DETAILED ACTION
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 .
Claim Objections
Claims 3 and 18-20 are objected to because of the following informalities:
In Claim 3 line 4, “unput” should be “input.”
In Claim 18 line 1, Claim 19 line 1, and Claim 20 line 1, “the method of” appears as if it should recite “the system of.”
Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The method of claim 1 is directed to a process, which is one
of the statutory categories of invention, and passes Step 1: Statutory Category- MPEP § 2106.03. However, the following limitations of Claim 1 recite steps that can be performed in the human mind or with pen and paper, therefore failing Step 2A Prong One. These limitations constitute mental processes because they describe acts of observation, evaluation, and judgement that can practically be performed in the human mind, or by a human using pen and paper as a physical aid.
Determining: (a) an identified type of transformation applicable to the media asset, and (b) a time period after which the identified type of transformation is to be applied
Claim 1 fails Step 2A Prong Two because the additional elements beyond the judicial exception do not integrate the judicial exception into a practical application. The claim does not recite a specific asserted improvement in computer technology, and, instead, uses a server/network to carry out content management decisions and to provide versions of content (MPEP § 2106.05(a)). The server and social media network are also used only to receive, determine, apply, and provide versions of content, amounting to instructions for applying the abstract idea on a computer (MPEP § 2106.05(f)). Furthermore, the claim does not impose meaningful limits on the computer components such that the method is tied to a particular machine; the additional elements are described at a high level of generality and can be implemented on any generic computing system (MPEP § 2106.05(b)). Lastly, although the claim recites “applying the transformation,” the claimed transformation merely changes information content of the media asset (e.g., data manipulation) and does not transform a particular article into a different state or thing in a technical sense (MPEP § 2106.05(c)).
Claim 1 also fails Step 2B, as these additional elements are well-understood, routine, and
conventional (WURC), adding nothing significantly more than the abstract idea itself (MPEP § 2106.07(a)(III)). The additional elements, including a server, social media network, and devices accessing content over the network, are WURC according to MPEP § 2106.05(d), where the courts have recognized computer functions such as receiving or transmitting data over a network to be WURC. Claim 13 contains identical subject matter, where the only additional element is control circuitry, which would also fail Step 2A Prong Two and Step 2B (see claim 1 analysis above). Therefore, it is also rejected.
The following elements of claims 2, 3, and 5-11 recite steps that can be performed in the human mind or with pen and paper, therefore failing Step 2A Prong One. These steps constitute mental processes because they describe acts of observation, evaluation, and judgement that a human can practically perform mentally.
Claim 2
Analyzing, by the server, the original version of the media asset to detect presence of facial regions or presence of text.
Claim 3
Receiving user interface input specifying: (a) the identified type of transformation applicable to the media asset, and (b) the time period.
Claim 5
Modifying settings of the social media network such that only the transformed version of the media asset is provided to the plurality of devices in response to a request for the media asset.
Claim 6
Modifying the settings of the social media network such that the original version of the media asset is accessible to a device from which the media asset was received for sharing.
Claim 7
Identifying a series of identified sequential transformations, and a set of time periods for applying the series of identified sequential transformations
Claim 8
And determining a maximum intensity level for the filter, wherein the maximum intensity level is reached upon completion of a number of sequential transformation.
Claim 9
Analyzing the original version of the media asset to identify and extract distinctive visual elements represented as feature vectors.
Claim 10
Analyzing the media asset using a facial recognition algorithm to identify one or more facial regions;
Segmenting the identified one or more facial regions within the media asset;
And selectively applying the transformation to only the segmented facial regions.
Claim 11
Monitoring access frequency of the media asset;
Determining an access rate based on the monitoring;
And based on the access rate, determining the time period for when to apply the transformation to the media asset.
