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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on is 10/17/2024 being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character "332" has been used to designate both the baling chamber and the drive roller in figure 3 and reference character "540" has been used to designate both Display and Produce model in figure 5. 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. 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.
Claim Objections
Claims 9, 11, and 13 are objected to because of the following informalities:
In claim 9, line 2, the term “predicted one or more values of the property” should be changed to, “predicted one or more values for the property” in order to maintain consistency and clarity throughout the claims.
In claim 9, line 5, the term “predicted one or more values of the property” should be changed to, “predicted one or more values for the property” in order to maintain consistency and clarity throughout the claims.
In claim 11, line 2, the term “predicted one or more values of the property” should be changed to, “predicted one or more values for the property” in order to maintain consistency and clarity throughout the claims.
In claim 13, line 3, the term “system to perform all of the steps of the computer-implemented method” should be changed to, “system to perform in order to avoid an insufficient antecedent issue and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function.
Claims 1, 4, and 13-14, recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 1; recites the limitation, “baling machinery configured to…..” [Line 2].
Claim 4; recites the limitation, “the baling machinery is configured to…..” [Line 2].
Claim 13; recites the limitation, “cause the processing system to…..” [Line 2-3].
Claim 14; recites the limitation, “A processing system for…..” [Line 1].
Claim 14; recites the limitation, “baling machinery configured to…..” [Line 2].
Claim 14; recites the limitation, “the processing system being configured to…..” [Line 3].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1, 4, and 13-14;
(i) “baling machinery” (Figs. 1-2, #118 and Fig. 3, #300. Paragraphs [0044-0053]- The illustrated first baling machinery 118 is a “rectangular baler” configured to produce rectangular bales. The baling machinery 118 as shown has a fore-and-aft extending baling chamber 132, within which the bales 80 of plant material 30 are prepared. The baling machinery 118 is depicted as an “in-line” type of baler, wherein portions of plant material 30 is picked up below and slightly ahead of the baling chamber 132 and then loaded up into the bottom of chamber 132 in a straight line path of travel. A pickup assembly 130 collects the plant material 30 and passes it to a stuffer chute assembly 133. The stuffer chute assembly 133 may extend generally rearward and upward from an inlet opening just behind the pickup assembly 130 to an outlet opening at the bottom of the baling chamber 132. In the particular illustrated embodiment, the baling machinery 118 is an “extrusion” type baler in which the bale discharge orifice at the rear of the baling machinery is generally smaller than upstream portions of the chamber, such that the orifice restricts the freedom of movement of a previous portion and provides back pressure against which a reciprocating plunger 134 within the baling chamber 132 can act to compress portions of plant material 30 into the next bale. The baling machinery is illustrated in Figs. 1-2, as #118 thus have sufficient structure or material wherein is a baler with a pickup assembly, a baling chamber, a plunger, and a discharge orifice.).
(ii) “processing system” (Paragraph [0119]- It will be understood that disclosed methods are preferably computer-implemented methods. As such, there is also proposed the concept of a computer program comprising code means for implementing any described method when said program is run on a processing system, such as a computer. Thus, different portions, lines or blocks of code of a computer program according to an embodiment may be executed by a processing system or computer to perform any herein described method. The processing system thus has sufficient structure or material wherein is a computer.).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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, 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over FRANZEN (US 20240130295 A1), hereinafter referenced as FRANZEN, in view of FRANZEN et al. (US 20210176918 A1), hereinafter referenced as FRANZEN2.
