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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 22 June 2026 has been entered.
Status of Claims
Claims 1, 3-7, 11, and 13-20 are examined herein. Claims 2, 8-10, and 12 are cancelled. Claims 1, 5-6, are 15-19 are amended.
Response to Amendment / Remarks
Any reference to the prior office action refers to the Final Rejection dated 20 April 2026.
All rejections under 35 U.S.C. 112(a) from the prior office action are withdrawn.
All rejections under 35 U.S.C. 112(b) from the prior office action are withdrawn.
Applicant's arguments, filed 22 June 2026, regarding the prior art from the prior office action have been fully considered. The prior art from the prior office action includes U.S. Pub. No. 2021/0138659 (Arora et al., hereinafter, Arora).
Firstly, Applicant asserts Arora is related to “a single session”; the Examiner disagrees. In FIG. 7 of Arora, the sensor events 232 are from a different mission than sensor events 234.
Regarding the Claims 1, 3-5, 11, and 13-20 arguments, Applicant argues Arora does not anticipate or suggest (1) “setting a cell of a predetermined size with respect to the risk area”, (2) “overlapping the plurality of restricted area maps”, (3) “assigning a weight to the cells “based on a type of the plurality of abnormal situations”” (4) “determining whether a total combined weight of overlapping cells of the risk areas…satisfies a predetermined confidence threshold”.
Applicant’s argument that Arora fails to anticipate or suggest generating defined cells as currently claimed has been considered but is moot because the new grounds of rejection for Claims 1, 3-5, 11, and 13-20 (see below) does not rely on Arora to teach generating defined cells. Furthermore, Applicant’s other arguments regarding cells, including “total combined cell weight” and “intersecting cellular regions” are moot for the same reason. The new grounds of rejection was necessitated by the amendment. All other arguments (i.e., those not related to “cells”) relating to these claims are not persuasive.
Applicant argues Arora is directed to “merely counting the total number of discrete data points within a cluster to pass a minimum quantity threshold”; this is not an accurate representation of the disclosure of Arora, and therefore not persuasive. Arora determines that a quantity of a type of sensor event (in Arora, no weight is given to certain events not meeting criteria during the evaluations while some events receive weight which is equivalent to “calculating a combined weight based on the severity or type of the underlying abnormality”) meets a distance threshold (i.e., “evaluating the geometric overlap”). Applicant argues “the Office Action fails to establish how Arora could conceivably meet the claimed features of “overlapping the plurality of restricted area maps””: the Examiner addressed this limitation on at least page 10 of the prior office action (at least by referencing at least FIG. 7 of Arora) and further has updated the rejection below to further explain the interpretation of Arora. Applicant argues Arora “merely maintains a database of raw historical event coordinates and applies a distance based clustering algorithm to that signal scatterplot of points” and for that reason is not “equivalent of generating independent, distinct map layers…”. Firstly, this argument is unpersuasive, because, as one of ordinary skill in the art would understand, coordinates in a database are equivalent to maps and also because Arora specifically discloses recording events on a map “During a cleaning mission, the autonomous mobile robot can encounter obstacles that can trigger an escape behavior or can cause the autonomous mobile cleaning robot to become stuck. Such events can be recorded on a map” (see at least [0004]).
Applicant’s arguments with respect to Claims 6 and 7 have been considered but are moot because the new grounds 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.
Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f), except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Traveling unit to in Claim 1
Storage unit to in Claim 11
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The structure for “traveling unit” is found on at least pages 21 and 22: one or two driving wheels and at least one motor, or equivalents thereof.
The structure for “storage unit” is found on at least page 19: “a volatile or non-volatile recording medium”, or equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Claim Objections
The claims are objected to because of the following informalities:
Claim 18: “each cell of the risk area” should be “each cell of the risk [[area]]areas” or “[[each]]the cell of [[the]]each risk area”.
Claim 18: “the cells of the risk area” should be “the cells of the risk [[area]]areas”.
Claim 18: “total combined weight of the risk area” should be “total combined weight of the overlapping risk [[area]]areas”.
Claim 18: “selecting the overlapping risk [[area]]areas”.
