Ten-X, LLC

Patent Trials and Appeals Board

Dec 28, 2020

2020005170 (P.T.A.B. Dec. 28, 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.
15/476,426 03/31/2017 Edwin Ng 103046.0010US1 4271
24392 7590 12/28/2020
FISH IP LAW, LLP
2603 Main Street
Suite 1000
Irvine, CA 92614
EXAMINER
DESAI, RESHA
ART UNIT PAPER NUMBER
3625
NOTIFICATION DATE DELIVERY MODE
12/28/2020 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):
Patents@fishiplaw.com
rfish@fishiplaw.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________________

Ex parte EDWIN NG, CHRISTOPHER DUMAS,
CHARLES McKINNEY, and WILLIAM WOLF RENDALL
__________________

Appeal 2020-005170
Application 15/476,426
Technology Center 3600
____________________

Before ANTON W. FETTING, JAMES P. CALVE, and
KENNETH G. SCHOPFER, Administrative Patent Judges.

CALVE, Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the decision
of the Examiner to reject claims 1, 5–10, and 14–19, which are all of the
pending claims.2 We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM.

1 “Appellant” refers to “applicant” as defined in 37 C.F.R. § 1.42. Appellant
identifies Auction.com, LLC as the real party in interest. Appeal Br. 2.
2 Claims 2–4 and 11–13 are cancelled. Appeal Br. 12, 13 (Claims App.); see
Final Act. 2.
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CLAIMED SUBJECT MATTER
Claims 1, 10, and 19 are independent. Claim 1 recites:
1. A method for generating recommendations related to an
online marketplace, the method being implemented by one or
more processors and comprising:
generating, based on training data associated with the
online marketplace, a model comprising a plurality
of classifiers; and
for a user of the online marketplace and an asset for sale
on the online marketplace:
computing outputs for the plurality of classifiers based
on user data associated with the user and asset
data associated with asset;
computing a user-asset propensity score based on the
plurality of classifier outputs, the user-asset
propensity score comprising a measure of a
likelihood that the user will submit a bid on the
asset;
comparing the computed user-asset propensity score
against a plurality of user-asset propensity
scores corresponding to a plurality of other
users; and
generating a recommendation regarding predicted
behavior of the user and a subset of the
plurality of other users based on the
comparison.

