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 .
Examiner Remarks
Claim 4 and analogous claims (11 and 17) are not rejected under 101 as the claims alter the interpretation of the abstract idea in claim 1. The requirement of the media type being generated being a webpage alters the media being generated to a format that one cannot create using the human mind possibly with pen and paper due to webpages requirement to be implemented digitally. However, claim 4 is not seen as integrating the invention into a practical application (see claim 4 under 101), thus rolling up claim 4 will not integrate other abstract ideas that could be present within the claims.
Claim Rejections - 35 USC § 112
Regarding 112(b):
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In regard to Claim 1:
Claim 1 recites the limitation "portion of the media" in “and facilitating a presentation of at least a portion of the media”. There is insufficient antecedent basis for this limitation in the claim. “the media” is indefinite as one of ordinary skill cannot determine what is being referred to by the phrase. Claim 1 recites multiple things of media including “a set of related media” for media assets or “generate media” for a request to generate or “generated media” for the media generated by the machine learning model. Without indication within the claims, one of ordinary skill in the art cannot tell what media is being referred to by “the media” in a phrase such as “portion of the media”. Amending the claim limitation to indicate which media is being referenced can help overcome the indefiniteness of the limitation. For the purposes of prosecution, 103 and 101 interpretations will interpret “the media” as referring to the media generated by the machine learning model using paragraph 15 of the specification as reference in claim 1 and dependent claims.
In regard to Claim 2:
Claim 2 recites the limitation “wherein the media”, which is rejected under 112b for the same reason “portion of the media” is rejected in claim 1, for one of ordinary skill in the art cannot determine which media is being referenced by “the media”.
In regard to Claim 4:
Claim 4 recites the limitation “wherein the media”, which is rejected under 112b for the same reason “portion of the media” is rejected in claim 1, for one of ordinary skill in the art cannot determine which media is being referenced by “the media”.
In regard to Claim 5:
Claim 5 recites the limitation “wherein the media”, which is rejected under 112b for the same reason “portion of the media” is rejected in claim 1, for one of ordinary skill in the art cannot determine which media is being referenced by “the media”.
In regard to Claim 6:
Claim 6 recites the limitation “presentation of the media”, which is rejected under 112b for the same reason “portion of the media” is rejected in claim 1, for one of ordinary skill in the art cannot determine which media is being referenced by “the media”.
In regard to Claim 7:
Claim 7 recites the limitation “associated with the media” and other recitations of “the media”, which is rejected under 112b for the same reason “portion of the media” is rejected in claim 1, for one of ordinary skill in the art cannot determine which media is being referenced by “the media”.
In regards to analogous claims:
Claims analogous to claims rejected under 112b, such as claim 8 being analogous to claim 1, are also rejected for containing the same rejected material as the rejected claim the claim is analogous to.
In regards to dependent claims:
Claims dependent upon a claim rejected under 112b are also rejected under 112b for being dependent upon a rejected claim
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 1-3, 5-10, 12-16, 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more.
In regards to Claim 1:
Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter?
Yes, the claim is directed towards a method, so a process.
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 1 recites the following abstract ideas:
executing the machine-learning model using a feature vector derived at least in part from the identification of the media asset and the media type, wherein the machine-learning model generates media associated with the media asset and of the media type;
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation. This claim is interpreted as generating media using an input of media (as the feature vector is noted to represent images ([Current Application 0028]: “For example, the one or more machine-learning models may receive a feature vector representing a text prompt as input and generate an output including a set of images. For another example, the one or more machine-learning models may receive a feature vector representing an image and generate an output including a set of images.”) thus is an image input just formatted for the application to a computer) in a computer. Generating media is an abstract idea for a human can generate media such as text, images, and audio using the human mind or possibly with pen and paper.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 1 recites the following additional elements:
receiving an identification of one or more media assets, wherein each media asset of the one or more media assets represents a set of related media;
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
generating a training dataset based on the identification of one or more media assets, wherein the training dataset includes a subset of the set of related media of each media asset of the one or more media assets
At a high level of generality, this is an activity of using an element as an “apply it” use (see MPEP 2106.05(f)).
