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
Election/Restrictions
Applicant’s response to the restriction requirement was received in the reply filed on July 22, 2026. Applicant elects, without traverse, Group I (Claims 1-17), for examination.
Status of Claims
Claims 1-17 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-17 are drawn to methods As such, claims 1-17 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
Claim 1 recites the following steps:
A method for estimating a popularity likelihood of an image, the method comprising:
extracting the feature vectors from the input image before the image is [shared]
identifying, using the feature vectors and a similarity calculator a predetermined number of nearest neighbors of sample images, the sample images having a known detrended popularity metric, the sample images including known popular images having a known detrended popularity percentile that is greater than a 50th percentile and known unpopular images where the known detrended popularity percentile is less than or equal to the 50th percentile; and
predicting, the popularity likelihood of the input image based, at least in part, on a number of the nearest neighbors that are known popular images relative to the predetermined number of the nearest neighbors.
Claim 10 recites the following steps:
A method for estimating a popularity likelihood of before the content is [shared]
extracting, the feature vectors from each frame;
applying, a sequential model to the feature vectors of the frames; and
predicting, using a probability classifier the popularity likelihood of the content using the model of the content and models of a plurality of sample contents, each sequential sample content having a known detrended popularity metric, the content including a plurality of known popular contents having a respective known detrended popularity percentile that is greater than a 50th percentile and a plurality of known unpopular contents where the respective known detrended popularity percentile is less than or equal to the 50th percentile.
These steps, under its broadest reasonable interpretation, encompass mathematical relationships. These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
Alternatively, these steps, under its broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) estimating the popularity of content based on a comparison to known popular content (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the "mental processes" subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 and 10 recite an abstract idea (Step 2A - Prong One: YES).
Step 2A - Prong Two:
This judicial exception is not integrated into a practical application. The claim(s) recite the additional elements/limitations of:
input image
the trained ML model
the computer
a social media platform
sequential input content
sequential model
The requirement to execute the claimed steps/functions listed above is equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. This/these limitation(s) do/does not impose any meaningful limits on producing the abstract idea and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Additionally, “Step 2A - Prong 2”, the recited additional element(s) of “feeding the input image into a trained machine learning (NIL) model running on a computer, the trained ML model configured to extract a plurality of feature vectors from the input image" and “with a decomposer running on a computer, decomposing the sequential input content into a plurality of frames; feeding the frames into a trained machine learning (ML) model running on the computer, the trained ML model configured to extract a plurality of feature vectors from each frame” serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A -Prong Two: NO).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above in "Step 2A - Prong 2", the requirement to execute the claimed steps/functions listed above is equivalent to adding the words "apply it" on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as "significantly more" (see MPEP 2106.05 (f)).
As discussed above in “Step 2A - Prong 2”, the recited additional element(s) of “feeding the input image into a trained machine learning (NIL) model running on a computer, the trained ML model configured to extract a plurality of feature vectors from the input image" and “with a decomposer running on a computer, decomposing the sequential input content into a plurality of frames; feeding the frames into a trained machine learning (ML) model running on the computer, the trained ML model configured to extract a plurality of feature vectors from each frame” serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more5' (see MPEP 2106.05(g, h)).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Regarding Dependent Claims:
Dependent claims 2, 3, 6, 7, 12 and 13 fail to include any additional elements and are further part of the abstract idea as identified by the Examiner.
Dependent claims 4, 5, 8, 9, 11 and 15-17 include additional limitations that are part of the abstract idea except for:
the trained ML model,
a large language model (LLM),
the computer is a first computer,
from a second computer in network communication with the first computer.
capturing the input image with a camera coupled to and/or in communication with the second computer.
a video content or an audio content.
the decomposer
audio file with a microphone
The additional elements of the dependent claims are equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
Dependent claims 4 and 14 include additional limitations that are part of the abstract idea except for:
feeding the sample images into the trained ML model..
feeding the respective frames of the sequential sample contents into the trained ML model;
The additional elements of the dependent claims serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h))
Prior Art
Examiner conducted a thorough search of the body of available prior art (see attached documents regards PTO-892 Notice of Reference Cited and PE2E Search History). Notably, Examiner discovered several patent literature documents that taught aspects of the invention, but no single disclosure taught “every element required by the claims under its broadest reasonable interpretation” [MPEP § 2131] to make a 35 USC § 102 rejection. Further, Examiner considered the individual elements of the recited claims taught across the prior art cited below, but did not find it obvious to combine such disclosures [MPEP § 2142] to make a 35 USC § 103 rejection. In particular, Bacus et al., U.S. Publication No. 8,712,937 Predicting popularity of electronic publications discloses, “method 300 identifies features relevant to popularity. In one embodiment, supervised learning module 220 identifies which features are most often associated with electronic media items that meet the popularity classification. In other words, supervised learning module 220 may determine the input feature representation of the learned function. The accuracy of the learned function depends strongly on how the input object is represented. For example, the input object may be transformed into a feature vector, which contains a number of features that are descriptive of the corresponding electronic media item,” it is silent with respect to “identifying, using the feature vectors and a similarity calculator running on the computer, a predetermined number of nearest neighbors of sample images.” Jin et al. U.S. Publication No. 2016/0379132 Collaborative feature learning from social media discloses “the act of extracting content item features from the learned features of the content item using machine learning” but fails to teach “predicting, with the computer, the popularity likelihood of the input image based, at least in part, on a number of the nearest neighbors that are known popular images relative to the predetermined number of the nearest neighbors.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RASHIDA R SHORTER whose telephone number is (571)272-9345. The examiner can normally be reached Monday- Friday from 9am- 530pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at (571) 270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RASHIDA R SHORTER/Primary Examiner, Art Unit 3626
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