Appeal Br. 12 (Claims App.).
REJECTIONS
Claims 1, 5–10, and 14–19 are rejected under 35 U.S.C. § 101 as
directed to a judicial exception without significantly more.
Claims 1, 5–10, and 14–19 are rejected under 35 U.S.C. § 103 as
unpatentable over Ravichandran (US 2011/0178831 A1, pub. July 21, 2011)
and Shivaswamy (US 2016/0078507 A1, pub. Mar. 17, 2016).
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ANALYSIS
Patent Eligibility of the Claims
Appellant argues the claims as a group. See Appeal Br. 5–8. We
select claim 1 as representative. See 37 C.F.R. § 41.37(c)(1)(iv).
Regarding claim 1, the Examiner analyzes each limitation of the claim
and determines the steps relate to generating recommendations related to a
marketplace, which is a method of organizing human activity and an abstract
idea. Final Act. 3. The Examiner determines that generating and computing
steps of the claim can be performed in the mind as mental processes. Id.
The Examiner also determines that the online marketplace links the
abstract idea to a particular technological environment of the internet and the
processors are generic computer components used as a tool to perform the
abstract idea. Id. at 3–4. The Examiner further determines that improving
targeted advertising improves the abstract idea rather than any technology by
sorting or filtering data to generate a recommendation, which is an abstract
idea. Ans. 3–5. The Examiner determines that increases in speed or more
efficient use of resources resulting from using general purpose computers for
automation does not improve computers or technology. Id. at 4–5.
Principles of Law
Section 101 of the Patent Act states:
Whoever invents or discovers any new and useful
process, machine, manufacture, or composition of matter, or
any new and useful improvement thereof, may obtain a patent
therefor, subject to the conditions and requirements of this title.
35 U.S.C. § 101. This provision contains an implicit exception: “Laws of
nature, natural phenomena, and abstract ideas are not patentable.” Alice
Corp. v. CLS Bank Int’l, 573 U.S. 208, 216 (2014).
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To distinguish patents that claim laws of nature, natural phenomena,
and abstract ideas from those that claim patent-eligible applications, we first
determine whether the claims are directed to a patent-ineligible concept. Id.
at 217. If they are, we consider the elements of each claim, individually and
“as an ordered combination,” to determine if additional elements “‘transform
the nature of the claim’ into a patent-eligible application” as an “inventive
concept” sufficient to ensure the claims in practice amount to significantly
more than a patent on the ineligible concept itself. See id. at 217–18.
The USPTO has issued guidance about this framework. 2019 Revised
Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019)
(“Revised Guidance”). Under the Revised Guidance, to determine whether a
claim is “directed to” an abstract idea, we evaluate whether the claim recites:
(1) any judicial exceptions, including certain groupings of abstract ideas
listed in the Revised Guidance (i.e., mathematical concepts, certain methods
of organizing human activities such as a fundamental economic practice, or
mental processes); and (2) additional elements that integrate the judicial
exception into a practical application (see MPEP §§ 2106.05(a)–(c), (e)–(h)
(9th ed. Rev. 10.2019 June 2020) (“MPEP”)). Id. at 52–55.
Only if a claim (1) recites a judicial exception and also (2) does not
integrate that exception into a practical application, do we then consider
whether the claim (3) adds a specific limitation beyond a judicial exception
that is not “well-understood, routine, conventional” in the field (see MPEP
§ 2106.05(d)) or (4) simply appends well-understood, routine, conventional
activities previously known to the industry, specified at a high level of
generality, to the judicial exception. Id. at 56.

