IP Library › Granted Patent US 12,204,437
Granted Patent B1
US 12,204,437 · App. 18/104,212 · Granted Jan 21, 2025

Techniques for visualizing browser test metrics

Inventors: Aditya Bhandari (San Jose, CA); Khawar Deen (Sunnyvale, CA); William Matthew Hoffman (Atlanta, GA); Nicholas Owen Pierson (Phoenix, AZ); Seerut Sidhu (Santa Clara, CA); Harnit Singh (Union City, CA)
Assignee: SPLUNK Inc.
G06F11/3664G06F11/3616
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Quick Facts
Patent No.
US 12,204,437
App. No.
18/104,212
Filed
Jan 31, 2023
Granted
Jan 21, 2025
Kind
B1
Art Unit
2196
USPC
717/125
Abstract

Techniques, which may be embodied herein as systems, computing devices, methods, algorithms, software, code, computer readable media, or the like, are described herein for comparing a set of metrics generated during a simulated user interaction with a website to metrics generated by observing real user interactions with the website. Simulated user interactions with a website can be used to diagnose a website's performance issues, but it can be difficult to determine whether the simulated interactions reflect the experience of real users. In addition, the simulated user interactions can be challenging to contextualize because the number of observed real user interactions may significantly outnumber the simulated interactions. A graphical user interface can help with the interpretation of these website interactions by using the real user interactions to properly contextualize the simulated results.

Claims (55)

1. A computer implemented method comprising:

accessing a set of controlled test metrics for a controlled browser test of a particular uniform resource locator (URL), the set of controlled test metrics comprising the particular uniform resource locator, a test time and one or more metrics calculated in response to a request to perform a browser test of the particular uniform resource locator (URL);

retrieving, from a data store, one or more sets of observed browser metrics using the particular uniform resource locator, each set of observed browser metrics of the one or more sets of observed browser metrics comprising an observed uniform resource locator, an observed time and the one or more metrics, where each set of observed browser metrics was calculated using user traffic to a host computing device hosting the particular uniform resource locator, each set of observed browser metrics having an observed time corresponding to a time period containing the test time;

calculating, for each metric of the one or more metrics and using the one or more sets of observed browser metrics, a distribution and at least one percentile; and

presenting, on a display device, a graphical user interface showing, for each metric, the set of controlled test metric, the distribution, and the at least one percentile.

2. The method of claim 1 , wherein retrieving the one or more sets of observed browser metrics further comprises:

transforming the particular uniform resource locator into a first plurality of normalized uniform resource locators;

locating, in the data store, a first plurality of sets of matching browser metrics with observed uniform resource locators that match the plurality of normalized uniform resource locators; and

retrieving the first plurality of sets of matching browser metrics as the one or more sets of observed browser metrics.

3. The method of claim 2 , further comprising:

comparing the first plurality of sets of matching browser metrics to a threshold;

responsive to a determination that the first plurality of sets of matching browser metrics is below the threshold, calculating a second plurality of normalized uniform resources locators;

locating, in the data store, a second plurality sets of matching browser metrics with observed uniform resource locators that match the second plurality of normalized uniform resource locators; and

retrieving the second plurality of sets of matching browser metrics as the one or more sets of observed browser metrics.

4. The method of claim 3 , wherein the second plurality of normalized uniform resource locators is larger than the first plurality of normalized resource locators.

5. The method of claim 1 , wherein the controlled browser test comprises simulated user interactions between a test computing device, generating the simulated user interactions, and the host computing device hosting the particular uniform resource locator.

6. The method of claim 5 , wherein each set of each set of observed browser metrics of the one or more sets of observed browser metrics was generated using user traffic from a geographic region.

7. The method of claim 6 , wherein the test computing device is located in the geographic region.

8. A computing device, comprising:

a processor; and

a non-transitory computer-readable medium having stored thereon instructions that, when executed by the processor, cause the processor to perform operations comprising:

accessing a set of controlled test metrics for a controlled browser test of a particular uniform resource locator (URL), the set of controlled test metrics comprising the particular uniform resource locator, a test time and one or more metrics calculated in response to a request to perform a browser test of the particular uniform resource locator (URL)

retrieving, from a data store, one or more sets of observed browser metrics using the particular uniform resource locator, each set of observed browser metrics of the one or more sets of observed browser metrics comprising an observed uniform resource locator, an observed time and the one or more metrics, where each set of observed browser metrics was calculated using user traffic to a host computing device hosting the particular uniform resource locator, each set of observed browser metrics having an observed time corresponding to a time period containing the test time;

calculating, for each metric of the one or more metrics and using the one or more sets of observed browser metrics, a distribution and at least one percentile; and

presenting, on a display device, a graphical user interface showing, for each metric, the set of controlled test metric, the distribution, and the at least one percentile.

9. The computing device of claim 8 , wherein the operations further comprise:

transforming the particular uniform resource locator into a first plurality of normalized uniform resource locators;

locating, in the data store, a first plurality of sets of matching browser metrics with observed uniform resource locators that match the plurality of normalized uniform resource locators; and

retrieving the first plurality of sets of matching browser metrics as the one or more sets of observed browser metrics.

10. The computing device of claim 9 , wherein the operations further comprise:

comparing the first plurality of sets of matching browser metrics to a threshold;

responsive to a determination that the first plurality of sets of matching browser metrics is below the threshold, calculating a second plurality of normalized uniform resources locators;

locating, in the data store, a second plurality sets of matching browser metrics with observed uniform resource locators that match the second plurality of normalized uniform resource locators; and

retrieving the second plurality of sets of matching browser metrics as the one or more sets of observed browser metrics.