Appropriate corrections are required.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-4, 11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Arora in view of U.S. Pub. No. 2019/0204851 (Afrouzi et al., hereinafter, Afrouzi).
Regarding Claim 1, Arora discloses A mobile robot (see at least [0044]-[0046] and FIG. 3A: robot 100) comprising:
a traveling unit to allow a body to move in a traveling area (see at least [0044]-[0046] and FIG. 3A: motors 114, drive wheels 112); and
a controller (see at least [0045] and FIG. 3A: controller 109) configured to:
analyze traveling state information while traveling in the traveling area according to a base map for the traveling area to set a risk area where a plurality of abnormal situations including stalling, wandering, or a collision occur (see at least [0025], [0082]-[0084], and [0088]-[0090]: “A sensor event can occur when one or more sensors of the sensor system of the robot 100 are triggered. A feature in the environment can be associated with the sensor event. A location of a sensor event can correspond to a location of the robot 100 when the sensor event occurs, or can correspond to a location of the feature detected by the sensor of the robot 100 for which the sensor event has occurred”; a sensor event is analogous to a risk area; multiple sensors can be triggered to determine the sensor event (i.e., a plurality of abnormal situations which could include at least a collision); “In some implementations, multiple sensors can be involved in a sensor event. For example, one of the proximity sensors 136a, 136b, 136c can detect an obstacle in the environment, and the image capture device 140 can also detect the same obstacle. A combination of data from the proximity sensor and the image capture device 140 can indicate a sensor event has occurred. A location of the sensor event can be determined based on the combination of data from the proximity sensor and the image capture device 140. Other combinations of sensors described herein could be used as the basis for a sensor event”);
create a plurality of restricted area maps by recording the risk area at each of a plurality of times of traveling (see at least [0010], [0097], [0101], [0107], and FIG. 7: details of sensor events are saved including which mission they occurred during, time of occurrence, and location; therefore, there are a plurality of maps corresponding to the plurality of missions);
calculate at least one restricted area candidate by setting …a predetermined size with respect to the risk area, overlapping the plurality of restricted area maps and determining whether a total combined weight of overlapping … risk areas, assigned based on a type of the plurality of abnormal situations, satisfies a predetermined confidence threshold (see at least [0092]-[0102], FIG. 6A, FIG. 6B, and FIG. 7: “In some implementations, sensor events 212 are considered to be in a subset that can serve as the basis for recommending a behavior control zone if the sensor events 212 are no more than a threshold distance 214 apart from one another” / the sensor events are shown surrounded in circles of the size of the threshold distance (i.e., setting a predetermined size with respect to the risk area); “In the example depicted in FIG. 6A, a cluster 216 of the sensor events 212 satisfies a threshold distance criterion. In particular, each sensor event 212 within the cluster 216 is no more than the threshold distance 214 from at least one other sensor event 212 within the cluster 216” / the threshold distance circles around the sensor events overlap (i.e., overlapping risk areas); “Referring to FIG. 6B, because only the cluster 216 satisfies both the threshold distance criterion and the threshold amount criterion, only the cluster 216 of the sensor events 212 is used for recommending a behavior control zone 222. The cluster 218 and the cluster 220 do not satisfy both these criteria and therefore are not used for recommending a behavior control zone” (i.e., summing amount is determining a total combined weight; amount criterion is a predetermined confidence threshold); “In the example depicted in FIG. 7, the cluster 243 includes the sensor events 234, which are triggered at an earlier time than the sensor events 232 are triggered” (i.e., FIG. 7 shows overlapping a plurality of restricted area maps because sensor events are from multiple missions); the weight assigned to sensor events of the same type is true/one and a different type may be false/zero);
update the base map by reflecting the at least one restricted area candidate as a restricted area in the base map to be used for subsequent traveling in the traveling area to generate an updated base map (see at least [0111], [0116], and FIG. 5: “the user selection corresponds to acceptance of the recommended behavior control zone such that the user-selected behavior control zone is identical to the recommended behavior control zone”; “the robot 100 can access data for the user-selected behavior control zone so that, during a mission, the robot 100 can be controlled in accordance with the user-selected behavior zone”); and
control the traveling unit to avoid the restricted area based on the updated base map (see at least [0015] and [0043]: “In some implementations, the behavior control zone can be a keep out zone, and the behavior initiated by the autonomous mobile robot can correspond to an avoidance behavior in which the autonomous mobile robot avoids the keep out zone”).
