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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 12/25/2025, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
In the future applicant is encouraged to submit each foreign reference as a unique document and to not submit them as one giant foreign reference.
Response to Amendment
This action is in response to amendments and remarks filed on 12/22/2025. Claim(s) 1-2, 4, 9, 14, and 16-18 have been amended. Claim(s) 7-8, 10-11, 13, and 15 have been cancelled. Claim(s) 1-6, 9, 12, 14, and 16-20 are pending examination. This action is made final.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 USC 102 and 35 USC 103 rejections of claims 1-20 is improper because the cited prior art fails to teach all claim limitations as taught/amended. Accordingly the claims are allowable.
Applicant’s arguments with respect to claim(s) 1-20 have been 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.
Regarding applicant’s argument A, the examiner finds it moot. With further search and consideration the examiner will now rely on newly cited art Regev (US PG Pub 2020/0249690) and Attar (US PG Pub 2019/0243379) as well as the previously cited art, Braidic, to teach the claim limitations. Erlich broadly teaches a system that does pathing for a robotic pool system, while Attar has teachings regarding the initial steps a robot takes, including going to an edge of an area first. In light of these newly cited arts the examiner would find the claims to be rejected under 35 USC 103, as obvious. Further detailed explanation and mapping can be found below in the section titled, “Claim Rejections – 35 USC 103.”
Claim Objections
Claims 14 and 16 objected to because of the following informalities:
Claims 14 recites “a first operation mode or a second operation mode” it should be “the first operation mode or the second operation mode”
Claims 16 recites “a first operation mode or a second operation mode” it should be “the first operation mode or the second operation mode”
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-6, 9, 12, 14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braidic (US PG Pub 2019/0085579) in view of Regev (US PG Pub 2020/0249690) and Attar (US PG Pub 2019/0243379).
Regarding claim 1, Braidic teaches a pool robot control method, comprising:
controlling the pool robot to perform a task in the target water region, ([0047]-[0048] teach the pool cleaning robot performing a task, i.e. patrolling/cleaning, in the target water region, i.e. a pool)
after it is determined that the pool robot completes the task, controlling the pool robot to perform a patrolling operation in the target water region, ([0047]-[0048] and [0054]-[0059] teach the robot patrolling the pool as it moves in a preset path)
in a process in which the pool robot performs the patrolling operation obtaining image information of the target water region captured by the pool robot, ([0042]-[0045] and [0048]-[0049] teach an aquatic robot that obtains images in a target region) wherein the pool robot operates in a first operation mode or a second operation mode in the target water region, ([0047]-[0048] teach the robot moving through the environment based on a mode of operation, this includes different kinds of pathing based on the best way to clean the pool)
wherein the first operation mode is a mode in which ([0047]-[0048] and [0054]-[0059] teach the robot pathing along a preset path between preset points. [0070] teaches that the robot can follow a “singular pathway” i.e. fixed route, along the pool surface to ensure that the pool is cleaned) wherein the image information comprises a frame of image or a plurality of frames of images; ([0048] teaches the robot collecting a series of images at it moves in the environment)
analyzing the image information to determine an analysis result; ([0048]-[0055] teach the system analyzing the image captured by the robot) and
determining a target position of a target object if the analysis result indicates that there is the target object in the target water region, ([0054]-[0055] teaches the robot system tracking the target objects, “debris” in the pool) and controlling the pool robot to move to the target position and perform a target operation corresponding to a type of the target object. ([0056]-[0057] teaches the system determining a track to the objects and performing an operation to remove the debris based on the type of debris)
Braidic does not teach wherein the task comprises performing the target operation along an edge of the target water region, performing the target operation in the target water region along a planned route, and performing the target operation along the edge of the target water region again; and the pool robot operates along a random route.
