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 (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.
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 statement (IDS) submitted on January 29th, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments and Amendments
Applicant’s arguments and amendments, filed January 29th, 2026, with respect to the rejection(s) of claims 1-12 and 14-23 under 35 U.S.C. 103 have been fully considered and are persuasive. However, upon further search and consideration, a new ground(s) of rejection is made in view of Lu et al. (“Autonomous Obstacle Legipulation with a Hexapod Robot”). Examiner notes the additional limitation of “legged robot” to describe the claimed robot and robot actions does limit the scope of the original claim(s), but does not distinguish the robot’s structure or functionality over a combination of the previously cited prior art and other mobile robots available in the art. Furthermore, newly added claims 21-23 have been rejected under 35 U.S.C. 103 as well, in light of the additional reference utilized for its teachings of a legged robot with navigation and object manipulation functionality.
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 1-12 and 14-23 are rejected under 35 U.S.C. 103 as being obvious over Benaim et al. (US Patent Pub. No. 2019/0126487 A1), herein “Benaim”, in view of Paxton et al. (“Evaluating Methods for End-User Creation of Robot Task Plans”), herein “Paxton”, and further in view of Liberg et al. (WO Patent Pub. No. 2006/043873 A1), herein “Liberg”, and Lu et al. (“Autonomous Obstacle Legipulation with a Hexapod Robot”), herein “Lu”, published November 12th, 2020.
Regarding Claims 1, 16 and 19, Benaim discloses a computer-implemented method, a system and a legged robot comprising:
receiving, by data processing hardware of a legged robot, first sensor data from one or more sensors of the legged robot, the first sensor data corresponding to an environment of the robot (See 0069, “[…] at least one sensor collects a plurality of images in a given scene where the target object or objects are present. The sensor may be a digital camera, an analog camera, a depth map sensor and the like.”);
determining, by the data processing hardware, based on the first sensor data, a first object in the environment and a second object in the environment (See 0003, “[…] robotic machine has to have capability of identifying objects in order to perform relevant tasks related to an object. Vision systems with different types of sensors combined with processing units executing image analysis software are widely used to identify objects in an operational scene.” See also 0134, “[…] to automatically identify different objects in a working environment scenario and to identify the objects’ location.”).
But does not explicitly disclose identifying, by the data processing hardware, a first legged robot action selected from a first set of legged robot actions to interact with the first object;
identifying, by the data processing hardware, a second legged robot action selected from a second set of legged robot actions to interact with the second object;
generating, by the data processing hardware, a mission to perform the first legged robot action and the second legged robot action; and
instructing, by the data processing hardware, performance of the mission by the legged robot.
Paxton, in a similar field of endeavor, teaches identifying, by the data processing hardware, a first action from a first set of actions associated with the first object (See Section III Reference B, “allows the end user to combine and parameterize operations […] can switch the robot into a compliant mode by pressing a TEACH button, and can enable autonomous execution by pressing the SERVO button […] task editor is accompanied by a 3D visualization of the robot, detected objects, and coordinate frames […]” Examiner notes the method shows detected objects are presented in the same interface where operations and actions are parameterized and selectable, meaning that the robot’s first selected action(s) are from a first set of actions associated with a target object);
identifying, by the data processing hardware, a second action from a second set of actions associated with the second object (See Section III Reference B as explained above, and also, “The resulting sorted list of grasp poses is used to generate motion plans in order of preference […] users can then frame the task plan as a sequence of high-level commands […]” Examiner notes each task plan is differentiable based on the target operation of different objects, thus including a second action from a different set of actions regarding a different object).
Liberg, in a similar field of endeavor, teaches generating, by the data processing hardware, a mission to perform the first action and the second action (See Pg. 11 Lines 15-22, “[…] robot program generator 8 adapted for generating a robot program for performing the work cycle based on the stored information about the workstation, including the preprogrammed robot code, the workstations selected by the user, and the order for which the robot shall assist the workstation […]”); and
instructing, by the data processing hardware, performance of the mission by the robot (See Pg. 6 Lines 16-23, “[…] one or more predefined movement paths to be followed by the robot when performing work at the workstation […] includes predefined movement path for carrying out the task.”).
