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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement filed 01/23/2025 fails to comply with the provisions of 37 CFR 1.98(a)(4) because it lacks the appropriate size fee assertion. It has been placed in the application file, but the information referred to therein has not been considered as to the merits.
The information disclosure statement (IDS) submitted on 01/30/2025 is being considered by the examiner.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show details of block diagrams in Fig. 2 and Fig. 3, namely labels of elements other than numerals, as described in the specification. Applicant is advised to include text labels of at least elements 29, 30, 32, 102, 110, 104, 108, 112, 229, 230, 232, 102, and 10. Additionally, Fig. 5 appears in the Drawings before Fig. 4A-4E. Applicant is advised to reorder the drawings numerically. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0010] recites “orc loud”; Applicant is advised to amend the term to “or cloud”
Paragraph [0032] recites “databased”; Applicant is advised to amend the term to “database”
Paragraph [0054] recites “One identified”; Applicant is advised to amend the term to “Once identified”
Paragraph [0066] recites “components to be identified 0 e.g. straw –”; Applicant is advised to amend the line to improve clarity and/or eliminate typographical errors.
Appropriate correction is required.
Claim Objections
Claim 17 is objected to because of the following informalities: Claim 17 recites “databased”; Applicant is advised to amend the term to “database”. Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claim 1 recites “an imaging device configured for capturing image data” and “analyze the image data to identify one or more material components within the image data.” Under the broadest reasonable interpretation, “material components” may be any component within the image data captured by an imaging device. Under the broadest reasonable interpretation, an imaging device may be a device mounted or coupled to the harvesting agricultural harvesting machine, an imaging device of a user device (see Specification, paragraphs [0050] and [0009]), or any other device capable of capturing image data. Thus, material components within the image data may be any materials having material characteristics including dimensional measures and color or hue, and enabling control signals for controlling operation of the agricultural harvesting machine based on the dimensional and color measure measures. That is, “material components” as recited in Claim 1 are limited by the features described in subsequent limitations, but still broadly describe many material components that may be visible in and around an agricultural harvesting machine.
For example, material components may include not only grain, straw, and residue, as disclosed in the Specification, but may also include materials of the agricultural harvesting machine itself, such as the grain bin or conveyor section, or materials of the landscape such as windrows or crop rows, all of which have dimensional measures and color which may be used to control operation of a harvesting machine. The specification discloses only a method of analysis of material components deposited on the ground. The quantity of experimentation needed to control the harvesting machine in response to determined dimension and color of other materials such as those listed above is undue. The specification does not disclose a method, for example, for controlling the harvesting machine in response to dimensions and colors of entire crop rows or a dimension and color of a fluid tank of the harvesting machine. That is, not all “material components” are enabled by the specification, even including material components containing features of subsequent limitations.
Dependent Claims 2-19 contain all subject matter of Claim 1 and do not further disclose how one of ordinary skill in the art would be enabled to make and/or use all claimed species of the invention, and thus are also rejected under 35 U.S.C. 112(a).
Claim 20 recites a method with steps corresponding to the elements of Claim 1 and thus contains all subject matter of Claim 1 and is also rejected under 35 U.S.C. 112(a).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8, 11-13, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 10255670 B1).
Regarding Claim 1, Wu teaches “A system for monitoring operation of an agricultural harvesting machine, the system comprising:
an imaging device configured for capturing image data” (Wu, column 42, lines 41-42 discloses “Instructions 3114 include the image sensor unit 50 capturing an image”; where an image sensor unit is an imaging device; where an image is image data) “and
one or more controllers” (Wu, column 43, lines 49-56 discloses “The image sensor units 50 also have transceiver circuits so that input signals (e.g. speed of travel, wind velocity) and output signals (e.g. “weed pattern found” or “uneven residue pattern found”) are transmitted either directly to another equipment on the agricultural vehicle (e.g. reel, spray nozzle to exercise actions) or they go to the central controllers in the cab or main computer hub of the agricultural vehicle”; where central controllers are one or more controllers), “configured to:
receive the image data from the imaging device” (Wu, column 42, lines 53-56 discloses “If there are no interrupts, there is a periodic request at 3128 to upload a pre-determined fraction of the unbiased data (e.g. no cropping, no triggers) to be checked offline”; where uploading a fraction of unbiased data is receiving image data);
“analyze the image data to identify one or more material components within the image data” (Wu, column 20, lines 23-26 discloses “FIG. 9 depicts another example Master Application, an embodiment of a method to determine the uniformity of the spread of dry fertilizer or other objects (e.g. residue) based on using color or texture or shape contrast”; where dry fertilizer or residue are material components within the image data);
