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 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
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.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/27/2026, was filed after the mailing of a First Office Action on the Merits but before the close of prosecution. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The application is a continuation of PCT/KR2024/003501 but is also claiming priority to the earlier filed foreign application KR10-2023-0087911. Because this application is a bypass application, examiner has checked and verified that the earlier filed Korean application supports the subject matter disclosed in the instant application. As such, the instant application is granted the earlier filing date of 07/06/2023.
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 05/22/2026 has been entered.
Status of Claims
This action is in response to Applicant’s Request for Continued Examination filed on 05/22/2026. Claims 1, 4-11, and 14-20 are pending and examined below.
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.
Claims 1, 4-5, 7-11, 14-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al., US 11378966 B2, herein referred to as Kim, and further in view of Fernando et al., US 20140031981 A1, herein referred to as Fernando.
Regarding claim 1, Kim discloses a first sensor (Col. 15 lines 1-3; robot may have a bumper as a first sensor), a second sensor (Col. 5 lines 48-54; system may include a camera as a second sensor), at least one memory (Fig. 1 item 170; system may include memory), at least one processor (Fig. 1 item 180; system may include a processor), acquire sensing data through the first sensor (Fig. 19, Col. 23 lines 8-45; sensing data may be obtained based on bumper sensor data), identify a plurality of driving locations based on the sensing data (Fig. 19, Col. 23 lines 8-45; bumper sensing data may be used for determining location data of the robot), acquire a plurality of photographed images through the second sensor (Fig. 19, Col. 23 lines 8-45; images may be captured of the surrounding environment at numerous points), store the plurality of driving locations and the plurality of photographed images in the at least one memory (Fig. 19, Col. 23 lines 8-45; bumper sensor data and images from the camera may be utilized to create a map of the environment which can be considered a storing of locations and images), identify a first time point corresponding to the event preventing driving (Fig. 11, Col. 24 lines 1-5; a fully stuck situation and its corresponding time of occurrence may be determined, a fully stuck situation may be considered one in which driving is prevented, including rotating), identify a second time point preceding the first time point by a threshold time (Figs. 10-11, Col. 18 lines 15-21; a preceding time point may be determined as one where a stuck situation has just occurred (see point 9 in Fig. 10), the differences between time points may be one second which can be considered a threshold), identify a driving location, among the plurality of driving locations, corresponding to the second time point (Fig. 10; positioning of the robot within the map may be determined at a previous time point before becoming just stuck (see point 9)), identify a photographed image, among the plurality of photographed images, corresponding to the second time point (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; image data may be used in conjunction with bumper data in order to generate a map of the environment, the image data may correspond to a position where the robot just becomes stuck (second time point)), register the event preventing driving based on event information, and wherein the event information comprises the driving location corresponding to the second time point and the photographed image corresponding to the second time point (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; the location and image data corresponding to when the robot first gets stuck (second point in time) may be registered to a location on a map), based on a location of the electronic apparatus corresponding to the driving location corresponding to the second time point included in the event information, drive along a path that evades the driving location corresponding to the second time point (Col. 16 lines 47-61; the robot may recognize a stuck situation (second time point/location corresponds to a stuck situation; see earlier rationale) and can avoid the situation in a future changed cleaning route, this avoidance may be considered as driving along a path (cleaning route) that avoids the stuck situation), identify a first driving location, among the plurality of driving locations, corresponding to the first time point (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; the location where the robot gets stuck (point 10 in Fig. 10) may be registered to a map, this location may be considered a first driving location), identify a first photographed image, among the plurality of photographed images, corresponding to the first time point (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; images that correspond to when the robot gets fully stuck may be registered to the map, these images are a portion of all the images used for registering and creating the map), wherein the driving location corresponding to the second time point comprises a second driving location (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; the location where the robot just gets stuck (point 9 in Fig. 10) may be registered to a map, this location may be considered a second driving location), wherein the photographed image corresponding to the second time point comprises a second photographed image (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; images that correspond to when the robot gets just stuck may be registered to the map, these images are a portion of all the images used for registering and creating the map), wherein the second time point precedes the first time point (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; the time point when the robot just gets stuck (point 9 in Fig. 10; considered second point, see claim 1 rationale) is before the time point when the robot gets fully stuck (point 10 in Fig. 10; considered first time point, see claim 1 rationale)), identify a target object related to the event preventing driving based on the second photographed image, and wherein the event information further comprises information regarding the target object (Figs. 9-11; Col. 17 lines 4-6; an obstacle that causes the robot to be stuck may be identified during a stuck event, the obstacle’s position may be recorded in the environment map), and registering the event preventing driving based on the event information (Figs. 9-11; Col. 17 lines 4-6; an obstacle that prevents driving (an event) may be registered into the map so that the robot can avoid a stuck situation in the future), but fails to disclose based on the target object being an object classified in advance as an unmovable object, register the event preventing driving based on the event information.
