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 .
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.
Claims 7, 8, 17, and 18 are 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. Claims 7 and 17 recite “a height of the soil-engaging assembly”; it is not clear whether this height is meant to be the same “a height of the soil-engaging assembly” recited in claims 6 and 16. For the purposes of examination, the claims will be interpreted as if the two heights are the same. Claims 8 and 18 are dependent on claims 7 and 17 and thus are indefinite for the same reasons.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-4, 6-14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ferrari et al. (US 10963751, cited in applicant IDS) in view of Taneja et al. ("Predicting soil organic matter and soil moisture content from digital camera images", cited in ISR).
Claim 1.
Ferrari et al. teaches:
a global positioning system (GPS) receiver
(Ferrari – Col. 9, lines 57-59) “the positioning device(s) 124 may be configured to determine the exact location of the work vehicle 10 and/or the implement 12 using a satellite navigation position system”
an optical sensor configured to capture an image of soil in an agricultural field
(Ferrari – Abstract) “imagery of such portion of the field captured by an imaging device.”
a controller operatively coupled in communication to the optical sensor and programmed, via computer-executable instructions
(Ferrari – Col. 1, lines 56-60 “The computing system includes one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, configure the computing system to perform operations.”
receive the image from the optical sensor
(Ferrari – Abstract) “imagery of such portion of the field captured by an imaging device.”
receive location data from the GPS sensor
(Ferrari – Col. 9, lines 57-59) “the positioning device(s) 124 may be configured to determine the exact location of the work vehicle 10 and/or the implement 12 using a satellite navigation position system”
associate the location data with the image
(Ferrari – Col. 10, lines 1-3) “the location data stored within the location database 122 may also be correlated to the image data stored within the image database 118.”
divide the image into a plurality of sub-images
(Ferrari – Col. 16, lines 24-25) “the method 200 may include extracting a plurality of patches from the image data.”
analyze the plurality of sub-images using a first machine learning (ML) model, the first ML model being trained to classify images into the following first classes: soil or residue
(Ferrari – Col. 13, lines 40-46) “the machine-learned crop residue classification model can be a multi-class classifier configured to output, for each patch, a multi-class crop residue classification that classified such patch as non-residue or as one of a plurality of different types of residue (e.g., “wheat residue” vs. “corn residue” vs. “soybean residue” vs. “soil”, etc.)”
classify, using the first ML model, each sub-image of the plurality of sub-images into one of the first classes
(Ferrari – Col. 13, lines 40-46) “the machine-learned crop residue classification model can be a multi-class classifier configured to output, for each patch, a multi-class crop residue classification that classified such patch as non-residue or as one of a plurality of different types of residue (e.g., “wheat residue” vs. “corn residue” vs. “soybean residue” vs. “soil”, etc.)”
generate an organic matter map, based on the associated GPS location data and the
(Ferrari – Col. 19, lines 34-37) “The total crop residue parameter value can also be expressed in the form of a field map for the field that describes, for each analyzed portion of the field, one or more corresponding crop residue parameter values.”
While Ferrari et al. teaches classification based on soil and residue, Ferrari et al. does not explicitly teach classification based on organic matter. However, Taneja et al. teaches:
process the sub-images classified in the soil class using a second ML model, the second ML model being trained to classify images into the following second classes: high organic matter or low organic matter
(Taneja – Abstract) “This study compared the performance of 22 different supervised regression and machine learning algorithms… in predicting SOM and SMC from soil images taken with a digital camera in the laboratory setting.”
the second ML model being trained, using supervised learning, on image data where a mean and standard deviation of RGB data of the image data provides an indication of an amount of organic matter in the imaged soil
(Taneja – Pages 775-776) “Color features were more important than textural features in SOM prediction. Also, the impact of mean values in SOM prediction accuracy was greater than that of median values.”
classify, using the second ML model, each sub-image of the sub-images classified in the soil class into one of the second classes
(Taneja – Abstract) “This study compared the performance of 22 different supervised regression and machine learning algorithms… in predicting SOM and SMC from soil images taken with a digital camera in the laboratory setting.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the crop residue classification model of Ferrari et al. with the further functionality of determining soil organic matter, as in the classification model of Taneja et al. Both Ferrari et al. and Taneja et al. are directed towards classifying soil based on obtained image data; therefore, a person of ordinary skill in the art would have recognized that these teachings could be combined with predictable results. One would have been motivated to do this because traditional approaches to determining soil organic matter content are time and labor intensive, leading to delays in making decisions (Taneja – Page 768).