Lu, in a similar field of endeavor, teaches a legged robot executing legged robot actions (See Section 2.2, “Through the use of control points, unique sequences of motion can be created for legged robots. The leg motion can be altered to allow for a modified leg end-effector, such as a gripper.”).
In view of Paxton, Liberg and Lu’ teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data as disclosed by Benaim, different sets of actions that may be parameterized and selectable as taught by Paxon, generating a robot program to be executed based on predefined parameters as taught by Liberg and applied to a legged robot as taught by Lu, with a reasonable expectation of success, since the robot system already includes the necessary components to conduct the environmental visualization, and the sensor perception and mission-based planning frameworks both operate within autonomous robotic systems that rely on environmental perception. Furthermore, this combination would predictably improve mission generation by using more accurate sensory inputs, which is a standard motivation in robotics to reduce uncertainty in various environments, and it has been held that making a machine portable or movable without producing new and unexpected results involves only skill routine in the art.
Regarding Claim 2, Benaim discloses the computer-implemented method of claim 1, further comprising:
instructing display of the graphical representation based on a location of the legged robot corresponding to a location of the first object (See 0046, “A display and/or a user input device […]” See also 0003, “[…] sensors and the dedicated software are designed and set to determine the physical location of the object relative to the sensors and/or to other objects or equipment in the scene.”).
But does not explicitly disclose generating a graphical representation of the first set of legged robot actions.
Liberg, in a similar field of endeavor, teaches generating a graphical representation of the first set of legged robot actions (See Abstract, “[…] a graphical generator (7), generating one or more graphical user interfaces on said display device […]” See also Pgs. 6-7 Lines 33-8, “[…] generate one or more graphical user interfaces for entering configuration data for the robot […] to generate said robot program based on said received configurations data for the robot […] define safety zones, robot tool, and robot and work object coordinate systems.”).
In view of Liberg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, the system to generate a graphical representation of a first set of actions, with a reasonable expectation of success, since graphical user interfaces (GUIs) in robotics often visualize planned missions relative to robot position, and would increase user comprehension and monitoring of the robot’s operations, thus improving usability without altering core functions.
Regarding Claim 3, Benaim discloses the computer-implemented method of claim 1, further comprising:
instructing display of the graphical representation (See 0046 and 0003 as referenced above).
But does not explicitly disclose generating a graphical representation based on performance of the first legged robot action.
Liberg, in a similar field of endeavor, teaches generating a graphical representation based on performance of the first legged robot action (See Abstract and Pgs. 6-7 Lines 33-8 as referenced above. See also Pg. 8 Lines 10-24, “[…] generating a graphical user interface including graphical information about the status of the selected workstations during operation […]” Examiner notes status of a workstation during operation includes robot performance).
In view of Liberg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, the system to generate a graphical representation based on performance of an action, with a reasonable expectation of success, since displaying progress or performance of a task is a well-known feedback mechanism, and would provide predictable user feedback that may aid debugging and situational awareness.
Regarding Claim 4, Benaim does not explicitly disclose the computer-implemented method of claim 1, further comprising:
receiving an input indicating selection of the first legged robot action, wherein identifying the first legged robot action is based on the input.
Liberg, in a similar field of endeavor, teaches receiving an input indicating selection of the first legged robot action, wherein identifying the first legged robot action is based on the input (See Abstract, “[…] allowing a user to select one or more predefined workstations and to specify the order in which the robot shall visit the selected workstations […] generating a robot program for performing the work cycle based on said predefined workstations […]”).
In view of Liberg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, the system to receive an input indicating the selection of an action, with a reasonable expectation of success, since this enables user input and increases user interaction with the robot’s operations to improve precision towards desired outcomes.
Regarding Claim 5, Benaim does not explicitly disclose the computer-implemented method of claim 1, further comprising:
generating a graphical representation based on the environment, wherein the graphical representation indicates the first set of legged robot actions and the first object; and
receiving, via an interaction with the graphical representation, an input indicating selection of the first legged robot action, wherein identifying the first legged robot action is based on the input.
Liberg, in a similar field of endeavor, teaches generating a graphical representation based on the environment, wherein the graphical representation indicates the first set of legged robot actions and the first object (See Abstract, and Pgs. 6-8 as referenced above. Examiner notes the graphical representation includes display and selection of predefined workstations in the environment); and
receiving, via an interaction with the graphical representation, an input indicating selection of the first legged robot action, wherein identifying the first legged robot action is based on the input (See Abstract as referenced above).
