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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. GB2411023.1, filed on 07/26/2024.
Information Disclosure Statements
The information disclosure statements (IDS) submitted on 09/15/2025, 12/16/2025, and 05/21/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement are being considered by the examiner.
Status of Claims
Claims 1-20 filed on 07/16/2025 are presently examined.
Double Patenting
Claims 1-4, 6-16, 18, and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/271,776, hereinafter referred to as ‘776. Although the claims at issue are not identical, they are not patentably distinct from each other. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Regarding claim 1, ‘776 discloses this is claims 1 and 5.
Regarding claim 2, ‘776 discloses this is claims 1 and 2.
Regarding claim 3, ‘776 discloses this is claims 2 and 3.
Regarding claim 4, ‘776 discloses this is claim 8.
Regarding claim 6, ‘776 discloses this is claims 9.
Regarding claim 7, ‘776 discloses this is claim 1.
Regarding claim 8-10, ‘776 discloses this is claims 10-12.
Regarding claim 11, ‘776 discloses this is claim 11.
Regarding claim 12, ‘776 disclose this in claim 4.
Regarding claim 13-15, ‘776 discloses this is claims 13-15.
Regarding claim 16, ‘776 discloses this is claim 16.
Regarding claim 18, ‘776 discloses this is claim 18.
Regarding claim 20, ‘776 discloses this is claims 20 and 5.
Claims 5 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/271,776 in view of Yin et al. ("Toward City-Scale Litter Monitoring Using Autonomous Ground Vehicles," in IEEE Pervasive Computing, vol. 21, no. 3, pp. 74-83 doi: 10.1109/MPRV.2022.3152926.). This is a provisional nonstatutory double patenting rejection.
Regarding claim 5, ‘776 fails to explicitly disclose The guidance system of claim 1, wherein analyzing the image data comprises analyzing one or more heat signatures depicted within the image data (claim 2 discloses a short wave infrared (SWIR) camera).
However, Yin teaches analyzing the image data comprises analyzing one or more heat signatures depicted within the image data. ([pages 0075-0078, especially page 77 section “litter identification using sunlight”] autonomous vehicle with a mounted thermal camera uses thermal image data to identify litter materials);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify ‘776 with Yin’s teaching of an autonomous vehicle that identifies litter materials with a thermal camera. One would be motivated, with a reasonable expectation of success, to use a thermal camera in order to combine additional image data channels and image processing to improve object identification in the field.
17 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/271,776 in view of Cudrano et al. (“Detection and mapping of crop weeds and litter for agricultural robots” 2022 AEIT). This is a provisional nonstatutory double patenting rejection.
Regarding claim 17, ‘776 fails to explicitly disclose The method of claim 16, wherein identifying and classifying a piece of refuse comprises identifying and classifying one or more of a plastic bottle, a cola can, a bicycle, a plastic bag, a beer can, and another piece of debris.
However, Cudrano teaches identifying and classifying a piece of refuse comprises identifying and classifying one or more of a plastic bottle, a cola can, a bicycle, a plastic bag, a beer can, and another piece of debris (examiner notes “another piece of debris” is broad. [Cudrano V-A] teaches beer bottles, cans).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify ‘776 with Cudrano’s teaching of automatic object detection and classification including detection of waste on the field and mapping that waste to a geospatial field map. One would be motivated, with reasonable expectation of success, to perform autonomous image acquisition and waste reporting and generate a geospatial unwanted object map of the field in order to improve plantation efficiency and support automated collection and disposal of the waste (Cudrano [abstract, VII conclusions] it’s a cost effective system for autonomous image acquisition and waste recognition and mapping which supports future automated collection and disposal of the waste).
19 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 19/271,776 in view of Pinto et al. (macro-litter categories by a multi-class Neural Network Marine Pollution Bulletin). This is a provisional nonstatutory double patenting rejection.
