Abstract: A collaboration platform for implicitly determining user collaborations in real time or near real time is disclosed. The collaboration platform monitors event records generated based on user interactions with various team-based tools/collaborative applications to generate collaboration scores that provide a real time picture of a user's interactions with other users, and a user's interactions with content/objects/projects in the tools/collaborative applications. The collaboration platform also monitors user interactions with specific sub-projects to determine at any given time the users actively involved in any given project or sub-project.

Abstract: Aspects of the present disclosure generally relates to a method including receiving group data and identifying at least a domain target for the at least a domain as a function of the domain-specific data. Also, the method may include generating a plurality of candidate schedules. Further, the method may include selecting one or more recommendations from the plurality of candidate schedules. Moreover, the method may include presenting, at a remote device, the one or more recommendations to a group, and tracking, by the computing device, a group's progress with regard to the optimal group schedule.

Abstract: A computer executed process for mimicking human dialog, referred to herein as a “humanoid” or “humanoid system,” can be configured to provide automated customer support. The humanoid can identify a support issue for a customer, as well as a customer support campaign corresponding to the support issue. The humanoid can identify at least one machine learning model associated with the customer support campaign and can communicate with the customer using the at least one machine learning model. The humanoid can execute a support action to resolve the support issue.

Abstract: Techniques to generate a digitally optimized schedule for a construction activity to meet a construction objective(s) of a construction project are disclosed. An artificial intelligence system receives a plurality of input data sets that impact the construction project. Each of the plurality of input data sets is processed to achieve the construction objective(s). The artificial intelligence system processes the plurality of input data sets using a respective ensemble of machine learning models. The artificial intelligence system generates machine learning validated intermediate output data sets corresponding to each of the plurality of input data sets. The artificial intelligence system implements a supervisory machine learning model to generate an optimized schedule for the construction activity based on the machine learning validated intermediate output data sets and the construction objective(s).

Abstract: A computing system configured to: (i) receive a request to create a project entity; create the project entity; establish a data connection between a first party and the project entity; and define the first party's default data retention settings; (ii) receive a request to create a set of data objects associated with the project entity; create each data object in the set; establish a respective data connection between the first party and each data object; and define the first party's data retention settings for the respective data object; (iii) after receiving an indication that a second party has accepted the first party's invitation to collaborate: establish a data connection between the second party and the project entity; define the second party's default data retention settings; establish a respective data connection between the second party and each data object; and define the second party's data retention settings for each data object to match the second party's default data retention settings.

Abstract: Systems and methods for task distribution and tracking in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, a method includes obtaining, by a task tracking server system, damage data indicating damage to a component of a rail car, determining, by the task tracking server system, a location of the damage on the component, determining, by the task tracking server system, task data based on the indicated damage and the component, wherein the task data indicates a repair to the component to correct the indicated damage, obtaining, by the task tracking server system and from a third-party database, ownership data indicating a responsible party for the component and a responsible party for the rail car, and generating, by the task tracking server system, a work order including the damage data and the task data.

Abstract: A customer service system that includes a customer device associated with a customer, an agent device associated with an agent, and a monitoring system. The monitoring system includes a memory that stores instructions and a processor that executes the instructions. The instructions, when executed by the processor, cause the monitoring system to monitor a conversation between the customer device and the agent device to identify a context of the conversation. Moreover, the instructions cause the monitoring system to identify a disclosure notice based at least part on the context of the conversation, in which the disclosure notice comprises an indication of a disclosure to be read in light of the context of the conversation.

Abstract: Historical activity data about backups and restorations are retrieved. Description files corresponding to cloud storage providers are received. Each description file includes a name of a cloud storage provider, a catalog listing cloud service options offered by the cloud storage provider, and pricing and descriptive information for the options. The historical activity data is mapped to the cloud service options. A set of cost figures is generated based on the mapping. Each cost figure represents a cost that would have been charged to a user, based on the historical activity data, by the cloud storage provider for storing the backups and accessing the backups for the restorations. The cloud storage providers are rated using the cost figures and the rated cloud storage providers are displayed in a user interface to allow the user to select a particular cloud storage provider to which the backups are to be migrated.

Abstract: Metric forecasting techniques and systems in a digital medium environment are described that leverage similarity of elements, one to another, in order to generate a forecast value for a metric for a particular element. In one example, training data is received that describes a time series of values of the metric for a plurality of elements. The model is trained to generate the forecast value of the metric, the training using machine learning of a neural network based on the training data. The training includes generating dimensional-transformation data configured to transform the training data into a simplified representation to determine similarity of the plurality of elements, one to another, with respect to the metric over the time series. The training also includes generating model parameters of the neural network based on the simplified representation to generate the forecast value of the metric.

Abstract: A system for automated origination of capital includes a rate processor, a revenue forecaster, and an offer processor. The rate processor generates prices for capital product offers to subscribers of a point-of-sale (POS) subscription service, where the prices are generated by employing probability of default (PD) values that are derived from historical POS data corresponding to each of the subscribers. The revenue forecaster is coupled to the rate processor and is configured to employ the configured to employ the historical POS data and locations of establishments corresponding to the each of said subscribers to predict future POS data for the establishments corresponding to the each of the subscribers, and to employ the future POS data to generate predicted total revenues corresponding to the each of the subscribers over a payback period.

