Phase 1: Initial contact and deep multimodal interaction Li Ming made an appointment and accessed the "Insight AI Empowerment Platform" through a designated employment and entrepreneurship service center or online platform. The system prompted him to ensure a quiet, well-lit environment and to use a device equipped with a high-definition camera and microphone. The system informed him that a comprehensive assessment would require a minimum of 30 minutes for the video conversation.

[0124] Video conversation starts: The multimodal data acquisition module starts working, collecting real-time video (facial expressions, full-body movements mainly of the upper body) and audio data of Li Ming during the conversation.

[0125] The integrated large model processing core's "(a) real-time multimodal perception and interaction function" was activated. The large language model (LLM) based on the Transformer architecture began a natural and fluent conversation with Li Ming. The LLM first gave an introductory opening, asking Li Ming to introduce his basic situation, the main responsibilities and achievements of his military career, and his initial thoughts and confusion after retirement. The LLM used its self-attention mechanism to understand the context of Li Ming's words and used its text generation capabilities to ask in-depth and targeted follow-up questions, such as: "What is the biggest challenge you encountered when you were responsible for the maintenance of technical equipment? How did you overcome it?" or "You mentioned that you are interested in starting a business. What specifically attracted you?" LTCs timing modeling and multimodal feature extraction: While LLM was talking with Li Ming, liquid time constant networks (LTCs) performed continuous timing modeling on the collected multimodal data streams.

[0126] Facial Expression and Emotion Analysis: LTCs process a video stream of Li Ming's facial expressions, capturing the dynamic changes and subtle key transitions in his emotions, such as joy and pride when recalling specific events in his military career (such as overcoming difficulties and receiving honors), or anxiety and confusion when discussing future uncertainties. For example, when the LLM asks him about his views on the high-tech industry, Li Ming verbally states, "I have some understanding and think it has great potential," but the LTCs might capture a flicker of uncertainty between his brows and a quick shift in his gaze.

[0127] Full Body Dynamics and Behavior Analysis: LTCs process Li Ming's body posture and gestures. For example, when describing how he organized a team to complete a task, he might sit more forward, gesture more expansively and forcefully, and speak more quickly. These are modeled by LTCs as behavioral traits of high engagement and confidence. On the other hand, when discussing unfamiliar business concepts, he might show more subtle movements or even slightly withdraw.

[0128] Speech feature analysis: LTCs process speech data and analyze how pitch, speaking rate, and energy change across different topics. For example, when discussing a technical area of ​​expertise, a speaker's speaking rate is steady and their tone is engaging.

[0129] These multimodal dynamic behavior temporal features output by LTCs will be processed by an adaptation layer to match the input requirements of the subsequent Transformer.

[0130] The first group of multi-head Transformers generates integrated reasoning and preliminary insight summaries: The conversation text content obtained by the LLM (Li Ming's answers and the system's questions) and the multimodal dynamic behavior temporal features extracted by the LTCs processed by the adaptation layer are fed into the attention mechanism of the first group of multi-head Transformers.

[0131] This mechanism uses different attention heads to focus on text semantic understanding (what Li Ming specifically said), emotional temporal feature integration (how his overall emotional state evolved while he spoke), and behavioral posture sequence pattern recognition (what his body language conveyed). Through deep integration and preliminary reasoning, the system begins to understand Li Ming's immediate state (such as his engagement in the current conversation turn and his emotional stability), his explicit needs (such as his explicit statement of "looking for a technical management job"), and initially captures his implicit intentions (such as inferring his potential desire for a leadership role through his particularly vivid description of "leading the team to overcome difficulties" and his positive non-verbal signals).

[0132] After a conversation has lasted at least 30 minutes, the system will first generate a preliminary, structured, and dynamic user insight summary with multimodal features. This summary may include: a temporal graph of Li Ming's emotional valence and arousal as he discusses different topics (such as "Military Achievements," "Local Challenges," and "Entrepreneurial Ideas"); an index of his notable nonverbal behaviors, such as facial expressions, gestures, and posture, when discussing key topics such as "Technical Challenges," "Teamwork," and "Future Plans"; and a preliminary assessment of cognitive load and engagement based on factors such as his reaction speed and verbal fluency. This summary will be continuously updated and enriched through subsequent interactions.

