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 .
Claim Rejections - 35 USC § 103
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 (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.
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.
Claim(s) 11-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Noh (US 20190179333), in view of Han (US 20150223659).
Claim 11, A method of autonomously processing floor surfaces with a mobile, self-propelled appliance, the method comprising the following method steps:
Noh, teaches a mobile self-propelled appliance for autonomous floor processing. Noh para [0062] states: “A mobile robot 100 according to an embodiment of the present invention refers to a robot capable of moving on its own using wheels and the like, and may be a domestic robot, a robot cleaner, etc. A robot cleaner having a cleaning function is herein after described as an example of the mobile robot…” Further, para [0070] states: “A suction port 110h to suction air may be formed on the bottom surface of the body 110…” Thus, the primary teaches an autonomously operating self-propelled floor-processing appliance.
“performing an exploratory tour by the mobile, self-propelled appliance in a designated floor processing area to create a surroundings map;”
The primary reference teaches creating a map while autonomously traversing the environment. Noh para [0066] states: “Referring to FIGS. 2 to 7, a mobile robot 100, 100a, or 100b includes a main body 110….” Para [0068] states: “The mobile robot 100, 100a, or 100b includes a travel drive unit 160 for moving the main body 110.” Para [0200] (corresponding to FIG. 29 map registration functionality) teaches object position storage and map registration, stating: “STORE POSITION INFORMATION, AND REGISTER OBJECT AREA IN MAP.”
However, to the extent Noh does not expressly teach an “exploratory tour” specifically for room/cleaning-area mapping as claimed, the secondary reference teaches this limitation. Han para [0028] states: “a method of controlling a robot cleaner… may include the steps of deriving a door location in a cleaning area through an image information, composing a cleaning map by detecting an obstacle in the cleaning area, creating a room information…” Han para [0031] further states: “the robot cleaner can create the obstacle map and the image information while running…” Thus, the secondary expressly teaches exploratory traversal for map generation,
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Noh to perform explicit exploratory mapping as taught by the secondary in order to improve environmental awareness and cleaning/navigation efficiency;
“detecting obstacles by way of a detection facility;”
The primary expressly teaches obstacle detection by sensors. NOH para [0032] states: “a sensor unit having one or more sensors configured to sense an object during the movement of the main body.” Para [0006] states: “The infrared sensor determines presence of an object… whereas the ultrasonic sensor… determines a distance to the object…” Para [0012] states: “The robot cleaner recognizes presence of an object in response to sensing, by an ultrasonic sensor…”
“classifying the obstacles as being passable or not passable;”
The primary expressly teaches this limitation. NOH para [0012] states: “determines whether the recognized object has a height that the robot cleaner is able to climb.” Para [0013] states: “If it is determined that the recognized object has a height that the robot cleaner is able to climb… otherwise, rotate by an angle of 90 degrees…” Para [0014] states: “if the object is a low door threshold… if a recognition result indicates that the robot cleaner is able to pass the door threshold, the robot cleaner moves over the door threshold.”
Additionally, para [0036] states: “since a mobile robot is able to determine an attribute of an object and adjust a traveling pattern according to the attribute of the object…”
“and selectively driving over an obstacle that is classified as passable and driving around an obstacle that is classified as not passable.”
Noh expressly teaches this limitation. NOH para [0013] states: “If it is determined that the recognized object has a height that the robot cleaner is able to climb, the robot cleaner may move in a straight forward direction… otherwise, rotate by an angle of 90 degrees.” Para [0014] states: “if the object is a low door threshold… the robot cleaner moves over the door threshold.”
Claim 12, The method according to claim 11, wherein the mobile, self-propelled appliance is a floor cleaning appliance being a suction robot, and/or a sweeping robot, and/or a mopping robot.
Noh, expressly teaches a floor cleaning appliance configured as a suction robot. Noh para [0062] states: “A mobile robot 100 according to an embodiment of the present invention refers to a robot capable of moving on its own using wheels and the like, and may be a domestic robot, a robot cleaner, etc. A robot cleaner having a cleaning function is herein after described as an example of the mobile robot…” Further, para [0070] states: “A suction port 110h to suction air may be formed on the bottom surface of the body 110…” and para [0072] states: “Dust is removed from the floor in a cleaning area by rotation of the brushes 134 and 135…”
Thus, the primary teaches a suction robot and sweeping robot configuration. It would have been obvious to additionally configure the appliance as a mopping robot because mopping functionality was well known in the floor-cleaning robot art and merely represents a predictable use of prior-art cleaning mechanisms with the autonomous navigation system of Noh.
