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 .
Summary
This Final Office Action in response to the communication received on January 2, 2026.
Claims 1, 13, and 19 have been amended.
Claims 1-20 are pending.
The effective filing date of the claimed invention is August 19, 2022.
Response to Amendment
Amendments to Claims 1, 13, and 19 are acknowledged.
Claim Objections
Claims 1, 13, and 19 are objected to because of the following informalities: Claim 1, 13, and 19 recite “one and more edge notes” in lines 3, 8, and 8, respectively. It is believed to be a typographical error, and that the claims were meant to recite “one or more edge nodes.” Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 – Statutory Categories
As indicated in the preamble of the claim, the examiner finds the claim is directed to a process, machine, manufacture, or composition of matter.(Claims 1-12 are processes and Claims 13-20 are machines). Accordingly, step 1 is satisfied.
Step 2A – Prong 1: was there a Judicial Exception Recited
Claim 1 (and similarly Claims 13 and 19) recites the following abstract concepts that are found to include “abstract idea.” Any additional elements will be analyzed under Step 2A-Prong 2 and Step 2B:
A method for managing data usage integrity, comprising;
receiving data connected with data usage of at least one user in a computer environment, wherein sad data relates to one and more edge notes being currently used (See 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
determining any other nodes that may be used in future based on being to said one or more edge notes data being currently used See 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
obtaining pricing information for said user relating to said data usage (See 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
segmenting said computer environment into a plurality of segment areas (See 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
calculating a payment price for each segment area based on any current edge nodes being used and by determining any future edge nodes being used based on other nodes being related to said one or more edge nodes being used (See 2106.04(a)(2)(I) Mathematical calculations, calculating the difference between local and average data values, In re Abele, 684 F.2d 902, 903, 214 USPQ 682, 683-84 (CCPA 1982));
determining an anomaly by comparing said current data usage and an associated pricing for each of said segmented areas to a preselected value (See MPEP 2106.04(a)(II) certain methods of organizing human activity, offer-based price optimization, OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362–63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015), and 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
predicting any anomaly for future edge nodes being used based on detecting an anomaly detected for said current data usage (See MPEP 2106.04(a)(II) certain methods of organizing human activity, offer-based price optimization, OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362–63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015), and 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
performing an end-to-end simulation check of said computer environment for said user when an anomaly has been detected to ascertain as whether said payment price for said segment may be inconsistent with a resource usage for affecting an end-to-end flow for said computer environment (See MPEP 2106.04(a)(II) certain methods of organizing human activity, offer-based price optimization, OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362–63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015), and 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)); and
allowing or preventing a user to use data based on said end-to-end simulation check and calculating and providing user payment when said user is allowed usage (See MPEP 2106.04(a)(II) certain methods of organizing human activity, offer-based price optimization, OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362–63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015), and 2106.04(a)(2)(III) mental processes, a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)).
Claim 1 (and similarly Claims 13 and 19) is directed to a series of steps for determining if a payment price is inconsistent with a resource usage, which is a fundamental economic principle and thus grouped as a certain method of organizing human interactions, being performed using mathematical calculations and mental processes. The mere nominal recitation of a computer environment, processor, computer-readable memory, and computer-readable tangible storage medium does not take the claim out of the method of organizing human interactions, mathematical calculations, and mental processes. Thus, Claim 1 (and similarly Claims 13 and 19) recites an abstract idea.
Step 2A – Prong 2: Can the Judicial Exception Recited be integrated into a practical application
Limitations that are indicative of integration into a practical application:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The identified abstract idea of exemplary Claim 1 (and similarly Claims 13 and 19) is not integrated into a practical application. The additional elements are: a computer environment, processor, computer-readable memory, and computer-readable tangible storage medium that implements the underlying abstract idea. These additional elements are broadly recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. Claim 1 (and similarly Claims 13 and 19) is directed to an abstract idea.
Step 2B – Significantly More Analysis
Claim 1 (and similarly Claims 13 and 19) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, steps a) receiving data, b) determining any other nodes, c) obtaining pricing information, d) segmenting the computer environment, e) calculating a payment price, f) determining an anomaly by comparing said data usage and said associated pricing, g) predicting any anomaly, h) performing an end-to-end simulation check of said computer environment when an anomaly has been detected, and i) allowing or preventing a user to use data, do not add significantly more to the exception because they amount to merely using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Claim 1 (and similarly Claims 13 and 19) is ineligible.
