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 .
Status of Claims
This Office Action is responsive to communications filed 5/26/2026
Claims 1 and 20 are amended.
Claims 1-5, 7-8, and 10-20 are presented for examination.
Response to Arguments/Remarks
Regarding rejections of claims 1 (and dependents) and 20 under §103
Applicant Argues
The cited combination of Case, Kim, and Yuan does not arrive at the limitations of claim 1. The cited combination of Case and Munich does not arrive at the limitations of claim 20.
Examiner Responds
Applicant’s arguments with respect to claims 1 (and dependents) and 20 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant’s arguments are over amended features and are rejected over newly discovered prior art, thus Applicant’s amendment necessitated the new ground(s) of rejections presented below.
Regarding rejections of claim 19 under §103
Applicant Argues
The cited combination of Case and Kim fails to teach a neural network that outputs a prediction regarding a failure of the mobile robot to complete the task and/or the mobile robot getting stuck. Case does not teach generating the prediction of a failure to complete the task or getting stuck using a neural network. Kim’s learning model does not perform any kind of path planning or avoidance spot determinations. Instead, the learning model of Kim is used to recognize the surrounding environment and the objects.
Examiner Responds
Applicant’s arguments have been fully considered but they are not persuasive.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Case was not relied on to teach generating the prediction of a failure to complete the task or getting stuck using a neural network. The examiner also disagrees that Kim’s learning model does not perform any kind of path planning, as Kim recites that the robot to which the neural network is applied “may determine the route and ravel plan” (Kim, [0103]).
As outlined in the previous office action, Case was relied upon to teach the bulk of the claim, to include updating a model associated with the environment wherein the model is configured receive data recorded by the mobile robot and to generate a prediction of a failure to complete a task or getting stuck. Case was not relied upon to teach that the model used to a operate the mobile robot may be a neural network. Kim, from the same field of endeavor as Case and the claimed invention, teaches to apply AI technology to a robot, such as a cleaning robot, that may do operations such as detecting/recognizing the surrounding environment and objects, generating map data, determining an operation, and determining a route and/or travel plan ([0099] AI applied to cleaning robot, [0101] teaches operations robot may perform). Kim also expressly discloses that the AI applied to the robot may be at least one artificial neural network ([0103]). In summary, Case teaches a method of operating a mobile robot that includes a model wherein the model is associated with the environment and the model is configured to output a prediction such as the robot getting stuck. Kim teaches that a neural network is a type of model that can be used to operate a mobile robot, such as the mobile robot taught by Case. Thus, the combination of Case in view of Kim teaches the elements of claim 19 as outlined in the previous office action.
Therefore, the rejection is maintained.
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 CASE1 in view of KIM2, in further view of PRISAMENT (US10173320B1).
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”)3
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 spends a most amount of time while performing the task ([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.
However, PRISAMENT in analogous art teaches methods for optimizing robot-implemented tasks based at least in part on historical task and location correlated duration data (Abstract). PRISAMENT teaches to generate a heatmap4 model associated with a mobile robot operating in an environment, the model being updated to reflect identified positions within the environment and the associated amount of time spent at the identified positions as well as the task performed (Col. 2, ll. 9-14: “implementations also include generating a heatmap for at least a portion of the spatial region served by the first robot based upon the historical task and location correlated duration data”, Col. 6, ll. 55-67 – Col. 7, ll. 1-2: “a heatmap generally provides a mechanism by which the relative amount of time that one or more robot spends performing certain tasks in different locations can be ascertained”, Figs. 2 & 5).
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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 PRISAMENT to the teachings of the CASE-KIM combination such that PRISAMENT’s method of using a heatmap to optimize future task perform would have been used with CASE-KIM’s learning model such that operating procedures would be modified based on the heatmap to optimize future task performance, as taught by PRISAMENT (Fig. 2, 206).
Regarding claim 2
CASE-KIM-PRISAMENT 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-PRISAMENT 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-PRISAMENT 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-PRISAMENT 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-PRISAMENT 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-PRISAMENT 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-PRISAMENT 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-PRISAMENT 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-PRISAMENT 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-PRISAMENT 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-PRISAMENT 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-PRISAMENT in further view of ERMAKOV.5
Regarding claim 5
CASE-KIM-PRISAMENT teaches the elements of claim 4 as outlined above.
