公开(公告)号：US12271423B1

公开(公告)日：2025-04-08

申请号：US17876412

申请日：2022-07-28

Applicant: Splunk, Inc.

Inventor： Julien Didier Jean Veron Vialard ,  Subhabrata Majumdar ,  Joseph Ari Ross

IPC: G06F17/00 ,  G06F7/00 ,  G06F16/22 ,  G06F16/901 ,  G06N3/08

Abstract: A computerized method is disclosed that includes operations of receiving incoming data including event data, extracting entities from the event data based on a graph ontology, generating a graph-based dense representation of each graph entity according to the graph ontology, wherein the graph-dense representations are stored in a vector database, computing relatedness scores between each of the entities, generating a listing of events related to a selected event, wherein the listing of events is ordered by corresponding relatedness scores, generating a graphical user interface illustrating the listing of events related to the selected event, and causing rendering of the graphical user interface on a display screen of a network device. Generating the graph-based dense representations may include training a graph neural network model on a corpus of metapaths to produce node embeddings.

公开(公告)号：US20250021767A1

公开(公告)日：2025-01-16

申请号：US18228654

申请日：2023-07-31

Applicant: Splunk Inc.

Inventor： Vedant Dharnidharka ,  Robert Riachi ,  Abraham Starosta ,  Alexander Sasha Stojanovic ,  Julien Didier Jean Veron Vialard ,  Rong Tan Wang ,  Poonam Yadav ,  Om Rajyaguru

IPC: G06F40/40 ,  G06F16/9032 ,  G06F40/211 ,  G06F40/30

Abstract: Implementations of this disclosure provide a machine learning model training system that receives user input being a natural language description of a search query, and packages and transmits the natural language description as a prompt to a plurality of large learning models (LLMs). The model training system also receives response from the plurality of LLMs being translations of the natural language descriptions to an executable search query and displays the translations to a user via a graphical user interface. The model training system receives user feedback via the graphical user interface that corresponds to indications as to whether each translation is correct, syntactically and/or semantically, and, in some examples, an indication of which response was preferred. The model training system also generates training data from the user input, translations generated by the plurality of LLMs, and user feedback, and subsequently, initiates training of a LLM using the training data.