Arora does not explicitly disclose calculate at least one restricted area candidate by setting a …a predetermined size with respect to the risk area, overlapping the plurality of restricted area maps and determining whether a total combined weight of overlapping …risk areas, assigned based on a type of the plurality of abnormal situations, satisfies a predetermined confidence threshold is calculate at least one restricted area candidate by setting a cell of a predetermined size with respect to the risk area, overlapping the plurality of restricted area maps and determining whether a total combined weight of overlapping cells of the risk areas, assigned based on a type of the plurality of abnormal situations, satisfies a predetermined confidence threshold.
Afrouzi, in the same field of cleaning robots, and therefore analogous art, explicitly teaches calculate at least one restricted area candidate by setting a cell of a predetermined size with respect to the risk area (see at least [0025] and [0027]: “In some embodiments, the processor of the robotic cleaning device generates a new grid map with new characteristics associated with each or a portion of the cells of the grid map at each work session”; “In some embodiments, the processor uses the aggregate map to predict areas with high risk of stalling, colliding with obstacles and/or becoming entangled with an obstruction. In some embodiments, the processor records the location of each such occurrence and marks the corresponding grid cell(s) in which the occurrence took place.”), overlapping the plurality of restricted area maps (see at least [0025]: “ the processor compiles the map generated at the end of a work session with an aggregate map based on a combination of maps generated during each or a portion of prior work sessions”) and determining whether a total combined weight of overlapping cells of the risk areas, assigned based on a type of the plurality of abnormal situations, satisfies a predetermined confidence threshold (see at least [0023], [0026]-[0027], and [0037]: “the processor may use environmental sensor data from more than one type of sensor to improve predictions of environmental characteristics. Different types of sensors may include, but are not limited to, obstacle sensors, audio sensors, image sensors, TOF sensors, and/or current sensors. In some embodiments, the processor may provide the classifier with different types of sensor data and over time the weight of each type of sensor data in determining the predicted output is optimized by the classifier”; “For example, the processor uses aggregated obstacle sensor data collected over multiple work sessions to determine areas with high probability of collisions or aggregated electrical current sensor of a peripheral brush motor to determine areas with high probability of increased electrical current due to entanglement with an obstruction. In some embodiments, the processor causes the robot to avoid or reduce visitation to such areas.”)
Combining the grid-based recording of Afrouzi with the coordinate-based recording of Arora, would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, with the motivation of using the known technique of grid-based recording of Afrouzi to improve the similar invention of Arora to provide a probability / scoring at every grid location (see at least Afrouzi [0023] and [0027]).
Regarding Claim 3, Arora and Afrouzi combination teaches Claim 1. Furthermore, Arora further discloses wherein, by analyzing the traveling state information, the controller is configured to determine whether each abnormal situation of the plurality of abnormal situations corresponds to one of the stalling, the wandering, or the collision (see at least [0012], [0057]-[0058], [0065], [0084], and [0088]-[0090]: obstacle detection events (i.e., collision events) are determined from analysis of sensor data; “For example, if the robot 100 includes a bump sensor, a sensor event can occur when the bump sensor is triggered. A location of the sensor event can correspond to a location of the robot 100 when the bump sensor is triggered, or can correspond to a location of contact between the robot 100 and an object in the environment that triggers the bump sensor. In further examples, if the robot 100 includes the image capture device 140, a sensor event can occur when the image capture device 140 captures imagery containing a particular object in the environment. The object can be an obstacle in the environment that the robot 100 can contact during navigation. A location of the sensor event can correspond to a location of the robot 100 when the image capture device 140 detects the object. Alternatively, the location of the sensor event can correspond to an estimated location of the detected object. The imagery captured by the image capture device 140 can be analyzed to determine a position of the object relative to the robot 100 such that the location of the object within the environment can be estimated. Sensor events can occur when other sensors of the robot 100 are triggered as well. For example, the sensor events can occur based on sensing performed by the proximity sensors 136a, 136b, 136c, the cliff sensors 134, the obstacle following sensor 141, the optical mouse sensor, the encoders, a brush motor controller, a wheel motor controller, a wheel drop sensor, an odometer, or other sensors of the sensor system”) to record location of the risk area and a type of the abnormal situation in a corresponding restricted area map among the plurality of restricted area maps (see at least [0089]-[0090] and FIG. 7: “In some implementations, multiple sensors can be involved in a sensor event. For example, one of the proximity sensors 136a, 136b, 136c can detect an obstacle in the environment, and the image capture device 140 can also detect the same obstacle. A combination of data from the proximity sensor and the image capture device 140 can indicate a sensor event has occurred. A location of the sensor event can be determined based on the combination of data from the proximity sensor and the image capture device 140. Other combinations of sensors described herein could be used as the basis for a sensor event”; “Alternatively, the location of the sensor event can correspond to an estimated location of the detected object. The imagery captured by the image capture device 140 can be analyzed to determine a position of the object relative to the robot 100 such that the location of the object within the environment can be estimated”).