CASE-KIM-PRISAMENT 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-PRISAMENT 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-PRISAMENT in further view of MUNICH.6
Regarding claim 12
CASE-KIM-PRISAMENT 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-PRISAMENT 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-PRISAMENT combination such that MUNICH’s user confirmation request could be used with CASE-KIM-PRISAMENT’s display for the purposes of allowing a user to configure a mobile robot.
Regarding claim 18
CASE-KIM-PRISAMENT 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-PRISAMENT 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-PRISAMENT combination such that MUNICH’s recommendation to move an object could be used with CASE-KIM-PRISAMENT’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-PRISAMENT in further view of HERLANT.7
Regarding claim 17
CASE-KIM-PRISAMENT 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-PRISAMENT 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-PRISAMENT combination such that HERLANT’s recommendation to move a docking station could be used with the user interface and docking station of CASE-KIM-PRISAMENT’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 CASE8 in view of KIM.9
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 [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 CASE10 in view of MUNICH11 in view of PRISAMENT (US10173320B1).
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 MUNICH 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 MUNICH to CASE such that MUNICH’s user confirmation to a recommendation would 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.
The CASE-MUNICH combination is not relied on the mode associated with the environment to incorporate the first data being a histogram model.
However, PRISAMENT in analogous art teaches methods for optimizing robot-implemented tasks based at least in part on historical task and location correlated duration data (Abstract). PRISAMENT teaches to generate a heatmap12 model associated with a mobile robot operating in an environment, the model being updated to reflect identified positions within the environment and the associated amount of time spent at the identified positions as well as the task performed (Col. 2, ll. 9-14: “implementations also include generating a heatmap for at least a portion of the spatial region served by the first robot based upon the historical task and location correlated duration data”, Col. 6, ll. 55-67 – Col. 7, ll. 1-2: “a heatmap generally provides a mechanism by which the relative amount of time that one or more robot spends performing certain tasks in different locations can be ascertained”, Figs. 2 & 5).
CASE-MUNICH teaches a method of operating a mobile robot system wherein the method comprises updating at least one of a model associated with the environment to incorporate data collected by the mobile robot as it navigates an environment to perform a task. PRISAMENT teaches a known heatmap model that is generated and updated using data that identifies a position within the environment, a task performed at the position, and a duration spent at the position performing the task. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine PRISAMENT with CASE-MUNICH such that PRISAMENT’s heatmap model would have been used with CASE-MUNICH’s method such that the CASE-MUNICH’s model associated with the robot’s environment would have been the heatmap model configured to identify positions, as well as the task and the duration at the position, within the environment as taught by PRISMENT. PRISAMENT teaches that future task performance can be optimized based on the heatmap, thereby providing the motivation to combine (Fig. 2, 206).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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
/MARK A CONNOLLY/Primary Examiner, Art Unit 2115 7/24/26
1 CASE is a prior art reference cited in the previous office action.
2 KIM is a prior art reference cited in the previous office action.
3 Examiner notes that the struck through claim limitations are used to show what the cited reference is not being relied on for.
4 Examiner’s note: the claimed “histogram model” is being interpreted as a heatmap in view of Applicant’s disclosure [0069]: “FIG. 4A shows a simple histogram model 200 that indicates a relative proportion or amount of time spent by the mobile robot at various positions […] In the histogram model 200, darker shaded cells indicate regions within the environment 40 that the mobile robot spends the most time, whereas lighter shaded cells indicate regions within the environment 40 that the mobile robot 20 spends relatively less time.”
5 ERMAKOV is a prior art reference cited in the previous office action.
6 MUNICH is a prior art reference cited in the previous office action.
7 HERLANT is a prior art reference cited in the previous office action.
8 CASE is a prior art reference cited in the previous office action.
9 KIM is a prior art references cited in the previous office action.
10 CASE is a prior art reference cited in the previous office action.
11 MUNICH is a prior art reference cited in the previous office action.
12 Examiner’s note: the claimed “histogram model” is being interpreted as a heatmap in view of Applicant’s disclosure [0069]: “FIG. 4A shows a simple histogram model 200 that indicates a relative proportion or amount of time spent by the mobile robot at various positions […] In the histogram model 200, darker shaded cells indicate regions within the environment 40 that the mobile robot spends the most time, whereas lighter shaded cells indicate regions within the environment 40 that the mobile robot 20 spends relatively less time.”
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