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 action is responsive to RCE filed 9/16/2026.
This action is made Non-Final.
Claims 1-13, 15-20 are pending in the case. Claims 1, 13, and 20 are independent claims. Claims 1, 2, 4-13, 15, 17-20 are amended.
Response to Arguments
Regarding claims 1-12 and 20, Applicant’s arguments are fully considered and are persuasive. Claims 1-12 and 20 are allowed. Regarding claims 13 and 16-19, Applicant’s arguments are fully considered and are moot. Applicant is directed to the updated rejection of the claims wherein the Examiner details how the new combination of references teach every feature of the claims.
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) 13 and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engin et al (Causal Machine Learning by Creative Insights, Netflix Technology Blog, January 11, 2023, 20 pages, from IDS filed 6/23/2025 hereinafter referred to as Engin) in view of Madeline et al (AVA: The Art and Science of Image Discovery at Netflix, Netflix Technology Blog, February 2018, 12 pages from IDS dated 6/23/2025 hereinafter referred to as Madeline) and further in view of Niedt et al (USPUB 20230164403 A1 hereinafter referred to as Niedt).
Claim 13:
Engrin teaches A system comprising: at least one physical processor; and physical memory comprising computer-executable instructions (title: Engin discusses a process that is machine learning, which inherently involves a computer system) that, when executed by the at least one physical processor, cause the at least one physical processor to: access a first image associated with a first media item (Pg 1: “at Netflix...promotional artwork...represents each title featured on our platform”); identify an association between the first image and a first image take fraction that indicates how well the first image correlates to views of the first media item (Pg 3, 4 and 6: “we have rich dataset of promotional artwork components and user engagement data...we represent the success of an artwork with the take rate: the probability of an average user to watch the promoted title after seeing its promotional artwork, adjusted for the popularity of the title...here are two promotional artwork assets from Unbreakable Kimmy Schmidt. We know that the image on the left performed better than the image on the right”); based at least on the association between the first image and the first image take fraction, train a machine learning (ML) model to predict which images will optimally correlate to views of the first media item (Pg 9-10: “Y: outcome variable (take rate)...W: a vector covariates (a subset of W) along with treatment effect heterogeneity is evaluated...2. Build a potential outcome model to predict Y give the W covariates. Y=q(X,W)+ε”); access an unprocessed image associated with a new media item that has not been processed by the trained ML model (Pg 1: “we can give our creative team data-driven insights to incorporate into their creative strategy, and help in their selection of which artwork to feature”); implement the trained ML model to calculate a predicted image take fraction for the unprocessed image to indicate how well the unprocessed image will correlate to views of the new, media item (Pg 14: “using the causal machine learning framework, we can...test and identify the various components of promotional artwork and gain invaluable creative insights...these insights will guide and assist our team of talented strategists and creatives to select and generate the most attractive artwork, leveraging the attributes that these models selected, down to a specific genre”).
Engin, by itself, does not seem to completely teach rank the unprocessed image and other images processed by the ML model are ranked based on the corresponding predicted image take fractions, and rank the unprocessed image and other images based on corresponding predicted image take fractions, and implement a supervised model to group the ranked images into thematic containers.
The Examiner maintains that these features were previously well-known as taught by Madeline.
Madeline teaches rank the unprocessed image and other images processed by the ML model are ranked based on the corresponding predicted image take fractions, and rank the unprocessed image and other images based on corresponding predicted image take fractions, and implement a supervised model to group the ranked images into thematic containers (pg 8-9: the next step is to surface “the best” image candidates from those frames through an automated artwork pipeline...they are automatically provided with a high quality image set to choose from...one way we identify the key character for a given episode is by utilizing a combination of face clustering and actor recognition to prioritize main characters and de-prioritize secondary characters or extras...we trained a deep-learning model to trace facial similarities from all qualifying candidate frames...to surface and rank the main actors of a given title”; an image shows an example of actor clusters, frame ranking and optimal selection allowing for a user to choose an image, the groups of ranked main characters, secondary actors and extras are equivalent to the claimed thematic containers).
Engin and Madeline are analogous art because they are from the same problem-solving area, identifying images to represent media content.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Engin and Madeline before him or her, to combine the teachings of Engin and Madeline. The rationale for doing so would have been to ensure only appropriate images are used to represent media content.
Engin and Madeline do not seem to completely teach determine that a user is associated with a preference for media items having a particular thematic attribute; identify, based on the preference, a particular thematic container corresponding to the particular thematic attribute; select, from the particular thematic container, a target image associated with the new media item; and cause the target image to be presented in a media-item selection user interface for the user.
The Examiner maintains that these features were previously well-known as taught by Niedt.
