Abstract: Screen intake assemblies and related methods of fabrication and operation that improve both operational performance and structural strength. The screen intake assembly can include one or more screen portions that run substantially and continually along the length of the screen assembly to provide increase fluid intake and uniform flow. The screen intake assembly can comprise a central manifold having an exterior manifold screen portion to provide additional filtering capacity. The screen intake assembly can be used alone or in combination with internal flow modifier structures to increase fluid intake and uniformity. Internal flow modifiers can include a perforated flow modifier pipe, an internal flow modifier pipe, and/or converging flow modifiers. The screen intake assembly can include cleaning systems that operate continuously or on demand.

Abstract: A cloud computing system can be configured to generate data models. A model optimizer of the cloud computing system can provision computing resources of the cloud computing system with a data model. A dataset generator of the cloud computing system can generate a synthetic dataset for training the data model. The computing resources can train the data model using the synthetic dataset. The model optimizer can store the data model and metadata of the data model in a model storage. The cloud computing system can receive production data from a data source by a production instance of the cloud computing system using a common file system. The production data can be processed using the data model by the production instance. The computing resources, the dataset generator, and the model optimizer can be hosted by separate virtual computing instances of the cloud computing system.

Abstract: A system for performing one or more steps of a method is disclosed. The method includes receiving a first legal clause, generating, using a segmentation algorithm, a first hidden Markov chain comprising a plurality of first nodes based on the first legal clause, each of the plurality of first nodes corresponding to an element of the first legal clause, generating, using the segmentation algorithm, a second hidden Markov chain comprising a plurality of second nodes based on the second legal clause, each of the plurality of second nodes corresponding to an element of the second legal clause, comparing each of the plurality of first nodes with each of the plurality of second nodes to identify a difference for each of the plurality of first nodes, determine, based on the comparison, whether the difference for each of the plurality of first nodes exceeds a predetermined difference threshold.

Abstract: Methods and Systems for providing a change to a voice interacting with a user are described. Information indicating a change that can be made to the voice can be received. The voice can be changed based on the information.

Abstract: Methods and systems for the dynamically re-clustering of nodes in clusters to provide optimal performance and/or the most efficient use of resources through the use of machine learning models. Specifically, the methods and systems may determine a cluster that optimally performs and/or has the most efficient use of resources based on a first machine learning model. The methods and system may then retrieve available substitute nodes from other domains and/or networks that may lie outside the cluster, but may nonetheless be available to, or accessed by the cluster. The methods and systems may then generate an additional plurality of clusters using one or more of the original nodes of the originally selected clusters and/or one or more of the available substitute nodes.

Abstract: A spectrometer is provided. In one implementation, for example, a spectrometer comprises an excitation source, a focusing lens, a movable mirror, and an actuator assembly. The focusing lens is adapted to focus an incident beam from the excitation source. The actuator assembly is adapted to control the movable mirror to move a focused incident beam across a surface of the sample.

Abstract: Apparatuses, systems and methods are described herein for separately collecting and storing form contents. Different information may be collected from a user on a form. Based on determining that portions of the form request private information, an extended reality (XR) environment may be provided to the user. The user may provide private information via an input method in the XR environment. The user may provide non-private information using a different input method outside of the XR environment. The private information may be processed and stored in a different database as compared to the non-private information. The database storing private information may have a higher security standard than the database storing non-private information.

Abstract: Techniques are disclosed for improving user experience of multimedia streaming over computer networks. More specifically, techniques presented herein reduce (or eliminate) latency in playback start time for streaming digital media content resulting from digital rights management (DRM) authorizations. A streaming media client (e.g., a browser, set-top box, mobile telephone or tablet “app”) may request a “fast-expiring” license for titles the streaming media client predicts a user is likely to begin streaming. A fast-expiring license is a DRM license (and associated decryption key) which is valid for only a very limited time after being used for playback. During the validity period of such a license, the client device requests a “normal” or “regular” license to continue accessing the title after the fast-expiring license expires.

Abstract: Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.

Abstract: In some implementations, a transaction card security system may detect a transaction card in an image, and may perform image processing to identify one or more character sequences or one or more designs that appear on the transaction card. The one or more character sequences or the one or more designs may identify an institution associated with the transaction card and/or a cardholder associated with the transaction card. The transaction card security system may identify the institution or the cardholder associated with the transaction card based on the one or more character sequences or the one or more designs, and may perform one or more actions relating to security of the transaction card based on detecting the transaction card in the image and identifying the institution or the cardholder associated with the transaction card.