Regarding claim 1, FRANZEN explicitly teaches a computer-implemented method for predicting a property of baled plant material (Fig. 1, #100 called the bale (wherein the second bale is baled plant material). Paragraph [0046]-FRANZEN discloses the controller 90 may then estimate a density of the respective radial layer 62 during formation of the second bale 100. The controller 90 may use machine learning techniques known to those skilled in the art to estimate the density of the respective radial layer 62. The density of the first bale 106, in combination with and/or modified by the moisture content of the respective radial layer 62, may be used to provide an accurate estimate of the density of the respective radial layer 62 during formation of the second bale 100 (wherein estimating is predicting).) produced by baling machinery (Fig. 1, #20 called the round baler.) configured to bale plant material present in a baling chamber (Fig. 1, #34 called the baling chamber. Paragraph [0025]-FRANZEN discloses the crop material is directed through the inlet 52 and into the baling chamber 34, whereby the forming belts 48 roll the crop material in a spiral fashion into the bale 100 having a cylindrical shape. The belts apply a constant pressure to the crop material as the crop material is formed into the bale 100 (wherein crop material is plant material).), the computer-implemented method comprising (Fig. 1, #90 called the controller. Paragraph [0035]-FRANZEN discloses the controller 90 may alternatively be referred to as a computing device, a computer, a control unit, a control module, a module, etc. The controller 90 includes a processor 92, a memory 94, and all software, hardware, algorithms, connections, sensors, etc., necessary to manage and control the operation of the baler system 20. As such, a method may be embodied as a program or algorithm operable on the controller 90.):
processing the sensor data (Fig. 1. Paragraph [0031]-FRANZEN discloses the baler system 20 further includes a bale weight sensor 72. The bale weight sensor 72 is operable to detect data related to a weight of the bale 100.), using a machine-learning algorithm (Fig. 1. Paragraph [0046]-FRANZEN discloses the controller 90 may use machine learning techniques known to those skilled in the art to estimate the density of the respective radial layer 62 (wherein machine learning techniques involve the use of machine-learning algorithms).), to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber (Fig. 1. Paragraph [0046]-FRANZEN discloses using the determined volume of respective radial layer 62 and the moisture content of the crop material forming the respective radial layer 62, in combination with the density of the completed first bale 106, the controller 90 may then estimate a density of the respective radial layer 62 during formation of the second bale 100. The controller 90 may use machine learning techniques known to those skilled in the art to estimate the density of the respective radial layer 62. The density of the first bale 106, in combination with and/or modified by the moisture content of the respective radial layer 62, may be used to provide an accurate estimate of the density of the respective radial layer 62 during formation of the second bale 100 (wherein estimating the density of the bale is predicting one or more values for the property of the baled plant material).).
Although FRANZEN explicitly teaches obtaining, a sensor positioned in the baling chamber, sensor data.
FRANZEN fails to explicitly teach obtaining, from a sensor positioned in the baling chamber, sensor data that changes responsive to a change in the property of plant material present in the baling chamber.
However, FRANZEN2 explicitly teaches obtaining, from a sensor positioned in the baling chamber (Fig. 1, #40 called characteristic sensor and #32 called baling chamber. Paragraph [0030]-FRANZEN2 discloses the location of the characteristic sensor 40 on the harvesting implement 24, e.g., the baler 24, may depend upon the specific characteristic being sensed. Further in paragraph [0030]-FRANZEN2 discloses the moisture sensor of the example embodiment any be positioned in or near the baling chamber 32 to sense the moisture content of the crop material 30 while or after being formed into the bale.), sensor data that changes responsive to a change in the property of plant material present in the baling chamber (Fig. 1, #72 called a bale weight sensor, #74 called a moisture sensor, and #34 called the baling chamber. Paragraph [0034]-FRANZEN2 discloses if the baler 24 includes a moisture sensor located in the baling chamber 32, then the data related to the characteristic of the crop material 30, e.g., the moisture content, is sensed at a time after the crop material 30 was gathered from the field 26 (wherein obtaining data that is response to a change in property of the plant material is when the characteristic sensor senses the data related to the characteristic).); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of FRANZEN2 of obtaining, from a sensor positioned in the baling chamber, sensor data that changes responsive to a change in the property of plant material present in the baling chamber.
