ThoughtSpot. has been granted a patent for a replay tool that operates in both learning and testing modes. This tool captures interaction workflows, establishes baseline response templates and times, and identifies anomalies by comparing testing data against these baselines, outputting messages for inconsistencies and response time deviations. GlobalData’s report on ThoughtSpot gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on ThoughtSpot, User behaviour analysis was a key innovation area identified from patents. ThoughtSpot's grant share as of June 2024 was 51%. Grant share is based on the ratio of number of grants to total number of patents.
Anomaly detection in recorded interaction workflows
The patent US12038824B2 outlines a method and device for analyzing interaction workflows between client devices and servers, focusing on response consistency and performance metrics. The method involves replaying recorded interaction workflows in both learning and testing modes using a replay tool. In the learning mode, the system collects test data to establish a baseline response template and response time. In the testing mode, the recorded workflows are replayed to measure response times and identify any discrepancies between the actual response body and the established baseline. If inconsistencies or performance issues are detected, the system generates anomaly messages to alert users to potential problems.
Additionally, the patent details the collection of server statistics during both learning and testing modes to further assess performance. The device is designed to compare testing-mode server statistics against learning-mode statistics, issuing alerts if the testing data falls outside expected ranges. The method also includes specific criteria for determining baseline response times and identifying anomalies based on key-value pair mismatches in response templates. This comprehensive approach aims to enhance the reliability and efficiency of client-server interactions by systematically identifying and addressing performance anomalies.
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