Detailed Action
This action is in response to the application filed 04/03/2023, in which:
Claims 1, 8 and 15 are the independent claims.
Claims 1-20 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/03/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Duplicate Claims, Warning
Applicant is advised that should Claim 1 be found allowable, Claim 15 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m). Dependent Claims 16-20 will also be objected based under dependency of the objected Claim.
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 U.S. Patent No. 17/513,423:
Although the Claims at issue are not identical, they are not patentably distinct from each other because each of instant Claims 1-7 is fully anticipated by corresponding patent Claims 1-7.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 1 further recites the method comprising:
determining, … based on the response data, a predicted reason the interaction session is likely to terminate before the user completes a target action (a human being can mentally apply evaluation to determine (based on specific data) a predicted reason the interaction session is likely to terminate for a specific constraint (such as before the user completes a target action/task))
determining, … based on the response data and the predicted reason determined …, an intervention action for increasing a probability that the user will complete the target action before the interaction session terminates action (a human being can mentally apply evaluation to determine (based on specific data) an intervention action for a specific constraint (such as for increasing a probability that the user will complete a target action/task before a session terminates))
Claim 1 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
sending one or more web pages for display to a user via a network during an interaction session between the user and an application, wherein the one or more web pages include elements for collecting response data from the user (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g)
receiving, via the web pages, response data from the user (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g)
… via a first machine-learning model … (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
… via a second machine-learning model … (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
performing, via the application, the intervention action (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional elements a and b fall within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Additional elements c-e are merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 2 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 2 further recites the method comprising of determining, … based on the response data, a next action the user is anticipated to perform in the application (a human being can mentally apply evaluation to determine (based on specific data) a next action the user is anticipated to perform in a specific environment). Claim 2 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
… via a third machine-learning model … (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
altering at least one aspect of the one or more web pages to facilitate user performance of the next action (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional elements a and b are merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 3 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 3 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 3 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the new sole additional element recited consists of … inputting a set of input features including a value … into the second machine-learning model (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Thus, the claim is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 4 recites the method of Claim 3. Claim 3 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 4 further recites the method comprising of determining, based on the updated response data, an updated value (a human being can mentally apply evaluation to determine (based on specific data) an updated value). Claim 4 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the new sole additional element recited consists of receiving, via the web pages, updated response data from the user (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Thus, the claim is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 5 recites the method of Claim 4. Claim 5 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 5 further recites the method comprising:
subtracting the value from the updated value to determine a difference (a human being can mentally apply evaluation to determine a difference via subtraction of specific values)
dividing the difference by a time interval to determine a rate of change (a human being can mentally apply evaluation to determine a rate of change via division with a specific time interval)
determining an updated time interval based on the rate of change (a human being can mentally apply evaluation to determine an updated time interval based on the determined rate of change)
Claim 5 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 6 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 6 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 6 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
opening, via the application, a messaging interface (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
establishing a network connection with a live support agent to allow the user to communicate with the live support agent through the messaging interface (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional elements a and b are merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 7 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 7 further recites the method comprising … wherein the intervention action is determined based further on the additional data (a human being can mentally apply evaluation to determine the intervention action based on specific data). Claim 7 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the new sole additional element recited consists of collecting, via the application, additional data that characterizes user behavior during the interaction session … (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Thus, the claim is subject-matter ineligible.
Regarding Claims 8-14:
Claims 8-14 incorporate substantively all the limitations of Claims 1-7 in a system (thus, a machine) and further recites one or more processors; and a memory storing one or more instructions that, when executed on the one or more processors, cause the system to (these claim limitations appear to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f)) and does not appear to integrate the abstract idea into a particular application; thus, the claim is subject-matter ineligible as it does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself); thus, Claims 8-14 are rejected for reasons set forth in the rejections of Claims 1-7, respectively.
Regarding Claims 15-20:
Claims 15-20 incorporate substantively all the limitations of Claims 1-2 and 4-7 in a method (thus, a process and (see Duplicate Claims, Warning)) and further recites no new limitations; thus, Claims 15-20 are rejected for reasons set forth in the rejections of Claims 1-2 and 4-7, respectively.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-4, 7, 8, 10-11, 14, 15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al., US-2016/0239867-A1, in view of Anderson et al., US-7,376,618-B1.