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Step 1: Is Claim 1 Within a Statutory Category?
Claim 1 recites a method, which is a statutory category of 35 U.S.C.
§ 101, namely, a process. Appeal Br. 4. Thus, we next consider whether
claim 1 recites a judicial exception.
Step 2A, Prong One: Does Claim 1 Recite a Judicial Exception?
We agree with the Examiner that claim 1 recites certain methods of
organizing human activity––fundamental economic practices, commercial
interactions of advertising and sales activities or behaviors, and some steps
can be performed in the human mind as mental processes and mathematical
concepts. See Final Act. 3; Revised Guidance, 84 Fed. Reg. at 52.
The method computes user-asset propensity scores for plural users to
predict the likelihood that each user will bid on a particular asset listed on an
online marketplace and generates a recommendation regarding the predicted
behavior to improve efficiency of the online marketplace by facilitating the
targeted advertising of specific users for specific assets. Spec. ¶¶ 9–12, 22.
Such recommendations and targeted advertising can improve throughput of
the online marketplace and increase transaction rates and bidding. Id. ¶ 12.
The preamble recites this purpose as “generating recommendations
related to an online marketplace.” Appeal Br. 12 (Claims App.).
Targeted marketing is a form of tailoring information based on data
known about a user and specific data, and it also is a fundamental practice.
See Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363,
1369–70 (Fed. Cir. 2015) (citing Alice, 573 U.S. at 219); Bridge & Post, Inc.
v. Verizon Commc’ns, Inc., 778 F. App’x 882, 887 (Fed. Cir. 2019) (targeted
marketing is a form of tailoring information based on data provided from a
user, and it is a fundamental practice).
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Generating a model of classifiers based on training data recites basic
data collection and parsing. Spec. ¶ 18. Training data is previous sales data
used to generate machine-learning models such as random forest models or
neural networks to predict future user behavior. Id. ¶ 36; id. ¶ 13 (machine-
learning algorithm is used to generate models based on a large set of training
data). Claim 1 recites no such technical details, however.
The Specification describes a “classifier” as follows:
A classifier can be a programmatic construct or algorithm
generated by the recommendation system to compute a
classification with respect to a particular outcome relating to a
user-asset combination. For instance, the classifier for a user-
asset combination can output a binary value indicating whether
it is likely that the user will submit a bid on the asset. The
classifier output can also indicate, for example, whether the
user is likely to be interested in the asset, etc. The plurality of
classifiers can be generated by the recommendation system
using the training data based on the identified set of user-asset
attributes. For instance, the recommendation system can parse
the training data to retrieve values of the user-asset attributes
and the classifiers can be generated using the training data
values of the user-asset attributes.
Spec. ¶ 18; see id. ¶ 40 (classifiers can be decision trees, artificial neurons,
perceptrons, or quadratic classifiers). Claim 1 recites none of these features.
“[T]he specification may be ‘helpful in illuminating what a claim is
directed to . . . [but] the specification must always yield to the claim
language’ when identifying the ‘true focus of a claim.’” Ericsson Inc. v.
TCL Commc’n Tech. Holdings Ltd., 955 F.3d 1317, 1325 (Fed. Cir. 2020)
(citation omitted); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138,
1149 (Fed. Cir. 2016) (“The § 101 inquiry must focus on the language of the
Asserted Claims themselves.”).
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As claimed, this step processes training data3 to generate a model of
classifiers without any details of the process or classifiers. Parsing data to
generate more data using models and data constructs like “classifiers” recites
the abstract idea above. See Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d
1350, 1355 (Fed. Cir. 2016) (“But merely selecting information, by content
or source, for collection, analysis, and display does nothing significant to
differentiate a process from ordinary mental processes, whose implicit
exclusion from § 101 undergirds the information-based category of abstract
ideas.”); In re TLI Commc’ns LLC Patent Litig., 823 F.3d 607, 613 (Fed.
Cir. 2016) (“[T]he claims . . . are simply directed to the abstract idea of
classifying and storing digital images in an organized manner. . . . [W]e
have applied the ‘abstract idea’ exception to encompass inventions
pertaining to methods of organizing human activity.”); Content Extraction
and Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1348
(Fed. Cir. 2014) (collecting data, recognizing certain data in the collected
data, and storing recognized data recite mental steps humans perform such
as banks reviewing checks, recognizing data in checks (e.g., amount,
account number, account holder), and storing data); see also Berkheimer v.
HP Inc., 881 F.3d 1360, 1367 (Fed. Cir. 2018) (“The parsing and comparing
of claims 1–3 and 9 are similar to the collecting and recognizing of Content
Extraction . . . and the classifying in an organized manner of TLI . . . .”); In
re Morsa, 809 F. App’x 913, 917 (Fed. Cir. 2020) (collecting information to
provide customized information and match advertisers with entities is a
fundamental economic practice of organizing human activity).

