DETAILED ACTION
This Office Action is in response to the arguments filed on 10/28/2025.
No claims currently amended or canceled.
Claims 1-20 are currently pending in this application and have been examined.
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 .
Allowable Subject Matter
Claims 4, 16, and 20 would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims, but are still rejected under 35 U.S.C. 101.
Response to Arguments
In reference to Applicant’s arguments on page(s) 6-10 regarding rejections made under 35 U.S.C. 103:
Claims 1, 2, 5-8, 13, 14 and 17-19 are rejected under 35 USC § 103 over an article by Gharibshah and Zhu entitled "User Response Prediction in Online Advertising" (referred to in the Office Action and herein as "Zhu") in view of US Pub. No. 2012/0059707 (Goenka) and in further view of an article by Lee et al. entitled Lee et al entitled "Estimating conversion rate in display advertising from past performance data" (Lee), claims 3 and 5 are rejected under 35 USC § 103 over Zhu, Goenka and Lee in further view of US Pub. No. 2018/0240015 (Martin), and claims 9-12 are rejected under 35 USC § 103 over Zhu, Goenka and Lee in further view of US Pub. No. 2013/019307 (Ahmed). Reconsideration and withdrawal of the rejections are respectfully requested for at least the following reasons.
According to the claimed subject matter, the claimed classifier is used as an initializer of the claimed user data to determine a set of user data clusters, where each cluster corresponds to a feature of user from the claimed cohort of users. In addition, according to the claimed subject matter, the claimed LR model is executed on the set of user data clusters determined based on the execution of the claimed classifier as an initializer of the claimed identified user data.
Zhu is devoid of any teaching, suggestion or disclosure of identifying user data that is an aggregation of data from interactions with a given content item by a cohort of users and that comprises information related to a label for each interaction, using a classifier as an initializer on the identified user data to determine a set of user data clusters, let alone executing a LR model on such a determined set of user data clusters. Rather, Zhu describes using its LR classifier to determine a user response, and using a LR classifier to determine a user response fails to disclose or suggest executing a classifier as an initializer on user data to determine a set of user data clusters, as is recited in each of the present claims.
The claimed subject matter not only recites determining the claimed set of user data clusters using the claimed classifier as an initializer, but further recites executing the claimed LR model on the claimed set of user data clusters to determine a predicted action by a user respective to the claimed content item. Zhu's is structurally and functionally different from the claimed subject matter, which executes the claimed LR on the claimed set of user data clusters.
Zhu's description of determining a user response based on its feature vector input is structurally and functionally different from the claimed predicted action determination, which is based on execution of the claimed LR model on the claimed set of user data clusters.
Indeed, Zhu is devoid of any disclosure of the claimed set of user data clusters,
determining the claimed set of user data clusters using the claimed classifier as an initializer on identified user data, and is further devoid of the claimed LR execution on the claimed set of user data clusters. At best, Zhu merely describes a LR classifier using feature vector input to determine a user response, which one of ordinary skill in the art would readily recognize is structurally and functionally different from the claimed subject matter, which has the claimed LR model being executed on the claimed set of user data clusters to determine the claimed predicted action.
Additionally, the Office Action concedes that Zhu fails to disclose: 1) the claimed determination of a set user data clusters, 2) the claimed LR model execution; 3) the claimed predicted action determination, and the claimed causing. Applicant agrees and submits that one of ordinary skill in the art would recognize that this further confirms that Zhu cannot and does not disclose the claimed execution of the claimed LR model on the claimed set of user data clusters and/or the claimed determination which determines the claimed predicted action based on the claimed LR model's execution on the claimed set of user data clusters.
Goenka has been reviewed and is not seen to remedy the deficiencies of Zhu noted herein and in the Office Action.
At 32 of Goenka, which is relied upon in the Office Action, Goenka merely describes receiving user data and generating user clusters. Goenka, like Zhu, is completely silent with respect to the claimed user data as an aggregation of data from interactions with a content item by a cohort of users and with comprises information related to a label of each interaction. Thus, like Zhu, Goenka cannot and does not disclose or suggest the claimed set of user data clusters determined based on the claimed execution of a classifier on the claimed user data.
Additionally, the Office Action concedes that Goenka, like Zhu, fails to disclose: 1) the claimed LR model execution; 2) the claimed predicted action determination, and 3) the claimed causing. Applicant agrees and submits that one of ordinary skill in the art would recognize that this further confirms that Zhu and Goenka cannot and does not disclose the claimed execution of the claimed LR model on the claimed set of user data clusters and/or the claimed determination which determines the claimed predicted action based on the claimed LR model's execution on the claimed set of user data clusters.
According to Lee, it explicitly clusters users based on a Euclidean distance similarity metric (i.e., Lee's explicit clustering) or implicitly clusters user based on cartesian products (i.e., Users X Publisher Type or Users X Publisher Type X Advertising Campaign). In other words, Lee's user clusters are formed based on a determined similarity metric or cartesian products. This is structurally and functionally different from the clamed subject matter which determines the claimed set of user data clusters based on execution of the claimed classifier which acts as an initializer of the claimed user data. Like Zhu and Goenka, Lee also fails to disclose the claimed classifier execution initializing the claimed user data and determining a set of user data clusters.
