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 .
Status of Claims
This action is in response amendment filed on 25 November 2026. Claims 1-4, 8, 12 -15 and 19 have been amended. Claims 1-20 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.
Step 1: The claims 1- 11 are a method, claims 12-18 are an apparatus/system and claims 19 -20 are a media. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A-Prong 1: independent claims (1, 12 and 19) recites receiving, an indication of a selected post including media content associated with a user; evaluating, trained on training data, a quality of the media content of the selected post, wherein the training data comprises any one or more of a profile of the user, a profile of a follower of the user, or an attribute of one or more previous posts associated with the user; generating, and based upon the evaluated quality, a modified post comprising modified media content, wherein a change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type when the evaluated quality is above a threshold, and wherein the first type of change is more substantive than the second type of change; transmitting, the modified post for consideration by the user; and receiving, an indication of a rejection or an acceptance of the modified post. The evaluating limitation as drafted is a process, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the reactions of generic computer components. That is, other the reciting “via a large language model”, nothing in the claim precludes the evaluating step from practically being performed in the human mind. For example, but for the “ a large language model” the claims encompass the user comparing that quality of the media content of the selected post. Thus, this limitation is a mental process.
The generating limitation, as drafted, is a process under broadest reasonable interpretation covers generating a modified media content in response to evaluating the quality of the media content which is adverting, marketing or sales activities or behaviors and business relation. These limitations fall within “Certain Methods Of Organizing Human Activity” for commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Simply put, these limitation merely describe generating a modified media content in repone to evaluating the quality of media content and posting on a social network for promoting content, which is clearly a business arrangement in its purest form. Claims 2-11, 13-18 and 20 merely provide additional abstract concepts and narrow the abstract idea of claims 1, 12 and 19. Further, claims 1-20, are recited at such a high level that the claimed steps amount to no more than a mental processes, such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because a human can select content that meets a specified criteria, acknowledge an agreement to promote content and authorize compensation.
Step 2A-Prong 2: The claims recite additional elements of receiving, and transmitting via interface steps of are recited at a high-level of generality (i.e., as a general means of gathering data for use evaluating and generating steps) and amount to mere data gathering which is a form of insignificant extra-solution activity. The large language model that performing the evaluating and generating steps is also recited at a high level of generality, and merely automates the generating modified content in response to evaluating the quality of the media content. The processor and memory for implement the recited steps is recited at ahigh level of generality, i.e., as generic processor and memory for performing the generic functions of processing data.
This generic processor and memory limitation is no more than mere instructions to apply the exception using a generic computer component. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component. The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component . Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to the abstract idea.
Step 2B: As discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim is ineligible
Further, the courts have consistently recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014). Examiner asserts that “filtered personalized social news feed” is not particular tool, thus it offers no more than presenting anything that includes the content that resulted from the agreement between the social network service and any third party.
In sum the combination of steps that receives indication, evaluate, generate media content and transmit the modified content to be posted in response to evaluated quality are at best is doing no more than generally linking the claims to network environment that sends and receives communications– see MPEP 2106.05(h). See also, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (transmitting content over a network).
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a generic computer components and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48.
