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 .
Status of Claims
This Office action is in response to the amendments filed on May 14, 2026. Claims 1-15 are currently pending, with Claims 1, 3-4, and 10-13 being amended, and Claims 14-15 being newly added.
Response to Amendments
In response to Applicant’s amendments, filed May 14, 2026, the Examiner withdraws the previous 35 U.S.C. 102 rejections.
Response to Arguments
Regarding Applicant's arguments, filed May 14, 2026, pertaining to the teachings of using an environmental sensor to gather data (see page 6 of instant arguments), the Examiner is unpersuaded. Afrouzi teaches that frequency distributions are determined for words when an image is sent from the robot, which means the robot still performs frequency distribution analysis (see at least Paragraphs [0506]-[0507] of Afrouzi). Afrouzi also teaches that the processor may approximate how many of a total number of data points scanned belong to each class, and the number of measurements within a specified area of interest may be used to indicate the presence of a wall, and the use of a probability distribution function can be used to interpret frequency data for lidar point cloud information by using the total counts within a space to predict the likelihood the peak corresponds to certain environmental information (see at least Paragraphs [0257], [0276], [0294] of Afrouzi). In other words, Afrouzi teaches that the processor is building a type of frequency distribution such that the robot can identify its environment. As such, the Examiner is unpersuaded, and maintains the corresponding rejections.
The remaining arguments are essentially the same as those addressed above and/or below and are unpersuasive for essentially the same reasons. Therefore, the corresponding rejections are maintained.
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.
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.
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.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2020/0225673 A1, to Ebrahimi Afrouzi, et al (hereinafter referred to as Afrouzi; previously of record), in view of U.S. Patent Publication No. 2017/0039694 A1, to Halata (hereinafter referred to as Halata; newly of record).
As per Claim 1, Afrouzi discloses the features of a method for controlling an industrial truck (100) in a warehouse (2) (e.g. Paragraphs [0008], [0159], [0349], [0418]; where the robot may include a controller/ processor, which controls operation of one or more components of the robot based on environmental characteristics inferred from sensory data; and where the robot may operate in a workspace),
the industrial truck (100) including an environment detection sensor (20) (e.g. Paragraphs [0154]-[0156]; where the robot may include a plurality of sensors (e.g., LIDAR, obstacle, temperature, imaging, camera, LED, etc., sensors), and the robot may use LIDAR and a depth cameras, and may receive and process data from internal and external sensors, to map the environment and localize the robot and establish a perimeter of the environment, sense obstacles, and conduct learning of images to determine the work environment), the method comprising:
reading in (S1) a point cloud of the warehouse (2) in the surroundings (4) of the industrial truck (100) (e.g. Paragraphs [0216], [0312]; where the processor of the robot may construct a point cloud map of two dimensional or three dimensional points by transforming each of the vectors into a vector space with a shared origin to determine parameters of the environment), wherein
the point cloud comprises point information generated using the environment detection sensor (20), at least a portion of the point information corresponding to at least one object (10) present in the surroundings (4) (e.g. Paragraphs [0154]-[0156], [0210], [0216], [0312]; where the depth camera on the robot provides data to an image processor for depth sensing, obstacle detection, presence detection, etc. for obstacles, and the LIDAR have a 360 degree field of view to construct a point cloud),
converting (S3) the point information into a frequency distribution of the point information (e.g. Paragraphs [0257], [0276], [0294]; that the processor may approximate how many of a total number of data points scanned belong to each class, and the number of measurements within a specified area of interest may be used to indicate the presence of a wall, and the use of a probability distribution function can be used to interpret frequency data for lidar point cloud information by using the total counts within a space to predict the likelihood the peak corresponds to certain environmental information);
determining (S4) a statistical distribution parameter of the point information in the frequency distribution (e.g. Paragraphs [0199], [0291], [0295], [0312], [0389]; Figures 35B-D, 62A-C; where the processor of the robot may use a statistical test to filter out points from the point cloud data, such as determining an aggregate or mean value or variance; and the processor generates a uniform phase probability distribution over the phase space, and may use adaptive resampling criteria including the variance of the weights with respect to a uniform distribution);
classifying (S5) the object (10) as a person (12) or a warehouse technology object (14) based, at least in part, on the statistical distribution parameter (e.g. Paragraphs [0157], [0219], [0353], [0363]; where the classification unit is configured to recognize objects under different conditions and may classify an object as being movable or dynamic, and may identify a particular person as occupying an area); and
emitting (S6) a control signal for controlling the industrial truck (100) in the warehouse (2), based at least in part, on a classification resulting from the classification step (S5) (e.g. Paragraphs [0157]-[0158], [0184], [0278]; where the processor of the robot may determine actions for the robot to execute based on the classification unit classifying the object).
