Abstract: A system for generating task schedules using an electronic device includes: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler is configured to: receive: a total work database configured to contain items representing work packages; a resources database configured to contain items representing resources required to fulfill items in the work packages; a constraints database configured to contain items representing constraints to fulfilling items in the work packages; and a scheduling objective database configured to designate a prime objective that is to be achieved by the optimum task schedule; provide a trained reinforcement learning engine for optimizing the task schedule based on inputs from the databases; and generate an optimum work package schedule to sequence the work packages using the trained reinforcement learning engine, wherein the optimum work package schedule maximizes the one or more prime objectives.

Abstract: Systems, methods, and apparatuses for completing an action requested by a customer are described herein. A requested action is received from the customer. A customer task for completion of the requested action to be performed by the customer is identified. An interaction board is generated that is accessible by the customer and accessible an employee associated with a provider. The interaction board is displayable via a graphical user interface and includes the customer task. An indication is received from the customer that the customer task has been completed prior to a customer interaction. The interaction board is updated to indicate that the customer task has been completed prior to the customer interaction. Information associated with the requested action is received from a customer device during the customer interaction. The interaction board is updated to include the information received from the customer device during the customer interaction.

Abstract: Systems and methods are directed to optimizing survey parameters using machine learning. A network system monitors user activity of a plurality of users with respect to an application and provides a notification to users of the plurality of users that satisfy a trigger condition for providing the notification. The network system obtains feedback corresponding to the notification, whereby the feedback indicates whether each of the users accepted, rejected, or ignored the notification. A machine learning model is then trained using input data obtained from the feedback to optimize on one or more parameters used by the network system in providing a future notification. Based on the machine learning model, the future notification is presented to a further set of users using the one or more optimized parameters.

Abstract: A method for verifying regenerative management practices in agricultural parcels includes: determining a regenerative carbon footprint value for a parcel that comprises a difference of a regenerative carbon footprint and a baseline carbon footprint, where the baseline carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under current management practices, and where the regenerative carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under regenerative management practices corresponding to a plan proposed by a grower; approving and publishing carbon credits according to the plan; for key dates corresponding to each of the regenerative management practices, processing remotely sensed images against corresponding crop curves to determine compliance/noncompliance indicators corresponding to the key dates; and storing the compliance/noncompliance indicators database, and determining at a verification date compliance with the plan.

Abstract: Techniques for forecasting long duration floating holidays in online traffic are described.

Abstract: A system for determining regenerative carbon footprint in agricultural parcels, including: a sequestration server, having: a management processor that builds first simulation inputs that correspond to baseline management practices, and that builds second simulation inputs that correspond to regenerative management practices; a crop simulation processor, that employs the first simulation inputs to simulate crop growth for a prescribed number of growing seasons to generate first outputs, and configured to employ the second simulation inputs to simulate crop growth for the prescribed number of growing seasons to generate second outputs; and a CO2E determination processor, that employs the first outputs and the second outputs to compute baseline annual carbon dioxide emissions and regenerative annual carbon dioxide emissions, and configured to subtract the baseline annual carbon dioxide emissions from the regenerative annual carbon dioxide emissions to yield a regenerative carbon footprint for the first agricultu

Abstract: Systems, computer program products, and methods are described herein for intelligent allocation of resources over multiple events into an auxiliary source retainer. The present invention may be configured to determine a total amount of resources for a resource distribution between an entity and a user and receive input indicating that the user provided a transferred amount of tangible resources to the entity. The present invention may be further configured to determine a difference between the transferred amount and the total amount, where the difference includes a primary amount of resources corresponding to primary tangible resources and a secondary amount of resources corresponding to secondary tangible resources. The present invention may be further configured to allocate, from a source retainer associated with the entity, the secondary amount of resources to an auxiliary source retainer associated with an auxiliary resource distribution instrument provided by the user.

Abstract: Methods and systems for prescription drug shipping selection are provided. The methods and systems include operations comprising: obtaining, by a server, activity data from a plurality of devices associated with a location, the activity data representing different types of activities that take place at the location over a threshold period of time; aggregating, by the server, the activity data to generate a location-based presence model for the location, the location-based presence model indicating likelihoods that a person is present at the location at a plurality of different time windows; and identifying, by the server, based on the location-based presence model, a time window for delivery of a perishable item to the location.

Abstract: Described is a system for monitoring provisions of services. The system includes a work order system for producing one or more work orders, an identity and access management system to verify information regarding a service provider and a system that receives a produced work order associated with a service provider upon receipt of a service request. The system requests information regarding service provider attributes from the identity and access management system, and receives from the identity and access management system information regarding at least some of the attributes of the service provider. The system determines a requisite level of expertise required for the service and determines whether the attributes received from the identity and access management system indicate that the service provider has the requisite level of attributes.

Abstract: A demand generation platform includes a salon finding application associated with a social media, wherein an intent for finding a salon is expressed by a user on the social media. The architecture includes an intent analyzer deciphering the intent of the user, a salon service application program interface (API) finding a salon based on the intent, and a database interface responding to a request for making an appointment with the salon.