Similarly, these claims store data, receive indication, evaluate the quality of the received media content and generate the modified content and transmit content by invoking computer systems as tools being used in an ordinary capacity to execute the abstract idea. Thus, these additional elements do not add significantly more to the abstract idea because they were simply applying the abstract idea on a computer system without sufficient recitation of details of how to carry out the abstract idea. The claims merely offer conventional computer systems to organizing Human Activity.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chung et al (US Pub. No., 2021/00270656 A1) in view of Cook (US Pub., No., 2024/0126794 A1)
With respect to claim 1, Chung teaches a method (paragraph [0004], discloses method configured to collect a set of training videos as training data ) comprising:
receiving, via a user interface, an indication of a selected post including media content associated with a user(Fig. 4, 402 discloses collect a set of training videos as training data, wherein the set of training videos are labeled with one or more labels based on one or more video quality metrics associated with an evaluation objective, paragraph [0027], discloses the collective past behavior of a set of users [an indication of a selected post] and paragraph [0037] discloses collect relatively large set of training data for training the machine learing model .., automatically collect at least some of the set of training videos for training a machine learning model .., autotmcially collect training videos from one or more page on social network system);
evaluating, rained on training data, a quality of the media content of the selected post, wherein the training data comprises any one or more of a profile of the user, a profile of a follower of the user, or an attribute of one or more previous posts associated with the user(Fig. 4, 402, discloses video quality metric assocted with an evaluation objective, identify the set of pages of the social networking system that are similar to the first set of pages.., paragraphs [0045]-[0046], discloses collect a st of training video as training data .., train a machine learing model e based on training data .., receive video to be evaluated .., assign a first video quality category of a plurality of video quality categories );
generating, and based upon the evaluated quality, a modified post comprising modified media content, a change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type in an instance in which the evaluated quality is above a threshold, and wherein the first type of change is more substantive than the second type of change ( paragraph [0027], discloses if the video has a low video quality level (or is assigned to a video quality category inducive of a low video quality level) [below threshold] , the my indicted that the video need to be change in some way [modified] before publishing, whereas a higher vide quality level may indict that the video is ready for publishing .., paragraph [0039], discloses training videos has been collected by the model training module 204 the set of training video may be filtered based on filtering in order to identify a set of qualified training videos and paragraph [0044], discloses determining the video quality by assigning videos into the one video quality category of the set of video quality categories: Excellent, [above threshold], Good, Fair and Poor [below threshold]…, various edits of the video to generate multiple version , each version can be evaluated for video quality to allow the publisher to publish the version with the desired or highest video quality) ; and
transmitting, via the user interface, the modified post for consideration by the user(paragraph [0027], discloses a video has a low video quality needs to be change in some in some way [modified] before publishing, paragraph [0036], discloses user that uploaded [transmitting] and/or created the video, such as demographic information of the user and paragraph [0042], discloses video quality predication module can receive a video that a user intend to publish or post , the user can continue to submit diffent version the videos the user is satisfied with the video quality predication .., [transmitting the modified post]).
Chung teaches the above elements including the machine learing model is a multi-stage model comprising deep neural network and a spares network (paragraph [0009]), training data for training one or machine learing models can be automatically collected (paragraph[0026]), determining whether or not the video are of a sufficiently high quality before publishing th videos (paragraph [0026]) and filtering criteria can include a threshold time filer such video that are short than a threshold length are filtered out (paragraph [0039]). Chung failed to teach the corrosinding machine learning module includes large language model (LLM) that used to train received data and corrosinding user intend to publish or post based different version is based on an indication of a rejection or an acceptance.
However, Cook teaches train large language model (LLM) (paragraphs [0055]-[0056], discloses a large language model (LLM) , as used is a deep learing algorithm that can recognize .., trained in large sets of data ) and receiving, via the user interface, an indication of a rejection or an acceptance of the modified post((paragraph [0077], discloses the user feedback indicates that an output of classifier was bad, then that the output of the corrosinding input may be removed [within the scope of receiving an indication of rejection or acceptance] and paragraph [0108], discloses generates images as output may be rejected if image quality is below a threshold value). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for machine learing model and publish or post based different version of Chung with large language model (LLM) and receiving input form of a prompt of Cook in order to tor recognize summarize, translate and predict efficiently and reduce publishing low quality of the images.
With respect to claim 2 , Chung in view of Cook teaches elements of claim 1, furthermore, Chung teaches the method further comprising:
causing to perform, based upon the indication of a comparative test based upon the selected post and the modified post(paragraph [0042], discloses video quality predication module can receive a video that a user intend to publish or post , the user can continue to submit diffent version the videos the user is satisfied with the video quality predication .., [transmitting the modified post] and paragraph [0044], discloses the video can implement various edits of the video to generate multiple versions). Chung failed to teach the corresponding publish/post is based on the rejection or acceptance, and retraining, based upon a result of the comparative test, the LLM model.
However, Cook teaches rejection or acceptance (paragraph [0108], discloses generates images as output may be rejected if image quality is below a threshold value)., and retraining, based upon a result of the comparative test, the LLM model (paragraphs [0055]-[0056], discloses a large language model (LLM) , as used is a deep learing algorithm that can recognize .., trained in large sets of data ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for machine learing model and publish or post based different version of Chung with large language model (LLM) and receiving input form of a prompt of Cook in order to tor recognize , summarize, translate and predict efficiently and reduce publishing low quality of the images.