However, Attar teaches “wherein the task comprises performing the target operation along an edge of the target water region, performing the target operation in the target water region along a planned route, and performing the target operation along the edge of the target water region again;” (Figs. 2 and 3B; and [0097]-[0100] and [0115]-[0118] teach the robotic cleaner being tasked with cleaning a pool, water region. The system begins by going to the edge of the pool and mapping it, this would perform the target operation along the edge. After the edge is done the robot uses the data gathered to a further operation along the pool, this would be akin to a cleaning path designed as a series of parallel lines, this would be performing the operation in the target region. After the inner part is clean the robot returns to the edge, as this is the end point of the cleaning pathway.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Braidic with Attar; and have a reasonable expectation of success. Both relate to the cleaning operation of pool robots and ensuring that the pool is cleaned effectively. By performing the perimeter/edge cleaning first the robot gains valuable information it can use to determine further cleaning metrics, such as time, duration, etc. This is taught in Attar [0116]. The use of these metrics allows the system to efficiently clean the pool. [0047] of Attar further shows that the edge of the pool/perimeter is useful in determining the direction of travel for the robot. This again allows for efficient mapping of the system.
The combination of Braidic and Attar does not teach the pool robot operates along a random route.
However, Regev teaches “the pool robot operates along a random route.” (0222] teaches a pool cleaning robot that operates with a random cleaning pathing.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Braidic and Attar with Regev; and have a reasonable expectation of success. All relate to the control of pool cleaning robots. Regev [0222] teaches that the random pathing of the robot can be as effective as the regular “raster scan cleaning path.” This random pathing is best used for simple cleaning tasks that are not as important. The random pathing does not ensure a full clean as taught by Braidic, [0003]. However, this random pathing element is well established in the art. As Braidic teaches in [0085] the random pathing is most effective in smaller pools. This random patrolling pathway still allows for Braidic to use its algorithm to further find the leaves as the system can randomly navigate until it spots a leaf, then perform a controlled navigation to the leaf/debris.
Regarding claim 2, Braidic teaches the method according to claim 1, wherein the obtaining image information of the target water region captured by the pool robot comprises: obtaining the image information of the target water region captured by the pool robot when the pool robot operates in the first operation mode, ([0042]-[0045] and [0048]-[0049] teach an aquatic robot that obtains images in a target region, while operating in a specific operation mode)
Braidic does not teach wherein the first operation mode is a mode in which the pool robot operates between a plurality of preset positions along a random route based on a preset time period.
However, Regev teaches “wherein the first operation mode is a mode in which the pool robot operates between a plurality of preset positions along a random route based on a preset time period.” (0222] teaches a pool cleaning robot that operates with a random cleaning pathing.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Braidic and Attar with Regev; and have a reasonable expectation of success. All relate to the control of pool cleaning robots. Regev [0222] teaches that the random pathing of the robot can be as effective as the regular “raster scan cleaning path.” This random pathing is best used for simple cleaning tasks that are not as important. The random pathing does not ensure a full clean as taught by Braidic, [0003]. However, this random pathing element is well established in the art. As Braidic teaches in [0085] the random pathing is most effective in smaller pools. This random patrolling pathway still allows for Braidic to use its algorithm to further find the leaves as the system can randomly navigate until it spots a leaf, then perform a controlled navigation to the leaf/debris.
Regarding claim 3, Braidic teaches the method according to claim 1, wherein the determining a target position of a target object if the analysis result indicates that there is the target object in the target water region, and controlling the pool robot to move to the target position and perform a target operation corresponding to a type of the target object comprises: if the analysis result indicates that there is the target object in the target water region, determining the target position based on the image information; ([0054]-[0055] teaches the robot system tracking the target objects, “debris” in the pool) and
controlling the pool robot to move to the target position and perform the target operation corresponding to the type of the target object. ([0056]-[0057] teaches the system determining a track to the objects and performing an operation to remove the debris based on the type of debris)
Regarding claim 4, Braidic teaches method according to claim 1, wherein the obtaining image information of the target water region captured by the pool robot comprises: obtaining the image information of the target water region captured by the pool robot when the pool robot operates in a second operation mode, ([0042]-[0045] and [0048]-[0049] teach the robot operating in a specific control mode and an aquatic robot that obtains images in a target region) wherein the second operation mode is a mode in which the pool robot operates between a plurality of preset positions along the fixed route based on a preset time period. ([0047]-[0048] and [0054]-[0059] teach the robot pathing along a preset path between preset points. [0070] teaches that the robot can follow a “singular pathway” i.e. fixed route, along the pool surface to ensure that the pool is cleaned.)
