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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/16/2025 has been entered.
Status of Claims
This Office Action is responsive to RCE filed on 12/16/2025.
Claims 6 and 9 are canceled.
Claims 1-5, 7-8, and 10-20 are presented for examination.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7-8, 10-11 and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over CASE (US20220151450A1) in view of KIM (US20210121035A1), in further view of YUAN (“A Gaussian Mixture Model Based Fast Motion Planning Method Through Online Environmental Feature Learning”, published 6/1/2022, https://ieeexplore.ieee.org/document/9786542. Accessed 1/13/2026.) (hereinafter – “CASE-KIM-YUAN”).
Regarding claim 1
CASE teaches a method for operating a mobile robot system, the mobile robot system including a mobile robot configured to perform a task in an environment using an operating procedure ([0005]: “document describes systems, devices, and methods for scheduling a mission for a mobile robot and controlling the mobile robot to execute the mission, such as traversing rooms of a user's home and clean floor areas therein”), the method comprising:
receiving first data that was recorded by the mobile robot at least in part using at least one sensor as the mobile robot navigates the environment to perform the task ([0130]: “controller circuit 109 uses data collected by the sensors of the sensor system to control navigational behaviors of the mobile robot 100 during the mission”; [0132]: “data produced during the mission can include persistent data that are produced during the mission and that are usable during a further mission. For example, the mission can be a first mission”);
updating a model associated with the environment to incorporate the first data by at least one of refining and training the model using the first data, [0132]: “the map can be a persistent map that is usable and updateable by the controller circuit 109 of the mobile robot 100 from one mission to another mission to navigate the mobile robot 100 about the floor surface 10”)1
at least one of:
modifying the operating procedure, based on the model, to generate a modified operating procedure for performing the task in the environment that improves a performance of the mobile robot ([0132-0133]: “controller circuit 109 can modify subsequent or future navigational behaviors of the mobile robot 100 according to the updated persistent map, such as by modifying the planned path or updating obstacle avoidance strategy […] controller circuit 109 is able to plan navigation of the mobile robot 100 through the environment using the persistent map to optimize paths taken during the missions”); and
determining, based on the model, and causing to be displayed to a user, a recommendation for improving the performance of the mobile robot when performing the task in the environment ([0157]: “mobile device520 may run a software application implemented therein (e.g., a mobile application) or a web-based service (e.g., services provided by the cloud computing system 530) to assist the user in creating or modifying the mission routines”; [0167]: “mobile device UI may offer the user pre-populated, and even personalized, suggestions”).
CASE is not relied on for the model being one of a histogram model, a mean shift clustering model, and a gaussian mixture model and configured to receive data recorded by the mobile robot and identify positions within the environment that the mobile robot spends a most amount of time while performing the task.
However, KIM in an analogous art teaches a robot cleaner and method of operating the same ([0002]: “disclosure relates to a robot cleaner using artificial intelligence”; [0014]: “object of the present disclosure is to provide a robot cleaner capable of performing cleaning by generating a cleaning plan based on cleaning record information”).
KIM teaches:
updating a model associated with the environment to incorporate the first data by at least one of refining and training the model using the first data by at least one of refining and training the model using the first data ([0099-0103]: Al technology is applied to a cleaning robot; robot may acquire state information using sensor information, may detect surround environment, may generate map data; robot may perform operations by using the learning model, robot may recognize the environment and the objects by using the learned model), the model being [0212]: "cleaning recording information may include information about a cleaning start data and time and a cleaning end date and time for each cleaning", emphasis added by the examiner; [0213]: "cleaning record information may include information about the cleaning degree"; [0251]: "processor may generate a cleaning plan based on the first cleaning record information", emphasis added by the examiner; the start and end time of a cleaning event provided by the cleaning recording information is used to determine a duration of a cleaning event, with multiple durations established for cleaning events the intelligent robot can then determine where it spends a most amount of time while performing a cleaning event and use the duration information to generate a cleaning plan).
CASE and KIM are analogous art to the claimed invention because they are from the same field of mobile cleaning robots.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply the teachings of KIM to the teachings of CASE such that KIM’s duration of a cleaning event could be used with CASE’s persistent map for the purposes of identifying the dirtier area “hotspots” disclosed by CASE ([0081]: mission optimizer for cleaning certain “hotspots” such as dirtier areas).
The CASE-KIM combination is not relied on for the model being one of a histogram model, a mean shift clustering model, and a gaussian mixture model.
However, YUAN in analogous art teaches a mobile robot with a learning system to improve task efficiency (Abstract: “learned information can be reused as prior knowledge in the next path planning to further improve the efficiency of path replanning”).
