Prosecution Insights
Last updated: August 17, 2026
Application No. 18/749,096

Look Suggester System Utilizing Favorited, Saved, and Liked Photos and Videos to Recreate Flattering Looks Based on User's Unique Facial Features

Final Rejection §101
Filed
Jun 20, 2024
Examiner
EGLOFF, PETER RICHARD
Art Unit
3715
Tech Center
3700 — Mechanical Engineering & Manufacturing
Assignee
ELC Management LLC
OA Round
2 (Final)
43%
Grant Probability
Moderate
3-4
OA Rounds
1y 2m
Est. Remaining
76%
With Interview

Examiner Intelligence

Grants 43% of resolved cases
43%
Career Allowance Rate
338 granted / 790 resolved
-27.2% vs TC avg
Strong +33% interview lift
Without
With
+32.7%
Interview Lift
resolved cases with interview
Typical timeline
3y 4m
Avg Prosecution
27 currently pending
Career history
826
Total Applications
across all art units

Statute-Specific Performance

§101
29.1%
-10.9% vs TC avg
§103
40.0%
+0.0% vs TC avg
§102
14.6%
-25.4% vs TC avg
§112
14.2%
-25.8% vs TC avg
Black line = Tech Center average estimate • Based on career data from 790 resolved cases

Office Action

§101
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 . Response to Amendment 2. In response to the amendment filed 10 April 2026, claims 1-9, 11-20, 22 and 23 remain pending. Claim Rejections – 35 USC § 101 3. 35 U.S.C. 101 reads as follows: 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. Claims 1-9, 11-20, 22 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 12 and 23 recite a method comprising: obtaining a three-dimensional face mesh representing a plurality of facial features corresponding to a face of a user; obtaining image data representing a face of another person and indicating a desired cosmetic appearance of the user; extracting facial features of the another person; comparing the extracted facial features of the another person to corresponding ones of the plurality of facial features of the user represented by the three-dimensional face mesh; based upon the comparison of the extracted facial features of the another person to the corresponding facial features of the user, generating a personalized look comprising a plurality of cosmetic products to be applied to the face of the user to replicate the desired cosmetic appearance on the plurality of facial features of the user; and causing an indication of the generated look to be presented to the user via one or more user interfaces at one or more computing devices accessible to the user. The limitations of obtaining a face mesh, obtaining image data, extracting facial features, comparing the extracted features, generating a look, and presenting the look, as drafted, constitutes a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components, akin to the steps of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group. That is, other than reciting the method is performed by one or more computers or processors, nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the “computer/processor” language, “obtaining”, “generating”, “extracting”, “comparing”, and “causing” in the context of this claim encompasses a user manually viewing the three-dimensional face mesh and image data, analyzing it to produce features of the image data, comparing the features to the features of the user, and producing and presenting a list of cosmetic products. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claims recite using a computer/processor to perform the claimed steps. The processor in these steps is recited at a high-level of generality (i.e., as a generic processor performing generic computer functions of obtaining data, analyzing it, and delivering results) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recite the extracting is performed by “applying one or more machine learning models”. These one or more models are generic and therefore amount to no more than utilizing a generic computer performed to the abstract idea, or generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). The claims are directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the use of one or more generic machine learning models to extract the facial features in a generic manner amounts to instructions to implement the abstract idea on a computer, or generally linking the use of the judicial exception to a particular technological environment. The claims are not patent eligible. Furthermore, the use of machine learning models to analyze facial image data amounts to well-understood, routine or conventional activity, as demonstrated by Prout (US Patent No. 11,178,956 B1) (column 6, line 51-63) and Kosecoff (US 2023/0101374 A1) (Par. 87). Dependent claims 2-9, 11, 13-20 and 22 recite the same abstract idea as in claim their respective parent claims, and only recite generic computer display technology (e.g. displaying virtual application, augmented reality, machine learning models, being used to perform further aspects of the abstract idea, such as depicting the face of the user, generating directions, receiving feedback, etc.) Therefore, these claims do not recite additional limitations sufficient to direct the claimed invention to significantly more. Response to Arguments 4. Applicant's arguments filed 10 April 2026 with respect to the section 101 rejection have been fully considered but they are not persuasive. Regarding Step 2A, Prong One, Applicant argues that the claims do not recite an abstract idea because they encompass AI in a way that cannot practically be performed in the human mind. However, it is noted that the claimed use of a machine learning model is not considered to be abstract in the rejection. Applicant further likens the claim to Example 39. This is not persuasive, since the claim in Example 39 set forth a series of steps for training and retraining a neural network based on facial image data. The instant claim merely recites use of a generic machine learning model to extract feature data in a generic, non-specific manner. This is more akin to Claim 1 of Example 48, which is ineligible because, similarly, it merely recites “using a deep neural network” to determine embedding vectors in a generic manner. Regarding Step 2A, Prong Two, Applicant again points to the use of a machine learning model to extract facial features and argues that this limitation along with the other limitations as a whole do not merely recite an abstract idea with the words “apply it”. However, Applicant is again directed to Claim 1 of Example 48. The guidance states that merely reciting use of a deep neural network does merely amount to a judicial exception with the words “apply it”, because only recites the idea of a solution or the outcome. Similarly, the instant claims only recite the idea or outcome of using machine learning to extract the feature data. The claims are also not analogous to Example 47, Claim 3, because that claim was directed to an improvement in networking technology through use of machine learning. The instant claims, on the other hand, would result at most in an improvement to delivering cosmetic recommendations, which is not a technical field. Regarding Step 2B, Prong Two, Applicant argues the rejection must show the specific ordered combination of steps was well-understood, routine or conventional at the time of filing. Applicant is directed to the rejection, above, which demonstrates that use of machine learning models to analyze facial image data was well-understood, routine or conventional. 5. Applicant’s arguments with respect to the rejections under section 103 have been fully considered and are persuasive. The 103 rejections have been withdrawn. Conclusion 6. 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 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. 7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER EGLOFF whose telephone number is (571) 270-3548. The examiner can normally be reached 9:00 AM – 5:00 PM, Monday through Friday Eastern. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai, can be reached at 571-272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Peter R Egloff/ Primary Examiner, Art Unit 3715
Read full office action

Prosecution Timeline

Jun 20, 2024
Application Filed
Jan 20, 2026
Non-Final Rejection mailed — §101
Apr 10, 2026
Response Filed
Jul 01, 2026
Final Rejection mailed — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
43%
Grant Probability
76%
With Interview (+32.7%)
3y 4m (~1y 2m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 790 resolved cases by this examiner. Grant probability derived from career allowance rate.

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