Abstract: Techniques described herein include providing payment and access infrastructure for an array of transportation services, such as a set of transit or mobility services that might be provided by one or more governments, public agencies, and/or other public entities. In one example, this disclosure describes a method that includes enabling, by a computing system, a user to register for mobility benefits using a verification system, wherein the mobility benefits are provided by a provider entity; receiving, by the computing system and from the verification system, information about the user; establishing, by the computing system, an account linked to the provider entity; detecting, by the computing system, a request to access mobility benefits; allocating, by the computing system, funds from the account to pay for the mobility benefits.

Abstract: A system comprises at least one computer device in communication with one or more server computers. The system converts unstructured text into usable order details, and uses instant messaging to notify recipients of the order details. The notices include electronic web links that launch a web interface that generates an editable, pre-populated electronic order ticket based on the order details. Submission of the electronic order ticket launches an auction during which the recipients may respond with offers to fulfill to the electronic order ticket. If after the auction more than one response is needed to fill the electronic order ticket, the system automatically allocates how and in which order the responses will be used to fill the electronic order ticket. If the electronic order ticket is not completely filled after the auction, the electronic order ticket is submitted to one or more additional exchanges for further filling.

Abstract: A system may include a computer readable medium and a processor communicatively coupled to the computer readable medium. The processor may be configured to: obtain a graphical image file, the graphical image file including an image, wherein the image includes a portion including textual characters, wherein each textual character of the textual characters is formed of line segments; and convert the graphical image file to at least one file including hardware directives that when executed cause a recreation of the image of the graphical image file to be drawn, wherein a size of the at least one file is smaller than the graphical image file.

Abstract: An occlusion clip comprising: (a) a spring; (b) a first runner including a first open top and a first open end adjacent the first open top, the first runner including a first interior camming surface partially delineating a first interior cavity that is open by way of the first open top and the first open end; (c) a second runner including a second open top and a second open end adjacent the second open top, the second runner including a second interior camming surface partially delineating a second interior cavity that is open by way of the second open top and the second open end, where the spring is configured to be coupled to the first runner and the second runner, where the first interior cavity is configured to receive a first portion of the spring, where the second interior cavity is configured to receive a second portion of the spring, where the first camming surface is configured to engage a first cam of the spring, and where the second camming surface is configured to engage a second cam of the sprin

Abstract: A computer-implemented method is disclosed that includes operations of receiving document to be classified, performing pre-processing operations on the document resulting in generation of a tokenized document, performing word embedding operations on the tokenized document resulting in generation of a vectorized document, performing text similarity operations on the vectorized document and each of one or more vectorized topics resulting in a set of one or more similarity scores, wherein a first similarity score indicates a level of similarity between the vectorized document and a first vectorized topic, and wherein each vectorized topic represents one of a predetermined set of topics and classifying the document into one of the predetermined set of topics based on the set of one or more similarity scores. Performing the word embedding operations includes mapping each token of the remaining subset to a multi-dimensional vector, with each multi-dimensional vector representing a semantic meaning of a token.