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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on September 4, 2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
In response to the Office Action mailed on June 5, 2026, independent claims 102 and 116 have been amended. Claims 103 and 117 have been canceled. No new matter was added. Accordingly, claims 102, 104-116, and 118-124 are currently pending.
Response to Arguments
Applicant’s arguments with respect to claim 102-244 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.
Applicant has amended independent claims 102 and 116 to include the following the subject matter recited in canceled claim 103:
wherein the pre-trained machine learning model is not retrained between generating the sample embedding and generating the candidate embedding
Applicant argues Petro fails to disclose or suggest the pre-trained machine learning model is not retrained between generating the sample embedding and generating the candidate embedding. Remarks, pages 6-7.
Applicant's submission of an information disclosure statement under 37 CFR 1.97(c) with the timing fee set forth in 37 CFR 1.17(p) on September 4, 2026, prompted the new ground(s) of rejection presented in this Office action.
Independent claims 102 and 116 are rejected under 35 U.S.C. 103 as being unpatentable over Petro et al., US 20240032525 A1 (hereinafter referred to as “Petro”) in view of Oami et al., US 20200034630 A1, (hereinafter referred to as “Oami”).
The combination of Petro and Oami discloses wherein the pre-trained machine learning model is not retrained between generating the sample embedding and generating the candidate embedding. Oami discloses a motion model is updated (i.e., step S89) after receiving a frame, performing object detection, transforming the detection result into a common coordinate system, predicting position of the object and associating the target with the tracker (steps s81-S89). Oami, Fig. 8. ¶¶ [0154]- [0164].
Oami shows that a model updating or retaining may occur after generating a candidate embedding. It would have been obvious to one with ordinary skill in the art at the time of the invention was filed to modify Petro with not retraining the pre-trained machine learning model between generating the sample embedding and generating the candidate embedding, as taught by Oami, in order to improve object detection accuracy. Oami, ¶ [0006].
Claims 104-115, and 118-124 depend on claims 1 and 16 respectively and are therefore unpatentable at least by virtue of their dependency.
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 102, 104, 106, 107, 109-116, 118-120, and 122-124 are rejected under 35 U.S.C. 103 as being unpatentable over Petro et al., US 20240032525 A1 (hereinafter referred to as “Petro”) in view of Oami et al., US 20200034630 A1, (hereinafter referred to as “Oami”).
Regarding claim 102 (Currently Amended), Petro discloses a method comprising: providing a sample image of a sample object to a user (Petro: Acquire image, block 500, at a block 502, a person may sort the images into various categories, such as grass, weed, boundary, etc., and tag or label or highlight the images or a portion thereof for further processing in a supervised learning embodiment. ¶ [0055], Fig. 5); receiving an indication from the user identifying the sample object as a user-created object type (Petro: At a block 502, a person may sort the images into various categories, such as grass, weed, boundary, etc., and tag or label or highlight the images or a portion thereof for further processing in a supervised learning embodiment. ¶ [0055], Fig. 5); generating a sample embedding describing the sample object by analyzing the sample image with a pre-trained machine learning model (Petro: computing system is configured to build a model based on sample data (e.g., training data, a training corpus, etc.) comprising the images which are acquired and sorted. ¶[0056] ); associating the indication and the sample embedding with the sample object (Petro: Fig. 5, 502); defining a support set of images including the sample image, wherein the support set comprises one or more images of sample objects across a dynamic set of one or more object types including the user-created object type (Petro: computing system is configured to build a model based on sample data (e.g., training data, a training corpus, etc.) comprising the images which are acquired and sorted. ¶[0056]); receiving a candidate image of an object of interest (Petro: Acquire image, step 600 Fig. 6); generating a candidate embedding describing the object of interest by analyzing the candidate image with the pre-trained machine learning model (Petro: At a block 604, processing circuit 44 is configured to run an inference algorithm on the downsized image using a trained model, such as a model trained using a deep learning algorithm, such as the illustrative model described in FIG. 5. At a block 606, processing circuit 44 may be configured to analyze threshold results to determine if the acquired image contains grass, weeds, boundary, etc. ¶ [0062]); identifying the object of interest based at least in part by comparing the candidate embedding to one or more sample embeddings associated with the one or more sample objects in the images of the support set (Petro: At a block 604, processing circuit 44 is configured to run an inference algorithm on the downsized image using a trained model, such as a model trained using a deep learning algorithm, such as the illustrative model described in FIG. 5. At a block 606, processing circuit 44 may be configured to analyze threshold results to determine if the acquired image contains grass, weeds, boundary, etc. ¶ [0062]).