11. The computing device of claim 10 , wherein the second plurality of normalized uniform resource locators is larger than the first plurality of normalized resource locators.

12. The computing device of claim 8 , wherein the controlled browser test comprises simulated user interactions between a test computing device, generating the simulated user interactions, and the host computing device hosting the particular uniform resource locator.

13. The computing device of claim 12 , wherein each set of each set of observed browser metrics of the one or more sets of observed browser metrics was generated using user traffic from a geographic region.

14. The computing device of claim 13 , wherein the test computing device is located in the geographic region.

15. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

accessing a set of controlled test metrics for a controlled browser test of a particular uniform resource locator (URL), the set of controlled test metrics comprising the particular uniform resource locator, a test time and one or more metrics calculated in response to a request to perform a browser test of the particular uniform resource locator (URL);

retrieving, from a data store, one or more sets of observed browser metrics using the particular uniform resource locator, each set of observed browser metrics of the one or more sets of observed browser metrics comprising an observed uniform resource locator, an observed time and the one or more metrics, where each set of observed browser metrics was calculated using user traffic to a host computing device hosting the particular uniform resource locator, each set of observed browser metrics having an observed time corresponding to a time period containing the test time;

calculating, for each metric of the one or more metrics and using the one or more sets of observed browser metrics, a distribution and at least one percentile; and

presenting, on a display device, a graphical user interface showing, for each metric, the set of controlled test metric, the distribution, and the at least one percentile.

16. The non-transitory computer readable medium of claim 15 , wherein the operations further comprise:

transforming the particular uniform resource locator into a first plurality of normalized uniform resource locators;

locating, in the data store, a first plurality of sets of matching browser metrics with observed uniform resource locators that match the plurality of normalized uniform resource locators; and

retrieving the first plurality of sets of matching browser metrics as the one or more sets of observed browser metrics.

17. The non-transitory computer readable medium of claim 16 , wherein the operations further comprise:

comparing the first plurality of sets of matching browser metrics to a threshold;

responsive to a determination that the first plurality of sets of matching browser metrics is below the threshold, calculating a second plurality of normalized uniform resources locators;

locating, in the data store, a second plurality sets of matching browser metrics with observed uniform resource locators that match the second plurality of normalized uniform resource locators; and

retrieving the second plurality of sets of matching browser metrics as the one or more sets of observed browser metrics.

18. The non-transitory computing medium of claim 17 , wherein the second plurality of normalized uniform resource locators is larger than the first plurality of normalized resource locators.

19. The non-transitory computing medium of claim 15 , wherein the controlled browser test comprises simulated user interactions between a test computing device, generating the simulated user interactions, and the host computing device hosting the particular uniform resource locator.

20. The non-transitory computing medium of claim 19 , wherein each set of each set of observed browser metrics of the one or more sets of observed browser metrics was generated using user traffic from a geographic region.

Assignments (3)
CHANGE OF NAME Recorded Jul 22, 2025
From: SPLUNK INC.
To: SPLUNK LLC
Reel/Frame 072170/0599 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 22, 2025
From: SPLUNK LLC
To: CISCO TECHNOLOGY, INC.
Reel/Frame 072173/0058 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 4, 2024
From: BHANDARI, ADITYA; DEEN, KHAWAR; HOFFMAN, WILLIAM MATTHEW; PIERSON, NICHOLAS OWEN; SIDHU, SEERUT; SINGH, HARNIT
To: SPLUNK INC.
Reel/Frame 069487/0821 →
References Cited (19)
US 7937344B2 · Baum et al. · 2011 [cited by applicant]
US 8112425B2 · Baum et al. · 2012 [cited by applicant]
US 8751529B2 · Zhang et al. · 2014 [cited by applicant]
US 8788525B2 · Neels et al. · 2014 [cited by applicant]
US 9215240B2 · Merza et al. · 2015 [cited by applicant]
US 9286413B1 · Coates et al. · 2016 [cited by applicant]
US 10102106B2 · Arguelles · 2018 [cited by examiner]
US 10127258B2 · Lamas et al. · 2018 [cited by applicant]
US 20160103758A1 · Zhao · 2016 [cited by examiner]
US 20170046254A1 · Buege · 2017 [cited by examiner]
US 20190098106A1 · Mungel et al. · 2019 [cited by applicant]
US 20200364133A1 · Vidal · 2020 [cited by examiner]
US 20210083961A1 · Polishchuk · 2021 [cited by examiner]
US 20220269579A1 · Hoenig · 2022 [cited by examiner]
Splunk Enterprise 8.0.0 Overview, available online, retrieved May 20, 2020 from docs.splunk.com. [cited by applicant]
Splunk Cloud 8.0.2004 User Manual, available online, retrieved May 20, 2020 from docs.splunk.com. [cited by applicant]
Splunk Quick Reference Guide, updated 2019, available online at https://www.splunk.com/pdfs/solution-guides/splunk-quick-reference-guide.pdf, retrieved May 20, 2020. [cited by applicant]
Carraso, David, “Exploring Splunk,” published by CITO Research, New York, NY, Apr. 2012. [cited by applicant]
Bitincka, Ledion et al., “Optimizing Data Analysis with a Semi-structured Time Series Database,” self-published, first presented at “Workshop on Managing Systems via Log Analysis and Machine Learning Techniques (SLAML)”… [cited by applicant]