DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 7/7/2023. Acknowledgment is made with respect to a claim of priority to PCT Application PCT/US2023/025261 filed on 6/14/2023 and Provisional Application 63/351,845 filed on 6/14/2022.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claim 1 recites the limitation “obtaining, from the first machine-learned model, a respective weight of the plurality of weights, the respective weight indicative of an incremental probability of the request conditioned on rendering of the input element” (emphasis added) which should read as “obtaining, from the first machine-learned model, a respective weight of the plurality of weights, the respective weight indicative of an incremental probability of the request for a resource conditioned on rendering of the input element” (emphasis added) so that it is clear that the referenced request is the “request for a resource” that is previously mentioned in the claim. Dependent claims 2-18 depend on objected claim 1, and are also objected to by virtue of this dependency. The same objection applies equally to the same limitation in independent claims 19 and 20. Appropriate correction is required.
Applicant is advised that should claim 3 be found allowable, claim 14 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
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 (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). 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 integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
obtaining, … a plurality of weights associated with the plurality of user sessions by, for a respective user session of the plurality of user sessions: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of obtaining or determining a plurality of weights for user sessions, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally assign a weight of “1” for when a user clicked an advertisement during a session.
obtaining, … a respective weight of the plurality of weights, the respective weight indicative of an incremental probability of the request conditioned on rendering of the input element: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of obtaining or determining a plurality of weights for user sessions based on an incremental probability, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally assign a weight of “1” for when a user clicked an advertisement during a session and based on a probability.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively comprising an interaction with an input element rendered at a user device and a request for a resource associated with the input element”, “using a first machine learning model”, “inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session”, “from the first machine-learned model”, and “updating, based on the plurality of weights, a second machine-learned model to optimize candidate proposals for participation in a real-time content selection process for populating a user interface with one or more selected input elements”.
The additional elements of “using a first machine learning model”, “from the first machine-learned model”, and “updating, based on the plurality of weights, a second machine-learned model to optimize candidate proposals for participation in a real-time content selection process for populating a user interface with one or more selected input elements” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the first generic ML model is broadly used to obtain or determine weights and how the generic second ML model is broadly updated to optimize candidate proposals. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional element “obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively comprising an interaction with an input element rendered at a user device and a request for a resource associated with the input element” and “inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “using a first machine learning model”, “from the first machine-learned model”, and “updating, based on the plurality of weights, a second machine-learned model to optimize candidate proposals for participation in a real-time content selection process for populating a user interface with one or more selected input elements” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the first generic ML model is broadly used to obtain or determine weights and how the generic second ML model is broadly updated to optimize candidate proposals. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional element “obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively comprising an interaction with an input element rendered at a user device and a request for a resource associated with the input element” and “inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
determining, based on the plurality of weights, one or more parameters of the first machine-learned model: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining model parameters based on weight, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “calibrating, using the one or more parameters, the second machine-learned model” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the second ML model is broadly calibrated based on parameters. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
generating, based on the plurality of weights, a semi-supervised training dataset comprising session data descriptive of the plurality of user sessions: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a training dataset based on weights, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “training, using the semi-supervised training dataset, the second machine-learned model” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic second ML model is broadly trained using the dataset. