DETAILED ACTION
This non-final Office action is responsive to amendments filed March 19th, 2026. Claims 1, 3, 5-6, 10-11, 13, 15-16, and 20 have been amended. Claims 1-20 are presented for examination.
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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/ has been entered.
Response to Arguments
Applicant's arguments regarding claim rejections under 35 USC 101 filed 03/19/26 have been fully considered but they are not persuasive.
On pages 7-12 of the provided remarks, Applicant argues that the claims satisfy the patent-eligibility requirements of Step 2A and are therefore patent-eligible under 35 USC 101. Citing paragraph [0003-0006] of the as-filed Specification, Applicant argues that the amended claims present a technical solution to the identified technical problem. On pages 8-9 of the provided remarks, Applicant argues that the claims overcome the problem associated with placing irrelevant keyword-matched content citing. Specifically, Applicant argues “By correlating (e.g., cross-referencing) the baseline and special audiences, the system identifies a geographically-aligned and interest-aligned set of users that conform to the configuration of the target audience to provide relevant content that is likely to be consumed and not simply ignored.” Examiner respectfully disagrees and asserts that the argued cross-referencing, cited within paragraph [0042] and [0067] of the as-filed Specification by “correlating [a] baseline audience and [a] special audience” is not a technical improvement as the correlation of audiences is a mental process of observation, judgment, and evaluation of the human mind. Applicant’s argument is not persuasive.
Continuing on page 9 of the provided remarks, Applicant argues that the claims overcome the technical problem of ensuring topics indicated in the end-user data records and the topic terms of interest are contextually-relevant. Specifically, Applicant argues that “executing a classifier trained on extracted contextually-relevant terms at bid-time to place only relevant content for end-users that conform to characteristics of the configured target audience” overcome the argued technical problem. Examiner respectfully disagrees and asserts that the execution of the classifier trained on extracted contextually-relevant terms does not present a technical improvement as the claimed training to predict a probability that a lookalike audience accessing a webpage corresponds to the target audience are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. The high-level recitation of “applying the classifier on the ranked-order list of context terms and user identifiers” is applicable to a judgement and evaluation of the human mind. Applicant’s argument is not persuasive.
On pages 9-10 of the provided remarks, Applicant argues that the system leverages limited user device information to place relevant content for end users. Citing paragraphs [0034], [0073], and [0075] of the as-filed Specification, Applicant argues “the system overcomes the technical problems of cybersecurity mechanisms stripping detailed “information about the users for privacy purposes” that limit “the details about the users available for identifying ways to categorize and target the users” relied upon by conventional systems (e.g., for behavioral targeting) by training the classifier to learn relationships between the ranked-order list of context terms and the available user-device information.” Examiner respectfully disagrees and asserts that the argued method by Applicant does not overcome the argued problem of applying stripped user information to place relevant content for end users. The ranked-order list, per Applicant’s citations is still utilizing the limited user information to place content for the end user through the cited user identifier. Per cited [0034] of the as-filed Specification, the trainer is applied to the identified association between the end-user device and the content. This identified association between user device and content is further directed to a mental process observation and judgement. While Applicant argues that the trainer is applied to the identifier, further cited paragraphs [0073] and [0075] do not include the identifier but the content associated with the ranked-order list. Therefore, the applied method does not overcome the argued technical problems. Applicant’s arguments are not persuasive.
On pages 10-12 of the provided remarks, Applicant argues that the amended claims reflect the argued improvements. Beginning on page 10 of the provided remarks, Applicant argues the cited portion of independent claim 1 “address the problems associated with keyword matching that conventional systems rely on by reciting the features described in the Specification related to training classifier on ranked-order lists of context terms associated with the target audience.” Examiner respectfully disagrees and asserts that the claimed “receiving one or more configuration inputs” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. The claimed “identifying … a set of target users” and “identifying … a ranker-order list of content terms” are recited with a high-level of generality such that this function is directed to a mental observation, judgment, and evaluation of the human mind. Finally, as stated above, the claimed “training … a classifier to predict a probability” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. The high-level recitation of “applying the classifier on the ranked-order list of context terms and user identifiers” is applicable to a judgement and evaluation of the human mind. The claimed “by a computer” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019). Applicant’s arguments are not persuasive.
Continuing on page 11 of the provided remarks, Applicant argues “the claims address the problems associated with placing content when user information is limited by reciting the features described in the Specification related to (i) training classifier on user identifier associated with user devices corresponding to the target audience in addition to ranked-order lists of context terms associated with the target audience and (ii) executing the classifier at bid time using the user identifier and the set of features of the available webpage to cause placement of a content.” Examiner respectfully disagrees and asserts, as stated above, that item (i) as claimed does not overcome the technical problem argued by Applicant. Further, regarding item (ii), the amended generation of a probability “based upon the user identifier associated with the user device and a set of features of the available webpage” is recited with a high-level of generality such that the generated probability is an evaluation and judgment of the human mind. Applicant’s arguments are not persuasive.
