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 .
Applicant’s Response
In Applicant’s response dated 03/31/2026, Applicant amended Claims 1 and 7, canceled Claims 13, 14 and 23; added Claim 24 and argued against all objections and rejections previously set forth in the Office action dated 01/13/2026.
In light of applicant’s amendments and remarks, the previously set forth rejections are withdrawn.
In light of applicant’s amendments, the previously set forth objection to claim 7 is withdrawn.
Status of the Claims
Claims 1 – 12, 15 – 22 and 24 are rejected under 35 U.S.C. 103.
Examiner Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Specification
The abstract of the disclosure is objected to because it exceeds 150 words in length. Correction is required. See MPEP § 608.01(b).
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 recites “comparing the received production parameters to at least one predefined demanded production parameter range”
This claim should me amended to recite “comparing the received input data of the plurality of production parameters to at least one predefined demanded production parameter range” to avoid antecedent basis problems.
Appropriate correction is required.
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 – 4, 6, 7, 11, 12, 16 – 22 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (US 2015/0331402) (hereinafter, Lin) (cited in IDS dated 10/20/2023) in view of Buggenthin et al. (US 2020/0230884) (hereinafter, Buggenthin).
Regarding Claim 1,Lin teaches a computer-implemented method for quality assessment of an object produced by at least one 3D printer using 3D printing processes (See Lin’s Abstract and par 0022), the method comprising:
receiving input data of a plurality of production parameters pertaining to the production of the object (Lin in par 0021, teaches that the embedded sensor(s) 116 in the 3D printer can monitor print progress and collect data on a number of key indicators, such as ambient machine, part and raw material temperatures, compressive, tensile, shear, bending and torsional stresses, visual indicators captured via cameras and audible indicators captured via microphones. Lin in par 0041 and Fig. 5, further teaches that during the 3D printing, 3D print progress can be monitored at 508. This can involve, collecting key indicators using one or more sensors in the 3D printer at 510);
comparing the received production parameters to at least one predefined demanded production parameter range (Lin in par 0022, teaches that the closed-loop feedback configuration 118 can compare sensor output to the 3D print profile and dynamically alter the 3D print parameters to optimize the 3D print on the fly. Lin in par 0031 and Fig. 4, further teaches the closed-loop feedback technique to dynamically alter a 3D print profile. During a 3D print, a closed-loop feedback system can be used to dynamically alter the 3D print profile 400 and print parameters in response to sensor input. Lin in par 0041 – 0042, further teaches that during the 3D printing, 3D print progress can be monitored at 508. This can involve, collecting key indicators using one or more sensors in the 3D printer at 510);
determining printing quality based on the degree of agreement between the production parameters and the at least one predefined demanded production parameter range (Lin in par 0034 – 0035 and Fig. 1, further teaches that the database 140 can store every 3D geometry, 3D print profile, sensor output, and simulation along with user input on success and failure and failure modes of multiple 3D prints. As more data is received over time by the database 140, the fault analysis 124 can continually improve the model analytics 122 and the resulting 3D print profiles 150. The fault analysis component 124 can employ machine learning algorithms, which can be implemented on the onboard computer 112, a local server or in the cloud. Such machine learning algorithms can analyze the database 140 for trends that link the outcome of 3D prints with specific 3D geometries, 3D print profiles, materials and/or applications. User feedback on the success or failure of the print and the quality of the part can be solicited and used in the feedback loop. Lin in par 0041 – 0042, further teaches that during the 3D printing, 3D print progress can be monitored at 508. This can involve, collecting key indicators using one or more sensors in the 3D printer at 510. The key indicators of 3D print progress can be processed at 512 to determine if a change is needed while the 3D printing on the 3D printer hardware continues. If a change is needed, the 3D printer parameters can be altered at 514);
However, Lin does not specifically disclose wherein determining the printing quality includes assigning different weights to individual production parameters of the plurality of production parameters, wherein the degree of agreement is measured on a scale ranging from complete agreement over partial agreement to complete disagreement; and aborting the production of the object before completion of the production of the object based on the determining printing quality.