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the respective weight corresponds to a reward for updating the second machine-learned model” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the data indicative of one or more characteristics of the respective user session comprises at least one of: device data and input element data” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the device data comprises at least one of a browser type, a device identifier, or data indicative of an account associated with the user device” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the input element data comprises at least one of a form of the input element, a subject matter of the input element, one or more visual characteristics of the input element, or one or more audio characteristics of the input element” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the one or more parameters comprise a value indicative of at least one of: one or more total effects, one or more impression query effects, one or more click query effects, one or more calibrated content effects, one or more impression content effects, or one or more click content effects” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the plurality of user sessions are associated with at least one of a first user group, a second user group, and a third user group, wherein the first user group comprises one or more user sessions associated with no rendering of a user input element associated with a first content provider; wherein the second user group comprises one or more user sessions associated with the rendering of a user input element associated with the first content provider on a user interface and not obtaining data indicative of a user interacting with the user input element; and wherein the third user group comprises one or more user sessions associated with the rendering of a user interface element associated with the first content provider and obtaining data indicative of a user interacting with the user interface element” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the one or more parameters are indicative of the one or more impression query effects, corresponding to a difference in conversion probability associated with the second user group and the first user group” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 11
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the one or more parameters are indicative of the one or more click query effects, corresponding to a difference in conversion probability associated with the third user group and the second user group” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 12
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
wherein the one or more calibrated content effects are determined by taking the difference between a first total effect of the one or more total effects and a sum of a first impression query effect of the one or more impression query effects and a first click query effect of the one or more click query effects: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining differences and sums of effects to determine a calibrated content effect, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 13
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein a first total effect is equal to a sum of a first impression query effect of the one or more impression query effects, a first click query effect of the one or more click query effects, and a first calibrated content effect of the one or more calibrated content effects” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 14
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
generating, based on the plurality of weights, a semi-supervised training dataset comprising session data descriptive of the plurality of user sessions: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a training dataset based on weights, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “training, using the semi-supervised training dataset, the second machine-learned model” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic second ML model is broadly trained using the dataset. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 15
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
determining the incremental probability of the request conditioned on rendering of the input element is below a threshold incremental probability: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining if a probability is below a threshold, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “updating, based on the incremental probability being below the threshold incremental probability, the second machine-learned model to avoid proposals for participation in the real-time content selection process for populating the user interface with the input element” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic second ML model is broadly updated to avoid proposals. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 16
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
determining the incremental probability of the request conditioned on rendering of the input element is above a threshold incremental probability: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining if a probability is above a threshold, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “updating, based on the incremental probability being above the threshold incremental probability, the second machine-learned model to generate proposals for participation in the real-time content selection process for populating the user interface with the input element” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic second ML model is broadly updated to generate proposals. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 17
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional elements of “transmitting, to a user computing device, data to cause the input element to be rendered on a user interface; obtaining data indicative of user interaction with the input element” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”). The additional element of “updating the second machine-learned model based on the data indicative of the user interaction with the input element” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic second ML model is broadly updated based on certain data. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 18
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the one or more characteristics of the respective user session comprises at least one of (i) a user identifier, (ii) a timestamp of an exposure, (ii) an exposure descriptor, (iv) a timestamp of a next chronological exposure, or (v) a count of users performing specified target actions that occurred in an interval defined by the time of the exposure and the next chronological exposure” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 19
Claim 19 recites a system (step 1: a machine) using a processor and one or more non-transitory computer readable medium to perform the steps of claim 1, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and is thus rejected for the same reasons set forth in the rejection of claim 1.
Claim 20
Claim 20 recites one or more non-transitory computer readable media (step 1: a manufacture) using a processor to perform the steps of claim 1, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and is thus rejected for the same reasons set forth in the rejection of claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. § 103 as being obvious over Petrosso et al. (US 20210103876, A1, hereinafter “Petrosso”) in view of Richardson et al. (Richardson et al., “Predicting clicks: estimating the click-through rate for new ads”, May 8, 2007, WWW '07: Proceedings of the 16th international conference on World Wide Web, pp. 521-529, hereinafter “Richardson”).