Finally, Applicant argues of pages 11-12 of the provided remarks, “the claims facilitate a reduction in network traffic bandwidth and computational processing resources by only transmitting a selection instruction to cause placement of a content item on an available when the probability satisfies a placement threshold.” Examiner respectfully disagrees and begins by asserting that argued computational resource utilization technical problem is not present within the as-filed Specification. Therefore, under MPEP 2106.04(d)(I), the claims are not patent eligible at least because (i) the Specification does not present the argued technical problem. The 35 USC 101 rejection is maintained. Applicant’s arguments are not persuasive.
Applicant's arguments regarding claim rejections under 35 USC 103 filed 03/19/26 have been fully considered but they are not persuasive.
On pages 12-13 of the provided remarks, Applicant argues that the cited prior art does not disclose the amended claims. Specifically, on page 13 of the provided remarks, Applicant argues, “Qi does not suggest the features of training a machine-learning architecture for determining a probability of a lookalike model, and, at bid-time, executing the trained machine- learning architecture for determining a probability of a lookalike model using a bid request, as described in the claims.” Examiner begins by asserting that the above argument is moot as argued Qi was not cited to disclose the entirety of Applicant’s argument. Further, Applicant argues “Qi fails to describe any "probability of a lookalike audience for a webpage," much less in the context of training or executing a classifier or other model to generate such probabilities”. Examiner again argues that Applicant’s argument is moot as argued Qi was not cited to disclose the argued training or execution of a classier to generate such probabilities. Examiner asserts that the amended citations of both Qi and Dimitrov and newly cited Qu (U.S 8,655,695 B1) disclose the amended limitations. Applicant’s arguments are not persuasive.
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 non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
Step 1: Independent claims 1 (method), 11 (system), and dependent claims 2-10, and 12-20, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 1 is directed to a method (i.e. process) and claim 11 is directed to a system (i.e. machine).
Step 2A Prong 1: The independent claims recite dynamically placing content at user interfaces by determining audiences in real time for contextually relevant content distribution, comprising: receiving, by a computer, one or more configuration inputs via a user interface of a content-user, the one or more configuration inputs indicating a target audience and one or more context terms; identifying, by the computer, a first plurality of end-users obtained from a first plurality of end-user devices for a special audience, and a second plurality of end-users for a second plurality of end-user devices obtained from a database for a background audience; identifying, by the computer, a set of target users associated with the one or more context terms defining the target audience, by cross-referencing a first plurality of end-users of the special audience against the second plurality of end-users of the background audience; identifying, by the computer, a ranked-order list of context terms associated with each target end-user of the target audience; training, by the computer, a classifier to predict a probability that a lookalike audience accessing a webpage corresponds to the target audience by applying the classifier on the ranked-order list of context terms associated with the target audience and user identifiers associated with user devices that corresponds to the target audience; at bid-time, receiving, by the computer from a content exchange server, a bid request indicating (i) a user identifier associated with a user device at the bid-time and (ii) an available webpage; generating, by the computer, the probability that the lookalike audience accesses the available webpage corresponding to the target audience for the bid request using the trained classifier based upon the user identifier associated with the user device and a set of features of the available webpage as indicated by the bid request and in response to the probability satisfying a placement threshold, transmitting, by the computer, a selection instruction to the content exchange server causing placement of a content item on the available webpage. (Certain Method of Organizing Human Activity & Mental Process), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are determining audiences of contextually relevant content distribution by identifying a first plurality of end-users obtained from a first plurality of end-user devices for a special audience, and a second plurality of end-users for a second plurality of end-user devices obtained from a database for a background audience; identifying, a set of target users associated with the one or more context terms defining the target audience, by cross-referencing a first plurality of end-users of a special audience against a second plurality of end-users of a background audience; identifying, a ranked-order list of context terms associated with each target end-user of the target audience; predicting a probability of a lookalike audience for a webpage; and generating, the probability that the lookalike audience access the available webpage corresponding to the target audience for the bid request using the trained classifier based upon the user identifier associated with the user device and a set of features of the available webpage as indicated by the bid request, which is commercial interactions in the form of marketing. The Applicant’s claimed limitations are determining audiences of contextually relevant content distribution, which recite the abstract idea of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are placing content by determining audiences of contextually relevant content distribution by identifying a first plurality of end-users obtained from a first plurality of end-user devices for a special audience, and a second plurality of end-users for a second plurality of end-user devices obtained from a database for a background audience; identifying, a set of target users associated with the one or more context terms defining the target audience, by cross-referencing a first plurality of end-users of a special audience against a second plurality of end-users of a background audience; identifying, a ranked-order list of context terms associated with each target end-user of the target audience; predicting a probability that a lookalike audience accessing a webpage corresponds to the target audience; generating, the probability that the lookalike audience accesses the available webpage corresponding to the target audience for the bid request using the trained classifier based upon the user identifier associated with the user device and a set of features of the available webpage as indicated by the bid request, which are observations, judgements, and evaluations of the human mind. The Applicant’s claimed limitations are determining audiences of contextually relevant content distribution, which recite the abstract idea of Mental Process.
In addition, dependent claims 2-8, 10, 12-18, and 20 further narrow the abstract idea and recite further defining the prediction of the likelihood of the lookalike audience for the webpage; generating the special audience; selecting the background audience; determining a plurality of co-occurrence probabilities; updating the data record for the particular end-user; and generating the probability of the lookalike audience based on available webpage bids. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include commercial interactions such as marketing as well as mental processes. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas. Dependent claims 9 and 19 will be discussed in Prong 2 analysis below.
