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 .
Response to Amendment
The amendment filed on 07/28/2026 has been entered. Claims 1, 7, 8, 16, 17, 21 have been amended. Claims 2 and 9 have been canceled. Claims 1, 3-8, and 10-21 remain pending in the present application.
Claim Objections
Claim 1, 7, 16, 17, and 21 are objected to for grammatical errors due to improper amendments. Claim 1 and 17 respectively have been amended to include that the computer system is configured to “generate a signal for communicating communicate an alert…” and “generate a signal that communicates communicate an alert…”, and claims 7, 17, and 21 have been amended to state “..and distinguish it from other objects such as including the receiving vehicle.” The amended terms added to these claims with no corresponding canceling of terms within the claims create grammatical errors within the claims’ sentence structures.
In regard to interpreting the claims, since the claims were filed with no revision of the previous claim language commensurate with their presented arguments, the claim limitations objected to will be interpreted the same as presented in the previous Non-final Office Action pending correction via amendments within the claims by applicant.
Applicant is advised that should claims 17-21 be found allowable, claims 1, 3, and 5-7 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Interpretation
Particular notice is drawn to the final steps performed as part of the processes performed by the apparatus of the independent claims 1 and 17. These steps, in response to a prediction or determination, comprise “generating a signal.” This signal is either “for communicating an alert to a user that the at least a portion of the agricultural material will flow outside of the target area,” or “for controlling the unloading process to direct the flow of the agricultural material to the target area.” However, examiner notes that patentability for apparatus claims is determined based on the claim limitations defining what the invention is, not what it does. Therefore, in claims 1 and 17, the language that the signal is “for communicating an alert to a user that the at least a portion of the agricultural material will flow outside of the target area,” or “for controlling the unloading process to direct the flow of the agricultural material to the target area” is not given substantial patentable weight.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites the step of “determine from the image data and the ranging data that at least a portion of the agricultural material will flow outside of the target area”, and claims 8 and 17 similarly recite the step of “predicting an amount of the agricultural material that is likely to flow outside of a target area based on the image data and the ranging data”. These steps are considered abstract ideas in the form of mental processes as a person, when given data such as photos of agricultural material flowing towards a target area, is able to determine if said agricultural material will spill, and is able to predict how much material will spill.
This judicial exception is not integrated into a practical application because the additional components and extra-solution activity performed fail to integrate it into a practical application.
The claimed “unloading conveyor configured to transfer agricultural material” is a mere linking component that limits the mental process to be performed to the field of agricultural material transferred by conveyors. The claimed “image sensor positioned to capture image data corresponding to an outlet of the unloading conveyor and a target area” and “electromagnetic detecting and ranging module positioned to capture ranging data corresponding to the outlet of the unloading conveyor and the target area” are recognized as components that perform pre-solution activity in the form of mere data gathering as necessary to perform the mental process claimed.
The step of “generating a signal” is considered insignificant extra-solution activity in the form of a mere outputting of the result of the mental process claimed.
For the method of claim 8, it is additionally considered that this signal “communicates an alert” or “controls an unloading process”. Under a broadest reasonable interpretation of the claim language, the post-solution activity of “generating a signal that communicates an alert” is considered insignificant extra-solution activity. The generality of the claim language renders this activity to no more than necessary outputting of the recited judicial exception. There is not enough detail into how the communication of the alert is done that adds a meaningful limitation to the method as necessary to integrate the claimed mental process into a practical application.
The dependent claims do not add further post-solution activity necessary to integrate the mental process claimed into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are well-known and fail to demonstrate an inventive concept. Applicant states that agricultural systems that discharge harvested material are known ([0004]). While “these systems often lack monitoring and control of the discharged material” ([0004]), systems that do have monitoring and control of the discharged material are known in the art. Additionally, the practices of using imaging and radar data- such as performing segmentation on the imaging data, fusing imaging data with the radar data, etc.- are well known in the processing of agricultural material, and are not considered as more than well-understood activity in the art. Therefore, these elements fail to demonstrate an inventive concept, and claims 1-21 are ineligible under 101.