Furthermore, like Zhu and Goenka, Lee fails to disclose the claimed LR model execution and predicted action determination. Lee describes estimators estimating conversion rates at different levels of the cross-products of user, publisher and advertiser data hierarchies and then using logistic regression to combine the estimators' estimations at the different levels. See, § 1,6, § 3.2 and Figure 2 of Lee. In other words, at most, Lee's logistic regression is "executed" on conversion rate estimators to combine the conversion rate estimators' estimations. This is clearly structurally and functionally different from the claimed subject matter.
According to the claims, a logistic regression model is executed on the claimed set of user data clusters. Furthermore, the claimed predicted action by a user respective to the claimed content item is determined by executing the claimed logistic model on the claimed set of user data clusters. Lee's description of combining estimators' differing levels of estimations using logistic regression fails to disclose at least this claimed subject matter.
Therefore, Goenka and Lee not only fail to teach or suggest what is claimed, but also fail to cure the deficiencies in Zhu.
In view of at least the foregoing, the Applicant submits that Zhu, Goenka and Lee do not yield all of the elements in claim 1, and therefore Zhu, Goenka Lee cannot form the basis of a proper § 103 rejection. Furthermore and since Zhu, Goenka and Lee each fails to disclose each and every one of the elements of claim 1, none of the references can form the basis of a proper § 102 rejection, and no such rejection is raised by the Office Action. Similar arguments are also applicable with independent claims 13 and 18. Since each of the dependent claims recites all of the elements of its independent base claim, the arguments made herein are equally applicable to each dependent claim.
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that Zhu is “devoid of any teaching, suggestion or disclosure of identifying user data that is an aggregation of data from interactions with a given content item by a cohort of users and that comprises information related to a label for each interaction, using a classifier as an initializer on the identified user data to determine a set of user data clusters”. Examiner agrees. Prior art reference Zhu was not used to reject the limitation related to aggregating user data clusters from interactions with a given content item, prior art reference Goenka was used for the rejection of that limitation and proper motivation was provided to combine the use of the LR model execution of Zhu and the user data clusters of Goenka.
Applicant argues that Goenka does not teach the use of clustering user data based on labeled interactions. Examiner disagrees. Goenka, as cited, clusters user data after normalizing the data, and the clusters represent users with similar shopping or browsing histories. Moreover, Goenka does teach the use of users interacting with the content data in [0020]: “The data providers 106a and 106b are entities, such as a content publisher or data aggregator (e.g., BlueKai), that collects user data (i.e., information associated with the user's activities on the website, information inherently collected from a website, and/or user's interactions with the advertising)”. Goenka also teaches the use of user identifiers (i.e. a label of each user as they interact with the content) in [0022]: “As the data providers 106a and 106b collect a particular user's data, the data providers 106a and 106b associate the particular user's data to a unique user identification (i.e., a user ID), which is provided by data providers 106a and 106b and/or the data normalization system 102”. As the claims are currently written, Goenka reads on the limitations it was applied to.
Applicant argues that Lee’s teaching of clustering is not analogous to the claimed limitations since Lee using distance based clustering. Examiner disagrees. There is not explicit mention of how the clustering is performed in the claims aside from the mention that it is based on features of the user cohorts. The Applicant does not specify any particular clustering algorithm in their claims or specification. Accordingly, the Examiner interprets ‘clustering’ according to its broadest reasonable interpretation as encompassing the distance-based clustering described in the Lee reference. If Applicant has a different approach for clustering, it is recommended that the specific method of doing so is included in the claims.
Applicant argues that Lee does not provide any predictive action as a result of running the LR model on the cluster of user data. Examiner disagrees. Lee discloses using a logistic regression model to predict a conversion outcome for each ad impression (see p. 769, first full paragraph).
In light of the lack of amendments made on the claims, the rejections made under 35 U.S.C. 103 are maintained below.
In reference to Applicant’s arguments on page(s) 10-16 regarding rejections made under 35 U.S.C. 101:
Claims 1-20 are rejected under 35 U.S.C. § 101 for allegedly being non-statutory. In the Office Action, at pages 3-20, the Examiner alleges that claimed subject matter is directed to an abstract idea without significantly more. Applicant respectfully disagrees.
At Step 2A Prong One, the Examiner must determine whether the claim recites a judicial exception. The Examiner has concluded that the claims recite "a mental process" and "a mathematical calculation." However, this characterization improperly oversimplifies the claims and fails to account for their specific technical requirements.
As instructed in MPEP 2106, "examiners should be 'careful to avoid oversimplifying the claims' by looking at them generally and failing to account for the specific requirements of the claims." The recent August 4, 2025 USPTO Memorandum emphasizes this point, cautioning examiners "not to oversimplify claim limitations" when evaluating eligibility.
The claims are not directed to the abstract idea of performing a mathematical calculation or a mental process. Rather, the claims are directed to a specific technical solution to a specific technical problem: the technical challenge of enabling personalized recommendations using aggregated user data to maintain user data privacy. This is a technology-centric problem that did not exist before the advent of online content provisioning systems.
To the extent the Examiner relies on the "mental process" grouping of abstract ideas, this reliance is misplaced. According to MPEP 2106.04, a mental process is one that "can be performed in the human mind, or by a human using a pen and paper." The August Memorandum reminds examiners that "the mental process grouping is not without limits" and cautions examiners "not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind."
The claimed method cannot practically be performed in the human mind or with pen and paper. Specifically:
1. User data clustering and initialization using a classifier executing on identified user aggregation data to determine a set of user data clusters;
2. Predicted action determination using a logistic regression model on the set of user data clusters determined by the classifier;
3. -Digital content display at a client device that is based on information related to the determined predicted action and the content item.