Step 2A Prong 2: In this application, the above “receiving, by a computer, one or more configuration inputs via a user interface of a content-user, the one or more configuration inputs indicating a target audience and one or more context terms; receiving, by the computer from a content exchange server, a bid request indicating (i) a user identifier associated with a user device at the bid-time and (ii) an available webpage; in response to the lookalike probability satisfying a placement threshold, transmitting, by the computer, a selection instruction to the content exchange server causing placement of a content item on the available webpage” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “A computer; user interfaces; a database; user devices; a client device; a bid server; a content exchange server; A system for determining audiences of contextually relevant content distribution, comprising: a computer having at least one processor” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Independent claims 1 and 11 recite the following limitation, “training, by the computer, a classifier”. The “training, by the computer, a classifier” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
In addition, dependent claims 2-8, 10, 12-18, and 20 further narrow the abstract idea and dependent claims 9-10 and 18-19 additionally recite “transmitting, by the computer to a client device, instructions for displaying the target audience via the user interface of the client device” and “receiving, by the computer from a bid server, an availability list of a plurality of available webpages requesting bids” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and the claimed “the computer to a client device” and “the computer from a bid server” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “A computer; user interfaces; a database; user devices; a client device; a bid server; a content exchange server; A system for determining audiences of contextually relevant content distribution, comprising: a computer having at least one processor” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, method claims 1-10; and System claims 11-20 recite “A computer; user interfaces; a database; user devices; a client device; a bid server; a content exchange server; A system for determining audiences of contextually relevant content distribution, comprising: a computer having at least one processor”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0027-29 and Figure 1. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “receiving, by a computer, one or more configuration inputs via a user interface of a content-user, the one or more configuration inputs indicating a target audience and one or more context terms; receiving, by the computer from a content exchange server, a bid request indicating (i) a user identifier associated with a user device at the bid-time and (ii) an available webpage; in response to the lookalike probability satisfying a placement threshold, transmitting, by the computer, a selection instruction to the content exchange server causing placement of a content item on the available webpage” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Next, when the “training, by the computer, a classifier” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to generate a learning model does not add significantly more to the claim.
In addition, claims 2-8, 10, 12-18, and 20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 9-10 and 18-19 additionally recite “transmitting, by the computer to a client device, instructions for displaying the target audience via the user interface of the client device” and “receiving, by the computer from a bid server, an availability list of a plurality of available webpages requesting bids” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed “the computer to a client device” and “the computer from a bid server” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi (U.S 2018/0040035 A1) in view of Dimitrov (U.S 2021/0256568 A1) in view of Qu (U.S 8,655,695 B1).
Claims 1 and 11
Regarding Claim 1, Qi discloses the following:
A computer-implemented method for dynamically placing content at user interfaces by determining audiences in real time for contextually relevant content distribution, comprising [see at least Paragraph 0047 for reference to a method for creating target audiences using labeled content campaign characteristics; Paragraph 0053 for reference to an example method for using advertising labels to generate a target audience for a now content campaign; Figure 3 and related text regarding a conceptual illustration of a method for creating target audiences using labeled content campaign characteristics; Figure 4 and related text for creating target audiences using labeled content campaign characteristics]
receiving, by a computer, one or more configuration inputs via a user interface of a content-user, the one or more configuration inputs indicating a target audience and one or more context terms [see at least Paragraph 0030 for reference to the input module receiving information from a content provider regarding the content campaign for which the online system will generate the target audience wherein the information includes the content itself as well as keyword labels describing the content, the campaign, and the content provider; Paragraph 0053 for reference to the online system receiving a request to create a new content campaign on the online system; Paragraph 0054 for reference to the input module and keyword extraction module determining keywords associated with the new content campaign; Paragraph 0054 for reference to the content provider inputting the keywords through the input module; Figure 4 and related text regarding item 405 ‘Receive Request for Create New Advertising Campaign’ and item 410 ‘Determine Keywords Associated with New Advertising Campaign’]
identifying, by the computer, a first plurality of end-users obtained from a first plurality of end-user devices for a special audience, and a second plurality of end-users for a second plurality of end-user devices obtained from a database for a background audience [see at least Paragraph 0042 for reference to the targeting is generalized based on user characteristics as determined by the characteristics of a subset of users of the online system; Paragraph 0042 for reference to the audience may be a list of identifiers in the online system of particular users who have been determined in advance to meet the targeting criteria, or the audience may simply be identified as the targeting criteria itself and the users falling within the targeting criteria may be identified at impression time; Paragraph 0043 for reference to the audience recommendation module incorporates users of the online system targeted by the similar content campaigns as well as previously untargeted users to generate the target audience for the new campaign; Examiner notes the ‘similar content audiences’ as analogous to ‘special audience’ and the ‘untargeted users’ as analogous to ‘background users’]