Regarding claim 5, Braidic teaches the method according to claim 4, wherein the determining a target position of a target object if the analysis result indicates that there is the target object in the target water region, and controlling the pool robot to move to the target position and perform a target operation corresponding to a type of the target object comprises: when the pool robot operates in the second operation mode, determining the target position of the target object if the analysis result indicates that there is the target object in the target water region, and controlling the pool robot to move to the target position and perform the target operation corresponding to the type of the target object. ([0047]-[0048] and [0054]-[0059] teach the robot operating in the second pathing mode, and determining the location of a target object. The robot then navigates to the debris and performs an operation based on the type of debris)
Regarding claim 6, Braidic teaches the method according to claim 4, wherein the determining a target position of a target object if the analysis result indicates that there is the target object in the target water region, and controlling the pool robot to move to the target position and perform a target operation corresponding to a type of the target object comprises: after the pool robot operates in the second operation mode, marking positions of a plurality of target objects on a map if the analysis result indicates that there is the plurality of target objects in the target water region, ([0047]-[0048] teaches the system recording a series of debris objects in the pool as it navigates)
determining an operation route based on the positions of the plurality of target objects; ([0077]-[0078] teach the system determining a pathing for a plurality of objects) and
controlling the pool robot to move to the positions of the plurality of target objects along the operation route and perform a target operation corresponding to a type of each target object. ([0077]-[0079] teaches the robot moving along the selected pathing in order to perform the operation)
Braidic does not teach wherein the map is constructed in a process in which the pool robot operates in the target water region.
However, Regev teaches “wherein the map is constructed in a process in which the pool robot operates in the target water region” ([0215] teaches the system generating a map of a pool during operation in order to better operate)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Braidic and Attar with Regev; and have a reasonable expectation of success. All relate to the control of pool robots. As Regev teaches in [0107] the creation of a map of the pool allows for the system to better control the cleaning trajectory. The cleaning robot is not moving in the dark and can more effectively plan a route. As seen in Braidic, the robot can determine multiple debris fields in a row and better plan how to get to all debris. The use of a map would improve this functionality by knowing the entire pool structure.
Regarding claim 9, Braidic teaches a pool robot control method, wherein a pool robot is configured to perform a cleaning operation in a target water region, and a camera is disposed on the pool robot, wherein the method comprises: controlling the pool robot to perform a patrolling operation in a first operation mode or a second operation mode in the target water region, in the target water region, ([0047]-[0048] and [0054]-[0059] teach the robot moving through the environment based on a mode of operation, this includes different kinds of pathing based on the best way to clean the pool) enabling the camera to capture an image of the target water region in a process in which the pool robot performs the patrolling operation, ([0042]-[0043] teaches a camera capturing images of the water area where the robot operates) wherein the first operation mode is a mode of ([0047]-[0048] and [0054]-[0059] teach the robot pathing along a preset path between preset points. [0070] teaches that the robot can follow a “singular pathway” i.e. fixed route, along the pool surface to ensure that the pool is cleaned) wherein the image is used to determine whether there is a target object in the target water region, wherein the target object needs to be cleaned, ([0048]-[0055] teach the system analyzing the image captured by the robot and determining if there is an object in the target area) wherein the patrolling operation is performed after the pool robot completes a task, ([0047]-[0048] and [0054]-[0059] teach the robot patrolling the pool as it moves in a preset path)
controlling, if it is determined that there is the target object, the pool robot to move to a target position of the target object and perform the cleaning operation on the target object. ([0056]-[0057] teaches the system determining a track to the objects and performing an operation to remove the debris based on the type of debris)
Braidic does not teach wherein the task comprises performing the target operation along an edge of the target water region, performing the target operation in the target water region along a planned route, and performing the target operation along the edge of the target water region again; and performing the patrolling operation along a random route.
