DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The Amendment filed on 3/12/2026 has been received and entered. Application No. 18/299,684 Claims 1-23 are now pending. Claims 1, 8 & 15 have been amended. Claims 21-23 are new.
Response to Amendment
Applicant’s amendment necessitated new grounds of rejection.
This action is made final in view of the new grounds of rejection.
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.
Claim(s) 1, 2, 4-6, 8, 9, 11-13, 15, 16 & 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. Pub 2016/0019460) in view of KANEMOTO (U.S. Pub 2017/0160881) hereinafter Kan, in view of Medlock et al. (U.S. Pub 2012/0197825) hereinafter Med.
As per Claim 1, Li teaches A method, comprising, by a software application executing on a computing device: a prediction center executing on the computing device; (Abstract; Fig. 2, Fig. 8A wherein a method for predicting events on a mobile application wherien the prediction module can be implemented as an Android service for devices, such as smartphones, mobile devices. A service utilizing the prediction module (e.g., a process or thread) can run in the background and awake in response to an application request)
receiving the prediction from the prediction center; (Fig. 2, ¶38 wherein prediction module PM running on device D can receive a message M. For example, message M can be an event message reporting receipt of a message, a prediction request or query requesting a prediction, or some other type of message. In other embodiments, message M can be some other type of indication than a message; e.g., a method or other function of prediction module PM can be invoked to provide the information described as being in message M);
monitoring activity at the computing device to determine an accuracy of the prediction; (Fig. 2,Fig. 4, ¶42, ¶98 wherien prediction module PM can determine prediction score PS to predict occurrence of event E for application AP wherien three graphs 400, 430, 460, where each graph shows a number of event occurrences on the X (horizontal) axis and accuracy in terms of the best guess on the Y (vertical) axis, with accuracy ranging from 0; e.g., completely inaccurate to 1 e.g., completely accurate. Graph 400 at the top of FIG. 4 shows prediction accuracies for the Launcher application on Device A with respect to the prediction module (PM) predictor 402, Naïve Bayesian (NB) predictor 404, frequency (F) feature 406, day (D) feature 408, hour (H) feature 410, Markov (Mv) feature 412, Cross-Markov (CMv) feature 414, recency feature 416, and Poisson feature 418. In graphs 400, 430, 460 shown in FIG. 4, prediction module predictor data are shown using a line with *'s (asterisks) added and Naïve Bayesian predictor data are shown with is shown using a line with circles added)
generating prediction center feedback based on the accuracy; and (¶96 wherein Each prediction technique—component predictors, prediction module, and Naïve Bayesian predictor—was provided with an event stream; e.g., a log trace in a dataset in an online, or event by event, fashion. Each technique was requested to predict occurrence of an event after the event had been observed at least once, using the timestamp of the event as the query time. For the Device B dataset with collected Cell information, the Cell Tower and Cell Geo information was provided at query time.)
providing the prediction center feedback to the prediction center, (¶96, ¶97 wherein The ranking of the target event was recorded to calculate how often the target event was ranked as the top choice. The prediction technique was then permitted to learn from the event. This process was repeated for each event in the event stream)
wherein the prediction center feedback is constructed for updating the prediction center to improve accuracy of subsequent predictions associated with the prediction category. (Fig. 4, Fig. 5, ¶96, ¶97 wherein the accuracy of each prediction method generally increases as more event occurrences are observed wherein the prediction technique was then permitted to learn from the event. This process was repeated for each event in the event stream )
However, Li does not explicitly teach issuing, to a prediction center executing on the computing device, a request for a prediction associated with a prediction category;
Kan teaches issuing, to a prediction center executing on the computing device, a request for a prediction associated with a prediction category; (Fig.3, ¶49, ¶50, ¶102, ¶106, ¶121, ¶123, ¶124 wherein The server 11 provides a service of supporting a user to activate an intended application (hereinafter, referred to as activation supporting service) by presenting an application, which corresponds to a situation, to a user using the clients 12-1 to 12-n that can install and execute a plurality of applications wherein the information acquisition unit 231 generates application retention information including an application ID of an application installed in the client 12 and a user ID of a user retaining the application. The information acquisition unit 231 transmits the application retention information to the server 11 through the communication unit 210 and the network 13 wherien the model learning unit 142 learns an activation prediction model of predicting an activation probability of an application on the basis of a date and time outputting an activation score indicating an activation probability)