identifying, by the computer, a set of target users associated with the one or more context terms defining the target audience, by cross-referencing the first plurality of end-users of the special audience against a second plurality of end-users of the background audience [see at least Paragraph 0056 for reference to the audience recommendation module identifying the target audience associated with the similar campaign in order to generate a target audience for the new campaign; Paragraph 0056 for reference to the target audience for a similar campaign comprises a list of users of the online system to whom the content provider; Paragraph 0056 for reference to the audience recommendation module defines the target audience based on past engagement with content items on the online system, direct or indirect connections with other users of the online system, or installation of an application associated with the content provider; Figure 2 and related text regarding item 255 ‘audience recommendation module’; Figure 4 and related text regarding item 420 ‘Identify Target Audience for Each Similar Advertising Campaign’ and item 425 ‘Generate a Target Audience for New Advertising Campaign’]
identifying, by the computer, a ranked-order list of context terms associated with each target end-user of the target audience [see at least Paragraph 0033 for reference to the keyword extraction module ranking keywords based on the number of times the keyword appears in the content associated with the content provider; Paragraph 0035 for reference to determines similarity by looking for matching terms between the keywords associated with the new content campaign and the keywords associated with the existing campaigns; Paragraph 0035 for reference to where the keywords are ranked or assigned a weight representing the importance of the keyword , the similarity score may equal or exceed a threshold value when a threshold number of keywords at the top of the ranking (e. g., at least 3 in the top 15 highest ranked keywords) or the weighting (e. g., at least 2 of the top 10 highest weighted keywords, or requiring that at least the top two keywords in the weighting are included) are matched across the two campaigns; Paragraph 0043 for reference to the percentage of users targeted by other campaigns varies in proportion to the similarity scores calculated by the keyword comparison module; Figure 2 and related text regarding item 250 ‘keyword comparison module’]
predict a probability of a lookalike audience for a webpage by applying the ranked-order list of context terms associated with the target audience [see at least Paragraph 0044 for reference to the audience recommendation module identifies a second set of users of the online system that are similar to the previously identified users based on past engagement history (e. g., click through rates), demographic information, or direct or indirect connections with previously targeted users; Paragraph 0045 for reference to the audience recommendation module generates a target audience for the new content campaign by determining an overlap between two target audiences whose keywords are similar to the keywords associated with the new campaign; Paragraph 0045 for reference to audience recommendation module extracts the overlapping audience from the two target audiences and includes that group of users in the target audience for the new campaign; Paragraph 0052 for reference to the audience recommendation module uses “look - alike” targeting to determine additional users of the online system to add to the target audience based on similarities between those users and the subset of users that were previously targeted]
generating, by the computer, the probability that the lookalike audience accesses the available webpage corresponding to the target audience for the bid request [see at least Paragraph 0039 for reference to a content provider using a determination of similarity to make or adjust a bid amount; Paragraph 0045 for reference to audience recommendation module extracts the overlapping audience from the two target audiences and includes that group of users in the target audience for the new campaign; Paragraph 0052 for reference to the audience recommendation module uses “look - alike” targeting to determine additional users of the online system to add to the target audience based on similarities between those users and the subset of users that were previously targeted]
While Qi discloses the limitations above, it does not disclose training, by the computer, a classifier to predict a probability that a lookalike audience accessing a webpage corresponds to the target audience by applying the classifier on the ranked-order list of context terms associated with the target audience and user identifiers associated with user devices that correspond to the target audience; at a bid-time, receiving, by the computer from a content exchange server, a bid request indicating (i) a user identifier associated with a user device at the bid-time and (ii) an available webpage; generating, by the computer, the probability that the lookalike audience accesses the available webpage corresponding to the target audience for the bid request using the trained classifier based upon the user identifier associated with the user device and a set of features of the available webpage as indicated by the bid request and in response to the probability satisfying a placement threshold, transmitting, by the computer, a selection instruction to the content exchange server causing placement of a content item on the available webpage.
However, Dimitrov discloses the following:
receiving, by a computer, one or more configuration inputs via a user interface of a content-user, the one or more configuration inputs indicating a target audience and one or more context terms [see at least Paragraph 0041 for reference to a computer hosts a website that receives inputs from a client computer of an end - user (e.g., content generator) for defining a new content delivery campaign or for updating a previously generated campaign; Paragraph 0041 for reference to the inputs including input phrases; Figure 3 and related text regarding item 301 ‘User specifies in-context phrases and out-of-context phrases’]
identifying, by the computer, a set of target users associated with the one or more context terms defining the target audience [see at least Paragraph 0108 for reference to based on the user's input phrases and beacon phrases, the ultimately calculated webpage scores target specific keywords or demographics, and therefore the webpage scores may be customized per campaign]
identifying, by the computer, a ranked-order list of context terms associated with each target end-user of the target audience [see at least Paragraph 0043 for reference to based on the algorithmic scores discussed herein for the input phrases (e.g., in - context phrases, out - of - context phrases), the computer determines or updates a list of identified beacon phrases contextually relevant to the user’s campaign]
