43 results about "System validation" patented technology

Computerized systems and methods are described for executing personalized bundling processes. Methods include receiving user inputs specifying preferences for product bundles and utilizing a Real-Time Data Mesh (RTDM) to retrieve relevant real-time data. An Advanced Analytics and Machine Learning (AAML) Module analyzes these inputs alongside market data to generate personalized bundle recommendations. Recommendations are then displayed to the user via a Single Pane of Glass User Interface (SPoG UI) and, upon user confirmation, transferred as orders to a vendor system. Validation steps use algorithms within the AAML Module to ensure accuracy and relevance of the bundles. Real-time reports on user engagement and bundle success rates are generated. The system, accessible on multiple devices, integrates machine learning models that continually refine the bundling process based on evolving data patterns and user feedback, enhancing personalization and efficiency.

Techniques are described for validating build integrity of software products, such as applications or containers. More specifically, this disclosure describes a build integrity validation system that analyzes build artifacts resulting from a software build process to create source code assertions, and compares the assertions against the source code from which the build artifacts were produced. The build integrity validation system validates that a particular build artifact is producible by the source code to ensure that no additional code was introduced during the build process. The build integrity validation system may also reverse the analysis to validate that the source code is able to produce the build artifacts to ensure that no code was removed or modified during the build process. The build integrity validation system identifies and reports identified discrepancies between the source code and the build artifacts resulting from the software build process of the source code.

Systems and methods for generating generative AI based solution are disclosed. A system receives a request for generating the generative AI (GenAI) based solution. The system classifies the received request into solution patterns and performs actions corresponding to at least one of the solution patterns. The system extracts a metadata from the received request and the actions based on the type of GenAI based solution to be generated. Further, the system segments the extracted metadata into data segments and generates a vector representation of the data segments. The system generates the GenAI based solution corresponding to the received request based on the generated vector representation. The system validates the GenAI based solution large language model (LLM). The system continuously updates the LLM and the vector-based machine learning model with the validated GenAI based solution and user feedback. The system outputs the GenAI based solution on a user interface.

A system validates code ownership of software components identified in a build process. The system receives a pull request identifying a set of software components. The system analyzes code ownership of each software component using machine learning. The system provides features describing the software components as input to a machine learning model. The system determines based on the output of the machine learning model, whether the code ownership of the software component can be determined accurately. If the system determines that a software component identified by the pull request cannot be determined with high accuracy, the system may block the pull request or send a message indicating that the code ownership of a software component cannot be determined accurately.

An online system performs inference requests in conjunction with the model serving system to perform AI (text-based LLM or multi-modal transformer)-generated integration test variants. Instead of rigid code-specified integration tests, the LLM creates integration test variants that follow a specification. Given one or more files from the codebase of an application and a specification for integration testing, the LLM compiles an integration test including a series of actions (e.g., API calls) as runnable code and assertions about the state of the application after the actions are executed.

A system and method for data compaction with adaptive codebook statistical estimates. Training data sets determine sourceblock frequencies while a dynamic mismatch probability system continuously refines estimates based on observed patterns. Context-aware handling selects appropriate secondary encoding methods for different data types (text, binary, image, executable). Machine learning models predict optimal mismatch probabilities from extracted features. Edge-optimized training enables codebook development on resource-constrained devices with intelligent resource management. Differential updates transmit only changes between codebook versions, minimizing bandwidth usage. Federated learning enables multiple devices to contribute to shared codebooks while maintaining data privacy. A secure synchronization protocol with authentication and verification ensures codebook consistency. The distributed maintenance method provides systematic validation, conflict resolution, and optimization across device networks, enabling efficient encoding across heterogeneous systems.

In a virtual commissioning of an industrial automation system, an industrial system or process is simulated by a simulation engine (118) utilizing a digital twin (112) of the industrial system or process. A virtual data acquisition system (120) utilizes a simulation snapshot (128) to generate data communicated as a model input (140) to a virtual inference system (124) having an AI model (138) deployed on it. The virtual inference system (124) processes the model input (140) using the AI model (138) to generate a model output (142) communicated to a virtual automation controller (126). The virtual automation controller (126) processes the model output (142) using automation logic (116) running on it to generate control data (144) communicated to the simulation engine (118). A closed-loop behavior of the automation logic (116) responsive to interaction with the AI model (138) is thereby validated. Communications between the virtual system components (118, 120, 124, 126) are based on data models and / or runtime interfaces that match communications between corresponding physical system components (102, 104, 106, 108) of the industrial automation system.