“determine, for at least one of the one or more identified material components, one or more material characteristics” (Wu, column 42, lines 59-64 discloses “Instructions 3122 include exercising the instructions for the list of applications selected by the operator or set to be exercised (e.g. check that the pixels or image elements pass a threshold (e.g. for a color), then perform the pattern extraction algorithms, reach decisions, transmit decisions to agricultural vehicle)”; where image elements such as color are material characteristics);
“determine, in dependence on the material characteristic(s) for one or more of the identified material components, a first material quality measure indicative of a dimensional measure of identified material component(s)” (Wu, column 20, lines 29-34 discloses “In addition to color, there is texture contrast or shape contrast. For example, residue is thin sticks, spiky, stubs, or dried leaves compared to soil that is more uniform and refined. As another example dry fertilizer, slow-release granular fertilizer are larger in size than soil and thus are distinguishable from soil”; where size is a first material quality measure indicative of a dimensional measure);
“analyze the image data to determine a second material quality measure indicative of a color or hue associated with the image or image components thereof” (Wu, column 20, lines 23-26 discloses “FIG. 9 depicts another example Master Application, an embodiment of a method to determine the uniformity of the spread of dry fertilizer or other objects (e.g. residue) based on using color or texture or shape contrast. For example, residue is often lighter or yellow or greenish compared to soil”; where a color is a second material quality measure);
“determine, in dependence on the first and second material quality measures, a material quality metric” (Wu, column 27, lines 1-7 discloses “As residue exits the combine, it is spread out by the chopper. Imaging alerts operators to uneven spreading and adjustments can be made to ensure uniform ground cover by the residue. Automatic adjustment is implemented by a comparison of whether residue pattern differs from expected values, then the reel speed/height are adjusted when the difference is beyond a selected percentage”; where residue pattern difference is a material quality metric; where “expected values” indicates values, or metrics corresponding to the residue pattern); “and
generate and output one or more control signals for controlling operation of one or more operable components of or otherwise associated with the harvesting machine in dependence on the material quality metric” (Wu, column 42, lines 65-67 discloses “Instructions 3124 include receiving controller information, enacting decision (e.g. the hydraulics raise and lower cultivator frame, the cutting speed is reduced)”; where enacting a decision is generating and outputting control signals for controlling operations; where hydraulics is an operable component of the harvesting machine).
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Figure 9 of Wu
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Figure 30 of Wu
It would be obvious to one of ordinary skill in the art to combine the embodiments of Wu. In particular, Wu discloses the embodiments of Figure 30, the embodiment of Figure 9, and the embodiment of Figure 11. The embodiment of Figure 30 is an “example method of operating an imaging system and its accompanying bank (set) of algorithms” (Wu, column 3, lines 50-52). Wu, column 4, lines 18-20 discloses “Image data collected from different example applications or Master Applications that are described below, are correlated with the crop yield.” Thus, Figure 30 is a general flow chart for the imaging system of Wu, and it would be obvious to one of ordinary skill in the art to apply the particular Master Applications of the embodiments of Fig. 9 and Fig. 11 in combination with the method of Figure 30.
With respect to the embodiment of Figure 11, Wu, column 26, lines 13-15 and lines 29-33 disclose “One aspect of the invention embodiments is to enhance the crop yield (including vegetable yield) by estimating the size and amount of crops produced in each area of a field” and “In example FIG. 11, harvest loss analysis, performed in real time, based on providing an operator the real time visual images to check if the plants are cut appropriately while the operator remains in the cab.” Wu, column 26, lines 45-54 discloses “In some embodiments, the operator's visual check is automated by the sensor units performing analysis of the pattern of the cut plant, whether the image matches a predicted image for a properly-cut plant or for an expected ground residue image (or detecting if there are naked beans or loose pods on the ground in the captured image, or if the identified stalks are choppy and uneven in height). The decision of the analysis is transmitted to the cab or to the reel 128 to make adjustments or to alert the operator (e.g. different alert levels indicate severity of the problem).” With respect to the embodiment of Figure 9, Wu, column 20, lines 54-59 discloses “Corrective action and or alarm levels are raised based on a threshold of undesirable spread. The data from the captured images are also saved or uploaded to a cloud server or to the cab computers, then later used for offline analysis to correlate with the crop yield in that area of the field.”
Both Figure 9 and Figure 11 are directed to analysis of crop yield, and particularly directed to analysis of residue images. Thus, it would be obvious to one of ordinary skill in the art to apply the residue pattern analysis (column 26-27, Figure 11) to the uniformity analysis based on color and size (column 20, Figure 9). That is, it would be obvious to one of ordinary skill in the art that “a comparison of whether residue pattern differs from expected values” may be executed by the disclosed determination of uniformity in color, shape, and size of residue.
