Ex Parte Yeck et al

Patent Trial and Appeal Board

Nov 25, 2016

13625753 (P.T.A.B. Nov. 25, 2016)

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.
13/625,753 09/24/2012 Todd Yeck 94261-095610US-851324 5161
142801 7590 11/25/2016
Bio-Rad Laboratories, Inc.
Kilpatrick Townsend and Stockton LLP
2 Embarcadero Center
Suite 1900
San Francisco, CA 94111
EXAMINER
BORIN, MICHAEL L
ART UNIT PAPER NUMBER
1631
MAIL DATE DELIVERY MODE
11/25/2016 PAPER
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.
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte TODD YECK, NIELS KIRK THOMSEN,
and JOHN CLOPTON DUNAWAY1
Appeal 2015-003047
Application 13/625,753
Technology Center 1600
Before ERIC B. GRIMES, MELANIE L. McCOLLUM, and
RYAN H. FLAX, Administrative Patent Judges.
FLAX, Administrative Patent Judge.
DECISION ON APPEAL
This is a decision on appeal under 35 U.S.C. § 134(a) involving
claims directed to a method of (or program/system for) biochemical data
analysis. Claims 1—21 are on appeal as rejected under 35 U.S.C. § 103(a).
We have jurisdiction under 35 U.S.C. § 6(b).
We affirm with respect to the rejection as to claim 1 and reverse with
respect to the rejection as to claims 3, 4, 7, and 11.
1 We understand the Real Party in Interest to be Bio-Rad, Digital Biology
Center. App. Br. 3.
Appeal 2015-003047
Application 13/625,753
STATEMENT OF THE CASE
“The present invention relates generally to biochemical data analysis,
and more specifically to analysis of biochemical data using user-supplied
parameters.” Spec. 12. The Specification states,
[experimental] data can be voluminous with a wide variety of
characteristics, and consequently cumbersome to manage and
analyze. Current users often employ Excel, performing many
manual steps for importing data into spreadsheets, for selecting
categories of data from the entire dataset for evaluation and
comparison, and for providing macros for statistical calculations
and charting[,]
but this presents difficulties to users, is time consuming, and causes potential
errors and invokes business risk. Spec. 13.
The Specification describes an analysis system that can receive “an
experimental results dataset for a plurality of biological samples.” Spec.
138. Such a “dataset may comprise . . . various attributes (fields),” with
information such as “gender, age, disease condition, etc.” Spec. 139.
A system user selects an analyte for data analysis, e.g., blood plasma
levels of analyte “X,” using a graphical interface drop-down menu/box.
Spec. 142. Then, the user selects a “compare field,” for example, “gender,
ethnicity, sex, [or] disease condition.” Spec. 143. The Specification further
explains, “user supplied parameters and criteria are employed to generate
sub-groups of data for statistical analysis” where,
the sub-groups are based on the selected compare field. For
example, given a dataset for analysis having 900 total rows of
data: with 300 rows having a value of “lung cancer” for a
condition column, 300 rows having a value of “colon cancer” for
a condition column, and 300 rows having a value of “normal” for
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a condition column, three different sub-groups may be
determined.
Spec. 1 50.
Finally, “many options exist for the data to be analyzed, the
calculations to be performed on the data, and how the data and any
calculated values (e.g. statistical values) are to be displayed.” Spec. | 51.
“Various plots generated based on the statistical analysis may be displayed
for each sub-population of data.” Spec. 1 54. “The displayed graphs may be
scatter plots, bar charts, box and whisker charts, or any other suitable
graphical representation of statistical data.” Spec. 1 55.
The appealed claims can be found in the Claims Appendix of the
Appeal Brief. Claims 1, 14, and 18 are the independent claims. Claim 1 is
representative and reads as follows:
1. A method of biochemical data analysis, the method
comprising:
receiving, at a computer system, a dataset for a plurality of
biological samples, the dataset having a plurality of fields for
each biological sample, at least a portion of the dataset being
obtained from experiments involving the biological samples,
wherein the dataset includes:
a plurality of first fields, each first field including a
plurality of values, each value corresponding to a
respective characteristic of a respective biological sample,
and
a plurality of second fields, each second field
corresponding to a respective analyte and including a
plurality of concentrations of the respective analyte in the
experiments, each concentration corresponding to a
respective biological sample;
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displaying a list of the plurality of first fields in a first region of
a user interface (UI) page of the computer system in response to
a selection of a drop-down box in the UI page;
receiving a selection of a compare field from the list of the
plurality of first fields;
identifying, by the computer system, subgroups of the biological
samples in the dataset for statistical analysis based on the
plurality of values for the compare field, wherein biological
samples of a subgroup have a same value for the compare field;
displaying a list of the plurality of second fields in a second
region of the UI page;
receiving a selection of an analyte from the list of the plurality of
second fields for statistical analysis; and
providing, based on the received selections of the analyte and the
compare field, a display of information separated by subgroups
to convey statistical information for the selected analyte for each
subgroup of the compare field, wherein the information
separated by subgroups is displayed within a third region of the
UI page to facilitate visual comparison.