Buggenthin teaches wherein determining the printing quality includes assigning different weights to individual production parameters of the plurality of production parameters, wherein the degree of agreement is measured on a scale ranging from complete agreement over partial agreement to complete disagreement (Buggenthin teaches a method for monitoring a quality of an object of a 3D-print job series of identical objects (See Buggenthin abstract). Buggenthin in par 0024, further teaches that different influencing parameters to the manufacturing process are considered. The detection and predication value depends on data of different data sources which can be weighted or interconnected to each other representing the manufacturing process with different aspects. Buggenthin in par 0064 – 0067 and Fig(s). 4 – 7, further teaches that during a current printing process of a layer Li the layer quality indicator QI(Li) is determined. In a further step S2 the determined layer quality indicator QI(Li) of the currently printed layer is compared with a predetermined lower confidence layer LCL(Li) of the corresponding layer Li. If the layer quality indicator QI(Li) is above the lower confidence limit LCL the value is stored and the same process is performed for the next layer. If the layer quality indicator is equal or below the lower confidence limit a warning signal is generated, see step S3, and transmitted to the additive manufacturing process. Such a sequence of quality indicators 11 is shown in FIG. 6. Here the layer quality indicator QI(L1, . . . , Li−1) of the layers L1, . . . , Li−1, marked with reference sign 11, show the value between the upper and the lower confidence layer UCL, LCL. At layer Li the layer quality indicator QI(li) is lower than the lower confidence layer LCL(Li) and accordingly a warning signal is generated. In case the determined layer quality indicator Li shows a value above the upper confidence layer a warning signal can also be generated. In this case the warning signal can be used to adapt the settings of the manufacturing process e.g. by lowering the amount of material powder for this layer as this high confident layer indicates that the present settings for the layer can be optimized e.g. to save material. The warning value can trigger different actions); and
aborting the production of the object before completion of the production of the object based on the determining printing quality (Buggenthin in par 0027 – 0028, further teaches that a warning signal is generated, if layer quality indicators of subsequent layers show a common trend towards the lower confidence limit. This has the advantage that already before the lower confidence limit is reached countermeasures can be started to raise the layer quality of subsequent layers or to stop the complete printing process and scrap the partly printed object as early as possible. Buggenthin in par 0051 and Fig. 3, further teaches that the method ends in step S4, if all layers L1, . . . , Ln of the object 300 are printed and layer quality indicators QI(L1), . . . QI(Ln) are determined and evaluated. The method ends in step S5, if the print-job of the object is stopped before all layers L1, . . . , Ln are printed, e.g. as a consequence of a previous warning signal transmitted to the 3D-printer).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Buggenthin with the teachings as in Lin to interrupt the printing process of Lin as disclosed in Buggenthin. The motivation for doing so would have been to properly resolve a generated warning associated with the quality of the printing job )See Buggenthin’s Abstract, par(s) 0028 and 0051)
Regarding Claim 2, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1. Lin further teaches:
further comprising calibrating the at least one 3D printer based on the provided quality assessment results (Lin in par 0043 and Fig. 5, further teaches that the success/failure input can be provided at 520 as feedback to the fault analysis and the model analytics (e.g., using machine learning algorithm(s) to analyze trends over many 3D prints, e.g., as performed by one user or multiples users). The fault analysis process can employ one or more machine learning algorithms to analyze the database for trends that link outcomes of 3D prints with specific 3D geometries, 3D print profiles, materials or applications, and this analysis can be used to improve future model analytic processes and 3D print profiles).
Regarding Claim 3, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1. Buggenthin further teaches:
aborting the production of the object based on the provided quality assessment result (Buggenthin in par 0027 – 0028, further teaches that a warning signal is generated, if layer quality indicators of subsequent layers show a common trend towards the lower confidence limit. This has the advantage that already before the lower confidence limit is reached countermeasures can be started to raise the layer quality of subsequent layers or to stop the complete printing process and scrap the partly printed object as early as possible. Buggenthin in par 0051 and Fig. 3, further teaches that the method ends in step S4, if all layers L1, . . . , Ln of the object 300 are printed and layer quality indicators QI(L1), . . . QI(Ln) are determined and evaluated. The method ends in step S5, if the print-job of the object is stopped before all layers L1, . . . , Ln are printed, e.g. as a consequence of a previous warning signal transmitted to the 3D-printer).
