DETAILED ACTION
Claims 1-20 are pending. Claims dated 07/13/2026 are being examined.
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 .
Response to Arguments
35 U.S.C. § 101:
Applicant’s arguments filed 07/13/2026 with respect to the 101 rejections, in view of the amendments, have been considered, but they are not fully persuasive (but some dependent claims are patent eligible as reasoned herein).
Step 2A, Prong One:
Examiner maintains the bolded elements as outlined in the 101 section constitute mental processes. In pages 11-12 of remarks, Applicant argues the human mind is not capable of obtaining image frames from an imaging device, but the steps to obtain image frames were not treated as mental processes and were evaluated as additional elements.
Step 2A, Prong Two:
Examiner treats the steps to obtain image frames as data gathering. In pages 11-12 of remarks, Applicant argues the features of utilizing the stored parameters of the first mathematical representation to establish an initial search region for identifying the line-based features in the second image frame, provides a particular technical solution “eliminating full-frame re-initialization and reducing the computation required to localize the features”, and further the determining whether the second mathematical representation is valid, “so that an invalid new fit is discarded and retained prior parameters carry the tracking forward without reinitialization” is an improvement in the functioning of the visual tracking technology. The Examiner treats these steps under Step 2A, Prong One – mental processes.
With respect to claim 11-20, the machine elements and computer elements were evaluated and Examiner maintains these additional elements that do not integrate the recited judicial exception into a practical application.
In page 12 of remarks, Applicant argues in particular claims 7 and 15 regarding a control step. Previously, as per Applicant’s specification [0098] disclosing a driver controlling steering, the control of steering based on the lines of best fit under broadest reasonable interpretation (BRI), may merely reflect use of the abstract idea as information to guide human decision making, encompassing simply presenting the generated lines for a human to observe and act upon, and such controlling amounts to insignificant extra-solution activity. However, claim 1 was amended such that the claim no longer merely recites generating information or providing guidance for use by a human operator. Instead, the claim affirmatively recites one or more processors to perform the controlling, and thus the judicial exception is integrated into a practical application because the abstract idea is applied to control the operation of a physical machine. Accordingly, claims 7 and 15 are patent eligible. Claim 16 recites a steering control system configured to guide the vehicle, but as per above, the BRI of this limitation encompasses operation by a human operator (i.e., there is absent any requirement of an “autonomous” guiding operation or the guiding being done by processors) and does not overcome the 101 rejections.
Step 2B:
Examiner maintains the underlined additional elements are well-understood, routine, and conventional. Page 12 of remarks asserts the additional elements require a finding that “prior-frame parameters establishing the search region for the next frame, validity gating, retention of prior parameters upon an invalid fit, and steering control from the resulting line” must be shown under this analysis, but these limitations were treated as part of the abstract idea, not as additional elements.
35 U.S.C. § 103:
Applicant’s arguments filed 07/13/2026 with respect to claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 9, claim 9 recites “the stored parameters” but because claim 1 relies on 2 different stored parameters (stored parameters defining the first mathematical representation vs. stored parameters defining the second mathematical representation) it is not clear which of the two (or is different from the two) this limitation is referring to. For examination, this limitation is interpreted as the stored parameters of the first mathematical representation.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Claim 1) A method for tracking line-based features across sequential image frames, the method performed by one or more processors, comprising:
obtaining a first image frame from an imaging device;
identifying, in the first image frame, a plurality of line-based features, wherein the line-based features comprise straight or curved lines;
generating a first mathematical representation of the line-based features in the first image frame; storing parameters defining the first mathematical representation;
obtaining a second image frame from the imaging device;
utilizing the stored parameters of the first mathematical representation to establish an initial search region for identifying the line-based features in the second image frame;
identifying, in the second image frame, the plurality of line-based features based on the initial search region; and
generating a second mathematical representation of the line-based features in the second image frame;
determining whether the second mathematical representation is valid;
when the second mathematical representation is determined to be valid, storing parameters defining the second mathematical representation for use in establishing an initial search region in a third image frame; and
when the second mathematical representation is determined not to be valid, discarding the second mathematical representation and retaining the stored parameters defining the first mathematical representation for use in establishing the initial search region in the third image frame.
