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 .
Status of Claims
The office action is being examined in response to the application filed by the applicant on May 11th, 2026.
Claims 1 – 20 are pending and have been examined.
This action is made FINAL.
Response to Arguments
Applicants’ arguments filed on May 11th, 2026, have been fully considered but are not persuasive.
Regarding Applicants’ arguments against the 101 rejections on p. 8 – 12: Applicant argues that the claims improve the quality of electronic SWO data, the claims remain directed to the abstract idea of evaluating information and identifying deficiencies in information, which constitutes a mental process. The recitation of automated analysis does not remove the concept from the mental-process grouping because the underlying functions—reviewing a report, determining whether information is missing or complete, and communicating the result—are activities that can be performed through human observation, evaluation, and judgment. The fact that the claimed analysis is performed in the context of service work orders does not alter the character of the abstract idea.
Under Step 2A, Prong Two, the additional limitations do not integrate the abstract idea into a practical application. Receiving information through a UI, providing the result through the UI, and storing the report in a database merely constitute generic computer implementation, and/or generally link the abstract idea to a particular technological environment. The claims do not recite a specific improvement to the operation of the computer, user interface, database, or other technology. Rather, the asserted improvement concerns the quality and usefulness of the information contained in the SWO report, which is an improvement to the abstract information itself rather than to computer functionality.
Applicant’s reliance on the Specification’s discussion of improved downstream diagnostic-model training likewise does not establish eligibility because such benefits are not sufficient to transform the claimed information-evaluation process into a technological improvement and claim 1 does not require the SWO report to be used to train or improve a diagnostic model.
Finally, under Step 2B, considering the additional elements individually and as an ordered combination, the claimed UI, processor-executable instructions, automated analysis, and database merely implement the abstract idea using generic computer functions and do not amount to significantly more than the abstract idea itself. Accordingly, Applicants’ arguments do not overcome the rejection under 35 U.S.C. § 101, and the rejection is maintained.
Regarding Applicants’ arguments regarding the 102/103 rejections on p. 12 – 16: Applicants’ arguments regarding Rathore have been considered but are not persuasive. Applicant argues that Rathore merely processes generic organizational data and does not disclose receiving a service work order (“SWO”) report via a user interface, analyzing the SWO report for missing information and/or completeness, providing an indication through the UI, and storing the SWO report in a SWO database. However, the rejection does not rely solely upon Rathore’s disclosure of a generated data-quality report as corresponding to the claimed SWO report. Rather, as set forth in the Office Action, Rathore discloses receiving data for analysis, evaluating the received data according to data-quality metrics including whether information is missing, identifying proposed modifications to deficient data, and storing/processing such data within the disclosed data-quality system. See, e.g., Rathore ¶¶16, 21, 55 – 58, 69, and 75.
Applicant’s characterization of Rathore’s data as “organizational data” rather than a “SWO report” does not distinguish the claimed functionality where the relied-upon data is subjected to the same claimed operations. The claimed SWO report merely identifies the particular type or content of information being processed and does not require a materially different manner of computer processing. Likewise, Rathore’s disclosure is not limited to the particular examples of OSS, BSS, and DSS data identified by the Applicant.
Applicant’s arguments regarding claim 12 have also been considered but are not persuasive. Applicant argues that Mavrieudus extracts features from machine and service log data for predictive maintenance rather than for the same purpose as the claimed SWO completeness analysis. However, the rejection relies upon Mavrieudus for its teaching of extracting textual features from service-related textual data and using the extracted information to characterize or classify the data, rather than for the entirety of the underlying SWO workflow.
The fact that Mavrieudus may employ its feature extraction in a predictive-maintenance content does not negate the teaching relied upon or require the references to have identical purposes. The proposed combination applies Mavrieudus’s known textual feature-extraction technique to Rathore’s data-quality processing system to facilitate organization, characterization and labeling of service-related data. A person of ordinary skill in the art would have recognized that applying such feature extraction to historical records would predictably facilitate identification and organization of relevant information contained in those records.
Thus, the rejection is based upon the combined teachings of the references rather than requiring either reference individually to disclose claim 12 in its entirety.
With respect to the amended claim 20, Applicant’s arguments concerning the newly added limitations have been considered. To the extent the amended claim now requires features not disclosed by Rathore as presently applied—including the particular current servicing-session content, pre-saving/pre-filing NLP analysis of free-form SWO text, and/or UI-generated suggested text—the rejection has been reconsidered in view of the amended claim language. Any limitation not expressly or inherently disclosed by Rathore is addressed by the rejection set forth herein, as appropriate.
