Detailed Action
This action is in response to the amendment filed on 05/11/2026 for application 18/194,676, in which:
Claims 1, 8 and 15 are the independent claims.
Claims 1-4 and 8-11 are currently amended.
Claims 15-20 are cancelled.
Claims 1-14 and 21-26 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 .
Regarding the Duplicate Claims, Warning:
The Duplicate Claims, Warning rejections of the previous office action have overcome the objections due to Claim 15-20 being cancelled. The warning has been withdrawn.
Regarding the Duplicate Claims, Warning:
The Double Patenting rejection has been withdrawn in view of the Terminal Disclaimer.
Response to Arguments
Applicant's arguments filed 05/11/2026 have been fully considered but they are not persuasive.
Regarding the 35 USC § 101 Rejections:
Applicant's arguments regarding the 35 U.S.C. 101 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant traverses the rejections (Page 10), by respectfully submitting that the pending claims recite patent eligible subject matter under Section 101, and thus requests withdrawal of the Section 101
rejections. Applicant further notes that the that the present claims are clearly eligible for the reasons discussed herein, and that the present claims at least reflect a "close call" such that unpatentability cannot be established by a preponderance of the evidence, as required.
Examiner respectfully disagrees. The 35 U.S.C. § 101 rejection is not rendered moot due to a “close call” as the amended claims are directed to an abstract idea (Step 2A Prong 1) and do not integrate the abstract idea into a practical application (Step 2A Prong 2). The rejection follows the steps of the analysis as laid out in the MPEP which was followed for the previous and current examination (see MPEP 2106). Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1, similar independent claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. More specific details are discussed below within the responses and 35 USC § 101 Rejections.
Applicant asserts (Pages 10-11), that the claims are directed to a technical solution to problems arising in the technical field of software applications. The applicant further supports these assertions as the claims embody the solution described in the specification and recites the determining, by a first machine-learning model ... , determining, by a second machine-learning model ... , and performing, via the application, the intervention action ... limitations.
Examiner respectfully disagrees. Although the Claims are interpreted in light of the specification, limitations from the specification are not read into the Claims.
MPEP 2106.05(a) recites:
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology … the claim must include the components or steps of the invention that provide the improvement described in the specification
…
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below.
Applicant fails to show how any alleged technical improvement would be provided by anything more than the judicial exception on its own. Additionally, applicant fails to show how the claim includes components or steps that would provide the alleged improvement described in the specification. By MPEP 2106.05(f)(1), "the claim recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished". Moreover, the examiner maintains that the Claim does not impose any meaningful limits on the judicial exceptions. The two determining limitations noted above are being evaluated as mental processes; where by a first/second machine-learning model is being evaluated under MPEP 2106.05(f) as 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.
Applicant asserts (Pages 12-13), that the claims are eligible under the first prong of the Step 2A analysis because the Examiner has not established that the features recited in the claims fall within any of the groupings identified in the MPEP. The Examiner does not assert that the claims recite certain methods of organizing human activity or mathematical concepts. Rather, the Examiner asserts that the claims recite features that fall within the grouping of mental processes. However, Applicant submits that the steps recited in Applicant's claims, under their broadest reasonable interpretation, cannot practically be performed in the mind. Such as the determining, by a first machine-learning model ... , determining, by a second machine-learning model ... limitations. To remove the machine learning aspects from these claim features is improperly reductive, as the actions recited in the claims are performed by machine learning models that have been trained in a particular manner. Therefore, just as "obtaining predicted character probabilities from a trained neural network" cannot practically be performed mentally, Applicant submits that various operations performed by a machine learning model, including the features discussed supra as currently recited in Applicant's claims cannot practically be performed mentally. Applicant submits that the claims do not recite an abstract idea and are thus eligible under the first prong of the Step 2A inquiry.