These claims fail Step 2A Prong Two and Step 2B because the additional elements beyond the judicial exception, including a server, social media network, and devices accessing content over the network, do not integrate the judicial exception into a practical application and are WURC (see claim 1 analysis above). The other additional elements beyond the judicial exception, including a facial recognition algorithm and image generative ML model, also do not integrate the judicial exception into a practical application (see claim 1 analysis above) and are WURC. Haq et. al states that “Face recognition has emerged as one of the most prominent applications of image analysis and understanding” (Abstract), and Sordo et. al states that “Generative AI (genAI) has emerged as a powerful tool with the ability to create novel digital content, including images, text, and music” (Abstract), showcasing that they are well-known and in common use in the industry. Claims 14, 15, and 17-20 contain identical ineligible subject matter and are also rejected.
Claims 4 and 12 also fail Step 2A Prong Two and Step 2B because the additional elements beyond the judicial exception, including a social media network and devices accessing content over the network, do not integrate the judicial exception into a practical application and are WURC (see claim 1 analysis above). Claim 16 contains identical ineligible subject matter and is also rejected.
Claim Rejections - 35 USC § 102
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.
Claims 1, 5, 6, 12, 13, 17, and 18 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Lerios et. al (US 20150248442A1).
Regarding Claim 1, Lerios teaches a method (Lerios: Summary) comprising:
Paragraph 0005: “To allow for non-destructive editing of images within a social networking system, embodiments of the invention include systems, methods, and computer readable media to facilitate applying, to an image, transformations selected on a user device.”
receiving, at a server of a social media network, a media asset for sharing (Lerios: Fig. 4, Step 401 (shown below));
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providing an original version of the media asset for access by a plurality of devices via the social media network (Lerios: Fig. 4, Steps 405-406 (shown above));
determining: (a) an identified type of transformation applicable to the media asset, and (b) a time period after which the identified type of transformation is to be applied (Lerios: Detailed Description and Fig. 5, Steps 501-506 (shown below));
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Paragraph 0069: “If the social networking system 130 stores the transformations and associates them with the original image rather than applying the transformations and storing the altered image or images, the social networking system 130 may apply the transformations to a copy of the original image and provide the altered copy to a consuming client each time the image is requested. Thus, applying the transformations at the time that the image is requested may be a more efficient and versatile approach to processing user edits.”
applying the transformation to the media asset to generate a transformed version of the media asset (Lerios: Detailed Description);
Paragraph 0054: “Using a photo editing application, the user may modify the photo by applying transformations to the original image. Transformations may include discrete operations such as cropping, blurring, filtering, rotating, or any other modification of the original image. A transformation may comprise an operation (e.g., crop) and a set of parameters (e.g., a range of pixels to be included in the cropped image).”
providing the transformed version of the media asset for access by the plurality of devices via the social media network, wherein the transformed version of the media asset requires less storage space than the original version of the media asset (Lerios: Detailed Description);
Paragraph 0062: “In an embodiment, the social networking system 130 may store both original image copies and altered image copies for a current user device landscape in which both smart consuming clients and simple consuming clients are common, and gradually reduce storage space devoted to altered image copies in anticipation of smart consuming clients becoming predominant and simple consuming clients becoming obsolete.”
and wherein the original version of the media asset is unavailable for access by the plurality of devices via the social media network after providing the transformed version of the media asset for access by the plurality of devices via the social media network (Lerios: Detailed Description).
Paragraph 0068: “This concern may be addressed with authorized smart consuming clients that be trusted to prevent access to the original copy when privacy settings demand so.”
Regarding Claim 5, Lerios teaches the method of claim 1, further comprising:
retaining the original version of the media asset in storage of the social media network after providing the transformed version of the media asset for access by the plurality of devices via the social media network (see paragraph 0062 (shown above));
and modifying settings of the social media network such that only the transformed version of the media asset is provided to the plurality of devices in response to a request for the media asset (see paragraph 0068 and 0069 (shown above)).
Regarding Claim 6, Lerios teaches the method of claim 5, further comprising:
modifying the settings of the social media network such that the original version of the media asset is accessible to a device from which the media asset was received for sharing (Lerios: Detailed Description).
Paragraph 0055: “The various user devices 110 used to access images on the social networking system 130 may include publishing clients, e.g., devices that are used to upload original images and specify certain transformations to be applied to the original images, and consuming clients, e.g., devices that are used to view copies of altered images.”
Paragraph 0077: “Initially, a user of a publishing client requests an original image stored within the social networking system 130.
