Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This in in response to the Amendment filed on 10/29/25. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over unpatentable over Chen et al., US Pub. No.20210241120 in view of Eng et al., US Pub.20250045591.
As to claim 1, Chen discloses a computer-implemented method for utilizing a machine learning model configured to determine synthetic identity theft, the method comprising:
processing a plurality of user datasets (users at 102, 106 fig.1) to generate a set of
features for each user dataset, each set of features representative of a particular user
and generating a plurality of embeddings sets, each embedding set being
representative of a respective set of features and generating a plurality of synthetic user
datasets based on the plurality of datasets (using the machine learning models described herein to generate scores for particular entities or identities and determine whether the particular entities or identities are real or synthetic and the dynamic modeling system 103 adapts to data from the first data store 104, the second data store 108, and/or from users that is constantly changing, see fig.1, [0027] and [0032]),
combining the plurality of embeddings sets and the plurality of synthetic user datasets to
generate a training dataset, the training dataset comprising a plurality of user profiles
and training the machine learning model based on the generated training
dataset (analyzing records stored in the first data store 104 and/or the second data
store 108 and generate an output regarding the particular entities or identities and/or
perform one or more actions based on the performed analysis and/or the transmitted
and received data and information, see [0027] to [0028]); and
determining, via the machine learning model and in response to receiving a new user
profile, a determination of whether the new user profile is real or synthetic (generating
new insights into identity attributes to separate real identities from potential synthetic
identities based on records accessible to the systems, see [0014] and [0091]).
Chen does not specifically disclose the plurality of user profiles comprising synthetic profiles based on extracted features of the plurality of synthetic user datasets. However, in a similar network environment, Eng discloses the plurality of user profiles comprising synthetic profiles based on extracted features of the plurality of synthetic user datasets (trained to generate synthetic user profiles based on real variety of user data, see Eng’s [0024]). It would have been obvious to one of the ordinary skill in the art before the effective filing date of the invention was made to incorporate Eng’s teachings into the computer system of Chen to control data information because it would have enabled quote providers to respond to real user data without requiring that such real user data be provided to those quote providers (see Eng’s [0025]).
As to claim 2, Chen discloses the generating the plurality of synthetic user datasets
utilizes an adversarial network trained using the plurality of user datasets (see [0091] to
[0094]).
As to claim 3, Chen discloses the set of features comprises a set of first features, the
first features corresponding to a first category of user features, and wherein the method
further comprises: processing the plurality of user data sets to generate a set of second features for each user dataset, the second features corresponding a second category of user features and generating a second training dataset based on embeddings sets respective of the set of second features (using the OCAN to train based on a hypothesis that there is a population of known real identities, and that others are synthetic, see [0093] to [0094])
As to claim 4, Chen discloses the first category of user features comprises global
features and the second category of user features comprises local features (see [0026] to [0028]).
As to claim 5, Chen discloses receiving, from a user device, an access request, the
access request comprising a requesting user profile; determining, via the trained
machine learning model, a synthetic score respective of the requesting user profile; and
selectively granting the access request based on the synthetic score (see [0095] to
[0098]).
As to claim 6, Chen discloses the synthetic score is indicative of a likelihood that the
requesting user profile comprises synthetic data (synthetic profiles, see [0094] to
[0098]).
As to claim 7, Chen discloses a system comprising: a processor; and a non-transitory
computer readable medium stored thereon instructions that are executable by the
processor to cause the system to perform operations comprising:
processing a user dataset to derive: a first training set comprising identity data; a
second training set comprising feature data; and a third training set comprising encoded
data; supplementing the first training set with noise and generating, by a first generator,
a first set of synthetic user profiles based on the supplement first training set (using the machine learning models described herein to generate scores for particular entities or identities and determine whether the particular entities or identities are real or synthetic and using the encoder data, see fig.1, [0027]-[0028] and [0058])
generating, by a second generator, a second set of synthetic user profiles based on the
second training set and training each of the first generator and the second generator
based on output from a discriminator that takes, as inputs, the first set of synthetic user
profiles, the second set of synthetic user profiles, and the third training set (generating
new insights into identity attributes to separate real identities from potential synthetic
identities based on records accessible to the systems, see [0014] and [0091]).
Chen does not specifically disclose the first set of synthetic user profiles comprising partially synthetic profiles based on extracted features from the supplemental first training set and wherein the second set of synthetic user profiles comprising synthetic user profiles based on extracted features from the second training dataset. However, in s similar network environment, Eng discloses the first set of synthetic user profiles comprising partially synthetic profiles based on extracted features from the supplemental first training set and wherein the second set of synthetic user profiles comprising synthetic user profiles based on extracted features from the second training dataset (generating synthetic user profiles based on real variety of user data and the first trained machine learning model might output a plurality of different synthetic user profiles: one might have a slightly different address, another might have a slightly different model year of vehicle, see Eng’s [0024] and [0026]). It would have been obvious to one of the ordinary skill in the art before the effective filing date of the invention was made to incorporate Eng’s teachings into the computer system of Chen to control data information because it would have enabled quote providers to respond to real user data without requiring that such real user data be provided to those quote providers (see Eng’s [0025]).