Examiner respectfully disagrees. The examiner establishes the features being evaluated as abstract ideas under Subject Matter Eligibility Analysis Step 2A Prong 1 where both determining ... limitations (a-b) fall into “mental processes” group of abstract ideas; which is noted within the previous and current office action. The examiner notes the evaluations for a-b to be mental processes by noting how a human being can mentally apply evaluation to make the determinations within the parentheses. The machine-learning aspects/limitations are only applying the mental process on a computer; thus, they are being evaluated under MPEP 2106.05(f)) under Subject Matter Eligibility Analysis Step 2A Prong 2. Thus, the recited abstract ideas are ineligible under the first prong of the Step 2A.
Applicant asserts (Pages 13-15), that any alleged abstract idea is nevertheless integrated into a practical application. Applicant submits that the claims are eligible under Step 2A Prong 2 because various features of the claims integrate any alleged abstract idea due to improving software applications by using an ordered combination of machine learning models to proactively predict a reason why a user may fail to perform a target action in the application using a first machine learning model and proactively predict an intervention to perform within the application using a second machine learning based on the reason predicted by the first machine learning model. Applicant submits that this practical application reflects an improvement to the technical field of software applications. The practical application improves the technical field of software applications by "us[ing] an ordered combination machine-learning models to identify users who are likely to abandon use of an application, predict the reasons why those users are likely to abandon, and identify intervening actions that the application can perform to reduce the probability that the users will abandon the application" which "can accelerate application response time, promote efficient use of memory and network bandwidth, and enhance the Quality of Experience (QoE) for the user overall" (as supported via the specification). The applicant further supports their assertions by noting Alice/Mayo and that the present claims are not merely instructions to apply an exception using a generic computer component, but provide a specific improvement in software application technology that integrates any alleged exception into a practical application, as discussed above. Applicant submits that the claims are directed to a specific improvement in computer technology, and are thus eligible subject matter under Step 2A, Prong 2 of the Alice/Mayo test.
Examiner respectfully disagrees. For the reasons given below and in the 35 U.S.C. § 101 rejections, the claims are directed to an abstract idea (Step 2A Prong 1) and do not integrate the abstract ideas into a practical application (Step 2A Prong 2). The pending claims recite abstract ideas that fall in at least one of the permissible groups, and noted within the office action below in more details. The independent claims fail to recite the steps that achieve the improvement. The independent claims do not recite how to achieve the alleged improvement and with no steps on how to achieve an improvement by the determination as there is no particular solution to a particular problem; thus, the Claims are not a technical solution to a technical problem. The rejection follows the steps of the analysis as laid out in the MPEP which was followed for the previous and current examination (see MPEP 2106). Thus, the office action does not fail to establish a proper and well-supported prima facie case as the claims are explained to be not patentable via the Patent Subject Matter Eligibility steps within MPEP 2106. The claims do not integrate the judicial exception into a practical application nor amount to significantly more. The claim is not patent eligible. Although the Claims are interpreted in light of the specification, limitations from the specification are not read into the Claims.
Applicant asserts (Pages 15-18), as discussed with respect to Step 2A, Prongs 1 and 2 supra, the Specification provides sufficient details to show how the claimed features improve the technical field of software applications. The applicant then notes the specification (as noted above), to show the improvements accomplished by the two determining, by a first machine-learning model ... , determining, by a second machine-learning model ... limitations. The amended claims are similar to Cosmokey and BASCOM; thus, providing a technical improvement and significantly more and notes the determining, by a first machine-learning model ... , determining, by a second machine-learning model ... limitations to support the assertions that these claimed features non-conventional and non-generic but they also improve the technical field of software applications, as explained in detail above. Accordingly, Applicant respectfully submits that the claims are eligible under Step 2B of the Alice/Mayo test and are therefore directed to eligible subject matter under Section 101. Thus, for at least these additional reasons, Applicant respectfully requests withdrawal of this rejection.