Regarding Claim 12, Lerios teaches the method of claim 1, further comprising:
embedding metadata associated with the transformation into the transformed version of the media asset prior to providing the transformed version of the media asset for access by the plurality of devices via the social media network (Lerios: Fig. 5, Step 504 (shown above));
upon access to the media asset by the plurality of devices via the social media network, retrieving the embedded metadata; and based on the retrieved metadata, instructing, via the social media network, the plurality of devices accessing the media asset to maintain or reapply the transformation associated with the media asset to ensure consistency of the transformed media asset across the plurality of devices over the time period (Lerios: Fig. 6, Steps 605-608 (shown below)).
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Regarding Claim 13, Lerios teaches all of the limitations of Claim 1 above because Claim 13 recites a
system comprising instructions that cause control circuitry to perform substantially the same
functions as those of the method of Claim 1. Lerios teaches that the “computer system 700 includes a processor 702, a cache 704, and one or more executable modules and drivers, stored on a computer-readable medium, directed to the processes and features” (paragraph 0092).
Regarding Claim 17, Lerios teaches the system of claim 13, and additional limitations are met as in the consideration of claim 5 above.
Regarding Claim 18, Lerios teaches the system of claim 17, and additional limitations are met as in the consideration of claim 6 above.
Claim Rejections - 35 USC § 103
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.
Claims 2, 10, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lerios (US 20150248442A1) in view of Krishnaswamy and Besbris (US10114532B2).
Regarding Claim 2, Lerios teaches the method of claim 1, but fails to teach that determining: (a) the identified type of transformation applicable to the media asset, and (b) the time period after which the identified type of transformation is to be applied comprises: analyzing, by the server, the original version of the media asset to detect presence of facial regions or presence of text.
However, Krishnaswamy and Besbris teach a method for analyzing an image to detect facial regions using facial recognition techniques, identifying facial features such as eyes, mouth, eyebrows, and nose to determine a face class, and using the detected facial regions to determine appropriate image transformations (e.g., smoothing, brightness, noise reduction) that are particularly suitable for facial regions. Specifically, Krishnaswamy and Besbris explain that facial regions are detected by analyzing pixel characteristics and facial features, and that the detected class of the region is used to determine which transformations should be applied.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lerios’s system to be able to detect the presence of facial regions or text in a media asset. Krishnaswamy and Besbris teach that facial region detection enables more appropriate and higher-quality image transformations, improving visual results and user experience by tailoring transformations to image content rather than applying them uniformly. Krishnaswamy and Besbris emphasize that analyzing images to detect facial regions allows systems to select transformations that are better suited for faces, thereby reducing undesirable effects and improving output quality. Given these advantages, one of ordinary skill in the art would have been motivated to incorporate the facial region analysis into Lerios’s system to improve the determination of which transformation should be applied to a media asset, particularly in a social media context where images frequently contain faces and visual quality is important.
Regarding Claim 10, Lerios teaches the method of claim 1, but fails to teach that the method further comprises: analyzing the media asset using a facial recognition algorithm to identify one or more facial regions; segmenting the identified one or more facial regions within the media asset; and selectively applying the transformation to only the segmented facial regions.
However, Krishnaswamy and Besbris teach a method for analyzing an image using facial recognition techniques to identify one or more facial regions, segmenting the image into regions (including facial regions and facial features), and selectively applying image transformations only to the segmented facial regions, while optionally excluding other facial features or non-facial areas. Krishnaswamy and Besbris disclose that transformations such as smoothing, brightening, or noise reduction are applied to pixels of the detected facial region, and not to the entire image.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lerios’s system to include this facial recognition algorithm. Krishnaswamy and Besbris explain that selectively applying transformations to facial regions improves image quality by avoiding unnecessary or undesirable alterations to non-facial regions, while enhancing regions of primary interest (faces). Krishnaswamy and Besbris highlight technical benefits such as improved visual quality, reduced processing artifacts, and more consistent editing results. Given these advantages, one of ordinary skill in the art would have been motivated to incorporate the facial recognition algorithm into Lerios’s system to improve the quality and relevance of image transformations performed in a social media system, particularly for images containing people.