This application relates to a system, method, and non-transitory computer readable medium for implementing a queuing platform. In some embodiments, a user request is received and it determined whether the user request matches predetermined criteria. Embodiments can include assigning the user request to an item queue and issuing a ticket. In further embodiments, the ticket is validated and an access token is transmitted to the user device based on the validation of the ticket. A checkout request corresponding to the at least one user request is received and validated. In some embodiments, the user request is transmitted from the item queue to a checkout engine to complete a purchase of the item based on the validation of the access token received in the checkout request.

The present disclosure relates to a system (108) and a method (500) for network congestion management. The system (108) receives a request for determining a back-off time value for one or more UEs (104) from one or more network entities (110). The system (108) validates the request and transmits an error response when the validation is unsuccessful. The system (108) retrieves a set of operational data associated with the one or more UEs (104) from a database (210) or the one or more network entities (110). The system (108) predicts the back-off time values for the requested one or more UEs (104). The back-off time value may be predicted using an Artificial Intelligence (AI) engine (214) based on a set of parameters associated with the retrieved data. The system (108) transmits the predicted back-off time value to the one or more network entities (110).

Described herein is a system for enabling a user to perform complex goals using multiple skills / applications of an intelligent assistant device. Skills may register as consumers of an action or providers of an action, and the consumer skills may be configured to invoke provider skills to perform actions. The system receives a request to perform an action from a skill along with some action data. The system validates the action data, selects another skill to perform the action, and forwards the request to the selected skill to perform the action.

Using artificial intelligence (AI)-based techniques to detect glitches in audiovisual data can improve the reliability of glitch detection and reduce the cost of system validation and / or maintenance. An AI-based method for detecting glitches in an image can include extracting one or more features from the image; providing the image and the extracted features as inputs to a model; and generating, by the model, a classification output indicating whether the image is glitched. An AI-based method for detecting glitches in an audio data segment can include generating an image including a spectrogram of the audio data segment; providing, the image as input to a model; and generating, by the model, a classification output indicating whether the audio data segment represented by the image is glitched. Records of the glitches can be generated, and the validation status of a system-under-test (SUT) can be determined based on the records.

In an example implementation according to aspects of the present disclosure, a system, method, and storage medium comprising a processor, memory, and instructions to receive a fiducial mark, wherein the fiducial mark encodes a user identity, a face descriptor, a background descriptor, and a signature block. The system decodes the fiducial mark and validates the signature block against a public key repository system. The system validates the user identity against an identity management system and validates the face descriptor against a face descriptor repository. The system validates the background descriptor against a background descriptor repository and responsive to successful validation, allows a document to be shared in an online conference.

The present disclosure relates to continuous aircraft validation to improve production build efficiency. Systems, apparatuses, and methods can provide a validation system for an aircraft. The validation system includes a validation function subsystem to automatically validate operating conditions of one or more components of the aircraft based on airflow-related signals, hydraulic-related signals, and electrical signals and associated impedances, and to present the operating conditions via a display. The airflow-related signals can be obtained from a plurality of airflow fittings, the hydraulic-related signals can be obtained from a plurality of hydraulic fittings, and the electrical signals and associated impedances can be obtained from a plurality of electrical connectors. At least two of the airflow fittings, at least two of the hydraulic fittings, and at least two of the electrical connectors can be positioned in different aircraft sections prior to assembly of additional aircraft sections.

Embodiments of the present disclosure relate to a method, an apparatus, an electronic device, and a medium for data transfer. The method includes generating, based on metadata of to-be-transferred data and a blockchain including a data transfer record, an ownership certificate of an initiator of a transfer for the to-be-transferred data. The method further includes generating a new transfer record for validation by a blockchain system, where the new transfer record includes the ownership certificate and validation information associated with a receiver of the transfer. The method further includes transferring the to-be-transferred data to the receiver in response to that the new transfer record passes validation of the blockchain system. In this way, the data transfer record may be reliably stored in the blockchain, thereby providing reliable integrity protection and data traceability for a storage system.