As to claim 8, Chen discloses the discriminator is configured to receive a user profile
and to output a determination of whether the user profile comprises a synthetic identity
(identifying synthetic identities may also use suspected synthetic (see [0066] to [0067]).
As to claim 9, Chen discloses the discriminator is trained to identify the first set of
synthetic user profiles as fraudulent and to identify the second set of synthetic user
profiles as genuine (see [0066] to [0067]).
As to claim 10, Chen discloses the first generator is trained to cause the discriminator to
identify the first set of synthetic user profiles as genuine, and the second generator 1s
rained to cause the discriminator to identify the second set of synthetic profiles as
fraudulent (synthetic detecting, see [0066] to [0068]).
As to claim 11, Chen discloses receiving, from a user device, an access request, the
access request comprising a user profile, generating, via the discriminator, an indication
of whether the user profile comprises a synthetic user profile; and selectively granting
the access request based on the generated indication (see [0092] to [0094])
As to claim 12, Chen discloses the generated indication comprises a binary value, with
a first value indicative of a genuine determination and a second value indicative of a
synthetic determination, and the selectively granting comprises: granting the access
request in response to the generated indication being the first value, and denying the
access request in response to the generated indication being the second value (see
[0092] to [0094]).
As to claim 13, Chen discloses the generated indication comprises a scalar value
indicative of a likelihood that the user profile is the synthetic user profile, and the
selectively granting comprises: granting the access request in response to the
generated indication being below a threshold value, and denying the access request in
response to the generated indication being greater than or equal to the threshold value
( threshold criteria, see [0031]).
As to claim 14, Chen discloses a system comprising: a processor, a non-transitory
computer readable medium stored thereon instructions that are executable by the
processor to cause the system to perform operations comprising:
receiving an access request, the access request comprising a user profile; processing,
by an adversarial-trained discriminator, the user profile to determine whether the user
profile is false, the discriminator trained by: retrieving a set of true profiles; generating,
by a generator, a set of false profiles based on the set of true profiles (known synthetic identities may be identified or reported by financial institutions when they confirm fraud occurs related to a particular identity; however, false positives (for example, identities that are suspected as being synthetic but not confirmed as such) may not be verified or checked, see abstract, [0026]-[0027] and [0057]).
outputting, by the discriminator, a determination in response to an input of a profile from
either the set of true profiles or the set of false profiles; and adjusting, by a loss function,
at least one of the discriminator or the generator based on the output determination; and
in response to the determination, by the discriminator, that the user profile is false,
rejecting the access request (identifying organized fraud rings by propagating a risk or
likelihood of fraud based on fraudulent users that share identity information, see [0067]
to [0068]).
Chen does not specifically disclose the determination by the discriminator is based on a plurality of user profiles comprising a set of synthetic user profiles based on extracted features from the set of true profiles. However, Eng disclose the determination by the discriminator is based on a plurality of user profiles comprising a set of synthetic user profiles based on extracted features from the set of true profiles (submitting synthetic user profiles along with real user data (as requested by users), this process uses computerized processes to ensure that quote providers are not able to distinguish real and fake profiles and provide maximally accurate quotes, see [0027]-[0028] and [0054]). It would have been obvious to one of the ordinary skill in the art before the effective filing date of the invention was made to incorporate Eng’s teachings into the computer system of Chen to control data information because it would have enabled quote providers to respond to real user data without requiring that such real user data be provided to those quote providers (see Eng’s [0025]).
As to claim 15, Chen discloses the input profile is from the set of false profiles, and
wherein the adjusting the at least one of the discriminator or the generator comprises: in
response to the output determination indicating true, positively adjusting the generator;
and in response to the output determination false, negatively adjusting the generator.
As to claim 16, Chen discloses the input profile is from the set of true profiles, and
wherein the adjusting the at least one of the discriminator or the generator comprises:
response to the output determination indicating true, positively adjusting the
discriminator; and in response to the output determination false, negatively adjusting the
discriminator (see [0067] to [0068]).
As to claim 17, Chen discloses the generator is further trained by: supplementing the
retrieved set of true profiles with noise to generate a set of training profiles; generating a
set of training embeddings respective of the set of training profiles; and training the
generator with the set of training embeddings (see [0067] to [0069]).
As to claim 19, Chen discloses the discriminator is further trained based on local
features and global features from the set of true profiles and from the set of false
profiles (see [0067] to [0070]).
As to claim 20, Chen discloses in response to the determination, by the discriminator,
that the user profile is true, granting the access request (see [0068] to [0071]).
Allowable Subject Matter
Claim 18 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments, filed 10/29/25, with respect to the rejection(s) of claim(s) 1-17, 19 and 20 under 35 USC 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Eng et al., US Pub. No.20250045591.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to Khanh Dinh whose telephone number is (571) 272-3936. The examiner can normally be reached on Monday through Friday from 8:00 A.m. to 5:00 P.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's
supervisor, Cheema Umar, can be reached on (571) 270-3037. The fax phone number
for this group is (571) 273-8300.
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