Regarding claim 19, ‘776 discloses generating a geospatial map indicating a distribution of objects of interest throughout the geospatial map comprises indicating a distribution of different types of objects of interest throughout the geospatial map (claim 19) where the object may be refuse (claims 5 and 8).
‘776 may fail to explicitly disclose The method of claim 17, wherein generating a geospatial map indicating refuse distribution throughout the geospatial map comprises indicating a distribution of different types of refuse throughout the geospatial map.
However, Pinto teaches generating a geospatial map indicating refuse distribution throughout the geospatial map comprises indicating a distribution of different types of refuse throughout the geospatial map ([FIG 4b, page 6] maps produced by multiclass neural network. [section 3.2] discusses spatial distributions of the litter. Maps supply different type distributions of the litter.).
It would have been obvious to one of ordinary skill in the art to modify ‘776 with Pinto. One would be motivated with reasonable expectation of success to use Pinto’s teaching of categorizing different types of litter and their distribution within the geospatial map in order to advance automated categorization of litter to improve environment litter mapping (Pinto [page 2, left column] “a new perspective to advance the automated categorization of … litter on … images, for improving … litter mapping in the environment.”).
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 1-5 and 7-20 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 6 recites positive control over the agricultural vehicle based on received data, and is therefore eligible and not rejected under 35 USC 101. Examiner recommends Applicant integrate this feature recited in claim 6 into the independent claims to overcome the 35 USC 101 rejection.
101 Analysis – Step 1
Claims 1-15 are directed to a system comprising a processor, memory, instructions executed by the processor (i.e. a machine). Claims 16-19 are directed to a method. Claim 20 is directed to a vehicle (a machine). Therefore, claims 1-20 are within at least one of the four statutory categories.
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.
Independent claim 1 recites similar limitations as independent claims 16 and 20 and will be used as a representative claim.
Claim 1 is recited below and limitations that recite an abstract idea are emphasized in bolding below:
A guidance system for controlling operation of an agricultural vehicle, comprising:
at least one processor; and
at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the guidance system, during an agricultural process, to:
receive image data from an image sensor;
analyze the image data to identify and classify one or more pieces of refuse depicted within the image data;
receive GNSS location data;
responsive to identifying and classifying one or more pieces of refuse, log location data indicating locations of the one or more pieces of refuse; and
based at least partially on the image data and the logged location data, generate a geospatial map indicating locations of the one or more pieces of refuse on the geospatial map.
The examiner submits that the above bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. The bolded limitations in the context of this claim encompasses a person mentally analyzing image data to identify and classify one or more pieces of refuse depicted within the image data, logging or writing down the location data indicating the locations of the one or more pieces of refuse, and generating a geospatial map either mentally or with pen and paper indicating locations of the one or more pieces of refuse on said map. 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 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.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A guidance system for controlling operation of an agricultural vehicle, comprising:
at least one processor; and
at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the guidance system, during an agricultural process, to:
receive image data from an image sensor;
analyze the image data to identify and classify one or more pieces of refuse depicted within the image data;
receive GNSS location data;
responsive to identifying and classifying one or more pieces of refuse, log location data indicating locations of the one or more pieces of refuse; and
based at least partially on the image data and the logged location data, generate a geospatial map indicating locations of the one or more pieces of refuse on the geospatial map.
For the following reason(s), the examiner submits that the above underlined additional limitations do not integrate the above-noted abstract idea into a practical application.
The examiner submits that these additional limitations merely perform an insignificant extra-solution activity of data gathering, and a computer (processor, generic computer components) to perform otherwise mental judgements is not sufficient to integrate the abstract idea into a practical application.
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, 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. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processor or generic computer components to gather data and perform the otherwise mental judgements amounts to nothing more than applying the exception using generic computer components. Generally applying an exception using a generic computer component cannot provide an inventive concept. Further the additional limitations are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, merely use generic computer components in their ordinary capacity to perform an otherwise mental process or judgement, and do not amount to significantly more. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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).
