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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/13/2026 has been entered.
This Office Action is in response to claims filed on 07/13/2026
Claims 1, 3-8 and 10-20 and 22-23 are pending.
Claims 2, 9 and 21 are cancelled.
Claims 1, 4, 6, 8, 11, 13, 15, 17, 19 and 22 are amended.
Claim 23 is new.
Claim Rejections - 35 USC § 103
Applicant’s arguments and amendments, see remarks pages 9-11, filed 07/13/2026, with respect to the rejection(s) of claim(s) 1, 3-8, and 10-22 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 Therefore, the rejection has been withdrawn.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 necessitated by the claim amendments is made in view of Chengxi Li et al, NPL, “AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop”, Published: 7 February 2022.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-8, 10-20 and 22-22 are rejected under 35 U.S.C. 103 as being unpatentable over Mairi Elaine Kerin, NPL “INDUSTRY 4.0: PRODUCT DIGITAL TWINS FOR REMANUFACTURING DECISION-MAKING”, Published: June 2021, (hereafter Kerin), in views of Annaiyappa et al. US 2023/0206779 A1 (hereafter Annaiyappa) and in further views of Chengxi Li et al, NPL, “AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop”, Published: 7 February 2022 (hereafter Li).
Regarding claim 1. Kerin teaches a computer-implemented method for optimizing disassembly (Page 84, sec 3.6.2, optimized disassembly costs and process planning), the method comprising:
receiving, by a processor, object data associated with an object (Page 92, fig 3-6, data sensing and acquisition);
wherein each of the one or more digital twins are comprised of a plurality of object components Page 122, sec 4.3.8, DTs can now represent individual components, products (assets), operators, systems or processes referred herein as “entities”);
performing one or more simulations of the object utilizing the one or more digital twins (Page 122, DT as an integration of simulation) (Page 123, fig 4-3, simulation);
identifying one or more object components of the plurality of object components (Page 122, sec 4.3.8, DTs can now represent individual components, products (assets), operators, systems or processes referred herein as “entities”) (Page 176, sec 5.4.4, components could be automatically updated in the virtual model);
generating an optimized disassembly plan for the object based on the one or more object components, wherein the optimized disassembly plan includes one or more disassembly stages (Page 126, table 4-1, optimize disassembly sequences);
disassembling the object, wherein the object is disassembled using at least one of a plurality devices according to the optimized disassembling plan (Page 127, fig 4-4, physical entity, remanufacturing) (Page 129, the process of remanufacturing includes both disassembly and assembly operations), and wherein object data received is received from at least one of the plurality of devices utilized in real-time simulations during the disassembling of the object (Page 21, real time data on product manufacturing, data driven simulation); and
predicting one or more impacts associated with the disassembling of the object (Page 123, fig 4-3, performance predictions calculations).
Kerin does not teach generating one or more digital twins of the object based on the object data, object components which are separable or detachable from the object based on the one or more simulations.
Annaiyappa teaches generating one or more digital twins of the object based on the object data, object components which are separable or detachable from the object based on the one or more simulations. (Par 169, creating a digital twin of a rig based on a rig plan for disassembling a rig)(Par 171, simulating disassembly of the rig components)(Par 82, rig components, include structures, derrick, platform, support equipment).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Kerin to incorporate the teachings of Annaiyappa to generate a digital twin of an object and have each component separable one ordinary skill in the art would be motivated because it allows training individuals to perform activities of the assembly and disassembly of the rig (Annaiyappa, par 160)
Kerin and Annaiyappa do not teach wherein the object is disassembled using at least one of a plurality of smart devices, wherein object data received is received from at least one of the plurality of smart devices and is utilized in real-time simulations.
Li teaches wherein the object is disassembled using at least one of a plurality of smart devices, wherein object data received is received from at least one of the plurality of smart devices and is utilized in real-time simulations (Page 4, fig 1, real robot and virtual robots are used to assemble/disassemble, cyber space simulates the physical space (examiner’s note: robots are interpreted as smart devices)) (Page 3, Sec 3, robot collaborative manufacturing, having pose registration and motion planning, in real time operation).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Kerin and Annaiyappa to incorporate the teachings of Li to disassemble using smart devices and received data from smart devices used in real-time simulation one ordinary skill in the art would be motivated one ordinary skill in the art would be motivated because it reduces learning cost and intuitively control the robot in advance (Li, abstract).
