Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
2. Applicant's arguments filed 01/02/2026 have been fully considered but they are not persuasive. Applicant argues that the newly added limitations are not taught by Khavronin and Keng. The Examiner respectfully disagrees. Khavronin discloses “a plurality of one or more behavioral parameters associated with the classification mapping, the plurality of behavioral parameters being configured to match the plurality of communications network users to the one or more behavioral segments based on web browsing activity, the plurality of behavioral parameters comprising a probability percentage threshold that a page is relevant to a classification, a frequency count threshold with which a type of the classification is visited, and a recency time period threshold for when a visit occurred… as belonging to the one or more behavioral segments by comparing the probability score associated with each page against the probability percentage threshold included in the plurality of behavioral parameters , comparing the tracked frequency count value against the frequency count threshold included in the plurality of behavioral parameters” Specifically, Khavronin discloses monitoring webpages and applications users interact with, generating behavioral features and using those features to classify and segment users and then storing and retrieving behavioral segments and serving content based on these classifications (see ¶[0209]-[0212] and ¶[0313]). Khavronin further discloses a assigning relevance values and probabilities to content items and determining classification based on comparison against a threshold value. This means that Khavronin determines whether a page is relevant to a classification based on a relevance value compared against a threshold, therefore teaching a probability percentage threshold that a page is relevant to a classification (see ¶[0196] and ¶[0214]). Khavronin also discloses tracking a count of visits or interactions associated with a classification and determining significance based on that count. Khavronin and Keng do not disclose newly added “and comparing the recency time period value against the recency time period threshold included in the plurality of behavioral parameters.” However, this limitation is taught by newly cited Dille (US 2022/0131769).
Therefore, in view of the arguments above, the rejection of claims 1-18 is maintained.
Claim Rejections - 35 USC § 103
3. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
4. Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Khavronin (US 2022/0279220) in view of Keng (US 2015/0127653) and further in view of Dille (US 2022/0131769).
Regarding Claim 1:
Khavronin discloses a user classification system comprising:
a communications network (Khavronin: Fig. 2 and ¶[0062] depict/explain a communication interface in which the user may be connected to the content consumption managers via computer network connection);
a Front-End URL Handler (FEUH) establishing an entry point for content calls from a network user (Khavronin: Fig. 5 the user computer 530 has input which is handled and sent to the content consumption manager (CCM) which contains the machine learning model that classifies as disclosed in ¶[0023-0024]);
a Fast Retrieval (FR) store storing a set of behavioral segments (Khavronin: Fig. 12 step 1202, ¶[0053] stores historic behavior results);
and a classification engine comprising one or more processors and a memory storing instructions which, when executed by at least one processor in the one or more processors, cause the at least one processor to perform operations comprising (Khavronin: ¶[0062-0068] the CCM contains at least one processor):
identifying one or more behavioral segments used to characterize a plurality of communications network users, the one or more behavioral segments being configured via user inputs (Khavronin: ¶[0209]-[0212] discloses user interactions categorized as behavioral features and ¶[0313] discloses session events, consumption scores, unique users. These features are used to characterize users and these behavioral segments are explicitly stored, see Fig. 12 step 1202 showing historic behavior), a configuration of the one or more behavioral segments comprises:
the plurality of behavioral parameters being configured to match the plurality of communications network users to the one or more behavioral segments based on web browsing activity (Khavronin: ¶[0209]-[0212] teaches matching communication network users to behavioral segments based on their web browsing activity),
the plurality of behavioral parameters comprising a probability percentage threshold that a page is relevant to a classification (Khavronin: ¶[0196] and ¶[0214] teach classifying web pages, assigning relevancy scores and selecting/rejecting pages based on these scores, these relevancy scores are used to determine inclusion and are a probability threshold),
a frequency count threshold with which a type of the classification is visited (Khavronin: ¶[0313] discloses consumption score based on number of session events, this is tracking a count and it is recognized over a plurality of time periods, i.e., a frequency count threshold),
accessing a plurality of pages viewed by a communications network user (Khavronin: ¶[0313] the CCM obtains data packets which include webpages and applications which come from user data);
determining, using machine learning, topic tags for each page in the plurality of pages, the topic tags including a topical classification of each page that is determined based on at least one of one or more keywords, one or more common terms, and one or more important terms included in the each page (Khavronin: ¶[0206], ¶[0264-0265] and Fig. 17 1704 a neural network is used to look at the content contained within information objects that users interact with and use it to predict topics associated with the content that relate to it in the vector space);
mapping the topic tags to a feature space to generate tag features that include a numeric representation of each of the topic tags (Khavronin: ¶[0212-0260] describes the use of resource classifiers to combine multiple feature vectors (F1-F6) into a single feature vector. The act of combining features inherently reduces dimensionality by summarizing the information within a more compact representation);