training, by the computer, a classifier to predict a probability that a lookalike audience accessing a webpage corresponds to the target audience by applying the classifier on the ranked-order list of context terms associated with the target audience [see at least Paragraph 0021 for reference to the webserver 101 associates the content with specific context terms, such as keywords or beacon terms wherein the webpage data or content may include, for example, metadata, fingerprints for media of the webpage, webpage or server identifiers, content tags, or content containing or associated with such context terms; Paragraph 0024 for reference to the webserver training the machine - learning model on the corpus of webpage data to identify and generate various statistical associations between terms, phrases, metadata, or other information indicating the nature or context of each particular webpage; Paragraph 0025 for reference to the trained machine learning model determines the co-occurrence probabilities (and/or other statistical measures) that the input terms co-occur with the corpus terms and then outputs the corpus terms / phrases having probabilities satisfying a threshold; Paragraph 0041 for reference to the inputs from a user may include website URLs, or fingerprints for media (e.g., audio, visual, audiovisual) content having a high context correlated with the campaign content; Paragraph 0043 for reference to the user specifying input phrases (e.g., in-context phrases, out-of-context phrases), the computer determines or updates a list of identified beacon phrases contextually relevant to the user's campaign; Paragraph 0045 for reference to the algorithm of step 302 may calculate context scores for content in the corpus database based on probabilistic implications, word co-occurrences, and context scores to find high context websites; Paragraph 0112 for reference to the computer (or other computing device of the system) in some embodiments, receives or calculates a performance score that defines the likelihood that a placement of campaign content will be viewed or clicked - through or engaged with; Figure 3 and related text regarding identifying the context of a campaign and correlating the campaign content with third - party Internet content]
at a bid-time, receiving, by the computer from a content exchange server, a bid request indicating (i) a user identifier associated with a user device at the bid-time and (ii) an available webpage [see at least Paragraph 0034 for reference to a bid request may be the computer - implemented instructions to the computer to display and/ or distribute those URLs and gather bid inputs corresponding to the published URLs, which thereby trigger the computer to execute various processes described herein to generate and submit the inputted data to the API service via the API request; Paragraph 0105 for reference to at bid-time the computer receives a bidstream of bid requests from a server of the RTB service containing one or more URLs of webpages for which content generators, such as the user in the process; Paragraph 0112 for reference to the system considering other factors about the bid request including the IP address of the bid requester; Figure 1 and related text regarding item 114 ‘real time bidding server’; Figure 2 and related text regarding item 201 ‘Bidstream brings bid requests’; Figure 3 and related text regarding item 306 ‘Bidstream brings bid requests’]
generating, by the computer, the probability that the lookalike audience accesses the available webpage corresponding to the target audience for the bid request using the trained classifier based upon the user identifier associated with the user device and a set of features of the available webpage as indicated by the bid request [see at least Paragraph 0024 for reference to the webserver training the machine - learning model on the corpus of webpage data to identify and generate various statistical associations between terms, phrases, metadata, or other information indicating the nature or context of each particular webpage; Paragraph 0025 for reference to the trained machine learning model determines the co-occurrence probabilities (and/or other statistical measures) that the input terms co-occur with the corpus terms and then outputs the corpus terms / phrases having probabilities satisfying a threshold; Paragraph 0027 for reference to the webserver applies the machine-learning model on a bid stream of URLs for available webpages received from a RTB server; Paragraph 0045 for reference to the algorithm of step 302 may calculate context scores for content in the corpus database based on probabilistic implications, word co-occurrences, and context scores to find high context websites; Paragraph 0106 for reference to using an algorithm and campaign data the computer calculates the page context scores for the webpages that have been published in the bidstream by the RTB wherein the computer matches the available webpages published in the bidstream to calculate webpage context scores of the available webpages; Paragraph 0112 for reference to the system considering other factors about the bid request including the IP address of the bid requester; Paragraph 0112 for reference to the computer (or other computing device of the system) in some embodiments, receives or calculates a performance score that defines the likelihood that a placement of campaign content will be viewed or clicked - through or engaged with; Figure 3 and related text regarding identifying the context of a campaign and correlating the campaign content with third - party Internet content]
in response to the probability satisfying a placement threshold, transmitting, by the computer, a selection instruction to the content exchange server causing placement of a content item on the available webpage [see at least Paragraph 0007 for reference to the system providing real time decision making between campaigns and placement adjacent to Internet content; Paragraph 0112 for reference to the computer (or other computing device of the system) in some embodiments, receives or calculates a performance score that defines the likelihood that a placement of campaign content will be viewed or clicked-through or engaged with; Paragraph 0112 for reference to the computer transmitting bids when the performance score satisfies a pre-configured threshold performance score]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the lookalike determination method of Qi to include the training of a classifier and bid time execution of Dimitrov. Doing so would provide effective and efficient means for specifying a context in which content should appear and efficiently delivering content to contextually relevant web sites for collocation and presentation, as stated by Dimitrov (Paragraph 0005).
While the combination of Qi and Dimitrov disclose the limitations above, they do not disclose training, by the computer, a classifier to predict a probability that a lookalike audience accessing a webpage corresponds to the target audience by applying the classifier on the ranked-order list of context terms associated with the target audience and user identifiers associated with user devices that correspond to the target audience; generating, by the computer, the probability that the lookalike audience accesses the available webpage corresponding to the target audience for the bid request using the trained classifier based upon the user identifier associated with the user device and a set of features of the available webpage as indicated by the bid request.
