DETAILED ACTON
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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, 3-5, 7-8, 10-12, 14-15, 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sullivan; Jonathan et al. US 20220030323 A1 (hereafter Sullivan) and in further view of Panchaksharaiah; Vishwas Sharadanagar et al. US 10187677 B1 (hereafter Panchaksharaiah) and in further view of Chaturvedi; Yash et al. US 12380484 B1 (hereafter Chaturvedi).
Regarding claim 1, “a computer-implemented method for selecting or generating high quality supplemental content for a program, comprising: in response to determining, by at least one computer processor, a quality of at least one of supplemental content items exceeds a predetermined supplemental content quality threshold: identifying a first plurality of supplemental content items having a quality that is greater than the predetermined supplemental content quality threshold; analyzing, using at least one of machine learning (ML) models or large language models (LLMs), the first plurality of supplemental content items; extracting, using the at least one of the ML models or the LLMs, a first plurality of features from the first plurality of supplemental content items based on automatic content recognition; categorizing the first plurality of features into a first plurality of feature categories; presenting to a plurality of users a second plurality of the supplemental content items associated with the first plurality of features, the second plurality of the supplemental content items being a subset of the first plurality of the supplemental content items, the plurality of users being selected based on a predefined standard; calculating a user engagement metric for each of the first plurality of feature categories based on the presenting, wherein the user engagement metric for a feature category represents user engagement in response to being presented a supplemental content item associated with the feature category; identifying a subset of the first plurality of feature categories for the plurality of users based on a predetermined user engagement metric threshold; and transmitting supplemental content items associated with features belonging to the subset of the first plurality of feature categories to media devices associated with the plurality of users” (Sullivan para 174-181 determining the expected values for the plurality of supplemental advertisements includes using one or more of the above factors as inputs for an artificial neural network and receiving, as an output from the artificial neural network, the expected values for the plurality of supplemental advertisements; selecting a subset of supplemental advertisements from among the plurality of supplemental advertisements based on the subset having expected values above a threshold value; in advance of the upcoming content-modification opportunities, sending the subset of supplemental advertisements to be locally stored at the content-presentation device. As described above in Section I, this can involve the content-management system 108 obtaining a link from the supplemental-content delivery system 112 that points to the subset of supplemental advertisements. The content-management system 108 can transmit the link to the content-presentation device 104, and the content-presentation device 104 can receive the link, which it can use to retrieve the subset of the supplemental advertisements from the supplemental-content delivery system 112 and store the subset of the supplemental advertisements in a data-storage unit of the content-presentation device). With respect to “calculating a user engagement metric for each of the first plurality of feature categories based on the presenting, wherein the user engagement metric for a feature category represents user engagement in response to being presented a supplemental content item associated with the feature category; identifying a subset of the first plurality of feature categories for the plurality of users based on a predetermined user engagement metric threshold” Sullivan para 165 teaches the content-modification system uses to value the supplemental advertisements can include (i) demographics or other information about the expected audience of the content-presentation device, (ii) television channels or other characteristics of media content expected to be presented by the content-presentation device, (iii) a number of available impressions for a supplemental advertisement (e.g., based on frequency cap or pacing constraints specified by an advertisement campaign of the supplemental advertisement), (iv) a number of expected content-modification opportunities of the client-presentation device (e.g., based on the expected tuning of the device), and (v) various inventory rules (e.g., creative separation rules restricting whether a supplemental advertisement can be presented in connection with or in place of another particular type of advertisement). See also Sullivan teaching “content-modification system 100 can use the historical content consumption data for the content-presentation device 104 to determine the probability of performing a successful content-modification operation using the supplemental advertisement. For instance, because the historical content consumption data identifies which channels are viewed at the content-presentation device 104 and when they are viewed, the content-modification system 100 can aggregate this information over time to determine which channels are expected to be viewed at the content-presentation device 104 for a given day and when they are expected to be viewed. The content-modification system 100 can access advertisement schedules associated with those channels and times to identify content-modification opportunities corresponding to underlying advertisements that are available to be replaced by supplemental advertisements. And the content-modification system 100 can compare various characteristics of the identified underlying advertisements against various rules associated with the supplemental advertisements to determine the probability of a successful content-modification operations for each supplemental advertisement.”
