256 results about "Software as a service" patented technology

Systems and methods for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative AI model about structured data in a SaaS environment, enabling users to interact with and manipulate data through AI-assisted interfaces. One of the systems maintains a generative AI agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative AI agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AI. The systems and methods represent advancement in AI-driven SaaS customization and data analysis.

The invention relates to methods and systems for integrating generative artificial intelligence (AI) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining AI agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows AI agents to be added as platform users. The generative AI agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of AI outputs, and management of AI resources as limited assets. This approach enhances SaaS functionality by enabling AI-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

Systems, methods, and computer program products are disclosed for running applications in a serverless environment. Running applications in a serverless environment includes configuring a serverle96ss environment by integrating a set of rules into the serverless environment, the set of rules including at least one trigger and at least one trigger-responsive action for use in conjunction with a SaaS platform; obtaining a general address for the serverless environment; using the general address for conveying code to the configured serverless environment, for combination in an immutable manner with configuration information, thereby enabling formation of an immutable image, wherein the configuration information is adapted to invoke the at least one trigger, thereby initiating the at least one trigger-responsive action; and receiving a specific address in the serverless environment for reaching an actuated instance of the immutable image for incorporation with the SaaS platform.

An innovative system for integrating generative artificial intelligence (Al) capabilities within Software as a Service (SaaS) platforms. By leveraging advanced Al techniques, seamless data synchronization, intelligent task management, and predictive workflow optimization across multiple SaaS platforms are described. Key components include an Al-driven data synchronization system, an intelligent cross-platform task manager, and an adaptive user interface. The system also features a cross-departmental health monitoring and notification mechanism, alongside an intent¬ based interaction system. This solution enhances organizational efficiency, improves decision¬ making, and provides unprecedented levels of automation and insight in managing complex SaaS ecosystems.

A system and method measuring data, from user devices, regarding user interactions with a Software-as-a-Service (SaaS) product installed in each of the user devices, using a measurement module in the processor to generate measured data. The measured data is then modeled as a graph, using a graphing application in the processor, wherein the graph includes a plurality of varying metrics, each representing different attributes of a structure of the graph. A viral effectiveness index (VEI) as a single metric summarizing core graph attributes of the graph is determined from the plurality of the varying metrics of the graph using a viral effectiveness index (VEI) module in an analytics application in the processor.

The invention relates to methods and systems for integrating generative artificial intelligence (Al) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining Al agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows Al agents to be added as platform users. The generative Al agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of Al outputs, and management of Al resources as limited assets. This approach enhances SaaS functionality by enabling Al-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

Systems and methods for integrating generative artificial intelligence (Al) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative Al model about structured data in a SaaS environment, enabling users to interact with and manipulate data through Al-assisted interfaces. One of the systems maintains a generative Al agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative Al agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AL The systems and methods represent advancement in AI-driven SaaS customization and data analysis.

Techniques for recognizing phishing URLs on Software as a Service (SaaS) platforms are disclosed. A candidate URL is received. A determination is made that the candidate URL is a SaaS hosted URL. In response to the determination, the URL is evaluated using a model trained on SaaS hosted content. A remedial action is performed in response to determining that the received URL is a phishing URL.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

In a method for controlling access to customer data of a multi-tenant software as a service (SaaS) system, a SaaS agent at a user endpoint sends a request for storage credentials to a SaaS agent facing application interface (API) of a SaaS system cloud. The storage credentials provide access to a SaaS encrypted data store of the multi-tenant SaaS system. The SaaS agent receives the storage credentials from the SaaS agent facing API, and the SaaS agent uses the storage credentials to store customer data in the SaaS encrypted data store. A customer-controlled encryption key is maintained on a customer-controlled cloud hosting a key management system (KMS). The customer-controlled cloud is in communication with the SaaS agent facing API.

Various techniques for security controls across SaaS apps are disclosed. In some embodiments, a system / method / computer program product for providing security controls across SaaS apps includes collecting configuration settings for each of a plurality of Software as a Service (SaaS) applications (apps) for a SaaS security service, wherein the configuration settings are related to security for one or more of the plurality of SaaS apps; grouping each of the configuration settings into one of a plurality of categories and one of a plurality of subcategories; and determining that a configuration setting associated with at least one of the plurality of SaaS apps is not in compliance with a rule of a security policy.