A system may validate execution of a video game to identify the existence of a coding error or of cheating by a video game player. The system can record action events performed by a video game within an action event log, which may be provided to a validation system that can repeat the action events identified in the action event log. By comparing a state of the video game with a state generated by the validation system responsive to repeating the action events, the system can determine whether a coding error exists or if a user is cheating (e.g., using cheat software) when playing the video game. Further, the system can use isomorphic code enabling the validation system to repeat the action events independent of the system hosting the video game. Thus, the system hosting the video game may continue to execute the video game while validation occurs.

A method includes receiving files. Deployed models analyze the files to create results with corresponding data. The results identify suspicious files. The system calculates ranked results by ranking the results according to a confidence level, and clustered groups by clustering the results based on similar data. The system calculates classes by correlating the ranked results to the clustered groups. The classes indicate accurate results and inaccurate results. The system retrains the deployed models having the class indicating inaccurate results with new training sets to create retrained models. The system validates the retrained models when the retrained models identify one or more suspicious files from the receiving. The system compares the classes of the deployed models to new classes of the retrained models. The computerized system deploys the retrained models when the new classes of the retrained models indicate accurate results greater than the classes of the deployed models.

Techniques are described for validating build integrity of software products, such as applications or containers. More specifically, this disclosure describes a build integrity validation system that analyzes build artifacts resulting from a software build process to create source code assertions, and compares the assertions against the source code from which the build artifacts were produced. The build integrity validation system validates that a particular build artifact is producible by the source code to ensure that no additional code was introduced during the build process. The build integrity validation system may also reverse the analysis to validate that the source code is able to produce the build artifacts to ensure that no code was removed or modified during the build process. The build integrity validation system identifies and reports identified discrepancies between the source code and the build artifacts resulting from the software build process of the source code.

A system may validate execution of a video game to identify the existence of a coding error or of cheating by a video game player. The system can record action events performed by a video game within an action event log, which may be provided to a validation system that can repeat the action events identified in the action event log. By comparing a state of the video game with a state generated by the validation system responsive to repeating the action events, the system can determine whether a coding error exists or if a user is cheating (e.g., using cheat software) when playing the video game. Further, the system can use isomorphic code enabling the validation system to repeat the action events independent of the system hosting the video game. Thus, the system hosting the video game may continue to execute the video game while validation occurs.

A method and system are described for verifying site modifications using a drone system. The method involves generating an initial CAD 3D model using drone-captured data, altering it to reflect planned changes, and employing augmented reality to overlay modification plans on live video after site modifications to determine compliance with the as-planned design.

A computer system receives a request from a device to join a device social network supported by an enterprise device social media cloud. The request comprises a user packet that includes a network social name, enterprise credentials of the device, and device information metadata. The computer system validates the user packet. When the validation is successful, the computer system calls a directory service to create a directory service user entry corresponding to the device in the directory service. When the creation of the directory service user entry is successful, the computer system creates a device social network user corresponding to the device in the enterprise device social media cloud.

Methods, systems, and computer programs are presented for protecting restricted actions on encryption keys that control the management of data stored by a service provider. In some implementations, a system of the service provider receives a request to generate a data encryption policy (DEP) for data stored by the system of the service provider for a customer, the request including a reference to a customer key and an availability key. The customer key and the availability key are root keys for encrypting a data encryption key. The data encryption key is used to encrypt the data stored by the service provider for the customer. Further, destructive changes to the availability key require receiving an approval from an account of the service provider. The system of the service provider validates the DEP. The system of the service provider stores the DEP based on the validation.

This document describes apparatuses, systems, and techniques directed to a modular system validation platform for computing devices. The modular system validation platform includes an interface board for interfacing a host with a peripheral. The interface board includes an apparatus identifier, a first connector configured to couple to the host, and a second connector configured to couple to the peripheral. The interface board comprises interface circuitry that can be reconfigured to enable different peripherals to operate with the host using the same interface board. The interface circuitry enables the interoperability between the host and the peripheral by distributing power from the host to the peripheral and facilitating communications between the host and the peripheral. By using the reconfigurable interface board to test and troubleshoot the interoperability of the processor and the peripheral, resources, time and costs spent during the design and testing phases of computing devices may be minimized.