Finally, Wu discloses the advantages of combining different solutions to agricultural issues: Wu, column 46, lines 34-40 discloses “There are many advantages of the aforementioned framework to combine different solutions to agricultural issues. By using a framework approach and modular portable devices, multiple tasks and problems are treated by the same single framework and devices, allowing for shared resources and data and re-use, cost reduction, ease learning and upgrades.” Thus, Wu contains a motivation that would have lead one of ordinary skill in the art to combine the embodiments of the prior art to arrive at the claimed invention. Thus, Wu teaches the invention of Claim 1.
Regarding Claim 2, the combination of the embodiments of Wu teaches “A system of claim 1, wherein the imaging device is provided by a user device” ” (Wu, column 4, lines 58-62 discloses “Example units 50 include vision or mobile device or smartphone-type electronics, each making up a local image sensor unit 50 with a co-located CPU processor(s) (e.g. ARM processing chips with RAM and ROM)”; where a smartphone or mobile device is a user device).
Regarding Claim 3, the combination of the embodiments of Wu teaches “A system as claimed in claim 2, wherein the one or more controllers are provided as one or more processors of the user device” (Wu, column 4, lines 58-62 discloses “Example units 50 include vision or mobile device or smartphone-type electronics, each making up a local image sensor unit 50 with a co-located CPU processor(s) (e.g. ARM processing chips with RAM and ROM).”)
Regarding Claim 4, the combination of the embodiments of Wu teaches “A system of claim 1, wherein the identification of the one or more material components includes the performance by the one or more controllers of one or more pre-processing steps on raw image data received from the imaging sensor” (Wu, column 44, lines 38-44 discloses “914, Optionally add another form of data “compression.” Sum or average neighboring pixels to reduce size of image grid (e.g. for fast image processing) in real time. E.g. analyze only core strip part of image to focus on only central strip or crop the captured image, then further reduce size of strip from 2000×2000 to 50×50 (for example) by averaging and filtering”; where reducing size of image grid is pre-processing raw image data).
Regarding Claim 5, the combination of the embodiments of Wu teaches “A system of claim 4, wherein the one or more pre-processing steps include one or more of:(Wu, column 18, lines 10-17 discloses “In some embodiments, to enhance green (e.g. to detect weeds or crops), yellow (e.g. to detect residue) or other colors (e.g. to detect solid fertilizer, dyed spray, etc.), lens filters or software filters (e.g. Matlab or Java libraries color enhancement functions, manipulating the pixels and luminosity) are used to sharpen the contrast between the desired color and other colors, and then select the desired color itself”);
Regarding Claim 6, the combination of the embodiments of Wu teaches “A system of claim 4, wherein the one or more controllers are configured to identify the one or more material components through identification of one or more lines in the image data corresponding to a pixel sequence across a portion of the image data” (Wu, column 44, lines 38-44 discloses “914, Optionally add another form of data “compression.” Sum or average neighboring pixels to reduce size of image grid (e.g. for fast image processing) in real time. E.g. analyze only core strip part of image to focus on only central strip or crop the captured image, then further reduce size of strip from 2000×2000 to 50×50 (for example) by averaging and filtering”; where analyzing only core strip part of image is identifying material components through identification of one or more lines in the image data).
Regarding Claim 8, the combination of the embodiments of Wu teaches “A system of claim 1, wherein the one or more controllers are configured to analyze the one or more material components at one or more image orientations” (Wu, column 31, lines 23-27 discloses “FIG. 23 depicts an example mechanism to tilt (pivot) and/or rotate an image sensing unit 50 or the circuit board 1914. The example pivot joint 2400 includes an electrically controlled servo mechanisms for tilt (pivot) about a horizontal (X) axis and/or rotation about a central vertical (Y) axis”; where a rotatable image sensing unit indicates collected images at one or more image orientations).
Regarding Claim 11, the combination of the embodiments of Wu teaches “A system of claim 1, wherein the first material quality measure relates to a determined average length of identified material components, (Wu, column 24, lines 33-38 discloses “the image sensors in some embodiments, provide information to the CPU to first assess the average height, average color, average mass and other characteristics of the crop. Deviations from the average values are used by the processors to detect anomalous conditions to decide subsequent action”; where average height I an average length).