3 Training data is information about user activities regarding particular assets
(e.g., views, searches, saves as favorite, bids, offers, etc.). Spec. ¶ 15.
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The next steps of “computing outputs for the plurality of classifiers
based on user data associated with the user and asset data associated with
asset” and “computing a user-asset propensity score based on the plurality of
classifier outputs, the user-asset propensity score comprising a measure of
the likelihood that the user will submit a bid on the asset” recite the same
abstract idea. See Appeal Br. 12 (Claims App.). They recite calculations at
a high level of generality without using any particular algorithm or formula.
The Specification confirms the abstract nature of the steps. Training
data is parsed and binary values are computed for classifiers. Spec. ¶ 20. If
a user previously viewed an asset at a marketplace, a value of 1 is used, but a
value of 0 is used if they did not. Id. Plural classifier scores are aggregated,
e.g., 200, 300, or more, to compute a propensity score between 0 and 1. Id.
¶ 21. An aggregated user-asset propensity score of 0.4 indicates a greater
likelihood that a user will bid on an asset than a score of 0.2. Id.
“[A]nalyzing information by steps people go through in their minds,
or by mathematical algorithms, without more, [recites] essentially mental
processes within the abstract-idea category.” Elec. Power, 830 F.3d at 1354;
Digitech Image Techs., LLC v. Elec. for Imaging, Inc., 758 F.3d 1344, 1351
(Fed. Cir. 2014) (“Without additional limitations, a process that employs
mathematical algorithms to manipulate existing information to generate
additional information is not patent eligible.”); Credit Acceptance Corp. v.
Westlake Servs., 859 F.3d 1044, 1054–55 (Fed. Cir. 2017) (calculating a
credit score for loan applications as agents do is a fundamental economic
practice). Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d
1314, 1324 (Fed. Cir. 2016) (collecting borrower information to generate a
credit grading for loan pricing can be performed without a computer).
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“If a claim is directed essentially to a method of calculating, using a
mathematical formula, even if the solution is for a specific purpose, the
claimed method is nonstatutory.” Digitech, 758 F.3d at 1351 (quoting
Parker v. Flook, 437 U.S. 584, 595 (1978)). Here, claim 1 does not recite a
particular algorithm or formula to compute classifier outputs or to compute a
user-asset propensity score based on a plurality of classifier outputs.
The final steps compare computed user-asset propensity scores to one
another to generate a recommendation regarding predicted behavior of a user
and a plurality of other users. In one embodiment, the system recommends
the top five or ten users with the highest user-asset propensity scores for a
particular asset so targeted advertising can be generated for each of the users
and properties. Spec. ¶ 22. Such details are not claimed, however.
Essentially, the steps identify users who have an optimum likelihood
of bidding on an asset based on their previous bid activities. Optimization of
price and other parameters is a fundamental economic practice. OIP Techs.,
Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362 (Fed. Cir. 2015) (claims to
the concept of offer based pricing is similar to other fundamental economic
concepts found to be abstract ideas). In OIP, the method provided automatic
pricing for electronic commerce by gathering price testing statistics to learn
how potential customers respond to different offer prices and using the data
to set an optimum price for a product. Id. at 1363–64. Here, claim 1 parses
previous sales training data to learn how users responded to different assets
at different prices and to estimate a likelihood that such users may bid on
similar assets in the future thereby to optimize bids to the users. Spec. ¶ 12.
Merely identifying users who may be most likely to bid on an asset so
advertising can be targeted to those users recites the same abstract idea.
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Targeted advertisements based on information known about a user is a
fundamental practice long prevalent in our economic system. Intellectual
Ventures, 792 F.3d at 1369–70 (customizing information for users based on
information known about users (location, time of day, navigation data) is a
fundamental practice); In re Morsa, 809 F. App’x at 917 (collecting data and
using it to customize information is a fundamental economic practice of
organizing human activity); Bridge & Post, 778 F. App’x at 887 (targeted
marketing using a persistent user identifier is a form of tailoring information
and a fundamental practice). Combining data collection, parsing, targeted
advertising, and price optimization does not make claim 1 any less abstract.
See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327 (Fed. Cir.
2017) (“Adding one abstract idea (math) to another abstract idea (encoding
and decoding) does not render the claim non-abstract.”).
Accordingly, we determine that claim 1 recites the abstract idea
identified above.
Step 2A, Prong Two: Integration into a Practical Application
We next consider whether claim 1 recites additional elements that
integrate the abstract idea into a practical application. Revised Guidance,
84 Fed. Reg. at 54 (Revised Step 2A, Prong Two). We determine claim 1
lacks additional elements that improve computers or other technology. The
one or more processors do not implement the abstract idea with a particular
machine or manufacture that is integral to the claim. They do not transform
or reduce a particular article to a different state or thing. They do not apply
the abstract idea in a meaningful way beyond merely linking it to a particular
technological environment. See Revised Guidance, 84 Fed. Reg. at 55 and
