Abstract: A method for determining optimal path arrangements for an infrastructure link between two geographic locations, in particular, in uneven terrain. The method includes modelling a geographic terrain containing the two geographic locations; optimizing an arrangement cost and a repair rate for two or more potential paths based on the modelled geographic terrain, an arrangement cost model, and a repair rate model, taking into account at least two design levels, wherein the arrangement cost model incorporates direction-dependent factor and direction-independent factor associated with the path arrangements; and determining the optimal path arrangements each including multiple path portions and respective design levels associated with the path portions based on the optimization.

Abstract: A system for path selection using vehicle route guidance includes computing device configured to receive a plurality of requests for a plurality of alimentary combinations and a plurality of destinations, wherein each request specifies an alimentary combination of the plurality of alimentary combinations to be assembled by at least an alimentary provider and a destination of the plurality of destinations, compute a projected nutritionally guided order volume using a first machine-learning process, determine a plurality of assembly times, the plurality of assembly times including an assembly time for each alimentary combination, as a function of the nutritionally guided order volume, generate a plurality of predicted routes as a function of the determined assembly times, and pair a predicted route of the plurality of predicted routes with a courier, wherein pairing further pairing, with the courier, the predicted route that optimizes an objective function.

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.

Abstract: In an example embodiment, a series of machine learned models are trained and utilized in conjunction with each other to improve the reliability of predictions of fuel costs. One of these models is specifically trained to learn the “gap” time for a particular retail location, meaning the amount of time between when the futures contract market price on a trading exchange making up the fuel blend has the most correlation with the retail price of that fuel blend (for that particular location). This greatly enhances the reliability of the predictions of fuel costs, and, as described in detail herein, these predictions may be used in a number of different applications in unique ways.

Abstract: Techniques are provided for generating a set of target hybrid seeds with optimal yield and risk performance, including a server receiving a candidate set of hybrid seeds along with probability of successful yield values, associated historical agricultural data and property information, and selecting a subset of the hybrid seeds that have probability of success values greater than a filtering threshold. The server generates representative yield values for hybrid seeds based on the historical agricultural data and risk values for each hybrid seed. The server generates a dataset of target hybrid seeds for planting based on the risk values, the yield values, and the properties for the target fields. The dataset of target hybrid seeds includes target hybrid seeds that meet a specific threshold for a range of risk values. The server causes display of the dataset of target hybrid seeds including yield values and risk values for the target fields.

Abstract: Systems and methods of the present disclosure facilitate managing a business. In some embodiments, the system includes a product data module and a plurality of business process modules executing on at least one processor of a server. The product data module may be configured to store at least one product description. Responsive to a first user, the system may associate a product class with a first description. Responsive to a second user, the system may associate a first product description with a first of the business process modules. The system may be configured to select a second business process module based on the product class and update the second business process module with the first product description.

Abstract: In one embodiment, a method includes using a first computing device to access digital data representing a contract set of contract documents and digital contract metadata, weight differences between data field values, use the weighted differences to calculate distance scores, use the distance scores and a temporal ordering of the contract documents to generate and cause displaying a time curve graph comprising a geometric shape that comprises time curves and a spatial proximity at a second computing device, a measure of each time curve calculated to indicate an amount of time between a creation of two contract documents, the spatial proximity calculated to indicate a metric of similarity between the contract documents, in response to determining that the geometric shape indicates a lack of convergence over a threshold amount of time, generate and cause displaying a notification indicating that the contract set is at risk at the second computing device.

Abstract: A method includes accessing a domain model plan that includes a time independent initial state and a time independent goal state and sequences of events that include a sequence of a first event, a second event and a third event with conditional interrelationships; accessing operational data that include a failure condition for the third event and a time constraint for the failure condition; issuing a control instruction to rigsite equipment to perform a drilling-related action for generating the third event; during performance of the drilling-related action for generating the third event, receiving sensor data germane to the failure condition; upon occurrence of the failure condition, timing a duration of the occurrence of the failure condition; comparing the duration to the time constraint for the failure condition; and, based on the comparing, deciding to re-plan the domain model plan or to continue execution of the domain model plan.

Abstract: An optimization apparatus for repeatedly obtaining a constraint violating solution, a constraint satisfying solution, and an approximate solution in a resource-constrained project scheduling problem performs setting an end time of a target optimization period at a point between a first finish time equal to a finish time of a latest version of the constraint violating solution and a second finish time equal to a finish time of a latest version of the constraint satisfying solution, followed by obtaining the approximate solution using a metaheuristic algorithm, making a determination as to whether the approximate solution violates constraint or satisfy the constraint, and performing, based on the determination, updating the first finish time with a finish time of the approximate solution when the approximate solution violates the constraint, and updating the second finish time with a finish time of the approximate solution when the approximate solution satisfies the constraint.