training a machine-learning model using the training dataset, the machine-learning model being configured to generate content associated with a particular media asset;
At a high level of generality, this is an activity of using training dataset as an “apply it” use (see MPEP 2106.05(f)).
receiving a request to generate media representing the particular media asset, wherein the request includes an identification of a media asset and a media type;
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
and facilitating a presentation of at least a portion of the media
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 1 recites the following additional elements:
receiving an identification of one or more media assets, wherein each media asset of the one or more media assets represents a set of related media;
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
generating a training dataset based on the identification of one or more media assets, wherein the training dataset includes a subset of the set of related media of each media asset of the one or more media assets
At a high level of generality, this is an activity of using an element as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “apply” or equivalent does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. The generic recitation of generating a training dataset does not integrate into a practical application.
training a machine-learning model using the training dataset, the machine-learning model being configured to generate content associated with a particular media asset;
At a high level of generality, this is an activity of using training dataset as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “training a machine-learning model” using a training dataset does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
receiving a request to generate media representing the particular media asset, wherein the request includes an identification of a media asset and a media type;
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
and facilitating a presentation of at least a portion of the media
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)). This is a well understood, routine conventional activity of presenting or transmitting data (see MPEP 2106.05(d)(2)(example iv. Presenting offers and gathering statistics and example i. Receiving or transmitting data over a network)).
In regards to Claim 2:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 2 recites the following abstract ideas:
wherein the media includes one or more strings, images, or video segments representative of a characteristic of the particular media asset
This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. Noting examples of the media output generated to strings, images, or video segments does not integrate into a practical application.
In regards to Claim 3:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 3 recites the following abstract ideas:
wherein the one or more media assets includes the particular media asset
This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. Noting elements of the media assets received include a particular media asset does not integrate into a practical application.
In regards to Claim 4 (Not Rejected but listed for clarity):
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 4 recites the following additional elements:
wherein the media includes one or more webpages characterizing portions of the particular media asset
At a high level of generality, this is an activity of using machine learning model as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 4 recites the following additional elements:
wherein the media includes one or more webpages characterizing portions of the particular media asset
At a high level of generality, this is an activity of using machine learning model as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “machine-learning model generates media” where the media is a webpage does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
In regards to Claim 5:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 5 recites the following abstract ideas:
wherein the media includes promotional material for a related media of the set of related media of the particular media asset
This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. Noting examples of the media output generated to strings, images, or video segments does not integrate into a practical application.
In regards to Claim 6:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 6 recites the following abstract ideas:
further comprising: generating metrics associated with a presentation of the media, wherein the metrics include an identification of a quantity instances in which the media is presented and an identification of one or more users that accessed the media;
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as observation and evaluation. Counting the amount of times something is presented (such as the media) and who looked at the media is a task performable by a person using their mind and eyes.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 6 recites the following additional elements:
and facilitating presentation of a graphical user interface associated with the particular media asset, the graphical user interface including a graphical representation of the metrics and one or more controls configured to modify the generation of subsequent media associated with the particular media asset
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 6 recites the following additional elements:
and facilitating presentation of a graphical user interface associated with the particular media asset, the graphical user interface including a graphical representation of the metrics and one or more controls configured to modify the generation of subsequent media associated with the particular media asset
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)). This is a well understood, routine conventional activity of presenting or transmitting data (see MPEP 2106.05(d)(2)(example iv. Presenting offers and gathering statistics and example i. Receiving or transmitting data over a network)).
