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
This action is in response to amendments and remarks filed on 08/06/2025. The examiner notes the following adjustments to the claims by the applicant:
Claims 1-2, 8 and 17 are amended;
No claims are cancelled or added.
Therefore, Claims 1-20 are pending examination, in which Claims 1, 8 and 17 are independent claims.
In light of the instant amendments and arguments:
The objection to Claims 1, 8 and 17 for informalities is maintained.
The objection to Claim 2 under 35 U.S.C. 112(d) is withdrawn.
The rejection to Claims 1-20 under 35 U.S.C. 101 is maintained.
Further examination resulted in a new rejection of Claims 1-20 under 35 U.S.C. § 103, as detailed below.
THIS ACTION IS MADE FINAL. Necessitated by amendment.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
To overcome the 35 U.S.C. § 103 rejection, the applicant has amended each independent claim to include the additional underlined limitations: "each classification goal being distinguishable by an objective of the classification goal, the objective corresponding to a plan for the plurality of crop bales…generate a signal utilized with an agricultural machine to perform an operation on the crop bale in a field based on the category of the two or more categories of the selected classification goal that the crop bale is grouped.“ ;
“In addressing Nona and features of claim 1 relating to classification goals, the Office Action cites paragraph 39 of Nona, and specifically features relating to the use of two or more bale parameters to determine a bale quality parameter, the use of look-up tables, and an algorithm. (Office Action at page 9.) Yet, nothing in this cited portion of Nona relates to ""... receive a selection of a classification goal from a plurality of classification goals ..." as recited in claim 1. The Applicant further respectfully submits that such features of paragraph 39 also neither teach nor suggest features of amended independent claim 1 of "... each classification goal being distinguishable by an objective of the classification goal, the objective corresponding to a plan for the plurality of crop bales ...." Indeed, to the contrary, as paragraph 37 discusses, Nona is instead merely related to "... the quality of the subsequently produced bales." (Nona at 35.)”;
“The Applicant respectfully submits that the deficiencies of Nona are not cured by Ghosh. For example, Ghosh relates to laying down bales in the context of textile manufacturing by describing a technique for grouping cotton bales to create consistent bale mixes for spinning operations. (Ghosh at pages 1-6.) Similar to Nona, Ghosh is also devoid of any teaching or suggestion of the receipt of a selection of a classification goal from a plurality of classification goals, each classification goal being distinguishable by an objective corresponding to a plan for the plurality of crop bales.”.
Applicant's arguments A., B. and C. appear to be directed to the instantly amended subject matter. Accordingly, they have been addressed in the rejections below.
Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
As described in MPEP § 2106, the analyses as to whether a claim qualifies as eligible subject matter under 35 U.S.C. § 101 includes the following determinations:
(1) Whether the claim is to a statutory category, i.e. to a process, machine, manufacture or composition of matter ("Step 1")- see MPEP §§ 2106, subsection III, and 2106.03.
(2) If the claim is to a statutory category, whether the claim recites any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes) ("Step 2A, Prong One") - see MPEP §§ 2106, subsection III, and 2106.04.
(3) If the claim recites a judicial exception, whether the claim recites additional elements that integrate the judicial exception into a practical application ("Step 2A, Prong Two") - see MPEP §§ 2106, subsection III, and 2106.04.
(4) If the claim does not recite additional elements that integrate the judicial exception into a practical application, whether the claim recites additional elements that amount to significantly more than the judicial exception ("Step 2B") – see MPEP §§ 2106, subsection III, and 2106.05.
Step 1: Claims 1-7 are a system, Claims 8-16 are a method, and Claims 17-20 are a computer implemented method. Thus, each independent claim, on its face, is directed to one of the four statutory categories of 35 U.S.C. §101 (MPEP 2106.03).
Claim 1 is considered a representative independent claim. The examiner has determined, the following analysis is applicable to each independent claim. With regard to Claim 1:
A system for grouping crop bales, the system comprising: a constituent sensor for analyzing, for each crop bale of a plurality of crop bales, a plurality of properties of a crop material of the crop bale; at least one processor; and a memory device coupled to the at least one processor, the memory device including instructions that when executed by the at least one processor cause at least one processor to: receive a selection of a classification goal from a plurality of classification goals, each classification goal being distinguishable by an objective of the classification goal, the objective corresponding to a plan for the plurality of crop bales, each of the plurality of classification goals (1) having two or more categories, and (2) assigns a weightage to the plurality of properties, wherein the weightage assigned to at least one property of the plurality of properties for one classification goal of the plurality of classification goals is different than the weightage assigned to the at least one property for at least another classification goal of the plurality of classification goals; record, in a database and for each crop bale of the plurality of crop bales, a dataset corresponding to the plurality of properties; assign, using a k-means clustering analysis, the dataset of each crop bale of the plurality of crop bales to a cluster of a plurality of clusters, each cluster of the plurality of clusters corresponding to one category of the two or more categories of the selected classification goal; determine a location of a centroid for each cluster of the plurality of clusters utilizing the weightage assigned for the selected classification goal; and group each crop bale of the plurality of crop bales into a category of the two or more categories of the selected classification goal based on the cluster to which the dataset associated with the crop bale was assigned; and generating a signal utilized with an agricultural machine to perform an operation on the crop bale in a field based on the category of the two or more categories of the selected classification goal that the crop bale is grouped.
