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 patent application as filed on 4/8/2026
This action is made Final.
Claims 1-13, 15-20 are pending in the case. Claims 1, 13, and 20 are independent claims. Claims 1, 13, 15-20 are amended, and claim 14 has been canceled.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 2, 4, 11-13 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant remarks that the rejection under 35 USC 102(a)(2) is improper (pages 8-9). Applicant’s argument is moot in view of the 103 rejection of said claims necessitated by the new claim amendment. Applicant further remarks that “while Madeline may disclose ranking and clustering candidate frames, including actor-based prioritization and diversity clustering, it does not disclose the claimed step of grouping the ranked images into thematic containers” (pages 9-10). The Examiner disagrees. As discussed in detail in the rejection of the claims, Madeline discusses frame ranking and optimal selection allowing for a user to choose an image, with the groups of ranked main characters, secondary actors and extras being equivalent to the claimed thematic containers. Madeline presents an image showing an example of actor clusters (e.g., main characters, secondary actors) which, as discussed above, are equivalent to the claimed thematic containers. This is consistent with the scope of the claimed “thematic buckets” as discussed in the specification paragraph 0009 “the thematic containers may include containers for at least one of: images with specific characters”.
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) 1- 6 and 9-13, 15-20 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).
Claim 1:
Engin teaches A computer-implemented method comprising: accessing at least one image associated with a media item (Pg 1: “at Netflix...promotional artwork...represents each title featured on our platform”); identifying an association between the accessed image and an image take fraction that indicates how well the accessed image correlates to views of the associated 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 identified association between the accessed media item image and the corresponding image take fraction, training a machine learning (ML) model to predict which images will optimally correlate to views of the associated 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”); accessing 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”); and implementing the trained ML model to predict an image take fraction for the unprocessed image to indicate how well the unprocessed image will correlate to views of the new, unprocessed 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 the 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 the 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.
Therefore, it would have been obvious to combine Engin and Madeline to obtain the invention as specified in the instant claim(s).
Claim 2:
Engin teaches wherein the ML model is configured to identify one or more patterns in the unprocessed image and match those identified patterns to patterns associated with the accessed image (pg 9-10: Engin’s variable W, representing a vector of covarities, is equivalent to the claimed patterns which are utilized in the ML model).
Claim 3:
Engin, by itself, does not seem to completely teach filtering images that are to be processed by the ML model to ensure that the images are usable by the ML model.
The Examiner maintains that these features were previously well-known as taught by Madeline.
Madeline teaches filtering images that are to be processed by the ML model to ensure that the images are usable by the ML model (Pg 11: Filters for Maturity).
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 obtain the benefit of ensuring 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 4:
Engin teaches the image take fraction indicates a percentage of views of the associated media item relative to a number of impressions of the accessed image (pg 4-5: “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...we look at user engagement patterns and see whether or not these engagements with artworks resulted in a successful title selection”).
Claim 5:
Engin, by itself, does not seem to completely teach the ML model comprises a deep learning model that is configured to analyze a plurality of images and a corresponding plurality of image take fractions to indicate how well the plurality of images correlates to views of the associated media items.
The Examiner maintains that these features were previously well-known as taught by Madeline.Madeline teaches the ML model comprises a deep learning model that is configured to analyze a plurality of images and a corresponding plurality of image take fractions to indicate how well the plurality of images correlates to views of the associated media items (pages 8-9: “we outline some of the key elements we use to surface the best images for a given title...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 deprioritize secondary characters or extras...we trained a deep-learning model to trace facial similarities from all qualifying candidate frames tagged with frame annotation to surface and rank the main actors of a given title without knowing anything about the cast members”).
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 6:
Engin, by itself, does not seem to completely teach ranking each of the plurality of images based on the predicted image take fractions.
The Examiner maintains that these features were previously well-known as taught by Madeline.
Madeline teaches ranking each of the plurality of images based on the predicted image take fractions (Page 8: Image Ranking).
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 9:
Engin, by itself, does not seem to completely teach recropped versions of the accessed image result in different image take fractions for the associated media item.
The Examiner maintains that these features were previously well-known as taught by Madeline.
Madeline teaches recropped versions of the accessed image result in different image take fractions for the associated media item (Pg 8: Composition Metadata).
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 10:
Engin, by itself, does not seem to completely teach the ML model is configured to process the recropped versions of the accessed image as separate images that are each associated with the media item.