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to utilize the teaching of information acquisition of Kan with the teaching of enabling event prediction of Li because Kan teaches an improved information acquisition unit that acquires first information indicating a current situation including a current date and time and a current position of a user, and a selection unit that selects a presented application, which is an application program presented to the user, on the basis of a use history, a profile of the user, and the first information, the use history being a use history of an application program of the user and including second information indicating a situation in activation which situation includes a date and time and a position of the user in activation of each application program to improve convenience of a user in utilization of an application program. (¶5, ¶6)
Li as modified previously taught providing the prediction center feedback to the prediction center. However, Li as modified does not explicitly teach wherein providing the prediction center feedback to the prediction center comprises: in accordance with a determination that the prediction category is a first prediction category, providing the prediction center feedback to the prediction center, wherein the prediction center feedback is assigned to a first set of prediction engines associated with the first prediction category; and in accordance with a determination that the prediction category is a second prediction category, different from the first prediction category, providing the prediction center feedback to the prediction center, wherein the prediction center feedback is assigned to a second set of prediction engines , different from the first set of prediction engines, associated with the second prediction category.
Med teaches wherein providing the prediction center feedback to the prediction center comprises: (¶23 wherien the step of generating text predictions comprises generating text predictions using at least two predictors. Preferably, generating text predictions using at least one of the at least two predictors comprises generating text predictions based upon the user text input, generating a second set of text category predictions and generating a set of new text predictions by weighting the text predictions from the second predictor by the second set of category predictions)
in accordance with a determination that the prediction category is a first prediction category, (¶9 wherein the system comprising a text prediction engine comprising at least one predictor and configured to receive text input into the device by a user and to generate text predictions using the at least one predictor, a classifier configured to receive the input text and to generate at least one text category prediction, and a weighting module configured to receive the text predictions and the at least one category prediction and to weight the text predictions by the category predictions to generate new text predictions for presentation to the user)
providing the prediction center feedback to the prediction center, wherein the prediction center feedback is assigned to a first set of prediction engines associated with the first prediction category; and (¶8, ¶9, ¶15, ¶33, ¶34, ¶39 wherein the category predictions can be graded to give broad category predictions and finer category predictions within those broad categories. For example, sport as a broad category can be split into any number of sub-categories, and these sub-categories can be further divided. If a sub-category of sport is football, this sub-category could be split into further sub-categories such as football clubs, players, managers etc. The system of the present invention can therefore predict accurately, from the user inputted text, a number of categories that this text relates to. The system can then hone the text predictions generated by a text prediction engine (that generates, preferably, context based predictions) by decreasing the probabilities of predictions which are unlikely to occur given the category predictions for the user inputted text The system comprises a plurality of text sources 1, 2, 3, each text source comprising at least one, and preferably a plurality of, documents. Each text source 1, 2, 3 is a body of electronic text for which there exists a category label referring to some aspect of the nature of the text. The category label could refer to a particular language, to a particular topic (e.g. sport, finance etc.), to a particular genre (e.g. legal, informal, etc.), to a particular author, to a particular recipient or set of recipients, to a particular semantic orientation, or to any other attribute of the text that can be identified. The text sources are used to train one or more predictors 6, 7, 8 and a classifier 9 wherein Each predictor 6, 7, 8 is trained by a text source 1, 2, 3, where each text source is used to train a separate predictor. Wherein The classifier 9 is trained by a training module 5 using the text sources 1, 2, 3, passed through the Feature Vector Generator 4 wherien given the three categories of Sport, Finance and Politics, three individual TAP classifiers would be trained)
in accordance with a determination that the prediction category is a second prediction category, different from the first prediction category, (¶15 wherein the at least one adaptive prediction system comprises a second text prediction engine comprising at least one predictor and configured to receive the input text and to generate text predictions using the at least one predictor, a second classifier configured to receive the input text and to generate at least one text category prediction, a second weighting module configured to receive the text predictions from the second text prediction engine and the at least one category prediction from the second classifier and to weight the text predictions by the category predictions to generate new text predictions.)