Step 2A, Prong 1:
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
The examiner submits that, under its broadest reasonable interpretation, the foregoing bolded limitations constitute a combination of mathematical-concepts and mental-processes. Specifically, Claim 1 recites the general idea of using a generic computer [“at least one processor; and a memory device coupled to the at least one processor, the memory device including instructions that when executed by the at least one processor cause at least one processor to:”] for grouping bales (of animal feed) into classifications and sub-classifications [“grouping crop bales…for each crop bale of a plurality of crop bales, a plurality of properties of a crop material of the crop bale… each classification goal being distinguishable by an objective of the classification goal, the objective corresponding to a plan for the plurality of crop bales, each of the plurality of classification goals (1) having two or more categories…and group each crop bale of the plurality of crop bales into a category of the two or more categories of the selected classification goal based on the cluster to which the dataset associated with the crop bale was assigned”] by assigning different weightings to various properties of each bale [“assigns a weightage to the plurality of properties, wherein the weightage assigned to at least one property of the plurality of properties for one classification goal of the plurality of classification goals is different than the weightage assigned to the at least one property for at least another classification goal of the plurality of classification goals”]; performing a statistical analysis on the data [“assign, using a k-means clustering analysis, the dataset of each crop bale of the plurality of crop bales to a cluster of a plurality of clusters, each cluster of the plurality of clusters corresponding to one category of the two or more categories of the selected classification goal”]; and documenting this data in a database [“record, in a database and for each crop bale of the plurality of crop bales, a dataset corresponding to the plurality of properties”].
These steps can be done by a combination of the mathematical and mental skills of a person, in combination with pen and paper, as needed. Thus, the claim recites a simple process, which under its broadest reasonable interpretation, recites a combination of abstract ideas capable of being performed by person [See MPEP § 2106.04(a)(2)].
Step 2A, Prong 2:
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer or processor to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The examiner submits that the foregoing underlined additional limitations do not integrate the above-noted abstract idea into a practical application. The examiner contends that the additional limitations of “a constituent sensor for analyzing” and “receive a selection of a classification goal from a plurality of classification goals” are associated with data gathering and thus constitute insignificant extra-solution activity [MPEP 2106.05(g)], that merely provides new data for analysis and processing. In addition, the additional limitation “generating a signal utilized with an agricultural machine to perform an operation on the crop bale in a field based on the category of the two or more categories of the selected classification goal that the crop bale is grouped” merely links the judicial exception, in a general manner, to a particular technological field of use [MPEP 2106.05(h)]: specifically, operating a baling machine.
The additional limitation “a memory device coupled to the at least one processor, the memory device including instructions that when executed by the at least one processor cause at least one processor to:” is recited at such a high level of generality as to be the equivalent of a generic computing device on which a judicial exceptions is applied:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
The courts have deemed that implementation of an abstract idea by a generic computer is equivalent to human performing the abstract idea
Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
On the other hand, courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257-59, 113 USPQ2d 1097, 1105-07 (Fed. Cir. 2014).
Thus, these additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The examiner further submits that the aforementioned additional elements in Claim 1 are not sufficient to amount to significantly more than the judicial exception for the same reason discussed above for Step 2A, Prong 2. Specifically, the additional limitations of “a constituent sensor for analyzing”, “receive a selection of a classification goal from a plurality of classification goals” and “generating a signal utilized with an agricultural machine to perform an operation on the crop bale in a field based on the category of the two or more categories of the selected classification goal that the crop bale is grouped” simply link the judicial exception to a particular technological environment (i.e., processing crop bales) such that the claim as a whole is no more than a drafting effort designed to monopolize the exception [MPEP 2106.04(d)(2) and 2106.05(e)]. Nor does the use of a generic computer [i.e., “a memory device coupled to the at least one processor, the memory device including instructions that when executed by the at least one processor cause at least one processor to:”] to analyze data provide an inventive concept. Hence, the claim is not patent eligible.
The examiner finds that independent Claims 8 and 17 include the same limitations as Claim 1 associated with “grouping crop bales” using a generic computer (discussed above under Step 2A, Prong 1). Thus, each of these claims, under its broadest reasonable interpretation, constitute an abstract idea comprised of a combination of “mathematical concepts” and “mental processes”.
Dependent: Claims 2-7, 9-16 and 18-20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. The claimed invention is directed to additional abstract ideas associated with “mathematical concept” and/or “methods of organizing human activities”:
• planning crop bale formation using agricultural harvesting machine (Claim 2);
• categories relate to nutritional content of a bale (Claims 3, 12 & 18);
• classification relates to bale storage and nutritional categories (Claims 4, 13 & 19);
• classification relates to animal feeding plan and the type of animal (Claims 5, 14 & 20);
• selecting nutritional properties (Claims 6 and 15);
• determine relative feed value (Claims 7 and 16);
• implement k-means analysis (Claim 9);
• determine bale location and displaying (Claim 10).
Therefore, Claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 6-13 and 15-19 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Nona et al. (US 2023/0032085 A1, henceforth Nona) and Gosh et al. ("A technique of cotton bale laydown using clustering algorithm", henceforth Gosh).
