Abstract: Disclosed herein are systems and methods for identifying and transmitting electronic requests to a suitable service provider. In one method, a server receives, from a computing device, a request to verify data via at least one service provider; executes a first computer model to identify a likelihood of verification success of the data; responsive to the likelihood of verification success satisfying a threshold, executes a second computer model to determine a service provider to transmit the request based on a first attribute associated with the data, a type of verification associated with the request, and a second attribute associated with the set of service providers and an impact value associated with each service provider; and responsive to determining the service provider from the set of service providers, transmitting, by the processor via an application programming interface, the data to the service provider.

Abstract: A method includes obtaining a transcript of multiple dialog acts during a multi-party communication session and extracting, from the transcript, a plurality of extractive action items each associated with a task expected to be completed within a timeframe to begin after the multi-party communication session ends. The method also includes generating, using an abstractive action item identification model configured to receive the extractive action items extracted from the transcript, one or more abstractive action items. Each abstractive action item is associated with a respective group of one or more of the extractive action items that are associated with a same respective task. For each of the one or more abstractive action items, the method also includes presenting, in one or more graphical user interfaces, information related to the corresponding abstractive action item.

Abstract: A system and method are disclosed for a warehouse management system having a task database and a server comprising a processor and memory. The system groups one or more tasks from the task database into one or more groups, estimate the latest feasible start time for each group of the one or more groups and assigns a priority for the one or more tasks in each group based, at least in part, on the estimated latest feasible start time for each group.

Abstract: A system and method are disclosed for displaying a visualization of tasks to be performed across one or more time intervals. Embodiments include receiving two or more tasks from a task database comprising one or more attributes in a table format, the table having two or more rows and two or more columns, adding a column to each of the two or more tasks comprising the volume of tasks to be cumulatively performed during a time interval and adding a column to each of the two or more tasks comprising the cumulative volume due during the time interval. Embodiments also include displaying, on a graphical user interface, a visualization of the cumulative volume due and the cumulative planned curve representing the volume of tasks to be performed across one or more time intervals.

Abstract: Automated and dynamic client and product/account onboarding assembly line for a firm dynamically configures a product/account type in any business line from any user/application channel of the firm. The client and product/account onboarding assembly line comprises changeable framework that dynamically configures each step according to the particular requirements of the product/account of the client. The assembly line can comprise two dynamic stages that together build any product/account type and test beds that automatically detect and discard any incorrectly assembled outputs.

Abstract: Various embodiments of the present disclosure provide a casino patron engagement system that facilitates engagement among casino employees and casino patrons to engender patron loyalty.

Abstract: A method of processing experience-content data within natural language narratives comprises the steps of: capturing experience-content data from of one or more sources of information, analyzing the captured experience-content data so as to identify narrative structures, and linking each of the identified narrative structures to an actant category among a set of predetermined actant categories constituting an actantial scheme. The capturing step implements a conversational agent that is programmed to capture additional experience-content data, until all actant categories of the actantial scheme are filled with captured experience-content data.

Abstract: A computer system for channel incrementality, including tracking, using first data associated with at least one user device, at least one characteristic of a user, and tracking, using second data associated with the at least one user device, at least one first interaction with at least two tracked communications. The system further generates weight data for at least two channels which weighs an impact that each channel has on the user's tendency to complete a transaction. The system receives information on a completed transaction and apportions the transaction based on the weight data.

Abstract: Methods, systems, and devices are described. A user journey may be initiated by a user device, and a trace may be generated for the user journey. The trace may include one or more spans that each correspond to at least one service of a set of services that provide functionality to the user journey (e.g., as components of an application). Based on the generated trace including the one or more spans, both diagnostic information and user-specific transaction information may be collected for the user journey. For example, the diagnostic information may include information associated with a service (e.g., how long the service took to complete) and the user-specific transaction information may include information related to the user, such as a location of the user, a type of device used for a transaction, among other examples. The diagnostic information and user-specific transaction information may be stored and processed by a system.

Abstract: This application relates to apparatus and methods for generating and implementing a machine learning model in electronic delivery systems to determine delivery routes and times. In some examples, a computing device generates a machine learning model comprising a plurality of indexed binary trees. Each indexed binary tree determines either a first value, or a second value, based on comparing an input to a condition value. The machine learning model can generate prediction values based on the determined values of all indexed binary trees. The machine learning model is trained with historical data. Once trained, the machine learning model's performance is evaluated. Based on the evaluation, the machine learning model may be further refined. Once the machine learning model's performance satisfies requirements, the computing device employs the machine learning model to determine vehicle delivery routes and estimated delivery times.

Abstract: Machine learning based systems and methods are disclosed herein for automatically classifying digital calendar events. A calendar tracking application (app) receives a first set of digital calendar data of a first period. A machine learning model may generate classification data comprising the first set of digital calendar data classified according to predefined meeting categorie(s). The machine learning based system and methods provide various benefits, including: (1) categorizing one or more user digital calendars into multiple pre-defined categories and reporting these in a GUI based dashboard; (2) using a machine learning based model trained on categorized data (as opposed to mechanistic rules only) to improve the predictive accuracy of the categorization; and (3) using the combination of machine learning based model and mechanistic rules to automate the categorization.

