CTNF 19/191,804 CTNF 97465 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This is a Non-Final rejection on the merits of this application. Claims 1-20 are currently pending, as discussed below. Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art. Information Disclosure Statement The information disclosure statement (IDS) filed on 08/20/2025 is being considered by the examiner. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim (s) 1-2, 4, 6-8, 13-14, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nishida (JP2020-193503A_English Translation) in view of Kocer et al. (US 2021/0357664 A1 hereinafter Kocer) . Regarding claim 1 (similarly claims 13 and 20), Nishida teaches A method (see at least Abstract [0001]: operation support method of work machine) comprising: accessing an image captured by a camera of a work machine moving within an environment; (see at least Fig. 6, 7, 9 [0027, 0054-0067]: the acquisition unit (i.e. imaging sensor capable of capturing an image) acquires image information regarding movement range Rm of the movement unit 40) identifying one or more objects in the image; (see at least Fig. 6, 7, 9 [0054-0067]: the motion control unit identifies an obstacle from the image information acquired by the acquisition unit. That is, the control unit identifies an obstacle. Examiner introduces Tawari for teaching limitations of identifying one or more objects in the image below.) selecting an object of interest from among the one or more identified objects based on a ranking including the importance metric and the dimensionality metric associated with the one or more identified objects; (see at least Fig. 6, 7, 9 [0054-0067]: the motion control unit identifies an obstacle from the image and identifies the object (i.e. selects an object of interest), Examiner notes that when only one object/obstacle is detected, the object/obstacle is rank highest among itself and is selected as an object of interest. Examiner introduces Kocer for teaching limitation below) determining a shortest distance in a range of potential distances between the work machine and the object of interest based on at least a height associated with the object of interest and an angle associated with the height; (see at least Fig. 8, 10 [0046-0066, 0095]: the motion control unit identifies the type of obstacle and identifies the range of a person when the obstacle is a person, further identifies the range of moving unit to specify the relative shortest distance Ds (i.e. threshold distance) between the person and the moving unit wherein the relative shortest distance Ds are calculated based on a range of person and the range of the moving unit. The motion control unit causes the moving unit to perform an avoidance operation by comparing whether a relative distance between the detected person and the moving unit is less than a threshold distance (D1-D3) as shown in S77-S80 of Fig. 10. The first to third distances D1 to D3 may be changed according to the type of obstacle and when the obstacle is a person, the distances may be made larger than in other cases. That is, upon determining an object type is a person, the motion control unit identifies a range of the person and subsequently sets/computes a moving range based on the object type.); and responsive to the shortest distance in the range of potential distances being less than a threshold distance, causing the work machine to perform a safety action. (see at least Fig. 8, 10 [0046, 0061-0066]: in response to determining the relative distance Ds is the third distance D3 or less (i.e. shortest distance in the range), the motion control unit changes the movement course of the movement unit(i.e. safety action)). It may be alleged that Nishida does not explicitly teach accessing a ranking of a plurality of object types, the ranking including: (i) an importance metric which ranks objects based on object types; and (ii) a dimensionality metric which ranks objects having standardized dimensions higher than objects having non-standardized dimensions; Kocer is directed to an autonomous obstacle monitoring and vehicle control system and method, Kocer teaches accessing a ranking of a plurality of object types, the ranking including: (i) an importance metric which ranks objects based on object types; and (ii) a dimensionality metric which ranks objects having standardized dimensions higher than objects having non-standardized dimensions; (see at least Fig. 3A-3B [0053-0089]: The obstacle recognition module 310 identifies and indexes obstacles and includes a prioritizing module 318 that is configured to assign priorities to obstacles based on a catalog set of priorities, priority rules, user input priorities or user input priority rules or the like. In one example, archived obstacles such as humans, livestock or the like have a higher priority relative to other identified obstacles including, for instance, brush, washouts, rocks, saturated or soaked areas of the field or the like. As described herein, the assigned priority of an identified object changes the operation of the agricultural system 100, for instance with the vehicle operation module 306 of the autonomous agricultural system controller 104. In one example, the identification of a human proximate to a determined path of the agricultural system 100 is given a high priority while other obstacles such as livestock, brush, fence or rocks or the like proximate to the determined path are given a lower priority (and optionally scaled lower priorities with livestock higher than brush or similar inanimate obstacles). For instance, the prioritizing module 318 assigns a higher priority to particular types of obstacles, for instance, humans or the like. Examiner notes that humans and live stocks generally have more uniform dimensions than rocks/brushes that can vary in size and shapes.) selecting an object of interest from among the one or more identified objects based on a ranking including the importance metric and the dimensionality metric associated with the one or more identified objects; (see at least Fig. 3A-3B [0053-0089]: In one example, the identification of a human proximate to a determined path of the agricultural system 100 is given a high priority while other obstacles such as livestock, brush, fence or rocks or the like proximate to the determined path are given a lower priority (and optionally scaled lower priorities with livestock higher than brush or similar inanimate obstacles). For instance, the prioritizing module 318 assigns a higher priority to particular types of obstacles, for instance, humans or the like.) Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Nishida’s operation work support system and method of a work machine to incorporate the technique of prioritizing/rank objects based on object types/catalog/rules and relative distance to the vehicle in order to affect an operation of the agricultural system as taught by Kocer with reasonable expectation of success and doing so would improve both vehicle operation safety and efficiency in collision control such that the system can focus attentions and resources on objects that posses the greatest safety risk while improving decision making efficiency. Regarding Claim 2 (similarly claim 14) , the combination of Nishida in view of Kocer teaches The method of claim 1 (similarly claim 13), further comprising: Nishida further teaches determining, as the angle associated with the height, an angle between the camera and the object of interest; (see at least [0043-0044]: the relative distance Ds from the person (i.e. object) to the moving portion is specified by the plane coordinates of the measurement points 14a to 14c and the person and the angle from the image sensor)(Chen US 2019/0318481A1 see at least Fig. 6B-6C [0057]: searching for the dynamic pitch angle includes calculating the distance from the ADV to the object based on D=h/tan(alpha+beta), where beta is the dynamic pitch angle to be calculated, D is the calculated distance, alpha is a previous calibration angle from a camera optical axis to the ground, and h is an estimated height of the object. That is, angle between the camera and the object can be determined) and accessing an uncertainty associated with the height, wherein the range of potential distances between the work machine and the object of interest is determined further based on the uncertainty associated with the height. (see at least Fig. 5 [0035-0076]: the obstacle identification unit identifies the type of obstacle based on image information and wherein the obstacle is a person, the characteristic points of a person are extracted and a predetermined range based on the characteristic points is specified as the range (i.e. uncertainty associate with the height) of the person wherein the person identification unit identifies the foot of the person and the area from the foot to a predetermined height as the range of the person. The distance specifying unit specifies the relative shortest distance between the obstacle and the moving unit (i.e. work machine) by calculation. The shortest relative distance from the person to the moving unit is calculated according to the range of the person specified by the person identified unit and the range of the moving unit. The relative distance Ds (i.e. range of potential distances) from the person to the moving portion of the work machine is specified by the plane coordinates of the measurement points and the person and the angle from the image sensor) Regarding claim 4 (similarly claim 16), the combination of Nishida in view of Kocer teaches The method of claim 2 (similarly claim 14), Nishida further teaches wherein the height associated with the object of interest and the uncertainty associated with the height is determined based on geographical characteristics of the environment or observable biographic characteristics of the object of interest. (see at least Fig. 5 [0035-0039, 0076]: the range of the person (i.e. height and associated uncertainty) may be calculated based on the image information such as foot, or skeleton information (i.e. observable biographic characteristics of the person) other than the foot of the person). Regarding claim 6 (similarly claim 18), the combination of Nishida in view of Kocer teaches The method of claim 1 (similarly claim 13), further comprising: Nishida further teaches determining the threshold distance based on an object type associated with the object of interest (see at least Fig. 8, 10 [0046-0066, 0095]: the motion control unit identifies the type of obstacle and identifies the range of a person when the obstacle is a person, further identifies the range of moving unit to specify the relative shortest distance Ds (i.e. threshold distance) between the person and the moving unit wherein the relative shortest distance Ds are calculated based on a range of person and the range of the moving unit. The motion control unit causes the moving unit to perform an avoidance operation by comparing whether a relative distance between the detected person and the moving unit is less than a threshold distance (D1-D3) as shown in S77-S80 of Fig. 10. The first to third distances D1 to D3 may be changed according to the type of obstacle and when the obstacle is a person, the distances may be made larger than in other cases. That is, upon determining an object type is a person, the motion control unit identifies a range of the person and subsequently sets/computes a moving range based on the object type.) Regarding claim 7 (similarly claim 19), the combination of Nishida in view of Kocer teaches The method of claim 5 (similarly claim 18), further comprising : Nishida further teaches determining the threshold distance further based on one or more of: a speed of the work machine, a direction the work machine is moving, a work machine type associated with the work machine, and dimensions of the work machine. (see at least Fig. 7-10 [0044-0067]: the distance specifying unit specifies the relative shortest distance between the obstacle and the moving unit (i.e. work machine) by calculation. The shortest relative distance from the person to the moving unit is calculated according to the range of the person specified by the person identified unit and the range of the moving unit (i.e. a work machine type associated with work machine). The relative distance Ds from the person to the moving portion of the work machine is specified by the plane coordinates of the measurement points and the person and the angle from the image sensor) Regarding claim 8, the combination of Nishida in view of Kocer teaches The method of claim 1, Nishida further teaches wherein performing the safety action includes one or more of: generating an audio alert, generating a haptic alert, adjusting a speed of the work machine, stopping the work machine, and adjusting a direction of the work machine. (see at least Fig. 8, 10 [0046-0052, 0061-0066]: The avoidance control unit controls the moving unit to reduce the moving speed of the moving unit, stopping the rotation of the vehicle body, stopping and/or changing a movement course of the movement unit) . 