Regarding Claim 4, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1. Lin further teaches:
further comprising inspecting the produced object based on the provided quality assessment results (Lin in par 0042 – 0043 and Fig. 5, further teaches that the key indicators of 3D print progress can be processed at 512 to determine if a change is needed while the 3D printing on the 3D printer hardware continues. If a change is needed, the 3D printer parameters can be altered at 514. A check is made at 516 to see if the 3D printing has completed. Once the 3D print is done, user input regarding success or failure of the 3D print can be obtained at 518. This can be done through a user interface and can include either a Boolean input or more detailed input information (e.g., gradations of quality between complete success and total failure and/or details of points in time of the printing or locations on the object where the 3D printing result was not adequate). In any case, this success/failure input can be provided at 520 as feedback to the fault analysis and the model analytics).
Regarding Claim 6, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1. Lin further teaches:
wherein the at least one production parameter is at least one out of a group, the group comprising material parameters, environmental parameters and printing parameters (Lin in par 0021, teaches that the embedded sensor(s) 116 in the 3D printer can monitor print progress and collect data on a number of key indicators, such as ambient machine, part and raw material temperatures, compressive, tensile, shear, bending and torsional stresses, visual indicators captured via cameras and audible indicators captured via microphones. Lin par 0033, further teaches that a variety of sensors 450 can be embedded within the system that measure print parameters. Sensors can include, but are not limited to, temperature sensor(s) to measure ambient and material temperatures, humidity sensor(s), pressure sensor(s), strain gauges to measure compressive, tensile, shear, bending and torsional stresses during print, imaging system(s), video capture system(s), and thermal imaging system(s).).
Regarding Claim 7, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1. Lin further teaches:
wherein the at least one production parameter is obtained during print execution or before print execution (Lin in par 0021, teaches that the embedded sensor(s) 116 in the 3D printer can monitor print progress and collect data on a number of key indicators, such as ambient machine, part and raw material temperatures, compressive, tensile, shear, bending and torsional stresses, visual indicators captured via cameras and audible indicators captured via microphones).
Regarding Claim 11, Lin teaches an apparatus for quality assessment of an object produced by at least one 3D printer using 3D printing processes (See Lin’s Abstract and par 0022), comprising: an input unit (See Lin’s par 0052, keyboard and pointing device); a processing unit (See Lin’s par 0052, computer); and an output unit (See Lin’s par 0052, monitor), wherein the input unit, the processing unit and the output unit are configured to carry out the method according to claim 1 (See the above rejection of Claim 1).
Regarding Claim 12, Lin teaches a system for providing quality assessment of an object produced by at least one 3D printer using 3D printing processes (See Lin’s Abstract and par 0022), comprising: an apparatus according to claim 11 (See the above rejection of claim 11); and a web server configured to interface with a user (Lin in par 0048, further teaches that the apparatus can employ various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures), wherein the system is configured to provide a graphical user interface to the user (Lin in par 0038 and Fig. 5, further teaches presenting a user interface (e.g., on the 3D printer or a computing device communicatively coupled therewith) through which the inputs can be entered, or receiving the inputs from another program).
Regarding Claim 16, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1. Lin further teaches:
wherein the receiving of the input data is performed via an input unit (Lin in par 0038 and Fig. 5, further teaches presenting a user interface (e.g., on the 3D printer or a computing device communicatively coupled therewith) through which the inputs can be entered, or receiving the inputs from another program).
Regarding Claim 17, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1. Lin further teaches:
wherein the comparing the received at least one production parameter to the predefined demanded production parameter range is performed via a processing unit (Lin in par 0022, teaches that the closed-loop feedback configuration 118 can compare sensor output to the 3D print profile and dynamically alter the 3D print parameters to optimize the 3D print on the fly. Lin in par 0031 and Fig. 4, further teaches the closed-loop feedback technique to dynamically alter a 3D print profile. During a 3D print, a closed-loop feedback system can be used to dynamically alter the 3D print profile 400 and print parameters in response to sensor input).
Regarding Claim 18, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1. Lin further teaches:
wherein the determining of the printing quality based on the degree of agreement between the production parameter and the predefined demanded production parameter range is performed via a processing unit (Lin in par 0034 – 0035 and Fig. 1, further teaches that the database 140 can store every 3D geometry, 3D print profile, sensor output, and simulation along with user input on success and failure and failure modes of multiple 3D prints. As more data is received over time by the database 140, the fault analysis 124 can continually improve the model analytics 122 and the resulting 3D print profiles 150. The fault analysis component 124 can employ machine learning algorithms, which can be implemented on the onboard computer 112, a local server or in the cloud. Such machine learning algorithms can analyze the database 140 for trends that link the outcome of 3D prints with specific 3D geometries, 3D print profiles, materials and/or applications. User feedback on the success or failure of the print and the quality of the part can be solicited and used in the feedback loop. Lin in par 0041 – 0042, further teaches that during the 3D printing, 3D print progress can be monitored at 508. This can involve, collecting key indicators using one or more sensors in the 3D printer at 510. The key indicators of 3D print progress can be processed at 512 to determine if a change is needed while the 3D printing on the 3D printer hardware continues. If a change is needed, the 3D printer parameters can be altered at 514).