Abstract: A system and method are disclosed for augmented planogram planning. Embodiments include a server comprising a processor and memory and an instruction builder. Embodiments further include a mobile device comprising a processor and a memory and configured to display instructions on the mobile device comprising rendered instruction blocks and at least one of the instruction blocks comprises a planogram instruction block. Embodiments additionally include a digital display comprising a processor and memory and the digital display is configured to display graphics comprising the rendered instruction blocks.

Abstract: Disclosed are various embodiments for generating a sales lead from a video of a vehicle with a unique identifier is disclosed. The system includes a vehicle with a unique identifier, a communication network, and a field device. The field device is comprised of an IP camera equipped to take a video of the vehicle with the unique identifier. The field device is further operatively configured with a communication adapter to transmit the video through the communication network to a central system. The central system comprises a network connection configured to receive the video from the field device as well as an image analysis module capable of extracting text information from the video received from the field device. The central system is connected to a relational database configured with a data table of the unique identifiers, wherein the relational database is connected to the central system through the communication network.

Abstract: Systems and methods are provided for managing hybrid seeds for planting. One example computer-implemented method includes receiving a first dataset of hybrid seeds for planting on a target field, where the first dataset includes probability of success values and historical agricultural data for the hybrid seeds, and selecting a subset of hybrid seeds of the first dataset based on the probability of success values. The method also includes generating representative yield values for the subset of hybrid seeds based on the historical agricultural data, generating risk values for the subset of hybrid seeds based on the historical agricultural data, and generating a second dataset of hybrid seeds for planting based on the risk values, the representative yield values, and properties for the target field. The method further includes causing displaying the representative yield values and the risk values related to the second dataset of hybrid seeds for planting.

Abstract: A method and apparatus for oil and gas operations according to which a bottom hole trajectory of a drilling well is surveyed using a survey tool located uphole from a drill bit positioned at a bottom hole location. One or more offset wells are identified having a trajectory, or respective trajectories, closing on the bottom hole trajectory of the drilling well. An anti-collision steering strategy for the drilling well to avoid the one or more offset wells is determined.

Abstract: Various embodiments of the present disclosure provide a casino patron engagement system that facilitates engagement among casino employees and casino patrons to engender patron loyalty.

Abstract: A dynamic next-stop or next-item recommendation system that is built entirely from raw card transaction data logs. These data logs contain rich transaction data between cardholders and merchants. A query network approach is constructed for geometrical expressivity and automatically learns the inherent class-hierarchy. To ensure scalability and interpretability of the approach, merchants or entities are grouped into interpretable categories and propose a quadtree-based spatial decomposition of the underlying geography. A two-step recommendation process initiates: (1) predict next-merchant quadtree-box and category combination (2) recommend merchants within the predicted combination. This novel neural architecture may handle the hierarchical classification task in the first part of the recommendation system and compare the methods to previous state-of-the-art approaches in related areas.

Abstract: A cloud-based process stream analysis and grading engine for manufacturing and business applications. As a module contained within a comprehensive manufacturing quality suite, the grading engine functions are divided into two stages. For each unique part/process/feature data stream, Stage one automatically generates and stores a daily statistical summary record. These records are summarized from millions of raw data values fed into the engine across thousands of process streams. Stage two, the grading function, compares a user-specified time bounded analysis of each stream's summary history to the respective engineering specifications, resulting in a concatenated dual character grade—a letter and a number. There are a total of nine possible grade outcomes (A1, A2, A3, B1, B2, B3, C1, C2, and C3). The ABC portion of the grade ranks the Yield Potential. The 123 portion ranks the Yield Performance.

Abstract: Techniques are disclosed for managing workflows using block chains. An entire workflow may be stored as a finite state machine on a block chain. Each data block of the block chain may correspond to a state and related operation of a workflow. Each data block of the block chain may be used as an input to a subsequent state of the workflow. Thus, any input may be used to compute the workflow even if the input is not designed to be used by a workflow. Nested block chains, each corresponding to a different workflow or different part of a workflow may be used to increase performance of computing the workflows.

Abstract: Techniques are provided for generating target success group of hybrid seeds for target fields include a server receiving agricultural data records that represent crop seed data describing seed and yield properties of hybrid seeds and first field geo-location data for agricultural fields where the hybrid seeds were planted. The server receives second geo-locations data for target fields where hybrid seeds are to be planted. The server generates a dataset of hybrid seed properties that include yield values and environmental classifications for hybrid seeds and then a dataset of success probability scores that describe the probability of a successful yield on the target fields based on the dataset of hybrid seed properties and the second geo-location data. The server generates target success yield group of hybrid seeds and probability of success values based on success probability scores and a yield threshold. The server causes display of the target success yield group.