DETAILED ACTION
Notice of Pre-AIA or AIA Status.
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/01/2026 has been entered.
3. Accordingly, claims 1-20 filed on 04/08/2026 are pending and being examined. Claims 1, 10, and 16 are independent form.
4. The rejections of the claims under 35 USC § 112(a) have been withdrawn in view of the applicant’s amendment.
Claim Rejections - 35 USC § 102
5. 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 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.
6. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
7. Claims 16-17 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Gu et al (US 2022/0188598, hereinafter “Gu”).
Regarding claim 16, Gu discloses a system for performing facial recognition (the system for authenticating an individual using image feature templates generated by a machine learning model; see Abstract; see “template authentication sys. 102” of fig.1), the system comprising: a memory (see memory 206 of fig.2) for storing a plurality of enrolled facial recognition templates for a plurality of enrolled users (see para.73: “template authentication system 102 may train (e.g., initially train) the first machine learning model based on a training dataset that includes a plurality of images of a user (e.g., a plurality of facial images of a user). [...] In some non-limiting embodiments or aspects, template authentication system 102 may train the first machine learning model training based on the training dataset that includes a plurality of images of one or more users.” In other words, the template authentication system 102 is initially trained based on the training dataset which stores a plurality of facial images from a plurality of users to be enrolled.); a camera for capturing a current facial image of a person; a controller operatively coupled to the memory and the camera (see para.75: “template authentication system 102 may receive an input image of the user from user device 104 (e.g., an input image of the user captured with an image capture device,”), the controller configured to: determine whether the current facial image of the person matches one of the plurality of enrolled facial recognition templates (see para.75: “template authentication system 102 may use the first machine learning model to authentic the identity of the user”; see para.86: “template authentication system 102 may generate a current image feature template (e.g., an image feature template generated during the run-time process) for the user based on the input image of the user, compare the current image feature template for the user to the predicted image feature template for the user, and determine whether the current image feature template for the user corresponds to the predicted image feature template for the user.); when the current facial image of the person matches one of the plurality of enrolled facial recognition templates: identify the enrolled user of the plurality of enrolled users that matches the current facial image of the person; and update the enrolled facial recognition template for the matching enrolled user based on the current facial image of the person (para.77: “template authentication system 102 may retrain the first machine learning model after a positive authentication of the identity of the user”.).
Regarding claim 17, Gu discloses the system of claim 16, wherein the controller is configured to execute an artificial intelligence (AI) algorithm to determine whether the current facial image of the person matches one of the plurality of enrolled facial recognition templates (see para.86: “template authentication system 102 may generate a current image feature template (e.g., an image feature template generated during the run-time process) for the user based on the input image of the user, compare the current image feature template for the user to the predicted image feature template for the user, and determine whether the current image feature template for the user corresponds to the predicted image feature template for the user.).
Claim Rejections - 35 USC § 103
8. 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 of this title, 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.
9. Claims 1-8, 10-15, and 18-20 rejected under 35 U.S.C. 103 as being unpatentable over Gu et al (US 2022/0188598, hereinafter “Gu”) in view of Streit (US2021/0141896, hereinafter “Streit”).
Regarding claim 1, Gu discloses a method for performing facial recognition (the method and the system for authenticating an individual using image feature templates generated by a machine learning model; see Abstract; see “template authentication sys. 102” of fig.1), the method comprising:
storing a plurality of enrolled facial recognition templates for a plurality of enrolled users (see para.73: “template authentication system 102 may train (e.g., initially train) the first machine learning model based on a training dataset that includes a plurality of images of a user (e.g., a plurality of facial images of a user). [...] In some non-limiting embodiments or aspects, template authentication system 102 may train the first machine learning model training based on the training dataset that includes a plurality of images of one or more users.” In other words, the template authentication system 102 is initially trained based on the training dataset which stores a plurality of facial images from a plurality of users to be enrolled.);
capturing a current facial image of a person; producing a current facial recognition template for the person based at least in part on the current facial image of the person (see para.75: “template authentication system 102 may receive an input image of the user from user device 104 (e.g., an input image of the user captured with an image capture device,”);
executing an artificial intelligence (AI) algorithm to determine whether the current facial recognition template for the person matches one of the plurality of enrolled facial recognition templates (see para.75: “template authentication system 102 may use the first machine learning model to authentic the identity of the user”; see para.86: “template authentication system 102 may generate a current image feature template (e.g., an image feature template generated during the run-time process) for the user based on the input image of the user, compare the current image feature template for the user to the predicted image feature template for the user, and determine whether the current image feature template for the user corresponds to the predicted image feature template for the user.”), wherein the artificial intelligence (AI) algorithm is:
trained to recognize changes in facial appearance due to aging using a plurality of facial images of a training data set, where the training data set includes facial images that are not facial images of any of the plurality of enrolled users; trained based at least in part on the plurality of enrolled facial recognition templates for the plurality of enrolled users (Gu implicitly discloses this feature. See pata.77, lines 1-16: “template authentication system 102 may retrain the first machine learning model. [...] Template authentication system 102 may add the input image of the user to the plurality of images in the training dataset (e.g., the training dataset from which the machine learning model was initially trained) to provide an updated training dataset.” See para.50: “embodiments of the present disclosure allow for the template authentication system to accurately authenticate an individual based on an image of a physical characteristic of the individual, such as a facial image of the individual, when aspects of the physical characteristic of the individual have changed due to aging.”);
when the current facial recognition template for the person matches one of the plurality of enrolled facial recognition templates: identifying the enrolled user that corresponds to the matching one of the plurality of enrolled facial recognition templates; updating the enrolled facial recognition template for the matching enrolled user based on one or more differences between the current facial recognition template and the previous enrolled facial recognition template for the matching enrolled user; and retraining the artificial intelligence (AI) algorithm using the updated enrolled facial recognition template to refine the artificial intelligence (AI) algorithm’s ability to recognize changes in facial appearance due to aging, wherein retraining the artificial intelligence (AI) algorithm comprises retraining the artificial intelligence (AI) algorithm using the updated enrolled facial recognition template for the matching enrolled user (See pata.77, lines 1-16: “template authentication system 102 may retrain the first machine learning model. [...] Template authentication system 102 may add the input image of the user to the plurality of images in the training dataset (e.g., the training dataset from which the machine learning model was initially trained) to provide an updated training dataset.”).
As explained above, although Gu does not explicitly disclose “the artificial intelligence (AI) algorithm is: trained to recognize changes in facial appearance due to aging using a plurality of facial images of a training data set, where the training data set includes facial images that are not facial images of any of the plurality of enrolled users; trained based at least in part on the plurality of enrolled facial recognition templates for the plurality of enrolled users”, Gu implicitly discloses that. As a further rationale, in the same field of endeavor, that is, in the field of user authentication using a neural network, Streit explicitly teaches: the deep learning neural network (see fig.2B) is: trained to recognize changes in facial appearance due to aging using a plurality of facial images of a training data set, where the training data set includes facial images that are not facial images of any of the plurality of enrolled users; trained based at least in part on the plurality of enrolled facial recognition templates for the plurality of enrolled users. See fig.2B and para.259: “In one example, a threshold is set such that step 258 tests if a threshold match has been exceeded, and if yes, the deep learning neural network (e.g., classifier & prediction network) is retrained to include the new feature vectors being analyzed. According to some embodiments, retraining to include newer feature vectors permits biometrics that change over time (e.g., weight loss, weight gain, aging or other events that alter biometric information, haircuts, among other options.”). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Streit into the teachings of Gu and retrain the template authentication system of Gu to include the new feature vectors of an enrolled user on the new facial image of the enrolled user due to aging. Suggestion or motivation for doing so would have been to “improve the accuracy of the models that use encrypted inputs for classification” as taught by Streit, see Abstract. Therefore, the claim is unpatentable over Gu in view of Streit.
Regarding claim 2, 11, 20, the combination of Gu and Streit discloses, wherein when the current facial recognition template of the person matches one of the plurality of enrolled facial recognition templates, determining whether the matching enrolled user has access rights to a secure area, and if so, controlling an access control system to allow the matching enrolled user to access the secure area (Gu, para.89: “template authentication system 102 may perform an action associated with allowing or preventing access (e.g., access to an account of the user, access to a computer system, and/or the like) based on determining whether to authenticate the identity of the user associated with user device 104.”).
Regarding claim 3, 12, the combination of Gu and Streit discloses, wherein when the current facial recognition template of the person does not match any of the plurality of enrolled facial recognition templates, controlling the access control system to prevent the person from accessing the secure area (ibid.).
Regarding claim 4, 13, 19, the combination of Gu and Streit discloses, wherein the plurality of enrolled facial recognition templates and the updated enrolled facial recognition template each include a timestamp, wherein two or more of the timestamps are used to retrain the artificial intelligence (AI) algorithm to refine the artificial intelligence (AI) algorithm’s ability (Gu, see para.71: “template authentication system 102 may assign each image feature template for each point in time of the time interval with a time stamp for the respective point in time [of the time interval]”) to recognize changes in facial appearance due to aging (Gu, see para.50: “when aspects of the physical characteristic of the individual have changed due to aging”).