Regarding Claim 14, Lerios teaches the system of claim 13, and additional limitations are met as in the consideration of claim 2 above.
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Lerios (US 20150248442A1) in view of Alles et. al (US8190986B2).
Regarding Claim 3, Lerios teaches the method of claim 1 and a user interface specifying the identified type of transformation applicable to the media asset. However, Lerios does not teach that the user interface specifies the time period.
However, Alles teaches a non-destructive media presentation system in which a user interface receives user input defining transformation actions (e.g., visual or audio presentation changes) to be applied to the media output. Alles also teaches that the user input is recorded as commands in a synchronization (synch) file that includes timing information specifying when during playback or presentation the transformation should occur. The transformation commands are executed at the specified times to modify the presentation of the media without altering the original media asset.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lerios’s system to include a user interface that specifies the time period. Alles explains that allowing users to specify when a transformation is applied, rather than applying all transformations immediately, provides significant technical and experiential advantages. These advantages include greater user control over media presentation, the ability to create customized, time-based derivative media experiences, and non-destructive editing that preserves the original media asset while enabling flexible transformation behavior. Given these advantages, one of ordinary skill in the art would have been motivated to incorporate time-based user input for transformations to enhance user control and flexibility over how and when transformations are applied to shared media assets.
Regarding Claim 15, Lerios teaches the system of claim 13, and additional limitations are met as in the consideration of claim 3 above.
Claims 4, 7-8, 11, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lerios (US 20150248442A1) in view of Podlipnig and Boszormenyi (“A Survey of Web Cache Replacement Strategies”).
Regarding Claim 4, Lerios teaches the method of claim 1, but fails to teach that the method further includes permanently deleting, by the social media network, the original version of the media asset after the time period.
However, Podlipnig and Boszormenyi teach that cache replacement strategies remove objects permanently from storage when space constraints or policy conditions are met. Podlipnig and Boszormenyi explain that eviction or replacement entails removing objects to free storage capacity, thereby preventing future access to the evicted object.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lerios’s system to include permanently deleting the original version of the media asset. Podlipnig and Boszormenyi emphasize that permanent removal of stored objects is necessary to conserve limited storage resources, prevent cache pollution, and improve system scalability and performance. A person of ordinary skill in the art would have been motivated to incorporate such permanent deletion behavior into Lerios’s social-media storage system to efficiently manage storage once a transformed version has replaced the original.
Regarding Claim 7, Lerios teaches the method of claim 1. Lerios discloses applying a “sequence of transformations that produce a desirable effect on an image to which they are applied” (paragraph 0089) and storing the transformed version but fails to teach that each sequential transformation produces progressively smaller storage footprints.
However, Podlipnig and Boszormenyi teach progressive handling of stored objects, including replacement and partial caching strategies that reduce storage consumption over time, such as retaining only portions or reduced representations of objects as part of storage optimization policies. Podlipnig and Boszormenyi further teach that cache strategies often replace objects with smaller or lower-value representations over time, prioritizing space efficiency and ensuring that only the most relevant version remains stored.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lerios’s system to include a sequential transformation sequence that progressively reduces storage space. Podlipnig and Boszormenyi explain that incremental reduction of stored object size improves cache efficiency and scalability. A person of ordinary skill in the art would have been motivated to adapt this principle to Lerios’s system by applying progressively more compact transformations, ensuring reduced storage consumption over time.
Regarding Claim 8, Lerios in view of Podlipnig and Boszormenyi teaches the method of claim 7. Lerios discloses applying image transformations (e.g., cropping, blurring, grayscale conversion) with adjustable intensity parameters and applying a sequence of transformations. However, Lerios fails to teach progressively increasing filter intensity across sequential transformations and determining a maximum intensity level.