providing the prediction center feedback to the prediction center, wherein the prediction center feedback is assigned to a second set of prediction engines , different from the first set of prediction engines, associated with the second prediction category. (Fig. 2, ¶15, ¶73, ¶75, ¶77, ¶85, ¶86 wherien the at least one adaptive prediction system comprises a second text prediction engine comprising at least one predictor and configured to receive the input text and to generate text predictions using the at least one predictor, a second classifier configured to receive the input text and to generate at least one text category prediction, a second weighting module configured to receive the text predictions from the second text prediction engine and the at least one category prediction from the second classifier and to weight the text predictions by the category predictions to generate new text predictions wherien the weighting module 12 generates an M-dimensional weights vector from the M-dimensional confidence vector of the category predictions 10 and uses the weights vector to scale the text predictions 11 from the M predictors of the text prediction engine, thereby generating category-weighted text predictions. The category-weighted text predictions are inserted, by the weighting module, into a multimap and the p most probable predictions 13 are returned to the user of the system for selection and text input wherien predictor 6, 7, 8 can be an adaptive predictive system, such as that described in FIG. 1. The present system therefore defines a recursive framework that allows an arbitrary number of adaptive predictors to be structured in hierarchy. Such an example is now described with reference to FIG. 2. FIG. 2 schematically shows the adaptive prediction architecture of claim 1, where one of the predictors 26, 27, 28 of the text prediction engine 200 is an adaptive predictor 26. Each of the predictors 46, 47, 48 within this adaptive predictor 26 can be a single language model, a multi-language model or an adaptive prediction model. Thus, the adaptive prediction architecture defines a recursive framework that allows an arbitrary number of adaptive predictors to be structured in hierarchy wherien At the first level, three text sources 21, 22, 23 are used, representing three topics: sport, finance and politics. These sources and their respective categories are passed through the Feature Vector Generator 24 to the TAP training module 25 to yield a 3-class TAP classifier 29. The text sources 22, 23 representing the finance and politics categories are used to train single language models 27, 28 while the text source representing sport 21 is used to train an adaptive predictor 26 wherein This input text 34 is passed to the first-level text prediction text engine 200 and the second-level text prediction engine 400 to generate first-level text predictions 31 and second-level text predictions 51. The input text 34 is also passed to both the first-level TAP classifier 29 and the second-level TAP classifier 49 after being preprocessed into the TAP input format described above. Each classifier 29, 49 yields a three-element confidence vector. The input text 34 is also passed to the first-level text prediction text engine 200 and the second-level text prediction engine 400 to generate first-level text predictions 31 and second-level text predictions 51 wherein , the first-level classifier 29 distinguishes between the categories sport, finance and politics. Examiner is relying on additional paragraphs 15 and 85 to show a recursive of adaptive predictors with two separate text prediction engines that includes a plurality of predictors in each , and in addition the classifiers all for the different categories to exist.)