Whereas Sullivan teaches utilizing artificial neural network for analyzing supplemental content to be delivered to a subset of viewers based on particular viewer characteristics (i.e., predefined standard), Sullivan does not explicitly disclose feature categories as claimed. In an analogous art, Panchaksharaiah teaches identifying media objects in media streams that are responsive to a particular viewer interest and categorizing the content for targeted display (see col. 8:57-67 to col. 11:1-67).
In an analogous art, Chaturvedi teaches a motivation for modifying Sullivan with the teachings of Panchaksharaiah wherein Chaturvedi teaches providing supplemental content based on the category of identified objects in media content (Abstract; col. 2:30-67 to col. 6:1-4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sullivan for utilizing artificial intelligence for analyzing video content for selecting or generating high quality supplemental content for a program, comprising determining a quality of at least one of supplemental content items exceeds a predetermined supplemental content quality threshold by further incorporating known elements of Panchaksharaiah for identifying media objects in media streams that are responsive to a particular viewer interest and categorizing the content for targeted display because the teachings of the prior art to Chaturvedi would enable a person or ordinary skill in the art to appreciate the benefit for providing primary content with supplemental content based on the categories of identified objects in media content in order to tailor supplemental content is likely to be consumed by a viewer with affinity for the desired content.
Regarding claim 3, “wherein the at least one of the supplemental content items comprises one of an image, a graphics interchange format (GIF), or a video clip” is further rejected as discussed in the rejection of claim 1 wherein the prior art to Panchaksharaiah teaches video clip (see col. Col. 6:22-67 to col. 7:1-25).
Regarding claim 4, “wherein the feature category comprises a face related feature, a text related feature, or a theme related feature” is further rejected as discussed in the rejection of claim 1 wherein the prior art to Chaturvedi (col. 5:56-67 to col. 8:1-22 categorization of objects interpreted as themes); see also Panchaksharaiah teaches video clip (see col. Col. 6:22-67 to col. 7:1-25).
Regarding claim 5, “wherein the user engagement metric is calculated based on at least one of a conversion rate, a click-through rate (CTR), a sentiment of a user, or a streaming time” is further rejected as discussed in the rejection of claim 1 wherein the prior art to Chaturvedi col. 10:9-24 disclosing click through rate).
Regarding claim 7, “wherein the predefined standard for selecting the plurality of users is based on at least one of behavior information, customer data, demographic information, psychographic information, or technographic information” is further rejected as discussed in the rejection of claim 1 wherein the prior art to Sullivan para 44, 50, 138 disclosing demographics).
Regarding the system claim 8, 10-12, 14 and non-transitory computer readable media claim 15,17-19 the claims are grouped and rejected with the method claims 1, 3-5, 7 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of claim 1, 3-5, 7and because the steps of the method are easily converted into elements of computer implemented methods and systems by one of ordinary skill in the art.
Claim(s) 2, 9, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sullivan; Jonathan et al. US 20220030323 A1 (hereafter Sullivan) and in further view of Panchaksharaiah; Vishwas Sharadanagar et al. US 10187677 B1 (hereafter Panchaksharaiah) and in further view of Chaturvedi; Yash et al. US 12380484 B1 (hereafter Chaturvedi) and in further view of Osindero; Simon et al. US 20170300576 A1 (hereafter Osindero).