Regarding Claim 1, Nona discloses the limitations: a system for grouping crop bales {“A method for determining a quality of an agricultural bale produced by an agricultural baler”, Abstract}, the system comprising: a constituent sensor for analyzing, for each crop bale of a plurality of crop bales, a plurality of properties of a crop material of the crop bale {“sensors 121, 122, 123 that may be useful for determining relevant bale parameters that can be used for determining a bale quality parameter include, but are not limited to, moisture sensors, protein content sensors, bale weight sensors, or twine tension sensors”, ¶[0034]}; at least one processor; and a memory device coupled to the at least one processor, the memory device including instructions that when executed by the at least one processor cause at least one processor {“FIG. 3 schematically shows a system 100 according to an embodiment of the present invention. The system includes a controller 110, configured to receive input signals from a plurality of sensors 40, 121, 122, 123, and to provide output signals to, e.g., a display screen 130.”, ¶[0033]} to: receive a selection of a classification goal from a plurality of classification goals {a wide variety of bale parameters are discussed in ¶[0007-0008 & 0039], which one skilled in the art will appreciated can be grouped in different ways to define different categories}, each classification goal being distinguishable by an objective of the classification goal, the objective corresponding to a plan for the plurality of crop bales, each of the plurality of classification goals (1) having two or more categories {“Two, three or more of these and other useful bale parameters are used as a basis for determining the bale quality parameter. The bale quality parameter may, for example, be determined using a multi-dimensional lookup table”, ¶[0039], wherein one skilled in the art will appreciate that the bale parameter data captured by the various sensors in ¶[0033-0034], can be grouped in a variety of ways, such as the common approach of low, medium and high quality categorization: “The first and second bale parameters each represent a different physical property of the bale. The bale quality parameter is determined based on the respective bale parameter signals and is indicative of a perceived quality of the agricultural bale.”, ¶[0010]}, and (2) assigns a weightage to the plurality of properties, wherein the weightage assigned to at least one property of the plurality of properties for one classification goal of the plurality of classification goals is different than the weightage assigned to the at least one property for at least another classification goal of the plurality of classification goals {“The bale quality parameter may, for example, be determined using a multi-dimensional lookup table or an algorithm using all obtained bale parameter values as input. Such an algorithm may include one or more weighting factors that can be adjusted by the operator in order to assign different priorities to different bale parameters.”, ¶[0033]}; record, in a database and for each crop bale of the plurality of crop bales, a dataset corresponding to the plurality of properties {recording of data related to quality and drop-off location: “controller 110…if the bale quality parameter is only used for recording the perceived bale quality of each bale 340, then the bale quality parameter may not need to be communicated to the operator at all.”, ¶[0041], and “The bale quality parameter is then stored in a memory in association with the drop-off location. As a result, it becomes possible to draw a map indicating how bale quality may vary over a field, which information may, for example, be useful for planning future tillage and planting decisions.”, ¶[0044]}; and generate a signal utilized with an agricultural machine to perform an operation on the crop bale in a field {with regard to Fig. 3, sensor 40 & 121-123 are in communication with a controller and display screen: “to provide output signals to, e.g., a display screen 130. Typically, the controller 110 and the display screen 130 are located on the tractor pulling and powering the agricultural baler 10, while most of the sensors 121, 122, 123 are part of the agricultural baler 10.”, ¶[0033]} based on the category of the two or more categories of the selected classification goal that the crop bale is grouped {on skilled in the art will appreciate that if “moisture content, or moisture content distribution” leads to significant lowering of the “bale quality parameter” (¶[0039-0040]) the operator can choose to temporarily cease the baling process}.
Nona does not appear to explicitly recite the limitations: assign, using a k-means clustering analysis, the dataset of each crop bale of the plurality of crop bales to a cluster of a plurality of clusters, each cluster of the plurality of clusters corresponding to one category of the two or more categories of the selected classification goal; determine a location of a centroid for each cluster of the plurality of clusters utilizing the weightage assigned for the selected classification goal; and group each crop bale of the plurality of crop bales into a category of the two or more categories of the selected classification goal based on the cluster to which the dataset associated with the crop bale was assigned.
However, Gosh explicitly recites the limitation: {“cotton bale management using clustering algorithm”, Abstract} comprising: assign, using a k-means clustering analysis {section beginning on Pg. 809, Col. 2: “K-means Square Clustering Algorithm”}, the dataset of each crop bale of the plurality of crop bales to a cluster of a plurality of clusters, each cluster of the plurality of clusters corresponding to one category of the two or more categories of the selected classification goal; determine a location of a centroid for each cluster of the plurality of clusters utilizing the weightage assigned for the selected classification goal; and group each crop bale of the plurality of crop bales into a category of the two or more categories of the selected classification goal based on the cluster to which the dataset associated with the crop bale was assigned {“The method is based on the grouping cotton bales of similar kind into respective categories using k-mean square clustering algorithm. A set of 500 cotton bales were clustered into 5 categories by minimizing the total within-group squared Euclidean distance around the centroids. In order to cluster bales of different categories, 8 fibre properties, viz., strength, elongation, upper half mean length, length uniformity, short fibre content, micronaire, reflectance and yellowness of each bale have been considered”, Abstract}.
Nona and Gosh are analogous art because they both deal with crop management.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Nona and Gosh before them, to modify the teachings of Nona to include the teachings of Gosh to establish bale quality groupings, and categorization, using a standard algorithmic approach.
Regarding Claim 2, the combination of Nona and Gosh discloses all the limitations of Claim 1, as discussed supra. In addition, Nona explicitly recites the limitation: further including an agricultural harvesting machine {Fig. 1} configured to collect the crop material from a field and having a baling chamber for compression of the crop material during formation of the crop bale {crop compression described in ¶[0027] involving the coordinated operation of a pre-compression chamber 22, a stuffer unit 24, a bale chamber 26, a stuffer forks 28 and a plunger 30}, the constituent sensor being coupled to the agricultural harvesting machine {“to provide output signals to, e.g., a display screen 130. Typically, the controller 110 and the display screen 130 are located on the tractor pulling and powering the agricultural baler 10, while most of the sensors 121, 122, 123 are part of the agricultural baler 10.”, ¶[0033]}.