Applicant’s arguments against the references individually, and based upon their differing stated purposes, do not demonstrate that the proposed combination would have been beyond the level of ordinary skill or would have produced unpredictable results. Accordingly, Applicant’s arguments are not persuasive as to the limitations disclosed by Rathore, and the rejections are maintained to the extent set forth above.
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 – 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, and therefore does not recite patent-eligible subject matter. Claim 1 is the most representative of the independent claim set 1, 15 and 20.
Claims 1 – 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, and therefore does not recite patent-eligible subject matter. Claim 1 is the most representative of the independent claim set 1, 15 and 20.
Step 2A Prong 1: The abstract idea is defined by the elements of:
via a user interface (UI) (140), receiving entry of a SWO report (136);
applying at least one automated analysis to the SWO report to detect information missing from the SWO report and/or to generate a completeness score (138) for the SWO report;
via the UI, providing an indication (142) of the information missing from the SWO report and/or the completeness score for the SWO report; and
storing the SWO report in a SWO database (111)
These limitations collectively describe evaluating the completeness or quality of information and notifying a user of deficiencies. Determining whether information is missing from a report and assigning a complete score are activities that can be performed mentally by a human reviewer. A supervisor could read service work order reports, determine whether required information is missing from it, assign a completeness score, and then inform the service engineer of the missing information. Accordingly, the claim recites a mental process, which is one of the enumerated groupings of abstract ideas, refer to MPEP 2106.04(a).
Additionally, the claimed method concerns managing and improving service work order documentation within a workplace environment. Evaluating reports for completeness and directing a user to correct missing information constitutes managing human activity in the context of business or workplace operations. Therefore, the claim also falls within the grouping of certain methods of organizing human activity. Thus, claim 1 recites a judicial exception.
Step 2A Prong 2: For independent claim 1, the claims do not integrate an abstract idea into a practical application. The additional elements recited in claim 1 beyond the abstract idea include:
a non-transitory computer readable medium
at least one electronic processor
a user interface (UI)
a service work order (SWO) database
These elements are described at a high level of generality and perform generic computer functions such as receiving data, analyzing data, displaying results, and storing data. The claim does not (1) improve the functioning of the of the electronic processor, (2) improve the functioning of the SWO database, (3) improve user interface technology, (4) recite any specific algorithm that improves computer performance, or (5) modify the operation of a medical imaging device or any other machine. The claim therefore merely uses generic computer components as tools to automate the abstract idea of evaluating report completeness.
Accordingly, the abstract idea is not integrated into a practical application.
Step 2B: For independent claim 1, because claim 1 is directed to a judicial exception and does not integrate the exception into a practical application, the analysis proceeds to Step 2B to determine whether the claim includes additional elements that amount to significantly more than the abstract idea. Claim 1 does not include any such additional elements. As discussed above, the claim merely performs the abstract idea of evaluating completeness of a report using generic computing components. Therefore, claim 1 does not include an inventive concept sufficient to transform the abstract idea into a patent-eligible subject matter.
For dependent claims 2 – 14, 16 – 19, and 21 – 22, these claims cover or fall under the same abstract ideas of a mental process or certain methods of organizing human activity, such as:
artificial neural network (ANN) training, threshold filtering, retraining, database generation (claims 2 – 6 and 18 – 19)
natural language processing (NLP) analysis, detecting missing information, suggesting text (claims 7, 10 – 12, and 21 – 22)
diagnostic model, root cause determination, predicting maintenance operation (claims 13 – 14)
providing completeness scores and prompting update if below a threshold (claims 8 – 9 and 16 – 17)
Each group of dependent claims merely adds generic machine learning techniques and NLP analysis, threshold-based evaluation, feedback prompting, or predictive analytics—which are all forms of data analysis and mental processes implemented on generic computing components. None of the dependent claims integrate the abstract idea into a practical application or recite significantly more than the abstract idea. Accordingly, claims 1 – 22 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 – 11 and 13 – 22 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Rathore (US20180373579 A1).
