2165 results about "Multiple applications" patented technology

An example operation may include one or more of ingesting data from one or more websites via one or more application programming interfaces (APIs) and storing the data within a data store of a host platform, displaying one or more prompts on a user interface of a user profile page within a software application hosted by the host platform, receiving one or more responses to the one or more prompts, generating a travel itinerary based on execution of an artificial intelligence (AI) model on the data from the one or more websites, the one or more prompts, and the one or more responses, and displaying the travel itinerary via the user interface of the user profile page of the software application.

An example operation may include one or more of ingesting profile data from one or more websites via one or more application programming interfaces (APIs) and storing the profile data within a data store, displaying one or more prompts on a user interface of a user profile page within a software application hosted by a host platform, receiving one or more responses to the one or more prompts, determining a goal for a user of the user profile page based on execution of an artificial intelligence (AI) model on the profile data, the one or more prompts, and the one or more responses to the one or more prompts, and displaying an identifier of the goal via the user interface of the user profile page of the software application.

The present invention discloses a software platform designed to facilitate seamless interconnection between users and multiple applications in an application-agnostic and centralized manner. This novel system simplifies the user experience by providing a unified interface for receiving notifications from, and interacting with, a multitude of disparate applications. Central to the platform is a robust middleware that bridges communication gaps, eliminating the need for manual data reconciliation and ensuring streamlined operations.Key components of the system include an Ingester Module that standardizes data from various sources, an Action Card Creator that transforms this data into interactive notifications, a Notification Dispatcher that routes these notifications to appropriate platforms, and a Command Translation Module that interprets user interactions for actionable insights. The platform offers interoperability across various communication mediums, employing AI and machine learning for enhanced data processing, predictive personalization, and efficient notification dispatching.Aimed at enhancing operational efficiency, the system addresses challenges faced by enterprises due to fragmented application environments, offering a scalable solution that adapts to organizational needs. By centralizing communication and workflow management, the disclosed invention provides businesses with a consolidated approach to managing multiple application interactions, driving innovation, and reducing technical complexities.

The invention relates to the technical field of detection, and discloses a polyurethane material performance detection method, which comprises the following steps: clamping a material to be detected on a loading platform, cooperatively controlling and simulating the stress effect of a target application scene, and capturing a time sequence image of the microstructure of the surface of the material by adopting a visual unit; synchronously acquiring internal strain distribution data through a distributed strain sensing array, acquiring a dynamic stress response spectrum in combination with a piezoelectric mechanical sensor, and generating a multi-modal deformation characteristic data set; performing crack density and texture orientation analysis on the time sequence image, extracting a crack growth rate and a direction consistency coefficient, and marking a strain concentration area; calculating an apparent elastic modulus attenuation rate based on the dynamic stress response spectrum, establishing a material performance degradation model, generating a dynamic flexible performance index through a weighted fusion algorithm, and performing graded evaluation on the material; material detection is upgraded from a single mechanical test to a multi-physics coupling intelligent evaluation system, and accurate and reliable detection support is provided for multiple application scenes.

The present disclosure relates to methods and devices for combining and simultaneously starting a plurality of applications. In one example method, a plurality of applications are combined to generate a combined application. A user taps an icon of the combined application to simultaneously start and run an interface of each of the plurality of applications. Start speeds, color styles, and the like of the plurality of applications are synchronized by default. In addition, the interface of each of the plurality of applications is automatically displayed in a split-screen window, and each of the plurality of applications enables data sharing space for the plurality of applications.

The present invention generally relates to a virtual reality (VR) navigation system using artificial intelligence (AI), thus enabling providing of individualized and context-aware navigation experience in VR environments. It consists of an AI-inclusive central processing unit (CPU) that receives and processes real-time data from multiple hardware sensors deployed in a wearable VR headset, e.g., inertial measurement units (IMUs), eye-tracking sensors, biometric ones. This is AI-driven CPU that dynamically changes the navigation path and sensory outputs such as visual, audio, haptic based on user input / interaction & environmental context. It consists of a hand-held controller with numerous input interfaces and a wireless communication module, enabling it to be easily linked or integrated into several VR applications.