A multi-perspective user and entity behavior analytics (UEBA) system (“system”) builds and maintains interchangeable modules for predicting likelihoods of anomalous user behavior at the scope of an actor (i.e., a user or entity) of an organization within time periods. Each module comprises probability models and / or machine learning models as sub-modules that model actor behavior at various levels of granularity with respect to usage of Software-as-a-Service applications. The system generates anomalousness scores by decorrelating likelihoods output by each sub-module and uses the anomalousness scores to monitor and perform corrective action based on anomalous actor behavior to maintain security posture across the organization.

An example system includes a service provider, wherein the service provider is configured to: receive a connection request from an enterprise device via one or more communication networks, generate a route, a logical tunnel, and a first port number, instantiate, by the service provider, a service process configured to listen for network traffic at a first port associated with the first port number, store an association of the route to a logical tunnel interface for the logical tunnel with one of a plurality of virtual machines (VMs) and an association of the first port number with a source Internet protocol (IP) address obtained from the connection request, and forward, to the first port, an application request received from the enterprise at a second port associated with a second port number and via a tunnel established with the enterprise device.

Systems, methods, and computer-readable media for facilitating SaaS platform element interconnections include maintaining SaaS platform elements and maintaining a permissions manager associating the SaaS platform elements with entities. Each element is associated with an entity utilizing the SaaS platform, and the permissions manager associates a first entity with first and second SaaS platform elements. Operations further include generating a new workflow for the entity, including generating a first workflow block in the new workflow and associating it with a sub-element of the first SaaS platform element, generating a second workflow block in the new workflow and associating it with a sub-element of the second SaaS platform element, interconnecting the first and second workflow blocks, activating the new workflow, and preventing a second entity user from associating the first block with the first platform sub-element, from associating the second block with the second platform sub-element, or a combination thereof.

Systems, methods, and computer-readable media for facilitating SaaS platform element interconnections include maintaining SaaS platform elements and maintaining a permissions manager associating the SaaS platform elements with entities. Each element is associated with an entity utilizing the SaaS platform, and the permissions manager associates a first entity with first and second SaaS platform elements. Operations further include generating a new workflow for the entity, including generating a first workflow block in the new workflow and associating it with a sub-element of the first SaaS platform element, generating a second workflow block in the new workflow and associating it with a sub-element of the second SaaS platform element, interconnecting the first and second workflow blocks, activating the new workflow, and preventing a second entity user from associating the first block with the first platform sub-element, from associating the second block with the second platform sub-element, or a combination thereof.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

The present disclosure provides a method for deal management and blockchain execution is disclosed. The method utilizes a blockchain-based platform to securely store and verify data related to investing entities, including their risk tolerance, investment goals, and regulatory compliance. The system determines a subset of entities with shared interests in a specific investment, generating a set of profiles associated with each entity based on historical deal data. These profiles are used to inform investment decisions and monitor portfolio performance. The platform also verifies accreditation statuses of the investing entities through blockchain-based consensus rules, providing an immutable record of investor accreditation status. Additionally, the system provides real-time transaction records to generate accurate and up-to-date reports for investors and deal sponsors, leveraging blockchain-based data storage and verification capabilities.

The present invention provides a graphical tool to create custom multimedia workflows. The graphics tool is marketed as Experience Builder (EB). One implementation of EB is implemented as software as a service (SAAS) services featuring software for real-time collaboration on shared documents, data capturing, live video communications to support remote equipment and product repair, replacement, and maintenance services, and IT support services for products, services, and operations. Using EB enables authors to create their own visual instructions for customers and technicians. EB is a no-code workflow creation tool to develop process flows, such as, self-service guides, training guides, service bulletins, warranty process steps, and checklists. Optional generative AI is also available.

The present disclosure relates to techniques for remediating data assets stored on one more software as a service (SaaS) platforms from a centralized security enforcement platform. An integration component is configured to integrate SaaS accounts with the security enforcement platform. The security enforcement platform enables users to create remediation policies that target specified data assets stored on the SaaS accounts. In some scenarios, the automated remediation functions can be executed to perform bulk remediation on large-scale data assets while handling inheritance issues in full.

The invention belongs to the technical field of industrial internet, and particularly relates to an adaptive extension method and system for SaaS (Software as a Service) application based on the industrial internet. According to the method, intelligent resource scheduling and self-adaptive extension are realized when a SaaS application platform processes a user uploading service, the resource demand in a service period is predicted at first, the remaining resource state is monitored, and then resource scheduling is carried out on the basis of resource demand prediction, remaining completion time and resource distribution change. According to the method, resource allocation is optimized and missing resources are supplemented through distributed enhanced scheduling at the minimum total cost, and finally, resource allocation parameters are adjusted through continuous testing and evaluation, so that efficient service processing and optimal cost are ensured; through dynamic monitoring and intelligent scheduling, adaptive expansion of SaaS application resources is realized, the response speed and the resource utilization rate of the system are improved, and the operation and maintenance cost is reduced.

