13 results about "Behavioral pattern" patented technology

The disclosure describes a method of generating a target profile including the target's sequence of events (SOE) for a task. Such target profile sequence of events is derived from several source group's transactions, where any source group's transactions cannot be shared with other source groups but the derived target group's profile is the only information that is shared. Source-side information is periodically extracted for a plurality of sources that each interact with a plurality of targets. The information includes source stages, resources, and stage transition events for a task with a target. Source information is used to generate a set of normalized stages, and a set of normalized events for transitioning between the stages of the set of normalized stages. An artificial intelligence (AI) model is trained using the source information. The AI model can generate a target profile with target process information inferred using the trained model. The target process information can include the target's identifiers for each stage, an estimated duration of the stage, deliverables for the stage, and one or more stage transition events for the stage.

This invention relates to a slow disk simulation method and system based on data augmentation, belonging to the field of data processing technology. It solves the problems of insufficient realism, simplistic behavioral patterns, and inability to accurately reflect the complex characteristics of real slow disks in existing slow disk simulation methods. The method includes: calculating the correlation coefficients between key indicators based on multiple key indicators of the slow disk by collecting corresponding time-series data, and constructing a feature matrix model; performing data augmentation on the time-series data of each key indicator based on a dynamic time warping algorithm to generate augmented time-series data; validating the augmented time-series data based on the feature matrix model, and storing it in the augmented time-series dataset if the verification passes; matching the real-time time-series data of each key indicator of the target disk with the augmented time-series dataset, and injecting delays into the I / O requests of the target disk based on the matched augmented time-series data when a match is successful, to simulate slow disk behavior. This achieves slow disk simulation covering various real-world faults.

This invention relates to the field of secure communication application technology, specifically to a host asset identification and profiling method based on multimodal network feature fusion enhancement. First, a data collection agent is deployed at the core node of the target network to collect multimodal data through traffic mirroring and log crawling, and a cross-modal aligned multi-source network data representation is constructed. Next, a TCN-BiLSTM model combined with an attention mechanism is used to extract deep periodic features, and a GraphSAGE model is used to generate graph embedding vectors bound to topological relationships. A pre-trained BERT model is used to obtain global protocol semantic feature vectors and communication intent distribution. Subsequently, a modality-specific mapping is used to unify dimensions, and a two-layer mechanism of intra-modal self-attention and inter-modal mutual attention is constructed to dynamically allocate weights, fusing multimodal features to extract core features of host assets. Finally, the fused features are mapped to structured text, and a CLM strategy combined with LoRA technology is used to fine-tune the LLM, generating a natural language asset profile containing role positioning, behavioral patterns, and topological relationship logic.

We provide the system. [Solution] A device that receives information acquired from a video acquisition device, An artificial intelligence analysis device that analyzes behavioral patterns based on the aforementioned information and identifies specific behaviors, A device that detects identified behavior as problematic behavior if it exceeds a predetermined standard, A device that generates response procedures and warnings based on detected problematic behavior, A device that transmits the aforementioned warning to a portable terminal, A system that includes this.

We provide the system. [Solution] Information collection means for collecting user transaction history data and behavioral data, A data analysis means for analyzing the collected data and identifying the user's past purchasing trends and behavioral patterns, A proposal generation means for extracting relevant facilities based on the user's current location information and generating preferential treatment according to the analysis results, A means for sending the generated discount information to the user's terminal, A prompt generation means that uses a generative AI model to create prompt sentences related to user trends, A system that includes this.

This invention provides a fuzzy testing mutation scheduling optimization method based on the mayfly algorithm, belonging to the field of fuzzy testing. It includes: Step 1, constructing a lightweight mayfly population for each seed in the fuzzy testing process, where individuals in the population represent a probability distribution of a set of mutation operators; Step 2, selecting mutation operators for each seed in the queue according to a uniform probability distribution, performing mutation, and simultaneously collecting path coverage and crash detection as feedback information, and calculating fitness scores through normalization and weighting; Step 3, simulating diverse behavioral patterns of the mayfly population based on individual fitness scores, dynamically updating the probability distribution of mutation operators, and optimizing the probability distribution; Step 4, finally applying the optimized probability distribution to the next round of seed mutation to generate more effective test cases, and repeatedly executing the above steps to achieve continuous optimization of the mutation scheduling strategy, improving fuzzy testing coverage and vulnerability discovery capabilities.