Regarding Claim 12, the combination of the embodiments of Wu teaches “A system of claim 4, wherein the second material quality measure comprises a measure indicative of a color, or hue associated with the image or image components thereof calculated utilizing processed image data” Wu, column 20, lines 23-26 discloses “FIG. 9 depicts another example Master Application, an embodiment of a method to determine the uniformity of the spread of dry fertilizer or other objects (e.g. residue) based on using color or texture or shape contrast. For example, residue is often lighter or yellow or greenish compared to soil”; where a color is a second material quality measure).
Regarding Claim 13, the combination of the embodiments of Wu teaches “A system of claim 1, wherein the second material quality measure comprises an average color or hue value, (Wu, column 24, lines 33-38 discloses “the image sensors in some embodiments, provide information to the CPU to first assess the average height, average color, average mass and other characteristics of the crop. Deviations from the average values are used by the processors to detect anomalous conditions to decide subsequent action”; where average color is an average color value).
Regarding Claim 16, the combination of the embodiments of Wu teaches “A system of claim 1, wherein the one or more operable components of or otherwise associated with the harvesting machine include a user interface for providing a representation of the determined material quality metric; optionally wherein the user interface comprises a display screen of a user device” (Wu, column 19, lines 28-38 discloses “FIG. 7 depicts one example way of organizing and displaying the different applications. Example icons 80 represent the different Master Applications or other processes, and the icons are displayed on a screen 82, console screen, a laptop screen, a tablet and so on. By voice command or by tapping on a touchscreen, the operator selects an icon 80 to select a Master Application, or once the operator selects a Master Application, additional options are selectable (e.g. to select different image sensor units 50 for a visual check, monitoring or control, or calibration purposes).”)
Regarding Claim 17, the combination of the embodiments of Wu teaches “A system of claim 1, wherein the one or more operable components comprises a database; and wherein the one or more controllers are configured to add data to, or update data stored within the databased in dependence on the determined material quality metric for the generation and/or updating a mapped representation of a working environment for the machine, mapping material quality across the environment” (Wu, column 11, lines 37-43 discloses “In some embodiments, the software is user-programmable to add algorithms or applications, to make easy revisions to factory-installed code, to add features, e.g. to tag and track rice seeds instead of soybeans pods, update databases that contain data on the physical features of weeds or new forms of weeds”; it would be obvious to one of ordinary skill in the art that adding features may include updating determined material quality metric, or residue pattern difference, and is not limited to the exemplary rice seeds and weeds recited in column 11).
Regarding Claim 18, the combination of the embodiments of Wu teaches “A system of claim 1, wherein the one or more operable components include a control system for the harvesting machine itself, one or more components thereof, or a working implement operably coupled thereto” (Wu, column 43, lines 49-56 discloses “The image sensor units 50 also have transceiver circuits so that input signals (e.g. speed of travel, wind velocity) and output signals (e.g. “weed pattern found” or “uneven residue pattern found”) are transmitted either directly to another equipment on the agricultural vehicle (e.g. reel, spray nozzle to exercise actions) or they go to the central controllers in the cab or main computer hub of the agricultural vehicle”; where central controllers in the cab of the agricultural vehicle is a control system for the harvesting machine).
Regarding Claim 19, the combination of the embodiments of Wu teaches “An agricultural harvesting machine comprising or controllable under operation of the system of claim 1” (Wu, column 3, lines 61-66, discloses “This disclosure relates to an organic, portable guidance and monitoring system framework and devices that can be mounted on agricultural vehicles (e.g. planters, spray tractors, tillage tractors, windrowers, combines, vegetable harvester) to control and exercise a suite of crop management practices by using a portable set of imaging devices”).
Regarding Claim 20, Claim 20 recites a method with steps corresponding to the elements of the system recited in Claim 1. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Additionally, the rationale and motivation to combine the embodiments of the Wu references, presented in rejection of Claim 1, apply to this claim.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 10255670 B1) in view of Brueckner et al. (US 2014/0050364 A1).
Regarding Claim 7, Wu does not explicitly teach “A system of claim 1, wherein the one or more controllers are configured to identify a material component through comparison of the length of a candidate component with a threshold length.”
However, in an analogous field of endeavor, Brueckner teaches ““A system of claim 1, wherein the one or more controllers are configured to identify a material component through comparison of the length of a candidate component with a threshold length” (Brueckner, [0016] discloses “The individual objects can be processed further in a size-dependent manner by individual objects lying below a threshold value of one or several dimensions being treated as background and/or individual objects lying above a threshold value of one or several dimensions being divided up into several individual objects by local brightness scaling and an application of a cutting mask with sharper discrimination parameters than in the case of the previous application of the cutting mask.”)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to have modified the combined embodiments of Wu to incorporate the teachings of Brueckner by processing objects in a size-dependent matter and using a threshold value of dimension. One of ordinary skill in the art would be motivated to combine the Wu and Brueckner references in order to improve evaluation of harvested crops (Brueckner, [0006] discloses “According to the present invention, there is provided an improved method and apparatus for optically evaluating harvested crop in a harvesting machine.”) Accordingly, the combination of Wu and Brueckner discloses the invention of Claim 7.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 10255670 B1) in view of Henry et al. (US 11538180 B2).