Abstract: Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

Abstract: System, apparatus, and methods are described for monitoring communication sessions and determining to merge two or more of the communication sessions sharing a common ground into a common communication session. A common communication channel may be determined for the common communication session. The two or more communication sessions may be merged to generate the common communication session using the common communication channel.

Abstract: An exemplary system, method and computer-accessible medium can include receiving information regarding a purchase by a person, receiving financial account information regarding a financial account associated with the person that was used to fund the purchase, generating rewards information associated with the purchase, generating an authentication token(s) based on the financial account information, sending the rewards information and the authentication token(s) over a network, and receiving a confirmation, over the network, that a rewards account associated with the person has been updated.

Abstract: Disclosed herein are system, method, and apparatus for generating labels for k-means clusters. The method includes accessing a plurality of data records from a database repository, and storing the plurality of data records into at least one of primary or secondary memory associated with at least one computer processor performing the method, along with a cluster number for each data record. All data records having a same cluster number form a cluster, and each record has been categorized or designated a cluster number out of a total K number of clusters. The method includes for each of a plurality of classification features, performing cluster-based analysis for a first cluster with respect to a single feature to generate a single feature overlap score. The method includes sorting, grouping, and generating a naming label for the first cluster based on the predetermined number of features having the lowest overlap scores.

Abstract: Systems and methods for searching data are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a sample dataset and identifying a data schema of the sample dataset. The operations may include generating a sample data vector that includes statistical metrics of the sample dataset and information based on the data schema of the sample dataset. The operations may include searching a data index comprising a plurality of stored data vectors corresponding to a plurality of reference datasets. The stored data vectors may include statistical metrics of the reference datasets and information based on corresponding data schema. The operations may include generating, based on the search and the sample data vector, one or more similarity metrics of the sample dataset to individual ones of the reference datasets.

Abstract: A computing system retrieves a credit check algorithm. The credit check algorithm utilizes one or more parameters for evaluation of a credit score of an individual. The computing system identifies a plurality of entities contributing parameters for the evaluation of the credit score of the individual. The computing system compiles the credit check algorithm into a plurality of components. Each component corresponds to a respective entity of the plurality of entities and each component generates an output unique to the respective entity. The computing system transmits each component to a respective entity of the plurality of entities. The computing system instructs each entity to share a respective output with each remaining entity. The computing system receives a credit score for the individual from each of the plurality of entities. Each credit score received from each entity is the same.

Abstract: Aspects described herein may allow for deploying third-party callback to be performed on a mobile device. For example, the mobile device may configure a third-party callback to be performed after a triggering event is detected by a remote device. The triggering event may relate to a remote device associated with the mobile device. A determination may be made as to whether the mobile device is within a pre-determined proximity of the remote device. If the two devices are in proximity and if the mobile device receives a message that indicates the triggering event has been detected, the mobile device may perform the third-party callback. In this way, a user's need to request services conveniently and accurately is facilitated.

Abstract: System, apparatus, and methods are described for monitoring communication sessions and determining to merge two or more of the communication sessions sharing a common ground into a common communication session. A common communication channel may be determined for the common communication session. The two or more communication sessions may be merged to generate the common communication session using the common communication channel.

Abstract: A playback application is configured to analyze audio frames associated with transitions between segments within a media title to identify one or more portions of extraneous audio. The playback application is configured to analyze the one or more portions of extraneous audio and then determine which of the one or more corresponding audio frames should be dropped. In doing so, the playback application can analyze a topology associated with the media title to determine whether any specific portions of extraneous audio are to be played outside of a logical ordering of audio samples set forth in the topology. These specific portions of extraneous audio are preferentially removed.

Abstract: Systems and methods are provided for training a model using machine learning. An exemplary method may include providing, by the model in a training session, an action to an environment to receive feedback from the environment. The method may also include generating, by a behavior simulator, a plurality of predicted outcomes from the environment resulting from the action. The method may further include training the model, using at least a subset of the predicted outcomes, to generate a set of candidate models. The method may include receiving actual feedback from the environment and determining whether the actual feedback matches one of the predicted outcomes in the subset. Responsive to the determination that the actual feedback matches one of the predicted outcomes in the subset, the method may include using, in a new training session, the candidate model in the set corresponding to the matched predicted outcome.