Dependent claim 2-5, 7-15, and 17-19 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, merely use generic computer components in their ordinary capacity to perform an otherwise mental process or judgement or data gathering, and do not amount to significantly more.
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 1-4, 6, 8-9, 11, 15-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Eichhorn et al. (US 20220026226 A1) in view of Cudrano et al. (“Detection and mapping of crop weeds and litter for agricultural robots” 2022 AEIT), hereinafter referred to as Eichhorn and Cudrano, respectively.
Regarding claims 1, 16, and 20, Eichhorn discloses A guidance system for controlling operation of an agricultural vehicle, comprising:
at least one processor ([0050-0053] [Example 8] central processing unit in the vehicle operation system); and
at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor ([0050-0053] [0056-0058] [0077-0080] non-transitory memory and software executed by processors for the methods of agricultural sensing, recording, mapping, and guidance during field operations disclosed), cause the guidance system, during an agricultural process, to:
receive image data from an image sensor ([0046-0047] [0053] [0061] [0075] vehicle-mounted cameras and other imaging sensors whose images, video, and point clouds are received by the processing system.);
receive GNSS location data ([0043] [0048] [0056-0058] [0074] GNSS/GPS data received from a positioning receiver and combines it with sensor data);
Eichhorn discloses the analysis of the image data using image CNN image recognition processing to identify and classify objects ([0047] [0059] [0061-0062] [0075-0076]), but does not explicitly teach the classification of refuse.
Eichhorn fails to explicitly disclose analyze the image data to identify and classify one or more pieces of refuse depicted within the image data.
Eichhorn discloses the calculation and sorting of the coordinates of each detected and classified object in a map and database ([0048] [0056-0059] [0074-0078] Example 10.), but does not explicitly teach this is responsive to identifying and classifying the piece(s) as refuse.
Eichhorn fails to explicitly disclose responsive to identifying and classifying one or more pieces of refuse, log location data indicating locations of the one or more pieces of refuse; and
Eichhorn discloses the use of sensor image data and GPS/GNSS coordinates to create and update the georeferenced digital object map ([0057-0059] [0072] [0074-0078]), but does not explicitly teach that these objects are pieces of refuse.
Eichhorn fails to explicitly disclose based at least partially on the image data and the logged location data, generate a geospatial map indicating locations of the one or more pieces of refuse on the geospatial map.
However, Cudrano teaches analyze the image data to identify and classify one or more pieces of refuse depicted within the image data ([III System Overview; V-A] its agricultural robot processes RGB-D field images with YOLOv4 and outputs an object class for each detection, classifying relevant objects as waste further classified into cans and beer bottles.);
responsive to identifying and classifying one or more pieces of refuse, log location data indicating locations of the one or more pieces of refuse ([III System Overview, S V-A and V-C] Cudrano stores the detected waste objects according to their detected coordinates); and
based at least partially on the image data and the logged location data, generate a geospatial map indicating locations of the one or more pieces of refuse on the geospatial map ([V-C] Cudrano maps the image-classified waste detections and coorindates into a full geospatial refuse map of the field.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Cudrano’s teaching of automatic object detection and classification including detection of waste on the field and mapping that waste to a geospatial field map. One would be motivated, with reasonable expectation of success, to perform autonomous image acquisition and waste reporting and generate a geospatial unwanted object map of the field in order to improve plantation efficiency and support automated collection and disposal of the waste (Cudrano [abstract, VII conclusions] it’s a cost effective system for autonomous image acquisition and waste recognition and mapping which supports future automated collection and disposal of the waste).
Regarding claim 2, Eichhorn discloses The guidance system of claim 1, wherein the image sensor comprises at least one of a thermal camera, a light detection and ranging (LiDAR) camera, a short wave infrared (SWIR) camera, a near infrared camera (NIR), an RGB camera, or a polarized camera ([0046-0047] RGB cameras and LIDAR sensors).