However, Podlipnig and Boszormenyi disclose systems in which content may be subjected to progressive, multi-level transformation or degradation policies, wherein successively stronger degrees of modification are applied over time or stages. Podlipnig and Boszormenyi explain that such transformations may be applied incrementally, with each level representing an increased degree of modification, and that such progression is governed by policy-defined thresholds that bound the extent of modification to maintain acceptable content quality. In addition, Podlipnig and Boszormenyi teach that reducing the fidelity or granularity of stored objects is a known approach to lowering storage requirements and prioritizing efficiency.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lerios’s system to include Podlipnig and Boszormenyi’s method. Podlipnig and Boszormenyi teach that applying progressive, staged transformations with increasing degrees of modification, under defined policy limits, provides controlled and predictable modification of content while avoiding unbounded degradation. Furthermore, Podlipnig and Boszormenyi discuss trade-offs between object fidelity and storage efficiency. A person of ordinary skill in the art would be motivated to increase filter intensity over time in Lerios’s system to further reduce storage usage while retaining a usable representation.
Regarding Claim 11, Lerios teaches the method of claim 1, but fails to teach that determining the time period for applying the transformation to the media asset comprises: monitoring access frequency of the media asset; determining an access rate based on the monitoring; and based on the access rate, determining the time period for when to apply the transformation to the media asset.
However, Podlipnig and Boszormenyi extensively teach frequency-based caching strategies, explaining that access frequency is a critical metric for deciding when objects should be retained, modified, or replaced.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lerios’s system to include Podlipnig and Boszormenyi’s frequency-based caching strategies. Podlipnig and Boszormenyi emphasize that access frequency is a reliable predictor of future use and should influence timing decisions. A person of ordinary skill in the art would be motivated to apply this principle to Lerios’s system so that transformations occur based on observed access frequency, improving efficiency and responsiveness.
Regarding Claim 16, Lerios teaches the system of claim 13, and additional limitations are met as in the consideration of claim 4 above.
Regarding Claim 19, Lerios teaches the system of claim 13, and additional limitations are met as in the consideration of claim 7 above.
Regarding Claim 20, Lerios in view of Podlipnig and Boszormenyi teaches the system of claim 19, and additional limitations are met as in the consideration of claim 8 above.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Lerios (US 20150248442A1) in view of Podlipnig and Boszormenyi, further in view of Gatys et. al (“A Neural Algorithm of Artistic Style”).
Regarding Claim 9, Lerios in view of Podlipnig and Boszormenyi teaches the method of claim 7, but fails to teach that the method includes generating a target stylized representation for the media asset, wherein the generating the target stylized representation comprises: analyzing the original version of the media asset to identify and extract distinctive visual elements represented as feature vectors; and generating the target stylized representation by inputting the feature vectors into an image generative machine learning model.
However, Gatys discloses a neural-network based system that analyzes an input image using a CNN, extracts hierarchical feature representations from multiple layers of the CNN that represent distinctive visual elements of the image, separates content and style representations using these feature vectors, and generates a new stylized image by inputting the extracted feature representations into an image generative machine-learning process that synthesizes an output image matching selected feature representations. Gatys explains that the CNN transforms an input image into feature maps that constitute content and style representations, and that a new image is synthesized by optimizing an image to match those feature-vector representations, thereby producing a target stylized representation of the original image.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Lerios’s system to include this image generative model. Gatys teaches that using feature-vector representations and generative neural networks enables the creation of high-quality stylized images that preserve content while altering appearance in a perceptually meaningful way. Gatys emphasizes that this approach provides superior visual quality and flexibility compared to traditional pixel-level filtering techniques, and that such generative models are broadly applicable to image transformation tasks. In light of these teachings, one of ordinary skill in the art would have been motivated to incorporate Gaty’s model into Lerios’s system in order to improve the quality and expressiveness of transformed images generated in response to media sharing. Applying Gatys’s generative stylization within Lerios’s sharing workflow would predictably result in more visually compelling stylized representations for shared media assets.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zara (US12455874B2) discloses systems and methods for dynamically modifying media assets by retrieving external data and generating updated media assets and associated metadata for presentation in various serving contexts. The reference generally relates to automated media asset transformation and catalog management.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM ADU-JAMFI whose telephone number is (571)272-9298. The examiner can normally be reached M-T 8:00-6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached at (571) 270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM ADU-JAMFI/Examiner, Art Unit 2677
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666