App. Br. 16 (Claims App’x).
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The following rejection is on appeal:
Claims 1—21 stand rejected under 35 U.S.C. § 103(a) over Ford,2
Robbins,3 and SigmaPlot4 or Cappione5 or Yakhini6 or Leban7 or Kelly8 or
Zeringue.9 Final Action 3.
Except where otherwise indicated, we adopt the Examiner’s findings
of fact, reasoning on scope and content of the prior art, and conclusions set
out in the Final Action and Answer. The findings of fact set forth below are
provided only to highlight certain evidence of record.
FINDINGS OF FACT
FF1. Ford disclosed:
The present invention relates to methods of formulating analyte
data databases (comprising, analyte data points, derived data, and
data attributes), the analyte data databases themselves, and to
2 U.S. Patent Application Pub. No. US 2002/0045808 A1 (published Apr.
18, 2002) (hereinafter “Ford”).
3 U.S. Patent Application Pub. No. US 2009/0307527 A1 (published Dec.
10, 2009) (hereinafter “Robbins”).
4 SigmaPlot, Exact Graphs and Data Analysis (brochure, update available at
http://www.sigmaplot.com/products/sigmaplot/produpdates/prod-
updates5.php) (dated 2010—11; see Ans. 9) (hereinafter “SigmaPlot”).
5 U.S. Patent Application Pub. No. US 2008/0263468 A1 (published Oct. 23,
2008) (hereinafter “Cappione”).
6 U.S. Patent Application Pub. No. US 2004/0080536 A1 (published Apr.
29, 2004) (hereinafter “Yakhini”).
7 U.S. Patent Application Pub. No. US 2010/0082634 A1 (published Apr. 1,
2010) (hereinafter “Leban”).
8 U.S. Patent Application Pub. No. US 2009/0248443 A1 (published Oct. 1,
2009) (hereinafter “Kelly”).
9 U.S. Patent Application Pub. No. US 2009/0024940 A1 (published Jan 22,
2009) (hereinafter “Zeringue”).
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methods for manipulating and analyzing the analyte data
databases.
Ford 1 5; and see claims 1—7; see also Final Rejection 3—8 and Ans.
2—8 (discussing Ford). Thus, Ford disclosed biochemical data
analysis utilizing a dataset of biological samples.
FF2. Ford disclosed:
A “database” is a collection of data points and data attributes
associated with each data point. Thus, an “analyte data points,
derived data, and data attributes database” is a database
comprising data points collected, e.g. by an analyte monitoring
device, data derived from the original data points and the data
attributes associated with those data points or the derived data.
A database may be limited to data points comprising
measurements of one or more analyte levels; those data points
may further be collected from one or more subjects.
Ford 1 61; and see claims 1—13, 73—74, 96—97; see also Final
Rejection 3—8 and Ans. 2—8 (discussing Ford). Thus, Ford disclosed a
dataset as a collection of data for biological samples, including
analyte data points, such as concentrations of analyte(s) as values.
FF3. Ford disclosed, “[ajnalyte measurements and derived data
points are collected and calculated, respectively, and may be
associated with one or more data attributes to form a database,” and
analyte attributes include, but are not limited to:
chronological information . . . ; user perspiration levels ...;[]
device operating temperature . . . ; user body temperature; user
skin conductance; environmental variables (e.g., temperature,
temperature changes, humidity, sun exposure, etc.) and number
and type (e.g., hyperglycemic or hypoglycemic) of alarm
events [;]
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. . . various activities affecting analyte levels, such as caloric
intake and/or output (e.g., food, physical activity, etc.), sleep and
administration of medications, including the dose and time
thereof^;]
. . . analyte values[;]. . . and
. . . subject identifiers, i.e. characteristics associated with a
particular subject [such as:]... (1) a subject code (e.g., a numeric
or alpha-numeric sequence); (2) demographic information such
as race, gender and age; (3) physical characteristics such as
weight, height and body mass index (BMI); (4) selected aspects
of the subject’s medical history (e.g., number of pregnancies,
disease states or conditions, etc.); and (5) disease-associated
characteristics such as the type of analyte disorder, if any; the
type of medication used by the subject, if any; and the presence
or absence of surrogate analyte markers.