With respect to claim 3 , Chung in view of Cook teaches elements of claim 1, furthermore, Chung teaches the method wherein the profile of the user comprises any one or more of a budget, duration, location of the user or audience, target audience, or modality of an offered service(paragraph [0030], discloses profile information, demographic information, location, geo-fenced area, etc.) .
With respect to claim 4 , Chung in view of Cook teaches elements of claim 1, furthermore, Chung teaches the method wherein the attribute of one or more previous posts comprises any one or more of a heading, wording, formatting, visual or audio enhancements, audience reach, or optimal time of audience engagement(paragraph [0031], discloses the content module 104 cam divide its user into different sets based on various attributes of the users [(e.g., age, ethnicity, income language, etc., )) .
With respect to claim 5 , Chung in view of Cook teaches elements of claim 1, furthermore, Chung teaches the method wherein the selected post is based upon an existing post on a media platform(paragraph [0031], discloses the content module can select content item for presentation to a user based on interest of the user).
With respect to claim 6 , Chung in view of Cook teaches elements of claim 1, furthermore, Chung teaches the method wherein the first type of change comprises one or more optimized views of the media content(paragraph [0044], discloses various edits of the video to generate multiple version , each version can be evaluated for video quality ).
With respect to claim 7 , Chung in view of Cook teaches elements of claim 6, furthermore, Chung teaches the method wherein the second type of change comprises a single optimized view of the media content(paragraph [0044], discloses various edits of the video to generate multiple version , each version can be evaluated for video quality ) .
With respect to claim 8 , Chung in view of Cook teaches elements of claim 1, furthermore, Chung teaches the method further comprising:
receiving, via the user interface, an indication of a request to generate a new post including media content(paragraph [0025], discloses create content that will be of interest of users);
generating, based upon the assessment, the new post(paragraph [0066], discloses a new object of a particular type is created ..); and
transmitting, via the user interface, the new post to the user(paragraph [0042], discloses video quality predication module can receive a video that a user intend to publish or post , the user can continue to submit diffent version the videos the user is satisfied with the video quality predication .., [transmitting the modified post]).
Chung teaches the above elements including assessing, based upon any one or more of the profile of the user, an account of the user, a historical advertiser of the user, the attribute of one or more previous posts, or guidance provided by the user, the received indication of the request(paragraphs [0030]-[0031] , discloses social networking system can include data about users, user identifiers, social connection, social interaction, profile information .., data sore training data for training one or mor machine learing modes one or mor trained learing models one or more video to be evaluated by the trained machine learing models and the like). Chung failed to teach the corrosinding machine learning module includes large language model (LLM) that used to train received data
However, Cook teaches train large language model (LLM) (paragraphs [0055]-[0056], discloses a large language model (LLM) , as used is a deep learing algorithm that can recognize .., trained in large sets of data ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for machine learing model and publish or post based different version of Chung with large language model (LLM) and receiving input form of a prompt of Cook in order to tor recognize , summarize, translate and predict efficiently and reduce publishing low quality of the images.
With respect to claim 9 , Chung in view of Cook teaches elements of claim 1, furthermore, Chung teaches the method wherein the media content comprises advertisement generation data(paragraph [0039], discloses videos (e.g., disply advertismetn in videos)).
With respect to claim 10 , Chung in view of Cook teaches elements of claim 9, furthermore, Chung teaches the method wherein the media content of the selected post comprises any one or more of a caption, an image or a video(paragraph [0036], discloses thumbnail assocted the video, closed-caption data for the video..).
With respect to claim 11 , Chung in view of Cook teaches elements of claim 2, furthermore, Chung teaches the method wherein the comparative test comprises an AB test(Fig. 5, 510, dislcies train a machine learing mode based on the labeled set of training data [ab test] or split testing]) .