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, 16, and 21 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. In these claims, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). Applicant is recommended to remove the term “such as the receiving vehicle” from these claims.
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.
Claims 1, 6-8, 17, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Puryk et al. (US 20210195840 A1) in view of Obropta et al. (US 20180047177 A1).
Regarding claim 1, Puryk teaches a system comprising:
an unloading conveyor configured to transfer agricultural material ([0109]: spout 89 as shown in Fig. 15A);
an image sensor positioned to capture image data corresponding to an outlet of the unloading conveyor and a target area ([0055] and [0107]: image devices 10 and 12 capture image data, such as agricultural material exiting the spout and entering the target area as shown in Fig. 6A);
an electromagnetic detecting and ranging module positioned to capture ranging data corresponding to the outlet of the unloading conveyor and the target area ([0085]: perception system includes lidar and radar); and
a computing system ([0106]: unloading controller 1024), configured to:
identify the agricultural material moving between the outlet of the unloading conveyor and the target area using the image data ([0055]: material is captured via “cameras…supported so as to capture images of the spout or other discharge point” such that "an unloading controller may analyze the captured images (perception signals) to determine the current fill state of the container during unloading of material from the gathering/harvesting vehicle into the container"; [0070-0072]: fill state includes “material unloading location” such that where the material flows into the receiving container is captured via images as seen in Figs. 6A-8A, modeled via the arrow in Figs. 2C-5C, and used to update the grain fill map in Figs 6B-8C);
determine from the image data that at least a portion of the agricultural moving between the outlet of the unloading conveyor and the target area will flow outside of the target area ([0064] and [0087]: the fill model uses data from perception system to “predict the amount of material spilling from a container”; [0070-0072]: the fill model operates at each single time step to update the fill profile based on the current fill profile and the "material unloading location", which includes a prediction of spillage amount as seen in Figs. 2B-2C. Thus, it is recognized that at a current time step such as T3 in Fig. 5C, the fill model will update to determine that material that is currently moving between the outlet of the unloading conveyor and the target area will flow outside of the target area and result in a spillage), and
in response to determining that the at least a portion of the agricultural material will flow outside of the target area, generate a signal for controlling control the unloading process to direct the flow of the agricultural material to the target area ([0078]: “outputs control signals” to adjust the filling of the container, i.e. target area).
The primary embodiment of Puryk explicitly uses image data for its determination ([0086]: primary implementation uses cameras; [0085]: “the perception signals produced by such perception systems may be signals that represent images or signals that represent point clouds”). Although ranging sensors, such as lidar and radar, are included in the perception system ([0085]), Puryk does not disclose performing operations using both image and ranging data in a single embodiment
However, given that sensor systems using exclusively imaging cameras are prone to failure, such as when objects impede the line of sight of the camera ([0091]: “dust or chaff that may impede or inhibit the imaging”), it would have been obvious to one of ordinary skill in the art the effective date of filing to use both image data and ranging data when performing its operations based on a reasonable expectation of success and motivation to ensure operations are performed with as accurate data as possible. This allows the radar data to be of use in situations where image data is unreliable due to dust or other difficulties, and vice versa for when radar data is unreliable.
Although Puryk does use ranging data to determine a location of the agricultural material ([0100]: point clouds are used), it is used to determine the location of the agricultural material within the fill container, and not explicitly the location of the agricultural material moving between the outlet of the unloading conveyor and the target area.