In regards to Claim 7:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 7 recites the following abstract ideas:
executing the machine-learning model using a new feature vector derived from an identification of the new version of the media and the information associated with the media, wherein the machine-learning model generates new media;
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation. This claim is interpreted as generating media using an input of media (as the feature vector is noted to represent images ([Current Application 0028]: “For example, the one or more machine-learning models may receive a feature vector representing a text prompt as input and generate an output including a set of images. For another example, the one or more machine-learning models may receive a feature vector representing an image and generate an output including a set of images.”) thus is an image input just formatted for the application to a computer) in a computer. Generating media is an abstract idea for a human can generate media such as text, images, and audio using the human mind or possibly with pen and paper.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 7 recites the following additional elements:
generating a graphical user interface associated with the particular media asset, wherein the graphical user interface includes information associated with the particular media asset and information associated with the media;
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)).
receiving a request to generate a new version of the media based on the information associated with the media;
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
and facilitating a presentation of the new media
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 7 recites the following additional elements:
generating a graphical user interface associated with the particular media asset, wherein the graphical user interface includes information associated with the particular media asset and information associated with the media;
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)). This is a well understood, routine conventional activity of presenting or transmitting data (see MPEP 2106.05(d)(2)(example iv. Presenting offers and gathering statistics and example i. Receiving or transmitting data over a network)).
receiving a request to generate a new version of the media based on the information associated with the media;
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
and facilitating a presentation of the new media
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)). This is a well understood, routine conventional activity of presenting or transmitting data (see MPEP 2106.05(d)(2)(example iv. Presenting offers and gathering statistics and example i. Receiving or transmitting data over a network)).
In regards to Claim 8:
Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter?
Yes, the claim is directed towards a machine.
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 8 recites the same abstract ideas as analogous claim 1.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 8 further recites the following additional elements on top of claim 1:
A system comprising: one or more processors; a non-transitory computer-readable medium storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations including
At a high level of generality, this is an activity of using a computer as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 8 further recites the following additional elements on top of claim 1:
A system comprising: one or more processors; a non-transitory computer-readable medium storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations including
At a high level of generality, this is an activity of using a computer as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a computer or computer parts appears to be an implementation of the abstract idea on a computer, so merely using a computer as a tool to perform the abstract idea.
In regards to Claim 9:
This claim is analogous to claim 2 in regards to 101.
In regards to Claim 10:
This claim is analogous to claim 3 in regards to 101.
In regards to Claim 11 (Not Rejected but listed for clarity):
This claim is analogous to claim 4 in regards to 101.
In regards to Claim 12:
This claim is analogous to claim 5 in regards to 101.
In regards to Claim 13:
This claim is analogous to claim 6 in regards to 101.
In regards to Claim 14:
This claim is analogous to claim 7 in regards to 101.
In regards to Claim 15:
This claim is analogous to claim 8 in regards to 101.
In regards to Claim 16:
This claim is analogous to claim 2 in regards to 101.
In regards to Claim 17 (Not Rejected but listed for clarity):
This claim is analogous to claim 4 in regards to 101.
In regards to Claim 18:
This claim is analogous to claim 5 in regards to 101.
In regards to Claim 19:
This claim is analogous to claim 6 in regards to 101.
In regards to Claim 20:
This claim is analogous to claim 7 in regards to 101.
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.
Claims 1-3, 6-10, 13-16, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Figueira et al (“Survey on Synthetic Data Generation, Evaluation Methods and GANs”), referred to as Figueira in this document, and further in combination with Dela Rosa et al (US 0240406477 A1), referred to as Dela Rosa in this document, and further in combination with Banh et al (“Generative artificial intelligence”), referred to as Banh in this document.
Regarding Claim 1:
Figueira teaches:
training a machine-learning model using the training dataset, the machine-learning model being configured to generate content associated with a particular media asset;
[Figueira 3.3 page 11]: “For example, if one wants synthetic images of cars (assuming that images of cars were used in the training data [training a machine-learning model using the training dataset, the machine-learning model being configured to generate content associated with a particular media asset as this notes the GANs are trained using datasets to produce what the training set is about]), one cannot force a vanilla GAN to do so. This happens because there is no control over the latent space representation. That is, the GAN maps point from latent space to the original domain, but the features in the latent space are not interpretable by the user. As such, one does not know from which range of points to sample in order to produce examples of a certain class. This is an obvious disadvantage of using GANs in labeled datasets. An interesting idea is to make the GAN dependent on a class label, which allows generated data to be conditioned on class labels.”