Claim 2 (and similarly Claims 14 and 20) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III). For the additional limitation of a database, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 3 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III). For the additional limitation of a database, the examiner refers to the "apply it" rationale of MPEP 2106.05(f).
Claim 4 (and similarly Claim 15) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 5 (and similarly Claim 16) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 6 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 7 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 8 (and similarly Claim 17) recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 9 recites the abstract idea of mental processes. See MPEP 2106.04(a)(2)(III).
Claim 10 (and similarly Claim 18) recites the abstract idea of organizing human activity. See MPEP 2106.04(a)(2)(II).
Claim 11 recites the abstract ideas of mathematical calculations and organizing human activity. See MPEP 2106.04(a)(I) and 2106.04(a)(2)(II).
Claim 12 recites the abstract idea of mathematical calculations and organizing human activity. See MPEP 2106.04(a)(I) and 2106.04(a)(2)(II).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pat 9,432,521 “Bhagat”, in view of US Pat Pub 20240104652 “Sabharwal”, in view of US Pat Pub 2022/0335340 “Moustafa”.
As per Claims 1, 13, and 19, Bhagat discloses a method, computer system, and computer program product for managing data usage integrity, comprising;
receiving data connected with data usage of at least one user in a computer environment, (Bhagat: Column 2, lines 18-31, The total categorized data usage for a time period may be determined by summing the data usage in each category. The data amount from the call detail records may be compared to the total categorized data usage. The difference between the call detail records data transfers and the total categorized usage may determine uncategorized data usage. Such uncategorized data usage may include, for example, using the portable user device to browse webpages on the Internet, transfer unauthorized files, access unauthorized services, and so on.);
obtaining pricing information for said user relating to said data usage (Bhagat: Column 2, lines 32-44, the cost analysis may be performed based on billing structures specific to each country to reduce the cost of transferring content.);
determining an anomaly by comparing said current data usage and an associated pricing for each of said segmented areas to a preselected value (Bhagat: Column 8, lines 35-60, Next, the remaining uncategorized data usage may be analyzed (block 322), and excessive use based on the uncategorized data usage may be identified from the analysis (block 324). The uncategorized data usage may be analyzed using, for example, statistical data analysis techniques to determine whether the uncategorized data usage is excessive and, in some embodiments, unauthorized use of the cellular network. In some embodiments, the statistical analysis techniques may include one or more of the following: data pre-processing techniques for detection, validation, error correction, and completion of missing data, incorrect data, or both; calculation of statistical parameters such as averages, quantiles, performance metrics, probability distributions, and other suitable statistical parameters; determination of models, probability distributions or both for various business activities such that activities may be represented in terms of parameters or probability distributions; computation of user profiles, such as user profiles that describe a user's usage of various services; time-series analysis of time-dependent data; clustering and classification to determine patterns and associations in the uncategorized data usage; and matching algorithms to detect anomalies in the behaviors of transactions and users as compared to previously known models and algorithms and, in some embodiments, to detect and eliminate false alarms, estimate risks, and predict further transactions and users.);
performing an end-to-end simulation check of said computer environment for said user when an anomaly has been detected to ascertain as whether said payment price for said segment may be inconsistent with a resource usage for affecting an end-to-end flow for said computer environment (Bhagat: Column 7, lines 35-60, Next, the remaining uncategorized data usage may be analyzed (block 322), and excessive use based on the uncategorized data usage may be identified from the analysis (block 324). The uncategorized data usage may be analyzed using, for example, statistical data analysis techniques to determine whether the uncategorized data usage is excessive and, in some embodiments, unauthorized use of the cellular network. In some embodiments, the statistical analysis techniques may include one or more of the following: data pre-processing techniques for detection, validation, error correction, and completion of missing data, incorrect data, or both; calculation of statistical parameters such as averages, quantiles, performance metrics, probability distributions, and other suitable statistical parameters; determination of models, probability distributions or both for various business activities such that activities may be represented in terms of parameters or probability distributions; computation of user profiles, such as user profiles that describe a user's usage of various services; time-series analysis of time-dependent data; clustering and classification to determine patterns and associations in the uncategorized data usage; and matching algorithms to detect anomalies in the behaviors of transactions and users as compared to previously known models and algorithms and, in some embodiments, to detect and eliminate false alarms, estimate risks, and predict further transactions and users.); and
allowing or preventing a user to use data based on said end-to-end simulation check and calculating and providing user payment when said user is allowed usage (Bhagat: Column 11, lines 9-15, The data warehouse 506 may be a collection of computing resources that collectively operate to run one or more data collections (e.g., databases). Such data collections may be operated and managed by utilizing appropriately configured API calls. This, in turn, may allow a user of the computing resource environment 500 to maintain and potentially scale the operations in the database.).