In the same field of endeavor, Obropta teaches using ranging data to determine the agricultural material moving between the outlet of the unloading conveyor and the target area ([0090]: 3d scan of the crops occurs as the agricultural material moves between the outlet and the target area as seen in Fig. 2A, and is used geolocate the crops; [0068]: “one or more sensors within sensor package 211b may obtain measurements of harvested specialty crops as they transition from conveyor 209 to truck 213”, emphasis added, as seen in Fig. 2A).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to use the ranging data of Puryk to located the crops based on a reasonable expectation of success and motivation of the advantages afforded from using ranging data, such as increased accuracy from “high resolution depth information” ([0090] of Obropta), the ability to geotag the crops in the field as they are being harvested ([0090] of Obropta, and ability to better track agricultural material that is subject to conditions such as wind as it is being outputted into the target container.
Regarding claim 6, Puryk teaches:
using the image data and the ranging data to detect an edge of a receiving vehicle ([0100] and [0115-0116]: point clouds from ranging data identify crop with respect to the edge of a vehicle, and image data identifies and localizes the “container position” as points);
and defining the target area using the edge of the receiving vehicle ([0115-0116]: container position is the target area as defined by its length, perimeter, edge, etc.).
Regarding claim 7, Puryk teaches:
wherein the computing system is configured to use the image data and the ranging data to detect the agricultural material moving between the outlet of the unloading conveyor and the target area and distinguish it from other objects such as including the receiving vehicle ([0100]: image data– such as the visualizations shown in Fig. 6A that captures agricultural material moving between the spout and the container– and point clouds, i.e. ranging data, “of the container and its contents may identify what regions of the container filled with material”. This is recognized as a distinguishing of the detected material from the container of the vehicle it is filling).
Regarding claim 8, Puryk teaches a method, comprising:
an unloading conveyor of a farming machine transferring agricultural material ([0109]: spout 89 as shown in Fig. 15A);
an image sensor capturing image data corresponding to an outlet of the unloading conveyor and a target area ([0055] and [0107]: image devices 10 and 12 capture image data, such as agricultural material exiting the spout and entering the target area as shown in Fig. 6A);
an electromagnetic detecting and ranging module capturing ranging data corresponding to the outlet of the unloading conveyor and the target area ([0085]: perception system includes lidar and radar);
identifying, by the computing system, the agricultural material moving between the outlet of the unloading conveyor and the target area using the captured image data ([0055]: material is captured via “cameras…supported so as to capture images of the spout or other discharge point” such that "an unloading controller may analyze the captured images (perception signals) to determine the current fill state of the container during unloading of material from the gathering/harvesting vehicle into the container"; [0070-0072]: fill state includes “material unloading location” such that where the material flows into the receiving container is captured via images as seen in Figs. 6A-8A, modeled via the arrow in Figs. 2C-5C, and used to update the grain fill map in Figs 6B-8C);
predicting, by a computing system, an amount of the agricultural material moving between the outlet of the unloading conveyor and the target area that is likely to flow outside of a target area based on the captured image data ([0064] and [0087]: the fill model uses data from perception system to “predict the amount of material spilling from a container”; [0070-0072]: the fill model operates at each single time step to update the fill profile based on the current fill profile and the "material unloading location", which includes a prediction of spillage amount as seen in Figs. 2B-2C. Thus, it is recognized that at a current time step such as T3 in Fig. 5C, the fill model will update to determine that material that is currently moving between the outlet of the unloading conveyor and the target area will flow outside of the target area and result in a spillage); and
in response to the prediction, generating, by a computing system, a signal that controls an unloading process to change direction of the flow of the agricultural material to reduce an extent that agricultural material flows outside of the target area ([0078]: “outputs control signals” to adjust the filling of the container, i.e. target area).
The primary embodiment of Puryk explicitly uses image data for its determination ([0086]: primary implementation uses cameras; [0085]: “the perception signals produced by such perception systems may be signals that represent images or signals that represent point clouds”). Although ranging sensors, such as lidar and radar, are included in the perception system ([0085]), Puryk does not disclose performing operations using both image and ranging data in a single embodiment
However, given that sensor systems using exclusively imaging cameras are prone to failure, such as when objects impede the line of sight of the camera ([0091]: “dust or chaff that may impede or inhibit the imaging”), it would have been obvious to one of ordinary skill in the art the effective date of filing to use both image data and ranging data when performing its operations based on a reasonable expectation of success and motivation to ensure operations are performed with as accurate data as possible. This allows the radar data to be of use in situations where image data is unreliable due to dust or other difficulties, and vice versa for when radar data is unreliable.