Halata, in a similar field of endeavor, more explicitly teaches the features of converting (S3) the point information into a frequency distribution of the point information; and determining (S4) a statistical distribution parameter of the point information in the frequency distribution; and classifying (S5) the object (10) as a person (12) or a warehouse technology object (14) based, at least in part, on the statistical distribution parameter.
Halata teaches a method for detecting objects in a warehouse, where several planes of the warehouse can be determined using position intervals of predetermined size, where the maximum of the frequency distribution indicates how many pixels were detected at each specific position in that direction, to identify distinctive structures in the warehouse (e.g. Paragraphs [0015], [0017]-[0018], [0022]-[0023]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the obstacle recognition method of Afrouzi, with the feature of utilizing frequency distribution information in the system of Halata, in order to detect objects and perform spatial orientation (see at least Paragraph [0004] of Halata).
As per Claim 2, Afrouzi, in view of Halata, teaches the features of Claim 1, and Afrouzi further discloses the features of wherein
the point cloud read in comprises information of at least two objects (10) present in the surroundings (4) (e.g. Paragraphs [0155]-[0156]; where the robot may process image data to identify objects or faces in the image), and wherein the method comprises as a
further step a segmenting (S2) of the point cloud read in, into at least one segment which, of the point information from the point cloud read in, comprises point information associated with one object (10) of the at least two objects (10) (e.g. Paragraphs [0189], [078]; where the processor may use image-base segmentation methods to separate objects from one another), ‘…’.
Halata further teaches the features of in the conversion step (S3) the associated point information is converted into the frequency distribution.
Halata teaches a method for detecting objects in a warehouse, where several planes of the warehouse can be determined using position intervals of predetermined size, where the maximum of the frequency distribution indicates how many pixels were detected at each specific position in that direction, to identify distinctive structures in the warehouse (e.g. Paragraphs [0015], [0017]-[0018], [0022]-[0023]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the obstacle recognition method of Afrouzi, with the feature of utilizing frequency distribution information in the system of Halata, in order to detect objects and perform spatial orientation (see at least Paragraph [0004] of Halata).
As per Claim 3, Afrouzi, in view of Halata, teaches the features of Claim 1, and Halata further teaches the features of wherein determining the statistical distribution parameter is performed based, at least in part, on a scatter parameter in the frequency distribution.
Halata teaches a method for detecting objects in a warehouse, where several planes of the warehouse can be determined using position intervals of predetermined size, where the maximum of the frequency distribution indicates how many pixels were detected at each specific position in that direction, to identify distinctive structures in the warehouse (e.g. Paragraphs [0015], [0017]-[0018], [0022]-[0023]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the obstacle recognition method of Afrouzi, with the feature of utilizing frequency distribution information in the system of Halata, in order to detect objects and perform spatial orientation (see at least Paragraph [0004] of Halata).