Regarding claim 3, Eichhorn discloses The guidance system of claim 1, wherein the guidance system comprises at least one additional image sensor comprising at least one of a thermal camera, a light detection and ranging (LIDAR) camera, a short wave infrared (SWIR) camera, a near infrared (NIR) camera, an RGB camera, or a polarized camera ([0046-0047] RGB cameras and LIDAR sensors, combines RBG with at least laser rangefinder and uses sensor fusion.).
Regarding claim 4, Eichhorn discloses The guidance system of claim 1, wherein the analysis of the image data uses image CNN image recognition processing to identify and classify objects ([0047] [0059] [0061-0062] [0075-0076]), but does not explicitly teach the classification of refuse.
Eichhorn fails to explicitly disclose The guidance system of claim 1, wherein analyzing the image data comprises utilizing one or more machine learning models to identify and classify one or more pieces of refuse depicted within the image data.
However, Cudrano teaches analyzing the image data comprises utilizing one or more machine learning models to identify and classify one or more pieces of refuse depicted within the image data. ([III System Overview, V-A and V-C] its agricultural robot processes RGB-D field images with YOLOv4 and outputs an object class for each detection, classifying relevant objects as waste further classified into cans and beer bottles.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Cudrano’s teaching of automatic object detection and classification including detection of waste on the field. One would be motivated, with reasonable expectation of success, to perform autonomous image acquisition and waste reporting in order to improve plantation efficiency and support automated collection and disposal of the waste (Cudrano [abstract, VII conclusions] it’s a cost effective system for autonomous image acquisition and waste recognition and mapping which supports future automated collection and disposal of the waste).
Regarding claim 6, Eichhorn discloses The guidance system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the guidance system to, responsive to identifying and classifying one or more objects, automatically, without operator input, adjust operation of the agricultural vehicle ([0051] [0064-0065] [0071] [0080] automatic steering along avoidance paths and controls speed or stops in response to detected objects).
Eichhorn fails to explicitly disclose identifying and classifying one or more pieces of refuse.
However, Cudrano teaches identifying and classifying one or more pieces of refuse ([III System Overview, V-A and V-C] its agricultural robot processes RGB-D field images with YOLOv4 and outputs an object class for each detection, classifying relevant objects as waste further classified into cans and beer bottles.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Cudrano’s teaching of automatic object detection and classification including detection of waste on the field. One would be motivated, with reasonable expectation of success, to perform autonomous image acquisition and waste reporting in order to improve plantation efficiency and support automated collection and disposal of the waste (Cudrano [abstract, VII conclusions] it’s a cost effective system for autonomous image acquisition and waste recognition and mapping which supports future automated collection and disposal of the waste).
Regarding claim 8, Eichhorn discloses The guidance system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the guidance system to display the generated map on an input/output device of the guidance system ([0050] [0054] [0057-0058] [FIGs 5-7] the agricultural vehicle includes a display with a GUI for operational information.).
Regarding claim 9, Eichhorn discloses The guidance system of claim 8, wherein displaying the generated map on the input/output device of the guidance system comprises displaying the generated map on a display within the agricultural vehicle ([0050] [0054] [0057-0058] [FIGs 5-7] the agricultural vehicle includes a display with a GUI for operational information.).
Regarding claim 11, Eichhorn discloses The guidance system of claim 1, wherein receiving image data comprises receiving LIDAR image data ([0046] LIDAR is an option among many for image data.).
Regarding claim 15 The guidance system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the guidance system to provide captured image data depicting the identified one or more objects and the logged location data to a remote device ([0047-0048] [0056-0059] [0061-0062] [0074-0078]).
Eichhorn fails to explicitly disclose provide captured image data depicting the identified one or more pieces of refuse and the logged location data to a remote device.