101 Analysis – Step 1:
Independent claim 1 is directed to a method. Therefore, claim 1 is within at least one of the four statutory categories. Claim 1 will be used as a representative claim for the remainder of the 101 rejections.
101 Analysis – Step 2A, Prong I:
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
The Examiner submits that the foregoing bolded limitation(s) constitute “mental processes” – concepts performed in the human mind with the aid of pen and paper (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III) because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind, and/or directed towards mathematical concepts, i.e., determining a line of best fit using least-squares regression.
For example, ‘identifying…”, “generating…”, “storing…”, “utilizing…”, “identifying…”, and “generating…” encompasses a person with the aid of pen and paper identifying data points on an image and calculating a line of best fit (claimed “first mathematical representation”). A similar operation can be done on the second image aided by stored line of best fit parameters (claimed “second mathematical representation”). The claims are directed towards generating a line of best fit based on a previous line of best fit, and these determining of lines of best fit (e.g. via least squares regression) are fundamental mathematical operation(s) that encompasses operations performed mentally with pen and paper.
The steps including and following determining the validity of the mathematical representation, encompasses a person with the aid of pen and paper excluding or including data to avoid issues with outliers, noisy data points, or invalid image frames. Data filtering and organization encompasses operations performed mentally with pen and paper. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II:
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea(s) into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
For the following reason(s), the Examiner submits that the above identified additional elements do not integrate the above-noted abstract idea into a practical application.
Regarding, the additional limitations of “the method performed by one or more processors”, the processor is a generic computer component and also acts merely as a tool to perform the aforementioned abstract ideas and do not amount to significantly more than the judicial exception. See MPEP 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception.
The additional limitations of obtaining the first and second image frames amounts to mere data gathering which is a form of insignificant extra-solution activity. It has been held that limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea, see MPEP 2106.05.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, that reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B:
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field.
Regarding the computer elements:
As discussed with respect to Step 2A Prong Two, the additional elements of one or more processors in the claim amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)). The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding the data gathering steps:
It has been determined that such limitations are conventional as they merely consist of data gathering and data transmitting which are recited at a high level of generality. See OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); or buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Independent claims 11 and 16 are rejected for similar reasons as disclosed above, directed towards the abstract idea of generating lines of best fit. The additional elements of a processor in the claim amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)). The claims are specified to work in an agricultural environment with the line-based features being crop rows, but these limitations merely serve to generally link the use of the judicial exception to a particular technological environment or field of use, specifically to an agricultural environment. It has been held that limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (MPEP 2106.05(h)). Furthermore, Applicant’s specification [0100] discloses that the tracking of crop rows is not limiting, and may be applied to “tracking lane markings” etc., suggesting that the environment in which the line-based features are identified are not crucial to the claimed generation of a first/second mathematical representation.
Dependent claims 2-6, 8-10, 12-14, and 17-20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the generation of lines of best fit, and reciting/elaborating on additional insignificant extra-solution activities (data gathering and post-solution activities) or further describing machine(s) that contribute(s) only nominally or insignificantly to the execution of the claimed method (generally linking the field of use to an agricultural environment), all of which are well-known as exemplified by the cited art and case law herein. Further, as per Applicant’s specification [0098] disclosing a driver controlling steering, the control of steering based on the lines of best fit merely reflects use of the abstract idea as information to guide human decision making, encompassing simply presenting the generated lines for a human to observe and act upon, and such controlling amounts to insignificant extra-solution activity.
Allowable Subject Matter
Claims 1-10 would be allowable (with the rejected claims rewritten to overcome all rejection(s) under 35 U.S.C. 101 and/or 35 U.S.C. 112(b), set forth in this Office Action and to include all the limitations of the base claim and any intervening claims).