Petro does not explicitly disclose wherein the pre-trained machine learning model is not retrained between generating the sample embedding and generating the candidate embedding.
However, in the same field of endeavor, Oami discloses the claimed feature.
Oami discloses wherein the pre-trained machine learning model is not retrained between generating the sample embedding and generating the candidate embedding (The motion model is updated (i.e., step S89) after, receiving a first frame, setting a search area, transforming detection result into a common coordinate system, predicting position of the object and associating the target with the tracker (steps s81-S89). Oami, Fig. 8. ¶¶ [0154]- [0164]).
Oami shows that a model updating or retaining may occur after receiving a frame and generating candidate embedding. It would have been obvious to one with ordinary skill in the art at the time of the invention was filed to modify Petro with not retraining the pre-trained machine learning model between generating the sample embedding and generating the candidate embedding, as taught by Oami, in order to improve object detection accuracy. Oami, ¶ [0006].
Regarding claim 104 (Previously Presented), Petro discloses the method of claim 102, wherein the sample embedding comprises one or more predicted properties of the sample object (Petro: At a block 502, a person may sort the images into various categories, such as grass, weed, boundary, etc., and tag or label or highlight the images or a portion thereof for further processing in a supervised learning embodiment. ¶ [0055], Fig. 5), and wherein the candidate embedding comprises one or more predicted properties of the object of interest (Petro: At a block 606, processing circuit 44 may be configured to analyze threshold results to determine if the acquired image contains grass, weeds, boundary, etc. ¶ [0062]).
Regarding claim 106 (Previously Presented), Petro discloses the method of claim 104, wherein the one or more predicted properties of the candidate embedding comprises a first object score representing likelihood that the object of interest is a first object type (Petro: At a block 606, processing circuit 44 may be configured to analyze threshold results to determine if the acquired image contains grass, weeds, boundary, etc. ¶ [0062]).
Regarding claim 107 (Previously Presented), Petro discloses the method of claim 106, wherein the first object type is a crop and the method further comprises instructing an implement to not damage the object of interest (Petro: At a blocks 606 and 614).
Regarding claim 109 (Previously Presented), Petro the method of claim 104, wherein the one or more predicted properties of the candidate embedding further comprises a second object score representing likelihood that the object of interest is a second object type (Petro: At a block 606, processing circuit 44 may be configured to analyze threshold results to determine if the acquired image contains grass, weeds, boundary, etc. ¶ [0062]).
Regarding claim 110 (Previously Presented), Petro discloses the method of claim 109, wherein the second object type is a weed and the method further comprises instructing an implement to damage the object of interest (Petro: At a blocks 606 and 610).
Regarding claim 111 (Previously Presented), Petro discloses the method of claim 102, wherein comparing the candidate embedding to one or more sample embeddings comprises inputting the candidate embedding and one or more sample embeddings into a distance-based classification algorithm (Petro: The computing system may be configured to use one or more of a convolutional neural network, a Bayesian network, a nearest neighbor algorithm, reinforcement learning ¶ [0056]).
Regarding claim 112 (Previously Presented), Petro discloses the method of claim 111, wherein the distance-based classification algorithm comprises one or more of: a K-nearest neighbors’ algorithm, a decision tree algorithm, a random forest algorithm, or a neural network (Petro: The computing system may be configured to use one or more of a convolutional neural network, a Bayesian network, a nearest neighbor algorithm, reinforcement learning ¶ [0056]).
Regarding claim 113 (Previously Presented), Petro discloses the method of claim 102, wherein comparing the candidate embedding to one or more sample embeddings comprises utilizing one or more of: a support vector machine (SVM) algorithm, a classification algorithm, or regression algorithm (Petro: The system or method may further comprise applying he machine learning prediction model to predict a classification of a new image as comprising grass, a weed and/or a boundary. ¶ [0065]).