Using artificial intelligence (AI)-based techniques to detect glitches in audiovisual data can improve the reliability of glitch detection and reduce the cost of system validation and / or maintenance. An AI-based method for detecting glitches in an image can include extracting one or more features from the image; providing the image and the extracted features as inputs to a model; and generating, by the model, a classification output indicating whether the image is glitched. An AI-based method for detecting glitches in an audio data segment can include generating an image including a spectrogram of the audio data segment; providing, the image as input to a model; and generating, by the model, a classification output indicating whether the audio data segment represented by the image is glitched. Records of the glitches can be generated, and the validation status of a system-under-test (SUT) can be determined based on the records.

Systems and devices can include an error injection register comprising error injection parameter information. The systems and devices can also include error injection logic circuit to read error injection parameter information from the error injection register, and inject an error into a flow control unit (Flit); and protocol stack circuitry to transmit the Flit comprising the error on a multilane link. The injected error can be detected by a receiver and used to test and characterize various aspects of a link, such as bit error rate, error correcting code, cyclic redundancy check, replay capabilities, error logging, and other characteristics of the link.

Methods, systems, and computer programs are presented for protecting restricted actions on encryption keys that control the management of data stored by a service provider. In some implementations, a system of the service provider receives a request to generate a data encryption policy (DEP) for data stored by the system of the service provider for a customer, the request including a reference to a customer key and an availability key. The customer key and the availability key are root keys for encrypting a data encryption key. The data encryption key is used to encrypt the data stored by the service provider for the customer. Further, destructive changes to the availability key require receiving an approval from an account of the service provider. The system of the service provider validates the DEP. The system of the service provider stores the DEP based on the validation.

A requirements to test (R2T) system is implemented, which provides an automated system by which a user interface (UI)-test automation script package is generated and the generated test scripts therein are executed against software. A visualized workflow is translated into some machine-consumable formatted file. The translated workflow is utilized by an artificial intelligence driven automated R2T engine to discover paths through the workflow, a series of executable steps for the paths that detail how the software will be used, and ultimately test scripts that are generated using pre-defined validation templates. An automation platform executes the test scripts through the software associated with the workflow, which automatically captures evidence of the executed test scripts to fulfill computer system validation requirements. The R2T system provides an automated solution for test script creation and system validation to expedite the validation process and thereby streamline a software's time to market.

A computer-implemented method for validating supplier data is provided. The method includes accessing a spend management interface of a spend management system. The spend management interface may receive supplier data from a supplier computer via user interface widgets of the spend management interface. A country identifier is received from the supplier computer via a country widget and a currency identifier is received via a currency widget of the user interface widgets. One or more parameters associated with the country identifier and currency identifier are identified within a validation database of the spend management system and customized user interface widgets are generated based on the one or more parameters. The spend management interface is electronically updated to present the customized user interface widgets to the supplier computer. Supplier data is received from the supplier computer via the customized user interface widgets and the supplier data is programmatically validated.

Various embodiments set forth a method comprising validating, by a first computing system in a secured computing environment, a demand event generated by an operator, where the operator is authenticated to generate demand events within the secured computing environment and the demand event corresponds to a set of endpoints operating outside the secured computing environment, generating, by the first computing system, an authorization permit associated with the demand event, and sending, from the first computing system to a second computing system outside of the secured computing environment, (i) an indicator of the demand event, and (ii) the authorization permit, where the demand event is usable by the second computing system to generate a demand event command for the set of endpoints, and the authorization permit is usable by the set of endpoints to validate the demand event command.

A method for a computer aiding access to a service by a device provided with data acquirer and communicator. The service being accessible only subject to at least one condition. After having obtained identification data of a user, from the data acquirer and from an identification document, the identification data comprising at least one photograph of the user and a characteristic of the user, are transmitted to a validation system. In response, a validation confirming the validity of the identification document is received. A digital certificate comprising a photograph of the user and the characteristic obtained is then created and stored. The digital certificate configured to be used to obtain the characteristic and the photograph. The photograph is compared to a real face to validate the characteristic. The service being accessed in response to the validation of the characteristic.