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 1, the prior arts on record do not teach, describe, and/or suggest all the limitations as presented in the claim as a whole – specifically “determining whether the second mathematical representation is valid; when the second mathematical representation is determined to be valid, storing parameters defining the second mathematical representation for use in establishing an initial search region in a third image frame; and when the second mathematical representation is determined not to be valid, discarding the second mathematical representation and retaining the stored parameters defining the first mathematical representation for use in establishing the initial search region in the third image frame”.
Claims 2-10 are also potentially allowable as they are dependent on potentially allowable claim 1.
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 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 11-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hou (CN-111539303-A), in view of Zhang et al. (“Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method”, https://doi.org/10.1016/j.compag.2018.09.014.) and herein after will be referred to as Hou and Zhang, respectively.
Regarding claim 11, Hou teaches a method for tracking […] rows in […] environments, the method performed by one or more processors, comprising:
obtaining a first image frame from an imaging device mounted on a vehicle ([0011] S1, collects video data while driving; [0012] S2, determine the distortion parameters of the acquisition device used in S1, and perform distortion correction on the video data acquired in S1 based on the distortion parameters; [0013] S3, construct a lane line detection model and extract lane line images frame by frame);
identifying, in the first image frame, a plurality of […] rows ([0016] S33, perform pixel statistics on the lane line extraction results of S32 to obtain the horizontal distribution map of the pixel points of the left and right lanes, and obtain the initial position of the left and right lane lines based on the left and right peak values);
generating a first best fit line representing a lane […] in the first image frame ([0017] S34. Based on the initial positions of the left and right lane lines obtained in S33, a sliding window method is used to fit a quadratic polynomial to the pixels in the window using the least squares method; [0059] Figure 7b shows the fitting results of the lane line function on the left and right sides);
storing parameters defining the first best fit line ([0018] S35: Create a buffer to store the coefficients of the quadratic polynomial of the previous frame);
obtaining a second image frame from the imaging device ([0018] …next frame);
utilizing the stored parameters of the first best fit line to establish an initial location for identifying […] rows in the second image frame ([0018] S35: Create a buffer to store the coefficients of the quadratic polynomial of the previous frame);
identifying, in the second image frame, the plurality of […] rows based on the initial location ([0018] In the next frame, use the quadratic polynomial of the previous frame to search for nearby lane line pixels. Then, use the least squares method to construct a new fitting quadratic polynomial); and
generating a second best fit line representing the lane […] in the second image frame ([0018] Finally, update the coefficients of the new quadratic polynomial in the buffer for detection in the next frame).
Hou does not explicitly teach that the rows are “crop” rows in an “agricultural” environment, and that the best fit lines are “positioned within the lane between the adjacent crop rows”.
However, Zhang teaches that the rows are “crop” rows in an “agricultural” environment, and that the best fit lines are “positioned within the lane between the adjacent crop rows” (Abstract: Finally, a linear regression method based on least squares was employed to fit the crop rows; see also FIG. 11(b) below).
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FIG. 11(b) where the line of best fit is between a left crop row and a right crop row
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify the detected line-based features of the lane lines as taught in Hou, as modified, to substitute crop rows as taught in Zhang, with a reasonable expectation of success as the underlying principles of detecting path features (e.g. lane lines or crop rows), and determining a line of best fit are the same in both contexts. Furthermore, Applicant’s specification [0100] discloses that the tracking of crop rows is not limiting, and may be applied to “tracking lane markings” etc. The substitution of crop rows for lane lines merely involves applying a known technique (line fitting) to a known analogous structure (crop rows instead of lane lines), yielding no more than predictable results (obtaining a line of best fit). Further, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify Hou to incorporate the teachings of Zhang to include the best fit lines to be positioned within the lane between the two adjacent crop rows, with a reasonable expectation of success because crop rows are conventionally planted in parallel adjacent rows, and it is well-known at the time of filing to detect three crop rows as taught in Zhang (Section 2.1. Image Acquisition). The modification merely applies the known spatial arrangement of the crop rows from Zhang to the known line of best fit determination of Hou, resulting in the predictable use of prior art elements according to their established functions.
Regarding claim 12, Hou, as modified, teaches the method of claim 11.
