DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ainscough et al. (US 2020/0380556 A1), hereinafter “Ainscough”, and in view of Birch et al. (US 2020/0042567 A1, Applicant’s submitted IDS filed 2/04/2026), hereinafter “Birch”, and further in view of Sussman et al. (US 2020/0311769 A1), hereinafter “Sussman”.
As per claim 1, Ainscough teaches a method comprising:
“selecting, by a central database system, a
(Ainscough teaches selecting the ML model 132, which is trained to predict multiple user actions based on the training istreams 124. The istream 124 encodes a sequence of actions captured during traversal of the navigable contents 120 of a website by multiple users)
“applying, by the central database system, the
(Ainscough teaches utilizing the trained RNN 232 to determine prediction data 134 by inputting live, real-word istream data 124 into the RNN 232 and cause the RNN 232 to produce corresponding prediction data 134. The prediction metadata 248 includes predicted values and/or probability that the user 109 will perform a conversion action at the specified webpage, probability that the user 109 will remain at the specified webpage for a predicted duration, a predicted next page, and so on)
“retraining the
(Ainscough teaches training the ML model 132 includes iteratively applying training istream data 224, evaluating errors between prediction data 134 produced by the ML model 132 and actual user actions of the training isteams 224, and adjusting, refining, and optimizing weights of the embedding layer 330, LSTM layer 332, and LSTM cell 333 accordingly)
Ainscough teaches the model is a machine learning model, but does not teach the model is a clustering model as claimed. However, Birch teaches a similar method for tracking user’s Web page navigation to predict next web page to be viewed by the user utilizing a clustering model to cluster navigation tree into a first cluster group and a second cluster group comprising similar web pages at [0086]-[0091]. Thus, it would have been obvious to one of ordinary skill in the art to combine Birch with Ainscough’s teaching because “when the keywords of the cluster group are topically related to one or more keywords of content viewed by the user (e.g., the keywords of the currently displayed content having a similarity metric above a threshold level with respect to the keywords of the cluster group), the system may determine that the user is carrying out the task, and provide one or navigation suggestions on the user interface of the web browser”, as suggested by Birch at [0090].
Birch also teaches at [0091] the steps of determining similarity between consecutive web pages and comparing the similarity to a threshold, but does not explicitly teach “filtered to remove consecutive related web pages with a below-threshold similarity” as claimed. However, Sussman teaches at [0006]-[0007], [0130]-[0139] a method for predicting web browsing behavior of consumers including the steps of calculating similarity between consecutive web pages, comparing the similarity to a configurable threshold and filtering to remove consecutive related webpage based on the threshold similarity. Thus, it would have been obvious to one of ordinary skill in the art to combine Sussman with Ainscough-Birch’s teaching in order to “provide accurate and efficient methods and systems for predicting behaviors of consumers during a pre-specified period. In turn, the predictions may be used to enhance, optimize and adjust marketing and advertising efforts that are unlikely to result in purchases and, in turn, increase the overall revenue. Reduce the required processing power for server” as suggested by Sussman at [0010].
As per claim 2, Ainscough-Birch and Sussman teach the method of claim 1 discussed above. Birch also teaches: wherein “training the cluster model using the training data set comprises: determining a number of identified actions performed by the plurality of historical users; generating vectors representing respective web page addresses of the web pages viewed by the historical users; and applying the cluster model to the generated vectors and the number of identified actions, the cluster model clustering the generated vectors into a number of clusters corresponding to the number of identified actions” at [0087]-[0091], [0118]-[0124].
As per claim 3, Ainscough-Birch and Sussman teach the method of claim 1 discussed above. Birch also teaches: wherein “applying the cluster model to the web pages viewed by the target user and the predicted next web page to be viewed by the target user comprises: generating vectors representing respective web page addresses of the web pages viewed by the target user and the predicted next web page; and applying the cluster model to the generated vectors and a number of identified actions performed by the plurality of historical users, the cluster model clustering the generated vectors into a number of clusters corresponding to the number of identified actions” at [0087]-[0091], [0118]-[0124].
As per claim 4, Ainscough-Birch and Sussman teach the method of claim 1 discussed above. Birch also teaches: wherein “identifying that the target user is performing the new action comprises identifying a new cluster output by the cluster model in response to applying the cluster model to the web pages viewed by the target user and the predicted next web page” at [0050]-[0052], [0087]-[0091], [0118]-[0124].
As per claim 5, Ainscough-Birch and Sussman teach the method of claim 4 discussed above. Birch also teaches: wherein “identifying the new cluster comprises determining a center of the new cluster is at least a threshold distance from each center of clusters output by the cluster model corresponding to the number of identified actions” [0087]-[0091], [0118]-[0124].
As per claim 6, Ainscough-Birch and Sussman teach the method of claim 1 discussed above. Ainscough also teaches: “incrementing a number of the actions performed by the plurality of historical users based on the new action; generating a training data set comprising the first training data set, the web pages viewed by the target user, and the new action; and retraining the cluster model using the training data set” at
As per claim 7, Ainscough-Birch and Sussman teach the method of claim 1 discussed above. Birch also teaches: “modifying, by the central database system, an interface displayed to the target user to include a web element to direct the target user to the predicted next web page in response to determining that an observed next web page viewed by the target user is unrelated to the identified action” at [0050]-[0052].