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to utilize the teaching of inputting text into small screen devices of Med with the teaching of enabling event prediction of Li as modified because Med teaches a improved system which employs a machine learning technique, classification, to make real-time category predictions for sections of text entered by a user. The system uses the category predictions to reorder and/or select the text predictions generated by a text prediction engine. The generated text predictions can then be displayed for user selection to input text into an electronic device wherein Reordering the text predictions by category predictions offers the advantage of placing predictions that are more likely to be relevant to the current textual topic/genre etc. (¶7, ¶8)
As per Claim 2, the rejection of claim 1 is hereby incorporated by reference; Li as modified further teaches wherein the prediction center provides predictions for a plurality of prediction categories, and the plurality of prediction categories includes the prediction category. (¶49, ¶50, ¶70, ¶106, ¶121, ¶124 wherein The server 11 provides a service of supporting a user to activate an intended by presenting an application, which corresponds to a situation, to a user using the clients 12-1 to 12-n that can install and execute a plurality of applications wherein The model learning unit 142 performs learning of a model of predicting an activation probability of each application on the basis of an application use history of each user which history is acquired from each client 12 and a user vector of each user wherien the model learning unit 142 generates a date-and-time activation prediction model of outputting an activation score indicating an activation probability (likeliness to be activated) of each application at a specified date and time; as taught by Kan)
As per Claim 4, the rejection of claim 1 is hereby incorporated by reference; Li as modified further teaches wherein improving the accuracy of subsequent predictions associated with the prediction category comprises: providing the prediction center feedback to at least one of the one or more prediction engines, (¶96, ¶97 wherein Each prediction technique—component predictors, prediction module, and Naïve Bayesian predictor—was provided with an event stream; e.g., a log trace in a dataset in an online, or event by event, fashion. Each technique was requested to predict occurrence of an event after the event had been observed at least once, using the timestamp of the event as the query time. For the Device B dataset with collected Cell information, the Cell Tower and Cell Geo information was provided at query time wherein The ranking of the target event was recorded to calculate how often the target event was ranked as the top choice. The prediction technique was then permitted to learn from the event. This process was repeated for each event in the event stream; as taught by Li)
wherein the one or more prediction engines update, based on the prediction center feedback, respective confidence levels that are advertised to the prediction center with regard to generating predictions for the prediction category. (Fig. 4, Fig. 5, ¶96, ¶97, ¶98 wherein the accuracy of each prediction method generally increases as more event occurrences are observed wherein the prediction technique was then permitted to learn from the event. This process was repeated for each event in the event stream wherien three graphs 400, 430, 460, where each graph shows a number of event occurrences on the X (horizontal) axis and accuracy in terms of the best guess on the Y (vertical) axis, with accuracy ranging from 0; e.g., completely inaccurate to 1 e.g., completely accurate; as taught by Li)
As per Claim 5, the rejection of claim 1 is hereby incorporated by reference; Li as modified further teaches wherein: the prediction indicates that a particular action will be taken by a user of the computing device, and (¶17, ¶24 wherien Predictive (or adaptive) user interfaces (UIs) for mobile devices can dynamically optimize the interaction flow or UI layout for specific actions that a user is likely to perform wherein the prediction module can be initially provided with prior data related to the application. The prior data can be based on a model of one or more general users, prior event captures, and/or prior user interaction with other, similar devices (e.g., interaction with an older mobile device that has been or is being replaced; as taught by Li)
monitoring the activity at the computing device comprises determining whether the particular action was taken by the user. (¶22 wherien An application can send interaction events to the prediction module via a simple API. These interaction events can be arbitrary and only meaningful to the application, e.g., they can be application names for a launcher, phone numbers dialed for a dialer or search queries entered in a search box. Then, the application can then query how likely each of the interaction events would occur at a given time. An application can then optimize a user interface based on the predicted results, e.g., sorting a list of options based on their prediction scores; as taught by Li)
As per Claim 6, the rejection of claim 1 is hereby incorporated by reference; Li as modified further teaches wherein the prediction is associated with a confidence level. (¶42 wherein prediction module PM can determine prediction score PS to predict occurrence of event E for application AP based on features F1, F2, . . . , event E, and/or application AP associated with prediction request PR; as taught by Li)
Claim 8 is similar in scope to Claim 1; therefore, Claim 8 is rejected under the same rationale as Claim 1.
Claim 9 is similar in scope to Claim 2; therefore, Claim 9 is rejected under the same rationale as Claim 2.
Claim 11 is similar in scope to Claim 4; therefore, Claim 11 is rejected under the same rationale as Claim 4.
Claim 12 is similar in scope to Claim 5; therefore, Claim 12 is rejected under the same rationale as Claim 5.
Claim 13 is similar in scope to Claim 6; therefore, Claim 13 is rejected under the same rationale as Claim 6.
Claim 15 is similar in scope to Claim 1; therefore, Claim 15 is rejected under the same rationale as Claim 1.
Claim 16 is similar in scope to Claim 2; therefore, Claim 16 is rejected under the same rationale as Claim 2.
Claim 18 is similar in scope to Claim 4; therefore, Claim 18 is rejected under the same rationale as Claim 4.