Hou also teaches wherein the parameters defining the first best fit line comprise coefficients of a polynomial equation ([0017] S34. Based on the initial positions of the left and right lane lines obtained in S33, a sliding window method is used to fit a quadratic polynomial to the pixels in the window using the least squares method; [0059] Figure 7b shows the fitting results of the lane line function on the left and right sides).
Regarding claim 13, Hou, as modified, teaches the method of claim 11.
Hou also teaches further comprising: tracking multiple lanes simultaneously by generating separate best fit lines for each lane visible in the image frames ([0017] S34. Based on the initial positions of the left and right lane lines obtained in S33, a sliding window method is used to fit a quadratic polynomial to the pixels in the window using the least squares method; [0059] Figure 7b shows the fitting results of the lane line function on the left and right sides).
Regarding claim 14, Hou, as modified, teaches the method of claim 11.
Hou, as modified, also teaches further comprising: applying a perspective warp to at least one of the first image frame and the second image frame to generate a bird's eye view of the crop rows (Hou [0022] In S32, a perspective matrix is used to convert the lane line area into a bird's-eye view; see rejection of claim 11 cited to Meier teaching the substitution of lane line features as crop rows).
Regarding claim 15, Hou, as modified, teaches the method of claim 11.
Hou also teaches further comprising further comprising: controlling steering of the vehicle based on the second best fit line to navigate the vehicle along the lane between adjacent crop rows (Hou [0099] In step S5, during vehicle operation, the camera lens distortion parameters obtained in step S2 are used to correct the video data from the dashcam. The model constructed in step S3 is used to detect lane lines in the video, and a quadratic polynomial fitting the lane lines is output. The distance calculation method in step S4 is used to calculate the deviation distance, lane curvature radius, and road direction, and to determine whether the safe distance is exceeded. If the safe distance is not exceeded, the lane line is marked in green; if it is exceeded, it is marked in red, thus providing a warning signal, as shown in Figures 8a and 8b; see rejection of claim 11 cited to Meier teaching the substitution of lane line features as crop rows).
Regarding claim 16, Hou teaches a system for visual tracking of […] rows in […] environments, comprising:
a vehicle configured to traverse a […] field ([0021] A vehicle-mounted monocular camera is used to collect driving video data);
an imaging device mounted on the vehicle and configured to capture sequential image frames of […] rows ([0011] S1, collects video data while driving; [0012] S2, determine the distortion parameters of the acquisition device used in S1, and perform distortion correction on the video data acquired in S1 based on the distortion parameters; [0013] S3, construct a lane line detection model and extract lane line images frame by frame);
a processor communicatively coupled to the imaging device and configured to: obtain a first image frame from the imaging device ([0011] S1, collects video data while driving; [0012] S2, determine the distortion parameters of the acquisition device used in S1, and perform distortion correction on the video data acquired in S1 based on the distortion parameters; [0013] S3, construct a lane line detection model and extract lane line images frame by frame);
identify, in the first image frame, a plurality of […] rows ([0016] S33, perform pixel statistics on the lane line extraction results of S32 to obtain the horizontal distribution map of the pixel points of the left and right lanes, and obtain the initial position of the left and right lane lines based on the left and right peak values);
generate a first best fit line representing a lane […] in the first image frame ([0017] S34. Based on the initial positions of the left and right lane lines obtained in S33, a sliding window method is used to fit a quadratic polynomial to the pixels in the window using the least squares method; [0059] Figure 7b shows the fitting results of the lane line function on the left and right sides);
store parameters defining the first best fit line ([0018] S35: Create a buffer to store the coefficients of the quadratic polynomial of the previous frame);
obtain a second image frame from the imaging device ([0018] …next frame);
utilize the stored parameters of the first best fit line to establish an initial location for identifying […] rows in the second image frame ([0018] S35: Create a buffer to store the coefficients of the quadratic polynomial of the previous frame);
identify, in the second image frame, the plurality of […] rows based on the initial location; and ([0018] In the next frame, use the quadratic polynomial of the previous frame to search for nearby lane line pixels. Then, use the least squares method to construct a new fitting quadratic polynomial)
generate a second best fit line representing the lane […] in the second image frame; and ([0018] Finally, update the coefficients of the new quadratic polynomial in the buffer for detection in the next frame)
a steering control system configured to guide the vehicle based on the second best fit line ([0099] In step S5, during vehicle operation, the camera lens distortion parameters obtained in step S2 are used to correct the video data from the dashcam. The model constructed in step S3 is used to detect lane lines in the video, and a quadratic polynomial fitting the lane lines is output. The distance calculation method in step S4 is used to calculate the deviation distance, lane curvature radius, and road direction, and to determine whether the safe distance is exceeded. If the safe distance is not exceeded, the lane line is marked in green; if it is exceeded, it is marked in red, thus providing a warning signal, as shown in Figures 8a and 8b; see rejection of claims 4 and 5 cited to Meier teaching the agricultural vehicle and crop rows).
