560 results about "Automated method" patented technology

Automatic generation of in-store product information and navigation guidance, using Augmented Reality (AR) and a Vision-and-Language Model (VLM) and multi-modal Artificial Intelligence (AI). An automated method includes: providing to the VLM images that are captured within a retailer venue by an electronic device that is a smartphone or an AR device or smart glasses; (b) automatically feeding into the VLM those images, or pre-sliced or pre-cropped image-portions of those images that were sliced or cropped using Machine Learning that performs object boundaries detection and not product recognition; (c) invoking the VLM to generate outputs of VLM analysis of content of those fed images or image-portions. The VLM outputs can be: VLM-based product recognition, VLM-based product-related information, VLM-generated virtual shopping assistance, VLM-generated in-store navigation guidance, or other VLM-generated outputs. Based on the VLM-generated outputs, the electronic device provides real-time information about products depicted in the images, VLM-generated shopping assistance, and VLM-generated in-store navigation guidance.

The invention relates to a public data automatic classification and grading method and system, and belongs to the field of data automatic classification and grading. Comprising the following steps: constructing a classification and grading vector knowledge base; for input data to be classified and graded, similarity matching is carried out on the basis of a nearest neighbor search algorithm and the content of the classification and grading vector knowledge base, and matching information of the field to which the data belongs and the grading rule is obtained; carrying out pre-training on the large model, and then carrying out adaptive fine tuning on the pre-trained large model based on the public data classification and grading vector knowledge base; and inputting data to be classified and graded and matching information of the classification and grading vector knowledge base into the large model for automatic classification and grading, and outputting a classification and grading result. According to the method, the problem that the existing classification and classification lack an automatic method and need to depend on a large amount of manual customized services is solved, automatic public data classification and classification are realized, and the generalization and efficiency of classification and classification are improved.

The current document is directed to methods and systems that provide an emotional compass that reflects the emotional states of human beings, dynamics of the emotional states over time periods, emotional synchronization between humans and between humans and automated interactive systems, and the degree to which the emotional state of one human is influenced or affected by the emotional state of another human or by an automated interactive system. The emotional compass can be used for continuous or intermittent emotional orientation by both automated systems and human analysts. The emotional awareness provided by the emotional compass facilitates continuous or intermittent strategy selection for achieving various different types of emotion-related results.

Embodiments relate to hazard detection in autonomous and semi-autonomous systems and applications. A transformer may use sampled image and LiDAR features to extract and decode a representation of whether there is a hazard at the 3D location corresponding to each initial transformer query, the shape of the hazard, and / or its class. These detections may be provided to one or more control components of an autonomous vehicle, which may use the detections to navigate, plan, or otherwise perform one or more operations (e.g., obstacle avoidance, lane keeping, lane changing, merging, splitting, etc.). Some embodiments employ an automated approach to derive ground truth data from sensor data collected by data collection vehicle(s), such as data representing detected static scene points, navigable space boundaries, or detected hazard objects. Accordingly, hazards such as road debris and other obstacles may be detected and ground truth data may be generated for a variety of sensing tasks.

Methods and apparatus for using an artificial intelligence engine to automatically predict what network slices will be needed are described. After the network slice prediction is made network slices are automatically generated in accordance with AI machine learned prediction. This new automated approach ensures the network slices are up to date and devices assigned the network slices properly as the new devices get activated on the network. This new approach can also add elasticity to the network slices based on the devices assigned to them by changing the amount of resources allocated to slices of a particular type as predicted resource needs change.

The present disclosure relates to electronic circuit design, and more specifically, to training a neural network to serve as the reward function for optimization-based approaches to PCB design automation. Embodiments may include generating, using a processor, one or more placed designs using a genetic optimization methodology including a reward function and adjusting the one or more placed designs and the reward function during the generating. Embodiments may further include routing the one or more placed designs using an auto-router to assign a routability score label and training a neural network, using the one or more placed designs and the routability score label, to extract one or more intermediate features from the one or more placed designs. Embodiments may also include predicting a routability of the PCB design based upon, at least in part, the one or more intermediate features.

The invention discloses a process automation method and device and electronic equipment. The method comprises the following steps: acquiring a user interface image; target detection and text recognition are conducted on the user interface image through a visual recognition model, visual information corresponding to the user interface image is obtained, and the visual information comprises interface element information and text information in the user interface image; receiving user request information, and processing the user request information and the visual information through a semantic understanding model to generate an operation instruction sequence; and converting the operation instruction sequence into an operation instruction, and automatically simulating a user operation behavior according to the operation instruction. According to the method and the device, the technical problems of relatively poor interface adaptability, relatively weak user intention understanding ability and insufficient robustness in a complex scene due to the fact that an RPA system in related technologies is generally based on fixed interface coordinates or control features are solved.

