Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is responsive to the Application filed on 6/29/2023. Claims 1-20 are pending in the case. Claims 1, 15 and 19 are independent claims.
Claim Objections
Claims 1, 16 and 19 are objected to because of the following informalities: the claims recite “the machine learning model outputting an effectiveness matching score indicative of effectiveness of matching the first piece of content the second piece of content with respect to a first metric based on the historical interaction information”, but missing “and” in between “the first piece of content the second piece of content”.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over “User Response Prediction in Online Advertising”, Gharibshah et al, 2/3/2021, in view of “Contextual Advertising by Combining Relevance with Click Feedback”, Chakrabarti et al, 2008.
Referring to claims 1, 16 and 19, Gharibshah discloses a system comprising: at least one processor; and at least one non-transitory computer-readable medium having instructions stored thereon, which, when executed by the at least one processor, cause the system to perform operations comprising:
accessing a first piece of content of a first content type and a second piece of content of a second content type; (page 8 of Gharibshah, two types of features for the content of Ads, such as Textual features vs. visual features, such as images, etc..)
feeding the first piece of content into a first generative artificial intelligence (GAI) model, (page 8 of Gharibshah, the text is fed through a CTR model, which can be a generative adversarial network based models as disclosed in page 23 of Gharibshah) the first GAI model outputting a first embedding corresponding to the first piece of content, (Fig. 5 and page 15 of Gharibshah, features are converted into embeddings) first embedding being a representation of a meaning of the first piece of content; (page 15 of Gharibshah, “it suggests to add one dimension to model parameters to allocate more than one embedding vector to features since pair of features incorporate different feature types information.”)
feeding the second piece of content into the first GAI model, the first GAI model outputting a second embedding corresponding to the second piece of content, (page 8 of Gharibshah, the visual features is fed through a CTR model, which can be a generative adversarial network based models as disclosed in page 23 of Gharibshah and Fig. 5 and page 15 of Gharibshah, features are converted into embeddings) the second embedding being a representation of a meaning of the second piece of content; (page 15 of Gharibshah, “it suggests to add one dimension to model parameters to allocate more than one embedding vector to features since pair of features incorporate different feature types information.”)
accessing historical interaction information regarding pieces of content; (page 24 of Gharibshah, “They separately built a logistic regression model for historical CTR values and another model for embedding vectors of images and textual information.”); and
based on <similarity> of the first piece of content and the second piece of content, passing the first and second pieces of content into a second GAI model to generate a combination piece of content. (page 8 of Gharibshah, “Textualfeatures. In search advertising, ads are displayed in the search result pages, incorporating textual data such as headline, relevant keywords and the body to highlight the details of promoted products. Many research proposes to treat click-through rate prediction task as the similarity learning between users’ query keywords and keywords of ads using their proposed text based similarity. For example, keywords in the title and body of advertisements [6, 33] and keywords in user queries are considered as two sources of data to extract textual features in many designed models [32, 41, 147]. Relying only on ad textual content and user query at character- and word-level, a deep CTR prediction model [32] collects data from textual letters of query along with the title, the description, and the ad URL. They are organized to feed into system as a one-hot-encoded matrix.”)
Gharibshah does not specifically disclose “feeding the first embedding and the second embedding into a machine learning model, the machine learning model outputting an effectiveness matching score indicative of effectiveness of matching the first piece of content the second piece of content with respect to a first metric based on the historical interaction information” and “the effectiveness matching score”.
However, Chakrabarti discloses feeding the first embedding and the second embedding into a machine learning model, the machine learning model outputting an effectiveness matching score indicative of effectiveness of matching the first piece of content the second piece of content with respect to a first metric based on the historical interaction information (page 4 of Chakrabarti, where the features are matched with each other based on user interactions with the features, such as clicking, and the matching quality of two separate features of the content such as ads and pages has matching rational score)
Gharibshah and Chakrabarti are analogous art because both references concern user interaction with features of content. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Gharibshah’s features’ similarity associated with contents with matching score of the features as taught by Chakrabarti. The motivation for doing so would have been improve matching different features of content for accuracy of user preferences.
Referring to claims 2 and 20, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the first GAI model and the second GAI model are an identical GAI model. (Fig. 5 and page 15 of Gharibshah, both features are identical models are used to convert features/content types into embedding)
Referring to claim 3, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the machine learning model is trained based on the historical interaction information. (page 24 of Gharibshah, “They separately built a logistic regression model for historical CTR values and another model for embedding vectors of images and textual information.”)
Referring to claims 4 and 17, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the operations further comprise: causing the generated combination pieces of content to be displayed in a graphical user interface of a client device presenting a first online platform, for selection by a user. (page 28 of Gharibshah, “The output are fed into a fully connected neural network to learn the probability of user’s click for a selected news piece”)
Referring to claims 5 and 18, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the first content type and the second content type are each a different one of an image, a text snippet, or a video. (page 8 of Gharibshah, two types of features for the content of Ads, such as Textual features vs. visual features, such as images, etc..)