Regarding Claim 4, Arora and Afrouzi combination teaches Claim 1. Furthermore, Arora further discloses wherein the traveling unit includes a plurality of wheels to allow the body to move (see at least FIG. 3A: wheels 112), and wherein the plurality of abnormal situations are determined based on the traveling state information on the plurality of wheels (see at least [0089]: “the sensor events can occur based on sensing performed by the proximity sensors 136a, 136b, 136c, the cliff sensors 134, the obstacle following sensor 141, the optical mouse sensor, the encoders, a brush motor controller, a wheel motor controller, a wheel drop sensor, an odometer, or other sensors of the sensor system”).
Regarding Claim 11, Arora and Afrouzi combination teaches Claim 1. Furthermore, Arora further discloses further comprising a storage unit to store the plurality of restricted area maps (see at least [0055] and FIG. 3A: memory storage element 144).
Regarding Claim 13, Arora and Afrouzi combination teaches Claim 1. Furthermore, Arora discloses wherein the controller is configured to assign a different weight to each type of the plurality of abnormal situations (see at least [0097]-[0102]: with two types of abnormal situations the first type of event being considered for a first behavior zone has a weight of true/one and the second type of event has a weight of false/zero).
Regarding Claim 14, Arora and Afrouzi combination teaches Claim 1. Furthermore, Arora discloses wherein the controller is configured to: transmit information regarding the at least one restricted area candidate to an external device (see at least [0119]-[0122], [0131], FIG. 9, and FIG. 10A: “a user interface 310 for a user computing device, e.g., the user computing device 188, presents a notification 312 indicating that a behavior control zone is recommended. The notification 312 indicates that the robot 100 has been getting stuck in the same spot lately, e.g., due to obstacles in the vicinity of the spot detected by obstacle detection sensors of the robot 100.”); and record a selected restricted area candidate from the at least one restricted area candidate as a restricted area in the updated base map (see at least [0111], [0116], and FIG. 5: “the user selection corresponds to acceptance of the recommended behavior control zone such that the user-selected behavior control zone is identical to the recommended behavior control zone”; “the robot 100 can access data for the user-selected behavior control zone so that, during a mission, the robot 100 can be controlled in accordance with the user-selected behavior zone”).
Regarding Claim 15, Arora and Afrouzi combination teaches Claim 11. Furthermore, the Arora and Afrouzi combination further teaches (with the same motivation to combine as Claim 1) wherein the controller is configured to: display the cells of the risk area with respect to a location point where each abnormal situation of the plurality of abnormal situations has occurred (see at least Afrouzi [0027]: “the processor records the location of each such occurrence and marks the corresponding grid cell(s) in which the occurrence took place”); and define an area where the cells of the risk areas overlap in the plurality of restricted area maps as the at least one restricted area candidate (see at least Afrouzi [0027]: “For example, the processor uses aggregated obstacle sensor data collected over multiple work sessions to determine areas with high probability of collisions or aggregated electrical current sensor of a peripheral brush motor to determine areas with high probability of increased electrical current due to entanglement with an obstruction. In some embodiments, the processor causes the robot to avoid or reduce visitation to such areas”; the overlapping cells are made the area to avoid in Afrouzi, based on the two references one of ordinary skill in the art would arrive at the claim invention).
