Abstract: An envelope separator is provided with a robotic arm and a support table where the robotic arm can fit fingers between envelopes to collect groups of envelopes from an envelope feeder and then separate those groups and allow for easy packaging of the envelopes into boxes. Preferably, the robotic arm accomplishes the packaging process as well by placing the groups of envelopes into boxes and continuing to gather and place successive groups of envelopes in successive boxes.

Abstract: A method for molding a seamless rebonded foam article with an internal cavity is provided. The method may include moving a middle mold to a middle mold form position within the mold cavity to press into a first portion of rebond foam particles, and moving a top mold to a top mold form position within the mold cavity to press into the second portion of rebond foam particles. The method may further include injecting steam into the mold cavity at a middle mold fluid port interface of the middle mold such that the steam passes into the mold cavity to interact with the first and second portions of rebond foam particles via at least one middle mold flow hole in the middle mold, and evacuating the steam via a vacuum source coupled to the top mold and the bottom mold.

Abstract: Techniques are described herein for evaluating user activities on a website for detecting and alerting anomalies with the website. For example, an alerting system may determine a set of time-windows for each of a set of attribute value(s) and use a set of performance metrics and/or web session events for the time-windows from historical web sessions to detect an anomaly in a current time-window. The alerting system may determine an evaluation range based on weighted performance ranges, and alert a client indicating a performance anomaly if a performance metric at the time of evaluation are out of the evaluation range. Attribute value(s) related to a website issue can be identified based on a correlated to anomalous web session events or to an anomalous time-window.

Abstract: A drill guide for facilitating placement of a surgical staple into pre-drilled holes formed in a patient may include a support structure and a plurality of guide members coupled to the support structure. Each of the guide members may have a proximal end and a distal end disposed opposite one another along a longitudinal axis of the guide member. Each of the guide members may include a tubular portion extending from the proximal end toward the distal end of the guide member and a track portion extending from the tubular portion toward the distal end of the guide member. The tubular portion may define a through passage extending along the longitudinal axis of the guide member. The track portion may define an open channel in communication with the through passage and extending along the longitudinal axis of the guide member.

Abstract: System and methods for bone fusion are disclosed. The systems and method are directed to forming complementary surfaces on bones on either side of an interphalangeal joint and limiting the relative movement thereof so as to promote fusion of the joint.

Abstract: A method for molding a seamless rebonded foam article with an internal cavity is provided. The method may include moving a middle mold to a middle mold form position within the mold cavity to press into a first portion of rebond foam particles, and moving a top mold to a top mold form position within the mold cavity to press into the second portion of rebond foam particles. The method may further include injecting steam into the mold cavity at a middle mold fluid port interface of the middle mold such that the steam passes into the mold cavity to interact with the first and second portions of rebond foam particles via at least one middle mold flow hole in the middle mold, and evacuating the steam via a vacuum source coupled to the top mold and the bottom mold.

Abstract: A method includes receiving, with a computing device, a first client request from a first client that identifies a machine learning model and a sensor. The method includes sending, with the computing device, a call to a server to apply the identified machine learning model to a set of data from the identified sensor, in response to the first client request. The method includes receiving, with the computing device, a second client request from a second client that identifies a same machine learning model and sensor as the first client request. The method includes sending, with the computing device, response data from the identified machine learning model to both the first client and the second client without sending an additional call to the server in response to the second client request.

Abstract: Systems, methods, and corresponding computer-readable media for multi-modal hierarchical semantic search of electronic documents. Systems in accordance with the disclosure comprise search engine controllers, any desired numbers of search engine interface controllers, and persistent data stores accessible by the search engine controllers. The controllers are configured to parse searchable document data sets to extract document object data sets. Each document object data set can represent textual and/or image feature of the document. For each document object data sets the controller generates at least one enriched document object data set, and forms at least one of a document graph or index. In response to search requests, the search controller compares document graphs or indexes to identify the most similar documents.

Abstract: Techniques are described herein for evaluating user activities on a website for detecting and alerting anomalies with the website. For example, an alerting system may determine a set of time-windows for each of a set of attribute value(s) and use a set of performance metrics and/or web session events for the time-windows from historical web sessions to detect an anomaly in a current time-window. The alerting system may determine an evaluation range based on weighted performance ranges, and alert a client indicating a performance anomaly if a performance metric at the time of evaluation are out of the evaluation range. Attribute value(s) related to a website issue can be identified based on a correlated to anomalous web session events or to an anomalous time-window.