Regarding Claim 3, the combination of Nona and Gosh discloses all the limitations of Claim 1, as discussed supra. In addition, Nona explicitly recites the limitation: wherein at least one of the plurality of classification goals is a nutritional content goal for the crop bales, and wherein each category of the two or more categories for the nutritional content goal is assigned to a different range of nutritional content {quality parameters include color/color distribution, moisture content and protein content and distribution directly relate to nutritional content, which varying combination reflect the nutritional value of the crop: “the first or second bale parameter represents one of a bale size, a bale shape, a bale weight, a bale color, a bale color distribution, a slice thickness distribution, twine tension, twine integrity, protein content, protein content distribution, moisture content, or moisture content distribution. Two, three or more of these and other useful bale parameters may be used as a basis for determining the bale quality parameter.”, ¶[0033]; and “Two, three or more of these and other useful bale parameters are used as a basis for determining the bale quality parameter. The bale quality parameter may, for example, be determined using a multi-dimensional lookup table”, ¶[0039]}.
Regarding Claim 4, the combination of Nona and Gosh discloses all the limitations of Claim 1, as discussed supra. In addition, Nona explicitly recites the limitation: wherein at least one of the plurality of classification goals is a storage plan {“Agricultural balers are used to consolidate and package crop material so as to facilitate the storage and handling of the crop material for later use”, ¶[0003], and “the controller further receives a location signal from a location sensor, such as a GPS sensor, the location signal being indicative of a drop-off location of the agricultural bale when it is released from the agricultural baler. The bale quality parameter is then stored in a memory in association with the drop-off location. As a result, it becomes possible to draw a map indicating how bale quality may vary over a field, which information may, for example, be useful for planning future tillage and planting decisions.”, ¶[0018]}, and wherein the two or more categories of the storage plan relate to a moisture content and a physical weight of the crop bale {quality parameters include color/color distribution, moisture content and protein content and distribution directly relate to nutritional content, which varying combination reflect the nutritional value of the crop: “the first or second bale parameter represents one of a bale size, a bale shape, a bale weight, a bale color, a bale color distribution, a slice thickness distribution, twine tension, twine integrity, protein content, protein content distribution, moisture content, or moisture content distribution. Two, three or more of these and other useful bale parameters may be used as a basis for determining the bale quality parameter.”, ¶[0033]}.
Regarding Claim 6, the combination of Nona and Gosh discloses all the limitations of Claim 1, as discussed supra. In addition, Nona explicitly recites the limitation: wherein the plurality of properties comprises at least two of: a moisture content, a dry matter content, an acid detergent fiber, a neutral detergent fiber, and a crude protein {“the first or second bale parameter represents one of a bale size, a bale shape, a bale weight, a bale color, a bale color distribution, a slice thickness distribution, twine tension, twine integrity, protein content, protein content distribution, moisture content, or moisture content distribution. Two, three or more of these and other useful bale parameters may be used as a basis for determining the bale quality parameter.”, ¶[0033]}.
Regarding Claim 7, the combination of Nona and Gosh discloses all the limitations of Claim 1, as discussed supra. In addition, Nona explicitly recites the limitation: wherein the plurality of properties includes a relative feed value for each crop bale {bale quality parameter: “the first or second bale parameter represents one of a bale size, a bale shape, a bale weight, a bale color, a bale color distribution, a slice thickness distribution, twine tension, twine integrity, protein content, protein content distribution, moisture content, or moisture content distribution. Two, three or more of these and other useful bale parameters may be used as a basis for determining the bale quality parameter.”, ¶[0033]}.
Regarding Claim 8, Nona discloses the limitations: a method for grouping crop bales {“A method for determining a quality of an agricultural bale produced by an agricultural baler”, Abstract}, the method comprising: sensing, by a constituent sensor, and for each crop bale of a plurality of crop bales, a plurality of properties of a crop material of the crop bale {“sensors 121, 122, 123 that may be useful for determining relevant bale parameters that can be used for determining a bale quality parameter include, but are not limited to, moisture sensors, protein content sensors, bale weight sensors, or twine tension sensors”, ¶[0034]}; recording, by at least one controller having a processor {110, Fig. 3; “A controller on board the tractor may calculate the bale quality parameter based on sensor data received from the camera 40 and sensors 121, 122, 123 or may just receive a calculated bale quality parameter from a processor on the agricultural baler 10”, ¶[0035]}, for each crop bale, the sensed plurality of properties as a dataset {recording of data related to quality and drop-off location: “controller 110…if the bale quality parameter is only used for recording the perceived bale quality of each bale 340, then the bale quality parameter may not need to be communicated to the operator at all.”, ¶[0041], and “The bale quality parameter is then stored in a memory in association with the drop-off location. As a result, it becomes possible to draw a map indicating how bale quality may vary over a field, which information may, for example, be useful for planning future tillage and planting decisions.”, ¶[0044]}; receiving, by the least one controller, a signal indicating a classification goal selected from a plurality of classification goals {a wide variety of bale parameters are discussed in ¶[0007-0008 & 0039], which one skilled in the art will appreciated can be grouped in different ways to define different categories}, each classification goal of the plurality of classification goals having two or more categories, and being distinguishable by an objective of the classification goal, the objective corresponding to a plan for the plurality of crop bales {“Two, three or more of these and other useful bale parameters are used as a basis for determining the bale quality parameter. The bale quality parameter may, for example, be determined using a multi-dimensional lookup table”, ¶[0039], wherein one skilled in the art will appreciate that the bale parameter data captured by the various sensors in ¶[0033-0034], can be grouped in a variety of ways, such as the common approach of low, medium and high quality categorization: “The first and second bale parameters each represent a different physical property of the bale. The bale quality parameter is determined based on the respective bale parameter signals and is indicative of a perceived quality of the agricultural bale.”, ¶[0010]}, wherein at least one classification goal of the plurality of classification goals assigns a weightage to at least one property of the plurality of properties that is different than a weightage assigned to the at least one property by at least another classification goal of the plurality of classification goals {“The bale quality parameter may, for example, be determined using a multi-dimensional lookup table or an algorithm using all obtained bale parameter values as input. Such an algorithm may include one or more weighting factors that can be adjusted by the operator in order to assign different priorities to different bale parameters.”, ¶[0033]}; generating, by the at least one controller, a map for display on a display, the map indicating the category to which the dataset for at least one crop bale was assigned {“FIG. 3 schematically shows a system 100 according to an embodiment of the present invention. The system includes a controller 110, configured to receive input signals from a plurality of sensors 40, 121, 122, 123, and to provide output signals to, e.g., a display screen 130.”, ¶[0033]}; and generate a signal utilized with an agricultural machine to perform an operation on the crop bale in a field {with regard to Fig. 3, sensor 40 & 121-123 are in communication with a controller and display screen: “to provide output signals to, e.g., a display screen 130. Typically, the controller 110 and the display screen 130 are located on the tractor pulling and powering the agricultural baler 10, while most of the sensors 121, 122, 123 are part of the agricultural baler 10.”, ¶[0033]} based on the category of the two or more categories of the selected classification goal that the crop bale is grouped {on skilled in the art will appreciate that if “moisture content, or moisture content distribution” leads to significant lowering of the “bale quality parameter” (¶[0039-0040]) the operator can choose to temporarily cease the baling process}.