Regarding claim 114 (Previously Presented), Petro discloses the method of claim 102, further comprising collecting the sample image (Petro: Fig. 5 500).
Regarding claim 115 (Previously Presented), Petro discloses the method of claim 102, where the sample object and the object of interest are plants (Petro: ¶ [0062]).
Regarding claim 116 (Currently Amended), claim 102 is substantially similar to claim 116. Therefore, claim 116 is rejected for the same reasons as claim 102.
Regarding claim 118 (Previously Presented), claim 104 is substantially similar to claim 118. Therefore, claim 118 is rejected for the same reasons as claim 104.
Regarding claim 119 (Previously Presented), claim 106 is substantially similar to claim 1119. Therefore, claim 119 is rejected for the same reasons as claim 106.
Regarding claim 120 (Previously Presented), claim 109 is substantially similar to claim 120. Therefore, claim 120 is rejected for the same reasons as claim 109.
Regarding claim 122 (Previously Presented), claim 111 is substantially similar to claim 122. Therefore, claim 122 is rejected for the same reasons as claim 111.
Regarding claim 123 (Previously Presented), Petro discloses the system of claim 116, further comprising a camera configured to collect the sample image (Petro: FIG. 2B also illustrates a field of view 56 of camera 36 ¶ [0030]).
Regarding claim 124 (Previously Presented), Petro discloses the system of claim 116, further comprising a display configured to display the sample image (Petro: Acquire image, block 500, at a block 502, a person may sort the images into various categories, such as grass, weed, boundary, etc., and tag or label or highlight the images or a portion thereof for further processing in a supervised learning embodiment. ¶ [0055]).
2. Claims 105, 108 and 121 are rejected under 35 U.S.C. 103 as being unpatentable over Petro in view of Oami and Sibley et al., US 11425852 B2, (hereinafter referred to as “Sibley”).
Regarding claim 105 (Previously Presented), Petro and Oami do not explicitly disclose the method of claim 104, wherein the one or more predicted properties of the object of interest comprises at least one of size or shape of the object of interest.
However, Sibley discloses wherein the one or more predicted properties of the object of interest comprises at least one of size or shape of the object of interest (Sibley: the system 311 can determine the size and stage of growth of an agricultural object 302. Col. 10:20-30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Petro and Oami with wherein the one or more predicted properties of the object of interest comprises at least one of size or shape of the object of interest, as taught by Sibley, in order to improve the efficiency of plant classification and detection.
Regarding claim 108 (Previously Presented), Petro and Oami do not explicitly disclose the method of claim 106, wherein the first object type is a crop and the method further comprises instructing an implement to damage the object of interest.
However, Sibley discloses wherein the first object type is a crop and the method further comprises instructing an implement to damage the object of interest (Sibley: destroying the object, pruning or harvesting the object, or a combination thereof. Col: 10:49-62).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Petro and Oami with wherein the first object type is a crop and the method further comprises instructing an implement to damage the object of interest, as taught by Sibley, in order to improve the treatment of agricultural objects.
Regarding claim 121 (Previously Presented), Petro and Oami do not explicitly disclose the system of claim 116, further comprising a laser and a control system configured to direct the laser at the object of interest.
However, Sibley discloses a laser and a control system configured to direct the laser at the object of interest (Sibley: treatment system 311 having a device configured to sense an individual object and its stage of growth, access its treatment history, and ... shine a light source such as a laser onto the individual object. Col 10:49-62. Receive instructions to point and shine a laser, through the treatment head 472, to treat a target position and location on the ground terrain relative to the treatment unit 470. Col. 26:20-25).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Petro and Oami with a laser and a control system configured to direct the laser at the object of interest, as taught by Sibley, in order to order to improve the treatment of agricultural objects.
Conclusion
Applicant's submission of an information disclosure statement under 37 CFR 1.97(c) with the timing fee set forth in 37 CFR 1.17(p) on September 4, 2026, prompted the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 609.04(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEIRDRE L BEASLEY whose telephone number is (571)270-0452. The examiner can normally be reached Monday-Friday 8 a.m. -5 p.m.
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, Chris Kelley can be reached at (571) 272-7331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DLB/Patent Examiner, Art Unit 2482
/CHRISTOPHER S KELLEY/Supervisory Patent Examiner, Art Unit 2482
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