Claim 2.
The combination of Ferrari et al. and Taneja et al. teaches all the limitations of claim 1, as discussed above. Ferrari et al. further teaches:
where the global positioning system (GPS) sensor and the residue and organic matter sensing system are attached to the vehicle
(Ferrari – Col. 7, lines 58-59) “the imaging devices 104 may be specifically installed at locations on the work vehicle 10”
(Ferrari – Col. 9, lines 54-55) “a positioning device(s) 124 installed on or within the work vehicle 10”
determine an
(Ferrari – Col. 13, lines 40-46) “the machine-learned crop residue classification model can be a multi-class classifier configured to output, for each patch, a multi-class crop residue classification that classified such patch as non-residue or as one of a plurality of different types of residue (e.g., “wheat residue” vs. “corn residue” vs. “soybean residue” vs. “soil”, etc.)”
transmit in real-time the organic matter result to the vehicle
(Ferrari – Col. 16, lines 13-14) “method 200 can be performed iteratively in real-time as new images are received from the imaging devices”
Ferrari et al. does not explicitly teach an organic matter result; however, Taneja et al. teaches:
determine an organic matter result of the soil in the image
(Taneja – Abstract) “This study compared the performance of 22 different supervised regression and machine learning algorithms… in predicting SOM and SMC from soil images taken with a digital camera in the laboratory setting.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 1.
Claim 3.
The combination of Ferrari et al. and Taneja et al. teaches all the limitations of claim 2, as discussed above. Ferrari et al. further teaches:
an agricultural implement coupled to the vehicle
(Ferrari – Col. 19, lines 3-7) “controlling the operation of at least one of a work vehicle or an implement as the implement is being towed by the work vehicle across a field based at least in part on the crop residue parameter value.”
wherein the agricultural implement comprises a soil-engaging assembly, wherein the soil-engaging assembly is configured to perform a soil manipulation operation on the soil
(Ferrari – Col. 19, lines 24-28) “adjusting one or more operating parameters associated with the ground-engaging elements of the implement 12, including, for example, down force, angle or position relative to the ground (e.g., height), and/or other operational parameters associated with the ground-engaging elements.”
transmit a recommendation signal to adjust at least one operating parameter of the agricultural implement, based on the organic matter result
(Ferrari – Col. 19, lines 24-28) “adjusting one or more operating parameters associated with the ground-engaging elements of the implement 12, including, for example, down force, angle or position relative to the ground (e.g., height), and/or other operational parameters associated with the ground-engaging elements.”
Claim 4.
The combination of Ferrari et al. and Taneja et al. teaches all the limitations of claim 3, as discussed above. Ferrari et al. further teaches:
automatically adjust at least one parameter selected from the group consisting of the at least one operating parameter of the agricultural implement and at least one operating parameter of the vehicle
(Ferrari – Col. 19, lines 3-7) “controlling the operation of at least one of a work vehicle or an implement as the implement is being towed by the work vehicle across a field based at least in part on the crop residue parameter value.”
Claim 6.
The combination of Ferrari et al. and Taneja et al. teaches all the limitations of claim 3, as discussed above. Ferrari et al. further teaches:
wherein the agricultural implement comprises a lift mechanism operable to adjust a height of the soil-engaging assembly to a desired height during operation
(Ferrari – Col. 19, lines 24-28) “adjusting one or more operating parameters associated with the ground-engaging elements of the implement 12, including, for example, down force, angle or position relative to the ground (e.g., height), and/or other operational parameters associated with the ground-engaging elements.”
Claim 7.
The combination of Ferrari et al. and Taneja et al. teaches all the limitations of claim 6, as discussed above. Ferrari et al. further teaches:
wherein the controller is programmed to automatically adjust at least one parameter selected from the group consisting of a height of the soil-engaging assembly and a ground speed of the vehicle
(Ferrari – Col. 11, lines 58-61) “the controller 102 may be configured to increase or decrease the operational or ground speed of the implement 12 to affect an increase or decrease in the crop residue coverage.”
(Ferrari – Col. 19, lines 24-28) “adjusting one or more operating parameters associated with the ground-engaging elements of the implement 12, including, for example, down force, angle or position relative to the ground (e.g., height), and/or other operational parameters associated with the ground-engaging elements.”