Regarding Claim 16, Arora and Afrouzi combination teaches Claim 15. Furthermore, the Arora and Afrouzi combination further teaches (with the same motivation to combine as Claim 1) wherein the area where the cells of the risk areas overlap is extended to be set as the restricted area candidate (see at least Arora [0108] and FIG. 7: “In some implementations, instead of the cluster 248 being used as the basis for a recommended behavior control zone, two distinct clusters 248a, 248b are used as the basis for the recommended behavior control zone 262. Each cluster 248a, 248b independently satisfies the criteria for recommending a behavior control zone. The sensor events 232 in the cluster 248a and the sensor events 232 in the cluster 248b together, however, may not satisfy a certain criterion, e.g., the distance threshold criteria. For example, the closest sensor events 232 in the cluster 248a and in the cluster 248b may be separated by a distance greater than the distance threshold for the distance threshold criteria. As a result, two distinct behavior control zones are recommended, one being recommended for the cluster 248a, and another being recommended for the cluster 248b. In some implementations, if two or more recommended behavior control zones satisfy a behavior control zone separation criterion, the two or more recommended behavior control zones are combined with one another to form a single recommended behavior control zone. The resulting recommended behavior control zones for the clusters 248a, 248b, for example, can be separated by a distance no more than a threshold distance for the behavior control zone separation criterion. As a result, the recommended behavior control zones are combined with one another to form a single recommended behavior control zone, i.e., the recommended behavior control zone 262, covering both the cluster 248a and the cluster 248b.”; Arora teaches expanding the area, in Arora the area expansion is to areas beyond the distance threshold circles, one of ordinary skill in the art would consider this analogous to extending beyond a directly impacted cell of Afrouzi / would find it obvious to include more than just a directly impacted cell based on the teachings of both references).
Regarding Claim 17, many limitations in Claim 17 are substantially similar to Claim 1 and Claim 17 is rejected for at least the same reasons as Claim 1. Furthermore, Arora discloses the limitations A method for controlling a mobile robot (see at least [0079] and FIG. 5), collecting traveling state information while traveling in a traveling area according to a base map for the traveling area (see at least [0012]-[0013] and [0088]-[0090]: “the sensor events can be obstacle detection events in which one or more sensors of the autonomous mobile robot is triggered. In some implementations, the one or more sensors can include at least one of a proximity sensor, a bump sensor, an image capture device, a cliff sensor, a wheel encoder, a wheel motor controller, a brush motor controller, a wheel drop sensor, an odometer, or an optical mouse sensor.”; “the subset of the sensor events can include error events. The error events can include at least one of: a wheel drop event in which a drive wheel of the autonomous mobile robot extends from the autonomous mobile robot beyond a threshold distance, a wheel slip event in which the drive wheel of the autonomous mobile robot loses traction with a floor surface across with the autonomous mobile robot moves, a wedge event in which the autonomous mobile robot is wedged between an obstacle above the autonomous mobile robot and the floor surface, or a robot stuck event in which the autonomous mobile robot moves into a region in the environment and is unable to exit the region”; “In some implementations, the error events include a robot stuck event in which the robot 100 moves into a region in the environment and is unable to exit the region.”); controlling the mobile robot to avoid the restricted area based on the updated base map (see at least [0015] and [0043]: “In some implementations, the behavior control zone can be a keep out zone, and the behavior initiated by the autonomous mobile robot can correspond to an avoidance behavior in which the autonomous mobile robot avoids the keep out zone”).