Regarding Claim 1:
Sinha teaches:
A method, comprising:
(Sinha, Abstract, “Online shopping cart analysis is described. In one or more implementations, a model is built is usable to …”; Page 1, [0012], “FIG. 4 is a flow diagram depicting a procedure …”. Sinha teaches an online shopping cart analysis procedure which is depicted in Figure 4; thus, interpreted by the examiner as a method for analyzing an online store via a model for the likelihood of a customer to return to purchase after abandoning a shopping cart).
sending one or more web pages for display to a user via a network during an interaction session between the user and an application, wherein the one or more web pages include elements for collecting response data from the user;
(Sinha, Fig. 5; Page 9, [0091], “Input/output interface(s) 508 … allow a user to enter commands and information to computing device 502, and also allow information to be presented to the user …”; Page 10, [0095], “… transmit to … 502 … via a network …”; Fig. 2: 202; Page 4, [0037], “FIG. 4 … content for configuring the web pages of the online store is represented by online store content 202”; Page 4, [0033], “… Historic data about the identified customers (… shopping sessions) is collected ... tracks and collects data describing interactions of the identified customers with the online store (e.g., Store browsing data), attributes of items left in shopping carts abandoned by the identified customers (e.g., shopping cart data), interactions of the identified customers with content …”. Figure 5 depicts an implementation for the online shopping cart analysis application; where application is sending the web pages to be displayed to a user via a network. The user is able to enter and receive information from the display (interpreted by the examiner as interacting) for the online store comprising shopping carts, clickstreams, sessions, etc. as response data of the users’ interaction sessions within the web pages (which include elements for collecting response data (ex. store browsing data, shopping cart data, etc.)).
receiving, via the web pages, response data from the user,
(Sinha, Fig. 4: 404; Page 5, [0043], “… the term clickstream data also indicates a location of a user's cursor relative to objects displayed on a webpage. In other words, the clickstream data indicates where a cursor hovers on a webpage”. The online store browsing data that is collected from the web pages (which were displayed to the users) is received as click stream data (indicates location of cursor relative to the objects (elements/content) of the web pages; thus, receiving, via the web pages, response data from the user).
determining, via a first machine-learning model based on the response data, a predicted reason the interaction session is likely to terminate before the user completes a target action;
(Sinha, Fig. 4: 406 -“… model to compute, based on the collected data, a likelihood of the customer to return to purchase …”; Page 5, [0051], “… customer classification model module 212 represents functionality to generate models that are usable for predicting whether a customer will return to the online store to purchase the items in left in an abandoned cart”, Page 6, [0053], “
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”. Within an online shopping cart interaction session there is a customer utilizing the online store within a current session, abandoning a shopping cart (end of the current session. start of after abandoning cart session), and returning to purchase the abandoned items (end of abandoning cart session). The first machine-learning model is generated to predict whether a customer will return to the online store to purchase the abandoned items (where the examiner associates purchasing of the abandon items as completion of a target action). The machine learning model calculates the probability of returning to purchase (P(return to purchase) to categorize the customer into a classification (ex. true abandoners (non-customers), prospects (true customers, etc.); thus, the system determines a predicted reason the interaction session is likely to terminate before the user completes a target action (where the model is generating a probability of the customer to return or not return to purchase)).
determining, … based on the response data and the predicted reason determined by the first machine-learning model, an intervention action for increasing a probability that the user will complete the target action before the interaction session terminates; and
(Sinha, Fig. 4: 408 -“Associate the customer with a marketing segment based in part on the likelihood of the customer to return to purchase the unpurchased items in the online shopping cart”; Page 2, [0019], “… Advertising content that is customized for the advertising segment is then delivered to the customer.”. Customers are segmented into different marketing segments based on their likelihood to return to purchase (target action); thus, interpreted by the examiner as based on users response data and the predicted reason determined by the first machine-learning model (where the predicted reason is based on historical data). The segmented groups are done to increase a probability that the user (prospect user/true customer) completes the target action).
performing, via the application, the intervention action.