Nona does not appear to explicitly recite the limitations: assigning, using a k-means clustering analysis, the dataset of each crop bale of the plurality of crop bales to a cluster of a plurality of clusters, each cluster of the plurality of clusters corresponding to one category of the two or more categories of the selected classification goal; determining a location of a centroid for each cluster of the plurality of clusters utilizing the weightage assigned for the selected classification goal; and grouping each crop bale of the plurality of crop bales into a category of the two or more categories of the selected classification goal based on the cluster to which the dataset associated with the crop bale was assigned.
However, Gosh explicitly recites the limitation: {“cotton bale management using clustering algorithm”, Abstract} comprising: assigning, using a k-means clustering analysis {section beginning on Pg. 809, Col. 2: “K-means Square Clustering Algorithm”}, the dataset of each crop bale of the plurality of crop bales to a cluster of a plurality of clusters, each cluster of the plurality of clusters corresponding to one category of the two or more categories of the selected classification goal; determining a location of a centroid for each cluster of the plurality of clusters utilizing the weightage assigned for the selected classification goal; and grouping each crop bale of the plurality of crop bales into a category of the two or more categories of the selected classification goal based on the cluster to which the dataset associated with the crop bale was assigned {“The method is based on the grouping cotton bales of similar kind into respective categories using k-mean square clustering algorithm. A set of 500 cotton bales were clustered into 5 categories by minimizing the total within-group squared Euclidean distance around the centroids. In order to cluster bales of different categories, 8 fibre properties, viz., strength, elongation, upper half mean length, length uniformity, short fibre content, micronaire, reflectance and yellowness of each bale have been considered”, Abstract}.
Regarding Claim 9, the combination of Nona and Gosh discloses all the limitations of Claim 8, as discussed supra. Nona does not appear to explicitly recite the limitations: wherein the step of assigning comprises assigning, using a k-means analysis, each crop bale of the plurality of crop bales to a cluster of a plurality of clusters, each cluster of the plurality of clusters corresponding to one of the two or more categories of the selected classification goal, and wherein a determination of a location of a centroid for each cluster of the plurality of clusters utilizes the weightage assigned for the selected classification goal.
However, Gosh explicitly recites the limitation: wherein the step of assigning comprises assigning, using a k-means analysis {section beginning on Pg. 809, Col. 2: “K-means Square Clustering Algorithm”}, each crop bale of the plurality of crop bales to a cluster of a plurality of clusters, each cluster of the plurality of clusters corresponding to one of the two or more categories of the selected classification goal, and wherein a determination of a location of a centroid for each cluster of the plurality of clusters utilizes the weightage assigned for the selected classification goal {“The method is based on the grouping cotton bales of similar kind into respective categories using k-mean square clustering algorithm. A set of 500 cotton bales were clustered into 5 categories by minimizing the total within-group squared Euclidean distance around the centroids. In order to cluster bales of different categories, 8 fibre properties, viz., strength, elongation, upper half mean length, length uniformity, short fibre content, micronaire, reflectance and yellowness of each bale have been considered”, Abstract}.
Regarding Claim 10, the combination of Nona and Gosh discloses all the limitations of Claim 9, as discussed supra. In addition, Nona explicitly recites the limitation: further including: determining, using a location system, a location for each crop bale of the plurality of crop bales {“controller 110…if the bale quality parameter is only used for recording the perceived bale quality of each bale 340, then the bale quality parameter may not need to be communicated to the operator at all.”, ¶[0041], and “The bale quality parameter is then stored in a memory in association with the drop-off location. As a result, it becomes possible to draw a map indicating how bale quality may vary over a field, which information may, for example, be useful for planning future tillage and planting decisions.”, ¶[0044]}; and displaying the map on the display, the map further including the location of the at least one crop bale {“FIG. 3 schematically shows a system 100 according to an embodiment of the present invention. The system includes a controller 110, configured to receive input signals from a plurality of sensors 40, 121, 122, 123, and to provide output signals to, e.g., a display screen 130.”, ¶[0033]}.
Regarding Claim 11, the combination of Nona and Gosh discloses all the limitations of Claim 8, as discussed supra. In addition, Nona explicitly recites the limitation: further comprising: displaying the map and the icon on the display {“FIG. 3 schematically shows a system 100 according to an embodiment of the present invention. The system includes a controller 110, configured to receive input signals from a plurality of sensors 40, 121, 122, 123, and to provide output signals to, e.g., a display screen 130.”, ¶[0033]}. Nona does not appear to explicitly recite the limitations: further comprising: assigning, based on the assigned category, an icon to the at least one crop bale, the icon including a visual indicator for display on the map that is associated with the category that the at least one crop bale was assigned, the visual indicator being visually distinctive from another visual indicator that is associated with a different category of the two more categories.
