181 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.

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.

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.

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.

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.

Methods and systems of providing a universal privileged access management solution to enable users to securely share web applications through remote browser isolation (RBI). The web applications may be hosted in a SAAS (Software As A Services) cloud or in a private network. For privileged access of web applications that do not support SSO, the universal privileged access management solution implements cloud-based software services including Remote Browser Isolation (RBI), Zero Trust Network Access (ZTNA) and password vault. For privileged access of web applications that support SSO, the universal privileged access management solution implements cloud-based software services including Remote Browser Isolation (RBI) and ID brokers. In the access authentication process, by leveraging the data loss prevention (DLP) feature of RBI, the actual access credentials for accessing web applications are not disclosed to the user for mitigating security risks.

The invention discloses an adjustment method for an SAAS platform service rule and an interpreter system. The invention relates to the technical field of a multi-tenant software as a service (Software as a Service) platform. Starting a rule definition interface; and receiving an interface edited by a user in a graphical manner or calling a template in the rule template library, and automatically defining and constructing a business rule. Meanwhile, format check and grammar verification are carried out on the service rules; and if the rule has a problem, returning error information to the user interface. A business rule passing verification is analyzed through an RETE algorithm interpreter module and converted into executable rule logic (rule program); according to the technical scheme of the invention, the tenants can quickly define and adjust the rules according to own business requirements without waiting for the support of a development team, so that the flexibility and response speed of the business are greatly improved. The tenants can flexibly define and adjust the business rules, and personalized requirements are met.

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.

Technology for applying artificial intelligence to decide when to, and / or when not to, send a consumer of a computer system a communication recommending that the computer system be revised to include a more recent version of at least one of the following: a hardware component (for example, microprocessor(s)) and / or a software component (for example, an updated version of an app). The computer system, that is subject to modernization, may be owned outright by the consumer, or it may be purchased as a service (for example, infrastructure as a service, software as a service, package of cloud services). Some embodiments focus on modernization recommendations specifically tailored to cloud orchestration software that deploys containers.

A software as a service (SaaS) platform sends, to an end user device, a message of a messaging conversation between the end user device and a first client device associated with a first user account of the SaaS platform. The message is associated with a first telephone number provisioned by the SaaS platform. The messaging conversation is associated with a messaging conversation identifier. The end user device is associated with a second telephone number. The first client device is associated with a third telephone number. a first request to place a voice call is received, via a first application programming interface (API) call from the first client device, based on the messaging conversation identifier. Responsive to receiving the first request, the voice call to the second telephone number is placed. The voice call is associated with the first telephone number provisioned by the SaaS platform.

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 transmitting data between a mobile communication device and a server. The method includes running a mobile application on the mobile communication device. The mobile application is hosted on the mobile communication device through the server as a Software as a Service (SaaS). The method further includes transmitting data associated with the mobile application between the mobile communication device and the server, in which transmission of the data between the mobile communication device and the server is monitored through the server.