(Sinha, Fig. 4: 410 -“ Control marketing activities directed at the customer according to the segment with which the customer is associated”; Page 2, [0019], “… Advertising content that is customized for the advertising segment is then delivered to the customer.”. The application performs the specific marketing strategy based on the segment the customer was associated with; thus, performing the intervention action (ex. pop up message/coupon which are considered intervention actions as they are intervening the customers experience with a new event to increase the return to purchase probability)).
Sinha teaches determining (with a first ML model based on the response data) a predicted reason the session is likely to terminate before the user completes a target action and also an intervention action to apply to increase a probability that the user will complete the target action before session termination. Nevertheless, Sinha does not explicitly teach the determination of the intervention action via a second machine-learning model.
However, Anderson teaches:
determining, via a second machine-learning model based on the response data and the predicted reason determined by the first machine-learning model …
(Anderson, Column 19, Lines 9-14 “… Statistical Model 116 may be … a cascaded model in which … fed into a second model which is trained specifically on such high scoring transactions …”. Anderson teaches a methodology of analyzing different data (including web page data for customer interactions); where the invention utilizes a statistical model which cascaded/sequential for statistics/analysis. Anderson teaches using the second machine-learning cascaded model (sequential model) which is based on the response data and the predicted reason (Fraud scores and reason codes) which depicts risk described by the first machine-learning model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize Sinha’s methodology of analyzing abandoned online shopping carts which teaches the determinations with the explicit teaching of the second machine-learning model of Anderson which explicitly incorporates the first machine learning model’s determination. One having ordinary skill in the art would have been motivated to implement this change before the effective filing date of the claimed invention, as this leads to accessibility, automation, analysis, detection, fine-tuned analysis, and incorporating decision systems (see Anderson, Column 5, Lines 39-45, “An advantage over previous methods stems from the accessibility of high-categorical data to a machine-trained statistical model. The invention also incorporates … using content mining approaches. Further the invention also includes the decision systems that use the results of such a predictive model …”; Column 19, Lines 9-14 “… Statistical Model 116 may be … a cascaded model in which … fed into a second model which is trained specifically on such high scoring transactions, and is thus more optimized to discriminate between risky and non-risky transactions in this upper range”).
Regarding Claim 3:
Sinha/Anderson teach the method of Claim 1 and Sinha/Anderson further teaches:
wherein determining the intervention action via the second machine-learning model includes:
inputting a set of input features including a value determined based on the response data into the second machine-learning model.
(Anderson, Fig. 1; Fig. 3: 306; Column 19, Lines 9-14 “… Statistical Model 116 may be … a cascaded model in which … fed into a second model which is trained specifically on such high scoring transactions …”. Anderson teaches a methodology of analyzing a webpage utilizing a statistical model which is cascaded/sequential for statistics/analysis (thus, using the second machine-learning) based on the response data and is shown within Fig. 1 where the set of input features (with different data types) are sent to the Statistical Model (116) as input (102). Fig. 3 shows the affinity values/database being used based on the response data which is inputted into the statistical model (Fig 3. 306) ).
The motivation of Claim 1’s combination of Sinha/Anderson is still maintained.
Regarding Claim 4:
Sinha/Anderson teach the method of Claim 3 and Sinha/Anderson further teach:
receiving, via the web pages, updated response data from the user; and
(Sinha, Fig. 4: 404; Page 5, [0043], “… the term clickstream data also indicates a location of a user's cursor relative to objects displayed on a webpage. In other words, the clickstream data indicates where a cursor hovers on a webpage”; Page 5, [0049], “When a customer abandons an online shopping cart, the browsing activity of the customer is observed. Taking into account the customers in-session behavior (e.g., the customer's browsing interactions with the online store over one or more shopping sessions), attributes of the online shopping cart and past interactions with the online shopping cart, and cross-channel interactions (e.g., interactions with promotional emails), a model is built to predict whether a customer will return to the online store to purchase the items in left in an abandoned cart”. The online store browsing data that is collected from the web pages (which were displayed to the users) is received as click stream data (indicates location of cursor relative to the objects (elements/content) of the web pages; thus, receiving, via the web pages, response data from the user and updated response data as it is clickstream (continuous) data which is observed in-session such as browsing activity).
determining, based on the updated response data, an updated value.