[0133] Phase 2: Deep Insight Analysis and Personalized Planning Deep processing of dynamic user insight summaries: The initially generated dynamic user insight summaries are fed into the “(b) Deep Insight Analysis and Personalized Planning Function” of the integrated large model processing core.

[0134] The second set of multi-head Transformer attention mechanisms begins to perform global context understanding and deep logical reasoning on this summary containing rich temporal and multimodal information.

[0135] Consistency Verification and Analysis of True Thoughts: This mechanism focuses on verifying the consistency between verbal information and non-verbal multimodal behavioral characteristics. For example, Li Ming verbally stated, "I am fully prepared for entrepreneurial risks." However, by analyzing the recurring frowns, clenched fists, and slightly rapid breathing patterns captured and recorded in summaries by LTCs during in-depth discussions of entrepreneurial failures and financial pressures, the system determined that there was a discrepancy between his verbal statement and his inner state. The system flagged this inconsistency and inferred that Li Ming's true tolerance for entrepreneurial risk might be lower than he expressed, or that he might still have significant concerns about it.

[0136] By analyzing multiple such key decision points, past experiences (for example, when he described the achievements of a certain technical breakthrough, although his language was humble, his non-verbal expression was full of pride and a sense of control) and future expectations, the system gradually outlined Li Ming's deeper psychological state, true thoughts and driving forces.

[0137] Core Competencies and Long-Distance Dependency Analysis: The second multi-head Transformer attention mechanism also integrates Li Ming's multimodally verified potential strengths throughout the conversation (based on the updated insight summary), as demonstrated through clear language logic (e.g., his organized explanation of complex technical principles), effective problem-solving strategies (e.g., his solutions to simulated management scenarios posed by LLM), and confident multimodal behavioral traits (e.g., his relaxed posture, precise gestures, and determined gaze when describing his expertise in equipment maintenance procedures). The system discovered that Li Ming not only possesses a technical background, but also demonstrates exceptional systematic thinking and logical expression when describing personnel training and mission planning, which may not have been fully reflected in his previous resume.

[0138] The system also detected that Li Ming mentioned his hobby of researching drones early in the conversation, and later in the conversation, when discussing emerging technologies, his voice LTCs model analysis showed that his tone was noticeably more enthusiastic and engaged, even though he had not explicitly identified this as a career path. This long-range dependency was captured by the system.

[0139] Personalized planning scheme generation: Based on the above in-depth analysis (especially the real ideas after consistency verification and the core capabilities and expertise of multimodal verification), and combined with the system's built-in "military career-local career skills mapping knowledge base", real-time updated career knowledge graph and entrepreneurial success factor model, the integrated large model processing core multi-head Transformer generation and reasoning capabilities began to customize a planning scheme for Li Ming.

[0140] Job search direction: The system might suggest: "Mr. Li Ming, based on comprehensive analysis, you possess proven capabilities and extensive experience in technical management, team leadership, and training. Considering your interest in high-tech fields and your outstanding performance in logical thinking and system planning, we recommend that you focus on the following positions: 1. 'Technical Project Manager' at a large technology company, responsible for coordinating technical projects across departments; 2. 'Operation and Maintenance Training Director' in the intelligent manufacturing field, responsible for developing and implementing technical personnel training systems. These positions can effectively combine your military management experience and technical background and provide stable development opportunities." Exploring entrepreneurial opportunities: Based on his interest in drones and his strengths in equipment maintenance and systems thinking, the system might suggest, "Furthermore, given your keen interest in drone technology, your professional experience in equipment maintenance management, and your demonstrated ability in systems planning, you might consider exploring entrepreneurial opportunities in the field of 'industrial drone application solutions,' for example, drone inspection and maintenance services for agriculture or security. Preliminary analysis shows that the market potential in this area is..., your strengths lie in..., the knowledge and skills you need to supplement include..., and the main risk points are... We can further explore the feasibility of this direction." Phase 3: Planning output and feedback closed-loop optimization Plan Presentation and Feedback Collection: The Insight AI Empowerment Platform, through its planning output and feedback iteration module, presented Li Ming with the preliminary job search and entrepreneurship plan. This plan was presented using clear text and charts (such as a competency match radar chart).