Abstract: An ad hoc supply chain collaborative computing environment management method includes selecting a collaboration model from amongst several models and establishing the environment in accordance with the selected model including defining a knowledge graph, a set of expected events to be received in an event log, and one or more predictive functions operating upon the different events in the event log to produce predictive values. The method also includes, registering actors in a supply chain as authenticated publishers or subscribers to the event log of different events published by different authenticated computing systems of the actors, authenticating different ones of the registered different actors prior the different actors publishing events to the event log, triggering predictive functions for newly published ones of the events in order to produce predictive values and providing read-only access to different states of the entities of the knowledge graph to authenticated subscribers.

Abstract: Systems and methods may be used to generate and use a merchant community graph generated based on merchant financial transaction data. Connections between merchants and other data within the merchant community graph can be used to detect fraud, target product offerings and or other advertisements, detect similar communities, generate dynamic attributes that may be used to develop machine learning models, and develop new user interfaces (UIs) and other features of an information service.

Abstract: An automated parameterized modeling and scoring intelligence system includes a parameterized score estimation software tool and a parameterized score optimization software tool. The parameterized score estimation software tool processes design metrics associated with a current project according to historical project data selected based on a similarity with at least some of the design metrics to determine a score estimation for the current project. The parameterized score optimization software tool processes the score estimation based on external application data retrieved from an external application to determine an expected yield for the current project. A user of the system may iterate against the score estimation or the expected yield by changing one or more of the parameters used to determine same. The iteration may result in a score estimation or expected yield different from the initial versions thereof, such as to identify an optimal design for the current project.

Abstract: A workflow generation system may receive communication data identifying a communication created by a user of a client device, and may process the communication data, with a machine learning model, to determine whether a workflow is needed and particular recipients to be included in the workflow. The machine learning model may be trained based on historical communication data, historical workflow data based on natural language processing, and historical response data based on a sentiment analysis. The workflow generation system may generate a proposed workflow when the workflow is determined to be needed and based on the particular recipients, and may provide data identifying the proposed workflow to the client device. The workflow generation system may receive an approval of the proposed workflow from the client device, and cause the communication to be provided to the particular recipients, in a particular order, based on receiving the approval of the proposed workflow.

Abstract: An AI-based process monitoring system access a plurality of data sources having different data formats to collect and analyze KPI data and shortlist KPIs that are to be used for determining the impact of automation of an automated process or sub-process. Information regarding an automated process is received and KPIs associated with the process and sub-processes of the process are identified. The identified KPIs are put through an approval process and the approved KPIs are presented to a user for selection. The user-selected KPIs are evaluated based on classification, ranking and sentiments associated therewith. The evaluations are again presented to the user along with a set of questionnaires wherein each of the questions has a dynamically controlled weight associated therewith. Based at least on the weights and user responses, a subset of the evaluated KPIs are shortlisted for use in evaluating the impact of process automation.

Abstract: A computer implemented method can include sending additional information concerning an upcoming event to a conversion unit for generation of an appointment reminder for the upcoming event that is to be sent to one or more endpoints. A telecommunication platform can process the appointment reminder to send a visual and/or audible signal to a user via one or more endpoint devices so that the reminder can be processed and responded to via at least one of the endpoint devices.

Abstract: A system for determining estimated alimentary element transfer time, the system comprising a computing device adapted to receive a plurality of alimentary elements and a plurality of destinations, determine an estimated transfer time, wherein determining includes retrieving a plurality of locations of a plurality of transfer apparatuses wherein the plurality of locations are associated with a plurality of transfer paths, generate a plurality of transfer times, determine an estimated transfer time as a function of the plurality of transfer times, generate an accuracy measure based on the estimated transfer time, wherein generating includes computing a plurality of transfer time variations, generate an accuracy measure based on a plurality of statistical parameters, and provide an estimated transfer time, wherein providing includes receiving a new alimentary element request, retrieving an estimated transfer time, retrieving the accuracy measure, and provide an estimated transfer time accuracy message.

Abstract: Systems and methods are provided for predicting the effects of acceleration on a probability of a successful completion of a project. Specifically, one or more machine learning algorithms can be trained to predict the success of a project from a plurality of features, including at least one feature that is a function of an end date of the project. A set of projects can be selected from projects having expected end dates that do not fall within a desired window of time, each project having a first probability of success given the current end date. A second probability is calculated for each project at a machine learning platform that quantifies a chance that the project will be successful if completed within the window of time. A difference between the first probability and the second probability is determined for each project, and the projects are selected according to the difference.

Abstract: A method for generating a communicable disease policy plan by using machine learning. The process identifies a number of workplace policies for a number of business entities. The workplace policies comprise a number of dimensions of data collected from a number of sources. The process collects employment data for each of the business entities. The employment data includes sick leave data about employees of the plurality of business entities. The process determines metrics for the sick leave data during a given time interval; simultaneously models the workplace policies and the metrics for the sick leave data to identify correlations among the number of dimensions of data and generalize rules for predicting effective workplace policies; predicts a number of effective workplace policies for a particular business entity; and generates a communicable disease policy plan for the particular business entity based on the number of effective workplace policies.

Abstract: Methods and systems for forecasting demand for a plurality of items are provided. In particular, the demand forecasting system and methods described herein are useful for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items. Forecast models are validated by evaluating actual demand against predicted demand and using that information to inform how future ensemble forecast will be generated. Forecasts may be broken down into smaller components to satisfy a variety of requests for data from client applications.