MPEP sections cited therein; Final Act. 3–4; Ans. 3–5.
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Appellant asserts claim 1 is not directed to an abstract idea because it
improves the functionality of an online marketplace system. Appeal Br. 5.
Appellant argues that comparing a user-asset propensity score against plural
other user-asset propensity scores and generating a recommendation for the
best users to bid on an asset instead of all users reduces the resources used to
generate a recommendation to the most likely bidders, improves throughput
of an online marketplace, and increases transaction rates and bidding activity
on the online marketplace while conserving resources for additional assets to
be listed on the marketplace. Id. at 5–6; Reply Br. 2.
These limitations cited by Appellant are features of the abstract idea
identified under Prong One and cannot serve as “additional elements” that
integrate that abstract idea into a practical application. Revised Guidance,
84 Fed. Reg. at 55 n.24; see also Alice, 573 U.S. at 221 (explaining that a
claim reciting an abstract idea must include additional features to ensure it
does not monopolize the abstract idea and noting Mayo’s holding that a
transformation into a patent-eligible application requires more than simply
stating the abstract idea with the words “apply it”); Synopsys, Inc. v. Mentor
Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“But, a claim for a
new abstract idea is still an abstract idea.”); Versata Dev. Grp., Inc. v. SAP
Am., Inc., 793 F.3d 1306, 1335 (Fed. Cir. 2015) (claims that improve an
abstract idea without improving computers were not patent eligible).
Even if we consider these features, they do not improve computers or
technology when recited at such a level of generality as in claim 1. Indeed,
the Specification indicates that hundreds of random classifiers are used to
calculate a propensity score using particular models, but claim 1 recites only
a “plurality” of classifiers and no particular model. Spec. ¶¶ 19, 21, 36, 41.
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In an analogous situation involving claims to analyzing user financial
data to identify financial risk using “an algorithm engine,” the court held:
Peculiar to this case is that the algorithm engine mentioned in
the claim is not claimed, identified, or explained. To be sure,
claiming an algorithm does not alone render subject matter
patent eligible. See Gottschalk v. Benson, 409 U.S. 63, 71–72 .
. . (1972). But a method for collection, analysis, and generation
of information reports, where the claims are not limited to how
the collected information is analyzed or reformed, is the height
of abstraction.
Clarilogic, Inc. v. FormFree Holdings Corp., 681 F. App’x 950, 954 (Fed.
Cir. 2017).
Here, the asserted improvement results from generic data collection
and analysis in an online bidding marketplace. Even if targeted advertising
was claimed, merely combining one abstract concept with another does not
improve technologies or tie the abstract idea to a particular machine integral
to the claim. See Morsa, 809 F. App’x at 917 (“Here, the claim recites both
targeted advertising and bidding to display the advertising, which are both
abstract ideas relating to customizing information based on the user and
matching them to the advertiser.”); Intellectual Ventures,792 F.3d at 1370
(“[T]he fact that the web site returns the pre-designed ad more quickly than a
newspaper could send the user a location-specific advertisement insert does
not confer patent eligibility[.]”); see also Evolutionary Intelligence LLC v.
Sprint Nextel Corp., 677 F. App’x 679, 680 (Fed. Cir. 2017) (“[T]he claims
are directed to selecting and sorting information by user interest or subject
matter, a longstanding activity of libraries and other human enterprises.”).
Here, claim 1 parses data, computes a user-asset propensity score, and then
generates a recommendation of a user implemented on generic processors.
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As the court held in a similar situation in Bridge & Post:
Bridge & Post does not claim to have invented new networking
hardware or software. Nor does it claim to have invented HTTP
header fields, user identifiers, encryption techniques, or any
other improvement in the network technology underlying its
claims. The specification states that the invention filled a need
for a system which would “ensure higher access rates, longer
browse times, and increased consumption of media” by users.
’747 col. 3 ll. 15–22. But each of these goals is in the abstract
realm—an improvement in the success or monetization of
tracking users with personalized markings in order to serve
advertisements—not an improvement in networking or
computer functionality. None of these alleged improvements
“enables a computer . . . to do things it could not do before.”
Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1305 (Fed.
Cir. 2018) (emphasis added). Such claims, whose focus is “not
a physical-realm improvement but an improvement in a wholly
abstract idea,” are not eligible for patenting.
Bridge & Post, 778 F. App’x at 889.
Accordingly, we determine that claim 1 lacks additional elements that
integrate the abstract idea into a practical application.
Step 2B: Does Claim 1 Include an Inventive Concept?
We next consider if claim 1 recites additional elements, individually,
or as an ordered combination, that provide an inventive concept. Alice, 573
U.S. at 217–18. This step is satisfied when claim limitations involve more
than well-understood, routine, and conventional activities known in industry.
Berkheimer, 881 F.3d at 1367; see Revised Guidance, 84 Fed. Reg. at 56
(explaining that the second step of the Alice analysis considers whether a
claim adds a specific limitation beyond the recited judicial exception that
also is not “well-understood, routine, conventional” activity in the field).

