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 Application
Claims 1-19, and 21 are pending. Claim 20 has been canceled. Claims 1, 12, and 21 are the independent claims. Claims 1, 12, and 21 have been amended. This office action is in response to the Amendments received on 02/05/2026.
Response to Arguments
With respect to Applicant’s remarks filed on 02/05/2026 “Applicant Arguments/Remarks Made in an Amendment” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
In response to the amendments to the claims, the rejection of claims 1-19 and 21 under 35 U.S.C § 112(b) have been withdrawn.
Applicant's arguments with respect to the reference, Zhang, relied upon in non-final office action filed on 11/05/2025, for the rejection of independent claims 1, 12, and 21 under 35 USC §103, (see page 9 of the Remarks, title “Independent Claim 1”), have been fully considered and are persuasive. However, the new amended claims have changed the metes and bound of the claims and therefore new ground of rejection has been applied with respect to the newly amended claims (See Office Action below). The applicant statement of argument regarding the dependent claims has been considered. This is the office’s stance that all of the claimed subject matter has been properly rejected.
Office Note: Due to applicant’s amendments, further claim rejections appear on the record as stated in the below Office Action.
It is the Office’ stance that all of applicant arguments have been considered.
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.
Claims 1-3, 5-8, 10-14, 16-19 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Williams et al., US 20220019970 A1, hereinafter Williams, in view of Sachdeva et al., US20200380876A1, hereinafter “Sachdeva”.
Regarding claim 1, 12 and 21, Williams teaches an autonomous drone (Abstract, “includes at least one aerial drone”) and a computing system for an autonomous drone, comprising: one or more processors; and one or more computer-readable media storing instructions that are executable to cause the computing system to perform operations, (Fig. 1F, Paragraph [0092], “Controller 102 (and/or flight controller 110) can include a processor 104, a memory 106”, Paragraph [0093], “The memory 106 can be an example of tangible, computer-readable storage medium that provides storage functionality to store various data and or program code associated with operation of the controller 102/drone 100,”), and a computer-implemented method comprising: determining, based on sensor data, an object of interest within a warehouse environment, wherein the sensor data comprises an image frame (([0071]-[0072]-[0082][0088], [0098], “image-based sensor 600 (e.g., a camera or a scanning array of photodetectors)”, [0110], “the controller 102 can be configured to employ computer vision to recognize image features that correspond to a reference point (e.g., endpoint) of a shelf or other structure within a storage facility 2100”,“ the controller 102 relies on a camera 118 in addition to the optical sensor 116 to detect the recognizable portion of the aisle,”); determining a drone location for the autonomous drone within the warehouse environment based on the location anchor (Paragraph [0087], “The controller can be configured to determine a position of the aerial drone based on one or more signals from the positioning system. The controller may be further configured to associate the determined position with a detected identifier.”, Paragraphs [0097] and [0100]); and generating a flight plan for the autonomous drone based on mission data indicating one or more inventory items to be scanned by the autonomous drone, the location anchor, and the drone location (Paragraph [0087], “The controller can also be configured to determine the flight path for the aerial drone based upon the determined position of the aerial drone and/or a determined position of the aerial drone relative to one or more markers or other reference points.”, Paragraph [0124], “to perform a task”, “For example, the user control interface system may contain commands for the drone 100, possibly given by a user through the user control interface system or automated by programming, which may be sent over the wireless network to be executed by the drone.”, “give instructions to the drone to fly to a certain location in the warehouse, …, scan an item,… ”).
William doesn’t explicitly teach determining one or more physical characteristics associated with the object of interest within the image frame. wherein at least one physical characteristic of the one or more physical characteristics comprises dimensions of the object of interest; Accessing map data comprising a dimensional layout of the warehouse environment and at least one object within the dimensional layout of the warehouse environment; generating, based on the dimensions of the object of interest, a location anchor by determining the dimensions of the object of interest correlate to the at least one object included within the dimensional layout of the warehouse environment; wherein the location anchor indicates an identifiable location within the warehouse environment when perceived by an autonomous drone at a subsequent time by comparing the image frame to a second image frame at the subsequent time.