[0141] Li Ming reviewed the proposal. He seemed to agree with the "Operations and Maintenance Training Director" role, but felt he lacked the software development experience required for the "Technical Project Manager" role. Regarding his entrepreneurial direction, he expressed strong interest in drones but expressed concerns about the market and financing. He input these ideas into the system's feedback interface.

[0142] Feedback processing and closed-loop optimization: Li Ming’s feedback is input into the integrated large model processing core as new context information.

[0143] The system triggers weight adjustments or content revisions to relevant items in the dynamic user insight summary. For example, it lowers the weight of skills strongly related to software development and increases the assessment of their interest and potential in training and drone applications. It also records their concerns about the market and financing.

[0144] The system re-runs the deep insight analysis and personalized planning functions.

[0145] Post-Iteration Planning: The new plan might focus more on refining the development path for "Operations and Maintenance Training Director" and provide suggestions for building relevant experience. For entrepreneurial initiatives, the system might be adjusted to "Drone Technology Application Training and Consulting Services" to mitigate initial market and financial risks, better integrate training experience and technical interests, and recommend relevant startup incubation resources and mentors.

[0146] Multiple rounds of iteration: This feedback-correction-replanning process can be carried out for multiple rounds until Li Ming feels clear and satisfied with the final plan.

[0147] Through deep multimodal interaction and consistency checking with the system, Li Ming not only sorted out his existing skills and experience, but more importantly, the system helped him identify true thoughts (such as his true attitude towards entrepreneurial risks) and potential strengths (such as his deep interest in drones and his ability to plan systems) that he might not have been aware of. This far surpasses the effectiveness of traditional consulting services based on questionnaires or simple interviews.

[0148] The system-generated plan wasn't a generic template; instead, it deeply integrated Li Ming's military experience, technical background, multimodally validated core competencies, genuine interests, and his true attitude toward risk. For example, combining his interest in drones with his training experience resulted in a more actionable and targeted entrepreneurial direction than his initial, vague idea.

[0149] After multiple rounds of feedback and iteration, Li Ming gained a stronger sense of identity and control over the final plan. The system not only provided direction but also explained the rationale, making his career transition decision more scientific and rational, significantly boosting his confidence in a successful transition.

[0150] After determining a direction, the system can also match them with the most appropriate training resources, industry mentors, and even potential entrepreneurial partners or investment opportunities based on their precise profile, bridging the "last mile" from awareness to action. For Li Ming, who served in the military for a long time, information about the local job market and entrepreneurial environment was relatively limited. The system, through its built-in knowledge graph and models, provides him with valuable external information references, compensating for this information asymmetry.

[0151] Through the above application cases, we can see how the "AI-based employment and entrepreneurship support system" described in the present invention, through its unique multimodal perception, deep insight and closed-loop optimization mechanism, can provide users facing career transformation with highly personalized, precise and effective support that far exceeds traditional methods.

[0152] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the concept described herein through the above teachings or techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be protected by the appended claims.

Claims

1. An artificial intelligence-based employment and entrepreneurship support system, characterized by: include: A multimodal data acquisition module, which is used to collect video data from a video conversation with a user lasting at least 30 minutes in real time. The video data includes the user's facial expression information and body dynamic information, and simultaneously collects the user's voice data; An integrated large model processing core configured to perform the following functions: Real-time multimodal perception and interaction functions: Performing natural language processing using a large language model based on the Transformer architecture. The large language model uses its self-attention mechanism to understand the conversation context and its text generation capabilities to conduct a fluent real-time video conversation with the user for the specified duration and obtain the text content of the conversation. Using a liquid time constant network to perform continuous time series modeling on the user's facial expression information flow, whole body dynamic information flow in the video data, and the acoustic feature flow in the voice data, the liquid time constant network processes continuous time dynamic information by solving ordinary differential equations and has a variable time constant coupled with a hidden state to extract the user's real-time multimodal dynamic behavior time series features; Combined with the first set of multi-head Transformer attention mechanisms, the conversation text content obtained by the large language model and the multimodal dynamic behavior temporal features extracted using the liquid time constant network are deeply integrated and preliminarily inferred to understand the user's immediate status, explicit needs, and preliminary implicit intentions, and generate a structured, dynamic user insight summary containing multimodal features; Deep insight analysis and personalized planning functions: receiving and processing the dynamic user insight summary; Combined with a second set of multi-head Transformer attention mechanisms, the dynamic user insight summary is subjected to global contextual understanding and deep logical reasoning. This reasoning process includes performing consistency checks between verbal information and non-verbal multimodal behavioral features. Based on these checks, the user's psychological state, behavioral patterns, and true thoughts are analyzed, as well as the long-range dependencies and core capabilities of the user throughout the interaction process. Based on the in-depth analysis results, the generation capability of the large model is used to generate personalized career development plans or entrepreneurial plans for users; The planning output and feedback iteration module is used to present the personalized career development plan or entrepreneurship plan to the user and receive user feedback. The feedback will drive the integrated large model processing core to perform closed-loop optimization, including updating the user insight summary and iterative adjustment of the personalized planning plan.

