The present disclosure relates to systems and methods for displaying vehicle information for an on-demand service. The method may include sending a request for on-demand service to a server. The method may further include obtaining information of a vehicle related to the request for on-demand service. The information of the vehicle may include color information of the vehicle. The method may further include generating, by a processor, a user interface based on the information of the vehicle. The user interface may include at least one user interface element corresponding to the color information of the vehicle.

The present disclosure relates to systems and methods for analyzing and extracting docket data related to a structured proceeding, for identifying docket entries associated with motions and docket entries associated with orders, and for identifying motions affected by orders. Embodiments provide for receiving docket data associated with a structured proceeding, the docket data including at least one docket entry. Embodiments also include identifying, by an automated analysis, docket entries associated with motions in the structured proceeding, and docket entries associated with orders in the structured proceeding. In embodiments, identifying the docket entries associated with orders includes identifying at least one order that includes a results-affecting decision affecting at least one motion. Embodiments further include linking, by the automated analysis, the affected at least one motion to the affecting order.

A log message classifier employs machine learning for identifying a corresponding parser for interpreting the incoming log message and for retraining a classification logic model processing the incoming log messages. Voluminous log messages generate a large amount of data, typically in a text form. Data fields are parseable from the message by a parser that knows a format of the message. The classification logic is trained by a set of messages having a known format for defining groups of messages recognizable by a corresponding parser. The classification logic is defined by a random forest that outputs a corresponding group and confidence value for each incoming message. Groups may be split to define new groups based on a recurring matching tail (latter portion) of the incoming messages. A trend of decreased confidence scores triggers a periodic retraining of the random forest, and may also generate an alert to operators.

Embodiments of the disclosure extract annotations from web pages. The annotations are combined with search results and/or advertisements to allow the user to better understand the content of the search result or advertisement landing web page. A visual snapshot of the web page is taken. Visual processing extracts information from the visual representation. The HTML, for the web page is also analyzed and various pieces of information extracted. The information from the visual processing is combined with the information extracted from the HTML. The combined information is evaluated and information for the annotations are selected. The annotations are then combined with the search results and/or advertisements.

A link-analyzing system (LAS) extracts information from a markup language (ML) document associated with a web page link. In some implementations, the information that is extracted includes at least: a) address content that is part of the link's destination address; and b) text that is associated with the link but that is not part of the destination address itself. The LAS generates feature information based on the address content and the text, and then uses a classification model to make a classification assessment for the link based on the feature information. In some implementations, the LAS can control a crawling engine based on the classification assessment. In some implementations, the LAS can revise a low-confidence classification assessment based on an examination of the classification assessments of a group of similar links described by the ML document. Other implementations use the above-described functionality to classify other parts of an ML document.

A link-analyzing system (LAS) extracts information from a markup language (ML) document associated with a web page link. In some implementations, the information that is extracted includes at least: a) address content that is part of the link's destination address; and b) text that is associated with the link but that is not part of the destination address itself. The LAS generates feature information based on the address content and the text, and then uses a classification model to make a classification assessment for the link based on the feature information. In some implementations, the LAS can control a crawling engine based on the classification assessment. In some implementations, the LAS can revise a low-confidence classification assessment based on an examination of the classification assessments of a group of similar links described by the ML document. Other implementations use the above-described functionality to classify other parts of an ML document.

Introduced here are computer programs and associated computer-implemented techniques for discovering the presence of filler words through tokenization of a transcript derived from audio content. When audio content is obtained by a media production platform, the audio content can be converted into text content as part of a speech-to-text operation. The text content can then be tokenized and labeled using a Natural Language Processing (NLP) library. Tokenizing/labeling may be performed in accordance with a series of rules associated with filler words. At a high level, these rules may examine the text content (and associated tokens/labels) to determine whether patterns, relationships, verbatim, and context indicate that a term is a filler word. Any filler words that are discovered in the text content can be identified as such so that appropriate action(s) can be taken.

Introduced here are computer programs and associated computer-implemented techniques for discovering the presence of filler words through tokenization of a transcript derived from audio content. When audio content is obtained by a media production platform, the audio content can be converted into text content as part of a speech-to-text operation. The text content can then be tokenized and labeled using a Natural Language Processing (NLP) library. Tokenizing/labeling may be performed in accordance with a series of rules associated with filler words. At a high level, these rules may examine the text content (and associated tokens/labels) to determine whether patterns, relationships, verbatim, and context indicate that a term is a filler word. Any filler words that are discovered in the text content can be identified as such so that appropriate action(s) can be taken.

The present invention relates to an in-browser editor. Specifically, embodiments of the present invention provide a system and method of editing content in a browser without having to rely on the particular in-browser editing technology, such as “contentEditable” and “innerHTML.” The system includes a parser that converts an input string representing content edits in the browser into a data structure known as an Intermediary DOM (document object model). The Intermediary DOM provides a completely accurate representation of editor content in real time and operates in tandem with the Browser DOM to render the edited content in the Intermediary DOM back into displayable content.

A computer-implemented system and method for processing messages using native data serialization/deserialization without any transformation, in a service-oriented pipeline architecture is disclosed. The method in an example embodiment that includes serializing or deserializing the request/response message directly into the format (specific on-the-wire data format or a java object) the recipient expects (either a service implementation or a service consumer or the framework), without first converting into an intermediate format. This provides an efficient mechanism for the same service implementation to be accessed by exchanging messages using different data formats.