Ca, Inc.

Universitat Politécnica De Catalunya

Victor Muntés Mulero

David Sánchez Charles

Steven Lloyd Greenspan

Jaume Ferrarons Llagostera

Marc Solé Simó

Sergio Gómez Villamor

Marc Solé Simo

Josep Lluis Larriba Pey

Josep Lluís Larriba Pey

Norbert Martínez Bazan

Provided is a computer system that includes a processor and a memory coupled to the processor, the memory including computer readable program code embodied therein that, when executed by the processor, causes the processor to generate a catalog that identifies a plurality of tasks that a plurality of network resources are available to perform, the network resources including Internet-of-things devices and human network resources and to generate, in response to receiving a request to perform a complex project, a solution path that includes an ordered list corresponding to selected ones of the plurality of tasks that are capable of aggregately performing the complex project, wherein the selected ones of the plurality of tasks define the solution path in an edge graph that include the plurality of tasks represented as edges therein.

Automated collaboration workflow generation in thing-sourcing environments

A network device associated with a database management system receives information associated with a customer support ticket. Based on information in the database management system, a direct relationship between the received customer support ticket and a customer support ticket in the database may be determined. A graph including nodes representing customer support tickets is generated based on information in the database. Edge prediction is performed on the graph to derive relationships among the nodes in the graph. A predictive relationship between customer support tickets is derived. A relationship data set based on the direct relationship between the customer support tickets and based on the predictive relationship between the customer support tickets is generated. The relationship data set associated with the customer support ticket is communicated to the user device.

Database management methods, systems, and devices for identifying related customer support tickets

A method includes performing, by a processor: receiving a document containing subject matter related to a course of action, the document comprising a plurality of sub-documents that are related to one another in a time sequence, converting the document to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words, applying a machine learning algorithm to the vectorized document to generate an estimated vectorized document, associating the estimated vectorized document with a reference document; predicting future subject matter contained in a future sub-document of the document based on the reference document, and adjusting the course of action responsive to predicting the future subject matter.

Document analysis system that uses machine learning to predict subject matter evolution of document content

A thing-sourcing project request including requirements for a thing-sourcing task that requires data input by a thing-sourcing device is received from a requestor device. A determination is made if real-time data is needed in order to complete the thing-sourcing task. In response to determining that real-time data is not needed, a determination is made if a similar thing-sourcing task has been previously completed. If not, the method determines if the thing-sourcing task can be completed using pre-existing data. If so, a data archive is searched for relevant pre-existing data that can be used to complete the thing-sourcing task. The thing-sourcing task is completed using the relevant pre-existing data, and a response to the thing-sourcing project request is transmitted to the requestor device.

Data collection and estimation using an internet of things

In a computer server, a ticket element generated by an issue tracking system responsive to a client request is received, where the ticket element includes a data field including data indicative of an attribute. A database including state data stored therein is accessed responsive to receipt of the ticket element. The state data relates a plurality of states that are specific to the attribute. One of the plurality of states related by the state data is identified as corresponding to a future state of the attribute based on a current state of the attribute, and a future probability of escalation of the ticket element is computed based on the future state of the attribute responsive to identification of the one of the plurality of states as corresponding thereto. An action indicator is provided in response to the client request.

Escalation prediction based on timed state machines

The present invention establishes a process for creating a set of structures that allows efficient storage and subsequent handling. The multigraph is represented using bitmaps with element counters and mappings between values and organized bitmaps to faciliate the handling of the multigraphs. The bits in the bitmaps represent two aspects of the multigraph: 1) indexing of all the objects of the multigraph as a function of their identifiers and 2) connectivity between objects of the multigraph, whether they are vertices or edges. Mappings allow, given a value, accessing the objects of the multigraph which contain such value. Multigraph operations are solved by accessing the mappings and applying logical operations on the bitmaps. This way of representing a graph allows efficiently performing graph operations such as: inserting a vertex or an edge, inserting an attribute, acquiring the incoming and outgoing edges of an attribute, etc.

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Barcelona, Spain

Victor Muntés Mulero