YUAN teaches updating a model associated with the environment, the model being one of a histogram model, a mean shift clustering model, and a gaussian mixture model (Abstract: “Gaussian mixture model (GMM) is proposed to allow robotic systems to efficiently extract feature nodes and generate collision-free paths considering the pedestrian density and the environmental structure”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply the teachings of YUAN to the teachings of the CASE-KIM combination such that YUAN’s gaussian mixture model could be used with CASE-KIM’s learning model to produce heatmaps for the purposes of improving future mission performance. The motivation for doing so would have been to reduce the learning time required (YUAN, Abstract: “proposed method can remarkably reduce the time taken to reach a goal and enhance the success rate of navigation in trapped and narrow environments).
Regarding claim 2
CASE-KIM-YUAN teaches the elements of claim 1 as outlined above. CASE also teaches:
providing the modified operating procedure to the mobile robot, the mobile robot being configured to perform the task in the environment again using the modified operating procedure ([0132]: “data produced during the mission can include persistent data that are produced during the mission and that are usable during a further mission […] the map can be a persistent map that is usable and updateable by the controller circuit 109 of the mobile robot 100 from one mission to another mission to navigate the mobile robot 100 about the floor surface 10”).
Regarding claim 3
CASE-KIM-YUAN teaches the elements of claim 1 as outlined above. CASE also teaches:
wherein the first data includes at least one of
(i) position data indicating positions of the mobile robot in the environment while performing the task ([0121]: sensor system can generate a signal indicative of a current location),
(ii) images of the environment while performing the task ([0127]: sensor system includes a camera to capture images), and
(iii) event data indicating events that occur while performing the task ([0148]: during a cleaning mission mobile robot tracks any operational events occurring during cleaning mission and a time spent cleaning).
Regarding claim 4
CASE-KIM-YUAN teaches the elements of claim 1 as outlined above. CASE also teaches:
updating a database associated with the environment to incorporate the first data by adding the first data to the database, the database storing a plurality of second data that was recorded during a plurality of performances by the mobile robot of the task in the environment ([0132]: persistent data used to update persistent map between first and further missions).
Regarding claim 7
CASE-KIM-YUAN teaches the elements of claim 1 as outlined above. CASE also teaches:
wherein the model is configured to receive data recorded by the robot and output a prediction regarding an event ([0148]: a time estimate could be calculated for a cleaning room).
Regarding claim 8
CASE-KIM-YUAN teaches the elements of claim 7 as outlined above. CASE also teaches:
wherein the model is configured to output a prediction regarding at least one of
(i) a failure of the mobile robot to complete the task ([0180]: “prioritized cleaning module 582 may prioritize cleaning areas based on locations and observabilities thereof […] may reduce cleaning time and thus avoid an unfinished mission”) and
(ii) the mobile robot getting stuck ([0187]: “avoidance spots may include hazardous areas where the mobile robot likely gets stuck”).
Regarding claim 10
CASE-KIM-YUAN teaches the elements of claim 1 as outlined above. CASE also teaches:
determined, based on the model, a modification to the operating procedure that would improve the performance of the mobile robot when performing the task in the environment ([0133]: “persistent data, including the persistent map, enables the mobile robot 100 to efficiently clean the floor surface 10 […] for subsequent missions, the controller circuit 109 is able to plan navigation of the mobile robot 100 through the environment using the persistent map to optimize paths taken during the missions”).
Regarding claim 11
CASE-KIM-YUAN teaches the elements of claim 10 as outlined above. CASE also teaches:
automatically modifying the operating procedure to incorporate the modification, thereby generating the modified operating procedure ([0133]: “for subsequent missions, controller circuit 109 is able to plan navigation of the mobile robot 100 through the environment using the persistent map to optimize paths taken during the missions”; [0182]: prioritized cleaning module used so mobile robot may prioritize dirtier areas over less dirty areas).
Regarding claim 13
CASE-KIM-YUAN teaches the elements of claim 10 as outlined above. CASE also teaches:
determining, based on the model, a region within the environment that the mobile robot should not enter while performing the task in the environment ([0133]: “persistent map enables the controller circuit 109 to direct the mobile robot 100 toward open floor space and to avoid nontraversable space”).
Regarding claim 14
CASE-KIM-YUAN teaches the elements of claim 10 as outlined above. CASE also teaches:
determining, based on the model, a region within the environment that the mobile robot should enter later than other regions within the environment when performing the task in the environment ([0169]: “controller circuit 512 may accordingly pause or suspend the mission, or reorder cleaning order of rooms”).
