Patent classifications
G06F40/183
Knowledge engine auto-generation of guided flow experience
Certain aspects of the present disclosure provide techniques for generating a user experience for a software program product based on a knowledge engine. Techniques for generating the user experience include a UI builder tool providing a set of tabular UI views and receiving in each tabular UI view corresponding input data for generating a calculation graph, a completeness graph, and a client UI view. Based on the input data, the UI builder tool and knowledge engine can generate a set of artifact files. The knowledge engine can generate and/or execute the calculation and completeness graphs as defined in the corresponding artifact files. The UI builder tool can generate an instance of the client UI view. With the generated calculation graph(s), completeness graph(s), and an instance of the client UI view, the user experience can be provided to a computing device.
Knowledge engine auto-generation of guided flow experience
Certain aspects of the present disclosure provide techniques for generating a user experience for a software program product based on a knowledge engine. Techniques for generating the user experience include a UI builder tool providing a set of tabular UI views and receiving in each tabular UI view corresponding input data for generating a calculation graph, a completeness graph, and a client UI view. Based on the input data, the UI builder tool and knowledge engine can generate a set of artifact files. The knowledge engine can generate and/or execute the calculation and completeness graphs as defined in the corresponding artifact files. The UI builder tool can generate an instance of the client UI view. With the generated calculation graph(s), completeness graph(s), and an instance of the client UI view, the user experience can be provided to a computing device.
Systems and methods for displaying representative samples of tabular data
The present disclosure generally relates to systems and methods that efficiently display tabular data (e.g., a large data set of a million or more rows of data with multiple data fields). More particularly, the present disclosure relates to systems and methods that compress the tabular data to a representative data set that maintains the data density and data variation of the original tabular data, and that display the representative data set with respect to clusters formed.
Systems and methods for displaying representative samples of tabular data
The present disclosure generally relates to systems and methods that efficiently display tabular data (e.g., a large data set of a million or more rows of data with multiple data fields). More particularly, the present disclosure relates to systems and methods that compress the tabular data to a representative data set that maintains the data density and data variation of the original tabular data, and that display the representative data set with respect to clusters formed.
Linguistically-driven automated text formatting
Systems and techniques for linguistically-driven automated text formatting are described herein. Data representing the linguistic structure of input text may be received from Natural Language Processing (NLP) Services, including but not limited to constituents, dependencies, and coreference relationships. A text model of the input text may be built using the linguistic components and relationships. Cascade rules may be applied to the text model to generate a cascaded text data structure. Cascaded data may be displayed on a range of media, including a phone, tablet, laptop, monitor, VR/AR devices. Cascaded data may be presented in dual screen formats to promote more accurate and efficient reading comprehension, greater ease in teaching native and foreign language grammatical structures, and tools for remediation of reading-related disabilities.
Linguistically-driven automated text formatting
Systems and techniques for linguistically-driven automated text formatting are described herein. Data representing the linguistic structure of input text may be received from Natural Language Processing (NLP) Services, including but not limited to constituents, dependencies, and coreference relationships. A text model of the input text may be built using the linguistic components and relationships. Cascade rules may be applied to the text model to generate a cascaded text data structure. Cascaded data may be displayed on a range of media, including a phone, tablet, laptop, monitor, VR/AR devices. Cascaded data may be presented in dual screen formats to promote more accurate and efficient reading comprehension, greater ease in teaching native and foreign language grammatical structures, and tools for remediation of reading-related disabilities.
Knowledge engine auto-generation of guided flow experience
Certain aspects of the present disclosure provide techniques for generating a user experience for a software program product based on a knowledge engine. Techniques for generating the user experience include a UI builder tool providing a set of tabular UI views and receiving in each tabular UI view corresponding input data for generating a calculation graph, a completeness graph, and a client UI view. Based on the input data, the UI builder tool and knowledge engine can generate a set of artifact files. The knowledge engine can generate and/or execute the calculation and completeness graphs as defined in the corresponding artifact files. The UI builder tool can generate an instance of the client UI view. With the generated calculation graph(s), completeness graph(s), and an instance of the client UI view, the user experience can be provided to a computing device.
Knowledge engine auto-generation of guided flow experience
Certain aspects of the present disclosure provide techniques for generating a user experience for a software program product based on a knowledge engine. Techniques for generating the user experience include a UI builder tool providing a set of tabular UI views and receiving in each tabular UI view corresponding input data for generating a calculation graph, a completeness graph, and a client UI view. Based on the input data, the UI builder tool and knowledge engine can generate a set of artifact files. The knowledge engine can generate and/or execute the calculation and completeness graphs as defined in the corresponding artifact files. The UI builder tool can generate an instance of the client UI view. With the generated calculation graph(s), completeness graph(s), and an instance of the client UI view, the user experience can be provided to a computing device.
COMPUTER DATA SYSTEM CURRENT ROW POSITION QUERY LANGUAGE CONSTRUCT AND ARRAY PROCESSING QUERY LANGUAGE CONSTRUCTS
Described are methods, systems and computer readable media for providing a current row position query language construct and array processing query language constructs and associated processing.
COMPUTER DATA SYSTEM CURRENT ROW POSITION QUERY LANGUAGE CONSTRUCT AND ARRAY PROCESSING QUERY LANGUAGE CONSTRUCTS
Described are methods, systems and computer readable media for providing a current row position query language construct and array processing query language constructs and associated processing.