Lightriver Technologies, Inc.

Frank M Massa

Timothy Wayne Masse

Daniel W Rose

Some embodiments enable the interpretation and transformation of unstructured human input to enable end-to-end service visibility across a transport network. Some embodiments include assembling and sending a prompt to a large language model (LLM) to interpret and transform examples of unstructured human input describing a circuit to network element (NE) information, where the NE information can be used to identify neighbor devices between different vendor products providing a service. The LLM can output data points that enable identifying neighboring devices on an end-to-end service path that includes multi-vendor and/or multigenerational devices. The data points used in conjunction with inventory data enables neighboring connections to be determined so that islands of different multi-vendor and/or multigenerational devices can be connected to neighbor devices along the end-to-end service path. The neighboring connections can be added to an inventory database enabling visibility end-to-end service path.

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Concord, CA, United States of America

Frank M Massa