DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) the following limitations that are considered to be abstract ideas:
Claims 1,11, 17
accessing a content page for a recipe, wherein the content page includes a title of the recipe, instructions for preparing the recipe, and a list of ingredients;
identifying one or more sponsorship opportunities at the content page;
identifying, by a one or more candidate sponsors for each sponsorship opportunity, wherein trained using a dataset of items related to a query to identify one or more candidate items for an ingredient related to the sponsorship opportunity;
selecting, for the sponsorship opportunity, a bidding sponsor of the one or more candidate sponsors and a candidate item associated with the bidding sponsor as a sponsored item;
providing, to the content page, a description of the sponsored item, and a request to generate content for a sponsored content page for the sponsorship opportunity;
generating instructions for presenting the sponsored content page based on a response wherein the sponsored content page incorporates the sponsored item; and
transmitting, to cause presentation of the sponsored content page to a user.
The limitations of independent claim 1, 11, and 17 as detailed above, as drafted, falls within “Certain Methods of Organizing Human Activity” specifically commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The applicant’s claims are directed to selecting sponsors and sponsored items for content and generating sponsored content for presentation to users. Accordingly, the claims recite an abstract idea
This judicial exception is not integrated into a practical application. In particular the claims recite the additional elements of:
machine learning model
Language model
Model serving system
Client device
Non-transitory computer readable storage medium
processor
The aforementioned additional generic computing elements perform the steps of the claims at a high level of generality (i.e. As a generic medium performing generic computer function of accessing, identifying, selecting, providing, generating, and transmitting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer, or to any other technology, or technical field. Their collective functions merely provide generic computer implementation.
Thus, taken individually and in combination, the additional elements do not amount to
significantly more than the above-identified judicial exception (the abstract idea).
The dependent claims 2-10, 12-16, and 18-20 appear to merely further limit the abstract and as such, the analysis of dependent claims 2-10, 12-16, and 18-20 results in the claims “reciting” an abstract idea. For example, claims merely further define or limit the content page. The claims the claims do not recite additional elements that integrate the exception into a practical application the additional elements do not amount to an inventive concept (significantly more) other than the above-identified judicial exception (the abstract idea).
Thus, based on the detailed analysis above, claims 1-20 are not patent eligible.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20is/are rejected under 35 U.S.C. 103 as being unpatentable over Byron et al. (US 2018/0341979) in view of Manavoglu et al. (US 2024/0256757)
Claims 1, 11, 17: Byron discloses a method comprising:
accessing a content page for a recipe, wherein the content page includes a title of the recipe, instructions for preparing the recipe, and a list of ingredients; (see for example [0054], The graphical user interface screen shown in FIG. 8 includes a brand portion 802 that displays the name of the advertiser, a product portion 804 that display one or more products, a recipe description portion 806 that describes the recipe, an ingredient list portion 808 that lists ingredients in recipe, a coupon portion 810 that offers coupons promotions and e-commerce, a social media portion 812 that lists recipes being selected by other users and a link to the recipe also see [0060], fig. 8)
identifying one or more sponsorship opportunities at the content page; (see for example [0023]. recipe recommendations that include ingredients sold by the food brand(s) being advertised are selected for a target consumer and also see [0054])
transmitting the instructions to a client device to cause presentation of the sponsored content page to a user.(see for example, [0054], he graphical user interface screen shown in FIG. 8 includes a brand portion 802 that displays the name of the advertiser, a product portion 804 that display one or more products, a recipe description portion 806 that describes the recipe, an ingredient list portion 808 that lists ingredients in recipe, a coupon portion, see also [0028], client device and also fig. 7 and 8)
But does not explicitly disclose identifying, by a machine-learning model, one or more candidate sponsors for each sponsorship opportunity, wherein the machine-learning model is trained using a dataset of items related to a query to identify one or more candidate items for an ingredient related to the sponsorship opportunity; selecting, for the sponsorship opportunity, a bidding sponsor of the one or more candidate sponsors and a candidate item associated with the bidding sponsor as a sponsored item; providing, to a model serving system hosting a machine-learning language model, the content page, a description of the sponsored item, and a request to generate content for a sponsored content page for the sponsorship opportunity;
generating instructions for presenting the sponsored content page based on a response received from the machine-learning language model wherein the sponsored content page incorporates the sponsored item; However Manavoglu discloses identifying, by a machine-learning model, one or more candidate sponsors for each sponsorship opportunity, wherein the machine-learning model is trained using a dataset of items related to a query to identify one or more candidate items for an ingredient related to the sponsorship opportunity; ([0031], it is contemplated that the generative model 114 can generate insights about the company from the supplemental content 204 identified by the supplemental content provision system 112 as well as identify and summarize information from the website of the company, such that the information provided to the user is personalized to preferences of the user.[0034], supplemental content of a provider is identified based upon the user input received at 304 and/or based upon search results identified by the search engine based upon the query. In an example, the supplemental content can be identified based upon outcome of a keyword auction [0055], the information can include an identity of a product, an identity of a service, an identity of a provider of the product, an identity of a webpage corresponding to the product or service, and so forth. At 1006, the generative model generates output based upon the supplemental content item and/or information received at 1002. At 1006, the supplemental content item is updated to include the output generated by the generative model.)