Systems, methods, and computer program products for using artificial intelligence (AI) models, machine learning, and large language models to identify root causes of issues in a computing environment where multiple applications and computing devices operate. One or more AI models may determine adverse trends from one or more issue metrics, where an issue metric corresponds to issues occurring in a computing system. The AI models may identify issues corresponding to the adverse trends. From the identified issues, the AI models may determine root causes from the issues and the impacted area information and process information from the issues. From the issues and the impacted area information and process information from the issues the AI models may determine recommendations for rectifying the issues. The relations between the root causes, the impacted area information, the process information, and the issues may be formatted and displayed as a traversable network graph.

The present application provides a shopping guide method and system based on a large language model, and the method is applied to the technical field of artificial intelligence. The method comprises: acquiring a training shopping guide requirement and a carried content enhancement configuration (S201); on the basis of the content enhancement configuration, generating a training prompt describing the training shopping guide requirement (S202); using a large language model to be trained to perform semantic analysis on the training prompt so as to determine a plurality of application interfaces required for responding to the training shopping guide requirement (S203); on the basis of a preset database, performing screening and summarization processing on returned results of the plurality of application interfaces to obtain predicted shopping guide information (S204); on the basis of the predicted shopping guide information and the training shopping guide requirement, performing parameter adjustment on the large language model to obtain a trained large language model (S205); and, when shopping guiding is conducted, on the basis of a prompt corresponding to a shopping guide requirement inputted by a user end, guiding the trained large language model to generate and output target shopping guide information. According to the method, target shopping guide information that is more in line with a user's natural language can be provided for the user end.

The invention discloses a large-model-driven intelligent calculation method and system for a water conservancy mechanism model. According to the method, natural language input, structured conversion and intelligent optimization calculation of a scheduling target are realized by integrating a field-enhanced large language model and a water conservancy professional mechanism model. The method comprises the steps of receiving a calculation target expressed by a user in a natural language, and analyzing and converting the calculation target into a constraint condition and a target function which can be recognized by a water conservancy mechanism model; a hydrological model, a hydraulic model, a hydrodynamic model and other models are called based on a workflow engine, and reverse calculation is carried out by adopting a hybrid optimization strategy of'coarse adjustment-fine adjustment-verification '; synchronously and visually displaying the parameter change and the result convergence state in the calculation process; and outputting a calculation result including parameter adjustment logic, standard conformity analysis and multi-scheme comparison. The system comprises a natural language interaction module, a target conversion module, an intelligent calculation engine module, a visualization module and a result generation module, and supports multiple application scenes such as multi-target scheduling, emergency decision making and ecological guarantee. Compared with a traditional scheme, the method has the advantages that the model use threshold is lowered, the dispatching efficiency and calculation transparency are improved, and the method is suitable for complex hydraulic engineering calculation tasks such as reservoir dispatching, cross-basin water transfer and flood control emergency.

Methods and apparatus for application aware memory patrol scrubbing techniques. The method may be performed on a computing system including one or more memory devices and running multiple applications with associated processes. The computer system may be implemented in a multi-tenant environment, where virtual instances of physical resources provided by the system are allocated to separate tenants, such as through virtualization schemes employing virtual machines or containers. Quality of Service (QoS) scrubbing logic and novel interfaces are provided to enable memory scrubbing QoS policies to be applied at the tenant, application, and / or process level. This QoS policies may include memory ranges for which specific policies are applied, as well as bandwidth allocations for performing scrubbing operations. A pattern generator is also provided for generating scrubbing patterns based on observed or predicted memory access patterns and / or predefined patterns.