However, Cudrano teaches provide captured image data depicting the identified one or more pieces of refuse and the logged location data to a remote device ([III System Overview, V-A and V-C] its agricultural robot processes RGB-D field images with YOLOv4 and outputs an object class for each detection, classifying relevant objects as waste further classified into cans and beer bottles, with the object image along with their detected GPS/GNSS coordinates. [1 introduction] sending field-waste information to farmer.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Cudrano’s teaching of automatic object detection and classification including detection of waste on the field and mapping that waste to a geospatial field map. One would be motivated, with reasonable expectation of success, to perform autonomous image acquisition and waste reporting and generate a geospatial unwanted object map of the field in order to improve plantation efficiency and support automated collection and disposal of the waste (Cudrano [abstract, VII conclusions] it’s a cost effective system for autonomous image acquisition and waste recognition and mapping which supports future automated collection and disposal of the waste).
Regarding claim 17, Eichhorn fails to explicitly disclose The method of claim 16, wherein identifying and classifying a piece of refuse comprises identifying and classifying one or more of a plastic bottle, a cola can, a bicycle, a plastic bag, a beer can, and another piece of debris.
However, Cudrano teaches identifying and classifying a piece of refuse comprises identifying and classifying one or more of a plastic bottle, a cola can, a bicycle, a plastic bag, a beer can, and another piece of debris (examiner notes “another piece of debris” is broad. [Cudrano V-A] teaches beer bottles, cans).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Cudrano’s teaching of automatic object detection and classification including detection of waste on the field and mapping that waste to a geospatial field map. One would be motivated, with reasonable expectation of success, to perform autonomous image acquisition and waste reporting and generate a geospatial unwanted object map of the field in order to improve plantation efficiency and support automated collection and disposal of the waste (Cudrano [abstract, VII conclusions] it’s a cost effective system for autonomous image acquisition and waste recognition and mapping which supports future automated collection and disposal of the waste).
Regarding claim 18, Eichhorn fails to explicitly disclose 18. The method of claim 16, wherein generating a geospatial map indicating the location of the piece of refuse on the geospatial map comprises generating a geospatial map indicating refuse distribution throughout the geospatial map.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Eichhorn in view of Cudrano further in view of Yin et al. ("Toward City-Scale Litter Monitoring Using Autonomous Ground Vehicles," in IEEE Pervasive Computing, vol. 21, no. 3, pp. 74-83 doi: 10.1109/MPRV.2022.3152926.), hereinafter referred to as Yin.
Regarding claim 5, Eichhorn discloses The guidance system of claim 1, wherein the analysis of the image data using image CNN image recognition processing to identify and classify objects ([0047] [0059] [0061-0062] [0075-0076]), but does not explicitly teach analyzing heat signatures.
Eichhorn fails to explicitly disclose analyzing the image data comprises analyzing one or more heat signatures depicted within the image data.
However, Yin teaches analyzing the image data comprises analyzing one or more heat signatures depicted within the image data. ([pages 0075-0078, especially page 77 section “litter identification using sunlight”] autonomous vehicle with a mounted thermal camera uses thermal image data to identify litter materials);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Yin’s teaching of an autonomous vehicle that identifies litter materials with a thermal camera. One would be motivated, with a reasonable expectation of success, to use a thermal camera in order to combine additional image data channels and image processing to improve object identification in the field.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Eichhorn in view of Cudrano further in view of Rovira-Mas (US 20040264763 A1), adhereinafter referred to as Rovira-Mas.
Eichhorn discloses The guidance system of claim 1, wherein generating a geospatial map indicating locations of the one or more objects on the geospatial map comprises generating a geospatial map ([0048] [0056-0059] [0072] [0074-0078]).
Eichhorn fails to explicitly disclose a geospatial map indicating locations of the one or more pieces of refuse.
