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 .
2. This is in response to the applicant response filed on 01/16/2026. In the applicant’s response, claims 1 and 10 were amended. Accordingly, claims 1-20 are pending and being examined. Claims 1, 10 and 16 are independent form.
Claim Rejections - 35 USC § 112
3. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
4. (New Matter) Claims 1-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
4-1. Regarding independent claim 1, the claim recites “wherein retraining the artificial intelligence (AI) comprises updating the enrolled facial recognition template for the matching enrolled user without adding facial images of the matching enrolled user to the training data set” in the last portion of the claim. However, support for this is not found throughout the applicant’s originally filed specification. Specification, see paragraph [0018], (the similar disclosure can be found in other paragraphs of the specification as well,) states:
[0018] The controller 18 is configured to determine whether a current facial image of a person captured by the camera 16 matches one of the plurality of enrolled facial recognition templates 14. When the current facial image of the person matches one of the plurality of enrolled facial recognition templates 14, the controller 18 is configured to identify the enrolled user of the plurality of enrolled users that matches the current facial image of the person and to update the enrolled facial recognition template for the matching enrolled user based on the current facial image of the person.
(The emphases added by the examiner.)
Specification, see paragraph [0019], similarly recited in other paragraphs as well, states:
“When the current facial image of the person matches one of the plurality of enrolled facial recognition templates 14, the controller 18 may retrain the AI algorithm 20 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, including for the enrolled user that matches the current facial image of the person and in some cases for other enrolled users.”
(The emphases added by the examiner.)
Under the broadest reasonable interpretation (BRI), the claimed inventions in the applicant’s originally filed specification empresses an enrolled facial recognition template which may be updated based on a current facial image of a person that matches one of the plurality of facial recognition templates enrolled for the person. However, the applicant’s originally filed specification does not disclose “retraining the artificial intelligence (AI) compris[ing] updating the enrolled facial recognition template for the matching enrolled user without adding facial images of the matching enrolled user to the training data set”. There is no disclosure anywhere in the specification that the artificial intelligence (AI) is retrained by “updating the enrolled facial recognition template for the matching enrolled user without adding facial images of the matching enrolled user to the training data set”.
Due to the reasons and the rationales set forth above, the newly added limitation is not examined and given a patentable weight.
4-2. The remaining claims 2-9 are dependent from claim 1, respectively, therefore, are rejected as being indefinite under 35 U.S.C. 112(a).
4-3. Regarding independent claim 10, the claim faces the similar issue set forth in the rejection of claim 1, therefore, is rejected as being indefinite under 35 U.S.C. 112(a). Likewise, its dependent claim 11-15 are rejected as being indefinite under 35 U.S.C. 112(a).
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 1-4, 6-8, and 10-20 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 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,”);
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 (see para.49: “Embodiments of the present disclosure may include a template authentication system that includes at least one processor programmed or configured to train a first machine learning model (e.g., a feature template authentication machine learning model) based on a training dataset of a plurality of images of one or more first users, [...] where each image feature template of the plurality of image feature templates is associated”. Also 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.”) 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 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.”);
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 (para.77: “template authentication system 102 may retrain the first machine learning model after a positive authentication of the identity of the user”. See para.50: “when aspects of the physical characteristic of the individual have changed due to aging.””),
Regarding claim 2, 11, 20, Gu 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 (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, Gu 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, Gu 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 (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 (see para.50: “when aspects of the physical characteristic of the individual have changed due to aging”).
Regarding claim 6, 14, Gu 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 (see the communication network 106 among user device 104, template authentication sys 102, and database 102a in fig.1).
Regarding claim 7, 15, Gu 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 (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, Gu discloses the method of claim 7, wherein the one or more characteristics correspond to one or more facial features of the person (ibid.).
Regarding claims 10, 16-19, the scope of each of them is broader than the scope of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
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. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Gu.
Regarding claim 5, although Gu does not explicitly disclose the claimed invention, Gu disclose it implicitly. 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.
10. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Gu in view of Zou et al (US 20200065563, hereinafter “Zou”).
Regarding claim 9, Gu 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 Gu 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 Zou.
Response to Arguments
11. Applicant’s arguments, filed on 01/16/2026, have been fully considered but they are not persuasive.
11-1. On page 8 of applicant’s response, regarding claim 1, applicant argues:
The Examiner cites paragraph [0050] to support that the algorithm is trained to recognize changes in facial appearance due to aging. However, paragraph [0050] only describes what the system can accomplish - the ability to authenticate individuals despite aging. Gu does not disclose how the system recognizes changes in appearance, let alone a method of doing so as recited in claim 1. Indeed, Gu does not describe how the artificial intelligence algorithm is trained to achieve the desired result.
The examiner respectfully disagrees with the applicant’s argument. First of all, the examiner respectfully points out that the argued element is “wherein the artificial intelligence (AI) algorithm is [] trained to recognize changes in facial appearance due to aging”. The claim does not recite “how the artificial intelligence algorithm is trained to achieve the desired result” argued by the applicant. Therefore, applicant’s contentions are not commensurate with the scope of the claim. As explained in the rejection of the claim, Gu explicitly discloses:
[0049] [...] Embodiments of the present disclosure may include a template authentication system that includes at least one processor programmed or configured to train a first machine learning model (e.g., a feature template authentication machine learning model) based on a training dataset of a plurality of images of one or more first users, [...] where each image feature template of the plurality of image feature templates is associated with a positive authentication of the identity of the one or more first users during the time interval, [...]
[0050] In this way, “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.”
(The emphases added by the examiner.)
In other words, the template authentication system of Gu is trained based on a training dataset of a plurality of images of one user so that the trained template authentication system can “accurately authenticate an individual based on an image of a physical characteristic of the individual [...] when aspects of the physical characteristic of the individual have changed due to aging.” Thus, Gu explicitly discloses the argued feature. The argument is unpersuasive.
11-2. On page 9 of applicant’s response, regarding claim 1, applicant argues:
The Examiner cites paragraph [0077] to describe the training limitations of claim 1. However, paragraph [0077] of Gu only discloses retraining using raw images not trained based on enrolled templates and is silent on training data that excludes enrolled users. Paragraph [0077] of Gu only describes post-authentication retraining, not initial training architecture.
(The emphases added by the examiner.)
First, the examiner respectfully points out that the applicant’s argument is not commensurate with the scope of the claim, namely, the claim does not recite the “training data that excludes enrolled users” instead the claim recites “storing a plurality of enrolled facial recognition templates for a plurality of enrolled users”. Second, Paragraph [0077], lines 1-16, Gu sates: “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.” Therefore, Gu discloses retaining the system by adding the matched input image in the existing training dataset. The argument is not persuasive.
Conclusion
12. THIS ACTION IS MADE FINAL. 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 extension fee 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.
13. 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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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