These operations recite specific computer processing, machine learning data analysis, and display of digital content at a user computing device that fall well outside the scope of what can be performed mentally.
With respect to the "mathematical concepts" grouping of abstract ideas, the August Memorandum cautions examiners to be careful to distinguish claims that recite an exception from claims that merely involve an exception. Referring to Examples 39 and 47, the August Memorandum distinguishes between a limitation that merely involve or rely on mathematical concepts and a limitation that sets forth or describes a specific mathematical concept. The Examiner contends that the claimed use of a logistic regression model on the set of user data clusters "covers a mathematical calculation. Application respectfully disagrees and submits that while a logistic regression model may involve or rely on mathematical concepts, no such mathematical concepts are set forth or described in the claims. As such, the limitation does not recite any exception.
Even if certain aspects of the claims could be characterized as involving abstract concepts, the claims do not "recite" or "set forth" a judicial exception as required for Step 2A Prong One analysis. As explained in MPEP 2106.04, subsection II(A)(1), there is an important distinction between claims that "recite" (i.e., set forth or describe) a judicial exception and claims that merely "involve" (i.e., are based on) an exception. Only claims that recite an exception require further eligibility analysis.
The claims here describe a specific technological system implementation involving determination of a predicted action of a user respective to a content item using a set of user data clusters determined using aggregated user data. The claims do not set forth or describe the abstract idea of mental process or mathematical concept.
The claims provide a specific improvement to the functioning of computer systems and to the technical field of digital content provisioning. According to MPEP 2106.04(d)(1) and MPEP 2106.05(a), a claim that improves computer capabilities or improves an existing technology integrates a judicial exception into a practical application.
The August Memorandum explains that "an important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome." The August Memorandum further instructs that when evaluating the improvements consideration, examiners should consider "whether the claim purports to improve computer capabilities or to improve an existing technology."
Here, the claims improve technology in several specific ways. First, the claims provide a technical solution for determining or predicting a user's action respective to a content item without relying on individual user data. As described at 0005-0006 of the present application's publication, U.S. Pub. No. 2022/0414493, conventional approaches rely on and are designed to exploit the ability to track user activity to predict user action respective to content; however, recent privacy guidelines and regulations impose prohibitions on the use of user tracking data. Determining a predicted action by an individual user without using tracking data for the user is an open problem that has not been adequately addressed by conventional content hosting and provisioning systems. The claims enable prediction of a user's action respective to a content item without relying on the individual user's data.
Second, the claims aggregate data at a "cohort" level. As described at 0007 of the present application's publication, rather than tracking whether or not a specific user clicked on a link, the present application provides a framework enabling the aggregation of such behavior by a plurality of users and the leveraging of such aggregated user data in determining a user's next action and/or recommending content for the user. As described at 0011 of the present application's publication, a novel computerized method and framework enables user action prediction and content provisioning/recommendation using logistic regression to leverage an aggregation of user data. The claims use a classifier as an initializer on an aggregation of data from interactions with a content item by a cohort to determine a set of user data clusters, each cluster corresponding to a feature of a user from the cohort of users. A logistic regression model is executed on the set of user data clusters to determine a predicted action by a user respective to the content item.
Third, the claims enable the provisioning of digital content using action prediction information respective to the content item imputed to the user using aggregations of cohort user data. As described at 0073 of the present application's publication, the novel computerized method and framework enables action predictions from aggregated user data, without compromising user data privacy, at a same or better accuracy as predictions made using individual user data and at an efficiency scale realizable for real-time web-based interactions.
The claims cause digital content to be displayed at a user's client device based on the predicted action determined by the logistic regression model using the set of user data clusters.
These improvements are analogous to those found eligible in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), where the Federal Circuit held that claims directed to a self-referential table for a computer database were eligible because they were "directed to a specific improvement to the way computers operate." Similarly, in McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016), the court found claims eligible where they provided a specific technological solution (automatic lip synchronization and facial expression animation) rather than merely claiming the idea of a solution.
The claims do not simply recite "apply it on a computer." They recite specific technological components working together in a specific way to achieve a specific technical result. This is sufficient to integrate any judicial exception into a practical application.
Furthermore, even if individual components might be known, "an inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces."
Even if the Examiner were to find that the claims recite an abstract idea not integrated into a practical application (which Applicant respectfully submits is incorrect and strongly disagrees), the claims provide an inventive concept under Step 2B that amounts to significantly more than any judicial exception.
The combination of elements- classifying and initializing aggregated user data into a set of user data clusters, which are then used by a logistic regression model to determine a predicted action by a specific user-represents a non-conventional arrangement of components that addresses the specific technical challenge of enabling prediction of an individual user's action respective to a digital content item using an aggregation of data and without violating the user's user data privacy. This ordered combination provides significantly more than any abstract idea.
Finally, the recent convening of an Appeals Review Panel (ARP) in Ex parte Desjardins (Appeal 2024-000567), is directly relevant to the instant case and supports a finding of eligibility. The ARP was convened specifically to review the Board's rejection of claims under 35 U.S.C. § 101, indicating heightened scrutiny of §101 rejections in technology-related cases. Like Desjardins, the instant case involves claims to improvements in computer system functionality. The institutional concern reflected in the Desjardins ARP proceedings counsels strongly in favor of reconsidering any § 101 rejection that does not fully account for the specific technical improvements and particular solutions recited in the claims. Given the parallels to the technology at issue in Desjardins, the preponderance of evidence favors a finding of eligibility here.