Bhagat fails to disclose a method, computer system, and computer program product for managing data usage integrity, comprising;
wherein said data relates to one or more edge nodes being currently used’
determining any other nodes that may be used in future based on being to said one or more edge notes data being currently used;
segmenting said computer environment into a plurality of segment areas;
calculating a payment price for each segment area based on any current edge nodes being used and by determining any future edge nodes being used based on other nodes being related to said one or more edge nodes being used;
predicting any anomaly for future edge nodes being used based on detecting an anomaly detected for said current data usage.
Sabharwal teaches a method, computer system, and computer program product for managing data usage integrity, comprising;
segmenting said computer environment into a plurality of segment areas (Sabharwal: [0030] The AI based cloud vendor arbitrage system 102 may include suitable logic, circuitry, interfaces, and/or code that may be configured to perform cloud vendor arbitrage using each of a plurality of metrices for each of a set of components associated with an application and infrastructure deployment. The plurality of metrices comprise at least one of a component type, a deployment date of a component, a deployment type, an instance type, a number of connections of the component, and a number of users associated with the component. Wherein components are understood to be components);
calculating a payment price for each segment area (Sabharwal: [0036] The server 106 may include suitable logic, circuitry, interfaces, and/or code that may be configured to store, maintain, and execute one or more software platforms and programs, such as AI programs and machine learning programs, data associated with metrices, and one or more databases (such as the database 108) that include, without limitation, historical data, price data for each of a set of components associated with an application and infrastructure deployment from the cloud vendors. ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhagat to include segmenting a computer environment into a plurality of segments as taught by Sabharwal, when managing data usage integrity as taught by Bhagat with the motivation of getting a firm handle on cloud costs (Sabharwal: [0004]).
Bhagat and Sabharwal fail to disclose a method, computer system, and computer program product for managing data usage integrity, comprising;
wherein said data relates to one or more edge nodes being currently used’
determining any other nodes that may be used in future based on being to said one or more edge notes data being currently used;
based on any current edge nodes being used and by determining any future edge nodes being used based on other nodes being related to said one or more edge nodes being used;
predicting any anomaly for future edge nodes being used based on detecting an anomaly detected for said current data usage.
Moustafa teaches a method, computer system, and computer program product for managing data usage integrity, comprising;
wherein said data relates to one or more edge nodes being currently used (Moustafa: [0151] At block 708 current and future candidate platforms that may be utilized for the end-to-end use case(s) may be identified.);
determining any other nodes that may be used in future based on being to said one or more edge notes data being currently used (Moustafa: [0151] At block 708 current and future candidate platforms that may be utilized for the end-to-end use case(s) may be identified.);
based on any current edge nodes being used and by determining any future edge nodes being used based on other nodes being related to said one or more edge nodes being used (Moustafa: [0084] In the example of FIG. 4, these virtual Edge instances include: a first virtual Edge 432, offered to a first tenant (Tenant 1), which offers a first combination of Edge storage, computing, and services; and a second virtual Edge 434, offered to a second tenant (Tenant 2), which offers a second combination of Edge storage, computing, and services. The virtual Edge instances 432, 434 are distributed among the Edge nodes 422, 424, and may include scenarios in which a request and response are fulfilled from the same or different Edge nodes. The configuration of the Edge nodes 422, 424 to operate in a distributed yet coordinated fashion occurs based on Edge provisioning functions 450. The functionality of the Edge nodes 422, 424 to provide coordinated operation for applications and services, among multiple tenants, occurs based on orchestration functions 460. [0187] Therefore, in some examples, if the data usage monitoring circuitry 1300 analyzes the metadata of the raw data for a retention decision, the data usage monitoring circuitry 1300 can factor the cyclical trend associated with the metadata/raw data into the analysis, prediction, learning, and/or retention decision.);
predicting any anomaly for future edge nodes being used based on detecting an anomaly detected for said current data usage (Moustafa: [0057] In some examples, the ADM system may evaluate (e.g., continuously evaluate) data within one or more non-target data streams to establish a baseline pattern (e.g., a nominal pattern, etc.) of one or more data streams being consumed by other AI application nodes over time. In some examples, the baseline pattern (or patterns) may be used in subsequent comparisons for anomaly or deviation detection.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhagat and Sabharwal to include received data relates to edge nodes being currently used as taught by Moustafa, when managing data usage integrity as taught by Bhagat and Sabharwal with the motivation of gathering of sufficient and relevant data to verify that a specific activity, tool, or task is performing as expected (or identifying a problem with the activity, tool, or task) (Moustafa: [0043]).