Although Puryk does use captured ranging data to determine a location of the agricultural material ([0100]: point clouds are used), it is used to determine the location of the agricultural material within the fill container, and not explicitly the location of the agricultural material moving between the outlet of the unloading conveyor and the target area.
In the same field of endeavor, Obropta teaches using ranging data to determine the agricultural material moving between the outlet of the unloading conveyor and the target area ([0090]: 3d scan of the crops occurs as the agricultural material moves between the outlet and the target area as seen in Fig. 2A, and is used geolocate the crops).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to use the ranging data of Puryk to located the crops based on a reasonable expectation of success and motivation of the advantages afforded from using ranging data, such as increased accuracy from “high resolution depth information” ([0090] of Obropta), the ability to geotag the crops in the field as they are being harvested ([0090] of Obropta, and ability to better track agricultural material that is subject to conditions such as wind as it is being outputted into the target container.
Regarding claim 17, Puryk teaches a system comprising:
an unloading conveyor configured to transfer agricultural material ([0109]: spout 89 as shown in Fig. 15A);
an image sensor positioned to capture image data corresponding to an outlet of the unloading conveyor and a target area ([0055] and [0107]: image devices 10 and 12 capture image data, such as agricultural material exiting the spout and entering the target area as shown in Fig. 6A);
an electromagnetic detecting and ranging module positioned to capture ranging data corresponding to the outlet of the unloading conveyor and the target area ([0085]: perception system includes lidar and radar); and
a computing system ([0106]: unloading controller 1024), configured to:
identify the agricultural material moving between the outlet of the unloading conveyor and the target area using the image data ([0055]: material is captured via “cameras…supported so as to capture images of the spout or other discharge point” such that "an unloading controller may analyze the captured images (perception signals) to determine the current fill state of the container during unloading of material from the gathering/harvesting vehicle into the container"; [0070-0072]: fill state includes “material unloading location” such that where the material flows into the receiving container is captured via images as seen in Figs. 6A-8A, modeled via the arrow in Figs. 2C-5C, and used to update the grain fill map in Figs 6B-8C);
predict an amount of the agricultural material moving between the outlet of the unloading conveyor and the target area that is likely to flow outside of a target area based on the image data and the ranging data ([0064] and [0087]: the fill model uses data from perception system to “predict the amount of material spilling from a container”; [0070-0072]: the fill model operates at each single time step to update the fill profile based on the current fill profile and the "material unloading location", which includes a prediction of spillage amount as seen in Figs. 2B-2C. Thus, it is recognized that at a current time step such as T3 in Fig. 5C, the fill model will update to determine that material that is currently moving between the outlet of the unloading conveyor and the target area will flow outside of the target area and result in a spillage); and
in response to the prediction of the computing system, generate a signal that controls control an unloading process to direct the flow of the agricultural material to the target area ([0078]: “outputs control signals” to adjust the filling of the container, i.e. target area).
The primary embodiment of Puryk explicitly uses image data for its determination ([0086]: primary implementation uses cameras; [0085]: “the perception signals produced by such perception systems may be signals that represent images or signals that represent point clouds”). Although ranging sensors, such as lidar and radar, are included in the perception system ([0085]), Puryk does not disclose performing operations using both image and ranging data in a single embodiment
However, given that sensor systems using exclusively imaging cameras are prone to failure, such as when objects impede the line of sight of the camera ([0091]: “dust or chaff that may impede or inhibit the imaging”), it would have been obvious to one of ordinary skill in the art the effective date of filing to use both image data and ranging data when performing its operations based on a reasonable expectation of success and motivation to ensure operations are performed with as accurate data as possible. This allows the radar data to be of use in situations where image data is unreliable due to dust or other difficulties, and vice versa for when radar data is unreliable.