Regarding Claim 19, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1. Lin further teaches:
wherein the providing of the quality assessment results of the object is performed via an output unit (Lin in par 0052, further teaches that to provide for interaction with a user, a computer having a display device is provided for displaying information to the user).
Regarding Claim 20, Lin in view of Buggenthin teaches the limitations contained in parent Claim 11. Lin further teaches:
wherein the processing unit comprises at least one processor (Lin in par 0046, further teaches that the data processing apparatus 600 also includes hardware or firmware devices including one or more processors 612, one or more additional devices 614, a computer readable medium 616, a communication interface 618, and one or more user interface devices 620).
Regarding Claim 21, Lin in view of Buggenthin teaches the limitations contained in parent Claim 12. Lin further teaches:
wherein the web server is configured to interface with a user via one or both of a webpage served by the web server or via an application program (Lin in par 0048, further teaches that the apparatus can employ various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures. Lin in par 0053, further teaches that the computing system includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with).
Regarding Claim 22, Lin in view of Buggenthin teaches the limitations contained in parent Claim 12. Lin further teaches:
wherein the system is configured to provide the graphical user interface to the user by one or both of a webpage or an application program (Lin in par 0053, further teaches that the computing system includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with).
Regarding Claim 24, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1. Lin further teaches:
wherein the at least one production parameter is obtained during print execution and before print execution (Lin in par 0021 – 0022, teaches that the embedded sensor(s) 116 in the 3D printer can monitor print progress and collect data on a number of key indicators, such as ambient machine, part and raw material temperatures, compressive, tensile, shear, bending and torsional stresses, visual indicators captured via cameras and audible indicators captured via microphones. On the other hand, certain 3D printing techniques can have monitoring of key indicators specific to the respective 3D printing techniques. For example, in stereolithography, oxygen content in the photopolymer resin can be a key indicator. Such feedback from the embedded sensor(s) 116 can be provided to the onboard computer 112, which can in turn adjust 3D print parameters that control the 3D print engine 114, as it prints a 3D object. Thus, the closed-loop feedback configuration 118 can compare sensor output to the 3D print profile and dynamically alter the 3D print parameters to optimize the 3D print on the fly).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Buggenthin and in further view of Minardi et al. (US 2017/0050382) (hereinafter, Minardi) (cited in IDS dated 10/20/2023).
Regarding Claim 5, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1.
However, Lin does not specifically disclose further comprising automatically rejecting the produced object based on the provided quality assessment results.
Minardi teaches a three-dimensional (3D) printer and method of 3D printing including receiving a 3D model of an object to be printed, receiving information including material properties of the materials to be extruded, and generating a set of sensor-based printer control parameters to print the object on the 3D printer based, at least in part, on sensor input (See Minardi’s Abstract).
Minardi in par 0063, further teaches that the sensor data may also be used to detect completely failed features, such as a missed polyline due to a clogged nozzle. In this instance the present invention may regenerate a path plan for those missed featured and re-execute after running the nozzle clean machine command.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Minardi with the teachings as in Lin and Buggenthin to re-execute the running after identifying a failure in Lin as disclosed in Minardi. The motivation for doing so would have been to adaptively change the 3D printing process on-the-fly during 3D printing, to improve build object quality and conformance to requisite design standards. Yields are improved and rejects are eliminated in many instances (See Minardi’s par 0066).
Claims 8 – 10 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Buggenthin and in further view of HASLAM et al. (US 2021/0346091) (hereinafter, Haslam).
Regarding Claim 8, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1.
However, Lin does not specifically disclose wherein the at least one 3D printer is integrated in a print farm.
Haslam teaches a generating a 3D physical model of a patient specific anatomic feature from 2D medical images (See Haslam’s Abstract).