With respect to claim 12, Chung teaches an apparatus (paragraph [0004], discloses system configured to collect a set of training videos as training data ) comprising:
one or more processors(Fig, 7, 702 dislcies processor) ; and
at least one memory storing instructions, that when executed by the one or more processor(Fig. 702, discloses processor and 714 system memory, paragraph [0021], discloses a computer system or computing device .. and paragraph [0080], dislcies a processor .., and one or more executable modules ..)) , cause the apparatus to:
receive, via a user interface, an indication of a selected post including media content associated with a user(Fig. 4, 402 discloses collect a set of training videos as training data, wherein the set of training videos are labeled with one or more labels based on one or more video quality metrics associated with an evaluation objective, paragraph [0027], discloses the collective past behavior of a set of users [an indication of a selected post] and paragraph [0037] discloses collect relatively large set of training data for training the machine learing model .., automatically collect at least some of the set of training videos for training a machine learning model .., autotmcially collect training videos from one or more page on social network system);
evaluate, trained on training data, a quality of the media content of the selected post, wherein the training data comprises any one or more of a profile of the user, a profile of a follower of the user, or an attribute of one or more previous posts associated with the user(Fig. 4, 402, discloses video quality metric assocted with an evaluation objective, identify the set of pages of the social networking system that are similar to the first set of pages.., paragraphs [0045]-[0046], discloses collect a st of training video as training data .., train a machine learing model e based on training data .., receive video to be evaluated .., assign a first video quality category of a plurality of video quality categories );
generate, and based upon the evaluated quality, a modified post comprising modified media content, wherein a change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type when the evaluated quality is above a threshold, and wherein the first type of change is more substantive than the second type of change (paragraph [0027], discloses if the video has a low video quality level (or is assigned to a video quality category inducive of a low video quality level) [below threshold] , the my indicted that the video need to be change in some way [modified] before publishing, whereas a higher vide quality level may indict that the video is ready for publishing .., paragraph [0039], discloses training videos has been collected by the model training module 204 the set of training video may be filtered based on filtering in order to identify a set of qualified training videos and paragraph [0044], discloses determining the video quality by assigning videos into the one video quality category of the set of video quality categories: Excellent, [above threshold], Good, Fair and Poor [below threshold]…, various edits of the video to generate multiple version , each version can be evaluated for video quality to allow the publisher to publish the version with the desired or highest video quality) ; and
transmit, via the user interface, the modified post for consideration by the user(paragraph [0027], discloses a video has a low video quality needs to be change in some in some way [modified] before publishing, paragraph [0036], discloses user that uploaded [transmitting] and/or created the video, such as demographic information of the user and paragraph [0042], discloses video quality predication module can receive a video that a user intend to publish or post , the user can continue to submit diffent version the videos the user is satisfied with the video quality predication .., [transmitting the modified post])
Chung teaches the above elements including the machine learing model is a multi-stage model comprising deep neural network and a spares network (paragraph [0009]), training data for training one or machine learing models can be automatically collected (paragraph[0026]), determining whether or not the video are of a sufficiently high quality before publishing th videos (paragraph [0026]) and filtering criteria can include a threshold time filer such video that are short than a threshold length are filtered out (paragraph [0039]). Chung failed to teach the corrosinding machine learning module includes large language model (LLM) that used to train received data and corrosinding user intend to publish or post based different version is based on an indication of a rejection or an acceptance.
However, Cook teaches train large language model (LLM) (paragraphs [0055]-[0056], discloses a large language model (LLM) , as used is a deep learing algorithm that can recognize .., trained in large sets of data ) and receiving, via the user interface, an indication of a rejection or an acceptance of the modified post(paragraph [0077], discloses the user feedback indicates that an output of classifier was bad, then that the output of the corrosinding input may be removed [within the scope of receiving an indication of rejection or acceptance] and paragraph [0108], discloses generates images as output may be rejected if image quality is below a threshold value). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for machine learing model and publish or post based different version of Chung with large language model (LLM) and receiving input form of a prompt of Cook in order to tor recognize , summarize, translate and predict efficiently and reduce publishing low quality of the images.
With respect to claim 13 , Chung in view of Cook teaches elements of claim 12, furthermore, Chung teaches the apparatus further comprising:
cause to perform, based upon the indication of a comparative test based upon the selected post and the modified post(paragraph [0042], discloses video quality predication module can receive a video that a user intend to publish or post , the user can continue to submit diffent version the videos the user is satisfied with the video quality predication .., [transmitting the modified post] and paragraph [0044], discloses the video can implement various edits of the video to generate multiple versions). Chung failed to teach the corresponding publish/post is based on the rejection or acceptance, and retraining, based upon a result of the comparative test, the LLM model.