Regarding claim 3. Kerin, Annaiyappa and Li teach the method of claim 1, further comprising:
identifying a level of damage associated with the object based, at least in part, on the one or more simulations (Kerin, Page 67, sec 3.4.2, 2D-code damage on readability and errors);
determining the level of damage exceeds a threshold level (Kerin, Page 172, fig 5-9, failure probability is higher); and
generating one or more recommendations, wherein the one or more recommendations are based on the level of damage exceeding the threshold level (Kerin, Page 173, formulate a quality metric to bolster data driven decision making for remanufacturing).
Regarding claim 4. Kerin, Annaiyappa and Li teach the method of claim 1, further comprising:
analyzing the object data associated with an environment, wherein the environment surrounds the object (Kerin, Page 12, fig 2-1, environment factors)(Kerin, Page 33, sec 2.5.3, environmental factors);
identifying an environmental condition associated with disassembling the object (Kerin, Page 123, fig 4-3, environment monitoring); and
predicting one or more impacts of the environmental condition on the environment (Kerin, Page 192, table 6-1, environmental impact of disassembly of component).
Regarding claim 5. Kerin, Annaiyappa and Li teach the method of claim 4, including: identifying a hazardous impact from the one or more impacts of the environmental condition (Kerin, Page 87, reducing waste and hazardous materials to landfill); and
recommending one or more hazard mitigation techniques associated with the hazardous impact (Kerin, Page 91, sec 3.7.3, waste to the environment, sustainability consciousness).
Regarding claim 6. Kerin, Annaiyappa and Li teach the method of claim 1, including:
identifying an object impact from one or more impacts, wherein the object impact inhibits at least one disassembly stage of the one or more disassembly stages of the optimized disassembly plan (Kerin, Page 172, safety limit set to minimize the risk, thus by minimizing it eliminates solutions that are not within the limit); and
determining one or more object stabilization tactics for the one or more disassembly stages inhibited by the object impact (Kerin, Page 176, fig 5-11, Alert HiVE owner to the need to consider EoL management) (Kerin, Page 130, limits the impact of the DT in reducing disassembly uncertainties)(Kerin, Page 192, different impact being determined), wherein the one or more object stabilization tactics include at least the one or more disassembly stages a stabilization is added and removed (Kerin, Page 20, minimizing environmental impact and optimizing resource efficiencies and profits)(Kerin, Page 26, waste minimization) (Kerin, Page 194, components may need to be cleaned, machined (additive or subtractive), repaired).
Regarding claim 7. Kerin, Annaiyappa and Li teach the method of claim 6, further comprising:
generating a new disassembly stage (Kerin, Page 172, minimize the risk, thus by minimizing it generates new stages);
replacing the at least one disassembly stage of the one or more disassembly stages inhibited by the object impact with the new disassembly stage (Kerin, Page 172, minimize the risk, thus by minimizing it replaces the non optimal); and
dynamically updating the optimized disassembly plan with the new disassembly stage (Kerin, Page 172, minimize the risk, thus by minimizing it dynamically updating).
Regarding claim 8. Kerin teaches a system for optimizing disassembly (Page 84, sec 3.6.2, optimized disassembly costs and process planning), the system comprising:
a memory (Page 63, computer generated information, thus using a computer having memory); and
a processor in communication with the memory (Page 63, computer connected to memory and processor), the processor being configured to perform operations comprising:
receiving object data associated with an object (Page 92, fig 3-6, data sensing and acquisition);
wherein each of the one or more digital twins are comprised of a plurality of object components Page 122, sec 4.3.8, DTs can now represent individual components, products (assets), operators, systems or processes referred herein as “entities”);
performing one or more simulations of the object utilizing the one or more digital twins ( (Page 122, DT as an integration of simulation) (Page 123, fig 4-3, simulation);
identifying one or more object components of the plurality of object components (Page 176, sec 5.4.4, components could be automatically updated in the virtual model);
generating an optimized disassembly plan for the object based on the one or more object components, wherein the optimized disassembly plan includes one or more disassembly stages (Page 126, table 4-1, optimize disassembly sequences);
disassembling the object, wherein the object is disassembled using at least one of a plurality devices according to the optimized disassembling plan (Page 127, fig 4-4, physical entity, remanufacturing) (Page 129, the process of remanufacturing includes both disassembly and assembly operations), and wherein object data received is received from at least one of the plurality of devices utilized in real-time simulations during the disassembling of the object (Page 21, real time data on product manufacturing, data driven simulation); and
predicting one or more impacts associated with the disassembling of the object (Page 123, fig 4-3, performance predictions calculations).
Kerin does not teach generating one or more digital twins of the object based on the object data, object components which are separable or detachable from the object based on the one or more simulations.