receiving a request to generate media representing the particular media asset, wherein the request includes an identification of a media asset
[Figueira 3.3 page 13]: “StackedGenerative AdversarialNetwork, StackGAN, proposed in [34] by Zhang et al., is another extension of GANs that can generate images from text descriptions [receiving a request to generate media representing the particular media asset, wherein the request includes an identification of a media asset as prompting the generation of the model would be requesting to generate the particular media asset when the prompt is about the particular media asset].”
executing the machine-learning model using a feature vector derived at least in part from the identification of the media asset and the media type, wherein the machine-learning model generates media associated with the media asset and of the media type;
The “using a feature vector derived at least in part from the identification of the media asset” is shown by models having inputs (as noted earlier by Figueira noting the ability to prompt GANs with inputs such as text descriptions or labels) and feature vector being an input ([Current Application 0028]: “For example, the one or more machine-learning models may receive a feature vector representing a text prompt as input and generate an output including a set of images. For another example, the one or more machine-learning models may receive a feature vector representing an image and generate an output including a set of images.”) and the “identification of the media asset” element was used earlier to determine the training data of the model, thus the feature vector being related or in part from elements identified media assets would be apparent as such elements are the material the model was created to work with.
[Figueira 3.1 GANs under the Hood page 9]: “A GAN is constituted by two models: a generator model G that tries to generate samples [executing the machine-learning model using a feature vector derived at least in part from the identification of the media asset and the media type, wherein the machine-learning model generates media associated with the media asset and of the media type] that follow the underlying distribution of the data. Nonetheless, these observations are suitably different from the ones in the dataset (i.e., they should not simply reproduce observations that already occur in the dataset). There is also a discriminator model D that, given an observation (from the original dataset or synthesized by the generator), classifies it as fake (produced by the generator) (Typically, the models used for the generator and discriminator are neural networks. As such, we normally refer to G and D as networks. However, in theory, the models need not be a neural network.”
Note the “of the media type” is shown by the teachings of Banh in claim 1 as shown for “and a media type”.
Figueira does not explicitly teach:
A method comprising:
receiving an identification of one or more media assets, wherein each media asset of the one or more media assets represents a set of related media;
generating a training dataset based on the identification of one or more media assets, wherein the training dataset includes a subset of the set of related media of each media asset of the one or more media assets;
and a media type
and facilitating a presentation of at least a portion of the media.
Dela Rosa teaches:
A method comprising: (the method is indicated by computer parts to show implementation of the method in a computer)
[Dela Rosa 0194]: “The machine 900 may include processors [processor] 904, memory [memory] 906, and input/output I/O components 908, which may be configured to communicate with each other via a bus 910.”
[Dela Rosa 0195]: “The instructions 902 may also reside, completely or partially, within the main memory 916, within the static memory 918, within machine-readable medium [computer readable storage medium] 922 within the storage unit 920, within at least one of the processors 904 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 900.”
receiving an identification of one or more media assets, wherein each media asset of the one or more media assets represents a set of related media;
generating a training dataset based on the identification of one or more media assets, wherein the training dataset includes a subset of the set of related media of each media asset of the one or more media assets;
[Dela Rosa 0250]: “Example 1 is a system comprising: at least one processor; at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: training a machine learning model by performing a set of operations comprising: accessing media content items [receiving an identification of one or more media assets, wherein each media asset of the one or more media assets represents a set of related media] associated with interaction functions initiated by users of an interaction system, wherein the media content items comprise images, videos, or content augmentations of the users posted on the interaction system enabling other users to view the posted media content items; generating training data [generating a training dataset based on the identification of one or more media assets] including labels for the media content items, wherein the labels are indicative of one or more characteristics of the media content items; extracting features from a media content item of the media content items; identifying additional media content items to include [wherein the training dataset includes a subset of the set of related media of each media asset of the one or more media assets] , in the training data based on the extracted features from the media content item; processing the training data using a machine learning model to generate a media content item output; and updating one or more parameters of the machine learning model based on the media content item output; and repeating the set of operations to retrain the machine learning model based on a retraining criterion being met.”
and facilitating a presentation of at least a portion of the media.