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that the claimed limitations cannot be performed in the human mind, specifically the limitations of user data clustering and initialization using a classifier executing on identified user aggregation data to determine a set of user data clusters, predicted action determination using a logistic regression model on the set of user data clusters determined by the classifier, and digital content display at a client device that is based on information related to the determined predicted action and the content item. Examiner disagrees. Firstly, the limitation of clustering and classifying data can reasonably be performed in the human mind. Clustering data is a mathematical process performed using various linear algebra techniques. Classifying data based on criteria is also something that can be reasonably performed in the human mind. Secondly, the limitation regarding predicting a user action reads: “determining, by the device, based on the execution of the LR model, a predicted action by a user respective to the content item”. At no point in this limitation is the prediction done using the linear regression model, but instead it is performed using the executed LR model, meaning that the prediction is based on the results of the LR model. Predicting an action based on calculated data, in this case the results of the LR model, can be seen as a mental process, specifically an observation. Thirdly, the limitation relating to displaying digital content was not flagged as being a mental process, but instead was flagged as an additional element.
Applicant argues that the instant application provides an improvement over the state of the art for a number of reasons. Examiner disagrees. Applicant argues that the technological improvement lies in the idea that specific user data isn’t being used or collected in order to predict user actions regarding a content item. Examiner disagrees; the inventive concept of the instant application is simply making a prediction with a higher level of abstracted data. Predicting a specific user’s action based on the aggregated cohort’s data is just applying a general trend to a user of a system. Without that user’s specific data being collected and processed this process is akin to any traditional machine learning prediction model. If there is an improvement to how the model is trained using the cohort level data, Examiner would recommend including language about that training in the claims.
Applicant argues that the instant application is similar to that of Enfish, LLC v. Microsoft Corp. and McRO, Inc. v. Bandai Namco Games Am. Inc. Examiner disagrees. Enfish, LLC v. Microsoft Corp. was found to be patent eligible because it introduced an improvement to database tables, that being the self-referential tables offered faster searching through the table to find specific data and more efficient storage of unstructured data in a database table. McRO, Inc. v. Bandai Namco Games Am. Inc. is related to the automation of 3D modeling specifically related to lip-synchronization. McRO, Inc. v. Bandai Namco Games Am. Inc. was found to not be directed to an abstract idea since none of the claim limitations can be practically performed in the human mind. This is not the case with the instant application as many of the claims state actions of making determinations, identifying data, and performing mathematical calculations.
Applicant argues that the claims do not recite generic computer components but instead recite specific technological components working together in a specific way. Examiner disagrees. Independent claims 1, 13, and 18 all recite some form of “a computing device” with independent claims 1 and 13 reciting that each limitation is performed “by the device”. Independent Claim 13 provides little insight into the makeup of the device other than “A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions”, which is simply a computer with a storage component and a processor. Without specific information about why the device is novel, this is seen as generic to one skilled in the art.
Applicant argues that the arrangement of components in the instant application is unconventional. Examiner disagrees. The instant application is conventional in that it gathers data, organizes/clusters the data, processes the data via logistic regression, and then outputs the results of that processing do a user device. There is nothing unconventional about organizing the components in this manner.
Applicant argues that the instant application is similar to that of Ex parte Desjardins. Examiner disagrees. The driving factor that decides Ex parte Desjardins was that the invention in that case was concerned with a machine learning model that continuously learned new data without forgetting any previously learned data. This is not the case for the instant application as it has nothing to do with explicitly keeping previously learned data.
In light of the lack of amendments made on the claims, the rejections made under 35 U.S.C. 101 are maintained below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1:
Independent claims 1 and 13 recite a method therefore falling into the statutory category of process. Independent claim 18 recites a computing device, therefore falling into the statutory category of product.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“identifying user data related to a content item, the user data being an aggregation of data from interactions with the content item by a cohort of users, the user data comprising information related to a label for each interaction”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses identifying user data related to content.
“executing a classifier as an initializer on the identified user data”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses classifying data.
“determining based on the execution of the initializer, a set of user data clusters, each cluster corresponding to a feature of a user from the cohort”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses determining clusters of data based on an initial classifier.
“executing a logistic regression (LR) model on the set of user data clusters determined based on the execution of the classifier as an initializer”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation.
“and determining, by the device, based on the execution of the LR model, a predicted action by a user respective to the content item”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses predicting a user’s action based on a calculated result.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“by a device”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (a computing device) (See MPEP 2106.05(f)).