Annaiyappa teaches generating one or more digital twins of the object based on the object data, object components which are separable or detachable from the object based on the one or more simulations. (Par 169, creating a digital twin of a rig based on a rig plan for disassembling a rig)(Par 171, simulating disassembly of the rig components)(Par 82, rig components, include structures, derrick, platform, support equipment).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Kerin to incorporate the teachings of Annaiyappa to generate a digital twin of an object and have each component separable one ordinary skill in the art would be motivated because it allows training individuals to perform activities of the assembly and disassembly of the rig (Annaiyappa, par 160).
Kerin and Annaiyappa do not teach wherein the object is disassembled using at least one of a plurality of smart devices, wherein object data received is received from at least one of the plurality of smart devices and is utilized in real-time simulations.
Li teaches wherein the object is disassembled using at least one of a plurality of smart devices, wherein object data received is received from at least one of the plurality of smart devices and is utilized in real-time simulations (Page 4, fig 1, real robot and virtual robots are used to assemble/disassemble, cyber space simulates the physical space) (Page 3, Sec 3, robot collaborative manufacturing, having pose registration and motion planning, in real time operation).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Kerin and Annaiyappa to incorporate the teachings of Li to disassemble using smart devices and received data from smart devices used in real-time simulation one ordinary skill in the art would be motivated one ordinary skill in the art would be motivated because it reduces learning cost and intuitively control the robot in advance (Li, abstract).
Regarding claim 10. Kerin, Annaiyappa and Li teach the system of claim 8, further comprising:
identifying a level of damage associated with the object based, at least in part, on the one or more simulations (Kerin, Page 67, sec 3.4.2, 2D-code damage on readability and errors);
determining the level of damage exceeds a threshold level (Kerin, Page 172, fig 5-9, failure probability is higher); and
generating one or more recommendations, wherein the one or more recommendations are based on the level of damage exceeding the threshold level (Kerin, Page 173, formulate a quality metric to bolster data driven decision making for remanufacturing).
Regarding claim 11. Kerin, Annaiyappa and Li teach the system of claim 8, further comprising:
analyzing the object data associated with an environment, wherein the environment surrounds the object (Kerin, Page 12, fig 2-1, environment factors)(Kerin, Page 33, sec 2.5.3, environmental factors);
identifying an environmental condition associated with disassembling the object (Kerin, Page 123, fig 4-3, environment monitoring); and
predicting one or more impacts of the environmental condition on the environment (Kerin, Page 192, table 6-1, environmental impact of disassembly of component).
Regarding claim 12. Kerin, Annaiyappa and Li teach the system of claim 11, including: identifying a hazardous impact from the one or more impacts of the environmental condition (Kerin, Page 87, reducing waste and hazardous materials to landfill); and
recommending one or more hazard mitigation techniques associated with the hazardous impact (Kerin, Page 91, sec 3.7.3, waste to the environment, sustainability consciousness).
Regarding claim 13. Kerin, Annaiyappa and Li teach the system of claim 8, including:
identifying an object impact from one or more impacts, wherein the object impact inhibits at least one disassembly stage of the one or more disassembly stages of the optimized disassembly plan (Kerin, Page 172, safety limit set to minimize the risk, thus by minimizing it eliminates solutions that are not within the limit); and
determining one or more object stabilization tactics for the one or more disassembly stages inhibited by the object impact (Kerin, Page 176, fig 5-11, Alert HiVE owner to the need to consider EoL management) (Kerin, Page 130, limits the impact of the DT in reducing disassembly uncertainties)(Kerin, Page 192, different impact being determined), wherein the one or more object stabilization tactics include at least the one or more disassembly stages a stabilization is added and removed (Kerin, Page 20, minimizing environmental impact and optimizing resource efficiencies and profits)(Kerin, Page 26, waste minimization) (Kerin, Page 194, components may need to be cleaned, machined (additive or subtractive), repaired).
Regarding claim 14. Kerin, Annaiyappa and Li teach the system of claim 13, further comprising:
generating a new disassembly stage (Kerin, Page 172, minimize the risk, thus by minimizing it generates new stages);
replacing the at least one disassembly stage of the one or more disassembly stages inhibited by the object impact with the new disassembly stage (Kerin, Page 172, minimize the risk, thus by minimizing it replaces the non optimal); and
dynamically updating the optimized disassembly plan with the new disassembly stage (Kerin, Page 172, minimize the risk, thus by minimizing it dynamically updating).