Wherein having FRANZEN’s bale data analysis and prediction system obtaining, from a sensor positioned in the baling chamber, sensor data that changes responsive to a change in the property of plant material present in the baling chamber.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and FRANZEN2 relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while FRANZEN2 by continuously sensing the characteristic and the location as the implement moves along not only the initial path, but also the harvest path, the accuracy of the set of estimated values may be improved. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and FRANZEN et al. (US 20210176918 A1), Paragraph [0010].
Regarding claim 4, FRANZEN in view of FRANZEN2 explicitly teach the computer-implemented method of claim 1, wherein:
FRANZEN further explicitly teaches the baling machinery (Fig. 1, #24 called the baler. Paragraph [0023]-FRANZEN discloses the example embodiment of the baler 24 described herein may include any type of baler 24, including but not limited to a large square baler 24, a small square baler 24, or a round baler 24.) is configured to bale a plurality of portions of plant material to form the baled plant material (Fig. 1, illustrates a plurality of portions of plant material. Paragraph [0026]-FRANZEN discloses the baler 24 includes a pick-up 28 located at a forward end of the baler 24, which gathers the crop material 30 from the ground. The pick-up 28 feeds the gathered crop material 30 to a baling chamber 32, which forms the crop material 30 into the bale, e.g., either a square bale or a round bale. The specific features and operation of the baler 24 related to gathering and forming the crop material 30 into bales are known to those skilled in the art (wherein crop material is plant material, and wherein the crop material and the bale are comprised of portions of crop material).);
FRANZEN fails to explicitly teach the sensor data comprises a portion of sensor data for each portion of plant material; and processing the sensor data comprises processing each portion of sensor data to predict a value for the property of each portion of plant material.
However, FRANZEN2 explicitly teaches the sensor data comprises a portion of sensor data for each portion of plant material (Fig. 3. Paragraph [0031]-FRANZEN2 discloses the characteristic sensor 40 senses data and communicates the data to a controller 42. The data may include information related to the value of the characteristic at each of a plurality of intervals. The intervals may include, but are not limited to, time intervals or distance intervals. In the example embodiment described herein, the characteristic sensor 40 senses data related to the moisture content of the crop material 30 at each of the intervals and communicates that data for each interval to the controller 42 (wherein a portion is an interval, so each interval is each portion of plant material).); and
processing the sensor data comprises processing each portion of sensor data to predict a value for the property of each portion of plant material (Fig. 3. Paragraph [0052]-FRANZEN2 discloses the set of estimated value of the characteristic of the crop material 30 throughout the field 26 may be generated in the form, but not limited to, a table or a predictive mapping of the field 26, both of which may provide estimated values of the crop characteristic at locations on the field 26. The predictive mapping of the file may be defined by modeling the field 26 using the sensed characteristic of the crop material 30 and the sensed location of the baler 24 at each of the plurality of intervals. The manner and form in which the set of estimated values of the characteristic of the crop material 30 is calculated and used by the controller 42 may include any suitable data analysis process that is capable of analyzing the characteristic data and the position data for each interval, and correlating those values into representative estimated values throughout the field 26 (wherein the estimated values are predicted values, wherein each portion of plant material is an interval of the field/collected crops and thus the predicted value for the property of a portion of plant material is the estimated value correspond to a certain interval.).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of FRANZEN2 of the sensor data comprises a portion of sensor data for each portion of plant material; and processing the sensor data comprises processing each portion of sensor data to predict a value for the property of each portion of plant material.
Wherein having FRANZEN’s bale data analysis and prediction system the sensor data comprises a portion of sensor data for each portion of plant material; and processing the sensor data comprises processing each portion of sensor data to predict a value for the property of each portion of plant material.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and FRANZEN2 relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while FRANZEN2 by continuously sensing the characteristic and the location as the implement moves along not only the initial path, but also the harvest path, the accuracy of the set of estimated values may be improved. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and FRANZEN et al. (US 20210176918 A1), Paragraph [0010].