Claim 12 is therefore rejected under 35 U.S.C. §103 as being unpatentable over Noh.
Claim 13, The method according to claim 11, which comprises classifying the obstacles with the aid of existing map data obtained by the exploratory tour.
Noh teaches map generation and obstacle registration in the map. Noh para [0200] corresponding to FIG. 29 states: “STORE POSITION INFORMATION, AND REGISTER OBJECT AREA IN MAP.” Further, para [0057] states: “FIG. 29 is a flowchart of a method for controlling a mobile robot according to an embodiment of the present invention.” Additionally, para [0033] states: “a mobile robot… includes an object recognition module configured to recognize… an object… and a travel control module configured to control driving… based on an attribute of the recognized object…”
However, Noh does not expressly teach classifying obstacles specifically using pre-existing map data generated from an exploratory tour,
Han teaches this limitation. Han para [0028] states: “composing a cleaning map by detecting an obstacle in the cleaning area, creating a room information…” Further, para [0034] states: “the door location deriving step may be started during the cleaning map composing step.” Para [0035] states: “the cleaning map composition and the door location derivation can be separately performed.”
Thus, the secondary teaches use of previously generated map information for environmental interpretation and classification,
It would have been obvious to one of ordinary skill in the art to utilize previously generated map data of the secondary reference within the obstacle classification framework of Noh to improve obstacle recognition reliability and contextual navigation efficiency.
Claim 14, The method according to claim 13, which comprises classifying the obstacles by comparing information from the exploratory tour and information from the detection of the obstacle.
Noh teaches using sensed obstacle information together with stored recognition information. Noh para [0032] states: “a sensor unit having one or more sensors configured to sense an object during the movement of the main body…” Further, para [0200] teaches: “STORE POSITION INFORMATION, AND REGISTER OBJECT AREA IN MAP.” Additionally, para [0199] states: “RECOGNIZE OBJECT SEQUENTIALLY WITH RESPECT TO PLURALITY OF IMAGES.”, however, Noh does not expressly teach comparing exploratory-tour information with current obstacle-detection information for classification purposes,
Han teaches comparing map-derived information and sensed environmental information. Han para [0028] states: “deriving a door location in a cleaning area through an image information, composing a cleaning map by detecting an obstacle in the cleaning area…” Further, para [0031] states: “the robot cleaner can create the obstacle map and the image information while running…”, thus, the secondary teaches simultaneous use of mapped environmental information and current sensor/image information, It would have been obvious to combine the references so that the obstacle recognition/classification system of Noh would further compare live obstacle detection information against previously generated environmental map information, as taught by the secondary, in order to improve classification accuracy and navigation decision-making.
Claim 15, The method according to claim 11, which comprises classifying the obstacles before making an attempt to drive over a respective obstacle.
Noh expressly teaches this limitation. Noh para [0012] states: “determines whether the recognized object has a height that the robot cleaner is able to climb.” Para [0013] further states: “If it is determined that the recognized object has a height that the robot cleaner is able to climb, the robot cleaner may move in a straight forward direction…”
Thus, the primary teaches classifying the obstacle before attempting traversal.
Claim 16, The method according to claim 11, which comprises, in order to perform a classification of the obstacles, automatically identifying with the mobile, self-propelled appliance rooms as such on a basis of information from the exploratory tour.
Noh teaches map generation and object registration but does not expressly teach identifying rooms from exploratory-tour information, Han expressly teaches this limitation. Han para [0020] states: “a cleaning can be done by room unit in a manner of recognizing a cleaning area by the room unit through a door.” Para [0028] states: “creating a room information for distinguishing a plurality of rooms partitioned with reference to the door from each other…” Further, para [0008] states: “a whole cleaning area can be partitioned into a plurality of Zones or rooms through doors.”, It would have been obvious to incorporate the room-identification functionality of the secondary reference into the autonomous obstacle classification/navigation system of Noh in order to provide contextual environmental understanding and improve cleaning efficiency.
Claim 17, The method according to claim 11, which comprises determining with the mobile self-propelled appliance a position of detected obstacles in the surroundings map.
