Dell Software Inc.

Patent Trials and Appeals Board

May 27, 2020

2020001473 (P.T.A.B. May. 27, 2020)

UNITED STATES PATENT AND TRADEMARK OFFICE
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
Address: COMMISSIONER FOR PATENTS
P.O. Box 1450
Alexandria, Virginia 22313-1450
www.uspto.gov
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
14/886,884 10/19/2015 Shree A. DANDEKAR 59961-P290US 2522
96061 7590 05/27/2020
Winstead PC
PO Box 131851
Dallas, TX 75313-1851
EXAMINER
CELANI, NICHOLAS P
ART UNIT PAPER NUMBER
2449
NOTIFICATION DATE DELIVERY MODE
05/27/2020 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address(es):
delldocket@winstead.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte SHREE A. DANDEKAR and MARK WILLIAM DAVIS
Appeal 2020-001473
Application 14/886,884
Technology Center 2400
BEFORE JAMESON LEE, SALLY C. MEDLEY, and
MICHAEL R. ZECHER, Administrative Patent Judges.
ZECHER, Administrative Patent Judge.
DECISION ON APPEAL
STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the
Examiner’s Final Action2 rejecting claims 1–20. Appeal Br. 6–19. We have
jurisdiction under 35 U.S.C. § 6(b).
We reverse.

1 We use the word Appellant to refer to “applicant” as defined in 37 C.F.R.
§ 1.42(a). Appellant identifies the real party in interest as Quest Software
Inc. Appeal Br. 3.
2 All references to the Final Action on appeal refer to the Final Action
mailed on February 15, 2019.
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Application 14/886,884

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CLAIMED SUBJECT MATTER
The disclosed invention generally relates to “data storage” and, in
particular, “to systems and methods for compression of time-series data.”
Spec. ¶ 1. According to the Specification, “[i]n an increasingly networked
world like in the Internet . . . more time-series data is being sent across
networks more frequently,” which “often necessitates greater bandwidth”
that “may be unavailable or expensive.” Id. ¶ 2. This could also negatively
impact network performance. Id.; see also id. ¶ 14 (disclosing that “the
transmission of time-series data from a source to a destination can create a
significant burden on networks and network-related resources”). One way to
address this problem is “to use an available compression algorithm to
compress time-series data before transmitting it over a network.” Id. ¶ 15.
This approach, however, also has its disadvantages. Id. As one example,
using “[l]ossless compression algorithms . . . cannot generally guarantee
compression of all input data sets.” Id. Consequently, the disclosed
invention provides “dynamically selecting time-series compression
algorithms for a wide range of time-series data. In certain embodiments,
time-series data streams emanating from a data source can be periodically
sampled and profiled to dynamically select a best-fit compression algorithm
for new or subsequent time-series data.” Id. ¶ 16.
Claims 1, 8, and 15 are independent. Independent claim 1 is directed
to “[a] method” implemented “by a computer system,” independent claim 8
is directed to “[a]n information handling system comprising a processor,
wherein the processor is operable to implement a method,” and independent
claim 15 is directed to “[a] computer-program product comprising a non-
transitory computer-usable medium having computer-readable program code
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embodied therein, the computer-readable program code adapted to be
executed to implement a method.” Appeal Br. 14, 16, 18 (Claims App’x).
Claims 2–7 directly or indirectly depend from independent claim 1, claims
9–14 directly or indirectly depend from independent claim 8, and claims 16–
20 directly depend from independent claim 15. Id. at 14–19.
Independent claim 1, reproduced below, is illustrative of the claimed
subject matter:
1. A method comprising, by a computer system:
receiving, from a data source, time-series data of a time-
series data stream produced by the data source;
identifying a target compression algorithm for the time-
series data, wherein the target compression algorithm is linked to
the data source in memory pursuant to a dynamically-
variable assignment;
compressing the time-series data using the target
compression algorithm;
transmitting the compressed time-series data to a
destination;
accessing a sample of the time-series data stream in
relation to a sample period;
determining a time density of time-series data production
by the data source over one or more intervals of the sample
period, wherein the time density of time-series data
production refers to a frequency with which the time-series data
is produced for compression such that greater production over
the one or more intervals constitutes a greater time density and
less production over the one or more intervals constitutes a lower
time density;
generating a time-series profile of the sample using the
time density;
comparing attributes of the time-series profile to stored
algorithm signatures comprising attributes of candidate
compression algorithms, the comparing comprising
identifying similarities between the time-series profile and the
attributes of the candidate compression algorithms;
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selecting a compression algorithm from among a plurality
of compression algorithms based at least in part on a result of the
comparing; and
causing subsequent time-series data received from the data
source to be compressed using the selected compression
algorithm.
Appeal Br. 14.