Regarding claim 15
CASE-KIM-YUAN teaches the elements of claim 10 as outlined above. CASE also teaches:
determining, based on the model, an object in the environment that should be avoided by the mobile robot while performing the task in the environment ([0185]: “path planning module 584 may identify one or more avoidance spots in the one or more areas, such as a clutter or an obstacle therein).
Regarding claim 16
CASE-KIM-YUAN teaches the elements of claim 10 as outlined above.
CASE also teaches determined, based on the model, a revised trajectory for performing the task in the environment that would improve the performance of the mobile robot when performing the task in the environment ([0185]: “path planning module 584 may identify one or more avoidance spots in the one or more areas, such as a clutter or an obstacle therein”, i.e., purpose of path planning model is to continually revise trajectory of robot while cleaning to avoid obstacles or clutter, thereby preventing the robot from getting stuck which would be a decrease in cleaning performance).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over CASE-KIM-YUAN in further view of ERMAKOV (US20070061043A1).
Regarding claim 5
CASE-KIM-YUAN teaches the elements of claim 4 as outlined above.
CASE-KIM-YUAN is not relied on for wherein the adding the first data to the database further comprises: uncompressing the plurality of second data; combining the first data with the uncompressed plurality of second data to generate combined data; and compressing the combined data.
However, ERMAKOV in analogous art teaches data compression for a robot device configured to perform a function (Abstract).
ERMAKOV teaches adding the first data to the database further comprises: uncompressing the plurality of second data; combining the first data with the uncompressed plurality of second data to generate combined data; and compressing the combined data ([0203]: forgoing data structure may facilitate processing by minimizing the amount of data for a map that is decompressed, updated, and subsequently recompressed).
ERMAKOV is analogous art to the claimed invention because they are from the same field of mobile robots and methods of operating thereof. The motivation to combine Munich and Ermakov is disclosed by Ermakov ([0201]: since data associated with the map may be accessed an updated as robot moves through the operating environment, a compression scheme that reduces the processing load associated with decompression and recompression may have utility). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Ermakov with the CASE-KIM-YUAN combination, as suggested by the prior art.
Claims 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over CASE-KIM-YUAN in further view of MUNICH (US20210124354A1).
Regarding claim 12
CASE-KIM-YUAN teaches the elements of claim 10 as outlined above. CASE also teaches:
causing to be displayed, to the user, the recommendation FIG. 6C below shows a user interface displaying a recommendation).
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CASE-KIM-YUAN is not relied on for the recommendation including the modification to the operating procedure, the operating procedure being modified to incorporate the modification in response to receiving an input from the user approving the recommendation.
However, MUNICH in an analogous art teaches construction a map of an environment based on mapping data produced by an autonomous cleaning robot in the environment during a first cleaning mission, including causing a display to present a visual representation of the environment based on the map, and a visual indicator of the label, and causing the autonomous cleaning robot to initiate a behavior associated with the label during a second cleaning mission (Abstract).
MUNICH teaches causing to be displayed, to the user, the recommendation including the modification to the operating procedure, the operating procedure being modified to incorporate the modification in response to receiving an input from the user approving the recommendation ([0003]: based on data collected by the robot, features in the environment, such as doors, dirty areas, or other features, can be indicated on the map with labels, and states of the features can further be indicated on the map; [0125]: before a label is provided, a user confirmation is requested).
MUNICH is analogous art to the claimed invention because they are from the same field of mobile robots and methods of operating thereof. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the teachings of MUNICH to the teachings of the CASE-KIM-YUAN combination such that MUNICH’s user confirmation request could be used with CASE-KIM-YUAN’s display for the purposes of allowing a user to configure a mobile robot.
Regarding claim 18
CASE-KIM-YUAN teaches the elements of claim 1 as outlined above. CASE also teaches:
causing to be displayed, to the user, the recommendation FIG. 6C above shows a user interface displaying a recommendation).
CASE-KIM-YUAN are not relied on for determined, based on the model, an object in the environment that should be moved from the environment; and causing to be displayed, to the user, the recommendation indication the object that should be removed from the environment.
However, MUNICH in an analogous art teaches construction a map of an environment based on mapping data produced by an autonomous cleaning robot in the environment during a first cleaning mission, including causing a display to present a visual representation of the environment based on the map, and a visual indicator of the label, and causing the autonomous cleaning robot to initiate a behavior associated with the label during a second cleaning mission (Abstract).
MUNICH teaches determining, based on the model, an object in the environment should be removed from the environment; and causing to be displayed, to the user, the recommendation indicating the object that should be removed from the environment ([0125]: "autonomous cleaning robot can transmit data to cause a request to change a state of another object in the environment to be issued to the user. For example, the request can correspond to a request to move an obstacle, to reorient an obstacle, to reposition an area rug, to unfurl a portion of an area rug, or to adjust a state of another object").