However, Cook teaches ejection or acceptance t(paragraph [0108], discloses generates images as output may be rejected if image quality is below a threshold value)., and retrain, based upon a result of the comparative test, the LLM model (paragraphs [0055]-[0056], discloses a large language model (LLM) , as used is a deep learing algorithm that can recognize .., trained in large sets of data ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for machine learing model and publish or post based different version of Chung with large language model (LLM) and receiving input form of a prompt of Cook in order to tor recognize , summarize, translate and predict efficiently and reduce publishing low quality of the images.
With respect to claim 14 , Chung in view of Cook teaches elements of claim 12, furthermore, Chung teaches the apparatus wherein the profile of the user comprises any one or more of a budget, duration, location of the user or audience, target audience, or modality of an offered service(paragraph [0030], discloses profile information, demographic information, location, geo-fenced area, etc.) .
With respect to claim 15 , Chung in view of Cook teaches elements of claim 12, furthermore, Chung teaches the apparatus wherein the attribute of one or more previous posts comprises any one or more of a heading, wording, formatting, visual or audio enhancements, audience reach, or optimal time of audience engagement(paragraph [0031], discloses the content module 104 cam divide its user into different sets based on various attributes of the users [(e.g., age, ethnicity, income language, etc., )) .
With respect to claim 16 , Chung in view of Cook teaches elements of claim 12, furthermore, Chung teaches the apparatus wherein the selected post is based upon an existing post on a media platform(paragraph [0031], discloses the content module can select content item for presentation to a user based on interest of the user).
With respect to claim 17, Chung in view of Cook teaches elements of claim 12, furthermore, Chung teaches the apparatus wherein the first type of change comprises one or more optimized views of the media content and wherein the second type of change comprises a single optimized view of the media content(paragraph [0044], discloses various edits of the video to generate multiple version , each version can be evaluated for video quality ) .
With respect to claim 18 , Chung in view of Cook teaches elements of claim 12, furthermore, Chung teaches the apparatus further comprising:
receive, via the user interface, an indication of a request to generate a new post including media content(paragraph [0025], discloses create content that will be of interest of users);
generate, based upon the assessment, the new post(paragraph [0066], discloses a new object of a particular type is created ..); and
transmit, via the user interface, the new post to the user(paragraph [0042], discloses video quality predication module can receive a video that a user intend to publish or post , the user can continue to submit diffent version the videos the user is satisfied with the video quality predication .., [transmitting the modified post]).
Chung teaches the above elements including assessing, based upon any one or more of the profile of the user, an account of the user, a historical advertiser of the user, the attribute of one or more previous posts, or guidance provided by the user, the received indication of the request(paragraphs [0030]-[0031] , discloses social networking system can include data about users, user identifiers, social connection, social interaction, profile information .., data sore training data for training one or mor machine learing modes one or mor trained learing models one or more video to be evaluated by the trained machine learing models and the like). Chung failed to teach the corrosinding machine learning module includes large language model (LLM) that used to train received data.
However, Cook teaches train large language model (LLM) (paragraphs [0055]-[0056], discloses a large language model (LLM) , as used is a deep learing algorithm that can recognize .., trained in large sets of data ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for machine learing model and publish or post based different version of Chung with large language model (LLM) and receiving input form of a prompt of Cook in order to tor recognize , summarize, translate and predict efficiently and reduce publishing low quality of the images.