As per Claim 4, Afrouzi, in view of Halata, teaches the features of Claim 1, and Afrouzi further discloses the features of wherein
converting the point information includes converting the point information into a histogram (e.g. Paragraphs [0184], [0203], [0229], [0257]; where the processor determines if the transformations between two vectors is inhomogeneous, and the processor of the robot determines low obstacle density areas and high obstacle density areas, and may execute a density-based clustering algorithm, to establish groups corresponding to the clusters, and create a graph of reachable depth vectors), wherein
determining the statistical distribution parameter is performed in the histogram (e.g. Paragraphs [0199], [0295], [0312]; Figures 35B-D, 62A-C; where the processor of the robot may use a statistical test to filter out points from the point cloud data, such as determining an aggregate or mean value or variance), and wherein
the distribution parameter is determined in the histogram based, at least in part, on a scatter parameter (e.g. Paragraph [0363], [0389]; where the processor may use a real-time classifier to identify the chance of traversing an area, by adjusting bias and variance with respect to a uniform distribution).
As per Claim 5, Afrouzi, in view of Halata, teaches the features of Claim 1, and Afrouzi further discloses the features of wherein
the point information of the point cloud comprises spatial point co-ordinates of the at least one object (10) present in the surroundings (4) (e.g. Paragraph [0300]; where the processor may determine a probability density function for localizing the robot using spatial coordinates), and wherein,
converting the spatial point coordinates includes converting spatial point coordinates into the frequency distribution (e.g. Paragraph [0207]; where the processor may transform the vectors into a shared coordinate system).
As per Claim 6, Afrouzi, in view of Halata, teaches the features of Claim 1, and Afrouzi further discloses the features of wherein
the point information of the point cloud read in comprises signal intensities of measurement signals reflected at the at least one object (10) present in the surroundings (4) for detecting the point cloud (e.g. Paragraphs [0272], [0352], [0556]-[0557]; where the depth sensor may use active or passive depth sensing methods, such as IR reflection intensity, and the intensity of the transmitter may be increased with the speed of the robot to observe at higher speeds), and wherein,
in the step (S3) of converting includes converting signal intensities into the frequency distribution (e.g. Paragraphs [0216], [0319], [0290]; where the system may construct a map using point cloud data by transforming each of the vectors into a vector space within a shared origin, and may transform an intensity of observed data into a classification problem; and where the processor uses a probability distribution value indicating how likely the robot is in a particular region and updates the distribution based on an observation probability distribution map).
As per Claim 7, Afrouzi, in view of Halata, teaches the features of Claim 1, and Afrouzi further discloses the features of wherein
the point information of the point cloud read in contains spatial speed co-ordinates of the at least one object (10) present in the surroundings (4) (e.g. Paragraphs [0272], [0352], [0556]-[0557]; where the depth sensor may use active or passive depth sensing methods, such as IR reflection intensity, and the intensity of the transmitter may be increased with the speed of the robot to observe at higher speeds), and wherein,
in the step (S3) of converting includes transferring spatial speed coordinates into the frequency distribution (e.g. Paragraphs [0216], [0319], [0290]; where the system may construct a map using point cloud data by transforming each of the vectors into a vector space within a shared origin, and may transform an intensity of observed data into a classification problem; and where the processor uses a probability distribution value indicating how likely the robot is in a particular region and updates the distribution based on an observation probability distribution map).
As per Claim 8, Afrouzi, in view of Halata, teaches the features of Claim 1, and Afrouzi further discloses the features of wherein
the object is classified as a person (e.g. Paragraphs [0157], [0219], [0353], [0363]; where the classification unit is configured to recognize objects under different conditions and may classify an object as being movable or dynamic, and may identify a particular person as occupying an area) and wherein
emitting the control signal includes sending a control signal for interrupting a working task being carried out by the industrial truck (100) to a working device (30) of the industrial truck (100) (e.g. Paragraphs [0157]-[0158], [0284], [0539]; where the processor of the robot may determine actions for the robot to execute based on the classification unit classifying the object; and where the robot may receive signals to interrupt operations, such as booting up, or may stop the robot when a person is detected walking quickly by the robot).
