Abstract: A computing system is disclosed for augmenting semantic retrieval using structured knowledge constraints. The system obtains content segments from one or more source documents and processes the content through a dual-indexing pipeline that generates vector embeddings and constructs a graph representation of inter-segment relationships. A vector index and a graph-based structure are maintained in parallel to support retrieval workflows. Upon receiving a search query, the system initiates vector-based retrieval to identify semantically similar segments and concurrently traverses the graph to identify contextually connected segments. Retrieved segments are cross-referenced using constraint resolution logic that evaluates identifier overlap, relationship proximity, and graph alignment metrics. The system adjusts relevance scores using adaptive weighting and structural validation and produces a unified ranking of content segments.

Abstract: A method for rule-based composition of user interfaces. A machine-learned rule repository is established based on previously observed combinations of UI states, UI features, and user features. A classifier classifies users into segments. Each segment includes users for which a combination of user features and UI states are defined. A first machine learning model (MLM) estimates a user segment-content preference including preferred UI content. A second MLM estimates a seen content-seen content similarity UI content preferences estimated according to prior UI content a user has seen. Based on the UI state and based on the user ID, rule-based recipes are obtained. Each rule-based recipe specifies a corresponding UI content suitable for an interaction between the user and the interface. A selected rule-based recipe is selected from the rule-based recipes. Specific UI content specified by the selected rule-based recipe is obtained, and the interface is updated with the specific UI content.

Abstract: Graph capabilities are embedded as a service (e.g., a software as a service (SAAS)) into relational databases or other databases so that graph analytics can be realized and relationships can be analyzed. A seamless graph analytics experience, using a graph analytics system, is provided to a database platform. Advantages include security and advance graph analytics can be run on data in database platforms (e.g., relational databases, kv-stores, etc.) The database platform console can be used to instruct the graph analytics to be performed on the data in the database platform. The insights and results from the graph analytics may be stored in the database platform as tables and can be joined with other data of the database platform and/or used in machine learning. A user of the database platform does not have to leave that environment to perform graph analytics on the data of the database platform.

Abstract: A method for rule-based composition of user interfaces. A machine-learned rule repository is established based on previously observed combinations of UI states, UI features, and user features. A classifier classifies users into segments. Each segment includes users for which a combination of user features and UI states are defined. A first machine learning model (MLM) estimates a user segment-content preference including preferred UI content. A second MLM estimates a seen content-seen content similarity UI content preferences estimated according to prior UI content a user has seen. Based on the UI state and based on the user ID, rule-based recipes are obtained. Each rule-based recipe specifies a corresponding UI content suitable for an interaction between the user and the interface. A selected rule-based recipe is selected from the rule-based recipes. Specific UI content specified by the selected rule-based recipe is obtained, and the interface is updated with the specific UI content.

Abstract: A method for rule-based composition of user interfaces involves obtaining a user identity (ID) of a user accessing an application using a user interface and obtaining a user interface (UI) state of the user interface. Based on the UI state and based on the user ID, a plurality of rule-based recipes are obtained. Each rule-based recipe specifies a UI content suitable for an interaction between the user and the user interface. The method further includes ranking each of the rule-based recipes of the plurality of rule-based recipes based on a likeliness that the rule-based recipe is suitable, given the UI state and the user ID, identifying, from the ranked plurality of rule-based recipes, a highest-ranked rule-based recipe, obtaining the UI content specified by the highest-ranked rule-based recipe, and updating the user interface with the UI content.

Abstract: This disclosure relates to facilitating communication between widgets of cross-platform applications. An exemplary system includes computing components configured to execute an application shell. The system is configured to cause the application shell to do the following. The application shell instantiates a cross-platform application comprising a plurality of application widgets. The application shell then determines that a first application widget of the plurality of application widgets executes instructions through an execution container of the cross-platform application. The application shell then receives a request comprising criteria from the first application widget, wherein the criteria specify an application widget to listen for communications from. The application shell then receives a communication from the second application widget and determines that the communication matches the criteria of the request by the first application widget.

Abstract: This disclosure relates to customizing deployment of an application to a user interface of a client device. An exemplary method generally includes training a model based on historical context information of a plurality of users by identifying correlations between the historical context information and a plurality of widgets and storing the correlations in the model. The method further includes receiving context information from the client device. The method further includes determining a user intent based on the context information using the model. The method further includes selecting one or more widgets to include in a custom user interface definition based, at least in part, on the user intent. The method further includes transmitting, to the user interface of the client device, the custom user interface definition.

Abstract: This disclosure relates to facilitating communication between widgets of cross-platform applications. An exemplary system includes computing components configured to execute an application shell. The system is configured to cause the application shell to do the following. The application shell instantiates a cross-platform application comprising a plurality of application widgets. The application shell then determines that a first application widget of the plurality of application widgets executes instructions through an execution container of the cross-platform application. The application shell then receives a request comprising criteria from the first application widget, wherein the criteria specify an application widget to listen for communications from. The application shell then receives a communication from the second application widget and determines that the communication matches the criteria of the request by the first application widget.

Abstract: Certain aspects of the present disclosure provide techniques for facilitating communication between widgets of cross-platform applications. An exemplary system is configured to instantiate a cross-platform application comprising a plurality of application widgets. The system is further configured to determine that a first application widget of the plurality of application widgets executes instructions through an execution container of the cross-platform application. The system is further configured to receive a request comprising criteria from the first application widget, wherein the criteria specify an application widget to listen for communications from. The system is further configured to receive a communication from the second application widget and determines that the communication matches the criteria of the request by the first application widget. The system is further configured to transmit to the first application widget an update containing data from the communication.