Claim 19 is similar in scope to Claim 5; therefore, Claim 19 is rejected under the same rationale as Claim 5.
Claim 20 is similar in scope to Claim 6; therefore, Claim 20 is rejected under the same rationale as Claim 6.
Claim(s) 3, 7, 10, 14 & 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Kan in view of Med as applied to claims 1, 6, 8, 13 & 15 above, and further in view of Szeto et al. (U.S. Pub 2017/0124487) hereinafter Szeto.
As per Claim 3, the rejection of claim 1 is hereby incorporated by reference; Li as modified previously taught prediction category, request. However, Li as modified does not explicitly teaches wherein issuing the request causes the prediction center to: identify, among a plurality of prediction engines, one or more prediction engines that are capable of providing predictions associated with the prediction category; provide, to the one or more prediction engines, information associated with the request; receive one or more predictions from the one or more prediction engines; aggregate the one or more predictions to generate the prediction.
Szeto teaches wherein issuing the request causes the prediction center to: identify, among a plurality of prediction engines, one or more prediction engines that are capable of providing predictions associated with the prediction category; (Fig. 2B, ¶243 an architectural overview of a deployable machine learning framework 250 based on multiple predictive engines, according to one embodiment. Here each of mobile application 270, website 272, and email campaign 274 sends user input, behavior, and/or other related data 261 to event server 262, continuously or in a batch mode. Instead of a single predictive engine, different engines, shown as engines 264, 265, 266, and 267, may be built for different purposes within the PredictionIO or machine learning server or platform 260)
provide, to the one or more prediction engines, information associated with the request; (¶263 wherein The determination and tuning of engine parameters is the key to generating good predictive engines. The evaluator component, also called an evaluation module, facilitates the engine tuning process to obtain the best parameter set. For example, in a classification application that uses a Bayesian algorithm, an optimal smoothing parameter for making the model more adaptive to unseen data can be found by evaluating the prediction quality against a list of parameter values to find the best value)
receive one or more predictions from the one or more prediction engines; (Fig. 3B, ¶250 wherein predictive engine 320 may respond to dynamic query 362 from user application 360. Query 362 may be in a predefined format, and predictive engine 320 may conduct further conversion of query data 362 before passing it to one or more trained algorithms or models 330 to 334, to trigger a predict function within each algorithm that has defined this particular function. As a result, each active algorithm or predictive model returns a predicted result in response to dynamic query 362. For example, the predicted result may be a list of product IDs, or a list of product recommendation scores associated with a list of product IDs.)
aggregate the one or more predictions to generate the prediction. (¶250 wherien The predicted results are passed to a serving component 340 of predictive engine 320. Serving component 340 further processes and aggregates the prediction results to generate a predicted result 345 for output back to user application 360.)
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to utilize the teaching of an improved learning model training and deployments with a rollback mechanism of Szeto with the teaching of enabling event prediction of Li as modified because Szeto teaches wherein Prediction result 445 and evaluation result 455 can be passed to other components within a PredictionIO or machine learning server. As discussed previously, a PredictionIO or machine learning server is a predictive engine deployment platform that enables developers to customize engine components, evaluate predictive models, and tune predictive engine parameters to improve performance of prediction results. A PredictionIO or machine learning server may also maintain adjustment history in addition to prediction and evaluation results for developers to further customize and improve each component of an engine for specific business needs. (¶259)
As per Claim 7, the rejection of claim 6 is hereby incorporated by reference; Li as modified previously taught the confidence level, the prediction center, the prediction. However, Li as modified does not explicitly teach wherein the confidence level affects a manner in which the prediction center aggregates the prediction with the at least one other prediction.
Szeto teaches wherein the confidence level affects a manner in which the prediction center aggregates the prediction with the at least one other prediction. (¶250 wherien Query 362 may be in a predefined format, and predictive engine 320 may conduct further conversion of query data 362 before passing it to one or more trained algorithms or models 330 to 334, to trigger a predict function within each algorithm that has defined this particular function. As a result, each active algorithm or predictive model returns a predicted result in response to dynamic query 362. For example, the predicted result may be a list of product IDs, or a list of product recommendation scores associated with a list of product IDs. The predicted results are passed to a serving component 340 of predictive engine 320. Serving component 340 further processes and aggregates the prediction results to generate a predicted result 345 for output back to user application 360.)