However, Gosh explicitly recites the limitation: further comprising: assigning, based on the assigned category, an icon to the at least one crop bale, the icon including a visual indicator for display on the map that is associated with the category that the at least one crop bale was assigned, the visual indicator being visually distinctive from another visual indicator that is associated with a different category of the two more categories {crop bale clustering, visual representation approach in Fig. 2}.
Regarding Claim 12, the combination of Nona and Gosh discloses all the limitations of Claim 8, as discussed supra. In addition, Nona explicitly recites the limitation: wherein at least one of the plurality of classification goals is a nutritional content goal for the crop bales, and wherein each category of the two or more categories for the nutritional content goal is assigned to a different range of nutritional content {quality parameters include color/color distribution, moisture content and protein content and distribution directly relate to nutritional content, which varying combination reflect the nutritional value of the crop: “the first or second bale parameter represents one of a bale size, a bale shape, a bale weight, a bale color, a bale color distribution, a slice thickness distribution, twine tension, twine integrity, protein content, protein content distribution, moisture content, or moisture content distribution. Two, three or more of these and other useful bale parameters may be used as a basis for determining the bale quality parameter.”, ¶[0033]; and “Two, three or more of these and other useful bale parameters are used as a basis for determining the bale quality parameter. The bale quality parameter may, for example, be determined using a multi-dimensional lookup table”, ¶[0039]}.
Regarding Claim 13, the combination of Nona and Gosh discloses all the limitations of Claim 12, as discussed supra. In addition, Nona explicitly recites the limitation: wherein at least one of the plurality of classification goals is a storage plan {“Agricultural balers are used to consolidate and package crop material so as to facilitate the storage and handling of the crop material for later use”, ¶[0003], and “the controller further receives a location signal from a location sensor, such as a GPS sensor, the location signal being indicative of a drop-off location of the agricultural bale when it is released from the agricultural baler. The bale quality parameter is then stored in a memory in association with the drop-off location. As a result, it becomes possible to draw a map indicating how bale quality may vary over a field, which information may, for example, be useful for planning future tillage and planting decisions.”, ¶[0018]}, and wherein the two or more categories of the storage plan relate to a moisture content and a physical weight of the crop bale {quality parameters include color/color distribution, moisture content and protein content and distribution directly relate to nutritional content, which varying combination reflect the nutritional value of the crop: “the first or second bale parameter represents one of a bale size, a bale shape, a bale weight, a bale color, a bale color distribution, a slice thickness distribution, twine tension, twine integrity, protein content, protein content distribution, moisture content, or moisture content distribution. Two, three or more of these and other useful bale parameters may be used as a basis for determining the bale quality parameter.”, ¶[0033]}.
Regarding Claim 15, the combination of Nona and Gosh discloses all the limitations of Claim 8, as discussed supra. In addition, Nona explicitly recites the limitation: wherein the plurality of properties comprises at least two of the following: a moisture content, a dry matter content, an acid detergent fiber, a neutral detergent fiber, and a crude protein { “the first or second bale parameter represents one of a bale size, a bale shape, a bale weight, a bale color, a bale color distribution, a slice thickness distribution, twine tension, twine integrity, protein content, protein content distribution, moisture content, or moisture content distribution. Two, three or more of these and other useful bale parameters may be used as a basis for determining the bale quality parameter.”, ¶[0033]}.
Regarding Claim 16, the combination of Nona and Gosh discloses all the limitations of Claim 8, as discussed supra. In addition, Nona explicitly recites the limitation: wherein the method further includes determining, by the at least one controller and using at least some of the plurality of properties, a relative feed value for each crop bale, and wherein the relative feed value is included in the dataset for each crop bale {bale quality parameter: “the first or second bale parameter represents one of a bale size, a bale shape, a bale weight, a bale color, a bale color distribution, a slice thickness distribution, twine tension, twine integrity, protein content, protein content distribution, moisture content, or moisture content distribution. Two, three or more of these and other useful bale parameters may be used as a basis for determining the bale quality parameter.”, ¶[0033]}.
