86 results about "Software as a service" patented technology

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 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.

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.

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.

Embodiments described herein comprise an advanced Software as a Service (SaaS) platform addressing challenges including trust, governance, quality, data accuracy, and transparency, ensuring full visibility of enterprise data warehouse data pipelines. These embodiments seamlessly integrate with cloud-based and legacy data platforms, enhancing visibility through monitoring data quality, tracking metadata changes, and generating exceptions for review. Data quality management allows for custom script creation to validate pipelines and business rules. Cloud-native connectors facilitate integration with one or more cloud-based services streamlining data delivery processes. Personalized notifications aid administrators in responsive action, while data profiling capabilities identify quality issues and provide distribution insights. Actionable data intelligence for privacy, security, and governance is enabled through data classification. Automation features encompass data quality checks, metadata monitoring, profiling, and classification, orchestrated by a flexible scheduler. The architecture leverages a scalable serverless design with independent microservices, dynamically scaling based on demand.

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.

The invention provides an SaaS tenant customized service method, and relates to the technical field of software as a service system architecture.The method comprises the steps that an independent database or table space is generated through a tenant identifier, configuration parameters are converted into JSON metadata in combination with a graphical tool and stored, and lightweight customization is achieved; according to the deep customization request, an independent server or container instance is distributed for a tenant, personalized codes are deployed, and the personalized codes and standard codes are separated for running through a dynamic loading mechanism; generating a unique second-level domain name based on the tenant identifier, and routing the request to a corresponding service node through DNS analysis and reverse proxy; and the standard tenants are supported to migrate to the customized tenants, configuration and service data are synchronized to a target instance, domain name resolution and proxy configuration are updated, and seamless switching of service paths is realized.

An innovative system for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. By leveraging advanced AI techniques, seamless data synchronization, intelligent task management, and predictive workflow optimization across multiple SaaS platforms are described. Key components include an AI-driven data synchronization system, an intelligent cross-platform task manager, and an adaptive user interface.

A method for enabling self-service capabilities in Software as a Service (Saas) applications comprises hosting, by a multi-tenant platform, signup and login pages for a SaaS builder's application. The multi-tenant platform receives authentication requests from prospective users of the SaaS builder's application and transmits authentication request payload to the SaaS builder's application for verification. Upon receiving verification acknowledgement from the SaaS builder's application, a self-service orchestration layer of the multi-tenant platform orchestrates user onboarding workflows including application roles, pricing plans, and tenant management. A telemetry API layer of the multi-tenant platform collects usage data from the orchestrated workflows and stores this collected usage data in at least one database. Finally, an analytics layer of the multi-tenant platform generates product-led growth analytics reports based on the stored usage data.

Systems and methods translate and govern directives across heterogeneous platforms. Directives are normalized into a common intermediate representation (OR) or converted via a direct semantic mapping engine into target-native, machine-executable directives without normalization. The system consults a platform capability database to synthesize target-compatible scripts, profiles, or rules where native primitives are absent. Ephemeral triggers cause the atomic activation of temporary directives that supersede baseline directives for a defined scope. A generalized rollback plan executes withdrawal upon expiry, utilizing a comprehensive taxonomy of termination mechanisms, including time-based schedules, event-driven checks, and state-restoration artifacts, to atomically restore the baseline. Adapters actuate across families such as cloud control planes, operating systems, loT / OT, and SaaS platforms. Both approaches share validation and governance engines supporting audit logging, atomic rollout, and continuous feedback.

Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host customers' information in a distributed environment that is not directly accessible to the customers. SaaS applications may store data in the form of relational tables. Snapshots for SaaS applications may maintain hierarchical relationships between computing objects and tables to enable cascading recovery. A data management system (DMS) may identify the computing object hierarchy for an organization's data as hosted by a SaaS application and also may identify the APIs associated with accessing each computing object, which may each include multiple tables. Tables that are accessed via a same API may be organized as logical entities. The DMS may store the hierarchical relationship between the tables, and as the tables are stored as logical entities, the DMS may organize the tables in the backup database based on which APIs are used to access the relevant tables.

A context-based chat message data loss prevention system (“DLP system”) detects sensitive chat messages communicated via Software-as-a-Service (“SaaS”) applications. The DLP system receives chat messages via SaaS connectors and buffers the chat messages in sliding windows that correspond to context of chat messages within UIs of the SaaS application. The DLP system then filters messages in the sliding windows and classifies the filtered messages with a language model. The resulting sensitive / non-sensitive classifications by the language model thus incorporate chat context for corresponding SaaS applications.