“causing, by the device, digital content to be displayed at a client device of the user based on the information related to the predicted action and the content item”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. post-solution activity of outputting/displaying data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “by a device” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
The additional element(s) of “causing, by the device, digital content to be displayed at a client device of the user based on the information related to the predicted action and the content item” is/are recited at a high level of generality and amount(s) to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying/outputting a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“communicating, over a network, information related to the predicted action to a provider of the content item”. This additional elements is recited at a high level of generality and amounts to extra- solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “communicating, over a network, information related to the predicted action to a provider of the content item” is recited at a high level of generality and amounts to extra- solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 2A: Prong 1 analysis:
Claim 3 recites in part:
“wherein the classifier operates at least one of a one-sided entropy objective, a Shannon entropy objective and a Naive Bayes (NB) entropy objective”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 4:
Step 2A: Prong 1 analysis:
Claim 4 recites in part:
“operating the classifier using a Naive Bayes (NB) entropy objective, the execution of the NB entropy objective causing the user data to be scaled by an order of magnitude, wherein the LR model is applied to the scaled user data”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 5:
Step 2A: Prong 1 analysis:
Claim 5 recites in part:
“determining, based on the execution of the initializer, a cluster of cohort data, wherein the execution of the LR model is based on the cluster of cohort data”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses determining clusters of data based on an initial classifier.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 6:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein each cluster corresponds to a feature of an interaction”. This additional element is directed to a particular field of use (feature engineering).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “wherein each cluster corresponds to a feature of an interaction” is directed to a particular field of use (feature engineering) (MPEP 2106.05(h)) and therefore does not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the user data is formatted as a feature vector”. This additional element is directed to a particular field of use (feature engineering).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “wherein the user data is formatted as a feature vector” is directed to a particular field of use (feature engineering) (MPEP 2106.05(h)) and therefore does not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 8:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the user data comprises a set of pairs of feature vectors and labels”. This additional element is directed to a particular field of use (feature engineering).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “wherein the user data comprises a set of pairs of feature vectors and labels” is directed to a particular field of use (feature engineering) (MPEP 2106.05(h)) and therefore does not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“receiving a request for information related to the content item, wherein the identification of the user data is based on the reception of the request”. This additional elements is recited at a high level of generality and amounts to extra- solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “receiving a request for information related to the content item, wherein the identification of the user data is based on the reception of the request” is recited at a high level of generality and amounts to extra- solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 10:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the request corresponds to a determination of analytics of a performance of the content item”. This additional element is directed to a particular field of use (performance analytics).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “wherein the request corresponds to a determination of analytics of a performance of the content item” is directed to a particular field of use (performance analytics) (MPEP 2106.05(h)) and therefore does not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 11:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the request corresponds to a content recommendation for the user”. This additional element is directed to a particular field of use (content recommendation).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “wherein the request corresponds to a content recommendation for the user” is directed to a particular field of use (content recommendation) (MPEP 2106.05(h)) and therefore does not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 12:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“requesting, over the network, third party digital content based information related to the predicted action and the content item”. This additional elements is recited at a high level of generality and amounts to extra- solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process.
“as the digital content to be displayed at the client device”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. post-solution activity of outputting/displaying data for use in the claimed process.
“receiving, over the network, the third party digital content”. This additional elements is recited at a high level of generality and amounts to extra- solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process.
“communicating, over the network, the third party digital content to the user”. This additional elements is recited at a high level of generality and amounts to extra- solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional elements of “requesting, over the network, third party digital content based information related to the predicted action and the content item”, “receiving, over the network, the third party digital content”, and “communicating, over the network, the third party digital content to the user” are recited at a high level of generality and amount to extra- solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The additional element(s) of “as the digital content to be displayed at the client device” is/are recited at a high level of generality and amount(s) to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying/outputting a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 13:
Due to claim language similar to that of Claim 1, Claim 13 is rejected for the same reasons as presented in the rejection of Claim 1, with the exception of the limitation(s) covered below.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor associated with a device, performs a method comprising”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (storage and processor) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor associated with a device, performs a method comprising” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 14:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“communicating, over a network, information related to the predicted action to a provider of the content item”. This additional elements is recited at a high level of generality and amounts to extra- solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “communicating, over a network, information related to the predicted action to a provider of the content item” is recited at a high level of generality and amounts to extra- solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 15:
Due to claim language similar to that of Claim 3, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 16:
Due to claim language similar to that of Claim 4, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 17
Due to claim language similar to that of Claim 5, Claim 17 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 18:
Due to claim language similar to that of claims 1 and 13, Claim 18 is rejected for the same reasons as presented in the rejection of claims 1 and 13.
Regarding Claim 19:
Due to claim language similar to that of Claim 14, Claim 19 is rejected for the same reasons as presented above in the rejection of Claim 14.