[Dela Rosa 0062]: “The interaction client 104 presents a graphical user interface [and facilitating a presentation of at least a portion of the media]”
One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Figueira and Dela Rosa. FIgueira and Dela Rosa are in the same field of endeavor of machine learning. One of ordinary skill in the art would have been motivated to combine Figueira and Dela Rosa in order to implement the invention within a computer ([Dela Rosa 0250]: “Example 1 is a system comprising: at least one processor; at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising…”) and to improve training by being able to automate the access to new data or large amounts of data ([Dela Rosa 0018]: “Example interaction systems address the issues described above by improving machine learning model training based on new data created by users on an online platform. The disclosed machine learning model processes can quickly access and process large amounts of new user data, reducing the time needed for model training and evaluation. Moreover, the interaction systems efficiently access and process content items, generate labels, and update training data sets, saving valuable time and resources.”) The use of a graphical user interface is motivated by the ability to enable a form of user interaction ([Dela Rosa 0083]: “In some examples, the system operating within the interaction client 104 determines the presence of a face within the image or video stream and provides modification icons associated with a computer animation model to transform image data, or the computer animation model can be present as associated with an interface described herein.“) and provides a method to evaluate quality of data ([Figueira 5 Synthetic Sample Quality Evaluation page 31]: “Anscombe constructed his quartet to demonstrate the importance of plotting the data when analyzing it. Back in 1973, it may have been difficult to create graphs with data, in part because of scarce and expensive computing resources. Today, however, it is quite easy, with hundreds of graphics libraries available for various programming languages. Thus, another method to evaluate the quality of synthetic data is to use graphical representations (e.g., box plots, histograms, violin plots).”).
Banh teaches:
and a media type;
[Banh Towards generative AI page 3]: “The application system functions as an interface for the user to interact with a GAI model. Prompting is an interaction technique and unique GAI property that enables end users using natural language to engage with and instruct GAI application (e.g., LLMs) to create desired output such as text, images, or other types (Dang et al., 2022; Liu & Chilton, 2022). Depending on the application, prompts vary in their modality and directly influence the mode of operation [and a media type].”
GAI (generative AI) includes GANs and GANs are also noted in Figueira to have the ability to do different media types in [Figueira 3.3 page 16]: “As can be seen in Section 3.1, there are no restrictions on whether the dataset must be an image, a video, music, or an ordinary tabular dataset. Nonetheless, different types of architectures must be considered depending on the task.”
One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Figueira and Banh. Figueira and Banh are in the same filed of endeavor of machine learning. One of ordinary skill in the art would have been motivated to combine Figueira and Banh in order to enable users to engage and instruct GAI ([Banh Towards generative AI page 3]: “The application system functions as an interface for the user to interact with a GAI model. Prompting is an interaction technique and unique GAI property that enables end users using natural language to engage with and instruct GAI application (e.g., LLMs) to create desired output such as text, images, or other types (Dang et al., 2022; Liu & Chilton, 2022).”).
Regarding Claim 2:
The method of claim 1 is taught by Figueira, Dela Rosa, Banh
Figueira teaches:
wherein the media includes one or more strings, images, or video segments representative of a characteristic of the particular media asset.
[Figueira 3 Generative Adversarial Networks page 9]: “GANs are currently capable of painting, writing [wherein the media includes one or more strings, images, or video segments representative of a characteristic of the particular media asset], composing, and playing, as we will see in this section.”
Regarding Claim 3:
The method of claim 1 is taught by Figueira, Dela Rosa, Banh
Figueira teaches:
wherein the one or more media assets includes the particular media asset.