As per claim 8, Ainscough-Birch and Sussman teach the method of claim 7 discussed above. Birch also teaches: wherein “the web element is an iframe, and wherein modifying the interface displayed to the target user to include the iframe to direct the target user to the predicted next web page comprises: causing the iframe having a hyperlink to the predicted next web page to be displayed at the interface” at [0050]-[0052].
As per claim 9, Ainscough-Birch and Sussman teach the method of claim 1 discussed above. Birch also teaches: “tracking that the target user navigated to the predicted next web page and a duration of time that the target user spent viewing the predicted next web page; and in response to determining that the duration of time exceeds a threshold duration, retraining the cluster model to strengthen a second association between the web pages viewed by the target user and the identified action” at [0056]-[0063].
As per claim 10, Ainscough-Birch and Sussman teach the method of claim 9 discussed above. Birch also teaches: “in response to determining that the duration of time does not exceed the threshold duration, retraining the cluster model to weaken the second association between the web pages viewed by the target user and the identified action” at [0056]-[0063].
As per claim 11, Ainscough-Birch and Sussman teach the method of claim 1 discussed above. Birch also teaches: “generating vectors representing web page addresses of the web pages viewed by the target user; and determining a combined vector using the generated vectors; wherein the predicted next web page to be viewed by the target user is determined based on the combined vector” at [0087]-[0091], [0118]-[0124].
As per claim 12, Ainscough-Birch and Sussman teach the method of claim 11 discussed above. Birch also teaches: wherein “determining the combined vector using the generated vectors comprises: calculating a plurality of similarity metrics between a web address of the latest viewed web page of the web pages viewed with web addresses of a set of the web pages viewed before the latest viewed web page; and identifying a subset of the generated vectors corresponding to a subset of the web page addresses having at least a threshold similarity metric with the web page address of the latest viewed web page, wherein the subset of the generated vectors is used to determine the combined vector” at [0087]-[0091], [0118]-[0124].
Claims 13-20 recite similar limitations as in claims 1-12 and are therefore rejected by the same reasons.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Patent No. 11,941,066. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of US Patent No. 11,941,066 contain every element of claims 1-20 of the instant application, as detailed in the mapping table below, and as such anticipate claims 1-20 of the instant application.
“A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). “ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Instant Application 19/216,728
Patent No. 11,941,066
A method comprising:
selecting, by a central database system, a cluster model trained based on actions of historical users within a domain and consecutive related web pages viewed by the historical users while performing the actions filtered to remove consecutive related web pages with a below-threshold similarity, the cluster model configured to predict a desired action to be performed by an acting user based on web pages viewed by the acting user;
applying, by the central database system, the cluster model to web pages viewed by a target user and a predicted next web page to be viewed by the target user to identify an action being performed by the target user; and
retraining the cluster model in response to determining that the target user is performing a new action different from actions performed by the plurality of historical users.
A method comprising:
identifying, by a central database system for each of a plurality of historical users, an action being performed by the historical user within a domain and a set of web pages viewed by the historical user while performing the action; generating, by the central database system, a training data set comprising, for each of the plurality of historical users, the identified action and the set of web pages viewed while the action is being performed; training, by the central database system, a cluster model using the training data set, the cluster model configured to predict a desired action to be performed by an acting user based on web pages viewed by the acting user; applying, by the central database system, a machine-learned model to web pages viewed by a target user to predict a next web page to be viewed by the target user; applying, by the central database system, the cluster model to the web pages viewed by the target user and the predicted next web page to be viewed by the target user to identify an action being performed by the target user; in response to determining that an observed next web page viewed by the target user is unrelated to the identified action being performed by the target user: modifying, by the central database system, an interface displayed to the target user to include a web element to direct the target user to the predicted next web page; and retraining the cluster model in response to determining that the target user is performing a new action different from actions performed by the plurality of historical users.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Patent No. 12,346,382. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of US Patent No. 12,346,382 contain every element of claims 1-20 of the instant application, as detailed in the mapping table below, and as such anticipate claims 1-20 of the instant application.
“A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). “ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Instant Application 19/216,728
Patent No. 12,346,382
A method comprising:
selecting, by a central database system, a cluster model trained based on actions of historical users within a domain and consecutive related web pages viewed by the historical users while performing the actions filtered to remove consecutive related web pages with a below-threshold similarity, the cluster model configured to predict a desired action to be performed by an acting user based on web pages viewed by the acting user;
applying, by the central database system, the cluster model to web pages viewed by a target user and a predicted next web page to be viewed by the target user to identify an action being performed by the target user; and
retraining the cluster model in response to determining that the target user is performing a new action different from actions performed by the plurality of historical users.
A method comprising:
identifying, by a central database system for each of a plurality of historical users, an action being performed by the historical user within a domain and a set of consecutive web pages viewed by the historical user while performing the action, each of the consecutive web pages corresponding to a web address and each web address corresponding to a web page of the consecutive web pages within a threshold similarity of the web address corresponding to each other web page of the consecutive web pages; generating, by the central database system, a training data set comprising, for each of the plurality of historical users, the identified action and the set of web pages viewed while the action is being performed; training, by the central database system, a cluster model using the training data set, the cluster model configured to predict a desired action to be performed by an acting user based on web pages viewed by the acting user; applying, by the central database system, the cluster model to web pages viewed by a target user and a predicted next web page to be viewed by the target user to identify an action being performed by the target user; and
retraining the cluster model in response to determining that the target user is performing a new action different from actions performed by the plurality of historical users.
Response to Arguments
Applicant’s arguments with respect to claims 1-20have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
June 11, 2026
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