aggregating the tag features to generate topic codes that have improved commonality with the one or more behavioral segments compared to the topic tags, the topic codes corresponding to a lower dimensional representation of the topic tags that summarizes content of the topic tags (Khavronin: ¶[0209-0214] discloses that webpages are chosen or not chosen based off of relevancy scores and feature extraction);
classifying the plurality of pages as pertaining to at least one of the topic codes by determining a probability score that each page is about-a particular topic code (Khavronin: ¶[0313] the CCM obtains data packets which include webpages and applications which come from user data, additionally each page has an event type that is identified later referred to as topics, these topics are aggregated and given relevancy scores);
tracking a frequency count value of each of the plurality of pages viewed by the communications network user that is classified as pertaining to each of the topic codes (Khavronin: ¶[0313] the CCM uses the obtained packets it generates a consumption score based on the number of session events generated by the organization over a time period and the unique number of users which is interpreted as tracking a count, additionally the CCM recognizes a surge in consumption scores (i.e. frequency) over the plurality of time periods (i.e. recency));;
determining a recency time period value for the plurality of pages associated with the tracked frequency count value (Khavronin: ¶[0313] the CCM uses the obtained packets it generates a consumption score based on the number of session events generated by the organization over a time period and the unique number of users which is interpreted as tracking a count, additionally the CCM recognizes a surge in consumption scores (i.e. frequency) over the plurality of time periods (i.e. recency));
characterizing, for each topic code, the communications network user as belonging to the one or more behavioral segments by comparing the probability score associated with each page against the probability percentage threshold included in the plurality of behavioral parameters , comparing the tracked frequency count value against the frequency count threshold included in the plurality of behavioral parameters, and comparing the recency time period value against the recency time period threshold included in the plurality of behavioral parameters (Khavronin: ¶[0209-0212] user interactions may categorized as behavioral features which come from the identified user content interaction/consumption monitoring);
serving content to the communications network user based on the characterizing of the communications network user (Khavronin ¶[0188] discloses that content is served to the user).
Khavronin does not explicitly disclose creating, via a user interface, a classification mapping between topics and the plurality of communications network users, and setting, via the user interface, one or more behavioral parameters associated with the classification mapping.
However, Keng discloses:
creating, via a user interface, a classification mapping between topics and the plurality of communications network users, and setting, via the user interface, one or more behavioral parameters associated with the classification mapping (Keng: p[0035], p[0099], p[0118] discloses that user inputs through GUIs are used to control analysis parameters, meaning the segmentation (behavioral clusters and thresholds) can be configured through a user interface), It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose identifying one or more behavioral segments used to characterize a plurality of communications network users, the one or more behavioral segments being configured via user inputs, a configuration of the one or more behavioral segments comprises: creating, via a user interface, a classification mapping between topics and the plurality of communications network users, and setting, via the user interface, one or more behavioral parameters associated with the classification mapping. Both Khavronin and Keng are directed toward systems that classify or segment users based on their behavior and interaction with online content. While Khavronin discloses monitoring user content interaction and generating features that can be used to classify data, it does not disclose configuring behavioral segments through a user interface. Keng does disclose this. The motivation for combining these references is “Quickly identifying popular or influential individuals and conversations becomes more difficult when the number of users and conversations within a social network grows” as disclosed in Keng’s background of invention.
The combination of Khavronin and Keng do not explicitly disclose a recency time period threshold for when a visit occurred. However, Dille discloses disclose a recency time period threshold for when a visit occurred (Dille: ¶[0059] discloses rate difference exceeds a threshold, ¶[0097] discloses fast and slow users evaluated over time windows in order to determine impact, this is a threshold logic that is tied to when activity occurred).
Khavronin, Keng and Dille are combinable because all three references address classifying users based on behavioral interaction with content. Khavronin teaches using browsing activity, relevance, frequency and recency to characterize users, Keng discloses configuring behavioral segments and associated parameters through a user interface and Dille teaches applying explicit time period and frequency thresholds to determine when user activity satisfies classification conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose recency time period threshold. The suggestion/motivation for doing so is “The detection system may further identify ranges of the timing distribution for users with slow and fast experiences on the set of webpages” as disclosed in ¶[0005] of Dille.
Regarding Claim 2:
The proposed combination of Khavronin, Keng and Dille further discloses the user classification system of claim 1, wherein the operations further comprise: providing a third-party user interface allowing a third-party to define the one or more behavioral segments (Khavronin: p[0209-0216] may include a webpage (i.e., a third party) where there is a graphical user interface, the user interacts with this interface which is where behavioral data comes from);
and receiving a third-party definition of the one or more behavioral segments (Khavronin: p[0209-0216] content interaction through these parties defines some of these behaviors and may even report it to the system, such as mouse clicks, dwell time, scroll velocity etc.)