Regarding claim 15. Kerin teaches a computer program product comprising:
one or more computer-readable storage media; and
program instructions stored on the one or more computer-readable storage media to perform operations comprising:
receiving object data associated with an object (Page 92, fig 3-6, data sensing and acquisition);
wherein each of the one or more digital twins are comprised of a plurality of object components Page 122, sec 4.3.8, DTs can now represent individual components, products (assets), operators, systems or processes referred herein as “entities”);
performing one or more simulations of the object utilizing the one or more digital twins ( (Page 122, DT as an integration of simulation) (Page 123, fig 4-3, simulation);
identifying one or more object components of the plurality of object components (Page 176, sec 5.4.4, components could be automatically updated in the virtual model);
generating an optimized disassembly plan for the object based on the one or more object components, wherein the optimized disassembly plan includes one or more disassembly stages (Page 126, table 4-1, optimize disassembly sequences);
disassembling the object, wherein the object is disassembled using at least one of a plurality devices according to the optimized disassembling plan (Page 127, fig 4-4, physical entity, remanufacturing) (Page 129, the process of remanufacturing includes both disassembly and assembly operations), and wherein object data received is received from at least one of the plurality of devices utilized in real-time simulations during the disassembling of the object (Page 21, real time data on product manufacturing, data driven simulation); and
predicting one or more impacts associated with the disassembling of the object (Page 123, fig 4-3, performance predictions calculations).
Kerin does not teach generating one or more digital twins of the object based on the object data, object components which are separable or detachable from the object based on the one or more simulations.
Annaiyappa teaches generating one or more digital twins of the object based on the object data, object components which are separable or detachable from the object based on the one or more simulations. (Par 169, creating a digital twin of a rig based on a rig plan for disassembling a rig)(Par 171, simulating disassembly of the rig components)(Par 82, rig components, include structures, derrick, platform, support equipment).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Kerin to incorporate the teachings of Annaiyappa to generate a digital twin of an object and have each component separable one ordinary skill in the art would be motivated because it allows training individuals to perform activities of the assembly and disassembly of the rig (Annaiyappa, par 160).
Kerin and Annaiyappa do not teach wherein the object is disassembled using at least one of a plurality of smart devices, wherein object data received is received from at least one of the plurality of smart devices and is utilized in real-time simulations.
Li teaches wherein the object is disassembled using at least one of a plurality of smart devices, wherein object data received is received from at least one of the plurality of smart devices and is utilized in real-time simulations (Page 4, fig 1, real robot and virtual robots are used to assemble/disassemble, cyber space simulates the physical space) (Page 3, Sec 3, robot collaborative manufacturing, having pose registration and motion planning, in real time operation).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Kerin and Annaiyappa to incorporate the teachings of Li to disassemble using smart devices and received data from smart devices used in real-time simulation one ordinary skill in the art would be motivated one ordinary skill in the art would be motivated because it reduces learning cost and intuitively control the robot in advance (Li, abstract).
Regarding claim 16. Kerin, Annaiyappa and Li teach the computer program product of claim 15, further comprising:
identifying a level of damage associated with the object based, at least in part, on the one or more simulations (Kerin, Page 67, sec 3.4.2, 2D-code damage on readability and errors);
determining the level of damage exceeds a threshold level (Kerin, Page 172, fig 5-9, failure probability is higher); and
generating one or more recommendations, wherein the one or more recommendations are based on the level of damage exceeding the threshold level (Kerin, Page 173, formulate a quality metric to bolster data driven decision making for remanufacturing).
Regarding claim 17. Kerin, Annaiyappa and Li teach the computer program product of claim 15, further comprising:
analyzing the object data associated with an environment, wherein the environment surrounds the object (Kerin, Page 12, fig 2-1, environment factors)(Kerin, Page 33, sec 2.5.3, environmental factors);
identifying an environmental condition associated with disassembling the object (Kerin, Page 123, fig 4-3, environment monitoring); and
predicting one or more impacts of the environmental condition on the environment (Kerin, Page 192, table 6-1, environmental impact of disassembly of component).
Regarding claim 18. Kerin, Annaiyappa and Li teach the computer program product of claim 17, including:
identifying a hazardous impact from the one or more impacts of the environmental condition (Kerin, Page 87, reducing waste and hazardous materials to landfill); and
recommending one or more hazard mitigation techniques associated with the hazardous impact (Kerin, Page 91, sec 3.7.3, waste to the environment, sustainability consciousness).