Ford 114—118 (section 2.5); and see claim 12; see also Final
Rejection 3—8 and Ans. 2—8 (discussing Ford). Thus, Ford disclosed
that analyte data points of its disclosed dataset are associated with
other information or values relating to the respective biological
subjects or biological data-gathering.
FF4. Ford disclosed:
“Data mining” refers to the process of selecting, exploiting,
modeling, etc., large amounts of data to uncover previously
unknown trends, patterns, and relationships within and among
various data points and data attributes. “Data aggregation” and
“data clustering” refers to the process of grouping data points on
the basis of one or more common attributes. Conversely, “data
segmentation” refers to the process of differentiating data into
discrete groups on the basis of one or more attributes.
Ford 1 61; and see claims 1, 9, 13, and 27; see also Final Rejection 3—
8 and Ans. 2—8 (discussing Ford); see also, e.g., Ford 1157
(disclosing a computer system receiving a dataset and request for
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database server resources). Thus, Ford disclosed a variety of ways of
organizing, analyzing, sub-grouping, filtering, and comparing data
points in a dataset of biological samples.
FF5. Ford disclosed:
Usually each networked computer system, PDA or PPC includes
a World Wide Web browser that provides a user interface to the
networked database server. The networked computer system,
PDA or PPC is able to construct search requests for retrieving
information from a database via a Web browser. With access to
a Web browser users can typically point and click to user
interface elements such as buttons, pull down menus, and other
graphical user interface elements to prepare and submit a query
that extracts the relevant information from the database.
Requests formulated in this manner are subsequently transmitted
to the Web application that formats the requests to produce a
query that can be used to extract the relevant information from
the database.
Ford 1158; see also Final Rejection 3—8 and Ans. 2—8 (discussing
Ford). Thus, Ford disclosed querying a system via, e.g., cursor
movements and mouse clicks at graphical objects, such as buttons and
pull-down menus, the system receiving the query, and the system
displaying relevant database information, e.g., a sub-group of data, in
response to the query.
FF6. Ford disclosed:
Graphical User Interface
In certain of the computer systems, an interface such as an
interface screen that includes a suite of functions is included to
enable users to easily access the information they seek from the
databases of the invention. Such interfaces usually include a
main menu page from which a user can initiate a variety of
different types of analyses (such as discussed above, for
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example, initiate a search for hypoglycemic events and related
attributes, followed by initiating a selected analysis to identify
salient factors). For example, the main menu page for the
databases generally include buttons for accessing certain types of
information, including, but not limited to, project information,
inter-project comparisons, times of day, events, dates, times,
ranges of analyte values, etc.
Ford 163—64; see also Final Rejection 3—8 and Ans. 2—8
(discussing Ford). Thus, Ford disclosed visual displays and a suite of
functions for users to show information about both analyte values,
e.g., concentrations, and related attribute information of the respective
biological samples, e.g., age, gender, disease condition, for
comparisons and analysis.
FF7. Ford disclosed its database design can be rational, relational,
and/or dimensional, where a relational database supports a set of
operations defined by relational algebra and includes tables composed
of columns and rows for the data of the database and typically
supports operations to select, join, and combine data. Ford Tflf 123—
26; see also Final Rejection 3—8 and Ans. 2—8 (discussing Ford).
FF8. Ford disclosed:
data sets may be aggregated, sorted, selected, sifted, clustered
and segregated by means of the attributes associated with the data
points. A number of database management systems and data
mining software programs exist which may be used to perform
the desired manipulations.
Relationships in the database can be directly queried and/or the
data analyzed by statistical methods to evaluate the information
obtained from manipulating the database.
For example, a distribution curve can be established for a
selected data set, and the mean, median and mode calculated
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therefor. Further, data spread characteristics, e.g. variability,
quartiles and standard deviations can be calculated.
The nature of the relationship between a particular variable and
analyte levels can be examined by calculating correlation
coefficients. Useful methods for doing so include but are not
limited to the following: Pearson Product Moment Correlation
and Spearman Rank Order Correlation.
Ford 130-33; see also Final Rejection 3—8 and Ans. 2—8
(discussing Ford). Thus, Ford disclosed a variety of ways of
organizing, manipulating, choosing, and filtering data points of a
database based on associated attributes and relationships between
biological sample attributes and respective analyte information.
Moreover, Ford disclosed developing and conveying statistical
information relating to and comparing these data points. Such
analyses are displayed to a user. See, e.g.. Ford H 147 and 154.
FF9. Robbins also disclosed drop-down menus for selecting fields
of information from a database. See, e.g., Robbins Fig. 9 (element
348), 140, claim 19; see also Ans. 5 (discussing Robbins).