07-21-aia AIA Claim (s) 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Nishida in view of Kocer and Townsend et al. (US 2022/0012911 A1 hereinafter Townsend) . Regarding claim 3 (similarly claim 15), the combination of Nishida in view of Kocer teaches The method of claim 2 (similarly claim 14), The combination of Nishida in view of Kocer does not explicitly teach wherein the angle between the camera and the object of interest is determined based on pixels representative of the object of interest in the image and features of the camera. Townsend is directed to system and method for analyzing an image to determine spacing between a person and an object, Townsend teaches wherein the angle between the camera and the object of interest is determined based on pixels representative of the object of interest in the image and features of the camera. (see at least Fig. 1 [0019-0023]: the distance from the position of camera to the person is determined/calculated based on known focal length of the camera, the height in pixels of the person from image, the height of images in pixel, the height of the camera, and the height of the person; and determining an angle between the first direction or line from the camera to the person) Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nishida and Kocer to incorporate the technique of determining the angle between the image sensor and the object based on pixels representative of the object in the image and features of the camera as taught by Townsend with reasonable expectation of success to ensure adequate spacing between object and person for safety and/or security in work place. (Townsend, [0002]) . 07-21-aia AIA Claim (s) 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Nishida in view of Kocer and Deyle et al. (US 2019/0329421 A1 hereinafter Deyle) . Regarding claim 5 (similarly claim 17), the combination of Nishida in view of Kocer teaches The method of claim 2 (similarly claim 14), The combination of Nishida in view Kocer does not explicitly teach wherein the height associated with the object of interest and the uncertainty associated with the height is determined based on an identifying tag on the object of interest. Deyle is directed to wireless tag detection and localization by a mobile robot, Deyle teaches wherein the height associated with the object of interest and the uncertainty associated with the height is determined based on an identifying tag on the object of interest. (see at least [0129]: the robot can be configured to detect and identify individuals where the robot can capture images or videos of the individuals using the cameras and can identify a height or size of the individual or the robot scan a badge of the individual and the individual can be identified with identity database. That is, the robot can determine and/or retrieve an individual’s height or size by identifying a tag on the individual.) Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nishida and Kocer to incorporate the technique of determining the height associated with the object is determined based on an identifying tag on the object as taught by Deyle with reasonable expectation of success to identify an object indicating potential risk to ensure operation safety . 07-21-aia AIA Claim (s) 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Nishida in view of Kocer and Haneda et al. (US 2019/0313576 A1 hereinafter Haneda) . Regarding claim 9, the combination of Nishida in view of Kocer teaches The method of claim 1, The combination of Nishida in view of Kocer does not explicitly teach receiving a location of the work machine at a time at which the image was captured; determining potential locations of the object of interest based on the range of potential distances between the work machine and the object of interest; and updating a map of the environment to include the potential locations of the object of interest. Haneda is directed to control device for autonomous work machine, Haneda teaches receiving a location of the work machine at a time at which the image was captured; (see at least [0074-0080]: the position calculating section acquires positional information indicating a position of the lawn mower at the time it captured the image) determining potential locations of the object of interest based on the range of potential distances between the work machine and the object of interest; (see at least [0074-0080]:the position calculating section calculates a position of an object in an image based on positional information associated with image data, an image capturing condition of the image (e.g. angle of view, at least one of pan angle, tilt angle and a roll angle, zoom factor and the like), and the geometrical arrangement of an image-capturing device in the lawn mower) and updating a map of the environment to include the potential locations of the object of interest . (see at least [0123-0125]: the map generating section may acquire positional information indicating a position of an object in the image, and generate map information associating the positional information of the object(s)). Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nishida and Kocer to incorporate the technique of receiving a location of the work machine at a time at which the image was captured; determining potential locations of the object based on the range of potential distances between the work machine and the object; and updating a map of the environment to include the potential locations of the object. updating a map of the environment to include the potential locations of the object as taught by Haneda with reasonable expectation of success to update obstacle locations in a map database to ensure operation safety and efficiency. Regarding claim 10, the combination of Nishida in view of Kocer and Haneda teaches The method of claim 9, The combination of Nishida in view of Kocer does not explicitly teach wherein the map of the environment is further updated to include the object type associated with the object of interest. Haneda is directed to control device for autonomous work machine, Haneda teaches wherein the map of the environment is further updated to include the object type associated with the object of interest. (see at least [0123-0125]:the map generating section acquires position information indicating a position of an object in the image and (ii) information about at least one of a plant, an animal, a microorganism, soil and waste that are included in each image) Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nishida and Kocer to include an object type associated with the object in map of the environment as taught by Haneda with reasonable expectation of success to update obstacle locations in a map database to ensure operation safety and efficiency . 