Examiner respectfully disagrees. This has been addressed above and below within the remarks and rejections. The noted features/limitations within the newly amended claims contain additional elements and abstract ideas but the additional elements are unable to integrate the judicial exception. Currently, the two types of additional elements fall within MPEP 2106.05 (f) and (g) for the independent claims (which is shown in Step 2A Prong 2). The pending claims recite abstract ideas that fall in at least one of the permissible groups, and noted within the office action below in more details. The independent claims fail to recite the steps that achieve the improvement. The limitations are unable to provide improvement as they are currently being evaluated as either abstract idea(s) or additional elements that fall within MPEP 2106.05. The claims are directed towards the improvement of an abstract idea. Improvements to an abstract idea are still considered to an abstract idea. Additionally, the Claims does not reflect any improvement in the functioning of a computer or hardware processor rather the additional elements merely use a generic computer component to perform the abstract idea or restricting the abstract idea to a particular technological environment or insignificant extra solution activities. Therefore, the claims do not integrate the judicial exception into a practical application nor amount to significantly more. The claim is not patent eligible. More specific details are discussed below within the responses and 35 USC § 103 Rejections. The amended independent claim rejections have been updated due to the amendments. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1, analogous independent Claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. More specific details are discussed below within the 35 USC § 101 Rejections.
Regarding the 35 USC § 103 Rejections:
Applicant's arguments regarding the 35 U.S.C. 103 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant traverses the 103 rejections (Pages 18-19), the combination of Sinha and Anderson does not teach, suggest, or otherwise render obvious all of the features recited in Claims 1 and 8 as currently amended. Accordingly, Applicant respectfully requests withdrawal of the rejection under Section 103 of Claims 1 and 8, as well as claims dependent thereon. The dependent claims depend upon one of the independent claims discussed above, and includes features recited in its base claim as well as any intervening claims. The additional cited references fail to overcome the deficiencies addressed above with respect to the independent claims. Accordingly, these claims are allowable for substantially similar reasons as discussed above and for their additional novel features recited therein.
Examiner respectfully disagrees. The previous combination does not teach the newly amended claims; however, due to the amendments the claims have been updated with a new prior art reference. Applicant’s arguments with respect to the independent claim(s) have 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. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1, analogous independent Claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. More specific details are discussed below within the 35 USC § 103 Rejections.
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, 3-8, 10-14, 21, and 23-26 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, … , a predicted reason the interaction session is likely to terminate before the user completes a target action, ... , based on training data comprising features of previous interaction sessions associated with labels indicating reasons why target actions were not completed during the previous interaction sessions, to determine the predicted reason from a set of reasons based on the response data (a human being can mentally apply evaluation to determine a predicted reason the interaction session is likely to terminate for a specific constraint (such as before the user completes a target action/task) which is based on specific data to determine the predicted reason from a specific set of reasons)
determining, … , an intervention action for increasing a probability that the user will complete the target action before the interaction session terminates, ... , based on corresponding training data determined from corresponding previous interaction sessions, to determine the intervention action based on the response data and the predicted reason ... (a human being can mentally apply evaluation to determine an intervention action within a specific constraint (such as for increasing a probability that the user will complete a target action/task before a session terminates) which is based on specific data)
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 one or more web pages, response data from the user (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g)
… by 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))
... based on providing the response data as input features to the first machine-learning model ... (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g)
... based on providing the response data and the predicted reason ... as corresponding input features to the second machine-learning model ... (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g)
… by a second machine-learning model … wherein the second machine-learning model has been trained ... (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))
... by displaying one or more elements within at least one of the one or more web pages (which is insignificant extra-solution activity of data display or output, 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 additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional elements a-b and d-e 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 and f-g are merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Additional element h falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of presenting offers and gathering statistics (MPEP 2106.05(d)(II): OIP Techs., 788 F.3d at 1362- 63, 115 USPQ2d at 1092-93). 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 further recites the method comprising of ... determined based on the response data ... (a human being can mentally apply evaluation to determine based on specific data). 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 … include a value ... (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
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 is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. 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 one or more 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 21-26:
Claims 21-26 incorporate substantively all the limitations of Claims 1-6 in a non-transitory computer readable medium (thus, a manufacture) and further recites no new limitations; thus, Claims 21-26 are rejected for reasons set forth in the rejections of Claims 1-6, respectively.