However, Qu discloses the following:
training, by the computer, a classifier to predict a probability that a lookalike audience accessing a webpage corresponds to the target audience by applying the classifier on the ranked-order list of context terms associated with the target audience and user identifiers associated with user devices that correspond to the target audience [see at least Col 10 lines 15-26 for reference to the received event data including behavioral logs including user ID, URL, and frequency of visits; Col 10 lines 39-54 for reference to specified (i.e., “target”) features being provided including users who respond to certain campaign or media; Col 10 lines 55-65 for reference to one or more user feature extractors 304 may be used for extracting users with each of the particular target features and their values; Col 12 lines 5-11 for reference to the model builder generates expanded look-alike user segments by developing a training set of instances represented as an input feature vector and a target value; Col 12 lines 14-38 for reference to the module builder modeling the task of generating expanded look-alike user segments as a classification task using a Naïve Bayes classifier built using a specific type of features; Col 12 lines 41-52 for reference to the training data set being generated using model specifications including the target class, a list of features used as independent features or predictors, etc.; Col 12 lines 53-60 for reference to the Naïve Bayes classifier uses the target classes and the list of sites pre-selected to be the most discriminating in separating the advertiser converters from the advertiser nonconverters; Examiner notes the ‘target class’ as analogous to the ‘user identifiers’ and the ‘list of sites’ as analogous to the ‘ranked-order list’]
generating, by the computer, the probability that the lookalike audience accesses the available webpage corresponding to the target audience for the bid request using the trained classifier based upon the user identifier associated with the user device and a set of features of the available webpage as indicated by the bid request [see at least Col 12 lines 12-14 for reference to the output of the learning task being a continuous value (e.g., the probability of the user clicking on an ad) or a class label (e.g., “predicted actor” denoting the user belonging to the actor class); Col 16 lines 4-20 for reference to the results of the Naïve Bayes Multinomal classification method that output the user ID and the probability of the user being a campaign actor given the visitation history of the user; Col 18 lines 35-38, 41-59, 60-64 for reference to an exchange may then facilitate the placement of advertisements from an advertiser onto cells provided by publishers by matching advertiser bids with publisher requests, based on segmentation information, among other factors; Col 18 lines 61-67 and Col 19 lines 1-10 for reference to a method of incorporating segmentation into the optimization process including creating a user roster based on the computer bids]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the classifier training method of Dimitrov to include the user identifier association of Qu. User segments offer additional information to optimization engines by identifying high-performing look-alike user segments suitable for targeting, as stated by Qu (Col 18 lines 38-41).
Regarding claim 11, the claim recites limitations already addressed by the rejection of claim 1. Regarding claim 11, Qi teaches a system for dynamically placing content at user interfaces by determining audiences in real time for contextually relevant content distribution, comprising a computer having at least one processor [Paragraphs 0015-0022 & Figures 1-2]. Therefore, claim 11 is rejected as being unpatentable over the combination of Qi, Dimitrov, and Qu.
Claims 2 and 12
While the combination of Qi, Dimitrov, and Qu disclose the limitations above, regarding Claim 2, Qi discloses the following:
further comprising applying, by the computer, a plurality of topic terms of the webpage to predict the probability of the lookalike audience for the webpage [see at least Paragraph 0044 for reference to the audience recommendation module identifies a second set of users of the online system that are similar to the previously identified users based on past engagement history (e. g., click through rates), demographic information, or direct or indirect connections with previously targeted users; Paragraph 0045 for reference to the audience recommendation module generates a target audience for the new content campaign by determining an overlap between two target audiences whose keywords are similar to the keywords associated with the new campaign; Paragraph 0045 for reference to audience recommendation module extracts the overlapping audience from the two target audiences and includes that group of users in the target audience for the new campaign; Paragraph 0052 for reference to the audience recommendation module uses “look - alike” targeting to determine additional users of the online system to add to the target audience based on similarities between those users and the subset of users that were previously targeted]
While Qi discloses the limitations above, it does not disclose further comprising applying, by the computer, the classifier on a plurality of topic terms of the webpage to predict a likelihood of the lookalike audience for the webpage.
However, Dimitrov discloses the following:
further comprising applying, by the computer, the classifier on a plurality of topic terms of the webpage to predict a likelihood of the lookalike audience for the webpage [see at least Paragraph 0024 for reference to the webserver training the machine - learning model on the corpus of webpage data to identify and generate various statistical associations between terms, phrases, metadata, or other information indicating the nature or context of each particular webpage; Paragraph 0025 for reference to the trained machine learning model determines the co-occurrence probabilities (and/or other statistical measures) that the input terms co-occur with the corpus terms and then outputs the corpus terms / phrases having probabilities satisfying a threshold; Paragraph 0045 for reference to the algorithm of step 302 may calculate context scores for content in the corpus database based on probabilistic implications, word co-occurrences, and context scores to find high context websites; Paragraph 0112 for reference to the computer (or other computing device of the system) in some embodiments, receives or calculates a performance score that defines the likelihood that a placement of campaign content will be viewed or clicked - through or engaged with; Figure 3 and related text regarding identifying the context of a campaign and correlating the campaign content with third - party Internet content]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the lookalike determination method of Qi to include the training of a classifier to analyze context terms of Dimitrov. Doing so would provide effective and efficient means for specifying a context in which content should appear and efficiently delivering content to contextually relevant web sites for collocation and presentation, as stated by Dimitrov (Paragraph 0005).
