Ex Parte Fadem et al

Patent Trial and Appeal Board

Feb 10, 2017

13228626 (P.T.A.B. Feb. 10, 2017)

United States Patent and Trademark Office
UNITED STATES DEPARTMENT OF COMMERCE
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APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
13/228,626 09/09/2011 Kalford C. Fadem 0103701.0580144 7908
26874 7590 02/14/2017
FROST RROWN TODD T T C EXAMINER
3300 Great American Tower UDDIN, MD I
301 East Fourth Street
CINCINNATI, OH 45202 ART UNIT PAPER NUMBER
2169
NOTIFICATION DATE DELIVERY MODE
02/14/2017 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.
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PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte KALFORD C. FADEM and MAUKTIK V. KULKARNI
Appeal 2016-002939
Application 13/228,626
Technology Center 2100
Before JOHN A. JEFFERY, DENISE M. POTHIER, and
JASON M. REPKO, Administrative Patent Judges.
REPKO, Administrative Patent Judge.
DECISION ON APPEAL
STATEMENT OF THE CASE
Appellants appeal under 35 U.S.C. § 134(a) from the Examiner’s
rejection of claims 1—11 and 13—20. App. Br. 5.1 Claim 12 has been
canceled. Id. We have jurisdiction under 35 U.S.C. § 6(b).
We reverse.
1 Throughout this opinion, we refer to (1) the Non-Final Action (“Non-
Final”) mailed October 6, 2014, (2) the Appeal Brief (“App. Br.”) filed May
4, 2015, (3) the Examiner’s Answer (“Ans.”) mailed November 17, 2015,
and (4) the Reply Brief (“Reply Br.”) filed January 15, 2016.
Appeal 2016-002939
Application 13/228,626
THE INVENTION
Appellants’ invention renders a diagnosis of a patient by fusing
biomarker data. Spec. 198. Biomarker data can include, for example, data
obtained through Evoked Response Potential (ERP) testing2 and magnetic
resonance imaging (MRI) data. Id. ]Hf 2, 98. In one embodiment, fusing
involves combining several determinations, where each determination uses a
different type of biomarker data. See, e.g., id. 1100. In these
determinations, the system uses algorithms to classify and diagnose patients
exhibiting the biomarkers contained in class-set definitions and feature-set
definitions. Id. ]f 98.
Claim 1 is reproduced below with emphasis:
1. An apparatus comprising:
(a) a ERP testing system, wherein the ERP testing system
is configured to administer an ERP test;
(b) a computer in communication with the ERP testing
system, wherein the computer is configured to access a database
of biomarkers, wherein the computer is configured to select at
least one class set definition and at least one feature set
definition, wherein the at least one class set definition includes a
class set based on a variable present in the database of
biomarkers, wherein the feature set definition comprises a
biomarker based on additional biomarker criteria, wherein the
computer is configured to form at least one training set definition
from the at least one class set definition and the at least one
feature set definition;
wherein the computer is further configured to
automatically render an original diagnosis by fusing biomarker
data using the training set definition,
2 An ERP test system is used to test for diseases or conditions within the
cerebral cortex. Spec. 12. The test system provides stimuli to a test subject,
while electrodes positioned on the subject’s head detect ERPs associated
with each stimuli. Id.
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Application 13/228,626
wherein at least some of the fused biomarker data is
acquired through the ERP testing system.
THE REJECTIONS
The Examiner relies on the following as evidence:
McDonough
Rabinowitz
Fadem
Mahesh
Oez
Monk
Helfman
US 2006/0135854 A9 June 22, 2006
US 2007/0027636 A1 Feb. 1, 2007
US 2007/0191727 A1 Aug. 16, 2007
US 2010/0076780 A1 Mar. 25, 2010
US 2010/0114598 A1 May 6, 2010
US 2010/0115288 A1 May 6, 2010
US 2011/0016432 A1 Jan. 20, 2011
Claims 1—4, 6, 7, 9, and 13—15 are rejected under 35 U.S.C. § 103(a)
as unpatentable over Mahesh and Fadem. Non-Final 3—7.