However, Fernando teaches based on the target object being an object classified in advance as an unmovable object, register the event preventing driving based on the event information (Paragraph 0134; static obstacles may be classified in an environmental map initially; static obstacles are unmovable). Therefore, from the teaching of Fernando, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified, with a reasonable expectation for success, the robotic system of Kim to include based on the target object being an object classified in advance as an unmovable object, register the event preventing driving based on the event information, as taught/suggested by Fernando. The motivation to do so would be to ensure proper classification of objects and obstacles within an environment so that the robot can quickly identify situations in which driving is prevented (true static obstacles, unmovable) compared to that of obstacles that may change over the course of time.
Regarding claim 4, Kim in view of Fernando renders obvious all the limitations of claim 1. Kim further discloses based on the target object being a predetermined object indicating an unmovable object, register the event preventing driving based on the event information (Figs. 9-11, 25A; Col. 17 lines 4-6; an obstacle that causes the robot to become stuck may be registered into the environment map, Fig. 25A shows couches that may be considered unmovable objects and are registered into the map as such).
Regarding claim 5, Kim in view of Fernando renders obvious all the limitations of claim 1. Kim further discloses the event information includes the first driving location, the second driving location, the first photographed image, and the second photographed image (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; a stuck event may be registered into an environmental map based on the robot’s locations and bumper and image sensor data, a stuck event may be determined by points 9 and 10 in Fig. 10 where bumper and image data at those points indicates the robot becoming stuck).
Regarding claim 7, Kim in view of Fernando renders obvious all the limitations of claim 1. Kim further discloses identify a third driving location, among the plurality of driving locations, corresponding to a third time point (Col. 25 line 55 to Col. 26 line 15; the robot may recognize it is stuck or is about to be stuck based on a stuck situation recognition model, recognition of being stuck may be based on previously obtained images which means that a robot recognizing a stuck situation from images may be considered as being related to a third time point and a third location, recognition of being stuck may be based on feature point data), identify a third photographed image, among the plurality of photographed images, corresponding to the third time point (Col. 25 line 55 to Col. 26 line 15; imaging may be used to determine where a robot is, if the image is recognized as one corresponding to a stuck situation, then the new image may be considered as a third photographed image corresponding to the third time point), based on the third driving location corresponding to the second driving location, acquire a degree of similarity between the second photographed image and the third photographed image (Col. 25 line 55 to Col. 26 line 15; the robot may be in a situation where the robot is just getting stuck which can correspond to the second driving location (see rationale in previous claims), this location may be considered a third location as it is at a different point in time, a stuck situation may be recognized based on a target feature vector which has given values, these feature vector values may be considered a threshold, the robot may be recognized as being stuck or just about to be stuck based on the recognition), and based on the degree of similarity being greater than or equal to a threshold value, drive along a path that evades the third driving location (Col. 25 line 55 to Col. 26 line 24; when the feature vectors are recognized as a stuck situation (threshold being met), then the robot may proceed to avoid the stuck situation by changing its traveling angle (direction) which can correspond to a change in a path).
Regarding claim 8, Kim in view of Fernando renders obvious all the limitations of claim 7. Kim further discloses acquire a plurality of driving directions corresponding to one or more directions in which the electronic apparatus moves (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; the robot may have a given traveling angle at points 9 and 10 in Fig. 10, these traveling angles are based on the cleaning path), based on identifying the event preventing driving, identify a first driving direction, among the plurality of driving directions, corresponding to the first time point (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; the robot may have a given traveling angle when the robot becomes fully stuck (point 10), the traveling angle at point 10 occurs at a given time point (first time point)), identify a second driving direction, among the plurality of driving directions, corresponding to the second time point (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; the robot may have a given traveling angle when the robot becomes just becomes stuck (point 9), the traveling angle at point 9 occurs at a given time point (second time point)), and wherein the event information includes the second driving location, the second driving direction, and the second photographed image (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55; a stuck situation may be registered as a location on a map, this stuck situation may include traveling directions, locations, and images that correspond to the robot just getting stuck (point 9) and being fully stuck (point 10)).