However, Attar teaches “wherein the task comprises performing the target operation along an edge of the target water region, performing the target operation in the target water region along a planned route, and performing the target operation along the edge of the target water region again;” (Figs. 2 and 3B; and [0097]-[0100] and [0115]-[0118] teach the robotic cleaner being tasked with cleaning a pool, water region. The system begins by going to the edge of the pool and mapping it, this would perform the target operation along the edge. After the edge is done the robot uses the data gathered to a further operation along the pool, this would be akin to a cleaning path designed as a series of parallel lines, this would be performing the operation in the target region. After the inner part is clean the robot returns to the edge, as this is the end point of the cleaning pathway.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Braidic with Attar; and have a reasonable expectation of success. Both relate to the cleaning operation of pool robots and ensuring that the pool is cleaned effectively. By performing the perimeter/edge cleaning first the robot gains valuable information it can use to determine further cleaning metrics, such as time, duration, etc. This is taught in Attar [0116]. The use of these metrics allows the system to efficiently clean the pool. [0047] of Attar further shows that the edge of the pool/perimeter is useful in determining the direction of travel for the robot. This again allows for efficient mapping of the system.
The combination of Braidic and Attar does not teach performing the patrolling operation along a random route
However, Regev teaches “performing the patrolling operation along a random route.” (0222] teaches a pool cleaning robot that operates with a random cleaning pathing.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Braidic and Attar with Regev; and have a reasonable expectation of success. All relate to the control of pool cleaning robots. Regev [0222] teaches that the random pathing of the robot can be as effective as the regular “raster scan cleaning path.” This random pathing is best used for simple cleaning tasks that are not as important. The random pathing does not ensure a full clean as taught by Braidic, [0003]. However, this random pathing element is well established in the art. As Braidic teaches in [0085] the random pathing is most effective in smaller pools. This random patrolling pathway still allows for Braidic to use its algorithm to further find the leaves as the system can randomly navigate until it spots a leaf, then perform a controlled navigation to the leaf/debris.
Regarding claim 12, Braidic teaches the method according to claim 9, comprising: if it is determined that there is the target object, controlling the pool robot to directly move to the target position of the target object and perform the cleaning operation on the target object. ([0056]-[0057] teach the robot moving directly to the debris to remove it)
Regarding claim 14, Braidic teaches the method according to claim 9, wherein the controlling the pool robot to perform a patrolling operation in a first operation mode or a second operation mode in the target water region comprises: ([0047]-[0048] teach the robot moving through the environment based on a mode of operation, this includes different kinds of pathing based on the best way to clean the pool) controlling the pool robot to perform the patrolling operation in the first operation mode,([0047]-[0048] and [0054]-[0059] teach the robot operating in a specific control mode)
Braidic does not teach wherein the first operation mode is a mode in which the pool robot operates between a plurality of preset positions along the random route based on a preset time period.
However, Regev teaches “wherein the first operation mode is a mode in which the pool robot operates between a plurality of preset positions along the random route based on a preset time period.” (0222] teaches a pool cleaning robot that operates with a random cleaning pathing.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Braidic and Attar with Regev; and have a reasonable expectation of success. All relate to the control of pool cleaning robots. Regev [0222] teaches that the random pathing of the robot can be as effective as the regular “raster scan cleaning path.” This random pathing is best used for simple cleaning tasks that are not as important. The random pathing does not ensure a full clean as taught by Braidic, [0003]. However, this random pathing element is well established in the art. As Braidic teaches in [0085] the random pathing is most effective in smaller pools. This random patrolling pathway still allows for Braidic to use its algorithm to further find the leaves as the system can randomly navigate until it spots a leaf, then perform a controlled navigation to the leaf/debris.