Regarding claim 20, Puryk teaches:
using the image data and the ranging data to detect an edge of a receiving vehicle ([0100] and [0115-0116]: point clouds from ranging data identify crop with respect to the edge of a vehicle, and image data identifies and localizes the “container position” as points);
and defining the target area using the edge of the receiving vehicle ([0115-0116]: container position is the target area as defined by its length, perimeter, edge, etc.).
Regarding claim 21, Puryk teaches:
wherein the computing system is configured to use the image data and the ranging data to detect the agricultural material moving between the outlet
of the unloading conveyor and the target area and distinguish it from other objects such as including the receiving vehicle ([0100]: image data, such as the visualizations shown in Fig. 6A that captures agricultural material moving between the spout and the container, and point clouds, i.e. ranging data, “of the container and its contents may identify what regions of the container filled with material”. This is recognized as a distinguishing of the detected material from the container of the vehicle it is filling).
Claims 3-4, 10-11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Puryk in view of Obropta as applied to claims 1, 8, and 17 above, and further in view of Bossaer et al. (US 20230132104 A1).
Regarding claim 3, Puryk teaches:
wherein the computing system is configured to use the image data to identify the agricultural material moving between the outlet of the unloading conveyor and the target area ([0055]).
Puryk does not teach that the system does this by performing image segmentation on the image data.
In the same field of endeavor, Bossaer teaches performing image segmentation on the image data ([0040]: semantic segmentation model, i.e. image segmentation, used to perform a classification that “distinguishes the crop … [from] any other background information”). Note that semantic segmentation is recognized as a type of image segmentation.
It would have been obvious to one of ordinary skill in the art at the effective date of filling to modify Puryk with the use of image segmentation based on a reasonable expectation of success and motivation to achieve a greater accuracy of distinction between the agricultural material and other background information being captured. Image segmentation is a well known technique, and the technical advantages, such as increased accuracy or more efficient operation, are also well known.
Regarding claim 4, Puryk teaches:
wherein the computing system is configured to use the image data to identify the agricultural material moving between the outlet of the unloading conveyor and the target area ([0055]).
Puryk does not teach that the system does this by performing semantic segmentation on the image data.
In the same field of endeavor, Bossaer teaches performing semantic segmentation on the image data ([0040]: semantic segmentation model used to perform a classification that “distinguishes the crop … [from] any other background information”).
It would have been obvious to one of ordinary skill in the art at the effective date of filling to modify Puryk with the use of semantic segmentation based on a reasonable expectation of success and motivation to achieve a greater accuracy of distinction between the agricultural material and other background information being captured. Image segmentation, such as semantic segmentation, is a well-known technique, and the technical advantages, such as increased accuracy or more efficient operation, are also well-known.
Regarding claim 10, Puryk teaches:
identifying the agricultural material moving between the outlet of the unloading conveyor and the target area ([0055]).
Puryk does not teach that this is done by enhancing the captured image data by performing image segmentation on the captured image data.
In the same field of endeavor, Bossaer teaches enhancing the captured image data by performing image segmentation on the captured image data ([0040]: semantic segmentation model, i.e. image segmentation, used to perform a classification that “distinguishes the crop … [from] any other background information”). Note that semantic segmentation is recognized as a type of image segmentation.
It would have been obvious to one of ordinary skill in the art at the effective date of filling to modify Puryk with the use of image segmentation based on a reasonable expectation of success and motivation to achieve a greater accuracy of distinction between the agricultural material and other background information being captured. Image segmentation is a well-known technique, and the technical advantages, such as increased accuracy or more efficient operation, are also well-known.
Regarding claim 11, Bossaer teaches:
wherein the image segmentation on the captured image data comprises semantic segmentation on the image data ([0040]).