Regarding Claims 2, 9, and 22:
Claims 2, 9, and 22 are subject-matter eligible and not rejected under 35 U.S.C. 101 as the limitations recite determining, via a third machine-learning model based on the response data, a next action the user is anticipated to perform in the application; and altering, based on the next action, at least one aspect of the one or more web pages to facilitate user performance of the next action, wherein the altering comprises one or more of: increasing a display size of an element within a web page of the one or more web pages; highlighting the element within the web page; or displaying instructions related to the next action near the element within the web page. Dependent Claims 2, 9, and 22 in combination with their respective Independent Claims provide a reasonable basis for integration into a practical application under Subject Matter Eligibility Analysis Step 2A Prong 2. Thus, Claims 2, 9, and 22 are subject-matter eligible.
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, 21, 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al., US-20160239867-A1, in view of Vijayaraghavan et.al, US-20140222503-A, in view of Li et al., “End-to-End Task-Completion Neural Dialogue Systems”.
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 one or more 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”; Page 4, [0037], “… the service provider 110 corresponds to an online store that is implemented in the form of a website having multiple web pages ..”. 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 one or more web pages (as the website contains multiple web pages), response data from the user).
determining, by a first machine-learning model based on providing the response data as input features to the first machine-learning model, a predicted reason the interaction session is likely to terminate before the user completes a target action, wherein the first machine-learning model has been trained, based on training data comprising features of previous interaction sessions...
(Sinha, Fig.3: 174; 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). The machine learning model is trained on previous interaction history (historical data) based on the collected (previous) data and is shown within Fig. 3: 174).
determining, … based on providing 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 which is provided with 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, specific associated labels, and displaying the intervention explicitly within a webpage.
However, Vijayaraghavan teaches:
…. based on training data comprising features of previous interaction sessions associated with labels indicating reasons why target actions were not completed during the previous interaction sessions, to determine the predicted reason from a set of reasons based on the response data;
(Vijayaraghavan, Figure 3: 172-174; Pages 3-4, Table 2; Page 3, Column 1-2, [0053], “A training and test data phase (172) partitions the data into training and test data ... This stage determines intent type based upon business needs, e.g. purchase, non-purchase, or purchase with assistance such as chat, self-serve purchase, browser, etc. Based upon the determined intent, a response variable, i.e. class label, is defined”. Figure 3: 172 notes the training data to determine intent type based on business needs based on features such as purchase history and based on the determined intent type the data is associated with a class label. The class label is interpreted as why target actions were not completed or completed during previous interactions as the features to decide intent type are based on business needs which entails the features. Table 2 shows the response variable (intent types) and the criteria for the categorization within a class label (such as “Used Proactive Chat but did not purchase” which is a reason why target actions (completed purchase) were not completed). Thus the class labels are associated with the training data to determine the predicted reason from a set of reasons based on the response data from the user session).
determining, ... based on the providing response data and the predicted reason determined by the first machine-learning model as corresponding input features to the ... to determine the intervention action based on the response data and the predicted reason determined by the first machine-learning model;
(Vijayaraghavan, Figure 3: 172-174; Figure 10. Figure 3 shows the corresponding inputs features determined by the first machine learning model to determine the intervention action (Figure 10: 106) which in this scenario is offering assistance via a chat or some offer to help the user meet requirements/expectations before leaving the site; thus, an intervention action to complete a purchase which is based on the response data and predicted intent (which is a predicted reasoning of the customers intent)).
… by displaying one or more elements within at least one of the one or more web pages.
