PatentNext Summary: Artificial Intelligence
(AI) typically involves certain common aspects such as training
data and AI models trained from that training data. Nonetheless, a
recent Patent Trial and Appeal Board (PTAB) decision found that it
is not always obvious to combine these common aspects to render an
AI-based medical device invention unpatentable.
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Artificial Intelligence (AI) typically involves certain common
aspects. This includes, for example, training data, AI training
algorithm(s) that use the training data to train an AI model, and
predictions and/or classifications as output from the trained AI
model.
Could a person of ordinary skill in the art (e.g., a computer
scientist) find it obvious to combine these common aspects to
arrive at any given AI-based invention?
The Patent Trial and Appeal Board (PTAB) recently answered
“no” to this question in Intel Corp. v. Health
Discovery Corp., IPR2021-00552, Final Written Decision, Paper
No. 38 (September 12, 2022).
Patent-at-Issue
Intel Corp. (“Intel”) had filed a petition to
institute an inter partes review (IPR) of U.S. Patent 7,542,959
(the “‘959 patent”).
As an overview, the ‘959 patent describes an AI-related and
medical device-related invention that uses Support Vector Machines
(SVM) and Recursive Feature Elimination (RFE) for selecting genes
capable of accurately distinguishing between medical
conditions.
Generally, an SVM is a known type of AI algorithm that finds a
“hyperplane” (i.e., a boundary) that distinctly
classifies mapped training data. RFE is a known type of AI
algorithm used to select features (columns) in a training dataset
that have an impact on an output prediction or classification.
The ‘959 patent describes the identification of a
determinative subset of features within a large set of features.
Such identification is performed by training the SVM to rank the
features according to classifier weights and where features are
removed to determine how their removal affects the value of the
classifier weights. Id. “The features having the
smallest weight values are removed, and a new support vector
machine is trained with the remaining weights.” ‘959
Patent, Abstract. “The process is repeated until a relatively
small subset of features remain that is capable of accurately
separating the data into different patterns or classes.”
Id.
Figure 2 shows a flowchart for using a support vector machine
(SVM) in accordance with the ‘959 patent.
As shown in Figure 2, the SVM is trained using training data to
generate an optimal hyperplane. ‘959 Patent at 16:51-17:4. Test
data is input into the trained SVM “to determine whether the
SVM was trained in a desirable manner.” Id. at
17:11-13. If not, the kernel selection is adjusted at step 224, and
the training process is repeated from step 208. Id. at
16:47-57. After the optimal kernel is selected, the SVM is further
optimized through feature selection to reduce the dimensionality of
feature space. See id. at 26:20-33.
The ‘959 patent uses RFE, where the feature corresponding to
the smallest weight in the new classifier is eliminated, and at
each iteration, a new classifier is trained with the remaining
features. Id. at 52:52-64.
Claim 1, which is representative claims-at-issue, and that
recites an SVM (“support vector machine” (bolded)), is
reproduced below.
A computer-implemented method for predicting patterns in
biological data, wherein the data comprises a large set of features
that describe the data and a sample set from which the biological
data is obtained is much smaller than the large set of features,
the method comprising:
identifying a determinative
subset of features that are most correlated to the patterns
comprising:
(a) inputting the data into a
computer processor programmed for executing support vector machine
classifiers;
(b) training a support
vector machine classifier with a training data set
comprising at least a portion of the sample set and having known
outcomes with respect to the patterns, wherein the classifier
comprises weights having weight values that correspond to the
features in the data set and removal of a subset of features
affects the weight values;
(c) ranking the features
according to their corresponding weight values;
(d) removing one or more features
corresponding to the smallest weight values;
(e) training a new classifier
with the remaining features;
(f) repeating steps (c) through
(e) for a plurality of iterations until a final subset having a
pre-determined number of features remains; and
generating at a printer or
display device a report comprising a listing of the features in the
final subset, wherein the final subset comprises the determinative
subset of features for determining biological characteristics of
the sample set.
Petitioner’s Grounds and Prior art
The Petitioner asserted two grounds of invalidity, both pursuant
to Section 103.
