Boomi, Inc.

Swagata Ashwani

Prakhar Amlathe

Michael Bachman

Chris Cappetta

Eric Fennell

Daniel Schwartz

Anil Enum

Shailendra Burman

Currently, technical expertise is required to construct data objects within integration processes. Disclosed embodiments enable automated generation of data objects using semantic comparisons and artificial intelligence. In particular, data may be converted into reference vector embeddings that define a semantic location of the data within a multi-dimensional vector space. User requests may be converted into input vector embeddings. The input vector embeddings may be semantically compared to the reference vector embeddings to identify semantically similar data, and related data may be identified using a graph database. This semantically similar and related data may then be used to generate a prompt, which may be input to a generative language model to automatically produce a data object for use in an integration process.

Automated generation of data objects using semantic comparisons and artificial intelligence

Currently, technical expertise is required to construct data maps between data sources and targets within integration processes. Disclosed embodiments enable automated generation of data maps using semantic comparisons and artificial intelligence. In particular, data may be converted into reference vector embeddings that define a semantic location of the data within a multi-dimensional vector space. User requests may be converted into input vector embeddings, based on source and target information. The input vector embeddings may be semantically compared to the reference vector embeddings, potentially using a graph database, to identify semantically similar data. This semantically similar data may then be used to generate a prompt, which may be input to a generative language model to automatically produce a data map for use in an integration process.

Automated data mapping using semantic comparisons and artificial intelligence

A substantial learning curve is required to construct integration processes in an integration platform. This can make it difficult for novice users to construct effective integration processes, and for expert users to construct integration processes quickly and efficiently. Accordingly, embodiments for building and operating a model to predict next steps, during construction of an integration process via a graphical user interface, are disclosed. The model may comprise a Markov chain, prediction tree, or an artificial neural network (e.g., graph neural network, recurrent neural network, etc.) or other machine-learning model that predicts a next step based on a current sequence of steps. In addition, the graphical user interface may display the suggested next steps according to a priority (e.g., defined by confidence values associated with each step).

Artificial-intelligence-assisted construction of integration processes

Growing community of inventors

Foster City, CA, United States of America

Swagata Ashwani