(Vijayaraghavan, Figure 14; Page 4, Column 1, [0063], “The database 23 captures user-related data concerning the user's visits to au online commerce Web site ... the users are provided with virtual assistance, if requested, during a session. In an embodiment of the invention, the virtual assistance may be in the form of Web-based support ...”. Once the intervention action is determined, the performing of the intervention action is taught to be provided via virtual assistance within at least one of the one or more web pages (also shown in Figure 14); thus, by displaying one or more elements within at least one of the one or more web pages).
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 teachings of specific associated labels, corresponding input feature to determine intervention actions, and displaying the elements for performing the intervention action within a web page from Vijayaraghavan. 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 proactive planning, model accuracy, personalized intervention, incremental learning and providing assistance (see Vijayaraghavan, Pages 5-6, [0083], “The output produced by the proposed model, as shown in FIG. 5, depicts the predictive accuracy of the model. As the Web visit progresses, the model accurately discovers the user's intent. Because the model incorporates the dynamic information and learns the intent of the user incrementally, suitable personalized intervention can be planned proactively which, in tum, helps to achieve sales goals. For example, suppose a user is visiting a product page, e.g. for laptops, and is viewing the product's details. At this stage, the user may need some help to know more information about the configuration of the system so that he can decide whether this meets his requirements or not. If the user's need or 'seek assistance intent' is proactively detected by the model, the seller can offer chat assistance to the user help him make the right decision at right time before quitting the website”).
Nevertheless, Sinha/Vijayaraghavan do not explicitly teach:
... via a second machine-learning model … wherein the second machine-learning model has been trained, based on corresponding training data determined from corresponding previous interaction sessions
However, Li teaches:
... via a second machine-learning model … wherein the second machine-learning model has been trained, based on corresponding training data determined from corresponding previous interaction sessions
(Li, Page 2, Figure 1: “Illustration of the end-to-end neural dialogue system: given user utterances, reinforcement learning is used to train all components in an end-to-end fashion”. Figure 1 shows the second machine learning model (DM model uses DQN: deep Q-network) which has been trained (via reinforcement learning) with corresponding training data (states for all actions) which is from corresponding previous interactions (as there are states/actions for the previous interaction sessions)).
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/Vijayaraghavan’s methodology of analyzing abandoned online shopping carts which teaches the determinations with the explicit teaching of Li’s second machine-learning model which explicitly incorporates the first machine learning model’s determination and trained based off previous interaction sessions. 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 robustness, performance for task completion, and maintain correct intent with the assistance model (see Li, Pages 8-9, Column 2: Paragraph 2-Column 1: Paragraphs 1, “This paper presents an end-to-end learning framework for task-completion neural dialogue systems. Our experiments, both on simulated and real users, show that reinforcement learning systems outperform rule-based agents and have better robustness to allow natural interactions with users in real-world task-completion scenarios. Furthermore, we conduct a series of extensive experiments to understand the impact of natural language understanding errors on the performance of a reinforcement learning based, task-completion neural dialogue system. Our empirical results suggest several interesting findings: 1) slot-level errors have a greater impact than intent-level errors; A possible explanation is related to our dialogue action representation, intent(slot-value pairs) ... Another reason is that the dialogue agent can still maintain a correct intent based on slot information even though the predicted intent is wrong ...”).
Regarding Claim 3:
Sinha/Vijayaraghavan/Li teach the method of Claim 1 and Li further teaches:
wherein the corresponding input features provided to the second machine-learning model further include a value determined based on the response data
(Li, Page 2, Figure 1: Semantic Frame. Figure 1 shows the Semantic Frame which is also provided to the second machine-learning model. The Semantic Frame is based on the users response data as it frames the user response data into a semantic frame such as a request/genre/date; thus, interpreted by the examiner as a value determine based on the response data).
The motivation of Claim 1’s combination of Sinha/Vijayaraghavan/Li is still maintained.