Abstract: Techniques are described herein for evaluating user activities on a website for detecting and alerting anomalies with the website. For example, an alerting system may determine a set of time-windows for each of a set of attribute value(s) and use a set of performance metrics and/or web session events for the time-windows from historical web sessions to detect an anomaly in a current time-window. The alerting system may determine an evaluation range based on weighted performance ranges, and alert a client indicating a performance anomaly if a performance metric at the time of evaluation are out of the evaluation range. Attribute value(s) related to a website issue can be identified based on a correlated to anomalous web session events or to an anomalous time-window.

Abstract: An example robot performs a scan to obtain image data of a given region. The robot performs image analysis on the image data to detect a set of undesirable objects, and generates a reference map that excludes the set of undesirable objects, where the reference map is associated with the location of the robot at the time of the scan.

Abstract: Examples of neural networks trained for multiple tasks are described herein. In some examples, a method may include determining a feature vector using a first portion of a neural network. In some examples, the neural network is trained for multiple tasks. Some examples of the method may include transmitting the feature vector to a remote device. In some examples, the remote device is to perform one of the multiple tasks using a second portion of the neural network.

Abstract: An example control system includes a memory and at least one processor to obtain image data from a given region and perform image analysis on the image data to detect a set of objects in the given region. For each object of the set, the example control system may classify each object as being one of multiple predefined classifications of object permanency, including (i) a fixed classification, (ii) a static and fixed classification, and/or (iii) a dynamic classification. The control system may generate at least a first layer of a occupancy map for the given region that depicts each detected object that is of the static and fixed classification and excluding each detected object that is either of the static and unfixed classification or of the dynamic classification.

Abstract: A method includes receiving, with a computing device, a configuration file and a client request to apply a machine learning model to a set of data from a sensor. The method includes performing, with the computing device, preprocessing on the set of data from the sensor based on the configuration file to generate preprocessed data. The method includes sending, with the computing device, a call to a model server to apply the machine learning model to the preprocessed data.

Abstract: The rack mounted weather shield is an awning. The rack mounted weather shield is configured for use with an automobile. The rack mounted weather shield attaches to a cargo rack. The rack mounted weather shield creates a weather protected space along a side of the automobile. The rack mounted weather shield is used for entering and exiting the automobile in inclement weather. The rack mounted weather shield extends and retracts. The rack mounted weather shield comprises a plurality of fans, a plurality of control circuits, and a remote control. Each of the plurality of fans creates the weather protected space on a side of the automobile. Each of the plurality of control circuits extends and retracts an individual fan selected from the plurality of fans. The remote control is a radio frequency device that controls the operation of the plurality of control circuits.

Abstract: Techniques are described herein for evaluating user activities on a website for detecting and alerting anomalies with the website. For example, an alerting system may determine a set of time-windows for each of a set of attribute value(s) and use a set of performance metrics and/or web session events for the time-windows from historical web sessions to detect an anomaly in a current time-window. The alerting system may determine an evaluation range based on weighted performance ranges, and alert a client indicating a performance anomaly if a performance metric at the time of evaluation are out of the evaluation range. Attribute value(s) related to a website issue can be identified based on a correlated to anomalous web session events or to an anomalous time-window.

Abstract: The invention generally relates to methods and systems for adding chemicals to a body of water through a skimmer basket in a strainer well associated with the body of water.

Abstract: A method includes receiving, with a computing device, a first client request from a first client that identifies a machine learning model and a sensor. The method includes sending, with the computing device, a call to a server to apply the identified machine learning model to a set of data from the identified sensor, in response to the first client request. The method includes receiving, with the computing device, a second client request from a second client that identifies a same machine learning model and sensor as the first client request. The method includes sending, with the computing device, response data from the identified machine learning model to both the first client and the second client without sending an additional call to the server in response to the second client request.

Abstract: A method includes receiving, with a computing device, a configuration file and a client request to apply a machine learning model to a set of data from a sensor. The method includes performing, with the computing device, preprocessing on the set of data from the sensor based on the configuration file to generate preprocessed data. The method includes sending, with the computing device, a call to a model server to apply the machine learning model to the preprocessed data.