Referring to claim 6, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the operations further comprise: receiving a text-based objective, wherein the feeding includes feeding the text-based objective into the machine learning model and wherein the passing includes passing the text- based objective into the second GAI model. (page 8 of Gharibshah, two types of features for the content of Ads, such as Textual features vs. visual features, such as images, etc..)
Referring to claim 7, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the operations further comprise: receiving an indication of desired audience, wherein the feeding includes feeding the indication of desired audience into the machine learning model and wherein the passing includes passing the indication of the desired audience into the second GAI model. (page 9 of Gharibshah, “The long-term user interaction can also be studied to create user profile behavior. It can not only provide indication of user intent change over time which can be used to improve the predication user responses, the popularity pattern regarding products can also be identified to remind users about a product according their previous interactions. In [40] authors consider a sequence of user activity events before and after the ad click and corresponding passed time-slot to investigate the potential user conversion intents. They analyzed the effect of elapsed time as a feature for conversion rate prediction and using targeting and retargeting2 paradigm for different users in online advertising systems.”)
Referring to claim 8, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the operations further comprise: accessing a document of an entity for which the combined of piece of content is being generated, wherein the feeding includes feeding data from the document into the machine learning model and wherein the passing includes passing the data from the document into the second GAI model. (page 4 of Gharibshah, “If ad content matches to user preferences, it encourages users to follow up promoted messages by generating the next clicks which can end up with desired activity such as a purchase.”)
Referring to claim 9, Gharibshah and Chakrabarti disclose the system of claim 4, wherein the machine learning model takes as input one or more features corresponding to the user. (page 8 of Gharibshah, “An event representing the user interaction with online advertising includes features from different actors like users, publishers, advertisers and the context in online advertising systems. A representative list of categorical features corresponding to user profile and behavior, advertisement and publisher’s web-page is provided in Table 2”)
Referring to claim 10, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the second GAI model generates at least one of a text color, text style, or text size of the combination piece of content. (page 40 of Gharibshah, “As shown in Table B.1, this information is characterized via features describing ad creatives and their appearance in the publisher web-pages, such as features about the content of web-pages, placement id, size, width and height, visibility status and format, as well as user information such as device types, user agent, browser information etc.”)
Referring to claim 11, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the effectiveness matching score is at least partially based on a similarity between the first piece of content and the second piece of content as determined based on a comparison between the first embedding and the second embedding. (page 8 of Gharibshah, “Textualfeatures. In search advertising, ads are displayed in the search result pages, incorporating textual data such as headline, relevant keywords and the body to highlight the details of promoted products. Many research proposes to treat click-through rate prediction task as the similarity learning between users’ query keywords and keywords of ads using their proposed text based similarity. For example, keywords in the title and body of advertisements [6, 33] and keywords in user queries are considered as two sources of data to extract textual features in many designed models [32, 41, 147]. Relying only on ad textual content and user query at character- and word-level, a deep CTR prediction model [32] collects data from textual letters of query along with the title, the description, and the ad URL. They are organized to feed into system as a one-hot-encoded matrix.”)
Referring to claim 12, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the historical interaction information includes interaction information retrieved from multiple different domains of an online platform. (as shown in Table 4 and page 12, user’s interaction, i.e., click-through is collected through multiple different application domain)
Referring to claim 13, Gharibshah and Chakrabarti disclose the system of claim 12, wherein the feeding the first piece of content includes feeding the first piece of content and a list of categories into the first GAI model, and the first embedding represents a selection of a category from the list of categories, the category determined by the first GAI model to be a closest match for the meaning of the content. (page 8 of Gharibshah, “Textualfeatures. In search advertising, ads are displayed in the search result pages, incorporating textual data such as headline, relevant keywords and the body to highlight the details of promoted products. Many research proposes to treat click-through rate prediction task as the similarity learning between users’ query keywords and keywords of ads using their proposed text based similarity. For example, keywords in the title and body of advertisements [6, 33] and keywords in user queries are considered as two sources of data to extract textual features in many designed models [32, 41, 147]. Relying only on ad textual content and user query at character- and word-level, a deep CTR prediction model [32] collects data from textual letters of query along with the title, the description, and the ad URL. They are organized to feed into system as a one-hot-encoded matrix.”)
Referring to claim 14, Gharibshah and Chakrabarti disclose the system of claim 12, wherein the feeding the first piece of content includes additionally providing the first GAI model with a text question about the first piece of content. (as shown in Table 4 and page 12 of Gharibshah, features including query)
Referring to claim 15, Gharibshah and Chakrabarti disclose the system of claim 1, wherein the machine learning model is trained offline using training data, the training data comprising prior pieces of content presented via an online platform that have been labeled with performance data regarding how the prior pieces of content were interacted with when presented via the online platform. (page 42 of Gharibshah, training offline)
Conclusion
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/HAIMEI JIANG/ Primary Examiner, Art Unit 2142
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