Hou does not explicitly teach that the rows are “crop” rows in an “agricultural” environment, and that the best fit lines are “positioned within the lane between the adjacent crop rows”.
However, Zhang teaches that the rows are “crop” rows in an “agricultural” environment, and that the best fit lines are “positioned within the lane between the adjacent crop rows” (Abstract: Finally, a linear regression method based on least squares was employed to fit the crop rows; see also FIG. 11(b) below).
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FIG. 11(b) where the line of best fit is between a left crop row and a right crop row
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify the detected line-based features of the lane lines as taught in Hou, as modified, to substitute crop rows as taught in Zhang, with a reasonable expectation of success as the underlying principles of detecting path features (e.g. lane lines or crop rows), and determining a line of best fit are the same in both contexts. Furthermore, Applicant’s specification [0100] discloses that the tracking of crop rows is not limiting, and may be applied to “tracking lane markings” etc. The substitution of crop rows for lane lines merely involves applying a known technique (line fitting) to a known analogous structure (crop rows instead of lane lines), yielding no more than predictable results (obtaining a line of best fit). Further, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify Hou to incorporate the teachings of Zhang to include the best fit lines to be positioned within the lane between the two adjacent crop rows, with a reasonable expectation of success because crop rows are conventionally planted in parallel adjacent rows, and it is well-known at the time of filing to detect three crop rows as taught in Zhang (Section 2.1. Image Acquisition). The modification merely applies the known spatial arrangement of the crop rows from Zhang to the known line of best fit determination of Hou, resulting in the predictable use of prior art elements according to their established functions.
Regarding claim 17, Hou, as modified, teaches the system of claim 16.
Hou also teaches wherein the imaging device comprises at least one of: a color camera, a depth camera, a lidar sensor, or an ultrasonic sensor ([0014] S31, weighted detection and recognition of yellow and white lane lines in HSL color space; [0021] A vehicle-mounted monocular camera is used to collect driving video data).
Regarding claim 19, Hou, as modified, teaches the system of claim 16.
Hou, as modified, also teaches wherein the processor is further configured to: maintain tracking of the crop rows during vehicle turns by compensating for changes in apparent position of crop rows within the image frames (Hou [0099] The model constructed in step S3 is used to detect lane lines in the video, and a quadratic polynomial fitting the lane lines is output. The distance calculation method in step S4 is used to calculate the deviation distance, lane curvature radius, and road direction, and to determine whether the safe distance is exceeded. If the safe distance is not exceeded, the lane line is marked in green; if it is exceeded, it is marked in red, thus providing a warning signal, as shown in Figures 8a and 8b; Hou [0100] the present invention can accurately calculate the position and deviation distance of the lane line in both straight and curved driving, and can provide early warning of whether the deviation from the lane line exceeds the safe distance. It can also resist certain adverse conditions such as changes in lighting and road shadows, and has strong robustness. It can be widely used in the safety warning links of various advanced driver assistance systems; see rejection of claim 16 cited to Zhang teaching the substitution of lane line features as crop rows).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Hou, in view of Zhang, in view of Boon (US-20100054538-A1) and herein after will be referred to as Boon.