As per Claims 2, 14, and 20, Bhagat discloses a method, computer system, and computer program product, wherein said anomaly is detected by comparing said data usage to a retrieved value of a previous history of said data usage, said value being retrieved from a database (Bhagat: Column 9, lines 15-44).
As per Claim 3, Bhagat discloses a method, wherein said anomaly is detected by comparing said pricing for said segment to a retrieved value of a previous history of said pricing, said value being retrieved from a database (Bhagat: Column 9, lines 15-44).
As per Claims 4 and 15, Bhagat fails to disclose but Sabharwal teaches a method and computer system, further comprising determining when said data usage is associated to more than one data provider and separating each data usage according to each provider, wherein each data usage associated with a different provider is established as a different component (Sabharwal: [0036]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhagat to include segmenting a computer environment into a plurality of segments as taught by Sabharwal, when managing data usage integrity as taught by Bhagat with the motivation of getting a firm handle on cloud costs (Sabharwal: [0004]).
As per Claims 5 and 16, Bhagat discloses a method and computer system, wherein different pricing is obtained for each component and said anomaly is determined for each component (Bhagat: Column 9, lines 15-44).
As per Claim 6, Bhagat discloses a method, wherein a plurality of users exists, and any anomaly is compared between said data users to determine a common pattern (Bhagat: Column 9, lines 15-44).
As per Claim 7, Bhagat discloses a method, wherein a plurality of users exists, and any anomaly is compared between data users nodes to determine a common pattern (Bhagat: Column 9, lines 15-44).
As per Claims 8 and 17, Bhagat discloses a method and computer system, wherein said computer environment is an edge environment (Bhagat: Column 11, lines 54-62).
As per Claim 9, Bhagat discloses a method, further comprising sending additional information to a data usage provider associated with said user (Bhagat: Column 11, lines 36-53).
As per Claims 10 and 18, Bhagat discloses a method and computer system, further comprising computing an amount to pay said data usage provider of said user based on data usage and pricing information; wherein said amount to pay is broken down per segment in an edge environment (Bhagat: Column 8, lines 35-60).
As per Claim 11, Bhagat discloses a method, wherein a final payment is calculated if no anomalies are detected (Bhagat: Column 8, lines 35-60).
As per Claim 12, Sultan discloses a method, wherein a final payment is calculated after an anomaly is detected but said anomaly has been resolved (Bhagat: Column 8, lines 35-60).
Response to Arguments
35 USC 101
Applicant's arguments filed January 2, 2025 have been fully considered but they are not persuasive.
Applicant argues that the independent claims are neither abstract nor can be performed in a human mind or through mental processes. Rather, these are activities that can be performed by a machine and involves training a machine, and that the claims include understanding and applying of an amount of data beyond what may be comprehensible by a single person.
While the applicant argues that a machine is trained, the independent claims do not recite machine learning or steps for training the machine learning. The use of a computer to perform computations or data analysis amounts to using a computer as a tool to perform the mental process. See MPEP 2106.04(a)(2)(III)(C). The current claims do not recite an improvement to the functioning of a computer, nor are they implemented with particular machine. See MPEP2106.04(d)(I). The use of a computer to perform the mental processes amounts to automating the task using generic computer elements, and is therefore ineligible.
Applicant further cites multiple court cases as being reasons for eligibility but fails to provide arguments about how the cases are similar to the cited cases.
35 USC 103
Applicant’s arguments, see Applicant Arguments/Remarks Filed in an Amendment, filed January 2, 2026, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US Pat 9,432,521 “Bhagat”, in view of US Pat Pub 20240104652 “Sabharwal”, in view of US Pat Pub 2022/0335340 “Moustafa”.
Conclusion
THIS ACTION IS MADE FINAL. 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 REVA R MOORE whose telephone number is (571)270-7942. The examiner can normally be reached M-Th: 9:00-6:00.
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/REVA R MOORE/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
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