Regarding Claim 4:
Sinha/Vijayaraghavan/Li teach the method of Claim 3 and Sinha further teach:
receiving, via the one or more 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 21 and 23-24:
Claims 21 and 23-24 incorporate substantively all the limitations of Claims 1 and 3-4, in a method (see Duplicate Claims, Warning)) and further recites no new limitations; thus, Claims 21, and 23-24 are rejected for reasons set forth in the rejections of Claims 1 and 3-4, respectively.
Claims 2, 9, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al., US-20160239867-A1, in view of Vijayaraghavan et.al, US-20140222503-A, in view of Li et al., “End-to-End Task-Completion Neural Dialogue Systems”, in view of Anderson et al., US-7,376,618-B1.
Regarding Claim 2:
Sinha/Vijayaraghavan/Li teach the method of Claim 1 and Vijayaraghavan:
determining … a next action the user is anticipated to perform in the application; and
(Vijayaraghavan, Figure 10: 104. Figure 10: 104 determines a next action (user’s intent for an action; thus, interpreted by the examiner as a next action the user is anticipated to perform) due to the intent being high on a specific page).
altering, based on the next action, at least one aspect of the one or more web pages to facilitate user performance of the next action, ...
(Vijayaraghavan, Figure 10: 106. Figure 10: 106 provides chatbot or some personalized offer to prevent the user from leaving the page (based on a next action); thus, altering at least one aspect of the webpage to facilitate the user of completing a target action).
wherein the altering comprises one or more of: increasing a display size of an element within a web page of the one or more web pages; ... or ...
(Vijayaraghavan, Figure 10. Vijayaraghavan teaches proactively altering the web page by offering a chat or some offer to aid the user from not leaving the site; thus, providing a chatbot/assistance within a web page leads to increasing a display size of the chatbot within a web page).
The motivation of Claim 1’s combination of Sinha/Vijayaraghavan/Li is still maintained.
Sinha/Vijayaraghavan/Li do not explicitly teach a third machine learning model for determining next actions.
However, Anderson teaches:
determining, via a third machine-learning model based on the response data, …
However, Anderson teaches:
(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).
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/Vijayaraghavan/Li’s methodology of analysis with the explicit teaching of the third machine-learning model of Anderson which explicitly incorporates the previous machine learning model’s determinations. 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 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 22:
Claim 22 incorporate substantively all the limitations of Claim 2 in a non-transitory computer readable medium and further recites no new limitations; thus, Claim 22 is rejected for reasons set forth in the rejections of Claims 2.
Claims 5, 12, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al., US-20160239867-A1, in view of Vijayaraghavan et.al, US-20140222503-A, in view of Li et al., “End-to-End Task-Completion Neural Dialogue Systems”, in view of Khosravi et. al, “Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances”.
Regarding Claim 5:
Sinha/Vijayaraghavan/Li teach the method of Claim 4. Sinha/Vijayaraghavan/Li 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/Vijayaraghavan/Li’s methodology of analysis 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 25:
Claim 25 incorporate substantively all the limitations of Claim 5 in a non-transitory computer readable medium and further recites no new limitations; thus, Claim 25 is rejected for reasons set forth in the rejections of Claims 5.
Claims 6, 13, 26 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al., US-20160239867-A1, in view of Vijayaraghavan et.al, US-20140222503-A, in view of Li et al., “End-to-End Task-Completion Neural Dialogue Systems”, in view of Bellini et. al, US-20180287898-A1.
Regarding Claim 6:
Sinha/Vijayaraghavan/Li teach the method of Claim 1. However, Sinha/Vijayaraghavan/Li 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 26:
Claim 26 incorporate substantively all the limitations of Claim 6 in a non-transitory computer readable medium and further recites no new limitations; thus, Claim 26 is rejected for reasons set forth in the rejections of Claims 6.
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.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/I.R./ Examiner, Art Unit 2122
/KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122
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