(Sinha, Fig. 4: 406. The model determines an updated value based on the updated response data (the new collected data)).
Regarding Claim 7:
Sinha/Anderson teach the method of Claim 1 and Sinha further teaches:
collecting, via the application, additional data that characterizes user behavior during the interaction session, wherein the intervention action is determined based further on the additional data.
(Sinha, Page 5, [0049], “When a customer abandons an online shopping cart, the browsing activity of the customer is observed. Taking into account the customers in-session behavior (e.g., the customer's browsing interactions with the online store over one or more shopping sessions), attributes of the online shopping cart and past interactions with the online shopping cart, and cross-channel interactions (e.g., interactions with promotional emails), a model is built to predict whether a customer will return to the online store to purchase the items in left in an abandoned cart”. The online store browsing data that is collected from the web pages (which were displayed to the users via the application) is received as click stream data; thus, the browsing activity/interactions is interpreted as additional data that characterizes user behavior during the interaction session, wherein the intervention action is determined based further on the additional data ).
Regarding Claims 8, 10-11, 14:
Claims 8, 10-11, 14 incorporate substantively all the limitations of Claims 1, 3-4, and 7, in a system and further recites one or more processors; and a memory storing one or more instructions that, when executed on the one or more processors, cause the system to (see Sinha, Fig. 5. Figure 5 shows the system (computing device) that contains the processing system of one or more processors (504), a memory storage (512), to cause the system to perform the application); thus, Claims 8, 10-11, 14 are rejected for reasons set forth in the rejections of Claims 1, 3-4, and 7, respectively.
Regarding Claims 15, 17, and 20:
Claims 15, 17, and 20 incorporate substantively all the limitations of Claims 1, 4, and 7 in a method (see Duplicate Claims, Warning)) and further recites no new limitations; thus, Claims 15, 17, and 20 are rejected for reasons set forth in the rejections of Claims 1, 4, and 7, respectively.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al., US-2016/0239867-A1, in view of Anderson et al., US-7,376,618-B1, in view of Amendjian et. al, US-2017/0032417-A1.
Regarding Claim 2:
Sinha/Anderson teach the method of Claim 1 and Sinha further teaches:
determining, via a third machine-learning model based on the response data, …
(Anderson, Column 19, Lines 9-14 “… Statistical Model 116 may be … a cascaded model in which … fed into a second model which is trained specifically on such high scoring transactions …”. Anderson teaches a methodology of analyzing different data (including web page data for customer interactions); where the invention utilizes a statistical model which is a cascaded/sequential model for statistics/analysis. Anderson teaches using the second machine-learning, third machine-learning, etc. (cascaded model (a sequential model)) which is based on the response data and the predicted reason (Fraud scores and reason codes) which depicts risk described by the first machine-learning model).
Nevertheless, Sinha/Anderson do not explicitly disclose:
determining … a next action the user is anticipated to perform in the application; and
altering at least one aspect of the one or more web pages to facilitate user performance of the next action.
However, Amendjian teaches:
determining … a next action the user is anticipated to perform in the application; and
(Amendjian, FIG. 1: 28-“PREDICT FUTURE STAGE/CHOOSE ACTION”; FIG. 4. Figure 1 shows the process for determining the current stage the user is within. Figure. 1: 28 shows predicting a next stage (interpreted as the next action by the examiner as noted by Figure 4: 84- “STAGE/ACTIONS”) for the user to commit within the application (interpreted by the examiner as anticipated) and leads to FIGURE 4 which is the process of finding the best action and displaying the selected action to increase likelihood of the user committing the final action).
altering at least one aspect of the one or more web pages to facilitate user performance of the next action.