Disclosed is a training data generation system for generating training data used to train machine learning models to inspect GenAI traffic to identify security and privacy concerns related to GenAI use. The training data generation system is seeded with initial prompts. The initial prompts include benign prompts, prompt injection attacks, and uploaded files. Each initial prompt is submitted to multiple GenAI applications to obtain responses. The corresponding prompts and responses are stored in a training data repository. Variations of the initial prompts are generated using, for example, one of the GenAI applications. Each variation is submitted to each of the GenAI applications as well, and the corresponding prompts and responses are stored. Another machine learning model, regex patterns, a combination, or the like may be used to label the prompts and responses in the training data repository to generate a large training data set quickly and efficiently.

The invention discloses a micro posture recognition method based on multi-modal feature fusion and fine adjustment, and relates to the field of computer vision and action recognition. The method is characterized in that a universal cross-modal knowledge fusion framework is provided, and multi-modal features are respectively extracted through a fine tuning network by using three kinds of modal information of video, skeleton and text. Meanwhile, a video-skeleton and text-skeleton fusion module is introduced to enhance the interactivity between modals. Compared learning is adopted to align feature distribution, and model training is optimized in combination with a freezing-fine tuning strategy, so that the calculation complexity is reduced, and the recognition efficiency is improved. According to the method provided by the invention, the problem of single-mode information loss can be solved, the perception capability of tiny attitude change is enhanced, the recognition precision and robustness are improved, and the method is suitable for multiple application scenes such as behavior monitoring, human-computer interaction and safety protection.

The subject matter described herein includes methods, systems, and computer readable media for early detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping. According to one method for early detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping includes obtaining user related information, wherein the user related information includes metrics derived from a user interacting with one or more applications executing on at least one user device; generating, using the user related information and a machine learning based model, a user assessment report including a prediction value indicating a likelihood that the user has a neurodevelopmental or psychiatric (neurodevelopmental / psychiatric) disorder and a prediction confidence value computed using relative contributions of the metrics to the prediction value generated using the machine learning based model; and providing the user assessment report to a display or a data store.

While AI models, like large language models, are powerful tools with multiple applications, they can be complex to use and can require a lot of resources to operate. The disclosed systems and methods provide tools to generate customized models (or AI agents) that are configured with features like tailored knowledge, capabilities, and instructions that make them faster, more efficient, and use less computational resources. AI agents may offer several technical advantages of improved efficiency, resource use, and connectivity. This disclosure describes systems and methods to configure, evaluate, generate, and deploy the custom models that can more efficiently run specific tasks. Disclosed systems and methods are configured to, for example, receive a query to generate a custom model, generate the AI agent custom model with the information in the query, and then resolve user queries more efficiently using the custom model.

Described herein are methods and systems for a conversational assistant that may be configured for use with one or more applications. The disclosed systems and methods may be configured as an outer loop / inner loop architecture incorporating one or more large language models (LLMs) and / or RAG functionality to support dialog-based interactions.

Described herein are devices, systems, methods, and processes for energy-efficient routing in a cloud environment. The system includes a load balancer that distributes incoming traffic across multiple application instances hosted within different availability zones (AZs) based on a preference for sustainable energy sources as communicated by a client device. The client device communicates its preference for sustainable energy sources through a sustainability flag included in a service request. The load balancer steers the service request to the suitable resource based on the sustainability flag. In subsequent interactions, the client device can send a service request with a configuration, such as a sticky cookie. The configuration may allow the load balancer to consistently direct requests from the same client device to the same resource. Accordingly, the embodiments enable energy-efficient routing, client preference for sustainable energy, and consistent service, and contribute to environmental protection.

The invention relates to the field of byte code unshelling storage, in particular to a byte code storage method and device of an Android unshelling machine and computer equipment. The method comprises the following steps: monitoring all application programs in a system in real time, and carrying out pseudo-call dynamic class loading to generate byte code structure feature information; performing service module identification on the byte code structure feature information, and performing module security sensitivity quantification so as to obtain a security sensitivity quantized value of each service module; based on the security sensitive quantized value of each service module, performing byte code symmetric encryption and confusion encryption on the multiple application program service modules so as to obtain a normalized module encryption code and a confusion encryption compression code; and carrying out code-by-code independent packaging processing on the normalized module encryption code and the confusion encryption compression code, and carrying out distributed storage so as to construct a distributed encryption unit storage structure. According to the invention, complete and efficient shelling preservation is realized.