Globally our environment comprises structures built to perform a meet different requirements including residential, commercial, retail, recreational and service infrastructure. Whilst, millions of tons of construction materials are deployed annually the quality control procedures in many instances have not changed to reflect today's demands. Accordingly, it would be beneficial to provide construction companies, engineering companies, infrastructure owners, regulators, etc. with means to automated testing / characterization of construction materials during at least one of its manufacture, deployment in construction and subsequent infrastructure life. It would be further beneficial for such automated methods to exploit self-contained data acquisition / logging modules allowing them to be employed with ease at the different points in the life cycle of a construction material and / or construction project.

Methods and systems for managing data processing systems that provide computer-implemented services are disclosed. A data processing system (e.g., device) may include and depend on the operation of its hardware and / or software components in order to provide the computer-implemented services. To manage the operation of a device, a device manager may obtain log data (e.g., sequences of log messages) for the components of the device that reflect the operational activity of the device and / or its components. The log data may be analyzed automatically (e.g., in real-time), using a repository of known log data sequences, to identify operational issues and / or corresponding responses to the operational issues. The responses may include compensatory and / or correctional actions for remediation of the operational issues. The automated methods may provide an efficient and cost-effective method for managing device operational issues that may reduce the likelihood of the device becoming impaired and / or interruptions in the computer-implemented services.

The invention provides a gastric cancer TNM staging automation method and system based on a large language model. The method comprises the steps that a medical data set related to gastric cancer of a patient is acquired; preprocessing the medical data set; inputting the preprocessed medical data set into a trained large language model; extracting examination conclusions in the preprocessed medical data set, splicing all diagnosis conclusions into a long text for input, and inserting special separators between different sources for distinguishing; performing feature extraction on the long text input to generate a feature vector; probability inference is carried out after suspicious remote transfer labeling in the feature vectors; carrying out conflict statement resolution and credibility judgment on the feature vectors, and then carrying out TNM stage classification; and outputting the TNM staging result of the patient. The method has the beneficial effects that full-process automatic processing from medical data set acquisition, preprocessing and feature extraction to final TNM stage classification is realized.

The invention discloses a cross-channel marketing automation method and system based on real-time behavior triggering. The method comprises the following steps: capturing a user behavior event flow in real time through a front-end acquisition unit; performing real-time mode matching on the event stream by utilizing a complex event processing engine, and generating a marketing trigger signal when a preset rule is matched; dynamically selecting an optimal channel for the user based on a real-time scoring model in response to the signal; the marketing content is sent to the selected channel through the unified execution module; and finally, tracking user feedback and updating model parameters and user preferences in real time to form closed-loop optimization. The corresponding system comprises a front-end data acquisition module, a streaming event processing module, a real-time intelligent decision-making module, a multi-channel execution module and a closed-loop optimization module which are used for implementing the steps. According to the method, millisecond-level intention perception, cost and experience optimal channel intelligent decision and data-driven adaptive optimization closed loop are realized, and the problems of delayed response, extensive channel and lack of self-adaption of traditional marketing are solved.

Embodiments relate to hazard detection in autonomous and semi-autonomous systems and applications. A transformer may use sampled image and LiDAR features to extract and decode a representation of one or more features of each point (e.g., refined height, range, driving condition, etc.) on a sampled surface (e.g., the road). These detections may be provided to one or more control components of an autonomous vehicle, which may use the detections to navigate, plan, or otherwise perform one or more operations. Some embodiments employ an automated approach to derive ground truth data from sensor data collected by data collection vehicle(s), such as data representing detected ground surface models, detected surface features, detected weather and / or surface condition labels, and / or detected per-point artifact labels. Accordingly, surface features such as ground surface heights along a predicted trajectory may be detected and ground truth data may be generated for a variety of sensing tasks.

The invention relates to the technical field of natural language processing, in particular to a browser automation method and system based on natural language processing. The invention particularly relates to a method and a system for generating an operation instruction by simplifying a webpage DOM (Document Object Model) structure and utilizing a GPT model. By monitoring and adapting to webpage structure changes in real time, the system can keep continuous effectiveness of automatic operation. Even if the page is subjected to large structure adjustment, the system can still automatically analyze and adjust the operation logic, and the intervention requirement of a user is reduced. And the circular updating and adapting module can quickly respond to webpage changes, so that the phenomenon of operation failure or interruption caused by page updating is reduced. Even if the DOM structure of the page is greatly changed, the system can quickly adjust, and successful execution of the task is ensured.