Regarding claim 13, FRANZEN in view of FRANZEN2 explicitly teach the computer-implemented method according to claim 1,
FRANZEN further explicitly teaches a computer program product comprising computer program code means which (Fig. 1. Paragraph [0039]-FRANZEN discloses the computer-readable memory 94 may include any non-transitory/tangible medium which participates in providing data or computer-readable instructions (wherein computer-readable instructions are a computer program product).), when executed on a computing device (Fig. 1, #90 called a controller. Paragraph [0038]-FRANZEN discloses the controller 90 may be embodied as one or multiple digital computers or host machines each having one or more processors 92, read only memory (ROM), random access memory (RAM), electrically-programmable read only memory (EPROM), optical drives, magnetic drives, etc., a high-speed clock, analog-to-digital (A/D) circuitry, digital-to-analog (D/A) circuitry, and any required input/output (I/O) circuitry, I/O devices, and communication interfaces, as well as signal conditioning and buffer electronics.) having a processing system, cause the processing system to perform all of the steps of (Fig. 1. Paragraph [0040]-FRANZEN discloses the controller 90 includes the tangible, non-transitory memory 94 on which are recorded computer-executable instructions, including a yield mapping algorithm 96. The processor 92 of the controller 90 is configured for executing the yield mapping algorithm 96. The yield mapping algorithm 96 implements a method of generating a yield map 98 and controlling the baler system 20, described in detail below.).
Regarding claim 14, FRANZEN explicitly teaches a processing system for predicting a property of baled plant material (Fig. 1, #100 called the bale (wherein the second bale is baled plant material). Paragraph [0046]-FRANZEN discloses the controller 90 may then estimate a density of the respective radial layer 62 during formation of the second bale 100. The controller 90 may use machine learning techniques known to those skilled in the art to estimate the density of the respective radial layer 62. The density of the first bale 106, in combination with and/or modified by the moisture content of the respective radial layer 62, may be used to provide an accurate estimate of the density of the respective radial layer 62 during formation of the second bale 100 (wherein estimating is predicting).) produced by baling machinery (Fig. 1, #20 called the round baler.) configured to bale plant material present in a baling chamber (Fig. 1, #34 called the baling chamber. Paragraph [0025]-FRANZEN discloses the crop material is directed through the inlet 52 and into the baling chamber 34, whereby the forming belts 48 roll the crop material in a spiral fashion into the bale 100 having a cylindrical shape. The belts apply a constant pressure to the crop material as the crop material is formed into the bale 100 (wherein crop material is plant material).), the processing system being configured to (Fig. 1, #90 called the controller. Paragraph [0035]-FRANZEN discloses the controller 90 may alternatively be referred to as a computing device, a computer, a control unit, a control module, a module, etc. The controller 90 includes a processor 92, a memory 94, and all software, hardware, algorithms, connections, sensors, etc., necessary to manage and control the operation of the baler system 20. As such, a method may be embodied as a program or algorithm operable on the controller 90. It should be appreciated that the controller 90 may include any device capable of analyzing data from various sensors, comparing data, making decisions, and executing the required tasks.):
process the sensor data (Fig. 1. Paragraph [0031]-FRANZEN discloses the baler system 20 further includes a bale weight sensor 72. The bale weight sensor 72 is operable to detect data related to a weight of the bale 100.), using a machine-learning algorithm (Fig. 1. Paragraph [0046]-FRANZEN discloses the controller 90 may use machine learning techniques known to those skilled in the art to estimate the density of the respective radial layer 62 (wherein machine learning techniques involve the use of machine-learning algorithms).), to predict one or more values for a property of the baled plant material produced by baling the plant material present in the baling chamber (Fig. 1. Paragraph [0046]-FRANZEN discloses using the determined volume of respective radial layer 62 and the moisture content of the crop material forming the respective radial layer 62, in combination with the density of the completed first bale 106, the controller 90 may then estimate a density of the respective radial layer 62 during formation of the second bale 100. The controller 90 may use machine learning techniques known to those skilled in the art to estimate the density of the respective radial layer 62. The density of the first bale 106, in combination with and/or modified by the moisture content of the respective radial layer 62, may be used to provide an accurate estimate of the density of the respective radial layer 62 during formation of the second bale 100 (wherein estimating the density of the bale is predicting one or more values for the property of the baled plant material).).
