DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to the communication filed on 04/30/2026.
Claims 1-7, 9-12 and 16-19 have been amended.
4. Claims 1-20 are currently pending and are considered below.
Continued Examination Under 37 CFR 1.114
5. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/30/2026 has been entered.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1, recites a non-transitory, processor-readable medium storing code configured to be executed by a processor, the code comprising instructions to cause the processor to:
access a text embedding;
identify a position of each website from a first plurality of websites in the text embedding;
identify a position of a target inventory in the text embedding space, the target inventory not being a website;
train a first machine learning model using the position of each website from the first plurality of websites in the text embedding space to convert at least one of (i) a position in the text embedding space to a position in a website embedding space or (ii) a position in the website embedding space to a position in the text embedding space;
access a second machine learning model, the second machine learning model trained to predict a conversion likelihood based on behavioral data representing user interactions with a second plurality of websites;
convert, using the first machine learning model, (i) the position of the target inventory in the text embedding space to a target position in the website embedding space or (11) a position of each website from the second plurality of websites in the website embedding space to a position of that website in the text embedding space, to produce vector data represents the target inventory and the second plurality of websites in a common embedding space;
predict the conversion likelihood using the second machine learning model, based on the vector data, and without using a cookie; and
facilitate delivery of an item of targeted content to the target inventory based on the conversion likelihood associated with the target inventory.
Claim 6, recites a non-transitory, processor-readable medium storing code configured to be executed by a processor, the code comprising instructions to cause the processor to:
identify a target inventory that is non-website inventory;
prompt a generative artificial intelligence (AI) with the target inventory, wherein the prompt causes the generative AI to produce keywords associated with the target inventory;
access a text embedding space;
assign a position in the text embedding space for the target inventory based on an output of the generative AI;
access behavioral data including, indications of a plurality of websites visited by a user and an indication of a conversion action;
map the behavioral data in a website embedding space based on locations of the plurality of websites;
train a first machine learning model, using the behavioral data in the website embedding space to predict a likelihood of conversion based on a position in the website embedding space;
convert a position of the target inventory in the text embedding space to produce a position of the target inventory in the website embedding space using a second machine learning model;
predict, without using a cookie, the likelihood of conversion of the target inventory, using the first machine learning model, based on the position of the target inventory in the website embedding space; and
facilitate delivery of the target inventory based on the likelihood of conversion.
Claim 9, recites a non-transitory, processor-readable medium storing code configured to be executed by a processor, the code comprising instructions to cause the processor to:
access a website embedding space;
access a text embedding space;
identify a position of a target inventory in the text embedding space, the target inventory not being a website;
define training data by assigning a position in the text embedding space to each website from a plurality of websites, each website from the plurality of website having a position in the website embedding space;
train a first machine learning model, using the training data, to output a position in the text embedding, given a position in the website embedding space;
convert a position of the target inventory in the text embedding space, using the first machine learning model to a target position in the website embedding space;
input the target position in the website embedding space to a second machine learning model to predict a conversion likelihood based on the position in the website embedding space and without using a cookie; and
facilitate delivery of an item of targeted content to the target inventory based on the conversion likelihood for the target inventory.
The steps of claim 1,
access a text embedding;
identify a position of each website from a first plurality of websites in the text embedding;
identify a position of a target inventory in the text embedding space, the target inventory not being a website;
train a first machine learning model using the position of each website from the first plurality of websites in the text embedding space to convert at least one of (i) a position in the text embedding space to a position in a website embedding space or (ii) a position in the website embedding space to a position in the text embedding space;
access a second machine learning model, the second machine learning model trained to predict a conversion likelihood based on behavioral data representing user interactions with a second plurality of websites;
convert, using the first machine learning model, (i) the position of the target inventory in the text embedding space to a target position in the website embedding space or (11) a position of each website from the second plurality of websites in the website embedding space to a position of that website in the text embedding space, to produce vector data represents the target inventory and the second plurality of websites in a common embedding space;
predict the conversion likelihood using the second machine learning model, based on the vector data, and without using a cookie; and
facilitate delivery of an item of targeted content to the target inventory based on the conversion likelihood associated with the target inventory.
The steps of claim 6,
identify a target inventory that is non-website inventory;
prompt a generative artificial intelligence (AI) with the target inventory, wherein the prompt causes the generative AI to produce keywords associated with the target inventory;
access a text embedding space;
assign a position in the text embedding space for the target inventory based on an output of the generative AI;
access behavioral data including, indications of a plurality of websites visited by a user and an indication of a conversion action;
map the behavioral data in a website embedding space based on locations of the plurality of websites;
train a first machine learning model, using the behavioral data in the website embedding space to predict a likelihood of conversion based on a position in the website embedding space;
convert a position of the target inventory in the text embedding space to produce a position of the target inventory in the website embedding space using a second machine learning model;
predict, without using a cookie, the likelihood of conversion of the target inventory, using the first machine learning model, based on the position of the target inventory in the website embedding space; and
facilitate delivery of the target inventory based on the likelihood of conversion.