Regarding claim 9, Kim in view of Fernando renders obvious all the limitations of claim 8. Kim further discloses identify a third driving direction, among the plurality of driving directions, corresponding to the third time point (Figs. 10-11, 19, Col. 25 line 55 to Col. 26 line 15; the robot may recognize it is stuck or is about to be stuck based on a stuck situation recognition model, recognition of being stuck may be based on previously obtained images which means that a robot recognizing a stuck situation from images may be considered as being related to a third time point, a third location, and a third traveling direction, recognition of being stuck may be based on feature point data), based on the third driving location corresponding to the second driving location and the third driving direction corresponding to the second driving direction, identify a degree of similarity between the second photographed image and the third photographed image (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55, Col. 25 line 55 to Col. 26 line 15; the robot may be in a situation where the robot is just getting stuck which can correspond to the second driving location and a second driving direction (see rationale in previous claims), this location and driving direction may be considered a third location and third driving location, respectively, as it is at a different point in time, a stuck situation may be recognized based on a target feature vector which has given values, these feature vector values may be considered a threshold, the robot may be recognized as being stuck or just becoming be stuck based on the recognition, imaging that corresponds to a stuck situation can indicate a given location and direction of travel (a robot re-encountering point 9/10 as shown in Fig. 10 would be at a given location with a similar travel direction)), and based on the degree of similarity being greater than or equal to a threshold value, drive along a path that evades the third driving location (Figs. 10-11, 19; Col. 24 lines 18-20, 49-55, Col. 25 line 55 to Col. 26 line 15; when the feature vectors are recognized as a stuck situation (threshold being met), then the robot may proceed to avoid the stuck situation by changing its traveling angle (direction) which can correspond to a change in a path).
Regarding claim 10, Kim in view of Fernando renders obvious all the limitations of claim 1. Kim further discloses generate a user interface indicating the first driving location (Figs. 10-11, 19; Col. 12 lines 46-50, Col. 24 lines 18-20, 49-55, Col. 25 line 55 to Col. 26 line 15, Col. 12 lines 46-50; a display unit may display obtained images by the robot, the images could include ones in which the robot is fully stuck which corresponds to a first driving location).
Regarding claims 11, 14-15, and 17-20, the claim limitations are similar to those in claims 1, 4-5, and 7-10 and are rejected using the same rationale as seen above in claims 1, 4-5, and 7-10.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as obvious over Kim in view of Fernando.
Regarding claim 6, Kim in view of Fernando renders obvious all the limitations of claim 1. Kim further discloses the first sensor comprises a bumper sensor (Col. 15 lines 1-3; the robot may have a bumper as a first sensor), wherein the second sensor comprises an image sensor configured to acquire photographic images (Col. 5 lines 48-54; the robot may include a camera as a second sensor which is able to acquire images), but fails to disclose the first sensor comprises one of a LiDAR sensor, an infra-red sensor, a three-dimensional (3D) depth camera, and a 3D visual sensor. However, Kim teaches the first sensor comprises one of a LiDAR sensor, an infra-red sensor, a three-dimensional (3D) depth camera, and a 3D visual sensor (Col. 6 lines 14-19, Col. 8 lines 42-45; the robot may use LiDAR for navigation purposes which can include positioning of the robot). Therefore, from the teaching of Kim, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified, with a reasonable expectation for success, the robotic system of Kim to include a specific type of sensor as the bumper sensor, as would be considered obvious based on the teachings/suggestions of Kim. The motivation to do so would be to use an obvious-to-try well-known sensor alternative such as LiDAR for localizing a robot in an environment. Furthermore, LiDAR can allow for a higher accuracy in localization and can lead to better control actions for the robot.
Regarding claim 16, the claim limitations are similar to those in claim 6 and are rejected using the same rationale as seen above in claim 6.
Response to Arguments
Applicant's arguments filed 05/22/2026 have been fully considered but they are not persuasive.
Applicant is arguing that the prior art fails to discloses the claim limitations. Specifically, Applicant is arguing that Kim and Fernando do not teach “registration of an event preventing driving based on the target object being an object classified in advance as an unmovable object” because the teaching can only be gleamed from the Applicant’s disclosure in the specification. However, under BRI, the claim limitation simply recites registering a static object that prevents driving. Although Kim only discloses registering an object as one that prevents driving (obstacle; by definition it prevents or prohibits driving), the teaching from Fernando is obvious because a static obstacle will always prevent driving from occurring, and pre-classifying these objects/obstacles as dynamic or static is an obvious feature to include because driving performance such as path planning, etc. will vary based on static or dynamic objects. For instance, a dynamic object may not necessarily prevent driving whereas a static object will always prevent/prohibit driving.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER ALLEN BUKSA whose telephone number is (571)272-5346. The examiner can normally be reached M-F 7:30 AM-4:30 PM.
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/CHRISTOPHER A BUKSA/Examiner, Art Unit 3658
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