Regarding claims 1 and 15:
Rathore discloses:
via a user interface (UI), receiving entry of a SWO report; [¶0040; Fig. 3]: A user interface is connected to the device. [¶0016; Fig. 1A]: The disclosure teaches a data quality system receiving metadata from a plurality of server devices associated with a system. Further, [¶0021]: Reports may be received and outputted by the system via a client device.
applying at least one automated analysis to the SWO report to detect information missing from the SWO report and/or to generate a completeness score for the SWO report; [¶0055 - 0058; Fig. 4]: The data quality system is able to perform analyses on the quality of data, as a result reducing and eliminating errors. The system aggregates the data related to specific topics and utilizes averages in order to determine a threshold granularity. [¶0069]: Techniques are set in place to identify whether data includes missing data.
via the UI, providing an indication of the information missing from the SWO report and/or the completeness score for the SWO report; and [¶0055 - 0058; Fig. 4]: The data quality system is able to perform analyses on the quality of data, as a result reducing and eliminating errors. Similarly, [¶0069]: teaches a set of techniques that may be used to process data and determine values for a plethora of data sets, including missing data.
storing the SWO report in a SWO database. [¶0016 – 0017; Fig. 1A]: Data is stored in a server device and may by stored in other sources as well.
Regarding claim 2:
Rathore discloses:
scoring the historical SWO reports according to a predetermined completeness score threshold; and [¶0067]: The data quality system is able to determine scores for predicted uses of data.
training at least one artificial neural network (ANN) (132) to detect information missing from the SWO report and/or to generate a completeness score for an SWO report, the training using historical SWO reports having a completeness score exceeding a predetermined completeness score threshold; [¶0065]: The data quality system may be coupled to machine learning in order to be trained on information identifying different data sets and sources.
wherein the at least one automated analysis includes the trained at least one ANN. [¶0065]: Machine learning is paired to the disclosure.
Regarding claim 3:
Rathore discloses:
generating the SWO database (111) storing the historical SWO reports having a completeness score exceeding predetermined completeness score threshold [¶0058]: The data quality system is responsible for storing and aggregating data to satisfy a certain threshold.
Regarding claim 4:
Rathore discloses:
receiving additional historical SWO reports; [¶0016; Fig. 1A]: The disclosure teaches a data quality system receiving metadata from a plurality of server devices associated with a system. Further, [¶0021]: Reports may be received and outputted by the system via a client device.
scoring the additional historical SWO reports according to a predetermined completeness score threshold; and [¶0067]: The data quality system is able to determine scores for predicted uses of data.
updating the SWO database (111) with the additional historical SWO reports having a completeness score exceeding predetermined completeness score threshold. [¶0014]: The data quality system may update data at a source of the data instead of each time data is utilized. “This conserves processing resources of hardware resources of an organization by reducing or eliminating a need to fix the data each time the data is used.”
Regarding claim 5:
Rathore discloses:
retraining the ANN (132) with the additional historical SWO reports stored in the SWO database (111). [¶0065]: The data quality system may be coupled to machine learning in order to be trained on information identifying different data sets and sources.
Regarding claim 6:
Rathore discloses:
receiving a user input from a service engineer (SE) indicative of the historical SWO report should be stored in the SWO database (111). [¶0043]: An input component is coupled to the disclosure, allowing the device to receive information via user input.
Regarding claim 7:
Rathore discloses:
wherein the at least one automated analysis includes: at least one natural language processing (NLP) analysis to detect information missing from the SWO report [¶0064]: The data quality system may use natural language processing to further identify terms/phrases.
Regarding claims 8 and 16:
Rathore discloses:
applying the at least one automated analysis to the SWO report to generate a completeness score for the SWO report, and the providing includes providing the completeness score for the SWO report. [¶0019]: The data quality system may use machine learning in order to effectively identify errors associated with pieces of data. [¶0022; Fig. 1B]: As a result, the system is able to automatically replace the detected error-containing data stored within the server(s).
Regarding claims 9 and 17:
Rathore discloses:
wherein, if the completeness score for the SWO report does not exceed the predetermined completeness score threshold, then the providing of the completeness score for the SWO report includes: outputting the indication to via the UI to update the SWO report. [¶0023; Fig. 1B]: The data quality system provides logistics and information to be displayed regarding the processed data.
Regarding claims 10:
Rathore discloses:
applying the at least one automated analysis to the SWO report to detect information missing from the SWO report, and the providing includes providing the detected missing information for the SWO report. [¶0055 - 0058; Fig. 4]: The data quality system is able to perform analyses on the quality of data, as a result reducing and eliminating errors. The system aggregates the data related to specific topics and utilizes averages in order to determine a threshold granularity. [¶0069]: Techniques are set in place to identify whether data includes missing data.