There is provided a configuration function for charging an application service provider coupled to a wireless communications network, the wireless communications network arranged to enable communication for one or more application services of the application service provider, the configuration function comprising: a receiver arranged to receive a subscription for charging notifications in respect of one or more application services of the application service provider; a processor arranged to derive charging analytics for the one or more application services from recorded data, the charging analytics including a prediction of the charging of the application service provider for use of the wireless communications network in a service area, by the one or more application services of the application service provider; and the processor further arranged to determine an automated charging trigger configuration for charging the application service provider for use of the wireless communications network based on the derived charging analytics.

Example systems and methods described herein relate to the automated generation of sample expressions. Metadata is accessed for each of a plurality of applications. The metadata includes a functional description of each application. Prompt data is provided to a generative machine learning model. The prompt data includes the metadata for each of the plurality of applications and an instruction to generate, for each of the plurality of applications, a plurality of sample expressions corresponding to user input provided to a digital assistant to invoke an action related to the application. One or more responses that were generated by the generative machine learning model based on the prompt data are processed to obtain output data including the plurality of sample expressions for each of the plurality of applications in a structured format. The output data is used to configure the digital assistant.

Implementations relate to identifying actions performed by a user while the user is interacting with multiple applications and providing a routine suggestion to the user based on the identified actions. While a user is interacting with one or more applications, the user actions are determined. The user actions are compared to one or more template routines and a template routine is selected that matches the actions of the user and provided to the user as a suggested routine. The suggested routine can be implemented by an automated assistant to perform the actions of the template by providing a corresponding command.

Systems and methods for translating a first coding language into a second coding language train a machine learning (ML) model on a first coding language specific data set relating to the first coding language, in which the ML model is trained to translate one or more code sets of the first coding language to respective one or more code sets of the second coding language; using the ML model, generate various unit test cases, in which the unit test cases run the one or more code sets of the second coding language in parallel with the one or more code sets of the first coding language; iteratively test and refine the ML model until a maturity threshold is reached; and upon reaching the maturity threshold, containerize the one or more code sets of the second coding language into one or more applications.

According to an embodiment, at least one processor of a multi-foldable electronic device, individually and / or collectively, is configured to control the electronic device to: display an execution screen of a first application on a first region, a second region and a third region of a flexible display; identify any of a second housing and a third housing being folded with respect to a first housing; display an execution screen of a first application on the second region and an execution screen of a second application on the first region and the third region, based on identifying that the second housing is folded with respect to the first housing; and display an execution screen of the first application on the first region and the second region and an execution screen of the second application on the third region, based on identifying that the third housing is folded with respect to the first housing.

The invention belongs to the field of satellite laser communication, and particularly relates to an optimal avoidance method for satellite laser communication under a sunshine or interference light source. Comprising the following steps: acquiring satellite historical position information based on a remote control telemetering receiver; establishing adaptive position prediction by adopting a UKF (Unscented Kalman Filter); determining whether a sunshine phenomenon occurs based on the predicted position information; and establishing a target optimization model of the optimal pointing path based on the minimum solar-solar evasion vector. According to the invention, calling of on-satellite computing resources can be avoided or high-precision blind tracking of the target can be realized in a remote control and telemetering unavailable stage, the dependence on the system is reduced, and the stability is improved; according to the method, the planning of a sun-sun avoidance autonomous path at a given angle can be realized, the most suitable pointing vector is found according to the shortest distance and the fastest speed, and the dependence on manpower and satellite computing resources is reduced; the time required for scanning and capturing again under the condition that the link is suddenly disconnected can be shortened by the system according to the predicted result; the method is multiple in application scenes and high in adaptability.