With respect to claim 19, Chung teaches a non-transitory computer-readable medium storing instruction that, when executed(paragraph [0004], discloses mom-transitory computer readable media configured to collect a set of training videos as training data and paragraph [0086], dislcies non-transitory storage medium for storing, encoding or carrying a series of instructions for execution by computer system ) cause:
receiving, via a user interface, an indication of a selected post including media content associated with a user(Fig. 4, 402 discloses collect a set of training videos as training data, wherein the set of training videos are labeled with one or more labels based on one or more video quality metrics associated with an evaluation objective, paragraph [0027], discloses the collective past behavior of a set of users [an indication of a selected post] and paragraph [0037] discloses collect relatively large set of training data for training the machine learing model .., automatically collect at least some of the set of training videos for training a machine learning model .., autotmcially collect training videos from one or more page on social network system);
evaluating, rained on training data, a quality of the media content of the selected post, wherein the training data comprises any one or more of a profile of the user, a profile of a follower of the user, or an attribute of one or more previous posts associated with the user(Fig. 4, 402, discloses video quality metric assocted with an evaluation objective, identify the set of pages of the social networking system that are similar to the first set of pages.., paragraphs [0045]-[0046], discloses collect a st of training video as training data .., train a machine learing model e based on training data .., receive video to be evaluated .., assign a first video quality category of a plurality of video quality categories );
generating, and based upon the evaluated quality, a modified post comprising modified media content, wherein a change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type when the evaluated quality is above a threshold, and wherein the first type of change is more substantive than the second type of change paragraph [0027], discloses if the video has a low video quality level (or is assigned to a video quality category inducive of a low video quality level) [below threshold] , the my indicted that the video need to be change in some way [modified] before publishing, whereas a higher vide quality level may indict that the video is ready for publishing .., paragraph [0039], discloses training videos has been collected by the model training module 204 the set of training video may be filtered based on filtering in order to identify a set of qualified training videos and paragraph [0044], discloses determining the video quality by assigning videos into the one video quality category of the set of video quality categories: Excellent, [above threshold], Good, Fair and Poor [below threshold]…, various edits of the video to generate multiple version , each version can be evaluated for video quality to allow the publisher to publish the version with the desired or highest video quality) ; and
transmitting, via the user interface, the modified post for consideration by the user(paragraph [0027], discloses a video has a low video quality needs to be change in some in some way [modified] before publishing, paragraph [0036], discloses user that uploaded [transmitting] and/or created the video, such as demographic information of the user and paragraph [0042], discloses video quality predication module can receive a video that a user intend to publish or post , the user can continue to submit diffent version the videos the user is satisfied with the video quality predication .., [transmitting the modified post])).
Chung teaches the above elements including the machine learing model is a multi-stage model comprising deep neural network and a spares network (paragraph [0009]), training data for training one or machine learing models can be automatically collected (paragraph[0026]), determining whether or not the video are of a sufficiently high quality before publishing th videos (paragraph [0026]) and filtering criteria can include a threshold time filer such video that are short than a threshold length are filtered out (paragraph [0039]). Chung failed to teach the corrosinding machine learning module includes large language model (LLM) that used to train received data and corrosinding user intend to publish or post based different version is based on an indication of a rejection or an acceptance.
However, Cook teaches train large language model (LLM) (paragraphs [0055]-[0056], discloses a large language model (LLM) , as used is a deep learing algorithm that can recognize .., trained in large sets of data ) and receiving, via the user interface, an indication of a rejection or an acceptance of the modified post(paragraph [0077], discloses the user feedback indicates that an output of classifier was bad, then that the output of the corrosinding input may be removed [within the scope of receiving an indication of rejection or acceptance] and paragraph [0108], discloses generates images as output may be rejected if image quality is below a threshold value). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for machine learing model and publish or post based different version of Chung with large language model (LLM) and receiving input form of a prompt of Cook in order to tor recognize , summarize, translate and predict efficiently and reduce publishing low quality of the images.
With respect to claim 20 , Chung in view of Cook teaches elements of claim 19, furthermore, Chung teaches the non-transitory computer-readable medium wherein the media content of the selected post comprises any one or more of a caption, an image or a video(paragraph [0036], discloses thumbnail assocted the video, closed-caption data for the video..).
Prior arts:
Chung et al (US Pub. No., 2021/00270656 A1) discloses systems, methods, and non-transitory computer-readable media can collect a set of training videos as training data, wherein the set of training videos are labeled with one or more labels based on one or more video quality metrics associated with an evaluation objective. A machine learning model is trained based on the training data. A video to be evaluated is received.