Regarding Claim 17, Nona discloses the limitations: a computer implemented {“The present invention further relates to an agricultural baler using one of the methods according to the present invention, and to computer programs including instructions which, when executed by a computer, cause the computer to carry out these methods.”, ¶[0002]} method for grouping crop bales {“A method for determining a quality of an agricultural bale produced by an agricultural baler”, Abstract}, the method comprising: determining, for each crop bale of a plurality of crop bales, a plurality of properties of the crop bale {“Two, three or more of these and other useful bale parameters are used as a basis for determining the bale quality parameter. The bale quality parameter may, for example, be determined using a multi-dimensional lookup table”, ¶[0039], wherein one skilled in the art can divide, and further subdivide, the plurality bale parameters measured by the sensors in ¶[0034]}, the plurality of properties including one or more measured properties of a crop material of the crop bale {using sensors to determine bale characteristics/properties: “sensors 121, 122, 123 that may be useful for determining relevant bale parameters that can be used for determining a bale quality parameter include, but are not limited to, moisture sensors, protein content sensors, bale weight sensors, or twine tension sensors”, ¶[0034]}; generating a database containing, for each of the plurality of crop bales, a dataset of the plurality of properties {recording of data related to quality and drop-off location: “controller 110…if the bale quality parameter is only used for recording the perceived bale quality of each bale 340, then the bale quality parameter may not need to be communicated to the operator at all.”, ¶[0041], and “The bale quality parameter is then stored in a memory in association with the drop-off location. As a result, it becomes possible to draw a map indicating how bale quality may vary over a field, which information may, for example, be useful for planning future tillage and planting decisions.”, ¶[0044]}; receiving a selection of a classification goal from a plurality of classification goals, each classification goal being distinguishable by an objective of the classification goal, the objective corresponding to a plan for the plurality of crop bales {“Two, three or more of these and other useful bale parameters are used as a basis for determining the bale quality parameter. The bale quality parameter may, for example, be determined using a multi-dimensional lookup table”, ¶[0039], wherein one skilled in the art will appreciate that the bale parameter data captured by the various sensors in ¶[0033-0034], can be grouped in a variety of ways, such as the common approach of low, medium and high quality categorization: “The first and second bale parameters each represent a different physical property of the bale. The bale quality parameter is determined based on the respective bale parameter signals and is indicative of a perceived quality of the agricultural bale.”, ¶[0010]}, each of the plurality of classification goals assigning a weightage to the plurality of properties, at least one classification goal assigning a weightage to two or more properties of the plurality of properties that is different than a weightage assigned to the two or more properties by at least another classification goal {“The bale quality parameter may, for example, be determined using a multi-dimensional lookup table or an algorithm using all obtained bale parameter values as input. Such an algorithm may include one or more weighting factors that can be adjusted by the operator in order to assign different priorities to different bale parameters.”, ¶[0033]}; generating a map for display that indicates a location of, and the visual indicator assigned to, at least some of the plurality of crop bales {“FIG. 3 schematically shows a system 100 according to an embodiment of the present invention. The system includes a controller 110, configured to receive input signals from a plurality of sensors 40, 121, 122, 123, and to provide output signals to, e.g., a display screen 130.”, ¶[0033]}; and generating a signal utilized with an agricultural machine to perform an operation on the crop bale in a field {with regard to Fig. 3, sensor 40 & 121-123 are in communication with a controller and display screen: “to provide output signals to, e.g., a display screen 130. Typically, the controller 110 and the display screen 130 are located on the tractor pulling and powering the agricultural baler 10, while most of the sensors 121, 122, 123 are part of the agricultural baler 10.”, ¶[0033]} based on the category of the two or more categories of the selected classification goal that the crop bale is grouped {on skilled in the art will appreciate that if “moisture content, or moisture content distribution” leads to significant lowering of the “bale quality parameter” (¶[0039-0040]) the operator can choose to temporarily cease the baling process}.
Nona does not appear to explicitly recite the limitations: assigning, using a k-means clustering analysis, the dataset of each crop bale of the plurality of crop bales to a cluster of a plurality of clusters, each cluster of the plurality of clusters corresponding to one category of the two or more categories of the selected classification goal; determining a location of a centroid for each cluster of the plurality of clusters utilizing the weightage assigned for the selected classification goal; and grouping each crop bale of the plurality of crop bales into a category of the two or more categories of the selected classification goal based on the cluster to which the dataset associated with the crop bale was assigned.
However, Gosh explicitly recites the limitation: {“cotton bale management using clustering algorithm”, Abstract} comprising: assigning, using a k-means clustering analysis {section beginning on Pg. 809, Col. 2: “K-means Square Clustering Algorithm”}, the dataset of each crop bale of the plurality of crop bales to a cluster of a plurality of clusters, each cluster of the plurality of clusters corresponding to one category of the two or more categories of the selected classification goal; determining a location of a centroid for each cluster of the plurality of clusters utilizing the weightage assigned for the selected classification goal; and grouping each crop bale of the plurality of crop bales into a category of the two or more categories of the selected classification goal based on the cluster to which the dataset associated with the crop bale was assigned {“The method is based on the grouping cotton bales of similar kind into respective categories using k-mean square clustering algorithm. A set of 500 cotton bales were clustered into 5 categories by minimizing the total within-group squared Euclidean distance around the centroids. In order to cluster bales of different categories, 8 fibre properties, viz., strength, elongation, upper half mean length, length uniformity, short fibre content, micronaire, reflectance and yellowness of each bale have been considered”, Abstract}.
Regarding Claim 18, the combination of Nona and Gosh discloses all the limitations of Claim 17, as discussed supra. In addition, Nona explicitly recites the limitation: wherein at least one of the plurality of classification goals is a nutritional content goal for the crop bales, and wherein each category of the plurality of categories for the nutritional content goal is assigned to a different range of nutritional content {quality parameters include color/color distribution, moisture content and protein content and distribution directly relate to nutritional content, which varying combination reflect the nutritional value of the crop: “the first or second bale parameter represents one of a bale size, a bale shape, a bale weight, a bale color, a bale color distribution, a slice thickness distribution, twine tension, twine integrity, protein content, protein content distribution, moisture content, or moisture content distribution. Two, three or more of these and other useful bale parameters may be used as a basis for determining the bale quality parameter.”, ¶[0033]; and “Two, three or more of these and other useful bale parameters are used as a basis for determining the bale quality parameter. The bale quality parameter may, for example, be determined using a multi-dimensional lookup table”, ¶[0039]}.
Regarding Claim 19, the combination of Nona and Gosh discloses all the limitations of Claim 17, as discussed supra. In addition, Nona explicitly recites the limitation: wherein at least one of the plurality of classification goals is a storage plan {“Agricultural balers are used to consolidate and package crop material so as to facilitate the storage and handling of the crop material for later use”, ¶[0003], and “the controller further receives a location signal from a location sensor, such as a GPS sensor, the location signal being indicative of a drop-off location of the agricultural bale when it is released from the agricultural baler. The bale quality parameter is then stored in a memory in association with the drop-off location. As a result, it becomes possible to draw a map indicating how bale quality may vary over a field, which information may, for example, be useful for planning future tillage and planting decisions.”, ¶[0018]}, and wherein the two or more categories of the storage plan relate to a moisture content and a physical weight of the crop bale {quality parameters include color/color distribution, moisture content and protein content and distribution directly relate to nutritional content, which varying combination reflect the nutritional value of the crop: “the first or second bale parameter represents one of a bale size, a bale shape, a bale weight, a bale color, a bale color distribution, a slice thickness distribution, twine tension, twine integrity, protein content, protein content distribution, moisture content, or moisture content distribution. Two, three or more of these and other useful bale parameters may be used as a basis for determining the bale quality parameter.”, ¶[0033]}.