Regarding Claim 9, Wu does not explicitly teach the system of Claim 9.
However, in an analogous field of endeavor, Henry teaches “A system of claim 8, wherein analyzing the one or more material components at one or more image orientations comprises determining an average length of identified crop material components at one or more image orientations” (Henry, [0080] discloses “For example, as described above, the computing system 110 may determine the length(s) of the identified residue piece(s). Thereafter, the computing system 110 may determine the average of the residue piece length(s) present within the imaged portion of the imaged or a statistical distribution of such length(s)”); “
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to have modified the combined embodiments of Wu to incorporate the teachings of Henry by determining average residue piece length in an image of residue. One of ordinary skill in the art would be motivated to combine the Wu and Henry references in order to improve accuracy of residue coverage determinations: Henry, [0026] discloses “Determining the residue length within the field based on the image gradients or texture depicted within captured images improves the accuracy the residue coverage determinations.” Accordingly, the combination of Wu and Henry discloses the invention of Claim 9.
Regarding Claim 10, the combination of Wu and Henry teaches “A system of claim 9, wherein the one or more controllers are configured to select a primary orientation for determination of the first material quality measure, the primary orientation being selected in dependence on the image orientation at which the highest average length for the material component(s) is determined, or the primary orientation being selected in dependence on the image orientation at which the largest number of material components are identified” (Henry, [0025] discloses “. Furthermore, the computing system may determine an image gradient orientation at each of the captured image. Based at least in part on the determined image gradient orientations, the computing system may identify one or more residue pieces present within the imaged portion of the field. Thereafter, the computing system may determine the length or longest dimension of each identified residue piece.”) The proposed combination as well as the motivation for combining the Wu and Henry references presented in the rejection of Claim 9, are incorporated into Claim 10 and are incorporated herein by reference. Thus, the apparatus recited in Claim 10 is taught by Wu and Henry.
Allowable Subject Matter
Claims 14 and 15 are rejected under 35 U.S.C. 112(a), but would be allowable if the rejection under 35 U.S.C. 112(a) is overcome, and rewritten to include all of the limitations of the base claim and any intervening claims.
Regarding Claim 14, none of the previously cited prior art teaches the invention of Claim 14. Wu discloses a size and color determination, and thus discloses first and second material quality measures (Wu, column 20, lines 29-34 discloses “In addition to color, there is texture contrast or shape contrast. For example, residue is thin sticks, spiky, stubs, or dried leaves compared to soil that is more uniform and refined. As another example dry fertilizer, slow-release granular fertilizer are larger in size than soil and thus are distinguishable from soil”; where size is a first material quality measure indicative of a dimensional measure); (Wu, column 20, lines 23-26 discloses “FIG. 9 depicts another example Master Application, an embodiment of a method to determine the uniformity of the spread of dry fertilizer or other objects (e.g. residue) based on using color or texture or shape contrast. For example, residue is often lighter or yellow or greenish compared to soil”; where a color is a second material quality measure). However, Wu does not explicitly teach “a comparison of the first and second material quality measures.” Wu and Brueckner teach comparing determined values to a threshold, but the cited prior art only teaches comparing a color value to a color threshold and a length value to a length threshold. The cited prior art does not teach comparing first and second material quality measures to each other as required by Claim 14. Even under the broadest reasonable interpretation, Claim 14 cannot be construed to mean a comparison of a first material quality measure to some other value and a second material quality measure to some other value because, as best understood in light of the specification, “a comparison of the first and second material quality measures” requires the first and second material quality measures be compared directly.
Thus, none of the previously cited prior art, alone or in combination, provides a motivation to teach the ordered combination of “A system of claim 1, wherein the material quality metric is determined in dependence on a comparison of the first and second material quality measures.”
Claim 15 depends from Claim 14 and thus contains all allowable subject matter of Claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Behnke (US 2009/0125197 A1) discloses a method for monitoring the quality of crop material using image analysis, particularly by measuring a proportion (by surface area) of undesired particles to the area of entire crop photographs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAROLINE TABANCAY DUFFY whose telephone number is (703)756-1859. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
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/CAROLINE TABANCAY DUFFY/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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