Regarding claim 1, Petrosso discloses [a] computer-implemented method, comprising: ([0003]; “systems and methods for evaluating and targeting entities”)
obtaining, using a first machine-learned model, a plurality of weights associated with the plurality of user sessions by, for a respective user session of the plurality of user sessions: ([0022]; “teach, with a teaching set, at least one first machine learning algorithm to generate one or more machine predicted results; 2) analyze one or more weights based on the one or more machine predicted results and the teaching set”; and [0084]; “The first machine learning model can include one or more parameters and one or more weight values that determine a magnitude of influence each parameter contributes to an output of the first machine learning model. The parameters and/or weights can be analyzed to determine an accuracy of the first machine learning model. Based on the analysis, the parameters and/or weights can be optimized (e.g., to improve accuracy metrics, reduce error metrics, or improve other metrics, such as bias metrics).”; and [0105]; and [0010]; “the system may generate a CCG engagement training set including data describing candidates, historic CCG-related communications, and historic engagement of the candidates with CCG-related recruitment communications”, wherein the historic engagements are interpreted as the user session/s”)
inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session; and ([0010]; “the system may generate a CCG engagement training set including data describing candidates, historic CCG-related communications, and historic engagement of the candidates with CCG-related recruitment communications”, wherein the historic engagements are interpreted as the user session/s”; and [0022])
obtaining, from the first machine-learned model, a respective weight of the plurality of weights ([0084]; “The first machine learning model can include one or more parameters and one or more weight values that determine a magnitude of influence each parameter contributes to an output of the first machine learning model. The parameters and/or weights can be analyzed to determine an accuracy of the first machine learning model. Based on the analysis, the parameters and/or weights can be optimized (e.g., to improve accuracy metrics, reduce error metrics, or improve other metrics, such as bias metrics).”; and [0105])
updating, based on the plurality of weights, a second machine-learned model to optimize candidate proposals for participation in a real-time content selection process for populating a user interface with one or more selected input elements ([0022]; “generate at least one second machine learning algorithm based on the one or more weights; 4) generate, via the at least one second machine learning algorithm, one or more machine-learned results; and 5) analyze a respective likelihood of interest in a CCG class of positions for each of one or more candidates based on the one or more machine-learned results”, which discloses updating a second ML mode based on weights from the first ML model for participation in a content selection process; and [0010]; “generate and train one or more machine learning models to accurately and precisely predict a likelihood of a candidate engaging with a CCG-related recruitment communication”; and [0092]; “Non-limiting examples of actions include, but are not limited to, generating particular language designed to provoke a response from one or more candidates, generating a ranking of candidates, …generating and rendering a graphical user interface”).
Petrosso fails to explicitly disclose but Richardson discloses obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively comprising an interaction with an input element rendered at a user device and a request for a resource associated with the input element; (Page 523, §4; “We collected information on a set of active ads in the Microsoft Web search engine. Each ad contains the following information: [Symbol font/0xB7] Landing page: The URL that a user is redirected to upon clicking the ad. [Symbol font/0xB7] Bid term (“keywords”): The query for which this ad should be displayed (this may be multiple words, e.g., “machine learning books”). [Symbol font/0xB7] Title: The ad title, shown to the user. [Symbol font/0xB7] Body: The text description of the ad. [Symbol font/0xB7] Display URL: The URL shown to the user at the bottom of the ad. [Symbol font/0xB7] Clicks: The number of times the ad has been clicked since it was entered into the system. [Symbol font/0xB7] Views: The number of times the ad has been seen since it was entered into the system, as described in section 3.”)
the respective weight indicative of an incremental probability of the request conditioned on rendering of the input element; and (Page 521, § 1; “we address the problem of estimating the probability that an ad will be clicked on, for newly created ads and advertising accounts. We show that we can use information about the ad itself (such as the length of the ad and the words it uses), the page the ad points to, and statistics of related ads, to build a model that reasonably predicts the future CTR of that ad”, wherein the weight is interpreted as the click through probability; and Page 522, §3; “As a simplification, we consider the probability that an ad is clicked on to be dependent on two factors: a) the probability that it is viewed, and b) the probability that it is clicked on, given that it is viewed”; and §4).
Petrosso and Richardson are analogous art because both are concerned with machine learning and predictive engagement. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning and predictive engagement to combine the session data and incremental probability of Richardson and the method of Petrosso to yield to the predictable result of obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively comprising an interaction with an input element rendered at a user device and a request for a resource associated with the input element and obtaining, using a first machine-learned model, a plurality of weights associated with the plurality of user sessions by, for a respective user session of the plurality of user sessions. The motivation for doing so would be to learn a model that accurately predicts the click-through rate for new ads (Richardson; Abstract).