Abstract: Store a subscription dataset for each tenant of a multi-tenant system, each of the subscription datasets having a common data format native to the system, the subscription datasets including billing data and not behavioral data. Determine primary features from a particular subscription dataset. Derive secondary features from the primary features. Generate a churn prediction model based on the primary features and the secondary features. Obtain a second subscription dataset, the second subscription dataset comprising billing data that is more recent than the particular subscription dataset. Identify, using the particular churn prediction model and the second subscription dataset, one or more subscribers as a churn-risk. Report the one or more subscribers identified as a churn-risk.

Abstract: One example operation may include retrieving meeting participant profiles associated with meeting participants and meeting topics assigned to a scheduled meeting set to occur at a scheduled meeting time for a time duration, creating the meeting, at the scheduled meeting time, with a number of meeting intervals, collectively which comprise the time duration of the meeting, and each of the meeting intervals are assigned a respective different meeting topic among the meeting topics, assigning the participant profiles to one or more of the meeting intervals, automatically initiating a start to the meeting at the scheduled meeting time, without any of the meeting participants being connected to the meeting via their respective computing devices, and creating one or more of audio and video content based on one or more of the meeting topics and one or more meeting data files.

Abstract: An objective of the present invention is to promote efficiency improvement of a business by a mobile device. A business analysis server that analyzes a business in a mobile device system conducting the business by controlling a mobile device on the basis of a scenario includes a control unit, and a storage unit including a business index value database that manages a business index value indicating an effect of the business and a business index value history database that manages a change in the business index value as time-series data. The control unit receives scenario execution information when a business index value is designated, analyzes a correlation between the business index value and the scenario by referring to the business index value history database and the scenario execution information, extracts a target scenario whose correlation value with the business index value satisfies predetermined conditions, and generates a correction scenario.

Abstract: Improvement of work is instructed based on an analysis result obtained by combining data having different timescales. A productivity improvement support system includes a time scale setting unit configured to, when 4M data having different time scales acquired from a target device contains data that satisfy a condition for detecting a state fluctuation, switch time scale of the 4M data to time scales according to a state fluctuation, a loss analysis calculation unit configured to analyze a production loss factor by using analysis model data in which the production loss factor of the target device when the condition is satisfied is determined, and a recommended work selection unit configured to select a recommended work when the production loss factor occurs from one or a plurality of recommended works by using recommended work data stored in association with the production loss factor.

Abstract: An automated method for determining a complexity of a task. The method includes extracting data from the plurality of historical support tickets to generate training data. The method trains a complexity model to predict a complexity value of a task associated with a support ticket using the training data. The method predicts, using the complexity model, the complexity value of a new task associated with a new support ticket.

Abstract: One example method of operation may include identifying a plurality of active project files in a database, identifying one or more deliverables included in the plurality of active project files, performing a simulation test based on a current set of allocated resources and a current set of deadlines associated with each of the one or more deliverables to determine whether the one or more deliverables will fail, determining the one or more deliverables includes one or more failed project risk metrics indicating that the one or more deliverables will fail to be completed by a corresponding deadline among the current set of deadlines, selecting one or more of the active project files which does not have a failed project risk metric, and reallocating resources, from the one or more of the active project files which does not have a failed project risk metric, to one or more of the active project files which has one or more failed project risk metrics.

Abstract: One example method of operation may include identifying a topics to apply to a meeting having a defined time window, creating meeting time intervals corresponding to the topics, transmitting meeting invites to user accounts identified as potential meeting attendees, identifying when the user accounts were active in the meeting, and determining the user accounts fulfilled a topic fulfillment obligation of one or more of the topics based on a user account presence during one or more of the meeting intervals.

Abstract: In energy markets in which bidding process is used to sell energy, it is important that a mechanism for deciding bidding amount is in place. State of the art systems in this domain have the disadvantage that they rely on simulation data, and also they make certain assumptions, and both the factors can affect accuracy of results when the systems are deployed and are expected to handle practical scenarios. The disclosure herein generally relates to energy markets, and, more particularly, to a method and a system for Reinforcement Learning (RL) based model for generating bids. The system trains a RL agent using historical data with respect to competitor bids places and Market Clearing Prices (MCPs). The RL agent then processes real-time inputs and generates bidding recommendations.

Abstract: The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of validating models guided by machine learning algorithms. The innovation can begin by receiving a risk model for validation having multiple sets of data. A first data set is selected from as an input. Outputs are generated for validation. One output can be generating a second set of analysis results using a comparable algorithm to the risk model. Another output can be generating a second set of variables and transformations using a machine learning algorithm and an un-transformed set of the selected variables to assess the set of selected transformations. Another output can be generating a third set of variables using one or more machine learning algorithms and an extended feature set of variables to assess the selected variables. The outputs are compared to the analysis results, coefficients, selected variables, and selected transformations. A report of the comparison is generated.