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Individually, and as an ordered combination, claim 1 recites no more
than the abstract idea identified above as implemented on generic processors
used as tools. See Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Canada
(U.S.), 687 F.3d 1266, 1280 (Fed. Cir. 2012) (“The district court correctly
held that without the computer limitations nothing remains in the claims but
the abstract idea of managing a stable value protected life insurance policy
by performing calculations and manipulating the results.”). Even accepting
Appellant’s assertions of improved throughput of the online marketplace as
a technological advance, claim 1 does not recite any features that generate
such efficiencies. See Berkheimer, 881 F.3d at 1369 (finding that claim 1
does not recite an inventive concept where the recited method of archiving
data included only parsing, analyzing, and comparing the data to previously
stored data and presenting the data for reconciliation if there is a variance
without also reciting limitations that eliminate redundancy of stored object
structures or effect a one-to-many change of linked documents in an archive
as Berkheimer argued were unconventional activities).
Here, the Specification describes machine-learning algorithms used to
generate models based on large training sets of certain types of user-asset
data to generate hundreds of randomly-selected classifiers and compute user-
asset propensity scores used for recommendations for targeted advertising.
Spec. ¶¶ 13–26. Even if the Specification provided a technical description
of the features, claim 1 recites well-understood, routine, and conventional
data processing activities. See In re Jobin, 811 F. App’x 633, 637–38 (Fed.
Cir. 2020) (generic technology did not impose a meaningful limitation on a
method of collecting, organizing, grouping, and storing data by survey or
crowdsourcing, individually or as an ordered combination).
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Even if the steps are groundbreaking, innovative, or brilliant, that is
not enough for eligibility. See Ass’n for Molecular Pathology v. Myriad
Genetics, Inc., 569 U.S. 576, 591 (2013); accord SAP Am., 898 F.3d at 1163
(“No matter how much of an advance in the finance field the claims recite,
the advance lies entirely in the realm of abstract ideas, with no plausibly
alleged innovation in the non-abstract application realm. An advance of that
nature is ineligible for patenting.”); Solutran, Inc. v. Elavon, Inc., 931 F.3d
1161, 1169 (Fed. Cir. 2019) (“[M]erely reciting an abstract idea by itself in a
claim––even if the idea is novel and non-obvious––is not enough to save it
from ineligibility.”).
Accordingly, we determine that claim 1 lacks an inventive concept
sufficient to transform the abstract idea into patent eligible subject matter
and we therefore sustain the rejection of claims 1, 5–10, and 14–19.
Claims 1, 5–10, and 14–19
Rejected Over Ravichandran and Shivaswamy
Appellant argues the claims as a group. See Appeal Br. 8–11. We
select claim 1 as representative. See 37 C.F.R. § 41.37(c)(1)(iv).
Regarding claim 1, the Examiner finds that Ravichandran teaches the
method substantially as claimed by computing a user-asset propensity score
based on a plurality of classifier outputs for a massive prediction grid of all
customers and products, and the user-asset propensity score measures the
likelihood a user will buy the asset. Final Act. 5–6. The Examiner finds that
Ravichandran uses an odds calculator and algorithm to improve chances of
making a sale and computes a user-asset propensity score for customers who
bought a product and those who did not to determine a likelihood that a user
will buy a product. Id. at 7 (citing Ravichandran ¶ 478).
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The Examiner relies on Shivaswamy to teach that it is known to
measure the likelihood that a user will submit a bid on an asset. Final Act.