Regarding claim 19, it is a system claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claim 20, it is a non-transitory computer readable medium claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claim 2, the rejection of claim 1 is incorporated and Petrosso discloses determining, based on the plurality of weights, one or more parameters of the first machine-learned model; and calibrating, using the one or more parameters, the second machine-learned model ([0022]; “1) teach, with a teaching set, at least one first machine learning algorithm to generate one or more machine predicted results; 2) analyze one or more weights based on the one or more machine predicted results and the teaching set; 3) generate at least one second machine learning algorithm based on the one or more weights; 4) generate, via the at least one second machine learning algorithm, one or more machine-learned results”).
Regarding claim 3, the rejection of claim 1 is incorporated and Petrosso discloses generating, based on the plurality of weights, a semi-supervised training dataset comprising session data descriptive of the plurality of user sessions; and training, using the semi-supervised training dataset, the second machine-learned model ([0022]; “1) teach, with a teaching set, at least one first machine learning algorithm to generate one or more machine predicted results; 2) analyze one or more weights based on the one or more machine predicted results and the teaching set; 3) generate at least one second machine learning algorithm based on the one or more weights; 4) generate, via the at least one second machine learning algorithm, one or more machine-learned results”; and [0110]; “One or more secondary training datasets can be generated, for example, to support unsupervised (e.g., unlabeled) training or supervised (e.g., labeled) training”).
Regarding claim 4, the rejection of claim 1 is incorporated and Petrosso discloses wherein the respective weight corresponds to a reward for updating the second machine-learned model ([0087]; “the machine learning model determines and outputs one or more most-weighted machine learning parameters that positively influenced a likelihood prediction, and may also identify one or more most-weighted machine learning parameters that negatively influenced a likelihood prediction”).
Regarding claim 5, the rejection of claims 1 and 3 are incorporated and Petrosso fails to explicitly disclose but Richardson discloses wherein the data indicative of one or more characteristics of the respective user session comprises at least one of: device data and input element data (Page 523, §4; “We collected information on a set of active ads in the Microsoft Web search engine. Each ad contains the following information: [Symbol font/0xB7] Landing page: The URL that a user is redirected to upon clicking the ad. [Symbol font/0xB7] Bid term (“keywords”): The query for which this ad should be displayed (this may be multiple words, e.g., “machine learning books”). [Symbol font/0xB7] Title: The ad title, shown to the user. [Symbol font/0xB7] Body: The text description of the ad. [Symbol font/0xB7] Display URL: The URL shown to the user at the bottom of the ad. [Symbol font/0xB7] Clicks: The number of times the ad has been clicked since it was entered into the system. [Symbol font/0xB7] Views: The number of times the ad has been seen since it was entered into the system, as described in section 3.”).
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Regarding claim 6, the rejection of claims 1 and 3 and 5 are incorporated and Petrosso fails to explicitly disclose but Richardson discloses wherein the device data comprises at least one of a browser type, a device identifier, or data indicative of an account associated with the user device (Page 523, §4; “We collected information on a set of active ads in the Microsoft Web search engine. Each ad contains the following information: [Symbol font/0xB7] Landing page: The URL that a user is redirected to upon clicking the ad. [Symbol font/0xB7] Bid term (“keywords”): The query for which this ad should be displayed (this may be multiple words, e.g., “machine learning books”). [Symbol font/0xB7] Title: The ad title, shown to the user. [Symbol font/0xB7] Body: The text description of the ad. [Symbol font/0xB7] Display URL: The URL shown to the user at the bottom of the ad. [Symbol font/0xB7] Clicks: The number of times the ad has been clicked since it was entered into the system. [Symbol font/0xB7] Views: The number of times the ad has been seen since it was entered into the system, as described in section 3.”).