Regarding Claim 3:
The proposed combination of Khavronin, Keng and Dille further discloses the user classification system of claim 2, wherein receiving the at least one behavioral segment includes receiving a classification mapping, and wherein the operations further comprise setting behavioral parameters associated with the classification mapping (Khavronin: p[0209-0216] wherein "duration of time users spend on an information object 112 and total engagement user has on the information object 112, the number of distinct user profiles accessing the information object 112 vs. total number of events for the information object 112, dwell time, scroll depth, scroll velocity, variance in content consumption over time, tab selections that switch to different information objects 112, page movements, mouse page scrolls, mouse clicks, mouse movements, scroll bar page scrolls, keyboard page movements, touch screen page scrolls, eye tracking data (e.g., gaze locations, gaze times, gaze regions of interest, eye movement frequency, speed, orientations, etc.), touch data (e.g., touch gestures, etc.), and/or the like. Identifying different event types associated with these different user content interaction behaviors (consumption) and associated engagement scores is described in more detail previously." is all interpreted as mapped data).
Regarding Claim 4:
The proposed combination of Khavronin, Keng and Dille further discloses the user classification system of claim 3, wherein at least one of the behavioral parameters includes a probability percentage that a page among the plurality of pages viewed by a communication network user related to a topic associated with one of the topic tags (Khavronin: p[0313] the CCM obtains these data packets which already include a plurality of behavioral data, the behavioral data are each given topics, aggregated and given a relevancy score which is interpreted as finding a probability that a page or pages that were viewed are related to topics and each other).
Regarding Claim 5:
The proposed combination of Khavronin, Keng and Dille further discloses the probability percentage relating to a frequency with which the page or the topic is seen by the communication network user (Khavronin: p[0313] the CCM obtains these data packets which already include a plurality of behavioral data, the behavioral data is processed and generates a consumption score based on the number of session events generated by the organization over a time period and the unique number of users which is interpreted as tracking a count, additionally the CCM recognizes a surge in consumption scores (i.e. frequency)).
Regarding Claim 6:
The proposed combination of Khavronin, Keng and Dille further discloses the user classification system of claim 4, wherein at least one of the behavioral parameters includes a probability percentage relating to a recency with which the page or the topic is seen by the communication network user (Khavronin: p[0313] the CCM recognizes a surge in consumption scores (i.e., frequency) over the plurality of time periods (i.e., recency)).
Regarding Claim 7:
Claim 7 is directed to the method claim corresponding to the system claim presented above in
claim 1 and is rejected under the same grounds.
Regarding Claim 8:
Claim 8 is directed to the method claim corresponding to the system claim presented above in
claim 2 and is rejected under the same grounds.
Regarding Claim 9:
Claim 9 is directed to the method claim corresponding to the system claim presented above in
claim 3 and is rejected under the same grounds.
Regarding Claim 10:
Claim 10 is directed to the method claim corresponding to the system claim presented above in
claim 4 and is rejected under the same grounds.
Regarding Claim 11:
Claim 11 is directed to the method claim corresponding to the system claim presented above in
claim 5 and is rejected under the same grounds.
Regarding Claim 12:
Claim 12 is directed to the method claim corresponding to the system claim presented above in
claim 6 and is rejected under the same grounds.
Regarding Claim 13:
Claim 13 is directed to the non-transitory machine-readable medium claim corresponding to the system claim presented above in claim 1 and is rejected under the same grounds.
Regarding Claim 14:
Claim 14 is directed to the non-transitory machine-readable medium claim corresponding to the system claim presented above in claim 2 and is rejected under the same grounds.
Regarding Claim 15:
Claim 15 is directed to the non-transitory machine-readable medium claim corresponding to the system claim presented above in claim 3 and is rejected under the same grounds.
Regarding Claim 16:
Claim 16 is directed to the non-transitory machine-readable medium claim corresponding to the system claim presented above in claim 4 and is rejected under the same grounds.
Regarding Claim 17:
Claim 17 is directed to the non-transitory machine-readable medium claim corresponding to the system claim presented above in claim 5 and is rejected under the same grounds.
Regarding Claim 18:
Claim 18 is directed to the non-transitory machine-readable medium claim corresponding to the system claim presented above in claim 6 and is rejected under the same grounds.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IAN SCOTT MCLEAN whose telephone number is (703)756-4599. The examiner can normally be reached "Monday - Friday 8:00-5:00 EST, off Every 2nd Friday".
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hai Phan can be reached at (571) 272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IAN SCOTT MCLEAN/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654