SPIKE ANALYSIS WITH CONTEXTUALLY INFORMED SPIKE EXPLANATIONS GENERATED BY USE OF LARGE LANGUAGE MODELS
Dossier Overview
Applicant
Meltwater News US Inc.
Inventor
Tuan TRAN; Emil Andreas KLINTBERG; Sushmita DAS; Julio ROMANO; Josephine Wing Yee CHAN; Hearad TEHRANCHIAN; Ebenezer ISAAC; Franck BABIN; Giorgio ORSI; Aditya JAMI; David DELGADO; Jinsong GUO; Georg GOTTLOB
IPC Classification
CPC Classification
This disclosure addresses deficiencies in existing methods for analyzing spikes in time-series data, particularly when dealing with vast document repositories. A method includes receiving a user specification of objects of interest, and by subsequently identifying spikes of mentions of these objects in the documents. The method includes retrieving metric data and context data related to mentions of objects of interest in relevant documents from a repository, both from spikes and other time intervals. By analyzing these documents, it is possible to pinpoint the key factors driving the spikes. Finally, use of an LLM provides capabilities to generate comprehensive explanations. This is achieved by submitting one or more prompts to LLM(s), where the prompts incorporate the specification of the objects of interest (or a reformulation thereof), the identified driving factors and a number of representative documents connected to the key driving factors.
Original (Chinese)
This disclosure addresses deficiencies in existing methods for analyzing spikes in time-series data, particularly when dealing with vast document repositories. A method includes receiving a user specification of objects of interest, and by subsequently identifying spikes of mentions of these objects in the documents. The method includes retrieving metric data and context data related to mentions of objects of interest in relevant documents from a repository, both from spikes and other time intervals. By analyzing these documents, it is possible to pinpoint the key factors driving the spikes. Finally, use of an LLM provides capabilities to generate comprehensive explanations. This is achieved by submitting one or more prompts to LLM(s), where the prompts incorporate the specification of the objects of interest (or a reformulation thereof), the identified driving factors and a number of representative documents connected to the key driving factors.
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