(Amendjian, FIG. 4: 94; FIG. 5: 110 & 112. Figure 4: 94, and FIG. 5: 110 & 112 show the generation and displaying of action/popup (altering) to increase likelihood user performance/completion of the next stage/action).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize Sinha/Anderson’s methodology of analyzing abandoned online shopping carts with the determining methodology for the next action to alter the webpage from Amendjian. One having ordinary skill in the art would have been motivated to implement this change before the effective filing date of to increase likelihood and facilitate user performance based on the abandoned online shopping cart analysis to increase influence user behavior(see Amendjian, Page 1, Column 1, [0003], “The various advantages and purposes of the exemplary embodiments as described above and hereafter are achieved by providing …. a method of detecting and generating online behavior from a clickstream including: learning a user's present stage of online behavior … predicting a user's future stage of online purchasing behavior; and providing a targeted online action to the user in conjunction with predicting the user's future stage of online purchasing behavior to influence the user to a next stage of online behavior”)
Regarding Claim 9:
Claim 9 incorporates substantively all the limitations of Claim 2 in a system and further recites one or more processors; and a memory storing one or more instructions that, when executed on the one or more processors, cause the system to (see Sinha, Fig. 5. Figure 5 shows the system (computing device) that contains the processing system of one or more processors (504), a memory storage (512), to cause the system to perform the application); thus, Claim 9 is rejected for reasons set forth in the rejections of Claim 2.
Regarding Claim 16:
Claim 16 incorporate substantively all the limitations of Claim 2 in a method (see Duplicate Claims, Warning)) and further recites no new limitations; thus, Claim 16 is rejected for reasons set forth in the rejections of Claims 2.
Claims 5, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al., US-2016/0239867-A1, in view of Anderson et al., US-7,376,618-B1, in view of Khosravi et. al, “Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances”.
Regarding Claim 5:
Sinha/Anderson teach the method of Claim 4. Sinha/Anderson fail to explicitly disclose:
subtracting the value from the updated value to determine a difference; dividing the difference by a time interval to determine a rate of change; and determining an updated time interval based on the rate of change.
However, Khosravi teaches:
subtracting the value from the updated value to determine a difference; dividing the difference by a time interval to determine a rate of change; and determining an updated time interval based on the rate of change.
(Khosravi, Page 1354-1355, Column 2: Paragraph 3-Column 1: Paragraph 1, “The amount of difference between the best method for PI construction and the other methods is demonstrated in Table VII. The percentage difference is the ratio of difference between the CWCs and the minimum of CWCs normalized by the minimum of CWCs Difference
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…”; Page 1346, Equations 36-38. Equations 36-38 shows the creation of calculating a CWC value to evaluate PIs (periodic intervals which are interpreted by the examiner as time interval (as a period is a length of time)); where equation 36 is the average width of the PIs, equation 37 is the normalized value to compare PIs, and CWC is the coverage width-based criterion. Thus, Equation 43 is subtracting the value from the updated value) and dividing the difference by a periodic range interval (shown in equation 37) to determine a rate of change for determining the best PI construction method (Table VII)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize Sinha/Anderson’s methodology of analyzing abandoned online shopping carts with the measuring of differences over a time interval taught within Khosravi. One having ordinary skill in the art would have been motivated to implement this change before the effective filing date of to construct and analyze prediction intervals, update prediction intervals, compare different intervals, handle different computational expense restrictions, etc. (see Khosravi, Page 1355, Column 1, Paragraph 2, “… The theoretical background of the delta, Bayesian, MVE, and bootstrap techniques was first studied to find the advantages and disadvantages of each method. Twelve synthetic and real-world case studies were implemented to assess the performance of each method for generating high-quality PIs. … Quantitative and comprehensive assessments were performed by using a hybrid measure related to the width and coverage probability of PIs. According to the obtained results, the delta technique generates the highest quality PIs, the Bayesian method is the most reliable for reproducing quality PIs, and the MVE method is the least computationally expensive method … selection and application of a PI construction method will depend on the purpose of analysis, the computational constraints, and which aspect of the PI is more important …”).