As per Claim 9, Afrouzi, in view of Halata, teaches the features of Claim 1, and Afrouzi further discloses the features of wherein
the object is classified as a warehouse technology object (14) (e.g. Paragraphs [0157], [0195], [0219], [0249]; where the classification unit is configured to recognize objects under different conditions and may classify an object as being movable or dynamic or static, and may identify furniture, obstacles, static objects, walls, etc.) and
emitting the control signal includes emitting a control signal for limiting a drive dynamics of the industrial truck (100) (e.g. Paragraphs [0284], [0335], [0407], [0539]; where the processor of the robot may determine actions for the robot to execute based on the classification unit classifying the object; and where the robot may receive signals to interrupt operations, such as booting up, or may stop the robot when a person is detected walking quickly by the robot; or the processor may instruct the robot to approach the object at a particular angle and/or driving speed).
As per Claim 10, Afrouzi, in view of Halata, teaches the features of Claim 1, and Afrouzi further discloses the features of wherein
classifying the object (10) is carried out based, at least in part, on a machine learning model (e.g. Paragraphs [0156], [0363]; where the identification of the object that is included in the image is trained through a deep learning method), wherein
at least one of the frequency distribution and the statistical distribution parameter is read into the machine learning model (e.g. Paragraphs [0369], [0397]; where the processor determines areas in which to operate based on data from prior work sessions and updates the movement plan based on new data; and the machine learning algorithm may be used to learn the features of different types of objects extracted from sensor data such that the machine learning algorithm may identify the most likely type of object observed at a location).
As per Claim 11, Afrouzi, in view of Halata, teaches the features of Claim 4, and Afrouzi further discloses the features of wherein
classifying the object (10) is carried out based, at least in part, on the machine learning model (e.g. Paragraphs [0156], [0363]; where the identification of the object that is included in the image is trained through a deep learning method), wherein
the histogram is read into the machine learning model (e.g. Paragraphs [0505]-[0506]; where a vector space model is used for representing histograms of word frequencies associated with the metadata of a digital image, and converting them word histograms to visual words).
As per Claim 12, Afrouzi discloses the features of a control unit (110) for controlling an industrial truck (100) that includes an environment detection sensor (20) (e.g. Paragraphs [0154]-[0156], [0159], [0349]; where the robot may include a controller/ processor, which controls operation of one or more components of the robot based on environmental characteristics inferred from sensory data; and where the robot may include a plurality of sensors (e.g., LIDAR, obstacle, temperature, imaging, camera, LED, etc., sensors), and the robot may may receive and process data from internal and external sensors, to map the environment and localize the robot and establish a perimeter of the environment, sense obstacles, and conduct learning of images to determine the work environment)
in a warehouse (2) (e.g. Paragraphs [0008], [0632]; where the robot may operate in a workspace), the control unit (110) comprising:
a first interface (112) (e.g. Paragraphs [0154]-[0155], [0216], [0312], [0363]; where the processor generates a spatial representation of the environment in the form of a point cloud of sensor data; and where the processor of the robot may receive and process data from internal or external sensors, execute commands based on the data received, and the robot may comprise a monitoring algorithm for receiving the data (i.e., the modules of the processor of the robot serve as an interface for receiving the data to be processed)) configured for
reading-in a point cloud of the warehouse (2) in the surroundings (4) of the industrial truck (100) (e.g. Paragraphs [0216], [0312]; where the processor of the robot may construct a point cloud map of two dimensional or three dimensional points by transforming each of the vectors into a vector space with a shared origin to determine parameters of the environment), wherein
the point cloud includes point information generated using the environment detection sensor (20), at least a portion of the point information corresponding to at least one object (10) present in the surroundings (4) (e.g. Paragraphs [0154]-[0156], [0210], [0216], [0312]; where the depth camera on the robot provides data to an image processor for depth sensing, obstacle detection, presence detection, etc. for obstacles, and the LIDAR have a 360 degree field of view to construct a point cloud); and
a second interface (114) for emitting a control signal for controlling the industrial truck (100) in the warehouse (2) (e.g. Paragraphs [0157]-[0158], [0184], [0349], [0546]; where the processor of the robot may determine actions for the robot to execute based on the classification unit classifying the object; and where a LED IR event may be detect as the robot traverses along a path within the environment, and the detection may be passed to a control module to adjust the path of the robot); wherein