In view of Liberg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, the system to receive an input indicating the selection of an action and generate a graphical representation of the environment, with a reasonable expectation of success, since creating a GUI to show objects and their actions sets while accepting a selection via interaction is an anticipated and conventional integration for improved usability in robotic systems.
Regarding Claim 6, Benaim does not explicitly disclose the computer-implemented method of claim 1, further comprising:
generating a graphical representation based on the environment, wherein the graphical representation indicates the first object; and
receiving, via an interaction with the graphical representation, an input defining the first set of legged robot actions.
Liberg, in a similar field of endeavor, teaches generating a graphical representation based on the environment, wherein the graphical representation indicates the first object (See Abstract, and Pgs. 6-8 as referenced above. Examiner notes each workstation includes objects involved in the robot’s work cycle); and
receiving, via an interaction with the graphical representation, an input defining the first set of legged robot actions (See Abstract as referenced above).
In view of Liberg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, the system to receive an input indicating the selection of an action and generate a graphical representation indicating an object in the environment, with a reasonable expectation of success, since creating a GUI to show objects and their actions sets while accepting a selection via interaction is an anticipated and conventional integration for improved usability in robotic systems.
Regarding Claim 7, Benaim further discloses the computer-implemented method of claim 1, wherein instructing performance of the mission by the legged robot comprises:
instructing performance of the first legged robot action by the legged robot at the first location using at least one of an arm of the legged robot or a leg of the two or more legs (See 0151, “Step 309 is performed when another iteration is needed. In such case, the Robot assisted Object-Learning vision system instructs the mechanical device to move the object to a next position in space.”).
But does not explicitly disclose instructing the legged robot to navigate to a first location using two or more legs of the legged robot.
Lu, in a similar field of endeavor, teaches instructing the legged robot to navigate to a first location using two or more legs of the legged robot (See Section 1, “To successfully traverse these unstructured terrains with unknown obstacles in the robot’s path, the robot is required to manipulate obstacles out of its way. Thus, if a legged robot platform is able to autonomously identify and manipulate an obstacle in its path, the robot can progress further in the environment.” See also Section 2.1, “[…] location of the object influences the final position of the intermediate and last control points for trajectory generation […]”).
In view of Lu’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, navigation of the robot using two or more legs, with a reasonable expectation of success, since it has been held that making a machine portable or movable without producing new and unexpected results involves only routine skill in the art.
Regarding Claim 8, Benaim further discloses the computer-implemented method of claim 1, wherein instructing performance of the mission by the legged robot comprises:
instructing determination of a location of the legged robot (See 0120, “[…] the positional information previously stored by the mechanical device is retrieved from the record and an estimation is performed as to the relative X,Y,Z position of the object with regards to the sensor […] if in a training stage the object was originally placed exactly in front of the center of the camera, and the match found in the training set corresponds to the image that was taken after either the sensor or object was displaced i,j,k cm in space, then this relative position can also be retrieved […]”); and
instructing performance of the first legged robot action by the legged robot based on the location of the legged robot corresponding to a location of the first object (See 0120 as referenced above).
Regarding Claim 9, Benaim does not explicitly disclose the computer-implemented method of claim 1, wherein generating the mission comprises:
scheduling the legged robot to perform the first legged robot action at a particular time period, wherein the legged robot performs multiple iterations of the mission.
Liberg, in a similar field of endeavor, teaches scheduling the legged robot to perform the first legged robot action at a particular time period, wherein the legged robot performs multiple iterations of the mission (See Abstract, “[…] allowing a user to select one or more of the predefined workstations and to specify the order in which the robot shall visit the selected workstations […]”).
In view of Liberg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, the mission generation stage to include scheduling of various actions, with a reasonable expectation of success, since planning for timed sequences to perform certain operations is a conventional extension to add temporal parameters that would increase system efficiency and reduce undesired behaviors in robot operations. Furthermore, it has been held that mere duplication of the essential working parts of a device or steps in a process involves only routine skill in the art.
Regarding Claim 10, Benaim does not explicitly disclose the computer-implemented method of claim 1, wherein the mission indicates an order of performance of the first legged robot action and performance of the second legged robot action.