The steps of claim 9,
access a website embedding space;
access a text embedding space;
identify a position of a target inventory in the text embedding space, the target inventory not being a website;
define training data by assigning a position in the text embedding space to each website from a plurality of websites, each website from the plurality of website having a position in the website embedding space;
train a first machine learning model, using the training data, to output a position in the text embedding, given a position in the website embedding space;
convert a position of the target inventory in the text embedding space, using the first machine learning model to a target position in the website embedding space;
input the target position in the website embedding space to a second machine learning model to predict a conversion likelihood based on the position in the website embedding space and without using a cookie; and
facilitate delivery of an item of targeted content to the target inventory based on the conversion likelihood for the target inventory.
As drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity. Given the broadest reasonable interpretation, the claim recites an apparatus storing code configured to be executed by a processor, the code comprising instructions to cause the processor to: leveraging textual inventories to identify positions of arbitrary inventories and leveraging website inventories to encode behavioral data. The above identified steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to improving targeting content to non-website (typically digital) inventories based on observed behavioral data. The sales activities and tailored/personalized marketing (accessing text embedding, identifying position of a target inventory and predicting position) including facilitating delivery of an item (which can also be considered to involve a mental process of organizing information).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as commercial interaction, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of processor, memory, first and second machine learning model. The processor and the memory is recited at a high level of generality (i.e., as a generic processor performing a generic computer functions of accessing a text embedding; identify a position of each website; identify a position of a target inventory in the text embedding; train a first machine learning model to convert at least one of (i) a position in the text embedding or (ii) a position in the website embedding; access a second machine learning model, to predict a conversion likelihood; predict the likelihood of conversion; and facilitate delivery of an item of targeted content, such that they amount to no more than mere instructions to apply the exception using generic computer components. As for the limitation training a machine learning model to convert a position and a likelihood of a conversion ,this feature is considered math, and therefore is a part of the abstract idea. Because the machine learning model in this claim is used as a tool for improving the abstract idea, rather than improving any technical feature or function, it is not sufficient to integrate the judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The 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 of processor, memory, first and second machine learning model. amount to no more than mere instructions to apply the exception using generic computer components. The additional elements are similar to the additional elements found by courts to be mere instructions to apply an exception because they do no more than merely invoke computers or machinery to perform an existing process such as: a common business method or mathematical algorithm being applied on a general purpose computer (Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 US 208, 223; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334); and requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considered as an ordered combination, the additional elements add nothing that is already present when the steps are considered separately. That is, a processors, a memory and the first and second machine learning model, performing commercial interactions including: accessing a text embedding; identify a position of each website; identify a position of a target inventory in the text embedding; train a first machine learning model to convert at least one of (i) a position in the text embedding or (ii) a position in the website embedding; access a second machine learning model, to predict a conversion likelihood; apply the first machine learning model and the second machine learning model to the position of the target inventory in the text embedding to predict the likelihood of conversion; and facilitate delivery of an item of targeted content, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, independent claims 1, 6 and 9 are not eligible.
As for dependent claims 2-5, 7-8 and 10-20 these claims recite limitations that further define the same abstract idea in claims 1, 6 and 9. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claim, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
8. Applicant's arguments filed on 04/30/2026 with respect to the rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
9. Applicant argued that “…Here, Applicant's as-filed Specification at paragraphs [0045] and [0050], for example, describes a technical problem arising from a conversion likelihood model trained on website data but not alternative (e.g., non-website) inventory data associated with, for example, connected TV (CTV) inventory, in-app mobile inventory, out of home (OOH) inventory, audio inventory, print inventory, non-digital inventory, etc. By converting website embeddings to an alternative inventory embedding (or vice versa), websites and alternative inventory can be represented in a common embedding space. As a result, the conversion likelihood model, trained on website data,
can process alternative inventory data without being retrained for that alternative inventory data. Applicant's as-filed Specification therefore sets forth a method for improving computational efficiency by reducing computer resource usage involved in retraining the conversion likelihood model, gathering / storing / labelling training data for alternative inventories, reducing the size of the conversion likelihood model (e.g., compared to an alternative conversion likelihood model that includes more nodes (e.g., weights and biases) to process data from multiple different embedding spaces), etc.
Amended independent claim 1 reflects the foregoing technical improvement by reciting, for example, "vector data that represents the target inventory and the second plurality of websites in a common embedding space."
Accordingly, Applicant respectfully submits that amended independent claim 1 (and, consequently, all claims depending therefrom) is patent eligible under 35 U.S.C. § 101…” Remarks pages 8-11
10. Examiner notes that there is no change to the claimed machine learning model or the way in which it is capable of functioning. Any improvement in regards to performing computational efficiency by reducing computer resource usage involved in retraining the conversion likelihood model, gathering / storing / labelling training data for alternative inventories, reducing the size of the conversion likelihood model (e.g., compared to an alternative conversion likelihood model that includes more nodes (e.g., weights and biases) to process data from multiple different embedding spaces), etc. that are processed by the machine learning model, are rooted solely in performing the identified abstract idea that is merely being applied with a general-purpose computer. The improvements are rooted solely in the abstract idea itself that is merely applied using a general-purpose computer and/or generic machine learning model. The rejection of claims 1-20 are maintained.
Conclusion
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARILYN G MACASIANO whose telephone number is (571)270-5205. The examiner can normally be reached Monday-Friday 12:00-9:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, llana Spar can be reached on 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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/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 06/27/2026
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