Claims 5, 14 and 20 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Nona, Gosh and Hamilton et al. (US 10,303,997 B2, henceforth Hamilton).
Regarding Claim 5, the combination of Nona and Gosh discloses all the limitations of Claim 1, as discussed supra. The combination of Nona and Gosh does not appear to explicitly recite the limitations: wherein at least one of the plurality of classification goals is an animal feeding plan, and wherein the weightage assigned to one or more properties of the plurality of properties for the animal feeding plan is determined based on an animal type that is to be feed by one or more of the plurality of crop bales.
However, Hamilton explicitly recites limitations: wherein at least one of the plurality of classification goals is an animal feeding plan, and wherein the weightage assigned to one or more properties of the plurality of properties for the animal feeding plan is determined based on an animal type that is to be feed by one or more of the plurality of crop bales {“The ability to trace or track parameters of each bale may be useful to an end user. Baled products, such as hay or silage, may be fed to livestock, and the quality of the feed may be important to the diet of the livestock. For example, a higher quality feed may be fed to certain livestock, whereas feed with lesser quality may go to a different type of livestock…It is also desirable to be able to label each bale with other important properties, such as moisture content and nutritional value…As a result, bale identification systems may be employed in the baling process for storing or otherwise retaining the parameters or quality of the crop so it can be provided to the end user…To identify a bale, it is known to attach a tag containing the information.“ Col. 1, Ln. 50 - Col. 2, Ln. 3}.
The combination of Nona and Gosh along with Hamilton are analogous art because they deal with processing of agricultural products.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Nona, Gosh and Hamilton before them, to modify the teachings of the combination of Nona and Gosh to include the teachings of Hamilton to provide the tracking data necessary to ensure that higher- and lower-quality hay goes to the appropriate type of animals, to provide the necessary nutrients in a cost-effective manner.
Regarding Claim 14, the combination of Nona and Gosh discloses all the limitations of Claim 12, as discussed supra. The combination of Nona and Gosh does not appear to explicitly recite the limitations: wherein at least one of the plurality of classification goals is an animal feeding plan, and wherein the weightage assigned to one or more properties of the plurality of properties for the animal feeding plan is determined based on an animal type that is to be feed by one or more of the plurality of crop bales.
However, Hamilton explicitly recites limitations: wherein at least one of the plurality of classification goals is an animal feeding plan, and wherein the weightage assigned to one or more properties of the plurality of properties for the animal feeding plan is determined based on an animal type that is to be feed by one or more of the plurality of crop bales {“The ability to trace or track parameters of each bale may be useful to an end user. Baled products, such as hay or silage, may be fed to livestock, and the quality of the feed may be important to the diet of the livestock. For example, a higher quality feed may be fed to certain livestock, whereas feed with lesser quality may go to a different type of livestock…It is also desirable to be able to label each bale with other important properties, such as moisture content and nutritional value…As a result, bale identification systems may be employed in the baling process for storing or otherwise retaining the parameters or quality of the crop so it can be provided to the end user…To identify a bale, it is known to attach a tag containing the information.“ Col. 1, Ln. 50 - Col. 2, Ln. 3}.
Regarding Claim 20, the combination of Nona and Gosh discloses all the limitations of Claim 19, as discussed supra. The combination of Nona and Gosh does not appear to explicitly recite the limitations: wherein at least one of the plurality of classification goals is an animal feeding plan, and wherein the weightage assigned to one or more properties of the plurality of properties for the animal feeding plan is determined based on an animal type that is to be feed by one or more of the plurality of crop bales.
However, Hamilton explicitly recites limitations: wherein at least one of the plurality of classification goals is an animal feeding plan, and wherein the weightage assigned to one or more properties of the plurality of properties for the animal feeding plan is determined based on an animal type that is to be feed by one or more of the plurality of crop bales {“The ability to trace or track parameters of each bale may be useful to an end user. Baled products, such as hay or silage, may be fed to livestock, and the quality of the feed may be important to the diet of the livestock. For example, a higher quality feed may be fed to certain livestock, whereas feed with lesser quality may go to a different type of livestock…It is also desirable to be able to label each bale with other important properties, such as moisture content and nutritional value…As a result, bale identification systems may be employed in the baling process for storing or otherwise retaining the parameters or quality of the crop so it can be provided to the end user…To identify a bale, it is known to attach a tag containing the information.“ Col. 1, Ln. 50 - Col. 2, Ln. 3}.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 12,024,331 B2 – Method of attaching a tag to a bale of hay. The tag information includes information on “primarily net energy, in vitro digestibility, acid detergent fiber, neutral detergent fiber, protein, and moisture” {¶[0006]}, with the aim of identifying which type of feed animal an individual bale provides optimum nutrition.
US 11,980,135 B2 – A system for tracking the field or fields that a particular crop bale came from, such that when a bale identifier is entered into the system it will identify the crop material in the bale, so that the bale can be fed to the appropriate animal.
US 10,717,099 B2 – An onboard system for marking crop bales with different color dyes corresponding the range of moisture content.
JP 6808436 B2 – A system attaching RFID-tag to a crop bale and embedding information in the tag on bale weight, moisture content and time harvested.
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/R.E.G./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665