MUNICH is analogous art to the claimed invention because they are from the same field of mobile robots and methods of operating thereof. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the teachings of MUNICH to the teachings of the CASE-KIM-YUAN combination such that MUNICH’s recommendation to move an object could be used with CASE-KIM-YUAN’s mobile robot for the purposes of allowing a user to configure a mobile robot operating space.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over CASE-KIM-YUAN in further view of HERLANT (US20220206507A1).
Regarding claim 17
CASE-KIM-YUAN teaches the elements of claim 1 as outlined above. CASE also teaches:
causing to be displayed, to the user, the recommendation FIG. 6C shows a user interface displaying a recommendation).
CASE-KIM-YUAN are not relied on for determining, based on the model, a recommended new location for a base station of the mobile robot within the environment that would improve the performance of the mobile robot when performing the task in the environment; and causing to be displayed, to the user, the recommendation indicating the recommended new location for the base station.
However, HERLANT in an analogous art teaches that a mobile robot system includes a docking station and a mobile cleaning robot (Abstract).
HERLANT teaches determining, based on the model, a recommended new location for a base station of the mobile robot within the environment that would improve the performance of the mobile robot when performing the task in the environment; and causing to be displayed, to the user, the recommendation indicating the recommended new location for the base station (FIG. 8A and [0114]: if current dock location 831 is determined to be unsuitable for docking, then the mobile device may determine one or more alternative locations for the docking station and display the one or more alternative locations on the map; [0116]: dock location identification module may identify one or more candidate dock locations based on the mobile robot's docking performance).
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HERLANT is analogous art to the claimed invention because it is from the same filed of mobile cleaning robots and methods of operating thereof. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply the teachings of HERLANT to the teachings of the CASE-KIM-YUAN combination such that HERLANT’s recommendation to move a docking station could be used with the user interface and docking station of CASE-KIM-YUAN’S mobile cleaning robot (see CASE, [0136] docking station) for the purposes of improving the efficiency of the mobile cleaning robot.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over CASE (US20220151450A1) in view of KIM (US20210121035A1).
Regarding claim 19
CASE teaches a method for operating a mobile robot system, the mobile robot system including a mobile robot configured to perform a task in an environment using an operating procedure ([0005]: “document describes systems, devices, and methods for scheduling a mission for a mobile robot and controlling the mobile robot to execute the mission, such as traversing rooms of a user's home and clean floor areas therein”), the method comprising:
receiving first data that was recorded by the mobile robot at least in part using at least one sensor as the mobile robot navigates the environment to perform the task ([0130]: “controller circuit 109 uses data collected by the sensors of the sensor system to control navigational behaviors of the mobile robot 100 during the mission”; [0132]: “data produced during the mission can include persistent data that are produced during the mission and that are usable during a further mission. For example, the mission can be a first mission”);
updating a model associated with the environment to incorporate the first data by at least one of refining and training the model using the first data ([0132]: “the map can be a persistent map that is usable and updateable by the controller circuit 109 of the mobile robot 100 from one mission to another mission to navigate the mobile robot 100 about the floor surface 10”), the model configured to receive data recorded by the mobile robot and output a prediction regarding at least one of (i) a failure of the mobile robot to complete the task ([0180]: “prioritized cleaning module 582 may prioritize cleaning areas based on locations and observabilities thereof […] may reduce cleaning time and thus avoid an unfinished mission”) and (ii) the mobile robot getting stuck ([0187]: “avoidance spots may include hazardous areas where the mobile robot likely gets stuck”); and
at least one of:
modifying the operating procedure, based on the model, to generate a modified operating procedure for performing the task in the environment that improves a performance of the mobile robot ([0132-0133]: “controller circuit 109 can modify subsequent or future navigational behaviors of the mobile robot 100 according to the updated persistent map, such as by modifying the planned path or updating obstacle avoidance strategy […] controller circuit 109 is able to plan navigation of the mobile robot 100 through the environment using the persistent map to optimize paths taken during the missions”); and
determining, based on the model, and causing to be displayed to a user, a recommendation for improving the performance of the mobile robot when performing the task in the environment ([0157]: “mobile device520 may run a software application implemented therein (e.g., a mobile application) or a web-based service (e.g., services provided by the cloud computing system 530) to assist the user in creating or modifying the mission routines”; [0167]: “mobile device UI may offer the user pre-populated, and even personalized, suggestions”).
CASE is not relied on for the model being a neural network.