[Figueira 3.3 page 13]: “StackedGenerative AdversarialNetwork, StackGAN, proposed in [34] by Zhang et al., is another extension of GANs that can generate images from text descriptions [wherein the one or more media assets includes the particular media asset as prompting the generation of the model would be requesting to generate the particular media asset when the prompt is about the particular media asset as noted previously in claim 1].”
Regarding Claim 6:
The method of claim 1 is taught by Figueira, Dela Rosa, Banh
Figueira teaches:
the graphical user interface including a graphical representation of the metrics
[Figueira 5 Synthetic Sample Quality Evaluation page 31]: “Anscombe constructed his quartet to demonstrate the importance of plotting the data when analyzing it. Back in 1973, it may have been difficult to create graphs with data [the graphical user interface including a graphical representation of the metrics], in part because of scarce and expensive computing resources. Today, however, it is quite easy, with hundreds of graphics libraries available for various programming languages. Thus, another method to evaluate the quality of synthetic data is to use graphical representations (e.g., box plots, histograms, violin plots).”
Dela Rosa teaches:
generating metrics associated with a presentation of the media, wherein the metrics include an identification of a quantity instances in which the media is presented and an identification of one or more users that accessed the media;
[Dela Rosa 0132]: “In some cases, the interaction system 100 can identify additional media content items based on text or captions added to the media content item, a number of times the media content item has been viewed by other users [generating metrics associated with a presentation of the media, wherein the metrics include an identification of a quantity instances in which the media is presented and an identification of one or more users that accessed the media as noting counting times has been viewed by other users means which user was viewing the content was being tracked], a number of times the media content item has been screenshotted by other users, the time when the media content item will expire and be automatically deleted (such as 24 hours after posting), and/or the like, and by identifying similarities with other media content items (as further described herein).”
Further support on metrics related to media is given in [Dela Rosa 0133]: “In some cases, the interaction system 100 can identify additional media content items based on metrics related to user interaction with the media content item (such as replies, mentions, and shares), information about the device used to create the media content item (such as the device model, operating system, and app version), an IP address of the device used to post or create the media content item which can be used to determine the user's approximate location, and/or the like, and by identifying similarities with other media content items (as further described herein).”
One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Figueira and Dela Rosa. Figueira and Dela Rosa are in the same field of endeavor of machine learning. One of ordinary skill in the art would have been motivated to combine Figueira and Dela Rosa in order to track and help identify additional media content ([Dela Rosa 0132]: “In some cases, the interaction system 100 can identify additional media content items based on text or captions added to the media content item, a number of times the media content item has been viewed by other users, a number of times the media content item has been screenshotted by other users, the time when the media content item will expire and be automatically deleted (such as 24 hours after posting), and/or the like, and by identifying similarities with other media content items (as further described herein).”)
and facilitating presentation of a graphical user interface associated with the particular media asset, the graphical user interface including a graphical representation of the metrics and one or more controls configured to modify the generation of subsequent media associated with the particular media asset
[Dela Rosa 0062]: “The interaction client 104 presents a graphical user interface”
[Dela Rosa 0083]: “In some examples, the system operating within the interaction client 104 determines the presence of a face within the image or video stream and provides modification icons associated with a computer animation model to transform image data [and facilitating presentation of a graphical user interface associated with the particular media asset, the graphical user interface including a graphical representation of the metrics and one or more controls configured to modify the generation of subsequent media associated with the particular media asset], or the computer animation model can be present as associated with an interface described herein. The system may implement a complex convolutional neural network on a portion of the image or video stream to generate and apply the selected modification. That is, the user may capture the image or video stream and be presented with a modified result in real-time or near real-time once a modification icon has been selected. Further, the modification may be persistent while the video stream is being captured, and the selected modification icon remains toggled. Machine-taught neural networks may be used to enable such modifications.”
The motivation to combine with Dela Rosa for graphical user interface is taught in claim 1.