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Regarding claim 7, the rejection of claims 1 and 3 and 5 are incorporated and Petrosso fails to explicitly disclose but Richardson discloses wherein the input element data comprises at least one of a form of the input element, a subject matter of the input element, one or more visual characteristics of the input element, or one or more audio characteristics of the input element (Page 523, §4; “We collected information on a set of active ads in the Microsoft Web search engine. Each ad contains the following information: [Symbol font/0xB7] Landing page: The URL that a user is redirected to upon clicking the ad. [Symbol font/0xB7] Bid term (“keywords”): The query for which this ad should be displayed (this may be multiple words, e.g., “machine learning books”). [Symbol font/0xB7] Title: The ad title, shown to the user. [Symbol font/0xB7] Body: The text description of the ad. [Symbol font/0xB7] Display URL: The URL shown to the user at the bottom of the ad. [Symbol font/0xB7] Clicks: The number of times the ad has been clicked since it was entered into the system. [Symbol font/0xB7] Views: The number of times the ad has been seen since it was entered into the system, as described in section 3.”)
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Regarding claim 8, the rejection of claims 1 and 2 are incorporated and Petrosso discloses wherein the one or more parameters comprise a value indicative of at least one of: one or more total effects, one or more impression query effects, one or more click query effects, one or more calibrated content effects, one or more impression content effects, or one or more click content effects ([0020]; “generating that the change in the profile increases a likelihood of interest in the CCG class of positions more than or equal to a threshold amount; and D) in response to the change increasing the likelihood of interest more than or equal to the threshold amount, adjusting the profile to facilitate communication with the particular candidate”).
Regarding claim 9, the rejection of claims 1 and 2 and 8 are incorporated and Petrosso fails to explicitly disclose but Richardson discloses wherein the plurality of user sessions are associated with at least one of a first user group, a second user group, and a third user group, wherein the first user group comprises one or more user sessions associated with no rendering of a user input element associated with a first content provider; wherein the second user group comprises one or more user sessions associated with the rendering of a user input element associated with the first content provider on a user interface and not obtaining data indicative of a user interacting with the user input element; and wherein the third user group comprises one or more user sessions associated with the rendering of a user interface element associated with the first content provider and obtaining data indicative of a user interacting with the user interface element (Page 522, §3; “As a simplification, we consider the probability that an ad is clicked on to be dependent on two factors: a) the probability that it is viewed, and b) the probability that it is clicked on, given that it is viewed … The relative probability of an ad being seen at different positions can be experimentally measured by presenting users with the same ad at various positions on the page. The CTR of the ad is simply the number of clicks divided by the total number of views.”).
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Regarding claim 10, the rejection of claims 1 and 2 and 8 and 9 are incorporated and Petrosso fails to explicitly disclose but Richardson discloses wherein the one or more parameters are indicative of the one or more impression query effects, corresponding to a difference in conversion probability associated with the second user group and the first user group (Page 522, §3; “As a simplification, we consider the probability that an ad is clicked on to be dependent on two factors: a) the probability that it is viewed, and b) the probability that it is clicked on, given that it is viewed … The relative probability of an ad being seen at different positions can be experimentally measured by presenting users with the same ad at various positions on the page. The CTR of the ad is simply the number of clicks divided by the total number of views.”).
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Regarding claim 11, the rejection of claims 1 and 2 and 8 and 9 are incorporated and Petrosso fails to explicitly disclose but Richardson discloses wherein the one or more parameters are indicative of the one or more click query effects, corresponding to a difference in conversion probability associated with the third user group and the second user group (Page 522, §3; “As a simplification, we consider the probability that an ad is clicked on to be dependent on two factors: a) the probability that it is viewed, and b) the probability that it is clicked on, given that it is viewed … The relative probability of an ad being seen at different positions can be experimentally measured by presenting users with the same ad at various positions on the page. The CTR of the ad is simply the number of clicks divided by the total number of views.”).
