Oracle International Corporation

Kathleen Maguire Candland

Hillel Noah Cooperman

Rohit Kumar Singh

Rozita Naghshin

Garrick Li

Carmen Ximena D'Arlach

Allison Marie Wayne

Blake T Sullivan

Mohammed Samji

Duncan Richard Mills

Sherry Mead

Thao Bich Nguyen

Melody Shores

Ray Soon Waldin, Jr

Lynn Munsinger

Joyce Etsuko Ohgi

Lancy Jude Silveira

Laney Jude Silveira

Richard Herbert Miller

Techniques for generating application interfaces based on semantic-level data are described herein. Application developers may semantically define an application flow. Based on the definition, a system may identify a template of a multipage navigable interface that is compatible with the flow. If multiple templates are compatible, then a template may be recommended or automatically selected based on the semantic information provided by the developer. The system may generate, based on the template and the semantic definition, a multipage navigable interface. The template may encapsulate some design and functional elements in a manner that is independent of the semantic definition.

Semantic design system

Techniques for suggesting a filter field based on a user input are disclosed. A system trains a machine learning model by obtaining historical data including sets of user search input, including a first search term and a first value for a first filter field. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Smart filters and search

Techniques for associating a selected search result, for a current query, with a recently executed prior query are disclosed. The system receives a first query from a user and presents a first set of search results. The system receives a second query from the user, subsequent to presenting the first set of search results, and presents a second set of search results. The system determines that a user selects a particular search result from the second set of search results. The system determines an association between the first query and the second query. Responsive to determining that the user selected the particular search result and the association between the first query and the second query, the system associates the particular search result with the first query. Subsequently, the system receives a new request for execution of the first query and, in response, presents at least the particular search result.

Associating search results, for a current query, with a recently executed prior query

Techniques for suggesting filters for query terms based on previously selected query results are disclosed. Common characteristics of previously selected query results are presented as a filter. A system trains a machine learning model by obtaining historical data including query characteristics and selected query results. Based on the historical data, the system trains the machine learning model to associate the first filter field with the first search term. The system receives a first query for execution. The system applies the machine learning model to the first query to identify the first filter field as a suggestion. The system: recommends the first field for filtering a first set of search results corresponding to the first query. Responsive to receiving user input selecting a first value for the first filter field, the system filters using the first value to generate a set of filtered search results, and presents the filtered search results.

Filter recommendation based on historical search result selection

Techniques for displaying a runtime-generated dashboard to a user are disclosed. A system receives user information regarding a user accessing a system and determine a plurality of system features available to the user based on the user information. The system assigns each of the plurality of system features a rank and orders the plurality of system features based on respective assigned ranks. The system displays a dashboard comprising multiple shortcuts corresponding respectively to the plurality of system features. The shortcuts are ordered based on ranks of respective corresponding system features. Concurrently with displaying the shortcuts, the system displays a heterogeneous plurality of interface elements corresponding respectively to the plurality of system features. Each interface element visually indicates a type of interaction associated with the respective system feature. At least two of the plurality of system features are associated with different types of interactions.

Runtime-generated dashboard for ordered set of heterogenous experiences

Systems and methods for providing access to actions and information are described in the present disclosure. According to one example, an embodiment of a method is described. The method comprises analyzing a position of a mouse pointer on a graphical user interface (GUI), the mouse pointer being controlled by a user-operated mouse. Also, a glyph is displayed on the GUI, wherein the glyph is associated with an object that is displayed on the GUI. The method also includes displaying a target in the location of the glyph when the mouse pointer is positioned within a field of the glyph. Furthermore, a dialog box is opened when a mouse click is detected and when the mouse pointer is within a field of the target.

Providing access to contextual actions and information

Growing community of inventors

San Francisco, CA, United States of America

Kathleen Maguire Candland