Claim 8.
The combination of Ferrari et al. and Taneja et al. teaches all the limitations of claim 7, as discussed above. Ferrari et al. further teaches:
the agricultural implement and the vehicle are equipped with a tractor implement management (TIM) system or an ISOBUS-compatible system
(Ferrari – Col. 12, lines 11-13) “the implement 12 can include or otherwise leverage an ISOBUS Class 3 system to control the speed of the work vehicle 10.”
the controller is programmed to automatically adjust the ground speed of the vehicle autonomously in response to a control signal transmitted by the controller to the vehicle
(Ferrari – Col. 12, lines 11-13) “the implement 12 can include or otherwise leverage an ISOBUS Class 3 system to control the speed of the work vehicle 10.”
Claim 9.
The combination of Ferrari et al. and Taneja et al. teaches all the limitations of claim 1, as discussed above. Ferrari et al. further teaches:
as part of the operation of determining the organic matter result of the soil in the image, reassemble the sub-images which have been classified in the soil class and into one of the second classes
(Ferrari – Col. 10, lines 23-26) “based on the location data and the associated image data, the controller 102 may be configured to generate a field map for the field that includes the geo-located images associated therewith.”
[Examiner’s Note: As discussed in further detail above, each image comprises patches, based on which the detection results are determined.
Claim 10.
The combination of Ferrari et al. and Taneja et al. teaches all the limitations of claim 2, as discussed above. Ferrari et al. further teaches:
the operations performed by the controller are performed iteratively as the vehicle traverses the agricultural field
(Ferrari – Col. 16, lines 13-14) “method 200 can be performed iteratively in real-time as new images are received from the imaging devices”
the organic matter map comprises a plurality of images captured by the optical sensor as the vehicle traverses the agricultural field
(Ferrari – Col. 10, lines 23-26) “based on the location data and the associated image data, the controller 102 may be configured to generate a field map for the field that includes the geo-located images associated therewith.”
Claim 11.
Rejected by the same rationale as claim 1.
Claim 12.
Rejected by the same rationale as claim 2.
Claim 13.
Rejected by the same rationale as claim 3.
Claim 14.
Rejected by the same rationale as claim 4.
Claim 16.
Rejected by the same rationale as claim 6.
Claim 17.
Rejected by the same rationale as claim 7.
Claim 18.
Rejected by the same rationale as claim 8.
Claim 19.
Rejected by the same rationale as claim 9.
Claim 20.
Rejected by the same rationale as claim 10.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Ferrari et al. and Taneja et al. as applied to claims 3 and 12 above, and further in view of Smith et al. (US 20130116895).
Claim 5.
The combination of Ferrari et al. and Taneja et al. teaches all the limitations of claim 1, as discussed above. Neither Ferrari et al. nor Taneja et al. explicitly teaches an approval input signal; however, Smith et al. teaches:
receive an approval input signal from an operator in response to transmitting the recommendation signal to adjust one or more operating parameters
(Smith – [0087]) “the tractor controller 50 transmits a permission request to the operator to begin initiating automatic tractor stopping.”
automatically adjust the one or more operating parameters in response to receiving the approval input signal
(Smith – [0088]) “If the operator approves the request to initiate automatic tractor stop, the tractor controller 250 begins automatic tractor stopping”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the tractor control system of Ferrari et al. with the permission request of Smith et al. Both Ferrari et al. and Smith et al. are directed to automated control systems for a tractor; therefore, a person of ordinary skill in the art would have recognized that the teachings could be combined with predictable results. One would have been motivated to do this so the operator can verify that there are no additional considerations which would make a determined parameter adjustment inadvisable (e.g., a rocky patch of ground, bystanders in the path of the tractor, etc.)
Claim 15.
Rejected by the same rationale as claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Loukili et al. (US 11259515) is directed to a detection system which applies a crop classifier which identifies locations of crop plants and weed plants based on image data.
Corpstein (US 12284933) is directed to a method of operating a tillage implement based on comparison of residue data obtained from a captured image to data stored in a curated library.
Nelan (US 20150310633) is directed to an image processing system which identifies a plurality of regions with similar soil characteristics.
Peshlov et al. (US 20200193589) is directed to a method for generating a field anomaly map based on image data obtained of an agricultural plot.
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/S.A.M./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