Regarding Claim 18, Arora and Afrouzi combination teaches Claim 15. Furthermore, the Arora and Afrouzi combination further teaches (with the same motivation to combine as Claim 1) wherein the calculating the at least one restricted area candidates comprises: assigning a weight to each cell of the risk area for each restricted area map (see at least Afrouzi [0026] and [0043]: scores are provided to different cells; furthermore, in Arora some areas are weighted zero/false/0 because they were not within the predetermined time period / number of missions, some areas are weighted one/true/1 because they are in the predetermined time period / number of missions) and summing the weights of the cells of the risk area overlapping with respect to the restricted area candidate (see at least Afrouzi [0026] and Arora [0093]-[0095]: different criteria, including summing to reach a criteria are taught); and
based on a total combined weight of the risk area being greater than or equal to a threshold value, selecting the risk area as the restricted area candidate (see at least Arora [0093]-[0096], [0099]-[0106], FIG. 6B, and FIG. 7: “The cluster 248 includes both the sensor events 232 and the sensor events 234. The cluster 248 satisfies the threshold amount criterion and the threshold distance criterion. A recommended behavior control zone 262 is accordingly defined”).
Regarding Claim 19, Arora and Afrouzi combination teaches Claim 18. Furthermore, Arora further discloses wherein a different weight is assigned to each type of the plurality of abnormal situations (see at least [0097]-[0102]: with two types of abnormal situations the first type of event being considered for a first behavior zone has a weight of true/one and the second type of event has a weight of false/zero).
Regarding Claim 20, Arora and Afrouzi combination teaches Claim 19. Furthermore, Arora further discloses further comprising: transmitting information regarding the at least one restricted area candidate to an external device (see at least [0119]-[0122], [0131], FIG. 9, and FIG. 10A: “a user interface 310 for a user computing device, e.g., the user computing device 188, presents a notification 312 indicating that a behavior control zone is recommended. The notification 312 indicates that the robot 100 has been getting stuck in the same spot lately, e.g., due to obstacles in the vicinity of the spot detected by obstacle detection sensors of the robot 100.”); and
receiving selection information of a selected restricted area candidate from the at least one restricted area candidate from the external device (see at least [0126], [0132]-[0134], and FIG. 10A: “The second indicator 320 thus represents a user selection of a behavior control zone based on the recommended behavior control zone. Referring to FIG. 10A, the user can interact with the user interface 310 and adjust the recommended behavior control zone represented by the first indicator 318. For example, the user can operate a user input device, e.g., a touchscreen, a mouse, trackpad, or other user input device, and make the user-selected modification of the shape, the size, the length, the width, or other geometric feature of the recommended behavior control zone. In the example shown in FIG. 10A, if the user interface 310 includes a touchscreen, the user can touch a corner 319 of the first indicator 318 and drag in a downward direction 321 to resize the recommended behavior control zone. Such resizing produces the user-selected behavior control zone represented by the second indicator 320.”),
wherein the updating the base map comprises updating the base map by recording, in the base map, the selected restricted area candidate as the restricted area (see at least [0134]: “At an operation 416, the user-selected behavior control zone is defined by the computing system 401.”; “At an operation 418, the computing system 401 determines that the robot 100 is proximate to the user-selected behavior control zone or is within the user-selected behavior control zone. And at an operation 420, the robot 100 initiates a behavior associated with the user-selected behavior control zone. The behavior can vary in implementations as described herein.”).
Claims 5 is rejected under 35 U.S.C. 103 as being unpatentable over Arora in view Afrouzi in further view of U.S. Pub. No. 2022/0414932 (Chen et al., hereinafter, Chen).
Regarding Claim 5, Arora and Afrouzi combination teaches Claim 4. Furthermore, Arora further suggests in case that the mobile robot does not travel for a predetermined time, the controller is configured to determine the abnormal situation as the stalling (see at least [0063] and [0088]: “the sensor events are error events in which errors associated with the robot 100 are triggered as the robot 100 moves about the floor surface 10”; “In some implementations, the error events include a wheel slip event in which one or more of the drive wheels 112 of the robot 100 loses traction with the floor surface 10 across which the robot 100 moves. The wheel slip event can be detected by one or more of the sensors of the robot 100, such as the encoders, the odometer, or other movement sensors of the robot 100”; “the sensor system can include encoders associated with the motors 114 for the drive wheels 112, and these encoders can track a distance that the robot 100 has traveled”). Furthermore, Afrouzi further teaches stalling as an issue (see at least [0027]).