Regarding claim 19. Kerin, Annaiyappa and Li teach the computer program product of claim 15, including:
identifying an object impact from one or more impacts, wherein the object impact inhibits at least one disassembly stage of the one or more disassembly stages of the optimized disassembly plan (Kerin, Page 172, safety limit set to minimize the risk, thus by minimizing it eliminates solutions that are not within the limit); and
determining one or more object stabilization tactics for the one or more disassembly stages inhibited by the object impact (Kerin, Page 176, fig 5-11, Alert HiVE owner to the need to consider EoL management) (Kerin, Page 130, limits the impact of the DT in reducing disassembly uncertainties)(Kerin, Page 192, different impact being determined), wherein the one or more object stabilization tactics include at least the one or more disassembly stages a stabilization is added and removed (Kerin, Page 20, minimizing environmental impact and optimizing resource efficiencies and profits)(Kerin, Page 26, waste minimization) (Kerin, Page 194, components may need to be cleaned, machined (additive or subtractive), repaired).
Regarding claim 20. Kerin, Annaiyappa and Li teach the computer program product of claim 19, further comprising:
generating a new disassembly stage (Kerin, Page 172, minimize the risk, thus by minimizing it generates new stages);
replacing the at least one disassembly stage of the one or more disassembly stages inhibited by the object impact with the new disassembly stage (Kerin, Page 172, minimize the risk, thus by minimizing it replaces the non optimal); and
dynamically updating the optimized disassembly plan with the new disassembly stage (Kerin, Page 172, minimize the risk, thus by minimizing it dynamically updating).
Regarding claim 21. (cancelled).
Regarding claim 22. Kerin, Annaiyappa and Li teach the method of claim 1, further comprising:
receiving additional object data associated with the object from one or more of the plurality of smart devices (Kerin, Page 92, Data processing and storage, preprocessed data) (Kerin, Page i, product simulator and DT prototype was designed to use internet of things (IoT) components), wherein the one or more of the plurality of smart devices includes at least an Internet of Things (IoT) device (Li, Page 3, sec 3.1, internet infrastructure for multi robot multi-client communications, thus the robots work on an internet infrastructure);
performing additional simulations utilizing the one or more digital twins and the additional object data received (Kerin, Page 123, Fig 4-3, Storage of data, becomes part of the loop for simulation to deliver performance); and
adjusting the optimized disassembling plan based on object impacts identified based on the additional simulations performed (Annaiyappa, Par 41, incorporate alternative tasks to reduce or eliminate impacts to the success of the overall rig plan)( Annaiyappa, Par 86, simulation can be used to determine impacts of changing the assembly or disassembly tasks, adjusted to optimize the rig tasks for disassembling the rig components).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Mairi Elaine Kerin, NPL “INDUSTRY 4.0: PRODUCT DIGITAL TWINS FOR REMANUFACTURING DECISION-MAKING”, Published: June 2021, (hereafter Kerin), in views of Annaiyappa et al. US 2023/0206779 A1 (hereafter Annaiyappa), in further views of Chengxi Li et al, NPL, “AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop”, Published: 7 February 2022 (hereafter Li), and in further views of Stuart A. Hoenig, NPL, “NEW TECHNOLOGY FOR IMPROVED FOUNDRY ENVIRONMENTS”, Published: August 1981 (hereafter Hoenig).
Regarding claim 23. Kerin, Annaiyappa and Li teach The method of claim 5, further comprising: implementing the one or more hazard mitigation techniques (Kerin, Page 87, reducing waste and hazardous materials to landfill).
Kerin, Annaiyappa and Li do not teach wherein the one or more hazard mitigation techniques include a dust capture system which is implemented utilizing an electrostatic fog within the environment to mitigate the hazardous impact associated with the disassembling of the object.
Hoenig teaches wherein the one or more hazard mitigation techniques include a dust capture system which is implemented utilizing an electrostatic fog within the environment to mitigate the hazardous impact associated with the disassembling of the object (Page 90, small particles would be collected in a dust collector, particle agglomerate) (Page 108, Fig 18, electrostatic fog) (Page 83, Charged Fog for Dust and Smoke Control).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Kerin, Annaiyappa and Li to incorporate the teachings of Hoenig to capture dust utilizing an electrostatic fog to mitigate the hazardous impact one ordinary skill in the art would be motivated because controlling smoke, fumes and hazards have been done with a variety of new and improved electrostatic techniques to help control of sulfur dioxide and keep a better air quality in the workspace (Hoenig, Page 82, abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGEL JAVIER CALLE whose telephone number is (571)272-0463. The examiner can normally be reached Monday - Friday 7:30 a.m. - 5 p.m..
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/A.C./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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