However, KIM in an analogous art teaches a robot cleaner and method of operating the same ([0002]: “disclosure relates to a robot cleaner using artificial intelligence”; [0014]: “object of the present disclosure is to provide a robot cleaner capable of performing cleaning by generating a cleaning plan based on cleaning record information”).
KIM teaches:
updating a model associated with the environment to incorporate the first data by at least one of refining and training the model using the first data by at least one of refining and training the model using the first data ([0099-0103]: Al technology is applied to a cleaning robot; robot may acquire state information using sensor information, may detect surround environment, may generate map data; robot may perform operations by using the learning model, robot may recognize the environment and the objects by using the learned model), the model being a neural network ([0103]: “The robot 100a may perform the above-described operations by using the learning model composed of at least one artificial neural network”).
CASE and KIM are analogous art to the claimed invention because they are from the same field of mobile cleaning robots.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply the teachings of KIM to the teachings of CASE such that KIM’s neural network could be used with CASE’s mobile robot controller and sensor system for the purposes of making the intelligent decisions disclosed by CASE ([0139]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over CASE (US20220151450A1) in view of MUNICH (US20210124354A1).
Regarding claim 20
CASE teaches a method for operating a mobile robot system, the mobile robot system including a mobile robot configured to perform a task in an environment using an operating procedure ([0005]: “document describes systems, devices, and methods for scheduling a mission for a mobile robot and controlling the mobile robot to execute the mission, such as traversing rooms of a user's home and clean floor areas therein”), the method comprising:
receiving first data that was recorded by the mobile robot at least in part using at least one sensor as the mobile robot navigates the environment to perform the task ([0130]: “controller circuit 109 uses data collected by the sensors of the sensor system to control navigational behaviors of the mobile robot 100 during the mission”; [0132]: “data produced during the mission can include persistent data that are produced during the mission and that are usable during a further mission. For example, the mission can be a first mission”);
updating at least one of a database and a model associated with the environment to incorporate first data ([0132]: “the map can be a persistent map that is usable and updateable by the controller circuit 109 of the mobile robot 100 from one mission to another mission to navigate the mobile robot 100 about the floor surface 10”);
determining, based on the at least one of the database and the model, a modification to the operating procedure, the modification including a region within the environment that the mobile robot should not enter while performing the task in the environment ([0133]: “persistent map enables the controller circuit 109 to direct the mobile robot 100 toward open floor space and to avoid nontraversable space”).
CASE also teaches displaying a recommendation to a user (FIG. 6C).
CASE is not relied on for causing to be displayed, to a user, a recommendation including the modification to the operating procedure, the operating procedure being modified to incorporate the modification in response to receive an input from the user approving the recommendation.
However, MUNICH in an analogous art teaches a mobile cleaning robot that can rely on data collected from previous missions to intelligently plan a path around an environment to avoid error conditions ([0005]).
MUNICH teaches causing to be displayed, to a user, a recommendation including the modification to the operating procedure, the operating procedure being modified to incorporate the modification in response to receiving an input from the user approving the recommendation ([0003]: based on data collected by the robot, features in the environment, such as doors, dirty areas, or other
features, can be indicated on the map with labels, and states of the features can further be indicated on
the map; [0125]: before a label is provided, a user confirmation is requested).
CASE and KIM are analogous art to the claimed invention because they are from the same field of mobile cleaning robots.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to apply the teachings of KIM to CASE such that KIM’s user confirmation to a recommendation could be used with CASE’s displayed recommendation for the purposes of allowing a user to have input to the configuration of the mobile cleaning robot system.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yoo et al. (US20150150429A1) teaches determined a degree of risk and type of stuck state before a cleaning robot is in the stuck state.
Burbank et al. (US20220047134A1) teaches determining a behavior modification of a mobile robot based on visual fiducial.
Millard (US11016491B1) teaches trajectory planning for mobile robots using machine learning.
Fichtl, S., et al. (“Learning Spatial Relationships From 3D vision Using Histograms”, published 9/29/2014. Retrieved from https://ieeexplore.ieee.org/abstract/document/6906902. Accessed on 1/14/2026) teaches mobile robot mapping using a histogram model.
Lakaemper, R. (“Simultaneous Multi-Line-Segment Merging for Robot Mapping using Mean Shift Clustering”, published on 12/15/2009. Retrieved from https://ieeexplore.ieee.org/abstract/document/5354828. Accessed on 1/14/2026) teaches mobile robot mapping using a mean shift clustering model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael V Farina whose telephone number is (571)272-4982. The examiner can normally be reached Mon-Thu 8:00-6:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571) 272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.V.F./Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
1 Examiner notes that the struck through claim limitations are used to show what the cited reference is not being relied on for.