The present disclosure provides a processing method for presenting an indicator hierarchy and classification of data in a spreadsheet. In this method, an indicator index structure S is constructed and a spreadsheet is used to present information based on user selections. The information includes an indicator and a hierarchy of the indicator, an indicator classification description and a hierarchy of the indicator classification description, an application scenario between an indicator and an indicator classification description, and other implicit attributes. This way, indicator descriptions can be neater and more readable. Data in a two-dimensional spreadsheet can be accurately presented and the spreadsheet is more comprehensible. In addition, indicators and indicator classification descriptions can be flexibly combined to present data in a more diversified, flexible, and personalized manner.

The present disclosure provides a processing method for presenting an indicator hierarchy and classification of data in a spreadsheet. In this method, an indicator index structure S is constructed and a spreadsheet is used to present information based on user selections. The information includes an indicator and a hierarchy of the indicator, an indicator classification description and a hierarchy of the indicator classification description, an application scenario between an indicator and an indicator classification description, and other implicit attributes. This way, indicator descriptions can be neater and more readable. Data in a two-dimensional spreadsheet can be accurately presented and the spreadsheet is more comprehensible. In addition, indicators and indicator classification descriptions can be flexibly combined to present data in a more diversified, flexible, and personalized manner.

A method includes receiving a document for displaying on a display of the client device. The method includes identifying a first portion and a second portion of the document having a first level of content hierarchy, and determining a first text corresponding to the first portion and a second text corresponding to the second portion of the document. The method includes generating a first outline entry including the first text and a second outline entry including the second text, and displaying a selectable entry point on a graphical user interface. The method includes, in response to a selection of the selectable entry point, displaying an outline window, including the first outline entry and the second outline entry, and navigating to the first portion of the received document or the second portion of the document in response to a selection of the first outline entry or the second outline entry.

A method includes receiving a document for displaying on a display of the client device. The method includes identifying a first portion and a second portion of the document having a first level of content hierarchy, and determining a first text corresponding to the first portion and a second text corresponding to the second portion of the document. The method includes generating a first outline entry including the first text and a second outline entry including the second text, and displaying a selectable entry point on a graphical user interface. The method includes, in response to a selection of the selectable entry point, displaying an outline window, including the first outline entry and the second outline entry, and navigating to the first portion of the received document or the second portion of the document in response to a selection of the first outline entry or the second outline entry.

A method for generating a pre-trained language model, includes: obtaining sample files; obtaining typography structure information and text information of the sample files by parsing the sample files; obtaining a plurality of task models of a pre-trained language model; obtaining a trained pre-trained language model by jointly training the pre-trained language model and the plurality of task models according to the typography structure information and the text information; and generating a target pre-trained language model by fine-tuning the trained pre-trained language model according to the typography structure information and the text information.

Systems for generating a USPTO office action response document based on an input USPTO Office Action document. A method implementation for constructing the USPTO office action response traverses a ruletree that has been dynamically formed from user answers to successive questions based on characteristics of the USPTO Office Action document. The ruletree comprises a first set of data items that pertains to an HCI and the ruletree further comprises a second set of data items that pertain to the target USPTO office action response document. Correspondence between the set of user answers to questions and passages in the USPTO office action response document are maintained in the ruletree. The ruletree expands and contracts during user interaction. To generate the USPTO office action response document, the ruletree is traversed in a sorted order, where traversal of a node generates a passage or passages in the generated USPTO office action response document.

Systems for generating a USPTO office action response document based on an input USPTO Office Action document. A method implementation for constructing the USPTO office action response traverses a ruletree that has been dynamically formed from user answers to successive questions based on characteristics of the USPTO Office Action document. The ruletree comprises a first set of data items that pertains to an HCI and the ruletree further comprises a second set of data items that pertain to the target USPTO office action response document. Correspondence between the set of user answers to questions and passages in the USPTO office action response document are maintained in the ruletree. The ruletree expands and contracts during user interaction. To generate the USPTO office action response document, the ruletree is traversed in a sorted order, where traversal of a node generates a passage or passages in the generated USPTO office action response document.

The methods and systems may improve the development of protocol documents used for clinical trials. The methods and systems may automatically estimate the likelihood of success or failure of executing a protocol document for a clinical study using a machine learning model that leverages several hundred thousand of past protocol documents and the outcomes of the clinical studies. The methods and systems may highlight sections of the protocol document that may increase a likelihood of an unsuccessful execution of the protocol document and may provide one or more recommendations to improve the highlighted sections of the protocol document.

A device including a memory and a processor. The processor receives electronic information. The processor analyzes the electronic information. The processor displays, based on analyzing the electronic information, the electronic information on a graphical user interface. The displayed electronic information is shown as a first electronic formation, and the graphical user interface is controlled by the processor. The device receives an electronic communication and changes the displayed electronic information to a second electronic formation displayed on the graphical user interface. The change from the first electronic formation to the second electronic formation occurs instantaneously.

Techniques include obtaining a location of a trigger word located in unstructured text of a natural-language-text document; determining, based on the location, a set of words following the trigger word in the unstructured text; conducting a lattice decoding operation for the set of words to determine a clause associated with the trigger word, the operation comprising: determining a clause decoding lattice for the set of words defining one or more paths, between the trigger word and the end of clause token, through the set of words; selecting a path of the clause decoding lattice; and determining, based on the path selected, the clause including one or more words of the set of words that correspond to the path selected; and generating and storing a data model object including the clause associated with the trigger word.