This invention discloses a method for mining and guiding behavioral patterns based on user data, belonging to the field of data retrieval technology. The key technical points include: acquiring the user's current behavioral data; obtaining behavioral codes based on the current behavioral data and a medical effectiveness rule sub-library in a historical consensus database; the historical consensus database being composed of data stored in a blockchain; obtaining effective behavioral sequences based on the behavioral codes and an effect association rule sub-library in the historical consensus database; and determining the action parameters in the effective behavioral sequences based on a phased rule sub-library in the historical consensus database. This invention encodes behavior using user behavioral data and combines it with a historical consensus database to filter effective behavioral sequences that fit the behavioral codes. It does not passively rely on general medical guidelines but generates behavioral sequences based on group patterns that conform to medical standards and are adapted to the user's action habits and recovery speed, guiding the user through training.

This invention relates to the field of trusted computing technology and discloses a terminal access authentication method and system based on trusted computing. The method includes: performing a step-by-step extension measurement on the hardware root of trust to obtain an initial trusted chain construction result; performing time-series dependency analysis on the construction result to obtain a runtime timing benchmark; using the time interval between the client key exchange parameters and the micro-timestamp of the digital certificate in the protocol handshake message as a behavioral pattern signal to evaluate the perturbation factorization of the system event sequence to obtain an endogenous temporal perturbation factor; performing an overlap test on the behavioral pattern signal and the endogenous temporal perturbation factor to obtain an identity state linkage indicator; determining the dynamic trust risk level of identity authentication based on the identity state linkage indicator, and making an authentication decision on the identity authentication process based on the dynamic trust risk level to obtain an identity authentication result. This invention can improve the accuracy of identity authentication decisions and the efficiency of adaptive response in complex attack scenarios.

This application relates to the field of software engineering technology and discloses a method for automated migration of customized code between PLM systems. The method includes: constructing a program profile containing a static semantic dependency graph and a dynamic execution trajectory graph; mining behavioral patterns in the dynamic execution trajectory graph and introducing intent entropy for quantitative evaluation to dynamically allocate migration strategies of overall behavior reconstruction or conservative step-by-step mapping; performing code transformation based on the allocated strategy; verifying the transformed code and triggering a feedback mechanism by identifying non-habitual migrations to achieve self-learning optimization of migration rules. Furthermore, this invention supports incremental migration, determining the impact domain of code changes through graph difference operations and migrating only the affected parts to adapt to version iterations. By deeply analyzing the business intent of the code and combining self-learning and incremental migration mechanisms, this invention significantly improves the automation level, accuracy, and version iteration efficiency of code migration.

We provide the system. [Solution] Information gathering means for acquiring and storing information from users, Analytical means for analyzing accumulated information and identifying behavioral patterns, A proposal generation means that generates improvement suggestions suitable for the user based on the aforementioned behavioral patterns, A notification means for notifying users of a reminder based on the proposal, A user interface means that provides the reminder to the user visually or audibly via a physical device, A system that includes this.

This invention discloses a method and system for mining diverse preference patterns based on training multiple reward models. Through deep learning training, corresponding preference patterns are mined from a preference dataset labeled with multiple different unknown preference patterns. Each preference pattern represents a behavioral pattern or task that a human group hopes the robot will follow. At the same time, corresponding reward models are trained for each preference pattern, and numerical values ​​are used to measure the degree to which the robot performs the task to meet human expectations. This enables the trained reward models with multiple preferences to enable different human groups to make the robot complete the expected tasks.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method accesses a plurality of data points. An individual data point of the plurality characterizes a pattern of activity associated with an account. The method splits the plurality of data points into clusters of similar data points. The method evaluates the clusters to detect that the individual data point has changed clusters in a manner indicative of an anomalous change to the pattern of activity. And, the method generates an electronic alert that the pattern of activity has changed anomalously.

This invention discloses an implicit multi-agent cooperative method and system based on spatiotemporal reasoning and adversarial reconstruction. First, at each time step, a graph attention network is used to encode the instantaneous spatial relationships between the agent and its neighbors, generating instantaneous spatial relationship embeddings. Second, the sequence of instantaneous spatial relationship embeddings is input into a GTrXL network, utilizing its recurrent memory units to capture long-term temporal dependencies and infer the agent's latent intentions. Next, adversarial self-supervised reconstruction training is performed, reconstructing randomly masked neighbor state information through a decoder, forcing the model to learn standardized behavioral patterns. Finally, a joint loss function is constructed based on a multi-agent proximal policy optimization algorithm to jointly optimize the network parameters. This invention employs multi-agent proximal policy optimization for training, achieving efficient training and distributed execution through Critic and Actor networks, enabling robust implicit cooperation without explicit communication.