Regarding Claim 7:
The method of claim 1 is taught by Figueira, Dela Rosa, Banh
Figueira teaches:
receiving a request to generate a new version of the media based on the information associated with the media;
[Figueira 3.3 page 13]: “StackedGenerative AdversarialNetwork, StackGAN, proposed in [34] by Zhang et al., is another extension of GANs that can generate images from text descriptions [receiving a request to generate a new version of the media based on the information associated with the media as prompting the generation of the model would be requesting to generate media].”
executing the machine-learning model using a new feature vector derived from an identification of the new version of the media and the information associated with the media, wherein the machine-learning model generates new media;
The “using a feature vector derived at least in part from the identification of the media asset” is shown by models having inputs (as noted earlier by Figueira noting the ability to prompt GANs with inputs such as text descriptions or labels) and feature vector being an input ([Current Application 0028]: “For example, the one or more machine-learning models may receive a feature vector representing a text prompt as input and generate an output including a set of images. For another example, the one or more machine-learning models may receive a feature vector representing an image and generate an output including a set of images.”) and the “identification of the media asset” element was used earlier to determine the training data of the model, thus the feature vector being related or in part from elements identified media assets would be apparent as such elements are the material the model was created to work with.
[Figueira 3.1 GANs under the Hood page 9]: “A GAN is constituted by two models: a generator model G that tries to generate samples [executing the machine-learning model using a new feature vector derived from an identification of the new version of the media and the information associated with the media, wherein the machine-learning model generates new media] that follow the underlying distribution of the data. Nonetheless, these observations are suitably different from the ones in the dataset (i.e., they should not simply reproduce observations that already occur in the dataset). There is also a discriminator model D that, given an observation (from the original dataset or synthesized by the generator), classifies it as fake (produced by the generator) (Typically, the models used for the generator and discriminator are neural networks. As such, we normally refer to G and D as networks. However, in theory, the models need not be a neural network.”
Dela Rosa teaches:
generating a graphical user interface associated with the particular media asset, wherein the graphical user interface includes information associated with the particular media asset and information associated with the media;
[Dela Rosa 0132]: “In some cases, the interaction system 100 can identify additional media content items [generating a graphical user interface associated with the particular media asset, wherein the graphical user interface includes information associated with the particular media asset] based on text or captions added to the media content item, a number of times the media content item has been viewed by other users, a number of times the media content item has been screenshotted by other users, the time when the media content item will expire and be automatically deleted (such as 24 hours after posting), and/or the like, and by identifying similarities with other media content items [and information associated with the media as this notes other media associated or similar has information that is grabbed] (as further described herein).”
Further support of information or metrics related to media being used in [Dela Rosa 0133]: “In some cases, the interaction system 100 can identify additional media content items based on metrics related to user interaction with the media content item (such as replies, mentions, and shares), information about the device used to create the media content item (such as the device model, operating system, and app version), an IP address of the device used to post or create the media content item which can be used to determine the user's approximate location, and/or the like, and by identifying similarities with other media content items (as further described herein).”
The graphical user interface is noted as being taught in in other claims by [Dela Rosa 0062]: “The interaction client 104 presents a graphical user interface”.
and facilitating a presentation of the new media
[Dela Rosa 0062]: “The interaction client 104 presents a graphical user interface [and facilitating a presentation of the new media]”
The motivation for the combination with Dela Rosa is the same as the motivations to combine with Dela Rosa in claim 1 and claim 6.
Regarding claim 8:
This claim is analogous to claim 1.
Regarding claim 9:
This claim is analogous to claim 2.
Regarding claim 10:
This claim is analogous to claim 3.
Regarding claim 13:
This claim is analogous to claim 6.
Regarding claim 14:
This claim is analogous to claim 7.
Regarding claim 15:
This claim is analogous to claim 1.
Regarding claim 16:
This claim is analogous to claim 2.
Regarding claim 19:
This claim is analogous to claim 6.
Regarding claim 20:
This claim is analogous to claim 7.