Cook (US Pub., No., 2024/0126794 A1) discloses an apparatus for generating a digital assistant is disclosed. The apparatus include at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive at least one user query from a user. The memory instructs the processor to extract a plurality of background data and a plurality of contextual data from the user dataset
Response to Arguments
Applicant's arguments of 35 U.S.C 103 rejections with respect to claim 1-20 filed on 25 November 2026 have been fully considered but they are not persuasive. Applicants’ arguments of the combination fails to teach or suggest “generating via the trained LLM model and based upon the evaluated quality, a modified post comprising modified media content, a change in the modified post of first type in an instance in which the evaluated quality is above a threshold, and the first type of change is more substantive than the second type of change” and “transmitting via the user interface the modified post for consideration by the user” and receive via the user interface an indication of a rejection or an acceptance of the modified post” is not persuasive.
While Chung teaches generating, and based upon the evaluated quality, a modified post comprising modified media content, a change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type in an instance in which the evaluated quality is above a threshold, and wherein the first type of change is more substantive than the second type of change ( paragraph [0027], discloses if the video has a low video quality level (or is assigned to a video quality category inducive of a low video quality level) [below threshold] , the my indicted that the video need to be change in some way [modified] before publishing, whereas a higher vide quality level may indict that the video is ready for publishing .., paragraph [0039], discloses training videos has been collected by the model training module 204 the set of training video may be filtered based on filtering in order to identify a set of qualified training videos and paragraph [0044], discloses determining the video quality by assigning videos into the one video quality category of the set of video quality categories: Excellent, [above threshold], Good, Fair and Poor [below threshold]…, various edits of the video to generate multiple version , each version can be evaluated for video quality to allow the publisher to publish the version with the desired or highest video quality) ; and transmitting, via the user interface, the modified post for consideration by the user(paragraph [0027], discloses a video has a low video quality needs to be change in some in some way [modified] before publishing, paragraph [0036], discloses user that uploaded [transmitting] and/or created the video, such as demographic information of the user and paragraph [0042], discloses video quality predication module can receive a video that a user intend to publish or post , the user can continue to submit diffent version the videos the user is satisfied with the video quality predication .., [transmitting the modified post]). Cook teaches receiving, via the user interface, an indication of a rejection or an acceptance of the modified post(paragraph [0077], discloses the user feedback indicates that an output of classifier was bad, then that the output of the corrosinding input may be removed [within the scope of receiving an indication of rejection or acceptance] and paragraph [0108], discloses generates images as output may be rejected if image quality is below a threshold value). The combination of the cited prior arts addressed the claimed limitation.
Applicants’ arguments of under the October 2019 Update to Subject Matter Eligibility guidance, the alleged abstract idea allegedly pertaining th the claims in indeed integrated into a practical application is not persuasive. Applicants’ futher argued the clamed invention provides “an improvements in the functioning of a computer or an improvement to another technology or technical field demonstrates that the claims are integrated into a practical application is not persuasive.
Based on the Alice/Mayo framework and recent USPTO guidance regarding 35 U.S.C 101, a method involving evaluating media content quality using an LLM and generating a modified post based on that evaluation is directed to an abstract idea without adding an "inventive concept, which falls within a "method of organizing human activity" or a mental process (evaluating, modifying content). The process of evaluating, modifying and receiving acceptance of content for a user interface constitutes analyzing and editing information, which is a fundamental, or business-related practice that can be performed in the human mind. Further, the claimed focused on receiving data, analyzing it, and modifying it based on a threshold is a data manipulation which courts often view these as routine, generic computer function.
The additional elements of the AI/LLM elements merely applies conventional AI techniques to a known process
Generic Use of LLM: The claim describes using a "trained LLM model" at a high level of generality, without specifying a new AI architecture, unique training technique, or non-conventional, specific improvement to the model itself.
Conventional Components: The steps—receiving via a user interface, evaluating via a model, transmitting for consideration, and receiving feedback—are standard, routine computer operations.
"Functional" Claiming: The claim defines the invention by its desired result ("modified post") rather than by a specific technical, computer-based solution.
Therefore, the additional limitation of LLM/AI is mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim is ineligible. Thus, the 35 U.S.C 101 rejection with respect to claim 1-20 is maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SABA DAGNEW/Primary Examiner, Art Unit 3621