B.yond, Inc.

Johnny Ghibril

Baris Abaci

Madhu Nunna

Santiago Molina

Bruno Morel

Rikard Kjellberg

Embodiments relate to intelligent entities for providing information service over a network in a telecommunication system. An intelligent element framework manages intelligent entities, which are modular and trained using artificial intelligence or machine learning algorithms to perform prediction or inference for different types of applications. The intelligent entities may communicate with each other via the intelligent element framework. For example, an intelligent entity may generate an output and provide the output for use by one or more other intelligent entities. Thus, the intelligent element framework may distribute portions of tasks for information service across multiple intelligent entities chained together, for example, in a directed graph configuration.

Decomposing tasks through artificial intelligence chaining

A model assessor retrieves a plurality of predicted outputs from a plurality of models, each predicted output generated using one of the models based on one or more data inputs and a regression model. The model assessor generates a candidate model, which includes as input 1) the one or more data inputs of a selected model of the plurality of models and 2) a predictive output of one or more other models of the plurality of models or one or more other data inputs. A correlation is computed between an actual output and a predicted output of the candidate model, and the model assessor determines if the correlation score exceeds a threshold criteria. If so, the selected model is replaced with the candidate model. Otherwise, the candidate model is deleted.

Self-intelligent improvement in predictive data models

A system and a method are disclosed for implementing distributed processing in edge nodes. In an embodiment, a respective edge node receives data from a client of the respective edge node. The respective edge node generates a prediction of a respective activity based on the data, and determines whether the prediction is valid by feeding the prediction into a validator module and receiving a validation response from the validator module. The respective edge node, in response to determining that the prediction is valid, activates a function.

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Boston, MA, United States of America

Johnny Ghibril