Niedt teaches determine that a user is associated with a preference for media items having a particular thematic attribute; identify, based on the preference, a particular thematic container corresponding to the particular thematic attribute; select, from the particular thematic container, a target image associated with the new media item; and cause the target image to be presented in a media-item selection user interface for the user (Fig 3A and 0032-38).
Engin and Niedt are analogous art because they are from the same problem-solving area, identifying images to represent media content.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Engin and Niedt before him or her, to combine the teachings of Engin and Niedt. The rationale for doing so would have been to ensure only appropriate images are used to represent media content.
Claim 16:
Engin, by itself, does not seem to completely teach the thematic containers include containers for at least one of: images with specific characters, images conveying specific genres, images conveying specific storylines, images conveying specific tones, or images conveying a specific type of shot.
The Examiner maintains that these features were previously well-known as taught by Madeline.
Madeline teaches the thematic containers include containers for at least one of: images with specific characters, images conveying specific genres, images conveying specific storylines, images conveying specific tones, or images conveying a specific type of shot (pg 8-9: the next step is to surface “the best” image candidates from those frames through an automated artwork pipeline...they are automatically provided with a high quality image set to choose from...one way we identify the key character for a given episode is by utilizing a combination of face clustering and actor recognition to prioritize main characters...we trained a deep-learning model to trace facial similarities from all qualifying candidate frames...to surface and rank the main actors of a given title”; an image shows an example of actor clusters, frame ranking and optimal selection allowing for a user to choose an image).
Engin and Madeline are analogous art because they are from the same problem-solving area, identifying images to represent media content.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Engin and Madeline before him or her, to combine the teachings of Engin and Madeline. The rationale for doing so would have been to ensure only appropriate images are used to represent media content.
Therefore, it would have been obvious to combine Engin and Madeline to obtain the invention as specified in the instant claim(s).
Claim 17:
Engin, by itself, does not seem to completely teach at least one of the images belongs to a plurality of different thematic containers.
The Examiner maintains that these features were previously well-known as taught by Madeline.
Madeline teaches at least one of the images belongs to a plurality of different thematic containers (pg 8-9: the next step is to surface “the best” image candidates from those frames through an automated artwork pipeline...they are automatically provided with a high quality image set to choose from...one way we identify the key character for a given episode is by utilizing a combination of face clustering and actor recognition to prioritize main characters...we trained a deep-learning model to trace facial similarities from all qualifying candidate frames...to surface and rank the main actors of a given title”; an image shows an example of actor clusters, frame ranking and optimal selection allowing for a user to choose an image).
Engin and Madeline are analogous art because they are from the same problem-solving area, identifying images to represent media content.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Engin and Madeline before him or her, to combine the teachings of Engin and Madeline. The rationale for doing so would have been to ensure only appropriate images are used to represent media content.
Therefore, it would have been obvious to combine Engin and Madeline to obtain the invention as specified in the instant claim(s).
Claim 18:
Engin, by itself, does not seem to completely teach the images in each thematic container are ranked based on the image's corresponding image take fraction.
The Examiner maintains that these features were previously well-known as taught by Madeline.
Madeline teaches the images in each thematic container are ranked based on the image's corresponding image take fraction (pg 8-9: the next step is to surface “the best” image candidates from those frames through an automated artwork pipeline...they are automatically provided with a high quality image set to choose from...one way we identify the key character for a given episode is by utilizing a combination of face clustering and actor recognition to prioritize main characters...we trained a deep-learning model to trace facial similarities from all qualifying candidate frames...to surface and rank the main actors of a given title”; an image shows an example of actor clusters, frame ranking and optimal selection allowing for a user to choose an image).
Engin and Madeline are analogous art because they are from the same problem-solving area, identifying images to represent media content.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Engin and Madeline before him or her, to combine the teachings of Engin and Madeline. The rationale for doing so would have been to ensure only appropriate images are used to represent media content.
Therefore, it would have been obvious to combine Engin and Madeline to obtain the invention as specified in the instant claim(s).
Claim 19:
Engin, by itself, does not seem to completely teach the images in the thematic containers to at least one user for selection and use with the associated media item.
The Examiner maintains that these features were previously well-known as taught by Madeline.
Madeline teaches the images in the thematic containers to at least one user for selection and use with the associated media item (pg 8-9: an image shows an example of actor clusters, frame ranking and optimal selection allowing for a user to choose an image).
Engin and Madeline are analogous art because they are from the same problem-solving area, identifying images to represent media content.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Engin and Madeline before him or her, to combine the teachings of Engin and Madeline. The rationale for doing so would have been to ensure only appropriate images are used to represent media content.
Therefore, it would have been obvious to combine Engin and Madeline to obtain the invention as specified in the instant claim(s).
Allowable Subject Matter
Claims 1-12 and 20 are allowed.
Claim 15 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.
Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed in the attached PTOL-892 form.
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/MOHAMMED H ZUBERI/Primary Examiner, Art Unit 2178
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