The Examiner maintains that these features were previously well-known as taught by Madeline.Madeline teaches the ML model is configured to process the recropped versions of the accessed image as separate images that are each associated with the media item (pg 3-5: we first came up with objective signals that we can measure for each and every frame of the video using Frame Annotations. As result, we can collect an effective representation of each frame of the video...every frame of video in a piece of content is processed through a series of computer vision algorithms to gather object frame metadata...as well as some of the contextual metadata that those frame(s) contain).
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 11:
Engin discloses tracking, as feedback, how well the unprocessed image correlated to views of the associated media item; and incorporating the feedback in the ML model when accessing future images and predicting future image take fractions (pg 4-5: we represent the success of an artwork with the take rate: the probability of a average user to watch the promoted title after seeing its promotional artwork...we look at user engagement patterns and see whether or not these engagements with artwork resulted in a successful title selection...we also utilize machine learning algorithms...for discovering high-level associations between image features and an artwork’s success).
Claim 12:
Engin teaches changing an artwork image for at least one media item based on the incorporated feedback (pg 6: we use machine learning algorithms to predict whether or not the artwork contains a face...every unit (an artwork) has some chance of getting treated...we calculate the propensity score...of having a face for samples with different covariates. If a certain subset of artwork...has close to a 0 or 1 propensity score for having a face, then we discard these samples from our analysis).
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 physical processor, cause the physical processor to: access at least one image associated with a media item (Pg 1: “at Netflix...promotional artwork...represents each title featured on our platform”); identify an association between the accessed image and an image take fraction that indicates how well the accessed image correlates to views of the associated 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 identified association between the accessed media item image and the corresponding image take fraction, train a machine learning (ML) model to predict which images will optimally correlate to views of the associated 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”); and implement the trained ML model to predict an image take fraction for the unprocessed image to indicate how well the unprocessed image will correlate to views of the new, unprocessed 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 the 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 the 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.
Claim 15:
Engin, by itself, does not seem to completely teach each thematic container is assigned a specific number of images that are to be taken from the associated media item and placed in each thematic container.
The Examiner maintains that these features were previously well-known as taught by Madeline.
Madeline teaches each thematic container is assigned a specific number of images that are to be taken from the associated media item and placed in each thematic container (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 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).
Claim 20:
Engrin discloses A non-transitory computer-readable medium comprising one or more computer- executable instructions (Title) that, when executed by at least one processor of a computing device, cause the computing device to: access at least one image associated with a media item (Pg 1: “at Netflix...promotional artwork...represents each title featured on our platform”); identify an association between the accessed image and an image take fraction that indicates how well the accessed image correlates to views of the associated 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 identified association between the accessed media item image and the corresponding image take fraction, train a machine learning (ML) model to predict which images will optimally correlate to views of the associated 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”); and implement the trained ML model to predict an image take fraction for the unprocessed image to indicate how well the unprocessed image will correlate to views of the new, unprocessed 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 the 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 the 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.
Claim(s) 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engin and Madeline in view of Krishnan et al (“Selecting the Best Artwork for Videos Through A/B Testing”, Netflix technology Blog, February 2018, 20 pages hereinafter referred to as Krishnan).
Claim 7:
Engin and Madeline discloses every feature of claim 1.
Engin, by itself, does not seem to completely teach the image take fraction includes, as a factor, an amount of time spent watching the media item.
The Examiner maintains that these features were previously well-known as taught by Krishnan.
Krishnan teaches the image take fraction includes, as a factor, an amount of time spent watching the media item (pg 7: amount of time spent watching being included in the take rate is discussed).
Engin and Krishnan 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 Krishnan before him or her, to combine the teachings of Engin and Krishnan. 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 Krishnan to obtain the invention as specified in the instant claim(s).
Claim 8:
Engin, by itself, does not seem to completely teach the image take fraction includes, as a factor, a property associated with the media item.
The Examiner maintains that these features were previously well-known as taught by Krishnan.
Krishnan teaches the image take fraction includes, as a factor, a property associated with the media item (pg 7: take rate potentially including a variety of associated data is discussed).
Engin and Krishnan 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 Krishnan before him or her, to combine the teachings of Engin and Krishnan. 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 Krishnan to obtain the invention as specified in the instant claim(s).
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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H ZUBERI whose telephone number is (571)270-7761. The examiner can normally be reached Mon – Th 10AM-8PM.
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/MOHAMMED H ZUBERI/Primary Examiner, Art Unit 2178
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