Regarding Claim 20:
Due to claim language similar to that of Claims 4 and 16, Claim 20 is rejected for the same reasons as presented above in the rejection of Claims 4 and 16.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 2, 5-8, 13, 14, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu and Gharibshah (Gharibshah, Zhabiz, and Xingquan Zhu. “User Response Prediction in Online Advertising.” ACM Computing Surveys, vol. 54, no. 3, May 2021, pp. 1–43. Crossref, https://doi.org/10.1145/3446662., hereinafter Zhu) in view of Goenka et al (US 20120059707 A1, hereinafter Goenka), and in further view of Lee et al (Kuang-chih Lee, Burkay Orten, Ali Dasdan, and Wentong Li. 2012. Estimating conversion rate in display advertising from past performance data. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12). Association for Computing Machinery, New York, NY, USA, 768–776. https://doi.org/10.1145/2339530.2339651, hereinafter Lee)
Regarding Claim 1:
Zhu teaches
A method comprising: identifying, by a device, user data related to a content item, the user data being an aggregation of data from interactions with the content item by a cohort of users, the user data comprising information related to a label for each interaction (Zhu [Section 2.1, figure 1]: “After getting relevant user information, such as user profile and their previous interaction, through DMPs”; (EN): Zhu defines a DMP as: “A software platform in online advertising designed to collect and analyze data for both advertisers and publishers. DMPs provide services to DSPs or SSPs to improve ad campaign efficiency”, in the Appendix);
executing, by the device, a classifier as an initializer on the identified user data (Zhu [Section 3, figure 2]: “The output can be considered as two types of user responses a) a scalar value of predicted score for an interaction between given user 𝑢𝑖 and item 𝐼𝑗 b) a ranked list of regular and promoted items ordered by predicted user response scores”; (EN): figure 2 illustrates that the output a) of the predicted score is the result of a classifier being run on the data);
Zhu does not distinctly disclose
determining, by the device, based on the execution of the initializer, a set of user data clusters, each cluster corresponding to a feature of a user from the cohort;
However, Goenka teaches
determining, by the device, based on the execution of the initializer, a set of user data clusters, each cluster corresponding to a feature of a user from the cohort (Goenka [0032]: “The clustering engine 202 receives the transformed user data and/or user lists generated by the data normalization engine 202 and generates user clusters and/or data clusters. The user clusters can indicate similarities between users. For example, a user cluster can represent users who share similar shopping or browsing histories”);
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the user response prediction methods of Zhu with the user data clustering method and apparatus of Goenka in order to provide a method for clustering data associated with users of a system or service for use in advertising or other user driven applications. The system presented in Goenka is beneficial for Zhu in that it makes identifying user clusters across data provided by different providers much simpler (Goenka [0017]: “Advantageously, the described system may provide for one or more benefits, such as identifying user clusters across user data provided by two different data providers 106 and making the user clusters easily traded with the data purchaser”).
Zhu + Goenka does not distinctly disclose
executing, by the device, a logistic regression (LR) model on the set of user data clusters determined based on the execution of the classifier as an initializer;
and determining, by the device, based on the execution of the LR model, a predicted action by a user respective to the content item
causing, by the device, digital content to be displayed at a client device of the user based on the information related to the predicted action and the content item.
However, Lee teaches
executing, by the device, a logistic regression (LR) model on the set of user data clusters determined based on the execution of the classifier as an initializer (Lee [Section 1, p. 6]: “Our conversion rate estimation method models the conversion event at different select levels of the cross-product of user, publisher and advertiser data hierarchies with separate binomial distributions and estimate the distribution parameters individually. These individual estimators are than combined using logistic regression.”; (EN): it can be seen in Fig 2 that the users are clustered into groups);
and determining, by the device, based on the execution of the LR model, a predicted action by a user respective to the content item (Lee [Section 1, p. ]: “In § 3, we describe the CVR estimators using the past performance observations across data hierarchies and also describe the use of logistic regression as a means of combining these individual rate estimates for improved predictive performance.”)
causing, by the device, digital content to be displayed at a client device of the user based on the information related to the predicted action and the content item (Lee [Section 1, p. 1]: “The advertiser’s main goal is to reach the most receptive online audience in the right context, which will then engage with their displayed ad and eventually take a desired action, identified by the type of the campaign, e.g., brand advertising or direct product marketing”; [Figure 1]: Figure 1 shows the ad flow between the decision engine that takes in all the relevant data, and the publisher web page, where the ads will eventually be displayed to users).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the user response prediction methods of Zhu + Goenka with the method of estimating conversion rate of display advertising of Lee in order to provide a method for clustering data associated with users of a system or service for use in advertising or other user driven applications. The system presented in Lee is beneficial for Zhu + Goenka in that it makes identifying user purchase intent across different clusters and content categories easier and allows for advertisers to present information to users so they can make a more informed decision about their potential purchase (Lee [Abstract]: “Finding the best ad impression, i.e., the opportunity to show an ad to a user, requires the ability to estimate the probability that the user who sees the ad on his or her browser will take an action, i.e., the user will convert. However, conversion probability estimation is a challenging task since there is extreme data sparsity across different data dimensions and the conversion event occurs rarely. In this paper, we present our approach to conversion rate estimation which relies on utilizing past performance observations along user, publisher and advertiser data hierarchies.”)
Regarding Claim 2:
Zhu does not distinctly disclose
The method of claim 1, further comprising: communicating, over a network, information related to the predicted action to a provider of the content item.
However, Goenka teaches
The method of claim 1, further comprising: communicating, over a network, information related to the predicted action to a provider of the content item (Goenka [0016]: “The data purchaser 108 interacts with the advertisement network 110 and the ad metric engine 112 and applies the user and data clusters to, for example, improve the effectiveness of its online advertising campaign”; [0019]: “The network 104 connects users, the data exchange system 102, the data providers 106a and 106b, the data purchaser 108, the advertisement network 110 and the ad metric engine”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the user response prediction methods of Zhu with the user data clustering method and apparatus of Goenka in order to provide a method for clustering data associated with users of a system or service for use in advertising or other user driven applications. The system presented in Goenka is beneficial for Zhu in that it makes identifying user clusters across data provided by different providers much simpler (Goenka [0017]: “Advantageously, the described system may provide for one or more benefits, such as identifying user clusters across user data provided by two different data providers 106 and making the user clusters easily traded with the data purchaser”).