However, Sachdeva teaches determining one or more physical characteristics associated with the object of interest within the image frame. wherein at least one physical characteristic of the one or more physical characteristics comprises dimensions of the object of interest ([0027], “the images associated with the one or more aisles include landmark features of respective aisle. [] the landmark feature of an aisle may include, [] length, height and width of rack pair forming the aisle, distance between opposing rack pair forming the aisle, height of shelves, number of shelves, number of rack columns, pallets and any other storage unit on the racks or obstacle.”, __reads on physical characteristics dimensions of the object of interest); Accessing map data comprising a dimensional layout of the warehouse environment ([0004]-[0006], “pre-existing map of the warehouse”) and at least one object within the dimensional layout of the warehouse environment ([0032], “the number of shelves may be derived from segmented images of the aisle or from the warehouse map.” “the warehouse map includes the warehouse layout design plan”, __pre-existing warehouse map includes the size of the inventory which reads on map data comprising at least one object within the dimensional layout__); generating, based on the dimensions of the object of interest, a location anchor by determining the dimensions of the object of interest correlate to the at least one object included within the dimensional layout of the warehouse environment ([0027], “The desired image capturing position is selected based on analysis of depth and height of the aisles of the warehouse.” “images associated with the one or more aisles include landmark features of respective aisle […] the landmark feature of an aisle may include, but are not limited to, edges, corners of the aisles, length, height and width of rack pair forming the aisle, distance between opposing rack pair forming the aisle, height of shelves, number of shelves, number of rack columns, pallets and any other storage unit on the racks or obstacle.”, __ Note: the desired image capturing position (reads on a location anchor), is selected/generated based on the depth and height of the aisles (reads on dimensions of the object of interest) by correlating to landmark features of the warehouse (reads an object within the dimensional layout of the warehouse) that is include in the pre-existing warehouse map__); wherein the location anchor indicates an identifiable location within the warehouse environment ([0027], “the desired location is entrance of respective one or more aisles included in the mission.”) when perceived by an autonomous drone at a subsequent time by comparing the image frame to a second image frame at the subsequent time ([0027], “The image analytics unit 114, is configured to combine the plurality of images associated with each of the one or more aisles respectively to analyze if the appropriate areas of the respective aisles are covered based on the type of mission. The image analytics unit 114 is configured to receive more images of the respective aisles included in the mission from the one or more image capturing devices 104 b after the initial path is generated during the ongoing mission if the appropriate areas of the aisles are not covered.” __Note: analyzing images of the aisles respectively, during the ongoing mission, to see if the appropriate areas of the respective aisles are covered reads on comparing the image frames at the subsequent time__, and also see, [0028], “In an exemplary embodiment of the present invention, a deep learning model may be trained using an image data of the warehouse prepared using images of the entire warehouse and the associated aisles captured by the UAV 104 or any other image capturing device in the past.”, [0029], “The path generation unit 116 is configured to generate a three dimensional (3D) grid map for respective one or more aisles included in the mission using the segmented images, a pre-existing map of the warehouse, identified landmark features and identified density of inventory.”, __Note: the process of analyzing the images is happening in real time during an ongoing mission, therefore the disclosure and cited paragraphs cover the location anchor perceiving by drone in subsequent time by comparing the image frames).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for drone flight planning within a warehouse as taught by Williams to generate a location anchor (desired image capturing position which is desired flying position/location) using dimensional layout of the warehouse by analyzing the images (in real time in the ongoing mission) that are indicative of objects of interest within the warehouse with respect to landmark features in the dimensional layout (e.g. height, length and width of the aisles) as taught by Sachdeva, with a reasonable expectation of success, with the motivation of optimizing a flight path and flight location of a drone in real time based on the inventory dimensional layout.
Regarding Claim 2 and 13, Wiliams in view of Sachdeva teaches the limitations of claims 1 and 12 (See rejections for claims 1 and 12), and Williams teaches wherein the mission data is obtained from a landing pad. (Paragraph [0122], “In one non-limiting configuration, the data may be transmitted through the wireless connection to an external processor, including a local processor such as a drone ground station,” __ drone ground station read on a landing pad__)
Regarding Claim 3 and 14, Williams in view of Sachdeva teaches the limitations of claims 1 and 12 (see rejections for claims 1 and 12), and William teaches wherein the computer-implemented method is indicative of a region of the warehouse environment (Paragraph [0063], “a system including an aerial drone with an optical sensor configured to scan identifiers based on a flight path of the aerial drone, wherein the aerial drone is configured to detect a marker located in proximity to (e.g., at or near) a portion (e.g., an end) of an aisle” __the mission data (flight path) is based on detecting a portion of an aisle (warehouse environment)__)