Regarding claim 18, Puryk teaches:
wherein the computing system is configured to use the image data to identify the agricultural material moving between the outlet of the unloading conveyor and the target area ([0055]).
Puryk does not teach that the system does this by performing semantic segmentation on the image data.
In the same field of endeavor, Bossaer teaches performing semantic segmentation on the image data ([0040]: semantic segmentation model used to perform a classification that “distinguishes the crop … [from] any other background information”).
It would have been obvious to one of ordinary skill in the art at the effective date of filling to modify Puryk with the use of semantic segmentation based on a reasonable expectation of success and motivation to achieve a greater accuracy of distinction between the agricultural material and other background information being captured. Image segmentation, such as semantic segmentation, is a well-known technique, and the technical advantages, such as increased accuracy or more efficient operation, are also well-known.
Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Puryk in view of Obropta as applied to claims 1 and 17 above, and further in view of Ellaboudy et al. (US 20210000006 A1).
Regarding claim 5, although Puryk teaches the use of image data and point clouds, i.e. ranging data ([0085] and [0100]), it does not teach temporally merging the image data and the ranging data.
In the same field of endeavor, Ellaboudy teaches:
temporally merging the image data and the ranging data ([0195]: “captured image data is fused with range data”).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Puryk to merge image and ranging data based on a reasonable expectation of success and motivation to increase the accuracy of the sensor system as resulting from the fusion of image and ranging data.
Regarding claim 19, although Puryk teaches the use of image data and point clouds, i.e. ranging data ([0085] and [0100]), it does not teach temporally merging the image data and the ranging data.
In the same field of endeavor, Ellaboudy teaches:
temporally merging the image data and the ranging data ([0195]: “captured image data is fused with range data”).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Puryk to merge image and ranging data based on a reasonable expectation of success and motivation to increase the accuracy of the sensor system as resulting from the fusion of image and ranging data.
Claims 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over Puryk in view of Obropta and Bossaer as applied to claim 10 and 17 above, and further in view of Ellaboudy et al. (US 20210000006 A1).
Regarding claim 12, although Puryk teaches the use of image data and point clouds, i.e. ranging data ([0085] and [0100]), it does not teach temporally merging the enhanced image data and the captured ranging data.
In the same field of endeavor, Ellaboudy teaches:
temporally merging captured image data and captured ranging data ([0195]: “captured image data is fused with range data”).
A skilled artisan would have been able to modify the prior combination with this merging, thereby merging the enhanced image data as is resulting from the prior combination with Bossaer. It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to merge image and ranging data based on a reasonable expectation of success and motivation to increase the accuracy of the sensor system as resulting from the fusion of image and ranging data.
Regarding claim 13, Puryk teaches the use of a second camera providing second image data ([0166]: second image device 12 as shown in Fig. 15A). Although Puryk teaches that this second image device produces second image data to identify the agricultural material moving between the outlet of the unloading conveyor and the target ([0055] and [0166]), it does not teach enhancing the derived second image data by performing image segmentation on the second image data.
However, Bossaer of the prior combination teaches enhancing image data by performing image segmentation on the image data ([0040]).
One of ordinary skill in the art would have been able to perform image segmentation on the second image data just as performed on the first image data in the prior combination, and it would have been obvious to do so at the effective date of filing based on a reasonable expectation of success and for the same motivations of performing image segmentation on the first image data, such as increased accuracy of the detection of material, faster operation, less processing power needed, or any of the well-known advantages of performing image segmentation.
Puryk does not teach that this second image data is derived from the captured ranging data.
In the same field of endeavor, Ellaboudy teaches image data is partially derived from the captured ranging data ([0209]: range data is used to locate where bounding boxes, which indicate agricultural material, are located in images).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination with the use of ranging data for deriving image data based on a reasonable expectation of success and motivation of more accurately locating the distance and location of agricultural material in images.
Regarding claim 14, Puryk teaches:
temporally merging the enhanced image data and the enhanced and derived second image data ([0166]: both sets of image data are merged to create a “virtual profile” of the agricultural material).