07-21-aia AIA Claim (s) 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Nishida in view of Kocer and Hurd et al. (US 2020/0326715 A1 hereinafter Hurd) . Regarding claim 11, the combination of Nishida in view of Kocer teaches The method of claim 1, further comprising: The combination of Nishida in view of Kocer does not explicitly teach responsive to determining the range of potential distances between the work machine and the object of interest, updating a training dataset for a machine learning model to include the range of potential distances, the image, and camera features. Hurd is directed to safety system for autonomous operation of agricultural vehicles using machine learning for detection and identification of object, Hurd teaches responsive to determining the range of potential distances between the work machine and the object of interest, updating a training dataset for a machine learning model to include the range of potential distances, the image, and camera features. (see at least Fig. 2 [0042-0046]: The neural network are constantly being trained to learn how to discern and distinguish items encountered by the autonomous agricultural machine as input data as input data is collected and as objects and terrain are recognized, characterized and confirmed wherein the neural network are capable of utilizing image data collected from camera and/ thermal imaging device to identify object and/or terrain in camera images by analyzing pixels from image data to determine spatial attribute such as distance to and movement of objects.) Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nishida and Kocer to incorporate the technique of updating a training dataset for a machine learning model to include the range of potential distances, the image, and camera features in response to determining the range of potential distances between the work machine and the object as taught by Hurd with reasonable expectation of success to enable autonomous or driverless vehicles to safely navigate through unpredictable operating conditions (Hurd, [0002]). Regarding claim 12, the combination of Nishida in view of Kocer and Hurd teaches The method of claim 11, further comprising: The combination of Nishida in view of Kocer does not explicitly teach training the machine learning model using the training dataset, the machine learning model configured to receive as input a new image including a given object of interest and information associated with camera features corresponding a camera that captured the new image and to output a predicted distance to the given object of interest. Hurd is directed to safety system for autonomous operation of agricultural vehicles using machine learning for detection and identification of object, Hurd teaches training the machine learning model using the training dataset, the machine learning model configured to receive as input a new image including a given object and information associated with camera features corresponding a camera that captured the new image and to output a predicted distance to the given object. (see at least Fig. 2 [0042-0046]: the neural network are constantly being trained to learn how to discern and distinguish items encountered by the autonomous agricultural machinery as input data is collected. A trajectory of the objects is calculated to further characterize the object and help determine the operational state of the autonomous agricultural machinery. The process then determines whether an operational state of the autonomous agricultural machinery must be altered in response to the calculated drivable pathway by applying the information obtained regarding any objects or terrain characteristics.) Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Nishida and Kocer to incorporate the technique of training the machine learning model using the training dataset, the machine learning model configured to receive an image including an object and information associated with camera features corresponding a camera that captured the image as input and to output a predicted distance to the object as taught by Hurd with reasonable expectation of success to enable autonomous or driverless vehicles to safely navigate through unpredictable operating conditions (Hurd, [0002]). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANA F ARTIMEZ whose telephone number is (571)272-3410. The examiner can normally be reached M-F: 9:00 am-3:30 pm EST. 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, Faris S. Almatrahi can be reached at (313) 446-4821. 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. /DANA F ARTIMEZ/Examiner, Art Unit 3667 /FARIS S ALMATRAHI/Supervisory Patent Examiner, Art Unit 3667 Application/Control Number: 19/191,804 Page 2 Art Unit: 3667 Application/Control Number: 19/191,804 Page 3 Art Unit: 3667 Application/Control Number: 19/191,804 Page 4 Art Unit: 3667 Application/Control Number: 19/191,804 Page 5 Art Unit: 3667 Application/Control Number: 19/191,804 Page 6 Art Unit: 3667 Application/Control Number: 19/191,804 Page 7 Art Unit: 3667 Application/Control Number: 19/191,804 Page 8 Art Unit: 3667 Application/Control Number: 19/191,804 Page 9 Art Unit: 3667 Application/Control Number: 19/191,804 Page 10 Art Unit: 3667 Application/Control Number: 19/191,804 Page 11 Art Unit: 3667 Application/Control Number: 19/191,804 Page 12 Art Unit: 3667 Application/Control Number: 19/191,804 Page 13 Art Unit: 3667 Application/Control Number: 19/191,804 Page 15 Art Unit: 3667 Application/Control Number: 19/191,804 Page 16 Art Unit: 3667
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