Regarding claim 5, the combination of Gu and Streit discloses the claimed invention. As explained above, Gu, paragraph [0003], states because “a [traditional] machine learning model may not be able to account for changes in the physical characteristics of an individual that occurs over time”, therefore, Gu, para.50, states: “embodiments of the present disclosure allow for the template authentication system to accurately authenticate an individual based on an image of a physical characteristic of the individual, such as a facial image of the individual, when aspects of the physical characteristic of the individual have changed due to aging.” Specifically, Gu, para.71, states: “template authentication system 102 may assign each image feature template for each point in time of the time interval with a time stamp for the respective point in time. For example, template authentication system 102 may assign the first image feature template with a first time stamp based on the first point in time, the second image feature template with a second time stamp based on the second point in time, and the third image feature template with a third time stamp based on the third point in time.” It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to appreciate that each of the differences among the first time, second time, and third time stamps represents an aging time difference of the enrolled user and would be usable to refine the first machine learning model’s ability to recognize changes in facial appearance due to aging. Suggestion or motivation for doing so would have been to train “a machine learning model be able to account for changes in the physical characteristics of an individual that occurs over time”, see Gu, para.3.
Regarding claim 6, 14, the combination of Gu and Streit discloses, wherein producing the current facial recognition template for the person comprises transforming the current facial image of the person into the current facial recognition template for the person (Gu, see the communication network 106 among user device 104, template authentication sys 102, and database 102a in fig.1).
Regarding claim 7, 15, the combination of Gu and Streit discloses, wherein transforming the current facial image of the person into the current facial recognition template for the person comprises extracting one or more characteristics from the current facial image and providing the extracted one or more characteristics to the current facial recognition template (Gu, see para.67:” For example, the image may include a facial image, such as an image of at least a portion of a face of an individual that may be used for identification and/or authentication of the identity of the individual. In some non-limiting embodiments or aspects, the feature template may be an n-dimensional vector, where the dimensions of the vector include values that are representative of features of an image.”)).
Regarding claim 8, the combination of Gu and Streit discloses the method of claim 7, wherein the one or more characteristics correspond to one or more facial features of the person (ibid.).
Regarding claim 10, claim 10 is a system claim which is analogue to the method claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
Regarding claim 18, claim 18 is dependent from claim 17 which is dependent claim 16. Since the all-essential elements among claims 16-18 are included by claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
10. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Gu in view of Streit and further in view of Zou et al (US2020/0065563, hereinafter “Zou”).
Regarding claim 9, the combination of Gu and Streit discloses the training data set which includes facial images with annotations of age, however does not disclose the training data set which includes facial images with annotations of gender and ethnicity. However, it would have been obvious and straightforward for one of ordinary skill in the art. As evidence, in the same field of endeavor, Zou teaches a face recognition neural network which is trained by the databases including variety of people having “many variations in terms of pose, age, illumination, ethnicity, and profession (e.g., actors, athletes, politicians)” (see para.43). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Zou into the teachings of the combination of Gu and Streit and annotate individual’s facial images with the physical characteristic of the individual including the age, gender, ethnicity, and profession taught by Zou. Suggestion or motivation for doing so would have been to “improve upon current vector matching techniques, especially in the facial recognition context” and “reduce the search space, thereby speeding up the facial recognition and/or improving its accuracy” as taught by Zou, cf., Par.11. Therefore, the claim is unpatentable over Gu in view of Streit and further in view of Zou.
Response to Arguments
11. Applicant's arguments filed on 04/08/2026 have been considered but are moot in view of the new ground(s) of rejection.
11-1. On page 10 of applicant’s response, regarding claim 1, the applicant argues:
[,]. Gu does not disclose or suggest training an artificial intelligence algorithm based at least in part on enrolled facial recognition templates. Absent such a disclosure the claim is novel over Gu.
The Examiner cites paragraph [0077] to describe the training limitations of claim 1. However, paragraph [0077] of Gu only discloses retraining using raw images,
The examiner respectfully disagrees with the argument. As explained in the rejections of the claims, Gu, see paragraph [0077], lines 3-5, states “template authentication system 102 may retrain the first machine learning model after a positive authentication of the identity of the user”; Streit, see fig.2B and paragraph [0259], states “In one example, a threshold is set such that step 258 tests if a threshold match has been exceeded, and if yes, the deep learning neural network (e.g., classifier & prediction network) is retrained to include the new feature vectors being analyzed. According to some embodiments, retraining to include newer feature vectors permits biometrics that change over time (e.g., weight loss, weight gain, aging or other events that alter biometric information, haircuts, among other options).” It is apparent that not only Gu discloses or suggesters the argument elements but also Streit does. Therefore, the rejections of the claims are proper.
11-1. On page 11 of applicant’s response, regarding claims 10 and 16-19, the applicant’ arguments have been considered, and then each of them in the instant office action has been separately explained and rejected. Therefore, the arguments are moot.
Conclusion
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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