FF10. Robbins disclosed the term “fields” refers to data associated
with subjects of a database. See, e.g., Robbins claim 15; see also Ans.
5 (discussing Robbins).
FF11. SigmaPlot disclosed “a scientific data analysis and graphing
software package” where data may be analyzed (and dynamically
updated) based on a selection of analyte data points from a table menu
and via an “Interactive Graph Wizard.” SigmaPlot 2; see also Ans. 5—
6 (discussing common features of data analysis and graphing software
packages and displays).
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FF12. SigmaPlot disclosed multiple windows of information (e.g.,
data points) and graphical displays on a single screen. SigmaPlot 2;
see also Ans. 5—6 (discussing common features of data analysis and
graphing software packages and displays).
FF13. SigmaPlot disclosed the ability to select “up to four
measured variables to quickly detect and demonstrate possible product
defects.” SigmaPlot 5.
FF14. Cappione disclosed, “[a] graphical user interface on a
computer for the analysis of location specific data and the presentation
of analysis results for visual comparison by a user” and shows a
display having multiple regions thereon for showing different sets of
data or analyses. Cappione Abstract, Figs. 15—18; see also Ans. 5—6
(discussing common features of data analysis and graphing software
packages and displays).
FF15. Yakhini disclosed, “[a]n interactive user interface that
allows a user to display microarray data, and other data, including
genetic, biochemical, and chemical data, in various ways to facilitate
human analysis of the displayed data within the context of the genetic,
biochemical, chemical, or other experiments from which the data is
obtained,” and “[t]he user interface provides a user with the ability to
rank and sort data on a row basis, as well as the ability to partition
columns into meaningful groups” and “allows a researcher to modify
ranking, partitioning, scaling, and other parameters of the display in
real time, in order to visually explore and navigate various different
relationships and correlations between individual data array.”
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Yakhini Abstract; see also Ans. 5—6 (discussing common features of
data analysis and graphing software packages and displays).
FF16. Leban disclosed, “[a] computer-implemented system,
method, and user interface for searching and organizing information,”
and “[information is organized and searched according to content,
and this organization is reflected directly in the user interface
provided to users for searching as well as the search results they are
shown.” Leban Abstract, Figs.; see also Ans. 5—6 (discussing
common features of data analysis and graphing software packages and
displays).
FF17. Kelly disclosed showing multiple windows of database
information, including drop-down menus for selecting data groups, on
a display. Kelly Figs. 5—19; see also Ans. 5—6 (discussing common
features of data analysis and graphing software packages and
displays).
FF18. Zeringue disclosed “[a] computer-implemented graphical
user interface system for generating a database query includes a create
region, a plurality of clause-specific regions and a navigation region”
and displays of multiple windows of database information. Zeringue
Abstract, Fig. 2; see also Ans. 5—6 (discussing common features of
data analysis and graphing software packages and displays).
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DISCUSSION
The rejection of claim 1 under 35 U.S.C. § 103(a) over Ford, Robbins,
and SigmaPlot or Cappione or Yakhini or Leban or Kelly or Zeringue.
The Examiner established a prima facie case that claim 1 would have
been obvious over the cited prior art. Appellants have not presented
persuasive evidence that this determination is incorrect. We address
Appellants’ arguments below.
“The combination of familiar elements according to known methods
is likely to be obvious when it does no more than yield predictable results.”
KSR Inti Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007). “[W]hen the
question is whether a patent claiming the combination of elements of prior
art is obvious,” the answer depends on “whether the improvement is more
than the predictable use of prior art elements according to their established
functions.” Id. at 417. “[T]he analysis need not seek out precise teachings
directed to the specific subject matter of the challenged claim, for a court
can take account of the inferences and creative steps that a person of
ordinary skill in the art would employ.” Id. at 418.
First, Appellants contend the Examiner’s use of the word “could”
instead of “would,” in relation to how one of ordinary skill in the art
could/would interpret and understand the teachings of the prior art was
improper. App. Br. 7—8. This is not persuasive. Although the rejection in
the Final Rejection uses the word “could” instead of “would,” the Examiner
provides a reason for combining the references: “to provide convenience of
comprehensive data analysis and display of the results.” (Final Rejection 7.)
In any event, Appellants have been well apprised of the Examiner’s
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determinations and rationale on the obviousness of the claimed subject
matter and have mounted a full and complete response thereto.
Appellants contend the Examiner’s cited prior art combination fails to
teach “displaying a list of the plurality of first fields in a first region of a user
interface (UI) page of the computer system in response to a selection of a
drop-down box in the UI page.” App. Br. 8. The argument is not
persuasive.