Regarding claim 18, Hou, as modified, teaches the system of claim 16.
Hou, as modified, does not explicitly teach wherein the processor is further configured to: filter best fit line data across multiple sequential image frames to improve robustness to missing or degraded frames.
However, Boon teaches wherein the processor is further configured to: filter best fit line data across multiple sequential image frames to improve robustness to missing or degraded frames ([0147] When an acceptable fit is found, the blind frame counter is reset to zero; [0148] The process 200 involves updating the blind frame and blind stretch values, and ceasing tracking if either value becomes too large. When tracking is ceased, the system enters back into search state, and restores the boundary/marking finding window or ROI to its maximum size and default location to search for markings anew without relying on an expected location for the markings based on marking detection in prior images; supported by [0143]-[0146] – Examiner interprets a skipping of the blind frames which occurs when the line fit is not acceptable is a filtering).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify Hou to incorporate the teachings of Boon to include wherein the processor is further configured to: filter best fit line data across multiple sequential image frames to improve robustness to missing or degraded frames, with a reasonable expectation of success since doing so would have achieved the benefit of better tracking by “accounting for blind frames and avoid using frame that did not produce an identifiable or acceptable lane marking or boundary, measured from the time of the last frame that did produce an acceptable lane marking or boundary” (Boon [0058]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Hou, in view of Zhang, in view of Fu (US-20210090274-A1), and herein after will be referred to as Fu.
Regarding claim 20, Hou, as modified, teaches the system of claim 16.
Hou does not explicitly teach further comprising: a spraying system configured to apply treatment to vegetation elements identified as weeds based on their position relative to the second best fit line.
However, Fu teaches further comprising: a spraying system configured to apply treatment to vegetation elements identified as weeds ([0200] Actuating a treatment mechanism (e.g., treatment mechanism 120) may include, for example, actuating a spray mechanism; [0206] Similarly, the control system 130 may select weeds above a threshold height for treatment, or with a more aggressive treatment).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify Hou, as modified, to incorporate the teachings of Fu to include further comprising: a spraying system configured to apply treatment to vegetation elements identified as weeds, with a reasonable expectation of success since doing so would have achieved the benefit of treating the weeds.
Hou does not explicitly teach vegetation elements identified as weeds based on their position relative to the second best fit line.
However, Zhang teaches vegetation elements identified as weeds based on their position relative to the second best fit line (Section 3. Results and Discussion: It is obvious that our method can better extract the crops even if the weed pressure is extremely high in the image in Fig. 1(c). This means that the proposed segmentation methods are appropriate for high weed densities and improves the accuracy of the posterior crop-row detection; Table 2 shows the detection accuracy for the various detection methods for various weed pressures; Section 3.2 All of this can be attributed to the shortest path method, which extracts the feature points closest to the real crop line and excludes the points considered as outliers from the estimate. Therefore, this method exhibits outstanding performance under high weed-pressure conditions; supported by FIGs. 9-10 with blue dots excluded from line –under broadest reasonable interpretation the weed exclusion from regression due to being an outlier/lies away from crop row reads on “based on their position relative to the second best fit line”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify Hou, as modified, to incorporate the teachings of Zhang to include that vegetation elements are identified as weeds based on their position relative to the second best fit line, with a reasonable expectation of success since doing so would have achieved the benefit of “improved fitting accuracy”, which exhibits outstanding performance under high weed-pressure conditions (Zhang Section 3.2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240202887 A1: Chien also discloses use of a previous image frame’s line of best fit to determine an initial search region in the next image frame
[0061] The method described in this embodiment considers a continuity of the lane line and does not require a complete window search for each frame of the foreground image. After processing a frame at the first moment, the region where the lane line is located at the second moment can be predicted based on the left lane line and the right lane line obtained at the first moment.
[0062] Based on characteristics of the left lane line and the right lane line, the region where the lane lines of the second moment are located is obtained based on the lane lines obtained at the first moment, thereby improving the efficiency of detecting the lane lines.
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 extension fee 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 date of this final action.
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/DAVIN SEOL/Examiner, Art Unit 3662
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