Regarding Claim 5 and 16, Williams in view of Sachdeva teaches the limitation of claims 1 and 12 (See rejections for claims 1 and 12), and although Williams teaches wherein generating the location anchor based on the object of interest comprises: determining, using a machine-learned model, the physical characteristics of an object in the sensor data; determining, using the machine-learned model, the object in the sensor data is the object of interest based on the physical characteristics; (Paragraph [0061], “the aerial drone is configured to detect a portion (e.g., an end) of an aisle before stopping or changing direction, wherein the portion of the aisle is detected based upon one or more identifiers disposed upon or near the portion of the aisle, such as using image processing, computer vision, and/or machine learning techniques") and determining, using the machine-learned model, the location of the object of interest. (Paragraph [0082], “the controller may be further configured to detect locations of the identifiers for the plurality of inventory items based on the image data, using image processing, computer vision, machine learning,”), for purpose of compact prosecution Sachdeva also teaches wherein generating the location anchor based on the object of interest comprises: determining, using a machine-learned model, physical characteristics of an object in the sensor data ([0014], “identifying landmark features of the warehouse and density of inventory by performing image segmentation and analytics on the captured plurality of images using one or more deep learning techniques.”, [0028], “ a deep learning model may be trained using an image data of the warehouse”, __Note: landmark features of an aisle reads on physical characteristic of an object in the sensor data__); determining, using the machine-learned model, the object in the sensor data is the object of interest based on the physical characteristics ([0027], [0028], “landmark feature of an aisle may include, but are not limited to, edges, corners of the aisles, length, height and width of rack pair forming the aisle, distance between opposing rack pair forming the aisle, height of shelves, number of shelves, number of rack columns, pallets and any other storage unit on the racks or obstacle”, [0047], “a deep learning model may be trained using an image data of the warehouse prepared using images of the entire warehouse and the associated aisles captured by the UAV”)”); and determining, using the machine-learned model, the location of the object of interest ( at least [0027] “The desired image capturing position is selected based on analysis of depth and height of the aisles of the warehouse. In an exemplary embodiment of the present invention, the desired location is entrance of respective one or more aisles included in the mission. In the exemplary embodiment of the present invention, the desired image capturing position is at a predefined height at the center of the aisle included in the mission.”, [0047], ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for drone flight planning within a warehouse by using obtained image data from the object of interest as taught by Williams to include machine learning technique to identify the location of the object of interest in the warehouse as taught by Sachdeva, with a reasonable expectation of success, with the motivation of improving the accuracy of detecting the object of interest and optimizing a flight path and flight location of a drone in real time based on the inventory dimensional layout.
Regarding Claim 6 and 17, Williams in view of Sachdeva teaches the limitations of claim 1 and 12 (See rejections for claim 1 and 12), and Williams wherein generating the flight plan further comprises, generating, using the location anchor, an initial trajectory of the autonomous drone wherein the initial trajectory is indicative of a first direction of travel based on the location anchor. (Paragraph [0087], “The controller may be further configured to associate the determined position with a detected identifier. For example, the controller can be configured to store respective positions for the detected identifiers. The controller can also be configured to determine the flight path for the aerial drone based upon the determined position of the aerial drone and/or a determined position of the aerial drone relative to one or more markers or other reference points.”).
Regarding Claim 7 and 18, Williams in view of Sachdeva teaches the limitations of claims 1 and 12 (See rejections for claims 1 and 12), and Williams teaches wherein the object of interest is a portion of an inventory shelving unit in the warehouse environment. (Paragraph [0110], “In another implementation shown in FIG. 21A, the controller 102 is configured to detect a recognizable portion 2108 (e.g., an end) of an aisle before stopping or changing direction. For example, the controller 102 can be configured to employ computer vision to recognize image features that correspond to a reference point (e.g., endpoint) of a shelf or other structure within a storage facility 2100,”).
Regarding Claim 8, Williams in view of Sachdeva teaches the limitations of claim 1 (See rejections for claim 1), and Williams teaches further comprising obtaining inventory data by scanning at least one inventory item of the one or more inventory items (e.g., [0054], [0074], [0097], “scan an identifier 504 (e.g., barcode) on an inventory item”, [0124], “scan an item”).
Regarding Claim 10, Williams in view of Sachdeva teaches the computer-implemented method of claim 2 (See rejections for claim 2), and Williams teaches further comprising: generating a dock flight plan, wherein the dock flight plan is indicative of the autonomous drone navigating to the landing pad; (Paragraph [0125], “automated mission profiles can be configured for using a single drone to complete a task or mission or for using multiple drones to complete a task or mission. For example, in some embodiments, a plurality of drones can be assigned to work in concert to perform a comprehensive warehouse inventory, wherein each drone can inventory a single shelf, rack, etc. before returning to a base station to recharge.” __the drone returns to its base (landing pad) after completing the mission__) and transmitting updated mission data to the landing pad, wherein the updated mission data is indicative of the inventory items scanned by the autonomous drone (Paragraph [0132], “The end device may comprise…drone base station…, or any other such suitable end device. With the received data, the end device may update the information running on its software, such as a GUI. This information may include…, barcode scans, …, location data, and/or other suitable information.”, Paragraph [0135]).