the control unit (110) (e.g. Paragraph [0418], [0447], [0600]; where the processor controls operation of one or more components of the robot based on environmental characteristics inferred from sensory data) is configured to:
convert the point information into a frequency distribution of the point information (e.g. Paragraphs [0257], [0276], [0294]; that the processor may approximate how many of a total number of data points scanned belong to each class, and the number of measurements within a specified area of interest may be used to indicate the presence of a wall, and the use of a probability distribution function can be used to interpret frequency data for lidar point cloud information by using the total counts within a space to predict the likelihood the peak corresponds to certain environmental information),
determine a statistical distribution parameter of the point information in the frequency distribution (e.g. Paragraphs [0199], [0291], [0295], [0312], [0389]; Figures 35B-D, 62A-C; where the processor of the robot may use a statistical test to filter out points from the point cloud data, such as determining an aggregate or mean value or variance; and the processor generates a uniform phase probability distribution over the phase space, and may use adaptive resampling criteria including the variance of the weights with respect to a uniform distribution),
classify the object (10) in a category as a person (12) or as a warehouse technology object (14) based, at least in part, on the statistical distribution parameter (e.g. Paragraphs [0157], [0219], [0353], [0363]; where the classification unit is configured to recognize objects under different conditions and may classify an object as being movable or dynamic, and may identify a particular person as occupying an area),
the control signal being based, at least in part, on the classification (e.g. Paragraphs [0156], [0297]; where the robot contains an object classifier unit for identifying a class to which a detected object belongs, and the processor of the robot my attempt to alter its path to avoid high density areas, or maneuver around an object).
Halata, in a similar field of endeavor, more explicitly teaches the features of convert the point information into a frequency distribution of the point information; and determine a statistical distribution parameter of the point information in the frequency distribution; and classify the object (10) in a category as a person (12) or as a warehouse technology object (14) based, at least in part, on the statistical distribution parameter.
Halata teaches a method for detecting objects in a warehouse, where several planes of the warehouse can be determined using position intervals of predetermined size, where the maximum of the frequency distribution indicates how many pixels were detected at each specific position in that direction, to identify distinctive structures in the warehouse (e.g. Paragraphs [0015], [0017]-[0018], [0022]-[0023]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the obstacle recognition method of Afrouzi, with the feature of utilizing frequency distribution information in the system of Halata, in order to detect objects and perform spatial orientation (see at least Paragraph [0004] of Halata).
As per Claim 13, Afrouzi discloses the features of an industrial truck (100) comprising: with the an environment detection sensor (20) (e.g. Paragraphs [0154]-[0156], [0159], [0349]; where the robot may include a controller/ processor, which controls operation of one or more components of the robot based on environmental characteristics inferred from sensory data; and where the robot may include a plurality of sensors (e.g., LIDAR, obstacle, temperature, imaging, camera, LED, etc., sensors), and the robot may may receive and process data from internal and external sensors, to map the environment and localize the robot and establish a perimeter of the environment, sense obstacles, and conduct learning of images to determine the work environment),
an output of the environment detection sensor being used to generate point information for a point cloud, at least a portion of the point information corresponding to at least one object e.g. Paragraphs [0154]-[0156], [0210], [0216], [0312]; where the depth camera on the robot provides data to an image processor for depth sensing, obstacle detection, presence detection, etc. for obstacles, and the LIDAR have a 360 degree field of view to construct a point cloud); and
a control unit (110) for emitting a control signal for controlling the industrial truck (100) (e.g. Paragraphs [0154]-[0156], [0159], [0349]; where the robot may include a controller/ processor, which controls operation of one or more components of the robot based on environmental characteristics inferred from sensory data), the control unit (110) being configured to:
convert the point information into a frequency distribution of the point information (e.g. Paragraphs [0257], [0276], [0294]; that the processor may approximate how many of a total number of data points scanned belong to each class, and the number of measurements within a specified area of interest may be used to indicate the presence of a wall, and the use of a probability distribution function can be used to interpret frequency data for lidar point cloud information by using the total counts within a space to predict the likelihood the peak corresponds to certain environmental information);