Liberg, in a similar field of endeavor, teaches the mission indicates an order of performance of the first action and performance of the second action (See Abstract as referenced above).
In view of Liberg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, the mission generation stage to indicate an order of performance between two actions, with a reasonable expectation of success, since Liberg directly teaches ordering of workstation visits that map to ordered actions in a mission. It would be obvious to use that existing teaching to indicate the order of performance for actions in a mission.
Regarding Claim 11, Benaim does not explicitly disclose the computer-implemented method of claim 1, wherein instructing performance of the mission by the legged robot comprises:
instructing the legged robot to navigate to a first location using two or more legs of the legged robot;
instructing the legged robot to perform the first legged robot action at the first location using at least one of the two or more legs;
instructing the legged robot to navigate to a second location from the first location using the two or more legs; and
instructing the legged robot to perform the second legged robot action at the second location using the at least one of the two or more legs.
Lu, in a similar field of endeavor, instructing performance of the mission by the legged robot comprises:
instructing the legged robot to navigate to a first location using two or more legs of the legged robot (See Section 1 and 2.1 as referenced above);
instructing the legged robot to perform the first legged robot action at the first location using at least one of the two or more legs (See Sections 2.1-2.2, “[…] extracts the height and width of the detected object and provides key points where the robot can interact with the object […] Additional key contact points can be specified for complex interaction motions such as combining lifting and pushing […] Spatial control of the leg allows unique leg movements for interacting with different objects […] key contact point from the object is fed into the control points of the curves, guiding the leg to interact with the object.”);
instructing the legged robot to navigate to a second location from the first location using the two or more legs (See Section 1 and 2.1 as referenced above); and
instructing the legged robot to perform the second legged robot action at the second location using the at least one of the two or more legs (See Sections 2.1-2.2 as referenced above).
In view of Lu’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, navigation to multiple locations and performance of robot actions at each location, since Lu directly teaches interaction actions that are tied to objects in proximity to the robot during navigation, and it has been held that making a machine portable or movable without producing new and unexpected results involves only routine skill in the art.
Regarding Claim 12, Benaim does not explicitly disclose the computer-implemented method of claim 1, wherein the first set of legged robot actions are different from the second set of legged robot actions.
Paxton, in a similar field of endeavor, teaches the first set of legged robot actions are different from the second set of legged robot actions (See Abstract, “Users were asked to perform pick-and-place assembly tasks with either SmartMoves or one of three simpler baseline versions of CoSTAR.” Examiner notes this explicitly shows different action strategies and sets).
In view of Paxtons’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, various types of robot actions that differ from each other based on interaction with objects, with a reasonable expectation of success, since it is obvious that different sets of actions can be applied to different objects or tasks, and it is only routine in the art to associate different objects with different action sets.
Regarding Claim 14, Benaim further discloses the computer-implemented method of claim 1, further comprising:
associating the first set of legged robot actions with the first object based on second sensor data (See 0120 as referenced above. Examiner notes the robot system and method derive positional and feature data from various sensors).
Regarding Claim 15, Benaim discloses the computer-implemented method of claim 1, further comprising:
matching the first object to a respective object of a set of objects (See 0153, “[…] system searches through the Objects Database 112 to identify a record that best matches the features extracted from the currently inspected object […] system determines when the match found is statistically significant. When the decision is that the match is significant, it is assumed that the object being inspected is identified as similar to the object in the Database, with the matching feature.”).
But does not explicitly disclose identifying the first set of legged robot actions based on the set of objects, wherein the set of objects associates the first object with the first set of legged robot actions.
Liberg, in a similar field of endeavor, teaches identifying the first set of legged robot actions based on the set of objects, wherein the set of objects associates the first object with the first set of legged robot actions (See Abstract as referenced above).
In view of Liberg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, the system to match objects to a respective set of objects, with a reasonable expectation of success, since matching a detected object to a stored object or workstation entry in memory allows a corresponding action or program to be retrieved as well. This is an expected, routine system for mapping observed objects and stored templates and further retrieving the associated action set.