Regarding claims 11:
Rathore discloses:
outputting the indication to via the UI to suggest text to add to the SWO report allow the SWO report to exceed the predetermined completeness score threshold. [¶0069]: “For example, the set of metrics may include… whether the data includes missing data, whether the data includes minimum and/or maximum value violations, “
Regarding claim 13:
Rathore discloses:
training a diagnostic model to determine a root cause of a fault of the medical imaging device; and [¶0065 – 0067]: Machine learning is paired to the data quality system in order to be trained on data to predict an intended use of the data—“data quality system 230 may process the data using machine learning to predict an intended use of the data and may then identify errors in the data based on the predicted intended use. “
using the trained diagnostic model to predict a maintenance operation to be performed on the medical imaging device. [¶0065 – 0067]: Machine learning is paired to the data quality system in order to be trained on data to predict an intended use of the data—“data quality system 230 may process the data using machine learning to predict an intended use of the data and may then identify errors in the data based on the predicted intended use. “
Regarding claim 14:
Rathore discloses:
data-mining completed SWO reports to determine a root cause of a fault of the medical imaging device. [¶019 – 0021]: The disclosure utilizes machine learning in order to identify errors associated with data within reports. Additionally, the data quality system “may fix errors in a particular manner for an intended use of the data, to satisfy a set of rules, to match other data with the same intended use, and/or the like. In some cases, the data quality system may output, for display via the client device, a report that indicates proposed modifications to the data and may request approval from a user of the client device prior to fixing errors related to the data.”
Regarding claim 18:
Rathore discloses:
retrieving historical SWO reports; [¶0040; Fig. 3]: A user interface is connected to the device. [¶0016; Fig. 1A]: The disclosure teaches a data quality system receiving metadata from a plurality of server devices associated with a system. Further, [¶0021]: Reports may be received and outputted by the system via a client device.
scoring the historical SWO reports according to a predetermined completeness score threshold; [¶0067]: The data quality system is able to determine scores for predicted uses of data.
training at least one artificial neural network (ANN) (132) to generate a completeness score for an SWO report, the training using historical SWO reports having a completeness score exceeding a predetermined completeness score threshold, wherein the at least one automated analysis includes the trained at least one ANN; and [¶0065]: The data quality system may be coupled to machine learning in order to be trained on information identifying different data sets and sources.
generating the SWO database (111) storing the historical SWO reports having a completeness score exceeding predetermined completeness score threshold. [¶0075 – 0077]: After generating the reports, the device processes the data towards the set of server devices to store the generated reports.
Regarding claim 19:
Rathore discloses:
receiving additional historical SWO reports; [¶0040; Fig. 3]: A user interface is connected to the device. [¶0016; Fig. 1A]: The disclosure teaches a data quality system receiving metadata from a plurality of server devices associated with a system. Further, [¶0021]: Reports may be received and outputted by the system via a client device.
scoring the additional historical SWO reports according to a predetermined completeness score threshold; [¶0067]: The data quality system is able to determine scores for predicted uses of data.
updating the SWO database with the additional historical SWO reports having a completeness score exceeding predetermined completeness score threshold; and [¶0014]: The data quality system may update data at a source of the data instead of each time data is utilized. “This conserves processing resources of hardware resources of an organization by reducing or eliminating a need to fix the data each time the data is used.”
retraining the ANN with the additional historical SWO reports stored in the SWO database. [¶0065]: The data quality system may be coupled to machine learning in order to be trained on information identifying different data sets and sources.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 12 is rejected under 35 U.S.C. 103 as being unpatentable over Rathore (US20180373579 A1) in view of Mavrieudus (US20200185085 A1).
Regarding claim 12:
Rathore does not disclose the limitation below. Thus, Mavrieudus teaches:
extracting textual features from the historical SWO reports to provide labels for the historical SWO reports. [¶0040]: Feature extraction is taught in order to identify component names/identifiers and then associate the extracted features to be classified by category.
It would have been obvious to one or ordinary skill in the art before the earliest effective filing date of the invention to have combined Rathore’s method and systems for processing data to improve the quality of datasets with extracting textual features in order to provide labels for historical SWO reports, as taught by Mavrieudus, in order to effectively and efficiently organize and label metadata corresponding to their respective groupings.
Claims 20 - 22 are rejected under 35 U.S.C. 103 as being unpatentable over Rathore (US20180373579 A1) in view of Mercer (US20150201912 A1).