REFERENCES
The prior art relied upon by the Examiner is set forth in the table
below.
Name3 Reference Date
Geiger US 2002/0101367 A1 Published Aug. 1, 2002
Thomas US 2013/0317659 A1 Published Nov. 28, 2013
Auerbach US 2013/0325690 A1 Published Dec. 5, 2013
Ju US 2014/0146188 A1 Published May 29, 2014
Hayner US 2014/0223010 A1 Published Aug. 7, 2014

REJECTIONS4
The Examiner’s rejections in the Final Action on appeal are set forth
in the table below.

3 For clarity and ease of reference, we only list the first named inventor.
4 The Examiner withdrew the rejection of dependent claims 7 and 14 under
35 U.S.C. § 112(b) as being indefinite. Ans. 3.
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Claims Rejected 35 U.S.C. §5 References
1–3, 5, 8–10, 12, 15–17, 19 103 Hayner, Thomas, Auerbach
4, 11, 18 103 Hayner, Thomas, Auerbach,
Geiger
6, 7, 13, 14, 20 103 Hayner, Thomas, Auerbach,
Ju

EXAMINER’S FINDINGS AND CONCLUSION
The Examiner finds that Hayner’s multiple sensors arranged in a
network structure teaches or suggests most of the limitations of independent
claim 1, except “time-series data” and “time density.” Final Act. 5–8 (citing
Hayner ¶¶ 6, 15, 17, 18, 29, 31, 32, 34–36, 42, Fig. 1 (sensor node 110
consisting of one or more sensors 100), Fig. 2 (bus 220)). The Examiner
turns to Thomas’s data items with associated time stamps that are uploaded
periodically to teach or suggest the claimed “time series data” and, in
particular, “accessing a sample of the time-series data stream in relation to a
sample period.” Id. at 6 (citing Thomas ¶¶ 8, 10). The Examiner turns to
Auerbach’s system that selects a compression scheme based on the time
density of the data to teach or suggest “time density” and, in particular,
“determining a time density of time-series data production by the data source
over one or more intervals of the sample period.” Id. at 6–7 (citing
Auerbach ¶¶ 5, 10, 48, 59, 60, 85–90). Of particular importance to this case
is the Examiner’s reliance on Hayner’s disclosure of using lossless or lossy

5 The Leahy-Smith America Invents Act, Pub. L. No. 112–29, 125 Stat. 284
(2011) (“AIA”), amended 35 U.S.C. §§ 102 and 103. Because the
application at issue contains claims having an effective filing date after
March 16, 2013, which is the effective date of the applicable AIA
amendments, we refer to the post-AIA version of 35 U.S.C. § 103.
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compression algorithms depending on whether a sensor signal contains
dynamic or steady-state data. Final Act. 5 (citing Hayner ¶¶ 35, 36); Ans. 5–
6, 8–10. According to the Examiner, it would have been obvious to a person
of ordinary skill in the art to combine the teachings of Hayner with those of
Thomas and Auerbach “to determine when events happened for analysis
purposes and to improve energy usage,” as well as “to optimize compression
algorithm selection.” Final Act. 6 (citing Thomas ¶¶ 5, 10), 7 (citing
Auerbach ¶ 87).
The Examiner relies on essentially the same findings and conclusion
identified above in the obviousness rejection of independent claim 1 to
support the obviousness rejection of independent claims 8 and 15.
Final Act. 8–9.

APPELLANT’S CONTENTIONS
The Appellant contends the Examiner’s obviousness rejection based
on the combined teachings of Hayner, Thomas, and Auerbach does not
account for the “stored algorithm signatures comprising attributes of
candidate compression algorithms,” as recited in independent claim 1.
Appeal Br. 7; Reply Br. 3. According to the Appellant, the Examiner directs
us to Hayner’s lossless or lossy compression algorithms to teach or suggest
this limitation, but does not explain how Hayner stores algorithm signatures
comprising attributes of these compression algorithms for comparison.
Appeal Br. 7. Appellant further contends that Hayner does not disclose the
remaining features of the “comparing” step recited in independent claim 1
because Hayner does not specify any attributes of the dynamic or steady-
state data contained within a sensor signal that are compared with stored
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attributes of the lossless and lossy compression algorithms. Appeal Br. 8–9;
Reply Br. 6–7.
Appellant relies upon the same arguments presented against the
Examiner’s obviousness rejection of independent claim 1 to rebut the
Examiner’s obviousness rejection of independent claims 8 and 15. Appeal
Br. 11.

ISSUE
The dispositive issue before us is whether the Examiner presents
sufficient evidence to support a finding that Hayner teaches “comparing
attributes of the time-series profile to stored algorithm signatures
comprising attributes of candidate compression algorithms, the comparing
comprising identifying similarities between the time-series profile and the
attributes of the candidate compression algorithms,” as recited in each of
independent claims 1, 8, and 15 (“the ‘comparing’ step”).