The two grounds each relied on three prior art references that
together taught all of the claim elements of the ‘959
patent:
Kohavi: Kohavi teaches a feature subset
selection method for selecting a relevant subset of features upon
which to focus a learning algorithm’s attention while ignoring
the rest. See Kohavi et al., “Wrappers for Feature
Subset Selection,” Artificial Intelligence 97, 273-324
(1997).
Boser: Boser teaches a “pattern
recognition system using support vectors”- i.e., an SVM. US
Patent No. 5,649,068, July 15, 1997, to Boser et al.
Hocking: Hocking teaches an iterative process
that removes variables based on weight-vector ranking until a
subset that provides the best regression is identified.
See Hocking et al., “Selection of the Best
Subset in Regression Analysis,” Technometrics, 9:4, 531-540
(1967).
In particular, Petitioner had argued that skilled artisans would
have been motivated to combine elements of these prior art
references to arrive at the claimed invention.
PTAB’s finding of No Motivation to
Combine
Even though the prior art references taught all elements, the
PTAB held that the Petitioner failed to show that a skilled artisan
would have combined the prior art in the manner cited by the
‘959 patent’s claimed invention.
The PTAB based its decision on Personal Web Techs., LLC v.
Apple, Inc., 848 F.3d 987, 993 (Fed. Cir. 2017), where the
Federal Circuit had found that even though a skilled artisan
may have understood that a set of prior
art references could be combined in a
specific claimed manner, it is not enough; instead, it must be
shown a skilled artisan would have known
to pick out the set of prior art references and combine them to
arrive at the claimed invention. IPR2021-00552, Final
Written Decision at 31.
The PTAB had agreed with the Petitioner that the prior art
references could be combined.
However, the PTAB found that the Petitioner had nonetheless
failed to provide sufficient evidence showing that a skilled
artisan would have been motivated to do
so
[W]e are not persuaded by Petitioner’s evidence and
contention that a skilled artisan would have had a motivation to
modify Kohavi’s wrapper method to rank the SVM features
according to their corresponding weight values as [recited by the
claims].
Intel Corp., IPR2021-00552, Final Written Decision at
26-27.
In particular, the PTAB found that the Petitioner’s evidence
and reasoning demonstrated “nothing more than a skilled
artisan, once presented with the separate pieces of highlighted
information in Kohavi, Boser, and Hocking, may have understood that
they could be combined in the manner claimed.” Id. at
27.
As to the specific AI technical features, the PTAB found that no
motivation was demonstrated “to modify Kohavi’s wrapper
method by changing the ranking used in
the feature subset selection algorithm
from an estimation of the performance of
an induction algorithm to classify data properly to a
variable–feature weight–used in the algorithm of an SVM to
classify data.” Id.
In particular, even though all elements of the claims-at-issue
were found in the prior art and where a skilled artisan may have
understood that such elements could have
been combined, the PTAB found that there was no evidence provided
that showed that a skilled artisan would
have made the combination as claimed by the ‘959 patent. At
most, “the combined Kohavi/Boser/Hocking disclosures suggest
that a skilled artisan, once presented with the separate pieces of
highlighted information from those references, may have understood
that they could be combined in the manner claimed, but that is not
enough because Petitioner has not shown persuasively why a skilled
artisan would have known to pick out those three references and
combine them to arrive at the claimed invention.” Id.
at 31 (citing Personal Web Techs., LLC v. Apple, Inc., 848
F.3d 987, 993 (Fed. Cir. 2017)).
Dissenting Opinion
It should be noted, however, that the decision was split 2-to-1,
where Judge Garth D. Baer broke from the majority in dissent.
Judge Baer argued that “Petitioner explained, with support
from its expert . that its proposed addition of Hocking’s
vector weight ranking criteria ‘applies a known technique
(Hocking’s variable selection) to a known device (Kohavi’s
RFE method using Boser’s SVM) which is ready for improvement to
yield predictable results.’ ” Intel Corp.,
IPR2021-00552, Final Written Decision at 41-42 (citing KSR
Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417 (2007)).
Because of this, Judge Baer agreed that the claimed invention
‘959 was nothing more than an obvious combination of known
techniques applied to a known device, yielding only predictable
results and thus obvious under KSR’s framework. Id. at
42.
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