Abstract: This application relates to systems and methods for generating desired or optimized event schedules. An example computer-implemented method of dynamically generating an event schedule includes: receiving ogle or more parameters for a series of live events to be held in a plurality of geographic regions; generating a schedule for the series of live events based on the one or more parameters; and automatically updating the schedule based on a change to the one or more parameters.

Abstract: Techniques to generate a digitally optimized schedule for a construction activity to meet a construction objective(s) of a construction project are disclosed. An artificial intelligence system receives a plurality of input data sets that impact the construction project. Each of the plurality of input data sets is processed to achieve the construction objective(s). The artificial intelligence system processes the plurality of input data sets using a respective ensemble of machine learning models. The artificial intelligence system generates machine learning validated intermediate output data sets corresponding to each of the plurality of input data sets. The artificial intelligence system implements a supervisory machine learning model to generate an optimized schedule for the construction activity based on the machine learning validated intermediate output data sets and the construction objective(s).

Abstract: A system for automated origination of capital includes a rate processor, a revenue forecaster, and an offer processor. The rate processor generates prices for capital product offers to subscribers of a point-of-sale (POS) subscription service, where the prices are generated by employing probability of default (PD) values that are derived from historical POS data corresponding to each of the subscribers. The revenue forecaster employs the historical POS data to predict future POS data for establishments corresponding to the each of the subscribers and employs the future POS data to generate predicted total revenues corresponding to the each of the subscribers over a payback period. The offer processor generates and transmits the capital product offers corresponding to the each of the subscribers, where the capital product offers comprise the payback period, the prices, and maximum dollar amounts that are a percentage of the predicted total revenues.

Abstract: Examples of cognitive procurement are described. In an example embodiment, procurement-specific data sources associated with at least one of a process, an organization, and an industry relevant for procurement operations are monitored. From the monitored procurement-specific data, an operation behavioral pattern is identified. Subsequently, a behavior model of an order is constructed using the operation behavioral pattern and a pre-existing behavior model library. A procurement interaction indicating a query for processing the order is received from a user. The order is tracked by the cognitive order concierge. Using the behavior model, a potential event relating to the order is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order. In an example, the user is notified as per the remediation requirement.

Abstract: A data engine may establish and maintain dynamic item subsets of items received from the item-tracking platforms. Users may define and customize the dynamic item subsets using a client application. The data engine may determine aggregated data about the dynamic item subsets for presentation in a graphical user interface of a client application executing on or otherwise accessible by the client devices.

Abstract: Aspects relate to methods and systems for exploiting value within certain domains. An exemplary method includes interrogating, using a remote device, a user for scheduling data and at least a domain, wherein the at least a domain includes at least one domain and no more than a predetermined maximum number of domains, receiving, using the remote device, the at least a domain from the user, interrogating, using the remote device, the user for domain-specific data associated with the at least a domain, receiving, using the remote device, the domain-specific data from the user, generating, using a computing device, a domain target for the at least a domain as a function of the domain-specific data, generating, using the computing device, a user schedule as a function of the domain target and the scheduling data, and displaying, using the remote device, the user schedule and the domain target to the user.

Abstract: This application relates to systems and methods for generating desired or optimized event schedules. An example computer-implemented method of dynamically generating an event schedule includes: receiving one or more parameters for a series of live events to be held in a plurality of geographic regions; generating a schedule for the series of live events based on the one or more parameters; and automatically updating the schedule based on a change to the one or more parameters.

Abstract: An electronic resource tracking and storage computer system communicates with computing systems operated by different participants. Computing systems store copies of a blockchain and have associated computing devices with sensors. A programmed rule set includes conditions to be met when cooperating to complete, in connection with a resource tracked via the blockchain, a modeled process including modeled tasks. A transceiver receives, from the computing devices, signed electronic data messages including identifiers and values from their respective sensors. Blockchain transactions including identifiers and value(s) in the respective messages are generated. Generated blockchain transactions are published for inclusion in blockchain's copies. Value(s) in the respective electronic data messages are validated against the set of programmed rules. Based on the validations' results, events are emitted to an event bus monitored by a management system.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for providing cross-domain access to calendar availability are presented. A request to schedule an event may be received. The request may comprise an identity of an organizer user account, the organizer user account associated with a first domain, and an identity of an invitee user account, the invitee user account associated with a second domain. An importance level of the organizer user account to the invitee user account may be determined. A determination may be made as to whether the organizer user account has access to events in an electronic calendar associated with the invitee user account based on the importance level. One or more events that the organizer account has access to may be surfaced in an event scheduling assistant user interface associated with the organizer user account.

Abstract: There is provided a system, method and computer program product for estimating a current value of an intangible asset, by obtaining a source file representing the intangible asset; analyzing the source file to identify at least one characteristic; classifying the source file to belong to at least one class based on the identified at least one characteristic; obtaining one or more evaluation parameters associated with the creator of the intangible asset from at least one first data source; obtaining any historical values associated with the intangible asset; predicting, using a machine learning model, a future value of the intangible asset based on the selected at least one class; the one or more evaluation parameter; and any obtained historical value; and estimating, by the machine learning model, the current value of the intangible asset based on the predicted future value and a pre-set time period.