Chen, in the same field of cleaning robots, and therefore analogous art explicitly teaches in case that the mobile robot does not travel for a predetermined time, the controller is configured to determine the abnormal situation as the stalling (see at least [0095]-[0098], Figure 6, Figure 7A: “Wheel slippage (e.g., a wheel on the mobile robot is turning, but the mobile robot is not substantially changing its position in the environment 100)”; “utilizes multimodal sensory inputs to detect if one or more robot wheels are in slipped state”; “using the OTS sensor to measure the robot motion from t to t+αt, can provide an important reference (e.g., that is used to compare with wheel encoder odometry measurements) for checking for wheel slip events”).
Substituting the specifically defined calculation of wheel slip disclosed by Chen for the more generic wheel slip calculation disclosed by Arora, would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, the motivation to combine the specific calculation of Chen with Arora is to use a known technique with predictable results from Chen for the generically disclosed determination of wheel slip based on sensors of Arora.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Arora in view Afrouzi in further view of Chen in further view of U.S. Pub. No. 2020/0150655 (Artes et al., hereinafter, Artes).
Regarding Claim 6, the Arora, Afrouzi, and Chen combination teaches Claim 5. Arora discloses many error events including “a robot stuck event in which the autonomous mobile robot moves into a region in the environment and is unable to exit the region” (see at least [0013] and [0088]), but does not explicitly disclose wherein, in case that a distance traveled by the mobile robot for a predetermined time is less than or equal to predetermined distance or a rotation current value of the at least one of the plurality of wheels is out of threshold range, the controller is configured to determine the abnormal situation as the wandering. One of ordinary skill in the art would consider the list of error events in Arora to be examples and would easily combine other known error events.
Artes, in the same field of mobile robots, and therefore analogous art, teaches wherein, in case that a distance traveled by the mobile robot for a predetermined time is less than or equal to predetermined distance or a rotation current value of the at least one of the plurality of wheels is out of threshold range, the controller is configured to determine the abnormal situation as the wandering (see at least [0090]: “A third example of a situation in which the robot can independently define exclusion regions has to do with regions in which the robot can only navigate with difficulty. Such a region may be, for example, a deep pile carpet on which numerous small obstacles such as, e.g. table and chair legs, are located. As movement is greatly impaired by the carpet and the table and chair legs essentially form a labyrinth for the robot through which it must navigate with particular precision, this situation is difficult for the robot to deal with. It may therefore happen that the robot is unable to liberate itself out of this situation or only after a longer period of time. The robot can recognize such a problematic area, e.g. from the spin of the wheels and/or from being occupied longer than usual in the area and with a great deal of maneuvering. In order to raise efficiency, this area can therefore be made into an exclusion region, thus avoiding the risk of the robot becoming stuck at this location and/or consuming a great deal of time and energy in following deployments. This area would then only be cleaned, for example, after receiving the explicit command of a user to do so”).
Combining the determination of a problematic area of Artes with Arora would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, one of ordinary skill in the art would be motivated to add an error event to Arora that relates to the problematic area of Artes to record areas that take large amounts of time and energy and consider controlling the robot to avoid those areas (see at least Artes [0090]).
Regarding Claim 7, the Arora, Afrouzi, Chen, and Artes combination teaches the limitations of Claim 6. Furthermore, Arora further discloses further comprising a bumper sensor, wherein the controller is configured to determine whether the collision occurs based on a signal detected by the bumper sensor (see at least [0012] and [0089]: “For example, if the robot 100 includes a bump sensor, a sensor event can occur when the bump sensor is triggered. A location of the sensor event can correspond to a location of the robot 100 when the bump sensor is triggered, or can correspond to a location of contact between the robot 100 and an object in the environment that triggers the bump sensor.”; “the sensor events can be obstacle detection events in which one or more sensors of the autonomous mobile robot is triggered. In some implementations, the one or more sensors can include at least one of a proximity sensor, a bump sensor, an image capture device, a cliff sensor, a wheel encoder, a wheel motor controller, a brush motor controller, a wheel drop sensor, an odometer, or an optical mouse sensor”).
Conclusion
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/A.R.M./Examiner, Art Unit 3658
/JASON HOLLOWAY/Primary Examiner, Art Unit 3658
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