Regarding Claim 17, Benaim further discloses the system of claim 16, wherein the execution of the instructions by the data processing hardware further causes the data processing hardware to:
identify the first set of legged robot actions using a machine learning model (See 0108, “[…] other classification models are used for the above stage for achieving same goal as described above. Such other classification models may be neural networks and deep learning models or the like.”).
Regarding Claim 18, Benaim further discloses the system of claim 16, wherein to determine the first object and the second object, the execution of the instructions by the data processing hardware further causes the data processing hardware to:
identify the first object using an object detection model (See 0108, “[…] other classification models are used for the above stage for achieving same goal as described above. Such other classification models may be neural networks and deep learning models or the like.”).
Regarding Claim 20, Benaim does not explicitly disclose the legged robot robot of claim 19, wherein the first legged robot action comprises a navigational behavior of the legged robot.
Liberg, in a similar field of endeavor, teaches the first legged robot action comprises a navigational behavior of the legged robot (See Pg. 6 Lines 17-23, “[…] default data includes one or more predefined movement paths to be followed by the robot when performing work at the workstation […] the predefined workstations include predefined movement path for carrying out the task.”).
In view of Liberg’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, an operational action of the robot to include navigational behavior, with a reasonable expectation of success, since it is an expected, routine functions for robots to be able to navigate within an environment, especially when they are further capable of mapping observed objects and determining actions with respect to the object(s).
Regarding Claims 21-23, Benaim does not explicitly disclose the computer-implemented method of claim 1, system of claim 16 and legged robot of claim 19, wherein each legged robot action of the first set of legged robot actions and the second set of legged robot actions comprises at least one of:
a particular action to move at least one leg of the legged robot to adjust a pose of the legged robot relative to a respective object (See Section 2.2 excerpt below);
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a particular action to move the leg to actuate the respective object (See Sections 2.1-2.2 as referenced above); or
a particular action to move a manipulator arm of the legged robot to at least one of actuate or grasp the respective object.
Lu, in a similar field of endeavor, teaches each legged robot action of the first set of legged robot actions and the second set of legged robot actions comprises at least one of:
a particular action to move at least one leg of the legged robot to adjust a pose of the legged robot relative to a respective object;
a particular action to move the leg to actuate the respective object; or
a particular action to move a manipulator arm of the legged robot to at least one of actuate or grasp the respective object.
In view of Lu’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, particular action(s) involving object manipulation or interaction using one or more legs of the robot, since Lu directly teaches interaction actions of the robot legs involving manipulation of objects in proximity to the robot during navigation, and it has been held that making a machine portable or movable without producing new and unexpected results involves only routine skill in the art.
Claim 13 is rejected under 35 U.S.C. 103 as being obvious over Benaim et al. (US Patent Pub. No. 2019/0126487 A1) in view of Paxton et al. (“Evaluating Methods for End-User Creation of Robot Task Plans”), Liberg et al. (WO Patent Pub. No. 2006/043873 A1) and Lu et al. (“Autonomous Obstacle Legipulation with a Hexapod Robot”) as applied to Claim 1 above, and further in view of Akan et al. (US Patent Pub. No. 2017/0320211 A1), herein “Akan”.
Regarding Claim 13, Benaim in view of Paxton, Liberg and Lu do not directly teach the computer-implemented method of claim 1, wherein the first object and the second object are a same type of object.
Akan, in a similar field of endeavor, teaches the first object and the second object are a same type of object (See 0087, “[…] specifies the sequence of workstations needed to accomplish the task, for example, to create a certain product. The user can select one or more of the defined workstations, and one workstation can be selected several times.” Examiner notes the robot will interact with objects at each workstation, and a workstation can be selected for the robot to operate several times, thus meaning the first and second object must by the same type of object).
In view of Akan’s teachings, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include, with the robot system and method of determining various objects in an environment of the robot using received sensor data and displaying a relative location of the robot and the object(s) as disclosed by Benaim, the various objects in the environment to be the same type of object, with a reasonable expectation of success, since any robot will be limited in scope to the type of objects it is able to interact with, and the objects being the same type simply means the robot is guaranteed to be capable of operating at that workstation.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bryant Tang whose telephone number is (571)270-0145. The examiner can normally be reached M-F 8-5 CST.
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/BRYANT TANG/Examiner, Art Unit 3658
/JASON HOLLOWAY/Primary Examiner, Art Unit 3658
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