Systems and methods for performing fingerprint-based data loss prevention (DLP) using information obtained from a cloud-native service are provided. In one example, DLP fingerprints are obtained by a security enforcement system (e.g., a network security appliance, a secure access service edge (SASE) platform, or a security service edge (SSE)). The DLP fingerprints may be generated locally by the security enforcement system or generated remotely from the security service edge based on a set of files stored in a cloud-native service, representing, for example, infrastructure-as-a-service (IaaS) (e.g., an object storage service) or Software-as-a-service (SaaS) (e.g., a file hosting service or a productivity platform). Based at least in part on the DLP fingerprints, DLP is performed on a file by a DLP service of the security enforcement system, in which the file is at rest on an endpoint protected by the security enforcement system or in transit through the security enforcement point.

The invention provides a system for intelligent interaction of a software as a service platform. The system comprises a large language model LLM module, a voice processing module, a model context protocol MCP communication layer and a context management module. According to the system, function operation of the SaaS platform is directly driven through natural language interaction, combination of a menu or button control of a traditional graphical user interface (GUI), an LLM module and historical data of a context management module is not needed, the intention recognition accuracy is larger than or equal to 92%, and repeated correction of a user is reduced.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

The invention provides a method and a device for generating a customized report of an SAAS (Software as a Service) system based on dynamic template configuration. The method comprises the following steps: acquiring a customized visual report template for realizing business data decoupling; editing the report template based on a preset editor, and generating a template configuration file; analyzing a structured marker in the template configuration file to generate a metadata description file; querying a business database through a unified data interface according to a data requirement defined by the metadata description file, and obtaining and assembling customized business data corresponding to the tenant ID; and at least based on the customized business data, controlling a rendering process through a dynamic position calculation algorithm, and dynamically filling the data to a corresponding position of the report template to generate a customized report. According to the method, the problems of code redundancy and difficulty in maintenance of the SAAS multi-tenant report are solved, zero code configuration is realized, and report generation efficiency and tenant personalized adaptation capability are improved.

At least one aspect of the technical solutions described herein relate to a system. The system can include one or more processors coupled with memory. The system can receive a message can include an identifier of a browser extension executing on the client device and a user identifier of a software-as-a-service (SaaS) application. The system can bind a subdomain identifier with the identifier of the browser extension. The system can transmit a uniform resource identifier (URI) including the subdomain identifier to the client device. The domain name service (DNS) request can include a host identifier of the client device. The system can access a DNS log of the DNS system to identify the host identifier using the subdomain identifier. The system can bind the identifier of the browser extension and the user identifier with the host identifier of the client device.

An emerging service in the private and non-profit sectors is to electronically facilitate engagement of employees and volunteers for various causes. The instant systems and methods provide a software as a service platform that provides user engagement via machine learning and artificial intelligence.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

A generative artificial intelligence (AI) pipeline has been created that employs aspects of natural language processing (NLP) to detect intents of web API calls and then summarizes the behavior expressed by the collective of intents. The pipeline uses a lightweight language model for intent classification of URLs corresponding to API calls in a time interval. The pipeline associates the intent classifications with metadata corresponding to the URLs and feeds this into another lightweight language model that summarizes the intent classifications and metadata. The natural language summarization describes exhibited behavior that can be understood by a wider audience than security experts. The capability to detect intents of API calls occurring in network traffic increases visibility and control of user behavior, particularly in Software-as-a-Service (Saas) environments. Furthermore, the enhanced visibility of user behavior with the created pipeline recognizes new and previously unseen API calls from live network traffic at enterprise scale.

Disclosed implementations provide a system of sensing modules that can operate in conjunction with a Software as a Service (SaaS) backend to detect and alert of various threats. The sensing modules can be installed at fixed locations or can be mobile. All sensing and detection processing can be accomplished by the sensing module or by several sensing modules cooperating on a mesh network or other network communication architecture. Privacy is preserved because all raw sensor data collected for analysis is processed locally at the sensing module(s) and deleted immediately following the completion of analysis. Accordingly, raw sensor data is inaccessible outside of the sensing module(s) which can be secured through hardware and software security techniques.