Abstract: This disclosure relates to customizing deployment of an application to a user interface of a client device. An exemplary method includes training a model based on historical context information of a plurality of users by identifying correlations between the historical context information and a plurality of user interface components. The method further includes receiving context information from the client device. The method further includes determining a user intent based on the context information using the model. The method further includes customizing one or more widgets by selecting one or more user interface components to include in the one or more widgets based on the user intent. The method further includes generating a custom user interface definition comprising the one or more widgets. The method further includes transmitting, to the user interface of the client device, the custom user interface definition.

Abstract: This disclosure relates to cross-platform applications that include native and non-native components on mobile devices. An exemplary method generally includes receiving a first workflow step definition including a first set of widgets to be loaded into an application shell. A mobile shell identifies a type of each widget in the first set of widgets (e.g., native or platform-agnostic) and loads each widget into the mobile shell based on the widget type. For a platform-agnostic widget, the mobile shell creates a platform-agnostic widget proxy service, which provides a runtime environment. The platform-agnostic widget may be loaded into the platform-agnostic widget proxy service and executes in the runtime provided thereby.

Abstract: This disclosure relates to customizing deployment of an application to a user interface of a client device. An exemplary method generally includes training a model based on historical context information of a plurality of users by identifying correlations between the historical context information and a plurality of widgets and storing the correlations in the model. The method further includes receiving context information from the client device. The method further includes determining a user intent based on the context information using the model. The method further includes selecting one or more widgets to include in a custom user interface definition based, at least in part, on the user intent. The method further includes transmitting, to the user interface of the client device, the custom user interface definition.

Abstract: This disclosure relates to customizing deployment of an application to a user interface of a client device. An exemplary method generally includes training a model based on historical context information of a plurality of users by identifying correlations between the historical context information and a plurality of widgets and storing the correlations in the model. The method further includes receiving context information from the client device. The method further includes determining a user intent based on the context information using the model. The method further includes selecting one or more widgets to include in a custom user interface definition based, at least in part, on the user intent. The method further includes transmitting, to the user interface of the client device, the custom user interface definition.

Abstract: This disclosure relates to cross-platform applications that include native and non-native components on mobile devices. An exemplary method generally includes receiving a first workflow step definition including a first set of widgets to be loaded into an application shell. A mobile shell identifies a type of each widget in the first set of widgets (e.g., native or platform-agnostic) and loads each widget into the mobile shell based on the widget type. For a platform-agnostic widget, the mobile shell creates a platform-agnostic widget proxy service, which provides a runtime environment. The platform-agnostic widget may be loaded into the platform-agnostic widget proxy service and executes in the runtime provided thereby.

Abstract: This disclosure relates to cross-platform applications that include native and non-native components on mobile devices. An exemplary method generally includes receiving a first workflow step definition including a first set of widgets to be loaded into an application shell. A mobile shell identifies a type of each widget in the first set of widgets (e.g., native or platform-agnostic) and loads each widget into the mobile shell based on the widget type. For a platform-agnostic widget, the mobile shell creates a platform-agnostic widget proxy service, which provides a runtime environment. The platform-agnostic widget may be loaded into the platform-agnostic widget proxy service and executes in the runtime provided thereby.

Abstract: A framework is presented that can be used to create and execute software applications that include a user interview. The framework includes run-time engines and a data repository. The run-time engines include an interview driver. The data repository includes interview instructions and model information. The interview driver generates or modifies an instantiated data model by using the interview instructions and model information to obtain information from a user. The interview instructions include flow control information, prompts, and user interface (UI) information. The flow control information specifies how to traverse a graph of the data model and can include, for example: pre-defined sequence, rules/heuristics (including idea of groups), graph-search, or combination; discover vs. explore data model element; once discovered, explore immediately vs. defer until later.

Abstract: A framework is presented that can be used to create and execute software applications that include a user interview. The framework includes run-time engines and a data repository. The run-time engines include an interview driver. The data repository includes interview instructions and model information. The interview driver generates or modifies an instantiated data model by using the interview instructions and model information to obtain information from a user. The interview instructions include flow control information, prompts, and user interface (UI) information. There are three types of prompts: AskExist, AskDetail, and AskChange. A prompt is associated with an element. A prompt is created manually or using a template. A prompt is customized based on info known about the user.

Abstract: A framework is presented that can be used to create and execute software applications that include a user interview. The framework includes run-time engines and a data repository. The run-time engines include an interview driver. The data repository includes interview instructions and model information. The interview driver generates or modifies an instantiated data model by using the interview instructions and model information to obtain information from a user. The interview instructions include flow control information, prompts, and user interface (UI) information. There are four types of UI views: Add View, Edit View, Summary View, and Detail View. The UI can have a theme.

Abstract: A framework is presented that can be used to create and execute software applications that include a user interview. The framework includes run-time engines and a data repository. The run-time engines include an interview driver. The data repository includes interview instructions and model information. The interview driver generates or modifies an instantiated data model by using the interview instructions and model information to obtain information from a user. The model information includes a meta-model, a data model, and an instantiated model. Once an instantiated model has been created, it can be used to generate an application-specific document, such as a tax form. A transformer and application logic are used to generate an instantiated application-specific model. The instantiated application-specific model and the document renderer are used to generate an application-specific document.

Abstract: A framework is presented that can be used to create and execute software applications that include a user interview. The framework includes run-time engines and a data repository. The run-time engines include an interview driver. The data repository includes interview instructions and model information. The interview driver generates or modifies an instantiated data model by using the interview instructions and model information to obtain information from a user. The interview instructions include flow control information, prompts, and user interface (UI) information. The model information includes a meta-model, a data model, and an instantiated model. Once an instantiated model has been created, it can be used to generate an application-specific document, such as a tax form. Since the application is executed based on the contents of the repository, the application can be modified by changing the contents of the repository.