Haslam in par 0179, further teaches that a Blockchain is also used to record the stage gates that the model passes through during processing, this allows us to automate the initiation of a print once all the required steps have taken place. This means that the network of distributed printers can be controlled via the Blockchain through the validation of a smart contract object in real time. Haslam in par 0183, further teaches that print scheduling can be operated and managed using a DApp executing smart contracts. This would be a Blockchain based application that sits between the data processor and the printer detecting when prints are ready to be sent to the printer. It is capable of identifying when the criteria for printing have been met and of deciding how best to arrange the printing on one or more printers. Haslam in par 0198 teaches the use of a print farm to deliver the printing order. Haslam in par 0204, further teaches that an input to this process is a pre classified piece of anatomy which is present in the object or objects to be printed. The output of the process is a selection of the specific printer that is best suited to the anatomy being printed.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Haslam with the teachings as in Lin and Buggenthin to have multiple printers with different capabilities in Lin as disclosed in Haslam. The motivation for doing so would have been to select a best suited printer based on the object to be printed (See Haslam’s par 0204).
Regarding Claim 9, Lin in view of Buggenthin teaches the limitations contained in parent Claim 1.
However, Lin does not specifically disclose wherein the object is marked based on the quality assessment results with a unique identifier.
Haslam teaches a generating a 3D physical model of a patient specific anatomic feature from 2D medical images (See Haslam’s Abstract).
Haslam in par 0182, further teaches encoding the properties of the data that is printed into a physical 23D printed object and a virtual record stored on the blockchain. This is facilitated by a QR code embedded in the 3D printed object and stored with the digital record. This allows the linking of a QR code to a specific print. Properties that are recorded include the 3D representation of the printable object and the information about the steps carried out by the Axial3D server to create this printable object. Therefore it is possible for anyone to verify that the appropriate quality control operations have been carried out.
Haslam in par 0184 – 0185, further teaches that hard linking allows for the linking of the original order, including the smart contract and data (e.g. image scans and user specifications) to the physical object used by the clinician and the auditing of all of the modifications of this data. This hard link is capable of being represented in a number of different ways. It can be a simple computational hash of the output of the software pipeline. It can also be embedded into a Quick Response (QR) code, NFC chip or RFID tag. This system allows us to move between the virtual world that created the 3D printed physical object and the physical world that the object is instantiated in.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Haslam with the teachings as in Lin and Buggenthin to include a QR code embedded in the 3d printed object of Lin as disclosed in Haslam. The motivation for doing so would have been to effectively provide a tool to access the digital information associated with the 3D printed object (See Haslam’s par 0182 and 0184).
Regarding Claim 10, Lin in view of Buggenthin and in further view of Haslam teaches the limitations contained in parent Claim 9. Haslam further teaches:
wherein the object is connected to its at least one production parameter using the unique identifier (Haslam in par 0182, further teaches encoding the properties of the data that is printed into a physical 23D printed object and a virtual record stored on the blockchain. This is facilitated by a QR code embedded in the 3D printed object and stored with the digital record. This allows the linking of a QR code to a specific print. Properties that are recorded include the 3D representation of the printable object and the information about the steps carried out by the Axial3D server to create this printable object. Therefore it is possible for anyone to verify that the appropriate quality control operations have been carried out).
Regarding Claim 15, Lin in view of Buggenthin and in further view of Haslam teaches the limitations contained in parent Claim 9. Haslam further teaches:
wherein the object is marked based on the quality assessment results with a bar code or a QR code (Haslam in par 0182, further teaches encoding the properties of the data that is printed into a physical 23D printed object and a virtual record stored on the blockchain. This is facilitated by a QR code embedded in the 3D printed object and stored with the digital record. This allows the linking of a QR code to a specific print. Properties that are recorded include the 3D representation of the printable object and the information about the steps carried out by the Axial3D server to create this printable object. Therefore it is possible for anyone to verify that the appropriate quality control operations have been carried out).
Response to Arguments
Applicant’s arguments, see remarks pages 1 – 2 filed on 03/31/2026 with respect to the rejection(s) of claim 1 under 35 U.S.C. 102(a)(1)/102(a)(2) 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 Buggenthin et al. (US 2020/0230884) (cited in 892 dated 01/13/2026).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIEL MERCADO VARGAS whose telephone number is (571)270-1701. The examiner can normally be reached M-F 8:00am - 4:00pm.
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/ARIEL MERCADO-VARGAS/ Primary Examiner, Art Unit 2118
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