As determined by the Examiner, the Ford reference disclosed a
database (or dataset) composed of data points for analytes (concentrations)
and related attributes of respective biological samples or subjects. FF1—
FF10. Further, as determined by the Examiner, Ford disclosed that such data
is accessed and viewed and manipulated by a user via a graphical user
interface connected to a computer system or network. FF5—6. Further, as
determined by the Examiner, Ford disclosed that a user queries and extracts
information, e.g., data mining, from the database using typical and well
known on-screen interactions, such as cursor clicks and drop-down menus.
FF4 and FF5. It would have been obvious to populate the disclosed drop­
down menus with the also disclosed analyte and attribute information (as
“fields,” or areas/spaces/regions, of data) for this purpose and it would
likewise have been obvious that the system must “receive” inputs, such as
selections of data or groups of data from such datasets, in order to provide
the data mining results requested by the user.
Appellants also argue that the cited prior art combination fails to teach
displaying the first and second fields in first and second regions of the UI
page and providing a display of information relating to selections of the first
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and second fields in a third region of the UI page. App. Br. 10. This
argument is not persuasive.
As discussed above, as determined by the Examiner, it would have
been obvious, based on Ford, to populate various drop down menus with
analyte data and attribute data or, put in the language of the claims, first
fields of biological sample characteristic values and second fields of
respective analyte concentration values of those biological samples. See
FF1—FF3, FF5; see also FF9 and FF10 (Robbins). Further, it would also
have been obvious to show these drop down menus on a display, as taught
by Ford. FF5—FF6. Further, it would have been obvious to sort, select and
segregate the data selected via these drop down menus to conduct statistical
analysis about relationships therebetween, as disclosed by Ford. FF8.
Moreover, it would have been obvious to also show a visual display of this
analysis, as disclosed by Ford, as, e.g., a distribution curve. FF8. As shown
by the various other references (see SigmaPlot, Cappione, Yaknini, Leban,
Kelly, and Zeringue), displaying the drop down menus and the visual
depiction of statistical analysis in various windows or, to use the language of
the claims, regions, on a screen would also have been obvious. FF11—FF18.
Finally, Appellants argue that the SigmaPlot reference is not prior art.
App. Br. 7. However, it is conceded by Appellants that the SigmaPlot
reference relates to (“lists”) a product for the years 2010—2011. Id. If the
brochure and product were available anytime in the year 2010, then the
relevant date for the reference can be no later than December 31, 2010,
which makes it prior art to the appealed claims.
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For the reasons above, we affirm the obviousness rejection of claim 1.
Because they are not separately argued, claims 2, 5, 6, 8—10, and 12—21 fall
with claim 1. See 37 C.F.R. § 41.37(c)(l)(iv).
The rejection of claims 3, 4, 7, and 11 under 35 U.S.C. § 103(a) over
Ford, Robbins, and SigmaPlot or Cappione or Yakhini orLeban or Kelly or
Zeringue.
“[W]hen obviousness is at issue,. . . [t]he examiner, and if later
involved, the Board, retain the ultimate burden of persuasion on the issue.
If, as a matter of law, the issue is in equipoise, the applicant is entitled to the
patent.” In re Oetiker, 977 F.2d 1443, 1449 (Fed. Cir. 1992) (J. Plager,
concurring).
Here, the Examiner has provided only a cursory explanation as to the
rationale for the rejection of these depending claims, basing it on the
contention that the limitations added in these claims are “minor in nature,”
“known per sef “slight constructional changes,” and/or “conventional
steps.” See Ans. 7. Appellants object to this unsupported, conclusory
rationale. App. Br. 13—14. We agree with Appellants.
“[Rjejections on obviousness grounds cannot be sustained by mere
conclusory statements; instead, there must be some articulated reasoning
with some rational underpinning to support the legal conclusion of
obviousness.” KSR 550 U.S. at 418 (quoting In re Kahn, 441 F.3d 977, 988
(Fed. Cir. 2006)). There may well be rational reasons, based on the prior art
of record, why these dependent claims would have been obvious over the
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cited prior art, but these reasons are not presented by the Examiner here. We
reverse the rejection of claims 3, 4, 7, and 11.
SUMMARY
The rejection under 35 U.S.C. § 103(a) over Ford, Robbins, and
SigmaPlot or Cappione or Yakhini or Leban or Kelly or Zeringue is affirmed
with respect to claims 1, 2, 5, 6, 8—10, and 12—21 and is reversed with
respect to claims 3, 4, 7, and 11.
TIME PERIOD FOR RESPONSE
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a).
AFFIRMED-IN-PART
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