selecting, for the sponsorship opportunity, a bidding sponsor of the one or more candidate sponsors and a candidate item associated with the bidding sponsor as a sponsored item; ( [0073] Advertisers can submit bids on an arbitrary block of text that gets embedded. A nearest neighbor similarity lookup can be performed in real-time or near real-time against user content and advertisers can be charged in proportion to the similarity. This model can be integrated into existing keyword auction mechanisms.)
providing, to a model serving system hosting a machine-learning language model, the content page, a description of the sponsored item, and a request to generate content for a sponsored content page for the sponsorship opportunity; ([0028] The computing system 102 additionally includes a data store 118. The data store 118 stores supplemental content 120 and user history data 122. For instance, the supplemental content provision system 112 can identify supplemental content from the supplemental content 120 based at least in part upon information in the user history 122. Further, as will be described in greater detail below, the generative model 114 can generate output based upon the user history 122. Such output can be employed by the supplemental content provision system 112 to identify and provide supplemental content for presentment on a webpage to a user [0050], the generative model 114 can also generate content that can be included in a supplemental content item (e.g., the generative model 114 can at least partially construct a supplemental content item). With reference now to FIG. 9, a GUI 900 of a supplemental content that includes at least portions thereof generated by the generative model 114 is illustrated.)
generating instructions for presenting the sponsored content page based on a response received from the machine-learning language model wherein the sponsored content page incorporates the sponsored item; ([0084, 0092] The generative model is configured to generate output based upon the prompt. The generative model is also configured to identify that text in the output is to be associated with a supplemental content item. The generative model is additionally configured to assign a hyperlink to the text in the output. The method also includes causing the output to be displayed upon a display of a client computing device, where the text in the displayed output has the hyperlink assigned thereto, and further where upon the hyperlink being hovered over, the supplemental content item is displayed concurrently with the output on the display. Also see [0066])
Therefore, 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, Byron to include identifying, by a machine-learning model, one or more candidate sponsors for each sponsorship opportunity, wherein the machine-learning model is trained using a dataset of items related to a query to identify one or more candidate items for an ingredient related to the sponsorship opportunity; selecting, for the sponsorship opportunity, a bidding sponsor of the one or more candidate sponsors and a candidate item associated with the bidding sponsor as a sponsored item; providing, to a model serving system hosting a machine-learning language model, the content page, a description of the sponsored item, and a request to generate content for a sponsored content page for the sponsorship opportunity; generating instructions for presenting the sponsored content page based on a response received from the machine-learning language model, in order to enable the user to quickly buy ingredients Claim 2,12, 18: Byron discloses the method of claim 1, wherein the presented sponsored content page includes a modified title incorporating the sponsored item and an indication that one or more sponsored items are included. ([0054], fig 7 and fig 8)
Claim 3, 13, 19: Byron discloses the method of claim 1, wherein the presented sponsored content page includes modified instructions indicating how to use the sponsored item in the recipe. [0060]
Claims 4, 14: Byron discloses the method of claim 1, further comprising: providing, to a multi-modal model, at least a request to generate an image describing the recipe that incorporates the sponsored item; [0022] and receiving a second response including the generated image, wherein the generated sponsored content page includes the generated image.[0022]
Claims 5, 15, 20: Byron discloses the method of claim 1, wherein the one or more candidate items for the ingredient related to the sponsorship opportunity represent a set of replacement items to replace the ingredient in the recipe. [0035]
Claims 6, 16: Byron discloses the method of claim 5, wherein identifying the one or more candidate sponsors further comprises:
obtaining a set of candidate items by the machine-learning model and corresponding replacement scores, wherein a replacement score for a respective candidate item is generated by applying the machine-learning model indicates whether the respective candidate item is a good replacement for the ingredient; ranking the set of candidate items according to the replacement scores; and selecting a subset of the set of candidate items as the one or more candidate items for the sponsorship opportunity. [0090]
Claim 7: Byron discloses the method of claim 1, wherein in the dataset of the items and the query for training the machine-learning model, the query is a particular item and the items are a set of replacement items for the particular item. [0023 and 0053]
Claim 8: Byron discloses the method of claim 1, wherein selecting the bidding sponsor further comprises: performing an auction process among the one or more candidate sponsors to obtain bids for the sponsorship opportunity associated with the content page, wherein the selected bidding sponsor is associated with a respective bid above a threshold bid or proportion among the bids obtained for the sponsorship opportunity. [0040]
Claim 9: Byron discloses the method of claim 1, further comprising:
mapping the list of ingredients to one or more items in an item catalog, and wherein transmitting the instructions further comprises transmitting instructions to cause presentation of the one or more items including the sponsored item to the user for purchase. [0060]
Claim 10: Byron discloses the method of claim 1, further comprising: obtaining feedback data to determine whether the user purchased the sponsored item after the presentation of the sponsored content page; and responsive to the determination that the user purchased the sponsored item, fine- tuning parameters of the machine-learning language model using the contents of the sponsored content page. [0033]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARNELL A POUNCIL whose telephone number is (571)270-3509. The examiner can normally be reached Monday - Friday 10:00 - 6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.A.P/Examiner, Art Unit 3622
/ILANA L SPAR/Supervisory Patent Examiner, Art Unit 3622
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