Regarding Claim 11, Williams in view of Sachdeva teaches the computer-implemented method of claim 2 (See rejection for claim 2), and Williams teaches wherein the autonomous drone is a first autonomous drone, wherein the mission data is obtained by the first autonomous drone and a second autonomous drone from the landing pad (Paragraph [0122], “transmit data to and receive data from the drone 100, such as image, …, command, and/or other data. In one non-limiting configuration, the data may be transmitted through the wireless connection to an external processor, including a local processor such as a drone ground station” __drone ground station reads on landing pad__, Paragraph [0125], “automated mission profiles can be configured for using a single drone to complete a task or mission or for using multiple drones to complete a task or mission.”).
Regarding claim 19, Williams in view of Sachdeva teaches the computer system of claim 12 (See rejections for claim 12), and Williams teaches further comprising obtaining inventory data by scanning inventory items, wherein the inventory items are indicative of the mission data. (Paragraph [0056], “the flight path causes the aerial drone to scan identifiers of inventory items” __flight path is mission data__).
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Sachdeva, further in view of Fong et al., US20140005933A1, hereinafter “Fong”.
Regarding claim 4 and 15, Williams in view of aforementioned prior arts teaches the limitations of claims 1 and 12 (See rejections for claims 1 and 12), however Williams in view of prior arts doesn’t explicitly teach determining the drone location based on the location anchor and dimensional layout of the warehouse environment.
Fong teaches determining the drone location based on the location anchor and dimensional layout of the warehouse environment (at east [0005], [0025], “estimates the location of the robot relative to those landmarks”, [0030], [0031]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for drone flight planning within a warehouse as taught by Williams in view of Sachdeva, to determining the drone location based on the location anchor and the dimensional layout/map of the environment as taught by Fong in order to improve the performance of the robot in generating a flight mission to the detected object of interest.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Williams, in view of Sachdeva, further in view of Skaff, US 20220303445 A1, hereinafter Skaff.
Regarding Claim 9, Williams in view of Sachdeva teaches the limitations claim 8 (See rejection for claim 8), and teaches wherein obtaining inventory data by scanning the at least one inventory item of the one or more inventory items comprise: obtaining, using a first camera, first sensor data indicative of the at least one inventory item of the one or more inventory items, wherein the first camera includes a wide angle view of the one and more inventory items; (Fig. 8A, Paragraph [0082], “the system can include a camera or multiple cameras (in addition to the optical sensor) on the aerial drone. The camera can have a wider field of view than the field of view of the optical sensor, which may also be a camera in some implementations. The controller may be configured to capture image data for a plurality of inventory items ”__camera with a wider field of view to capture image of the inventory items, reads on the limitation of the first camera__[0097], [0124]) obtaining, using a second camera, second sensor data indicative of a barcode on the at least one inventory item, wherein the second camera includes a narrow angle view of the barcode; (Paragraph [0117], “In order to detect identifiers (e.g., barcodes, QR codes, text, symbols, images, etc.), the aerial drone 100 must be able to align the optical sensor 116 with the identifier. In some embodiments, the aerial drone 100 can employ a wide field of view camera (e.g., camera 118) to collect image data, determine positioning of at least one identifier based upon the image data, and utilize the positioning information to align the optical sensor 116 with the identifier.”__ camera 118 reads on the first camera and optical sensor reads on the second camera__).
Williams in view of Sachdeva doesn’t teach determining that the at least one inventory item is associated with a misslot or a rescan based on the first and second sensor data.
Nevertheless, Skaff discloses an image capture systems and methods for inventory monitoring including detecting a misplaced product (Paragraph [0009, “detect a misplaced product”]) by autonomous robot (drone) (Paragraph [0010], “an autonomous robot capable of guiding itself through a store or warehouse.”, Paragraph [0142]) and teaches determining that the at least one inventory item is associated with a misslot or a rescan (Paragraph [0009], “detect a misplaced product”, Paragraph [0073], “localize product placement”) based on the first and second sensor data (Paragraph [0065], “The inventory cameras 340 can include one or more movable cameras, zoom cameras, focusable cameras, wide-field cameras, infrared cameras, ultraviolet cameras, or other specialty cameras to aid in product identification or image construction”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for drone flight planning within a warehouse as taught by Williams in view of Sachdeva, to include the image capture system and methods as taught by Skaff in order to use two cameras (wide- and narrow-view) to capture the image of an inventory item and the identifier to detect the misplaced/mislotted inventory item.
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.
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/H.H./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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