The invention discloses an automation method and system for an ADAS simulation test, and the method comprises the steps: generating a simulation test scene extraction signal value configuration file and a test case configuration file in a preset format according to a test case metadata file; generating a test case set file according to the test case configuration file; loading the test case set file in a test library DYNA4 project of the DYNA4 software; executing a simulation test based on the simulation test scene file and the test case set file in the test library DYNA4 project, and generating a simulation test result file; and extracting the signal value configuration file according to the simulation test scene, extracting the expected signal real-time numerical value and the expected signal numerical value from the simulation test result file, and judging whether the test case passes or fails according to a comparison result. According to the method, automation of the ADAS simulation test process can be achieved, setting and execution of a large number of test cases are completed in a short time, complex configuration scripts do not need to be written manually, and the operation process is simplified.

The invention provides a financial statement balance verification automation method based on a large language model. The method comprises the steps that S1, a table custom format conversion module is constructed; s2, constructing a typical sample library; s3, constructing a table verification information generation module; s4, connecting character calculators in series; step S5, generating a verification report; and S6, carrying out confidence coefficient test and dynamically updating the typical sample library. According to the method, the semantic understanding and numerical value verification accuracy of the table can be improved, efficient field adaptability is achieved, automatic report generation is achieved, user friendliness is achieved, and the enterprise management efficiency can be improved.

An automated method is provided for comparing a source database and a target database that are intended to be kept in synchronization with each other. An application processes transactions that are posted to the source database, and a synchronization process processes transactions that are posted to the target database. Change log data is chunked from a change log associated with the source database to create first chunks of change log data. Change log data is also chunked from a change log associated with the target database to create second chunks of change log data. A comparison engine compares representations of the first chunks of change log data with representations of the second chunks of change log data, wherein the second chunks of change log data correspond to the same respective first chunks of change log data that were made to the source database and kept in synchronization with the target database via the synchronization process. The source database and the target database are presumed to be in synchronization with each other via the synchronization process when the comparison indicates that the representations of the first chunks of change log data match the representations of the second chunks of change log data. The source database and the target database are presumed not to be in synchronization with each other when the comparison indicates that the representations of the first chunks of change log data do not match the representations of the second chunks of change log data. An action is performed when the comparison indicates that the representations of the first chunks of change log data do not match the representations of the second chunks of change log data. The action may be a corrective action that resynchronizes the target database with the source database, or that suspends or stops the application from processing transactions.

An automated method and system for modeling intrusion detection on an Internet of Things network preprocess received raw data packets. The raw data packets are converted to a packet capture file, which is converted to a comma separated value (CSV) file. The CSV file is filtered to create normal and abnormal CSV files. An attack is identified in the abnormal CSV file. Features associated with the attack are labeled and classified as an attack type. The abnormal and normal CSV files are merged. Redundant data is removed from the merged file, which is added to a concatenated file with other merged files. Features are extracted from the concatenated file and a machine language (ML) model is built using the extracted features. ML classifiers are determined for each merged file for the ML model to build an ensemble ML model. A dynamic ensemble of ML classifiers detects evolving attack types.

The invention provides an Android application task automation method based on a user interaction description diagram, and belongs to the technical field of automation. Comprising the steps that a first stage comprises user interaction description graph generation and aims at obtaining a user interaction description graph of an application; and outputting the user interaction description diagram of the application by taking the Android project as input. And the second stage comprises task execution path planning and adaptive interactive operation generation. According to the method, static program analysis is combined with the semantic reasoning ability of the LLM, the structured user interaction description graph is automatically constructed from the source code of the target application, the user interface elements, the interaction logic and the state jump relation are covered, and the problems of operation blindness and the like caused by lack of application prior knowledge before task execution of the LLM are solved. An initial operation instruction sequence can be generated in combination with user task requirements; through an adaptive instruction re-planning mechanism, state fault tolerance and path optimization in a task execution process are realized, and the task completion rate in a complex scene is remarkably improved.

An automated method is provided for comparing a first database table and a second database table with each other to produce a table of differences therebetween. The first database table and the second database table include one or more subsets of data that are intended to match each other. The first database table and the second database table each include multiple rows, each row having multiple columns. A first set of indicia is generated comprising indicia for a subset of the columns of each of the multiple rows of a subset of the first database table, and a second set of indicia is generated comprising indicia for a subset of the columns of each of the multiple rows of a subset of the second database table. The second set of indicia are generated without regard to an original order of the columns in the second database. The indicia in the first set of indicia are compared with the indicia in the second set of indicia on a row-by-row basis, wherein the comparing occurs without regard to an original order of the rows in the second database table. Any indicia in the first set of indicia that are not present in the second set of indicia are identified, thereby identifying a first set of mismatched indicia. Likewise, any indicia in the second set of indicia that are not present in the first set of indicia are identified, thereby identifying a second set of mismatched indicia. The table of differences is populated with the identified indicia. The second database table may be repaired by using at least one of the first set of mismatched indicia and the second set of mismatched indicia.