ANALYSIS
Section 103 Rejection Based on the Combined Teachings of
Hayner, Thomas, and Auerbach
Claims 1, 8, and 15
Based on the record before us, we discern error in the Examiner’s
obviousness rejection of independent claims 1, 8, and 15, each of which
recites, in relevant part, the “comparing” step.
As an initial matter, we agree with Appellant that the Examiner does
not explain adequately how Hayner stores algorithm signatures comprising
attributes of lossless or lossy compression algorithms that may be used for
comparison purposes, as required by independent claims 1, 8, and 15. See
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Appeal Br. 7. Hayner discloses that the “[d]ata generated by [its] sensor
network and passed to the central processing system 180 may be,” amongst
other things, “stored for later use in data storage 190.” Hayner ¶ 18, Fig. 1.
Hayner, however, is silent as to whether the data stored for later use in data
storage 190 includes attributes describing lossless or lossy compression
algorithms that may be used for comparison purposes.
To the extent the Examiner takes the position that Hayner’s lossless
and lossy compression algorithms, and the dynamic or steady-state data that
describes these compression algorithms, teaches “stored algorithm
signatures comprising attributes of candidate compression algorithms,” we
do not agree. See Ans. 9. Although Hayner discloses that dynamic or
steady-state data amounts to attributes that describe lossless or lossy
compression algorithms, the Examiner does not direct us to, nor can we find,
a disclosure in Hayner indicating that these attributes are stored (e.g., in data
storage 190) so that they later may be accessed and compared against data
within a sensor signal to determine whether that data includes similar
attributes. Stated differently, what is missing from the Examiner’s
obviousness analysis is an explanation as to whether Hayner stores attributes
of lossless or lossy compression algorithms, such that upon receiving a
sensor signal, the attributes of the data within that signal may be compared
against previously stored attributes. At best, Hayner discloses determining
whether a sensor signal includes dynamic or steady-state data and, based on
that determination, either applies the lossless compression algorithm if the
data is dynamic or, alternatively, applies the lossy compression algorithm if
the data is steady-state. Hayner ¶¶ 32, 34–36. In these two scenarios, there
is no comparison between the dynamic or steady-state attribute of the data
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contained with the sensor signal against previously stored attributes to
determine if there are similarities that might be indicative of whether to
select either a lossless or lossy compression algorithm.
We need not reach the merits of Appellant’s other arguments because,
as we explained above, the Examiner has not presented sufficient evidence
to support a finding that Hayner teaches the “comparing” step, as recited in
each of independent claims 1, 8, and 15. Accordingly, the Examiner has
erred in determining that the combined teachings of Hayner, Thomas, and
Auerbach renders the subject matter of independent claims 1, 8, and 15
unpatentable.

Claims 2, 3, 5, 9, 10, 12, 16, 17, and 19
By virtue of their dependency, claims 2, 3, 5, 9, 10, 12, 16, 17, and 19
include the same limitations as at least one of independent claims 1, 8, and
15. Therefore, for the same reason set forth above in our discussion of
independent claims 1, 8, and 15, the Examiner has erred in determining that
the combined teachings of Hayner, Thomas, and Auerbach renders the
subject matter of dependent claims 2, 3, 5, 9, 10, 12, 16, 17, and 19
unpatentable.

Remaining § 103 Rejections
Claims 4, 6, 7, 11, 13, 14, 18, and 20
By virtue of their dependency, claims 4, 6, 7, 11, 13, 14, 18, and 20
include the same limitations as at least one of independent claims 1, 8, and
15. As applied by the Examiner, neither Geiger nor Ju remedy the
deficiency in the Examiner’s proffered combination of Hayner, Thomas, and
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Auerbach identified above. Therefore, for the same reasons discussed above
with respect to independent claims 1, 8, and 15, the Examiner has erred in
determining that (1) the combined teachings of Hayner, Thomas, Auerbach,
and Geiger renders the subject matter of dependent claims 4, 11, and 18
unpatentable; and (2) the combined teachings of Hayner, Thomas, Auerbach,
and Ju renders the subject matter of dependent claims 6, 7, 13, 14, and 20
unpatentable.

CONCLUSION
For the foregoing reasons, the Examiner has erred in rejecting claims
1–20 as unpatentable under § 103. We, therefore, reverse the Examiner’s
decision to reject these claims.

DECISION SUMMARY
In summary:
Claims
Rejected
35 U.S.C. § References Affirmed Reversed
1–3, 5, 8–10,
12, 15–17,
19
103 Hayner, Thomas,
Auerbach
1–3, 5, 8–10,
12, 15–17,
19
4, 11, 18 103 Hayner, Thomas,
Auerbach, Geiger
4, 11, 18
6, 7, 13, 14,
20
103 Hayner, Thomas,
Auerbach, Ju
6, 7, 13, 14,
20
Overall
Outcome
1–20
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REVERSED