Regarding claim 15, Puryk teaches:
using the merged data to detect an edge of a receiving vehicle ([0100] and [0115-0116]: point clouds from ranging data identify crop with respect to the edge of a vehicle, and image data identifies and localizes the “container position” as points. This data is merged per the prior combination in claim 12); and
defining the target area using the detected edge of the receiving vehicle ([0115-0116]: container position is the target area as defined by its length, perimeter, edge, etc.).
Regarding claim 16, Puryk teaches:
using the merged data to detect the agricultural material moving between the outlet of the unloading conveyor and the target area and distinguish it from other objects such as including the receiving vehicle ([0100]: image data, such as the visualizations shown in Fig. 6A that captures agricultural material moving between the spout and the container, and point clouds, i.e. ranging data, “of the container and its contents may identify what regions of the container filled with material”. This is recognized as a distinguishing of the detected material from the container of the vehicle it is filling. This data is merged per the prior combination in claim 12).
Response to Arguments
Applicant's arguments filed 07/28/2026 have been fully considered.
Regarding the rejections of claims 1 and 17 under 35 USC 101 and the rejections of claim 7, 16, and 21 under 35 USC 112(b), applicant’s arguments pertaining to the amendments for the post-solution activity of claims 1 and 17, and pertaining to the correction of “such as” in claims 7, 16, and 21, are moot as the amended claims are not officially filed in a manner commensurate with applicant’s presented arguments. For example, applicant argues that claim 1 as amended “requires the computing system, ‘in response to determining that the at least a portion of the agricultural material will flow outside of the target area, communicate an alert to a user … or control the unloading process to direct the flow of the agricultural material to the target area.’” However, the amended claim 1 as officially filed reads as “in response to determining that the at least a portion of the agricultural material will flow outside of the target area, generate a signal for communicating communicate an alert to a user… or for controlling control the unloading process to direct the flow of the agricultural material to the target area.” Therefore, since the “generate a signal for” term has not been revised out of the claim, the interpretation of this claimed limitation remains as applied in the previously mailed non-final rejection. In a similar manner, the “such as” term has not been revised out of claims 7, 16, and 21.
Further regarding the rejections of claims 1, 8, and 17 under 35 USC 101, applicant argues that “the claims, as amended, are not directed to a mental process under Step 2A Prong One” as “determining the three-dimensional location of particles of agricultural material in flight from electromagnetic ranging data is not a process that can be practically performed in the human mind.” This is unpersuasive as the claimed “ranging data” has a wide ranging broadest reasonable interpretation, which includes well known visualizations and displays of 3D point clouds in which, when provided to a human as ranging data, can be used by said human to locate objects. Therefore, the process of determining the location of a particle based on ranging data is considered a mental process.
Further regarding the 101 rejection, applicant argues that “the claims, as amended, integrate any such exception into a practical application under Step 2A Prong Two” since “the control branch of each claim requires the computing system to use the result of the determination or prediction to control a physical unloading process so as to redirect the flow of agricultural material to the target area and thereby reduce spillage.” This is unpersuasive. Claims 1 and 17, as stated above, are only required to “generate a signal”. For claim 8, it is noted that the claim is drawn to a method, wherein the claimed post-solution activity is a conditional step of generating a signal that “communicates an alert” or “controls an unloading process”, emphasis added. When interpreting method claims, it is recognized that the broadest reasonable interpretation of said method need only perform one alternative step when the claim recites multiple alternative steps when claimed with the article or. Therefore, as interpreted above by the examiner, the method comprises “in response to the prediction, generating, by a computing system, a signal that communicates an alert that the amount of the agricultural material is likely to flow outside of the target area”. To this end, the claimed communicating of an error is generically described and, due to the lack of detail in regard to how the communication is performed, amounts to no more than a necessary outputting of the mental process of predicting that the material will spill. Therefore, since no controlling of “a physical unloading process” is required, the post-solution activity is not sufficient enough to integrate the mental process into a practical application.