Noh expressly teaches determining obstacle positions within a map. Noh para [0200] states: “STORE POSITION INFORMATION, AND REGISTER OBJECT AREA IN MAP.” Further, para [0066] describes the map generation functionality and para [0032] teaches sensor-based obstacle detection, thus, the primary teaches determining obstacle position in a surroundings map.
Claim 18 , The method according to claim 17, which comprises classifying obstacles near a door as door thresholds and classifying obstacles far from a door as furniture.
Noh teaches door-threshold recognition and object classification. Noh para [0014] states: “if the object is a low door threshold, the robot cleaner recognizes the door threshold…” Further, para [0016] states: “as a fan base has a height similar to or lower than a height of a door threshold…” Additionally, para [0036] states: “a mobile robot is able to determine an attribute of an object…”, however, Noh does not expressly teach using proximity to a door for classifying an obstacle as a threshold versus furniture, Han teaches identifying door locations and room boundaries. Han para [0028] states: “deriving a door location in a cleaning area…” Further, para [0037] states: “the recognition of the door location may be achieved not through the door itself but through a door frame.”, It would have been obvious to one of ordinary skill in the art to utilize the known door-location information of the secondary reference in the object-classification framework of Noh so that obstacles positioned near detected doors would be inferred as likely thresholds while obstacles positioned away from doors would likely correspond to furniture or room objects, thereby improving traversal decision accuracy.
Claim 19, The method according to claim 18, which comprises driving over obstacles near to a door and driving around obstacles far from a door before an attempt is made to drive over the obstacles far from a door.
Noh teaches driving over passable door-threshold-type obstacles and driving around non-passable obstacles. Noh para [0013] states: “If it is determined that the recognized object has a height that the robot cleaner is able to climb, the robot cleaner may move in a straight forward direction… otherwise, rotate by an angle of 90 degrees.” Further, para [0014] states: “if the object is a low door threshold… the robot cleaner moves over the door threshold.”, however, Noh does not expressly teach making such traversal decisions based on nearness to a detected door, Han teaches determining door locations and room structures. Han para [0028] states: “deriving a door location in a cleaning area…”, It would have been obvious to combine the door-location awareness of the secondary reference with the traversal/avoidance logic of Noh so that objects near doors, likely corresponding to thresholds, would be traversed, while objects away from doors would be treated more conservatively as furniture obstacles and avoided before attempted traversal, thereby improving operational reliability and preventing entrapment.
Claim 20 , A mobile, self-propelled appliance, comprising: a detection unit for detecting obstacles; an evaluation unit for classifying the obstacles as passable or not passable; and wherein the appliance is configured for performing the method according to claim 11.
Noh expressly teaches this limitation. Noh para [0032] states: “a sensor unit having one or more sensors configured to sense an object during the movement of the main body…” Further, para [0012] states: “determines whether the recognized object has a height that the robot cleaner is able to climb.” Para [0033] states: “a travel control module configured to control driving… based on an attribute of the recognized object…”, thus, Noh teaches a detection unit and evaluation/classification unit configured to perform the autonomous traversal method.
Claim 21, The appliance according to claim 20 configured as a floor cleaning appliance being a suction robot, and/or a sweeping robot, and/or a mopping robot.
Nohteaches a suction and sweeping floor-cleaning robot. Noh para [0062] states: “a robot cleaner having a cleaning function…” Further, para [0070] states: “A suction port 110h to suction air…” and para [0072] states: “Dust is removed from the floor… by rotation of the brushes…”, Thus, Noh teaches a suction and sweeping robot configuration. Configuring the same autonomous appliance additionally for mopping would have been an obvious variation well known in the robot-cleaning art.
Claim 22, The appliance according to claim 20, wherein said detection unit comprises sensors configured to determine distance measurement values and/or temporal changes of sensor values.
Noh expressly teaches this limitation. Noh para [0006] states: “The infrared sensor determines presence of an object and a distance to the object based on an amount of light reflected by the object or a time taken to receive the reflected light, whereas the ultrasonic sensor emits an ultrasound wave… and determines a distance to the object based on a difference between the time to emit the ultrasonic waves and the time for the ultrasonic waves to return…”, thus, Noh teaches sensors configured to determine distance measurements and temporal sensor-value changes.
Conclusion
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MASUD . AHMED
Primary Examiner
Art Unit 3657A
/MASUD AHMED/Primary Examiner, Art Unit 3657
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