Systems and methods for an automated method of detecting and reporting structural defects, comprising: obtaining image data of an aircraft, wherein the image data is captured from one or more fixed locations in an environment surrounding the aircraft; isolating one or more aircraft features from the obtained image data to generate isolated aircraft feature image data that includes isolated aircraft features isolated from one or more aircraft features or non-aircraft features; comparing the isolated aircraft feature image data to stored isolated aircraft feature image data; determining, by a processor configured to execute a deep neural network, one or more anomalies in the isolated aircraft feature image data, wherein the anomaly corresponds to a possible maintenance requirement at a determined location; and comparing the determined location of the possible maintenance requirement to a maintenance standard to generate a maintenance plan including one or more steps for repairing the possible maintenance requirement.

The invention discloses a robot process automation method based on deep learning, relates to the technical field of data processing, and solves the problem that it is difficult to construct a multi-modal prediction model, an identification classification model and an anomaly detection model to analyze business data. Various constructed models are difficult to be integrated into a robot process automation program; and finally, the performance of the robot process automation process is difficult to assess. According to the method, the multi-modal prediction model, the identification and classification model and the anomaly detection model are constructed by using the deep learning algorithm and are integrated into the robot process automation program to carry out automation of the service process, so that the service data can be analyzed more accurately, and the execution efficiency of the service process is improved.

The invention discloses a saturation multi-probe instrument nuclear calculation and processing automation method, a saturation multi-probe instrument nuclear calculation and processing automation system, a saturation multi-probe instrument nuclear calculation and processing automation terminal and a saturation multi-probe instrument nuclear calculation and processing automation medium, and belongs to the technical field of nuclear logging in oil-gas exploration. Data generated by the model is extracted, converted and integrated into a simulation spectrum database, and saturation parameters of the simulation data are automatically generated into the simulation spectrum database; meanwhile, saturation actual measurement logging data are preprocessed, saturation parameter results are automatically calculated, the water-containing saturation, the gas-containing saturation and the oil-containing saturation under each depth are solved, and the whole system basically achieves the automatic processing flow; according to the method, the dependence on manual processing is reduced, the consumption of human resources is reduced, the influence of human subjective judgment on results is reduced, the consistency of the results in the oil-gas exploration field is improved, important saturation parameters for oil-gas exploration are generated in batches, and the intelligent level in the oil-gas field is improved.

The invention discloses an automatic tightening method for vehicle screws, and relates to the technical field of automatic control, and the method comprises the steps: obtaining the dynamic information of a target vehicle and the initial position information of all target screw holes; based on the dynamic information, the mobile platform is controlled to perform synchronous following motion, so that the mobile platform and the target vehicle keep a preset relative position relationship; based on the initial position information, an execution mechanism installed on the mobile platform is controlled to position a preset target screw hole, the actual position of the preset target screw hole is obtained, and the preset target screw hole is determined from all the target screw holes based on a preset screening rule; determining target positions of all target screw holes based on the actual position and initial position information of the preset target screw holes; and the execution mechanism is controlled to move to target positions corresponding to all the target screw holes, and all the target screws are tightened.

The invention discloses a decision planning system-oriented simulation test scene automatic generation method. The method comprises the steps of scene modeling: constructing a scene model by using a deep learning model; constraint generation: based on an output result of the scene model, utilizing a reinforcement learning model to generate a constraint condition; performing optimization search: based on the scene model and the constraint conditions, generating scene parameters meeting constraints by adopting a Bayesian optimization algorithm; and scene evaluation: evaluating the system performance by using the deep reinforcement learning model. According to the simulation test scene automatic generation method facing the decision planning system, manual intervention is reduced through an automatic method, and the scene generation efficiency is improved; diversified and high-coverage test scenes are generated, the scene diversity is enhanced, and the robustness of the system is comprehensively tested; constraint processing is optimized by efficiently processing complex constraint conditions, and it is ensured that a generated scene meets system requirements; and comprehensive system performance evaluation is provided, multi-dimensional evaluation is realized, and the performance of the system in different scenes is reflected.