Regarding claim 12, the claim recites limitations already addressed by the rejection of claim 2.
Claims 3 and 13
While the combination of Qi, Dimitrov, and Qu disclose the limitations above, regarding Claim 3, Qi discloses the following:
further comprising generating, by the computer, the special audience based upon user data of each end-user in the first plurality of end-users of the special audience according to the one or more configuration inputs [see at least Paragraph 0042 for reference to the targeting is generalized based on user characteristics as determined by the characteristics of a subset of users of the online system; Paragraph 0042 for reference to the audience may be a list of identifiers in the online system of particular users who have been determined in advance to meet the targeting criteria, or the audience may simply be identified as the targeting criteria itself and the users falling within the targeting criteria may be identified at impression time; Paragraph 0056 for reference to the audience recommendation module identifying the target audience associated with the similar campaign in order to generate a target audience for the new campaign; Paragraph 0056 for reference to the target audience for a similar campaign comprises a list of users of the online system to whom the content provider; Paragraph 0056 for reference to the audience recommendation module defines the target audience based on past engagement with content items on the online system, direct or indirect connections with other users of the online system, or installation of an application associated with the content provider; Figure 2 and related text regarding item 255 ‘audience recommendation module’; Figure 4 and related text regarding item 420 ‘Identify Target Audience for Each Similar Advertising Campaign’ and item 425 ‘Generate a Target Audience for New Advertising Campaign’]
Regarding claim 13, the claim recites limitations already addressed by the rejection of claim 3.
Claims 4 and 14
While the combination of Qi, Dimitrov, and Qu disclose the limitations above, regarding Claim 4, Qi discloses the following:
further comprising updating, by the computer, the special audience according to additional user data received from one or more client devices [see at least Paragraph 0056 for reference to the target audience for a similar campaign comprises a list of users of the online system to whom the content provider; Paragraph 0056 for reference to the audience recommendation module defines the target audience based on past engagement with content items on the online system, direct or indirect connections with other users of the online system, or installation of an application associated with the content provider; Figure 2 and related text regarding item 255 ‘audience recommendation module’; Figure 4 and related text regarding item 420 ‘Identify Target Audience for Each Similar Advertising Campaign’ and item 425 ‘Generate a Target Audience for New Advertising Campaign’]
Regarding claim 14, the claim recites limitations already addressed by the rejection of claim 4.
Claims 5 and 15
While the combination of Qi, Dimitrov, and Qu disclose the limitations above, regarding Claim 5, Qi discloses the following:
further comprising selecting, by the computer, the background audience from the database according to a background feature indicated by the one or more configuration inputs [see at least Paragraph 0042 for reference to the targeting is generalized based on user characteristics as determined by the characteristics of a subset of users of the online system; Paragraph 0043 for reference to the audience recommendation module incorporates users of the online system targeted by the similar content campaigns as well as previously untargeted users to generate the target audience for the new campaign]
Regarding claim 15, the claim recites limitations already addressed by the rejection of claim 5.
Claims 6 and 16
While the combination of Qi, Dimitrov, and Qu disclose the limitations above, regarding Claim 6, Qi discloses the following:
wherein selecting the background audience includes, extracting, by the computer, a sample subset of user data records from the database for the second plurality of end-users of the background audience [see at least Paragraph 0024 for reference to the action logger tracking user actions on the online system; Paragraph 0024 for reference to the action logger recording a user’s interactions with advertisements of the online system; Paragraph 0024 for reference to data from the action log is used to infer interests or preferences of a user, augmenting the interests included in the user's profile and allowing a more complete understanding of user preferences; Paragraph 0042 for reference to the targeting is generalized based on user characteristics as determined by the characteristics of a subset of users of the online system; Paragraph 0050 for reference to the target audience may be generalized based on user characteristics as determined by a subset of users of the online system]
Regarding claim 16, the claim recites limitations already addressed by the rejection of claim 6.
Claims 7 and 17
While the combination of Qi, Dimitrov, and Qu disclose the limitations above, Qi does not disclose further comprising determining, by the computer, a plurality of co-occurrence probabilities for a plurality of topic terms in a plurality of corpus webpages.
Regarding Claim 7, Dimitrov discloses the following:
further comprising determining, by the computer, a plurality of co-occurrence probabilities for a plurality of topic terms in a plurality of corpus webpages [see at least Paragraph 0024 for reference to the machine - learning model determines co-occurrences and other statistical contextual data for various types of webpage data in the corpus database; Paragraph 0025 for reference to the machine learning model determines the co-occurrence probabilities (and/or other statistical measures) that the input terms co-occur with the corpus terms; Paragraph 0046 for reference to the algorithm of step 302 computes word probabilities, word co - occurrence counts, and word - to - word probabilistic implications using the input phrases; Figure 3 and related text regarding item 302 ‘Algorithm based on probabilistic implications and word co-occurrences computes context beacon phrase scores’]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the method of Qi to include the determination of co-occurrence probabilities of Dimitrov. Doing so would provide effective and efficient means for specifying a context in which content should appear and efficiently delivering content to contextually relevant web sites for collocation and presentation, as stated by Dimitrov (Paragraph 0005).
Regarding claim 17, the claim recites limitations already addressed by the rejection of claim 7.