Regarding Claim 5:
Zhu teaches
The method of claim 1, further comprising: determining, based on the execution of the initializer, a cluster of cohort data, wherein the execution of the LR model is based on the cluster of cohort data (Zhu [Section 4.1.2, p. 6; Eqn. 5]: “The conversion estimation is calculated at different levels of the hierarchy made from the cross product of levels in three hierarchical structures of users, publishers and advertisers via the maximum likelihood estimation as follows. Eqn. (5). where 𝐶𝑢𝑖 is the cluster that 𝑢𝑖 belongs to. 𝐶𝑝𝑗 and 𝐶𝑎𝑘 indicate the cluster of web-page 𝑝𝑗 and ad 𝑎𝑘 , respectively. The final estimation of the conversion rate value is then modelled using logistic regression from the linear combination of MLE estimators at different hierarchical levels.”; (EN): the logistic regression is run on the combination of MLE estimators, which utilize the clusters of user data.”).
Regarding Claim 6:
Zhu teaches
The method of claim 1, wherein each cluster corresponds to a feature of an interaction (Zhu [Section 3.2.1, p. 2]: “In [190] user behavior features are represented as a list of visiting events of ads, each of which are described by categorical features about goods, shop, and page categories of past user-defined time points. Each time point is described using multi-hot-encoding”; [Section 4.4.1, p. 1]: “Clustering methods have also been investigated in the literature for online advertising. As an unsupervised approach, clustering involves grouping sample data into related clusters based on similarity among data points”).
Regarding Claim 7:
Zhu teaches
The method of claim 1, wherein the user data is formatted as a feature vector (Zhu [Section 3, p. 2]: “Like typical machine learning problems, the input data should be described through feature vectors to capture the class correlation, meaning that features need to be discriminative for the prediction task. Therefore, during the second (learning) phase, features are extracted using different approaches, such as (1) using data fields to represent users, pages, etc. and create sparse features; or (2) using embedding based approaches to create dense features”).
Regarding Claim 8:
Zhu teaches
The method of claim 7, wherein the user data comprises a set of pairs of feature vectors and labels. (Zhu [Section 3, p. 2]: “After the pre-processing and labeling steps, data samples are described with series of features (fields) along with label (class) values which are normally specified as binary user response value such as 1 for click, conversion, purchasing, etc. and 0 otherwise”).
Regarding Claim 13:
Due to claim language similar to that of Claim 1, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of limitation(s) covered below.
Zhu does not distinctly disclose
A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor associated with a device, performs a method
However, Goenka teaches
A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by a processor associated with a device, performs a method (Goenka [0061]: “The system 500 includes a processor 510, a memory 520, a storage device 530, and an input/output device 540”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the user response prediction methods of Zhu with the user data clustering method and apparatus of Goenka in order to provide a method for clustering data associated with users of a system or service for use in advertising or other user driven applications. The system presented in Goenka is beneficial for Zhu in that it makes identifying user clusters across data provided by different providers much simpler (Goenka [0017]: “Advantageously, the described system may provide for one or more benefits, such as identifying user clusters across user data provided by two different data providers 106 and making the user clusters easily traded with the data purchaser”).
Regarding Claim 14:
Due to claim language similar to that of Claim 2, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 17:
Due to claim language similar to that of Claim 5, Claim 17 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 18:
Due to claim language similar to that of claims 1 and 13, Claim 18 is rejected for the same reasons as presented above in the rejection of claims 1 and 13, with the exception of limitation(s) covered below.
Zhu does not distinctly disclose
A computing device comprising: a processor configured to
However, Goenka teaches
A computing device comprising: a processor configured to (Goenka [0061]: “The system 500 includes a processor 510, a memory 520, a storage device 530, and an input/output device 540”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the user response prediction methods of Zhu with the user data clustering method and apparatus of Goenka in order to provide a method for clustering data associated with users of a system or service for use in advertising or other user driven applications. The system presented in Goenka is beneficial for Zhu in that it makes identifying user clusters across data provided by different providers much simpler (Goenka [0017]: “Advantageously, the described system may provide for one or more benefits, such as identifying user clusters across user data provided by two different data providers 106 and making the user clusters easily traded with the data purchaser”).
Regarding Claim 19:
Due to claim language similar to that of Claims 2 and 14, Claim 19 is rejected for the same reasons as presented above in the rejections of Claims 2 and 14.
Claim(s) 3 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu, Goenka, and Lee as applied to claims 1 and 13 above, and further in view of Martin et al (US 20180240015 A1, hereinafter Martin).
Regarding Claim 3:
Zhu + Goenka + Lee does not distinctly disclose
The method of claim 1, wherein the classifier operates at least one of a one-sided entropy objective, a Shannon entropy objective and a Naive Bayes (NB) entropy objective.
However, Martin teaches
The method of claim 1, wherein the classifier operates at least one of a one-sided entropy objective, a Shannon entropy objective and a Naive Bayes (NB) entropy objective (Martin [0115]: “FIG. 18 is a schematic diagram illustrating an exemplary Entropy Filter and Decision Process associated with the STM. The STM module 1600 uses a modified version of the Shannon Entropy Equations (Shannon, C. E. (2001)”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the user response prediction methods of Zhu + Goenka + Lee with the memory model for storing, retrieving, and recalling of aggregate data of Martin in order to provide a method for personalized learning and serves to track students achievements and goals. The benefit of Martin is that the system uses aggregated data and a specialized machine learning model based on an entropy function to parse data (Martin [0018]: “DALI's DNLN AI models parses these immense datasets utilizing artificial cognitive memory models that includes Working Memory (buffer) and a Short-Term Memory (STM) model that includes a unique machine learning (ML) trained entropy function to decipher, identify, tag, index, and store subject (academic) and non-subject communication and social data.”).