Claims 4-5, 11-12, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Figueira et al (“Survey on Synthetic Data Generation, Evaluation Methods and GANs”), referred to as Figueira in this document, and further in combination with Dela Rosa et al (US 0240406477 A1), referred to as Dela Rosa in this document, and further in combination with Banh et al (“Generative artificial intelligence”), referred to as Banh in this document, and further in combination with Khan (“Role of Generative AI for Developing Personalized Content Based Websites”), referred to as Khan in this document.
Regarding Claim 4:
The method of claim 1 is taught by Figueira, Dela Rosa, Banh
Figueira does not explicitly teach:
wherein the media includes one or more webpages characterizing portions of the particular media asset
Khan teaches:
wherein the media includes one or more webpages characterizing portions of the particular media asset
[Khan 3 Generative AI for Personalized Content-Based Websites & Challenges page 2]: “When applied to personalized web development, generative AI models can be trained on vast amounts of user data, including browsing history, preferences, and past interactions. These models can then generate personalized content, such as product recommendations, article suggestions, or website layouts [wherein the media includes one or more webpages characterizing portions of the particular media asset where the particular media asset is noted in claim 1 in regards to being related to the prompting], tailored to each user's unique profile. By leveraging generative AI, developers can create dynamic and adaptive websites that respond to users' everchanging needs and preferences.”
One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Figueira and Khan. Figueira and Khan are in the same field of endeavor of machine learning. One of ordinary skill in the art would have been motivated to combine Figueira and Khan in order to take advantage of generative AI to enable website changes such as in real time or to better show tailored experiences ([Khan Literature Review page 1]: “Fruitful research has been conducted in generative AI, highlighting the numerous advantages it holds for society. When offering tailored experiences, personalized content can considerably heighten user engagement and pleasure. Content on websites can change in real-time due to user preferences, browsing history, location, and other variables, resulting in an immersive and personalized online experience. Moreover, customer satisfaction grows alongside increased retention rates, enhanced conversions, and other positive outcomes [3].”)
Regarding Claim 5:
The method of claim 1 is taught by Figueira, Dela Rosa, Banh
wherein the media includes promotional material for a related media of the set of related media of the particular media asset.
[Khan 3 Generative AI for Personalized Content-Based Websites & Challenges page 2]: “When applied to personalized web development, generative AI models can be trained on vast amounts of user data, including browsing history, preferences, and past interactions. These models can then generate personalized content, such as product recommendations [wherein the media includes promotional material for a related media of the set of related media of the particular media asset], article suggestions, or website layouts, tailored to each user's unique profile. By leveraging generative AI, developers can create dynamic and adaptive websites that respond to users' everchanging needs and preferences.”
Figueira notes the ability to choose what appears in the generated material as noted in earlier claim teachings. Promotional material is seen as revering to some form of promotion, recommendation, advertisement or the like as noted in [Current Specification 0069]: “The media generation requestion may generate promotional media (e.g., advertisements, etc.) that promote the media asset or a media thereof, wiki’s (e.g., such as one or more documents including contextual details and/or of the media asset or media thereof, etc.), related media (e.g., new media of the media asset and/or alternative versions of media of the media asset, etc.) in same or similar style, etc.”
Motivation is the same as the motivation to combine with Khan in claim 4.
Regarding claim 11:
This claim is analogous to claim 4.
Regarding claim 12:
This claim is analogous to claim 5.
Regarding claim 17:
This claim is analogous to claim 4.
Regarding claim 18:
This claim is analogous to claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cretu et al (“Casting out Demons: Sanitizing Training Data for Anomaly Sensors”) is relevant art that discusses data sanitization as part of a preprocessing of information for a dataset. This is relevant as notes ideas covered by the data controller in the specification of the current application.
Arena et al (“St. Nicholas of Myra: Reconstruction of the Face between Canon and AI”) is relevant art as discusses using AI trained to reconstruct canon elements to reconstruct or generate media which matches elements of the specification of the current application.
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/C.D.D./Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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