Regarding Claim 12:
Claim 12 incorporates substantively all the limitations of Claim 5 in a system and further recites one or more processors; and a memory storing one or more instructions that, when executed on the one or more processors, cause the system to (see Sinha, Fig. 5. Figure 5 shows the system (computing device) that contains the processing system of one or more processors (504), a memory storage (512), to cause the system to perform the application); thus, Claim 12 is rejected for reasons set forth in the rejections of Claim 5.
Regarding Claim 18:
Claim 18 incorporate substantively all the limitations of Claim 5 in a method (see Duplicate Claims, Warning)) and further recites no new limitations; thus, Claim 18 is rejected for reasons set forth in the rejections of Claims 5.
Claims 6, 13, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al., US-2016/0239867-A1, in view of Anderson et al., US-7,376,618-B1, in view of Bellini et. al, US-20180287898-A1.
Regarding Claim 6:
Sinha/Anderson teach the method of Claim 1. Sinha/Anderson fail to explicitly disclose:
wherein performing the intervention action includes:
opening, via the application, a messaging interface; and establishing a network connection with a live support agent to allow the user to communicate with the live support agent through the messaging interface.
However, Bellini teaches:
wherein performing the intervention action includes:
opening, via the application, a messaging interface; and establishing a network connection with a live support agent to allow the user to communicate with the live support agent through the messaging interface.
(Bellini, Page 5, Column 2, [0051], “… The device can then generate a support ticket indicating the increased utilization, and transmit it to an electronic dashboard that can assign the ticket to a support agent to resolve the issue …”;Page 6. [0057], “The system 100 can include, access or interact with a customer support system 126. … The customer support system 126 can process ticket data to prioritize tickets 130 and assign tickets 130 to support agents … automatically respond to tickets or resolve tickets. … generate a notification based on a new ticket 130 or an existing ticket 130, and the notification can be sent to the client device 132, third party device 134, or RUS 102. … can refer to a customer support representative, a support technician, a device of a customer support representative or technician, or an agent executed by a processor of a device.”; FIG. 2. The live support agents are shown within Figure 2 which allows the support agent to respond or resolve tickets to the client/third party device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize Sinha/Anderson’s methodology of analyzing abandoned online shopping carts with live support agent messaging interface of Bellini. One having ordinary skill in the art would have been motivated to implement this change before the effective filing date of to offer real time support with cloud utilization, automatic scaling, resource utilization/allocation management and more (see Bellini, Page 3, Column 1, [0027], “The state information and resource utilization that characterize a cloud service can be measured with improved accuracy compared to those of a service running on a corresponding physical machine. Similarly, the execution environment of a cloud service can be controlled more precisely that that of a service running on a corresponding physical machine … This information and control can be used to improve the performance of the cloud service by applying configuration updates and resource allocations, based on the improved measurements, and utilizing the improved control features to apply those updates and resource allocations. The improved measurements can be used to more accurately predict the operation of the cloud service and can correspondingly improve the selection of the configuration updates and resource allocations … a review of the CPU utilization of the tenants can reveal peak CPU usage for two tenants at two different morning hours, resulting from the two tenants being in two different time zones, and it can be possible to allocate a higher CPU limit to each tenant during their peak operation time, providing a more efficient overall utilization of the machine CPU resource.”)
Regarding Claim 13:
Claim 13 incorporates substantively all the limitations of Claim 6 in a system and further recites one or more processors; and a memory storing one or more instructions that, when executed on the one or more processors, cause the system to (see Sinha, Fig. 5. Figure 5 shows the system (computing device) that contains the processing system of one or more processors (504), a memory storage (512), to cause the system to perform the application); thus, Claim 13 is rejected for reasons set forth in the rejections of Claim 6.
Regarding Claim 19:
Claim 19 incorporate substantively all the limitations of Claim 6 in a method (see Duplicate Claims, Warning)) and further recites no new limitations; thus, Claim 19 is rejected for reasons set forth in the rejections of Claims 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM RAHMAN whose telephone number is (703)756-1646. The examiner can normally be reached M-F 8am-5pm.
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/I.R./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122