However, Cudrano teaches generating a geospatial map indicating locations of the one or more pieces of refuse ([III System Overview, V-A and V-C] Cudrano maps the image-classified waste detections and coorindates into a full geospatial refuse map of the field.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Cudrano’s teaching of automatic object detection and classification including detection of waste on the field and mapping that waste to a geospatial field map. One would be motivated, with reasonable expectation of success, to perform autonomous image acquisition and waste reporting and generate a geospatial unwanted object map of the field in order to improve plantation efficiency and support automated collection and disposal of the waste (Cudrano [abstract, VII conclusions] it’s a cost effective system for autonomous image acquisition and waste recognition and mapping which supports future automated collection and disposal of the waste).
Eichhorn fails to explicitly disclose generating a geospatial map … comprises generating a three-dimensional geospatial map.
However, Rovira-Mas teaches generating a geospatial map … comprises generating a three-dimensional geospatial map ([0113-0115] [0188] [0200] [0222-0225] generates 3-D maps from stereo imagery, recording geographic locations for each map, transforms the points into global coordinates, merges local maps, renders a 3-D global agricultural field map for vehicle guidance).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Rovira-Mas’ teaching of generating a 3-d map of the geospatial area of the agricultural field. One would be motivated, with reasonable expectation of success, to generate a 3-dimensional map of the geospatial map of the agricultural field in order to provide inexpensive and reliably robust crop detection (Rovira-Mas [0004] “since no depth information can be efficiently obtained from conventional images, the robustness of such systems rely heavily on the accuracy of the camera calibration … expensive and unreliable” [0006] “there is a need for an accurate and reliable crop row detection system and method which is easy to operate and inexpensive to maintain.”).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Eichhorn in view of Cudrano further in view of Willgert (US 20150220086 A1), hereinafter referred to as Willgert.
Regarding claim 10, Eichhorn fails to explicitly disclose displaying the generated map on the input/output device of the guidance system comprises displaying the generated map on a display of a remote device.
However, Willgert teaches displaying the generated map on the input/output device of the guidance system comprises displaying the generated map on a display of a remote device ([0020] [0041] [0042] wireless communication between multiple local and remote devices for the display of detected object information including the GPS position data and map views).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Willgert’s teaching of wirelessly communicating the generated map onto a remote display device. One would be motivated, with reasonable expectation of success, to remotely display the generated map in order to improve the safety and efficiency of the machines used in the field (Willgert [0007] “improve the ability of operators and/or fleet managers to make lawn mowers operate safely and/or efficiently.”).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Eichhorn in view of Cudrano further in view of Kalra (US 20210264607 A1), hereinafter referred to as Kalra.
Regarding claim 12, Eichhorn discloses The guidance system of claim 1, wherein receiving image data comprises receiving image data representing different polarization states.
Eichhorn fails to explicitly disclose receiving image data comprises receiving image data representing different polarization states.
However, Kalra teaches receiving image data comprises receiving image data representing different polarization states ([0060] [0064-0065] [0084-0086] Klara captures polarization frames at different angles: 0, 45, 90, and 135, from a polarization camera. Those frames are processed by a CNN for object detection and segmentation.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Kalra’s teaching of using polarization in the detection and recognition of objects using a polarized CNN model. One would be motivated, with reasonable expectation of success, to include polarization in Eichchorn’s CNN model in order to improve object detection based on polarization vs intensity (Kalra [0156] “Transparent objects have more prominent textures in the polarization domain than in the intensity domain. This texture in the polarization texture can exploited with feature extractors and Polarized CNN models in accordance with embodiments of the present disclosure.”).
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Eichhorn in view of Cudrano further in view of Wu (US 10255670 B1), hereinafter referred to as Wu.
Regarding claim 13, Eichhorn discloses The guidance system of claim 1, wherein the image sensor is mounted on the agricultural vehicle ().
Eichhorn fails to explicitly disclose the image sensor is mounted to an extendable arm member attached to the agricultural vehicle.