Regarding Claim 15:
Due to claim language similar to that of Claim 3, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 3.
Claim(s) 9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu, Goenka, and Lee as applied to claim 1 above, and further in view of Ahmed (WO 2013019307 A1, hereinafter Ahmed).
Regarding Claim 9:
Zhu + Goenka + Lee does not distinctly disclose
The method of claim 1, further comprising: receiving a request for information related to the content item, wherein the identification of the user data is based on the reception of the request.
However, Ahmed teaches
The method of claim 1, further comprising: receiving a request for information related to the content item, wherein the identification of the user data is based on the reception of the request (Ahmed [Page 7, lines 4-9]: “the recommendation engine 14 also receives user content data. The user content data includes (1) content searches made by a user; (2) content requests made by the user; (3) Electronic Program Guide (EPG) requests made by the user; (4) content purchases/rentals made by the user, (5) when a user consumes auxiliary content when supplementing other content. Such information enables the recommendation engine to recommend content based on the user's content preferences, an attribute of the user”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the user response prediction methods of Zhu + Goenka + Lee with the content recommendation method(s) of Ahmed in order to provide a method of content recommendation that takes into account various facets of a given user and the different devices they have access to throughout the day. The benefit of combining Zhu + Goenka + Lee with Ahmed is to consider the different devices that a user could be consuming content on and make recommendations on those devices (Ahmed [Page 2, lines 5-8]: “If the content provider making the content recommendation assumes the same device in both instances, the content provider will miss the opportunity to recommend a wider variety of content, and thus miss the opportunity to gain greater revenue from the consumer”).
Regarding Claim 10:
Zhu + Goenka + Lee do not distinctly disclose
The method of claim 9, wherein the request corresponds to a determination of analytics of a performance of the content item.
However, Ahmed teaches
The method of claim 9, wherein the request corresponds to a determination of analytics of a performance of the content item (Ahmed [Page 7, lines 4-9]: “the recommendation engine 14 also receives user content data. The user content data includes (1) content searches made by a user; (2) content requests made by the user; (3) Electronic Program Guide (EPG) requests made by the user; (4) content purchases/rentals made by the user, (5) when a user consumes auxiliary content when supplementing other content. Such information enables the recommendation engine to recommend content based on the user's content preferences, an attribute of the user”).
The obviousness analysis of claim 9 applies equally here.
Regarding Claim 11:
Zhu + Goenka + Lee does not distinctly disclose
The method of claim 9, wherein the request corresponds to a content recommendation for the user.
However, Ahmed teaches
The method of claim 9, wherein the request corresponds to a content recommendation for the user (Ahmed [Page 7, lines 4-9]: “the recommendation engine 14 also receives user content data. The user content data includes (1) content searches made by a user; (2) content requests made by the user; (3) Electronic Program Guide (EPG) requests made by the user; (4) content purchases/rentals made by the user, (5) when a user consumes auxiliary content when supplementing other content. Such information enables the recommendation engine to recommend content based on the user's content preferences, an attribute of the user”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the user response prediction methods of Zhu + Goenka + Lee with the content recommendation method(s) of Ahmed in order to provide a method of content recommendation that takes into account various facets of a given user and the different devices they have access to throughout the day. The benefit of combining Zhu + Goenka + Lee with Ahmed is to consider the different devices that a user could be consuming content on and make recommendations on those devices (Ahmed [Page 2, lines 5-8]: “If the content provider making the content recommendation assumes the same device in both instances, the content provider will miss the opportunity to recommend a wider variety of content, and thus miss the opportunity to gain greater revenue from the consumer”).
Regarding Claim 12:
Zhu + Goenka + Lee do not distinctly disclose
The method of claim 11, further comprising: requesting, over the network, third party digital content based information related to the predicted action and the content item;
However, Ahmed teaches
The method of claim 11, further comprising: requesting, over the network, third party digital content based information related to the predicted action and the content item (Ahmed [Page 8, lines 7-12]: “The content recommendation provided by the recommendation engine 14 to the user 12 ultimately takes account of: (a) the user's profile (including the user's content profile, the user's device profile and the user's location profile) and (b) the content available for the devices current accessible to the user at the user's current location”);
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the user response prediction methods of Zhu and Goenka with the content recommendation method(s) of Ahmed in order to provide a method of content recommendation that takes into account various facets of a given user and the different devices they have access to throughout the day. The benefit of combining Zhu and Goenka with Ahmed is to consider the different devices that a user could be consuming content on and make recommendations on those devices (Ahmed [Page 2, lines 5-8]: “If the content provider making the content recommendation assumes the same device in both instances, the content provider will miss the opportunity to recommend a wider variety of content, and thus miss the opportunity to gain greater revenue from the consumer”).
Ahmed further teaches
receiving, over the network, the third party digital content (Ahmed [Page 8, lines 7-12]: “The content recommendation provided by the recommendation engine 14 to the user 12 ultimately takes account of: (a) the user's profile (including the user's content profile, the user's device profile and the user's location profile) and (b) the content available for the devices current accessible to the user at the user's current location”);
And communicating, over the network, the third party digital content to the user (Ahmed [Page 3, lines 15-17]: “In the illustrated embodiment of FIG. 1 , the user 12 can use one or more devices 18i- 18.sub.4 to consume content supplied though a network 20 by one or more content providers, illustratively depicted by content providers 22 and 24”).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/VINCENT GONZALES/Primary Examiner, Art Unit 2124