A computer-implemented method and system standardize code patterns within enterprise software environments. A code modernization system utilizes a Large Language Model (LLM) and a specialized prompt library. The library includes pattern recognition prompts to guide the LLM in identifying specific code patterns within selected software code, potentially using enterprise-specific context. It also includes standardized solution prompts to guide the LLM in generating replacement code conforming to predefined enterprise standards for the identified patterns. The system orchestrates communication, transmitting code and relevant prompts to the LLM and receiving identified patterns and subsequently the generated standardized replacement code. This automated approach facilitates improved code quality, consistency, maintainability, and can support code translation efforts within the enterprise.

The invention discloses a method for automatically constructing a pathological image data set and training cell nucleus detection and classification based on a space transcriptome technology, and belongs to the field of image processing and artificial intelligence auxiliary pathological diagnosis. According to the method, a spatial transcriptome public data set is obtained, and a data set containing image blocks, weak supervision / semi-supervision labels and cell nucleus boundary information is automatically constructed through preprocessing, deconvolution cell type annotation and cell nucleus instance segmentation, so that the dependence on manual annotation is reduced. Furthermore, a detection and classification model is designed, a multi-scale deformable attention encoder and a decoupled detection and classification decoder are adopted, a limited deformable cross attention mechanism is introduced into the classification decoder, KL divergence classification loss is combined, and instance-level cell nucleus categories are learned from region-level proportion labels. According to the method, end-to-end automation is realized, the cell nucleus detection and classification precision and efficiency are improved, and a high-quality pre-training model basis is provided for downstream pathological analysis.

The invention provides a software project multi-dimensional data analysis and resource scheduling automation method and system, and relates to the technical field of resource scheduling, and the method comprises the steps: collecting multi-dimensional data information, constructing a multi-dimensional data analysis model, and generating a project state evaluation index; predicting a resource demand by using a deep neural network; constructing a resource scheduling optimization model based on the prediction data, and optimizing a resource allocation scheme by using a genetic algorithm; and outputting and automatically executing the optimal resource scheduling scheme. According to the invention, intelligent management of the software project is realized, the resource utilization rate is improved, and the project delay risk is reduced.

The invention relates to the technical field of automation, and discloses a discrete energy-saving test automation method and device, equipment and a storage medium. The method comprises the following steps: performing multi-source heterogeneous signal acquisition and preprocessing on a power supply aging test process of discrete energy-saving power supply aging test equipment to obtain a multi-dimensional time sequence data matrix; decomposing the multi-dimensional time sequence data matrix into a temperature subspace, a vibration subspace and an electrical parameter subspace, and performing data feature fusion to obtain a power supply aging state fusion feature vector; performing power supply health assessment based on the power supply aging state fusion feature vector to obtain a global health index and a state evolution rate index; and performing adaptive control parameter analysis on the temperature control loop and the vibration control loop under dual event triggering conditions to obtain a target control scheme. According to the invention, the characterization capability of the aging state of the power supply is enhanced, more comprehensive and accurate global health assessment is formed, the control execution frequency is reduced, and the system energy consumption is reduced.

Intent-based automation that discovers automatable tasks and / or determines task variants in data is disclosed. Task capture data may be utilized to determine task variants in task mining data. Semantic understanding of user actions by artificial intelligence (AI) / machine learning (ML) model(s), for example, may be applied to determine the intent of the user rather than only focusing on what actions the user is performing on the computing system. Application logs and semantic understanding may be used to facilitate a more accurate determination of what the user actually intends to do. Task capture for individual user flows may be performed. Once these are captured, task capture algorithms and AI / ML models are used to determine which parts of the flows are similar and / or match and which parts are unique. The path through these flows can then be followed to build a process graph that includes decision points representing the unique flows.

The invention relates to the technical field of road construction automation and intelligent control, discloses an intelligent sensing and control road laying automation method and system, and aims to solve the problem of unstable construction quality caused by discontinuous sensing data, poor equipment collaboration and lack of closed-loop feedback in the prior art. The method comprises the steps that operation parameters and material state data of paving and compacting equipment are collected in real time through a multi-mode sensing module; performing space-time alignment and fusion on the multi-source heterogeneous data, and constructing a high-precision three-dimensional digital construction model; and on the basis of a model prediction control algorithm, combining the laying quality prediction model and multi-objective optimization to generate optimal equipment control parameters. According to the scheme, intelligent sensing, self-adaptive decision making and accurate control in the construction process are achieved, the pavement flatness, the compaction uniformity and the construction efficiency are remarkably improved, manual dependence and resource waste are reduced, and the construction safety and the engineering quality traceability are enhanced.