Application-manager software authenticates a user of a client device over a channel. The authentication operation is performed using a directory service. The application-manager software presents a plurality of applications in a GUI displayed by the client device. The plurality of applications depends on the authentication, the client device, and the channel. And the plurality of applications includes a thin application and a software-as-a-service (SaaS) application. The application-manager software receives a selection as to an application from the user. If the selection is for the SaaS application, the application-manager software provisions the SaaS application. The provision includes automatically logging the user onto an account with a provider of the SaaS application using a single sign-on and connecting the user to the account so that the user can interact with the SaaS application. If the selection is for the thin application, the application manager software launches the thin application.

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.

In an embodiment, a browser extension for a browser is installed at the first compute device. A first log indicating activities tracked by the browser extension as being performed at a software as a service (SaaS) application via the browser and by the user is generated. A representation of the first log is sent to a second compute device to cause the second compute device to perform cyber attestation by comparing (1) the first log and (2) a second log (a) sent to the second compute device via a third compute device associated with the SaaS application and (b) representing activities determined by the third compute device as being performed at the SaaS application via the browser and by the user.

Policy-based genomic digital data sharing facilitates a variety of sharing scenarios, including public access, tenant-to-tenant sharing, workgroup sharing, and access by external service providers. Genomic digital data can be published to the platform and controlled by access tokens that are generated based on access policies. The policies can support conditions that are evaluated at execution time and effectively place control of access to information in hands of the owning tenant. Sharing conditions can be easily specified to support various use cases, relieving administrators from excessive access control configuration.

An interactive data object (IDO) is generated by a software-as-a-service (SaaS) management platform responsive to a first client request from a client device. A second client request identifying user-defined IDO data for the IDO is received from the client device. The user-defined IDO data is related to a subscriber entity and one or more previous services consumed by the subscriber entity via the SaaS management platform. A contained IDO is determined based on the user-defined IDO data and contained data related to one or more new services for consumption by the subscriber entity. The contained IDO is configured to model first outcomes corresponding to the one or more new services provided by one or more third-party services providers based on one or more parameters. A notification indicating that first information pertaining to the IDO and the contained IDO is provided to an end user device.

Systems and methods are disclosed for agentless monitoring of third-party applications in a software as a service (SaaS) environment. A monitoring agentless application (MAA) initiates a service instance in a cloud-based computing environment of a SaaS provider and populates the service instance with simulated resources and simulated data that emulate an authentic SaaS environment while excluding sensitive information. Access credentials are provided to a third-party application, enabling the third-party application to operate within the service instance under realistic conditions. The MAA monitors actions performed by the third-party application with respect to the simulated resources to extract behavior data, such as resource access patterns, data collection frequency, configuration changes, or network communications. The behavior data may be analyzed to detect anomalous or malicious activity, thereby enabling behavioral analysis of SaaS applications without installing agents or exposing production environments.

Systems, methods, and computer-readable media for generating a contextual tutorial for a software workspace within a Software as a Service (SaaS) platform. Systems, methods, devices, and non-transitory computer-readable media may involve at least one processor configured to: access within the SaaS platform a customized software workspace including a plurality of software components therein; analyze the customized software workspace to determine a functional context of the plurality of software components; generate, using a generative artificial intelligence (AI) model, a contextual tutorial based on the determined functional context, wherein the tutorial includes a sequential narrative explaining the function and interrelationship of each of the plurality of software components within the customized software workspace; and output for presentation the generated contextual tutorial to a SaaS platform user via a user interface.

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.

A multi-view user and entity behavior analysis (UEBA) system ("system") constructs and maintains interchangeable modules for predicting the likelihood of anomalous user behavior at the range of organized participants (i.e., users or entities) over a time period. Each module includes a probabilistic model and / or a machine learning model as a sub-module that models participant behavior at various granularity levels with respect to the use of a software as a service application. The system generates anomaly scores by decorrelating the likelihoods output by each sub-module, and uses the anomaly scores to monitor and perform corrective actions based on anomalous participant behavior to maintain a security situation across organizations.

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 a method for deploying a software as a service (SaaS) application, in particular an authentication as a service (IDaaS) application. The method includes deploying a load distribution module, provided for receiving the connections to the application, and distributing the connections within a group of application nodes, each executing an instance of the application and being provided to perform at least one connection to the application. The method also includes deploying at least one master application node, and at least one iteration of of adjusting, by a master application node, the number of application nodes in the group based on an index representative of the total load of the application nodes of the group. The invention also relates to a computer program and a system implementing such a method.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing platform 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)—and implements associated architectural features that address a number of issues relating to data sharing, security, scalability, and other concerns.

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.

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.

A voice call between a customer device and a first client device associated with a first user account of a communication services platform is established via the communication services platform. A first request to transfer the voice call with the customer device to a second client device associated with the first user account is received via an application programming interface (API) call. Responsive to receiving the first request to transfer the voice call, the voice call between the customer device and the second client device associated with the first user account is established via the communication services platform.