Further regarding the 101 rejection, applicant argues that “under Step 2B, the claims, as amended, include additional elements that amount to significantly more than any alleged judicial exception” as “the specific combination of identifying in-flight agricultural material using image data and determining its location using ranging data is not well-understood, routine, or conventional, as evidenced by its absence from the cited prior art.” This is unpersuasive as the cited prior art does teach said limitations as evidenced in the rejection above and further evidenced in the response to applicant’s arguments below.
Regarding the rejection of the independent claims under 35 USC 103, applicant argues that the amended limitations are not taught by Puryk in view of Obropta. This argument is unpersuasive. In regards to the step of “identifying the agricultural material moving between the outlet of the unloading conveyor and the target area using the image data,” as cited above, Puryk teaches "cameras ... supported so as to capture images of the spout or other discharge point" as seen in Figs. 6A-8A such that "an unloading controller may analyze the captured images (perception signals) to determine the current fill state of the container during unloading of material from the gathering/harvesting vehicle into the container" ([0055]). This is done to specifically allow for a modeling of material entering the container as pictorially shown via the arrow in Figs. 2C-4C. Additionally, if it is deemed that the Puryk does not teach the identifying of the material, to which the examiner does not concede, the examiner further refers to the arrow in Figs. 2C-4C. Although this arrow is described to be the spout or auger location ([0070-0072]), given that the images of the spout also include the flowing material as pictorially shown, given that the auger location is used to denote where material will flow into the container, and absent any showing to the contrary, the identification of the auger or spout location is functionally equivalent to the identification of the material flowing from said auger or spout, especially since said location is used to denote the input of material as it is being outputted from the spout/auger and before it is landed in the container, and as there is no distinction made in Puryk between the spout location and where the material flows out of the spout/into the container. Alternatively, Obropta discloses image sensors ([0078-0082]) that identify crop material and its characteristics “as [harvested specialty crops] transition from conveyor 209 to truck 213” ([0068]).
In regard to the step of “determining a location of the agricultural material moving between the outlet of the unloading conveyor and the target area using the ranging data”, Obropta discloses ranging sensors that capture ranging data used for analyzing the depth, i.e. location, of crop ([0089-0093]), said ranging sensors may be positioned to “obtain measurements of harvested specialty crops as they transition from conveyor 209 to truck 213” ([0068]).
In regards to the amendment that the determining/predicting of agricultural material is made for material “moving between the outlet of the unloading conveyor and the target area”, applicant argues that “Puryk's "spillage" is a fundamentally different concept from the in-flight spillage monitoring recited by claims 1, 8, and 17 as amended” as Puryk only detects material that is within the container, but not material “in flight between the outlet of the unloading conveyor and the target area”. This is unpersuasive since, as stated above, Puryk identifies material and where it will enter the container, and also identifies the location of the spout/auger so as to determine where material will enter the container, which is functionally equivalent to the claim. Furthermore, the claims merely require determining/predicting whether “least a portion of the agricultural material moving between the outlet of the unloading conveyor and the target area will flow outside of the target area”. This is not the same as determining whether material will directly flow outside of the target area. For example, examiner considers the container being filled at T3 as seen in Fig. 5C. The fill model of Puryk predicts that a certain percentage of material that flows into the container at that time will eventually spill out of it. Thus, even if it were to be interpreted that Puryk provided no identification of material in flight, to which examiner does not concede, a determination/prediction that material will flow into the container and trigger an overfilling such that it spills out of the container still maps the amended claim limitation.
Conclusion
THIS ACTION IS MADE FINAL. 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 JACK R BREWER whose telephone number is (571)272-4455. The examiner can normally be reached 10AM-6PM.
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/JACK R BREWER/Examiner, Art Unit 3663 /ADAM D TISSOT/Primary Examiner, Art Unit 3663