Regarding claim 20:
Rathore discloses:
applying at least one automated analysis to before saving or filing the current SWO report, applying at least one automated analysis to free-form text entry field content of the current SWO report to detect information missing from the current SWO report, wherein the at least one automated analysis includes applying at least one natural language process (NLP) operation to the free-form text entry field content) [¶0040]: The data quality system is configured to apply text processing techniques, such as NLPs, computational linguistics, and text analysis prior to the data being saved in the data quality system;
via the SWO report entry UI, providing an indication of identifying the information detected missing from the information and suggesting text to add to the current SWO report [¶0044]: A communication interface may be paired with a transceiver component to include interfaces to other structures and interfaces (e.g., Ethernet interface, optical interface, Wi-Fi interface, etc.) Alternatively, [¶0070 – 0071]: The data quality system provides an indication that the data set may include an error, or that it violates a set of rules, as well as it provides a fix to the error related to the data; and
storing the completed SWO report in a SWO database [Fig. 1A; ¶0016 – 0017]: Data is stored in a server device and may be stored through other means of sources.
Rathore further discloses receiving metadata and reports from a plurality of server devices associated with the data quality system [¶0016; Fig. 1A]. However, Rathore does not disclose the element of a medical imaging device. Thus, Mercer teaches:
via a SWO report entry user interface (UI), receiving, from a service engineer (SE), entry of a current SWO report for a current servicing session of a medical imaging device (120) [Fig. 1; ¶0030]: a servicing support system for service engineers as well as service devices (e.g., a medical device) generates alerts using predictive failure models [Fig. 3; ¶0025, ¶0055]: Data from the medical imaging device is collected into the network/maintenance system in order to issue service engineers alerts regarding what is detected from sensors;
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data-quality system of Rathore to apply its automated analysis to service information associated with the servicing of a medical imaging device, as taught by Mercer. One of ordinary skill would have been motivated to make such a modification because Mercer teaches that medical imaging devices require servicing and maintenance by service engineers and that information concerning such maintenance and repairs may be maintained for subsequent use. Applying Rathore’s data quality techniques to such medical-imaging-service information would predictably improve the completeness and quality of information associated with servicing the medical imaging device, thereby facilitating reliable maintenance documentation and subsequent analysis.
Regarding claim 21:
The combination of Rathore and Mercer teach all the limitations of claim 20 respectively.
Rathore discloses:
applying the at least one automated analysis to the SWO report to detect information missing from the SWO report, and the providing includes providing the detected missing information for the SWO report. [¶0055 - 0058; Fig. 4]: The data quality system is able to perform analyses on the quality of data, as a result reducing and eliminating errors. The system aggregates the data related to specific topics and utilizes averages in order to determine a threshold granularity. [¶0069]: Techniques are set in place to identify whether data includes missing data.
Regarding claim 22:
The combination of Rathore and Mercer teach all the limitations of claims 20 and 21 respectively.
Rathore discloses:
outputting the indication to via the UI to suggest text to add to the SWO report allow the SWO report to exceed the predetermined completeness score threshold. [¶0069]: “For example, the set of metrics may include… whether the data includes missing data, whether the data includes minimum and/or maximum value violations, “
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Mukherjee (US7949444 B2) is pertinent because it is directed to “A natural language data extraction module extracts problem data and related solution data from the narrative data, and a database module populates an aircraft service information database with the extracted problem data and the related extracted solution data.”
Muthusamy (US20080215387 A1) is pertinent because it is directed to “a software process that provides: an increase in the productivity in the validation process; tracking of the validation inventory, paperless execution of validation protocols; automation of revalidation schedule with alert features; increased validation efficiency; and ensures compliance to FDA regulations.”
Wilczek (US20160092423 A1) is pertinent because it is directed to “computer-implemented template recognition using optical character recognition (OCR) technology, and more specifically to feedback validation of electronically generated forms for more accurate data extraction and categorization.”
Lott (US20050060317 A1) is pertinent because it is directed to “a method and system for improving the requirements engineering process. More specifically, the present invention relates to a method and system for the creation of rules documents and/or interface specifications and the automated generation of computer-implemented message transformation and/or validation software based on those business rules and interface definitions.”
Lin (US20210279566 A1) is pertinent because it is directed to “training a contrastive neural network in an active learning environment. More specifically, the embodiments relate identifying novel patterns in a new dataset for a prediction accuracy assessment to further train the contrastive neural network.”
Horowitz (US11568153 B2) is pertinent because it is directed to “evaluating natural language text. More particularly, in certain embodiments, the present disclosure is related to a narrative evaluator.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bill Chen whose telephone number is (571)270-0660. The examiner can normally be reached Monday - Friday 8:30am - 5:00pm.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BILL CHEN/Examiner, Art Unit 3626
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
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