Systems, computer program products, and methods are described herein for determining causes of network anomalies across a distributed network. The method includes determining one or more known indicator patterns from one or more error logs for one or more applications on a network. The method also includes receiving one or more performance indicators during an execution of an application. The method further includes comparing the one or more performance indicators to one or more known indicator patterns. The method also includes determining at least one of the one or more performance indicators that is malfeasant. The method also includes determining a malfeasant component of the network based on the one or more performance indicators that is malfeasant. The method also includes determining a remediation action to be executed in response to the comparison of the one or more performance indicators to one or more known indicator patterns.

The present invention extends to methods, systems, and computer program products for optimizing resource allocation in view of predicted network traffic patterns and predicted power consumption. Network packets defining a network traffic flow can be received at a platform over time. Metrics can be derived from one or more applications executing on resources of the platform and processing data contained in the network data packets. Model training data can be formulated from the metrics. A resource adjustment model can be trained using the model training data. Executing the model can be automated to adjust resource allocation at the platform. Additional network packets defining an additional network traffic flow can be received at a platform over time. Data contained in the additional network packets can be processed using the adjusted resource allocation.

Methods, computer readable media, and apparatuses for real-time application state monitoring, white list profile instantiation, behavioral detection and automatic cyber-attack defense are presented. For example, according to one aspect, multiple application behavioral metrics are received from multiple behavioral metric collection agents. At least some of the behavioral metrics are identified based on an association with an application profile or common entity. The identified behavioral metrics are utilized to evaluate whether behavioral conditions specified by application white lists, policies and or security policies associated with the common entity have been satisfied. Responsive to evaluating that a behavioral condition has been deviated, an alert is generated and communicated to one or more response devices associated with the common entity.

An artificial intelligence (AI)-based system for detecting malware in endpoint devices using a multi-source data fusion and method thereof are disclosed. The AI-based system collects at least one of: endpoint visibility data, publicly available information, and sandbox analysis data using the multi-source data fusion. By using the collected data, the AI-based system generates one or more numerical embeddings for at least one of: one or more files and one or more applications of each endpoint device using one or more AI models. The AI-based system is configured with the one or more AI models to generate one or more dynamic directed graphs based on the generated one or more numerical embeddings to compute a maliciousness risk score. The maliciousness risk score of at least one of the: one or more files and one or more applications is used for detecting the malware in the one or more endpoint devices.

Anycast addressing is utilized to support the connection of multiple application connectors fronting an application(s) to a network element and anycast routing of network traffic destined for the application(s). When an application is indicated for onboarding in a network fabric of a tenant, a network controller allocates virtual and anycast addresses to the application. Allocation of anycast addresses is per domain name and port / protocol combination. Upon determining that the application is available, the application connector(s) advertises reachability of the application via the anycast address. The network controller orchestrates configuration of a DNS entry that resolves the application name to its virtual IP address and destination NAT rules that translate the virtual IP address to the anycast address and the anycast address to the application's private IP address. Application network traffic can thus be forwarded to the application via any application connector that advertised the anycast address.

The present disclosure involves systems, software, and computer implemented methods for integrated data privacy services. An example method includes receiving a request to initiate an aligned purpose disassociation protocol for a purpose for an object instance. A determination is made as to whether a timestamp is stored for the purpose and the object instance that indicates an earliest time that the purpose can be disassociated from the object instance. The request is accepted in response to determining that no timestamp is stored for the purpose and the object instance that is greater than the current time. A status request is sent to applications that requests a status response that indicates whether an application can disassociate the purpose from the object instance. Status responses are received from at least some of the applications. A disassociation decision for the purpose and the object instance is determined based on the received status responses.

Methods, systems, and computer-readable storage media for restoring microservice persistencies. A disruptive event potentially affecting a computing system functionality and data synchronization of one or more applications and one or more microservices is determined. Each application and microservice stores respective data records in separate data tables. A restoration of the computing system functionality is determined. A synchronization process is initiated by using a synchronization identification engine. Data record synchronization is verified across a plurality of data tables, by matching record identifiers according to a call mapping between the applications and the microservices. The data record synchronization is restored across the plurality of data tables. An access to the applications and the microservices is reactivated.