Regarding claim 2, “further comprising: in response to determining a quality of none of the supplemental content items exceeds the predetermined supplemental content quality threshold: analyzing using at least one of the ML models or the LLMs, the supplemental content items; extracting using the at least one of the ML models or the LLMs, a second plurality of features from the supplemental content items based on automatic content recognition; categorizing the second plurality of features into a second plurality of feature categories; presenting to the plurality of users a third plurality of the supplemental content items associated with the second plurality of features, the third plurality of the supplemental content items being a subset of the supplemental content items; calculating a user engagement metric for each of the second plurality of feature categories based on the presenting; identifying a subset of the second plurality of feature categories for the plurality of users based on the predetermined user engagement metric threshold; generating, using an artificial intelligence tool, a supplemental content item having one or more features belonging to the identified subset of the second plurality of feature categories; and transmitting the generated supplemental content item to the media devices associated with the plurality of users” is further rejected on obviousness grounds as discussed in the rejection of claim 1 wherein the prior art to Sullivan, Panchaksharaiah, and Chaturvedi disclose all the elements of claim 2 except a quality of none of the supplemental content as claimed.
In an analogous art, Osindero teaches providing supplemental content that is dissimilar to the digital content requested (Abstract, para 24, 42-43).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sullivan, Panchaksharaiah, and Chaturvedi for utilizing artificial intelligence for analyzing video content for selecting or generating high quality supplemental content for a program, comprising determining a quality of at least one of supplemental content items exceeds a predetermined supplemental content quality threshold and identifying media objects in media streams that are responsive to a particular viewer interest and categorizing the content for targeted display because the teachings of the prior art to Chaturvedi would enable a person or ordinary skill in the art to appreciate the benefit for providing primary content with supplemental content based on the categories of identified objects in media content in order to tailor supplemental content is likely to be consumed by a viewer with affinity for the desired content by further incorporating known elements of Osindero for providing additional supplemental content that is visually congruent, or incongruent, with content requested by a user, such that the additional content is similar, or dissimilar, to the requested content from a visual standpoint in order to present content to a viewer that is displayed in ascending or descending order an further increase the advertising opportunities for content providers.
Regarding the system claim 9 and non-transitory computer readable media claim 16 the claims are grouped and rejected with the method claims 1, 2-5, 7 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of claims 1, 2-5, 7 and because the steps of the method are easily converted into elements of computer implemented methods and systems by one of ordinary skill in the art.
Claim(s) 6, 13, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sullivan; Jonathan et al. US 20220030323 A1 (hereafter Sullivan) and in further view of Panchaksharaiah; Vishwas Sharadanagar et al. US 10187677 B1 (hereafter Panchaksharaiah) and in further view of Chaturvedi; Yash et al. US 12380484 B1 (hereafter Chaturvedi) and in further view of GROVER; Matthew US 20230300388 A1 (hereafter Grover).
Regarding claim 6, Sullivan, Panchaksharaiah, and Chaturvedi are silent with respect to “wherein the presenting comprises conducting a multivariate testing on the first plurality of features with a plurality of hypotheses”. In an analogous art, Grover teaches the deficiency (see para 40, 52-57).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Sullivan, Panchaksharaiah, and Chaturvedi for utilizing artificial intelligence for analyzing video content for selecting or generating high quality supplemental content for a program, comprising determining a quality of at least one of supplemental content items exceeds a predetermined supplemental content quality threshold and identifying media objects in media streams that are responsive to a particular viewer interest and categorizing the content for targeted display because the teachings of the prior art to Chaturvedi would enable a person or ordinary skill in the art to appreciate the benefit for providing primary content with supplemental content based on the categories of identified objects in media content in order to tailor supplemental content is likely to be consumed by a viewer with affinity for the desired content by further incorporating known elements of Grover for providing additional supplemental content and conducting a multivariate testing on the first plurality of features with a plurality of hypotheses in order to determine an optimal presentation location.
Regarding the system claim 13 and non-transitory computer readable media claim 20 the claims are grouped and rejected with the method claims 1-7 because the steps of the method claims are met by the disclosure of the apparatus and methods of the reference(s) as discussed in the rejection of claims 1-7 and because the steps of the method are easily converted into elements of computer implemented methods and systems by one of ordinary skill in the art.
CONCLUSION
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/ALFONSO CASTRO/Primary Examiner, Art Unit 2421
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