Regarding claim 16, Braidic teaches the method according to claim 15, wherein the controlling the pool robot to perform a patrolling operation in a first operation mode or a second operation mode in the target water region comprises: ([0047]-[0048] teach the robot moving through the environment based on a mode of operation, this includes different kinds of pathing based on the best way to clean the pool): controlling the pool robot to perform the patrolling operation in the second operation mode, ([0047]-[0048] and [0054]-[0059] teach the robot operating in a specific control mode) wherein the second operation mode is a mode in which the pool robot operates between a plurality of preset positions along the fixed route based on a preset time period. ([0047]-[0048] and [0054]-[0059] teach the robot pathing along a preset path between preset points. [0070] teaches that the robot can follow a “singular pathway” i.e. fixed route, along the pool surface to ensure that the pool is cleaned.)
Regarding claim 17, Braidic teaches the method according to claim 9, wherein the controlling, if it is determined that there is the target object, the pool robot to move to a target position of the target object and perform the cleaning operation on the target object comprises: when the pool robot is controlled to perform the patrolling operation in the second operation mode, ([0047]-[0048] and [0054]-[0059] teach the robot operating in a specific control mode) controlling, if it is determined that there is the target object, the pool robot to move to the target position of the target object and perform the cleaning operation on the target object. ([0047]-[0048] and [0054]-[0059] teach the robot operating in the second pathing mode, and determining the location of a target object. The robot then navigates to the debris and performs an operation based on the type of debris)
Regarding claim 18, Braidic teaches the method according to claim 19 comprising: in a process of controlling the pool robot to perform the patrolling operation in the second operation mode, if it is determined that there is the target object, recording the target position of the target object, or after the pool robot is controlled to perform the patrolling operation in the second operation mode, if it is determined that there is a plurality of target objects, marking target positions of the plurality of target objects on a map, ([0047]-[0048] teaches the system recording a series of debris objects in the pool as it navigates)
the controlling, if it is determined that there is the target object, the pool robot to move to a target position of the target object and perform the cleaning operation on the target object ([0077]-[0078] teach the system determining a pathing for a plurality of objects) comprises: after the patrolling operation is completed, controlling the pool robot to move to the target positions of the plurality of target objects along an operation route and perform the cleaning operation on each target object. ([0077]-[0079] teaches the robot moving along the selected pathing in order to perform the operation)
Braidic does not teach wherein the map is constructed in a process in which the pool robot operates in the target water region.
However, Regev teaches “wherein the map is constructed in a process in which the pool robot operates in the target water region” ([0215] teaches the system generating a map of a pool during operation in order to better operate)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Braidic with Regev; and have a reasonable expectation of success. Both relate to the control of pool robots. As Regev teaches in [0107] the creation of a map of the pool allows for the system to better control the cleaning trajectory. The cleaning robot is not moving in the dark and can more effectively plan a route. As seen in Braidic, the robot can determine multiple debris fields in a row and better plan how to get to all debris. The use of a map would improve this functionality by knowing the entire pool structure.
Regarding claim 19, Braidic teaches the method according to claim 9, comprising: when the pool robot performs the cleaning operation at a water surface or a bottom of the target water region, controlling the pool robot to perform the patrolling operation in the target water region. ([0047] teaches the robot patrolling an aquatic environment. This environment comprises a pool, the robot cleans the entirety of the pool, surface and bottom, and patrols the rest)
Regarding claim 20, Braidic teaches the method according to claim 9, comprising: if it is determined that there is the target object, determining the target position of the target object ([0054]-[0055] teaches the robot system tracking the target objects, “debris” in the pool) and controlling the pool robot to move to the target position of the target object, wherein the image is used to determine the target position of the target object. ([0056]-[0057] teaches the system determining a track to the objects and performing an operation to remove the debris based on the type of debris)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS STRYKER whose telephone number is (571)272-4659. The examiner can normally be reached Monday-Friday 7:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571) 272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665