2. The artificial intelligence-based employment and entrepreneurship support system according to claim 1, characterized in that: The liquid time constant network used for continuous time series modeling is specifically configured to capture and characterize the dynamic patterns and key transition points of the user's emotions, posture, and voice characteristics evolving over time during a conversation of at least 30 minutes.

3. The artificial intelligence-based employment and entrepreneurship support system according to claim 1, characterized in that: When performing deep fusion and preliminary reasoning, the first group of multi-head Transformer attention mechanisms focuses on text semantic understanding, emotional temporal feature integration, and behavioral posture sequence pattern recognition through different attention heads to achieve multi-dimensional representation of the user's real-time status.

4. The artificial intelligence-based employment and entrepreneurship support system according to claim 1, characterized in that: The structured, dynamic user insight summary containing multimodal features includes data of at least the following dimensions: a time-series curve of changes in the user's emotional valence and arousal, an index of key non-verbal behavior segments associated with the conversation topic, and preliminary assessments of cognitive load and engagement indicators.

5. The artificial intelligence-based employment and entrepreneurship support system according to claim 1, characterized in that: When the second group of multi-head Transformer attention mechanisms performs consistency checks, it focuses on analyzing the match or conflict between the certainty and emotional tendencies of users' verbal statements when discussing key decision points, past experiences, or future expectations, and their inner states reflected by facial micro-expressions and body language extracted through the liquid time constant network.

6. The artificial intelligence-based employment and entrepreneurship support system according to claim 1, characterized in that: When analyzing a user's core competencies, the deep insight analysis and personalized planning function utilizes a second multi-head Transformer attention mechanism to integrate the user's potential strengths throughout the conversation, as indicated by language logic, problem-solving strategy demonstrations, and multimodal behavioral characteristics (such as confident posture and smooth gestures).

7. The artificial intelligence-based employment and entrepreneurship support system according to claim 1, characterized in that: The generation of the personalized plan is based on the core capabilities of the integrated large-scale model processing core, which is based on the user's real thoughts after consistency verification and in-depth analysis, combined with the built-in professional knowledge graph and entrepreneurial success factor model, and customized output through the generation and reasoning capabilities of multi-head Transformer.

8. The artificial intelligence-based employment and entrepreneurship support system according to claim 1, characterized in that: The feedback closed-loop optimization mechanism includes: when a user raises objections to a planning plan or provides additional information, the feedback will be input into the integrated large model processing core as new contextual information, triggering weight adjustments or content corrections to relevant items in the dynamic user insight summary, and re-running the deep insight analysis and personalized planning functions.

9. The artificial intelligence-based employment and entrepreneurship support system according to claim 1, characterized in that: The features output by the liquid time constant network after continuous time series modeling are processed by an adaptation layer to match the input dimension and representation requirements of the first group of multi-head Transformer attention mechanisms.

10. The artificial intelligence-based employment and entrepreneurship support system according to claim 1, characterized in that: After a video conversation with the user for at least 30 minutes, the integrated large model processing core will first generate a preliminary dynamic user insight summary, and in subsequent interactions or user feedback, it will continue to incrementally update and enrich this summary through its real-time multimodal perception and interaction functions.

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