determine a statistical distribution parameter of the point information in the frequency distribution (e.g. Paragraphs [0199], [0291], [0295], [0312], [0389]; Figures 35B-D, 62A-C; where the processor of the robot may use a statistical test to filter out points from the point cloud data, such as determining an aggregate or mean value or variance; and the processor generates a uniform phase probability distribution over the phase space, and may use adaptive resampling criteria including the variance of the weights with respect to a uniform distribution),
determine an inhomogeneity of the point information based, at least in part, on the statistical distribution parameter (e.g. Paragraphs [0184], [0203], [0229], [0257]; where the processor determines if the transformations between two vectors is inhomogeneous, and the processor of the robot determines low obstacle density areas and high obstacle density areas, and may execute a density-based clustering algorithm, to establish groups corresponding to the clusters, and create a graph of reachable depth vectors), and
classify the object (10) in a category as a person (12) or as a warehouse technology object (14) based, at least in part, on the inhomogeneity of the point information (e.g. Paragraphs [0276], [0293]-[0294], [0296], [0380], [0397]-[0398]; where the robot may determine the probability density function to predict if there is an opening in the wall of interest, or identify high and low obstacle density areas; and where the processor may identify people and/or pets based on obstacle density),
the control signal being based, at least in part, on the classification (e.g. Paragraphs [0156], [0297]; where the robot contains an object classifier unit for identifying a class to which a detected object belongs, and the processor of the robot my attempt to alter its path to avoid high density areas, or maneuver around an object).
Halata, in a similar field of endeavor, more explicitly teaches the features of convert the point information into a frequency distribution of the point information; and determine a statistical distribution parameter of the point information in the frequency distribution; and classify the object (10) in a category as a person (12) or as a warehouse technology object (14) based, at least in part, on the inhomogeneity of the point information.
Halata teaches a method for detecting objects in a warehouse, where several planes of the warehouse can be determined using position intervals of predetermined size, where the maximum of the frequency distribution indicates how many pixels were detected at each specific position in that direction, to identify distinctive structures in the warehouse (e.g. Paragraphs [0015], [0017]-[0018], [0022]-[0023]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the obstacle recognition method of Afrouzi, with the feature of utilizing frequency distribution information in the system of Halata, in order to detect objects and perform spatial orientation (see at least Paragraph [0004] of Halata).
As per Claim 14, and similarly for Claim 15, Afrouzi, in view of Halata, teaches the features of Claims 1 and 12, respectively, and Afrouzi further discloses the features of
determining an inhomogeneity of the point information based, at least in part, on the statistical distribution parameter (e.g. Paragraphs [0184], [0203], [0229], [0257]; where the processor determines if the transformations between two vectors is inhomogeneous, and the processor of the robot determines low obstacle density areas and high obstacle density areas, and may execute a density-based clustering algorithm, to establish groups corresponding to the clusters, and create a graph of reachable depth vectors), wherein
the object (10) is classified as the person (12) or the warehouse technology object (14) based, at least in part, on the inhomogeneity of the point information (e.g. Paragraphs [0276], [0293]-[0294], [0296], [0380], [0397]-[0398]; where the robot may determine the probability density function to predict if there is an opening in the wall of interest, or identify high and low obstacle density areas; and where the processor may identify people and/or pets based on obstacle density).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ghafarianzadeh, et al (U.S. 2019/0250626 A1), which teaches a method of determining objects that are blocking the path of a vehicle using frequency distribution of data using environmental sensors.
Suhre, et al (WO 2017/125369 A1), which teaches a method for detecting traffic on a roadway based on frequency distribution values.
Yonekawa (U.S. 2022/0172484 A1), which teaches a method for recognizing an environment in front a vehicle, where frequency distribution data is used to determine a feature map of the environment.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MERRITT LEVY whose telephone number is (571)270-5595. The examiner can normally be reached Mon-Fri 0630-1600.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached at (571) 272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MERRITT LEVY/Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
Prosecution Insights