Claims 8 and 18
While the combination of Qi, Dimitrov, and Qu disclose the limitations above, regarding Claim 8, Qi discloses the following:
for a particular end-user, identifying, by the computer, one or more historic webpages accessed by the particular end-user [see at least Paragraph 0024 for reference to the action logger tracking user actions on the online system; Paragraph 0024 for reference to the action logger recording a user’s interactions with advertisements of the online system; Paragraph 0024 for reference to data from the action log is used to infer interests or preferences of a user, augmenting the interests included in the user's profile and allowing a more complete understanding of user preferences; Paragraph 0044 for reference to the audience recommendation module identifies a second set of users of the online system that are similar to the previously identified users based on past engagement history (e.g., click-through rates)]
identifying, by the computer, a plurality of topic terms of the one or more historic webpages accessed by the particular end-user [see at least Paragraph 0035 for reference to determines similarity by looking for matching terms between the keywords associated with the new content campaign and the keywords associated with the existing campaigns; Paragraph 0043 for reference to the percentage of users targeted by other campaigns varies in proportion to the similarity scores calculated by the keyword comparison module; Figure 2 and related text regarding item 250 ‘keyword comparison module’]
updated, by the computer, a data record for the particular end-user to include the plurality of topic terms [see at least Paragraph 0024 for reference to the action logger recording a user’s interactions with advertisements of the online system; Paragraph 0024 for reference to data from the action log is used to infer interests or preferences of a user, augmenting the interests included in the user's profile and allowing a more complete understanding of user preferences]
Regarding claim 18, the claim recites limitations already addressed by the rejection of claim 8.
Claims 9 and 19
While the combination of Qi, Dimitrov, and Qu disclose the limitations above, regarding Claim 9, Qi discloses the following:
further comprising transmitting, by the computer to a client device, instructions for displaying the target audience via the user interface of the client device [see at least Paragraph 0057 for reference to the audience recommendation module presenting the target audience for the new campaign to the client device; Figure 4 and related text regarding item 430 ‘Present Target Audience to Client Device’]
Regarding claim 19, the claim recites limitations already addressed by the rejection of claim 9.
Claims 10 and 20
While the combination of Qi, Dimitrov, and Qu disclose the limitations above, Qi does not disclose receiving, by the computer from a bid server, an availability list of a plurality of available webpages requesting bids; and for each available webpage of a bid stream, generating, by the computer, the probability for the available webpage by applying the classifier on a plurality of topic terms of the available webpage.
Regarding Claim 10, Dimitrov discloses the following:
receiving, by the computer from a bid server, an availability list of a plurality of available webpages requesting bids [see at least Paragraph 0034 for reference to a bidstream is a data stream of published URLs available for bids to content generators interested in placing campaign content at those URLs and a bid request may be the computer - implemented instructions to the computer to display and/or distribute those URLs and gather bid inputs corresponding to the published URLs; Paragraph 0105 for reference to at bid - time the computer receives a bidstream of bid requests from a server of the RTB service, initiating the user’s content delivery campaign; Figure 2 and related text regarding item 201 ‘Bidstream brings bid requests’]
for each available webpage of a bid stream, generating, by the computer, the probability for the available webpage by applying the classifier on a plurality of topic terms of the available webpage [see at least Paragraph 0024 for reference to the webserver training the machine - learning model on the corpus of webpage data to identify and generate various statistical associations between terms, phrases, metadata, or other information indicating the nature or context of each particular webpage; Paragraph 0025 for reference to the trained machine learning model determines the co-occurrence probabilities (and/or other statistical measures) that the input terms co-occur with the corpus terms and then outputs the corpus terms / phrases having probabilities satisfying a threshold; Paragraph 0026 for reference to the machine learning model is trained and tuned for the end - user's context, the webserver then applies the machine - learning model on a bid stream of URLs for available webpages received from RTB server; Paragraph 0106 for reference to the computer calculates beacon phrase scores and page context scores for the webpages that have been published in the bidstream by the RTB; Paragraph 0107 for reference to by executing the same or similar algorithm that generated a beacon phrase score (as in prior steps 307-309), the computer may compute page scores for the webpage URLs published by the RTB, and a context score for each of the context phrases on a particular webpage; where the computation of the context scores may include calculating the probabilities of word co - occurrence, geometric means, and expected numbers of occurrences versus actual occurrences; Figure 3 and related text regarding identifying the context of a campaign and correlating the campaign content with third - party Internet content]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the method of Qi to include the determination of available webpages and bid stream processing of Dimitrov. Doing so would provide effective and efficient means for specifying a context in which content should appear and efficiently delivering content to contextually relevant web sites for collocation and presentation, as stated by Dimitrov (Paragraph 0005).
Regarding claim 20, the claim recites limitations already addressed by the rejection of claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dewet, Stephanie, and Jiafan Ou. "Finding users who act alike: transfer learning for expanding advertiser audiences." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019.
DOCUMENT ID
INVENTOR(S)
TITLE
US 2020/0126100 A1
Goyal et al.
Machine Learning-Based Generation of Target Segments
CN111882349A
Feng, Xhi-xiang
Data Processing Method, Device And Storage Medium
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/KRISTIN E GAVIN/Primary Examiner, Art Unit 3624
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