Claim 5 is rejected under 35 U.S.C. § 103(a) as unpatentable over
Fadem, Mahesh, and Rabinowitz. Non-Final 7—8.
Claim 8 is rejected under 35 U.S.C. § 103(a) as unpatentable over
Fadem, Mahesh, and McDonough. Non-Final 8—9.
Claims 10 and 11 are rejected under 35 U.S.C. § 103(a) as
unpatentable over Fadem, Mahesh, and Helfman. Non-Final 9-10.
Claims 16 and 18 are rejected under 35 U.S.C. § 103(a) as
unpatentable over Mahesh and Oez. Non-Final 10—14.
Claim 17 is rejected under 35 U.S.C. § 103(a) as unpatentable over
Mahesh, Oez, and Monk. Non-Final 14.
Claim 19 is rejected under 35 U.S.C. § 103(a) as unpatentable over
Mahesh, Oez, and Rabinowitz. Non-Final 14—15.
Claim 20 is rejected under 35 U.S.C. § 103(a) as unpatentable over
Mahesh, Oez, and McDonough. Non-Final 15—16.
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THE OBVIOUSNESS REJECTION OVER MAHESH AND FADEM
Contentions
In rejecting claim 1 under § 103, the Examiner finds that Fadem
automatically renders an original diagnosis by fusing biomarker data using a
training-set definition. Non-Final 3. The Examiner, however, finds that
Fadem does not form a training-set definition from the recited class-set and
feature-set definitions. Id. at 4.
In concluding that claim 1 would have been obvious, the Examiner
cites Mahesh for these and other features. Id. at 4—5. In particular, the
Examiner finds that (1) Mahesh’s report classification (step 304)
corresponds to the recited class-set definition, (2) Mahesh’s measurement of
a medical report’s severity (step 306) corresponds to the recited feature-set
definition, and (3) Mahesh’s record update (step 308) corresponds to the
recited training-set definition. Id.
Appellants contend that Fadem does not create a training-set
definition, and it would not have been obvious to add Mahesh’s teachings to
arrive at the claimed invention. App. Br. 11—12; Reply Br. 8—9. According
to Appellants, Mahesh receives pre-existing medical reports already
containing a diagnosis, and Fadem requires a training set to render a
diagnosis. App. Br. 11. In Appellants’ view, one of ordinary skill in the art
would not use Mahesh’s data to form Fadem’s training-set definition.
Id. at 11—12; Reply Br. 9.
Issues
(1) Under § 103, has the Examiner erred in rejecting claim 1 by
finding that Fadem and Mahesh collectively would have taught or suggested
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rendering an original diagnosis by fusing biomarker data using a training-set
definition formed from the recited class-set and feature-set definitions?
(2) Has the Examiner supported the obviousness conclusion with
articulated reasoning with some rational underpinning?
Analysis
Claim 1 recites, in part, three definitions: (1) a class-set definition, (2)
a feature-set definition, and (3) a training-set definition. For example,
Appellants’ system can define a “class set” by a height criterion to form a set
of patients less than six feet tall. Spec. 192. In addition, the system may
define a “feature set” based on a criterion unrelated to the one used to form
the class set. Id. ]Hf 94—95. The training-set definition can contain
instructions to identity patients matching the class-set definition, and then
extract features from those patients based on the feature-set definition.
Id. 196. The system uses the training-set definition to diagnose patients
exhibiting one of the biomarkers used in the class-set definition or feature-
set definition. Id. 198.
Although the recited definitions are not limited to these particular
examples in the disclosure (see id. 138), these embodiments nevertheless
inform our construction of claim 1. That is, under the plain meaning of its
terms, claim 1 requires, in part, forming a third definition (i.e., a training-set
definition) from two others related to classes and features (i.e., a class-set
definition and a feature-set definition), and using this third definition to
render a diagnosis.