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Regarding claim 12, the rejection of claims 1 and 2 and 8 and 9 and 10 are incorporated and Petrosso fails to explicitly disclose but Richardson discloses wherein the one or more calibrated content effects are determined by taking the difference between a first total effect of the one or more total effects and a sum of a first impression query effect of the one or more impression query effects and a first click query effect of the one or more click query effects. (Page 522, §3; “As a simplification, we consider the probability that an ad is clicked on to be dependent on two factors: a) the probability that it is viewed, and b) the probability that it is clicked on, given that it is viewed … The relative probability of an ad being seen at different positions can be experimentally measured by presenting users with the same ad at various positions on the page. The CTR of the ad is simply the number of clicks divided by the total number of views.”).
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Regarding claim 13, the rejection of claims 1 and 2 and 8 are incorporated and Petrosso fails to explicitly disclose but Richardson discloses wherein a first total effect is equal to a sum of a first impression query effect of the one or more impression query effects, a first click query effect of the one or more click query effects, and a first calibrated content effect of the one or more calibrated content effects (Page 522, §3; “As a simplification, we consider the probability that an ad is clicked on to be dependent on two factors: a) the probability that it is viewed, and b) the probability that it is clicked on, given that it is viewed … The relative probability of an ad being seen at different positions can be experimentally measured by presenting users with the same ad at various positions on the page. The CTR of the ad is simply the number of clicks divided by the total number of views.”).
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Regarding claim 14, the rejection of claim 1 is incorporated and Petrosso discloses generating, based on the plurality of weights, a semi-supervised training dataset comprising session data descriptive of the plurality of user sessions; and training, using the semi-supervised training dataset, the second machine-learned model ([0022]; “1) teach, with a teaching set, at least one first machine learning algorithm to generate one or more machine predicted results; 2) analyze one or more weights based on the one or more machine predicted results and the teaching set; 3) generate at least one second machine learning algorithm based on the one or more weights; 4) generate, via the at least one second machine learning algorithm, one or more machine-learned results”; and [0110]; “One or more secondary training datasets can be generated, for example, to support unsupervised (e.g., unlabeled) training or supervised (e.g., labeled) training”).
Regarding claim 15, the rejection of claim 1 is incorporated and Petrosso fails to explicitly disclose but Richardson discloses determining the incremental probability of the request conditioned on rendering of the input element is below a threshold incremental probability; and updating, based on the incremental probability being below the threshold incremental probability, the second machine-learned model to avoid proposals for participation in the real-time content selection process for populating the user interface with the input element (Page 522-523, §3; the section discloses a click through probability that is used to select or rank ads and select candidates with high probabilities and avoid low-probability candidates).
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Regarding claim 16, the rejection of claim 1 is incorporated and Petrosso fails to explicitly disclose but Richardson discloses determining the incremental probability of the request conditioned on rendering of the input element is above a threshold incremental probability; and updating, based on the incremental probability being above the threshold incremental probability, the second machine-learned model to generate proposals for participation in the real-time content selection process for populating the user interface with the input element (Page 522-523, §3; the section discloses a click through probability that is used to select or rank ads and select candidates with high probabilities and avoid low-probability candidates).
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Regarding claim 17, the rejection of claim 1 is incorporated and Petrosso discloses transmitting, to a user computing device, data to cause the input element to be rendered on a user interface; obtaining data indicative of user interaction with the input element; and updating the second machine-learned model based on the data indicative of the user interaction with the input element ([0044]; and [0092]; and [0010]; and [0022]).
Regarding claim 18, the rejection of claim 1 is incorporated and Petrosso fails to explicitly disclose but Richardson discloses wherein the one or more characteristics of the respective user session comprises at least one of (i) a user identifier, (ii) a timestamp of an exposure, (ii) an exposure descriptor, (iv) a timestamp of a next chronological exposure, or (v) a count of users performing specified target actions that occurred in an interval defined by the time of the exposure and the next chronological exposure (Page 522-523, §3 and 4).
The motivation to combine Petrosso and Richardson is the same as discussed above with respect to claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ghajar et al. (US 20180330278 A1).
Agost et al. (US 20190080260 A1).
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/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127