7; Ans. 7. The Examiner determines it would have been obvious to modify
Ravichandran’s method of computing a user-asset propensity score that
measures a likelihood that a user will purchase an asset to measure whether a
user will bid on an asset, as Shivaswamy teaches, to improve a product sale
listing for an auction. See Final Act. 7; Ans. 7.
Appellant argues that Shivaswamy’s model to suggest a starting point
for a price listing to include an opening bid price for an auction listing does
not measure a likelihood that a user will do anything much less that a user
will submit a bid on the asset. Appeal Br. 10. Appellant also argues that
increasing a likelihood of something occurring is not the same as measuring
a likelihood that it will occur as claimed. Id. at 10–11.
Ravichandran teaches an analytics facility 134 that converts customer
profiles into sales by using an odds calculator facility that fits information
about customers to an algorithm to improve the chances of making a sale.
Ravichandran ¶ 478. Ravichandran uses a massive grid of customers and
products to generate classification information including an “odds ratio” that
predicts the likelihood that a customer will buy a product based on user-asset
propensity scores and data of customers who bought products and customers
who did not. Id. The “odds ratio” that is computed by the odds calculator
facility measures the likelihood that users will purchase a particular asset.
See id. In combination with a system that models an auction listing, such as
Shivaswamy teaches, a product purchase also entails a successful bid for that
product. Thus, scoring a purchase in Ravichandran would score a bid for the
product if it was sold in an auction marketplace as Shivaswamy teaches.
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Shivaswamy teaches a price computation model that suggests starting
prices for items to be sold or auctioned. Shivaswamy ¶ 44. Shivaswamy
teaches that its price setting model “may be especially valuable in the case of
an auction listing, where a seller is often unsure of where to set an opening
bid and/or reserve bid price.” Id. Shivaswamy teaches price computation
models as desirable to use to set sales or bid prices to establish competitive
pricing that improves online sales and auction listings. Id. ¶¶ 42–44.
The Examiner proposes to modify Ravichandran’s odds calculator,
which calculates an “odds ratio” that measures the likelihood of a customer
buying a product, to measure the likelihood of a user bidding on a product,
as Shivaswamy teaches, to improve sales in auction marketplaces similarly.
See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417 (2007) (using a known
technique that improves one device to improve similar devices in the same
way is obvious unless its application is beyond the level of ordinary skill);
see also Final Act. 7; Ans. 7 (“Examiner is merely relying on Shivaswamy
to modify the purchase of Ravichandran to be a bid.”). The Examiner’s
articulated reasoning is supported by a rational underpinning based on the
teachings of both references as to the advantages of their technologies for
improving sales of products whether by purchase or auction in an online
marketplace. See Ravichandran ¶¶ 474–478; Shivaswamy ¶ 44; see also
DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464
F.3d 1356, 1368 (Fed. Cir. 2006) (an implicit motivation to combine exists
when an improvement makes a process more efficient, cheaper, or faster).
Thus, we sustain the rejection of claim 1 and claims 5–10 and
14–19, which fall with claim 1.

Appeal 2020-005170
Application 15/476,426

18
CONCLUSION
In summary:
Claims
Rejected
35 U.S.C.
§
Reference(s)/
Basis
Affirmed Reversed
1, 5–10,
14–19
101 Eligibility 1, 5–10,
14–19

1, 5–10,
14–19
103 Ravichandran,
Shivaswamy
1, 5–10,
14–19

Overall
Outcome
1, 5–10,
14–19

No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R.
§ 1.136(a)(1)(iv).
AFFIRMED