The Examiner finds that Fadem’s classification template corresponds
to the recited training-set definition. Ans. 3. In particular, Fadem discloses
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an ERP screening system. Fadem 1127, cited in Non-Final 3. In this
system, analysis computer 71 recognizes waveforms in screening tests to
diagnose neurological conditions. Fadem 1128. As part of this process,
Fadem matches the test results against a class template. Id. 1153.
According to the Examiner, Fadem does not form this template from a class-
set definition and a feature-set definition. Non-Final 4.
The Examiner’s rejection, however, does not contain sufficient
reasoning to support the conclusion that it would have been obvious to
modify Fadem with Mahesh to cure this deficiency. See id. at 3—5. Rather,
the Examiner merely cites and discusses each reference’s teachings without
explaining why the differences between the prior art and the claimed
invention would have been obvious. See id. For at least this reason, we
agree with Appellants that the rejection lacks the essential requirements of a
prima facie case of obviousness. See, e.g., App. Br. 9.
In the Answer’s “Response to Arguments” section, the Examiner
offers a quotation from Mahesh’s Abstract as a justification for combining
the references. Ans. 4 (reproducing the last three lines of Mahesh’s Abstract
without quotation marks). The quoted portion of Mahesh refers to a
healthcare practitioner querying a data store. Id. But the Examiner has not
explained how this query is related to the proposed combination. See id.
The passage quoted by the Examiner also refers to combining the
references to reflect the assigned classification and severity score. Id. But,
given the record, the quoted passage’s relevance to the Examiner’s proposed
combination (Non-Final 3—5) is unclear. Specifically, Mahesh extracts data
from a plain-text report to classify and measure the report’s severity.
Mahesh Fig. 3. But Mahesh’s plain-text reports already contain a diagnosis.
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Id. 114; accord App. Br. 11. The Examiner’s explanation (Ans. 4) lacks a
reason why Mahesh’s reports—or any data associated with the classification
(step 304) or severity measurement (step 306)—would have been combined
with Fadem to render another diagnosis automatically. Such reasoning is
necessary because Fadem uses a computer to render the diagnosis
automatically. Fadem 1128. And to this automated diagnosis, the stated
purpose of Mahesh’s teachings cited by the Examiner—i.e., to draw a
physician’s attention to certain information in a report (Mahesh 114)—
offers no apparent improvement.
Accordingly, Appellants’ argument that one of ordinary skill would
not have combined these teachings (App. Br. 11—12) is persuasive. Because
this issue is dispositive regarding the Examiner’s error in rejecting claim 1,
we need not address Appellants’ remaining arguments.
We, therefore, do not sustain the Examiner’s rejection of independent
claim 1 and dependent claims 2-4, 6, 7, 9, and 13—15 for similar reasons.
THE OBVIOUSNESS REJECTION OVER MAHESH AND OEZ
Contentions
The Examiner finds that Mahesh discloses every recited element of
claim 16 except transmitting a training-set definition to an expert-classifier
module.3 Non-Final 10—13. In concluding that claim 16 would have been
obvious, the Examiner finds that one of ordinary skill would have been
3 Although the Examiner finds that Mahesh lacks a temporary average and
cites Oez as teaching this feature (Non-Final 12—13), a temporary average is
not recited in claim 16. The claim language quoted by the Examiner (id.)
appears in the claim Amendments filed on June 13, 2013.
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motivated to incorporate this feature in Mahesh to “speed up” the
physician’s review of the record. Id. at 12—13. According to the Examiner,
the medical report’s classification and organization is a design choice.
Id. at 13.