However, Wu teaches the image sensor is mounted to an extendable arm member attached to the agricultural vehicle ([FIG. 2] [col 14, lines 5-20] [col 6, lines 50-59] an image sensor unit is mounted on the end of a rod attached above or to the side of a tractor cab, where the rod is extendable upward, downward, or outward. Sensor units are also mounted to rotatable, telescopic, or retractable fixture 1952 on the cab side, roof, or fender.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Wu’s teaching of mounting image sensors onto an extendable member attached to the agricultural vehicle cabin or boom. One would be motivated, with reasonable expectation of success, to mount image sensors there in order to extend sensor reach and coverage while allowing them to retract to avoid trees or wires (Wu [col 7, lines 1-10]).
Regarding claim 14, Eichhorn discloses The guidance system of claim 1, wherein the image sensor is mounted on the agricultural vehicle.
Eichhorn fails to explicitly disclose the image sensor is mounted to an extendable arm member attached to an implement.
However, Wu teaches the image sensor is mounted to an extendable arm member attached to an implement ([FIG. 3] [col 34, lines 25-33] [col 14, lines 20-30] image sensor attachment fixtures telescope out to the side of a vehicle boom, can be attached below, on top, or within a spray boom.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Wu’s teaching of mounting image sensors onto an extendable member attached to the agricultural vehicle cabin or boom. One would be motivated, with reasonable expectation of success, to mount image sensors there in order to extend sensor reach and coverage while allowing them to retract to avoid trees or wires (Wu [col 7, lines 1-10]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Eichhorn with Wu’s teaching of mounting image sensors onto an extendable member attached to the agricultural vehicle cabin or boom. One would be motivated, with reasonable expectation of success, to mount image sensors there in order to extend sensor reach and coverage while allowing them to retract to avoid trees or wires (Wu [col 7, lines 1-10]).
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Eichhorn in view of Cudrano further in view of Pinto et al. (macro-litter categories by a multi-class Neural Network Marine Pollution Bulletin), hereinafter referred to as Pinto.
Regarding claim 18, Eichhorn fails to explicitly disclose The method of claim 16, wherein generating a geospatial map indicating the location of the piece of refuse on the geospatial map comprises generating a geospatial map indicating refuse distribution throughout the geospatial map.
However, Pinto teaches generating a geospatial map indicating the location of the piece of refuse on the geospatial map comprises generating a geospatial map indicating refuse distribution throughout the geospatial map ([FIG 4b, page 6] maps produced by multiclass neural network. [section 3.2] discusses spatial distributions of the litter. Maps supply different type distributions of the litter.).
It would have been obvious to one of ordinary skill in the art to modify Eichhorn with Pinto. One would be motivated with reasonable expectation of success to use Pinto’s teaching of categorizing different types of litter and their distribution within the geospatial map in order to advance automated categorization of litter to improve environment litter mapping (Pinto [page 2, left column] “a new perspective to advance the automated categorization of … litter on … images, for improving … litter mapping in the environment.”).
Regarding claim 19, Eichhorn fails to explicitly disclose The method of claim 17, wherein generating a geospatial map indicating refuse distribution throughout the geospatial map comprises indicating a distribution of different types of refuse throughout the geospatial map.
However, Pinto teaches generating a geospatial map indicating refuse distribution throughout the geospatial map comprises indicating a distribution of different types of refuse throughout the geospatial map ([FIG 4b, page 6] maps produced by multiclass neural network. [section 3.2] discusses spatial distributions of the litter. Maps supply different type distributions of the litter.).
It would have been obvious to one of ordinary skill in the art to modify Eichhorn with Pinto. One would be motivated with reasonable expectation of success to use Pinto’s teaching of categorizing different types of litter and their distribution within the geospatial map in order to advance automated categorization of litter to improve environment litter mapping (Pinto [page 2, left column] “a new perspective to advance the automated categorization of … litter on … images, for improving … litter mapping in the environment.”).
Conclusion
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/M.R.H./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
