A semantic document generation system is described. The semantic document is composed of document details, people and meta-data. The semantic document is self-aware of the information it contains. The semantic document's structure and terms are governed by legal, logical and party related rules. A semantic contract can be created from a semantic document generation system. The semantic document generation system receives an indication of a type of a document to be generated and plurality of terms for the document from a plurality of sources. The terms are converted into triples. A plurality of rules governing the terms of the document is applied to the triples to generate a knowledge graph and determine whether terms from the different parties are compatible. The terms are determined to be compatible in a case where the plurality of rules governing terms of the document is satisfied. If at least one set of terms is non-compatible, the system reconciles the non-compatible terms in the generated knowledge graph until all the terms are compatible, and generates the document based at least on the reconciled knowledge graph.

Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. An improved approach has been described to automatically generate a log parser by analysis of the line content of a log. In addition, an efficient approach has been described to extract key-value content from the log content.

Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. An improved approach has been described to automatically generate a log parser by analysis of the line content of a log. In addition, an efficient approach has been described to extract key-value content from the log content.

A system and method for integrating machine learning algorithms to enrich documents in a content management system. The content management system includes a content services engine, a transformation engine to execute one or more transformations on documents stored in the content management system, and a machine learning services to apply a machine learning algorithm to the documents based on the one or more transformations.

A system and method for integrating machine learning algorithms to enrich documents in a content management system. The content management system includes a content services engine, a transformation engine to execute one or more transformations on documents stored in the content management system, and a machine learning services to apply a machine learning algorithm to the documents based on the one or more transformations.

The technology disclosed relates to accessing external data in spreadsheet cells and includes spreadsheet cells that can use formulaically defined external data, allowing users to utilize many cells as if they were one cell and to simplify or expand the type of calculations possible within a cell. The disclosed technology further includes a spreadsheet application that can perform looped calculations within the spreadsheet cells without the need to use an embedded programming language. Additionally the disclosed spreadsheet application can use named variables and formulaic cell designators for cell operations in which formulaically defined non-spreadsheet cell data variables and their values can be exposed and multiple values can exist in a cell, and the user can step through and see those different values.

The technology disclosed relates to accessing external data in spreadsheet cells and includes spreadsheet cells that can use formulaically defined external data, allowing users to utilize many cells as if they were one cell and to simplify or expand the type of calculations possible within a cell. The disclosed technology further includes a spreadsheet application that can perform looped calculations within the spreadsheet cells without the need to use an embedded programming language. Additionally the disclosed spreadsheet application can use named variables and formulaic cell designators for cell operations in which formulaically defined non-spreadsheet cell data variables and their values can be exposed and multiple values can exist in a cell, and the user can step through and see those different values.

Non-limiting examples of the present disclosure describe processing that generates intelligent inferences of authoring from analysis of attributes associated with a digital file being imported in an application/service. Examples described herein are configured to work with any type of application/service including an authoring application/service. For instance, a request to import a digital file is received in an application/service. The application/service may be configured to analyze the digital file and generate authoring inferences based on an analysis of attributes of the digital file. For example, a conversion data model may be utilized to identify a file type of the digital file, analyze attributes of the identified digital file (e.g. content portions, layout, formatting, metadata, etc.) and output file data in a format that is tailored for the application/service based on authoring inferences. A converted representation of the digital file is surfaced in the application/service based on output of the file data.

Non-limiting examples of the present disclosure describe processing that generates intelligent inferences of authoring from analysis of attributes associated with a digital file being imported in an application/service. Examples described herein are configured to work with any type of application/service including an authoring application/service. For instance, a request to import a digital file is received in an application/service. The application/service may be configured to analyze the digital file and generate authoring inferences based on an analysis of attributes of the digital file. For example, a conversion data model may be utilized to identify a file type of the digital file, analyze attributes of the identified digital file (e.g. content portions, layout, formatting, metadata, etc.) and output file data in a format that is tailored for the application/service based on authoring inferences. A converted representation of the digital file is surfaced in the application/service based on output of the file data.

A web experience augmentation system predicts, during a web browsing session of a user, augmentation data that the user is likely to want to view during the web browsing session. This prediction is based on both local content preferences for the user and global content preferences. The local content preferences for the user refer to an indication of the webpages accessed during the current web browsing session of the user. The global content preferences refer to analytics for webpages on a website obtained over an extended period of time that extends prior to the web browsing session of the user. The web experience augmentation system also modifies a webpage to which the user navigates to include the predicted augmentation data.