The Examiner further finds that Mahesh’s record updater 206
corresponds to the recited expert-classifier module. Id. at 12. In the
Examiner’s view, the limitation “thereby automatically generating an
original diagnosis” is an intended result and should not be given patentable
weight. Ans. 5. Nevertheless, the Examiner finds that Mahesh’s record
updater 206 performs this function. Id. at 4—5 (citing Mahesh || 22, 24, 30,
33-35).
Appellants argue that Mahesh does not generate an original diagnosis
automatically. App. Br. 12—13. According to Appellants, Mahesh’s system
accepts medical reports that must include a diagnosis. Id. at 12. In
Appellants’ view, this diagnosis is not original because the diagnosis was
extracted from a pre-existing medical report. Id. at 12—13.
Issue
Under § 103, has the Examiner erred in rejecting claim 16 by finding
that Mahesh would have taught or suggested executing an expert-classifier
module to generate an original diagnosis automatically?
Analysis
Claim 16 recites, in part, “wherein the computer is further configured
to execute the expert classifier module to thereby automatically generate an
original diagnosis . . . .”
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We disagree with the Examiner’s interpretation that the “thereby”
clause should not be given patentable weight (Ans. 5). The recited
automatic diagnosis gives meaning and purpose to the recited functions.
See Griffin v. Bertina, 285 F.3d 1029, 1033—34 (Fed. Cir. 2002), cited in
MPEP § 2111.04 (9th ed. Rev. 07.2015, Nov. 2015). Indeed, this diagnosis
is a fundamental characteristic of the claimed invention. The computer’s
recited function would have little meaning without generating this diagnosis.
See Griffin, 285 F. 3d at 1034. Accordingly, the phrase “to automatically
generate an original diagnosis” is limiting.
Furthermore, claim 16 expressly requires that the generated diagnosis
is original.
Given this understanding of claim 16, Appellants’ argument that
Mahesh does not generate an original diagnosis automatically (App. Br. 12—
13) is persuasive. The Examiner finds that Mahesh’s record updater 206
corresponds to the recited expert-classifier module. Non-Final 12. But the
Examiner has not shown that record updater 206 generates a diagnosis that is
original. See App. Br. 12—13.
In particular, Mahesh extracts data from a plain-text report to classify
and measure the report’s severity. Mahesh Fig. 3. Record updater 206 then
receives the reports from the classification module 202 and measurement
module 204. Id. 129. As discussed previously, Mahesh’s plain-text reports
contain a diagnosis. Id. 114.
The Examiner, however, has not shown that Mahesh’s record updater
206 generates the report’s original diagnosis or any other of its own, as
required by the claim. See Non-Final 12. Rather, the cited teachings merely
describe a record updater 206 that processes reports containing an existing
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diagnosis. Mahesh Tflf 22, 33—35, cited in Non-Final 12; see also
Mahesh 124, cited in Ans. 4. Specifically, record updater 206 assigns a
value to the report’s “storage property.” Mahesh. 129. Data store 128 uses
this property to store the medical report in the appropriate memory. Id. For
example, record updater 206 assigns the property of “short-term” to a
medical report containing events described as life threatening. Id. This
short-term designation merely causes the data store 128 to store the report in
cache memory for quick retrieval. Id. 134, cited in Non-Final 12.
Accordingly, Appellants’ argument that Mahesh does not generate an
original diagnosis (App. Br. 12—13) is persuasive.
Because this issue is dispositive regarding the Examiner’s error in
rejecting claim 16, we need not address Appellants’ remaining arguments.
We, therefore, do not sustain the Examiner’s rejection of claim 16 and
dependent claim 18 for similar reasons.
THE OTHER OBVIOUSNESS REJECTIONS
We also do not sustain the Examiner’s rejections of dependent claims
5, 8, 10, 11, 17, 19, and 20 for the same reasons discussed above in
connection with claims 1 and 16. The Examiner did not rely upon the
additional references to cure the deficiencies explained previously given the
record. See Non-Final 7—10, 14—15.
DECISION
We reverse the Examiner’s rejection of claims 1—11 and 13—20.
REVERSED
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