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to utilize the teaching of an improved learning model training and deployments with a rollback mechanism of Szeto with the teaching of enabling event prediction of Li as modified because Szeto teaches wherein Prediction result 445 and evaluation result 455 can be passed to other components within a PredictionIO or machine learning server. As discussed previously, a PredictionIO or machine learning server is a predictive engine deployment platform that enables developers to customize engine components, evaluate predictive models, and tune predictive engine parameters to improve performance of prediction results. A PredictionIO or machine learning server may also maintain adjustment history in addition to prediction and evaluation results for developers to further customize and improve each component of an engine for specific business needs. (¶259)
Claim 10 is similar in scope to Claim 3; therefore, Claim 10 is rejected under the same rationale as Claim 3.
Claim 14 is similar in scope to Claim 7; therefore, Claim 14 is rejected under the same rationale as Claim 7.
Claim 17 is similar in scope to Claim 3; therefore, Claim 17 is rejected under the same rationale as Claim 3.
Claim(s) 21, 22 & 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Kan in view of Med as applied to claims 1, 8 & 15 above, and further in view of Colagrosso et al. (U.S. Pub 2019/0179494) hereinafter Colag.
As per Claim 21, the rejection of claim 1 is hereby incorporated by reference; Li as modified previously taught the monitoring of activity at the computing device to determine the accuracy of the prediction and the generation of the prediction center feedback based on the accuracy. However, Li as modified does not explicitly teach are based on whether a user input detected by the computing device after the prediction is received indicates that the prediction held true.
Colag teaches the monitoring of activity at the computing device to determine the accuracy of the prediction and the generation of the prediction center feedback based on the accuracy are based on whether a user input detected by the computing device after the prediction is received indicates that the prediction held true. (¶89 wherien To refine the heuristics approach of the method 1200, the action predictor 119 may determine whether the user responded as intended to the selected pending action(s), and in implementations that use the expected response rule, whether the user response(s) to the selected pending action(s) matches any of the predicted responses. Thus, the action predictor 119 can monitor actions of the first user after the predictions to determine whether the predictions based on the heuristics rule(s) are accurate, and can modify the ranking rule and optionally the expected response rule if the predictions are not accurate. For example, if the first user does not respond as intended to the selected pending actions or provides responses different from the predicted response, the action predictor 119 may adjust either or both the ranking and expected response rules to provide more precise prediction in the future.)
It would have been obvious to one having ordinary skill in the art at the time the invention was filed to utilize the teaching of intelligent people-centric predictions in a collaborative environment of Colag with the teaching of enabling event prediction of Li as modified because Colag teaches an improved system and method are disclosed for predicting a collaborator that a user will likely collaborate with, based on collaboration attributes of potential collaborators. In an implementation, potential collaborators are identified from users of a cloud-based content management platform that have a relationship with the user and are associated with documents hosted by the cloud-based content management platform. The collaboration attributes of each potential collaborator are extracted from records of past collaboration with the user. Information identifying the predicted collaborators may be provided to the user to direct the user to documents associated with each predicted collaborator. (¶3)
Claim 22 is similar in scope to Claim 21; therefore, Claim 22 is rejected under the same rationale as Claim 21.
Claim 23 is similar in scope to Claim 21; therefore, Claim 23 is rejected under the same rationale as Claim 21.
Claim 23 is similar in scope to Claim 21; therefore, Claim 23 is rejected under the same rationale as Claim 21.
Response to Arguments
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection wherein Examiner points to new cited portions of Medlock to reinforce the teaching of different prediction engines . Colag is relied upon to teach the new added claims 21-23.
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 extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-Form 892 for listed of cited references.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGIE BADAWI whose telephone number is (571)270-7590. The examiner can normally be reached Monday thru Wednesday 9:00am - 5:00pm EST with Thursdays and Fridays off.
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/ANGIE BADAWI/Primary Examiner, Art Unit 2179