Although FRANZEN explicitly teaches a sensor positioned in the baling chamber.
FRANZEN fails to explicitly teach obtain, from a sensor positioned in the baling chamber, sensor data that changes responsive to a change in the property of plant material present in the baling chamber; and.
However, FRANZEN2 explicitly teaches obtain, from a sensor positioned in the baling chamber (Fig. 1, #40 called characteristic sensor and #32 called baling chamber. Paragraph [0030]-FRANZEN2 discloses the location of the characteristic sensor 40 on the harvesting implement 24, e.g., the baler 24, may depend upon the specific characteristic being sensed. Further in paragraph [0030]-FRANZEN2 discloses the moisture sensor of the example embodiment any be positioned in or near the baling chamber 32 to sense the moisture content of the crop material 30 while or after being formed into the bale.), sensor data that changes responsive to a change in the property of plant material present in the baling chamber (Fig. 1, #72 called a bale weight sensor, #74 called a moisture sensor, and #34 called the baling chamber. Paragraph [0034]-FRANZEN2 discloses if the baler 24 includes a moisture sensor located in the baling chamber 32, then the data related to the characteristic of the crop material 30, e.g., the moisture content, is sensed at a time after the crop material 30 was gathered from the field 26. In this time period between when the pick-gathered the crop material 30 in the field 26 and when the characteristic sensor 40 senses the data related to the characteristic of the crop material 30, then bale may have traveled a distance from the location on the field 26 from which the crop material 30 originated (wherein obtaining data that is response to a change in property of the plant material is when the characteristic sensor senses the data related to the characteristic).); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN of a processing system for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the processing system being configured to: process the sensor data, using a machine-learning algorithm, to predict one or more values for a property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of FRANZEN2 of obtain, from a sensor positioned in the baling chamber, sensor data that changes responsive to a change in the property of plant material present in the baling chamber; and.
Wherein having FRANZEN’s bale data analysis and prediction system obtain, from a sensor positioned in the baling chamber, sensor data that changes responsive to a change in the property of plant material present in the baling chamber; and.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and FRANZEN2 relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while FRANZEN2 by continuously sensing the characteristic and the location as the implement moves along not only the initial path, but also the harvest path, the accuracy of the set of estimated values may be improved. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and FRANZEN et al. (US 20210176918 A1), Paragraph [0010].
Regarding claim 15, FRANZEN in view of FRANZEN2 explicitly teach the processing system of claim 14;
FRANZEN further explicitly teaches a baling system comprising (Fig. 1, illustrates a baling system with #20 called the baler system. Paragraph [0021]-FRANZEN discloses a baler system is generally shown at 20. The baler system 20 may alternatively be referred to herein as a round baler 20.);
the baling machinery (Fig. 1, #20 called the round baler. Paragraph [0022]-FRANZEN discloses the round baler 20 includes a housing 32 forming a baling chamber 34. The housing 32 is attached to and supported by the frame 22. The housing 32 may include one or more walls or panels that at least partially enclose and/or define the baling chamber 34. The round baler 20 further includes a gate 36. The gate 36 is attached to and rotatably supported by the housing 32. The gate 36 is positioned adjacent a rearward end of the frame 22 and is pivotably moveable about a gate axis 38. The gate axis 38 is generally horizontal and perpendicular to a central longitudinal axis 40 of the frame 22. The gate 36 is moveable between a closed position for forming a bale 100 within the baling chamber 34, and an open position for discharging the bale 100 from the baling chamber 34 (wherein the baling machinery is the round baler).); and
the sensor positioned in the baling chamber of the baling machinery (Fig. 1. Paragraph [0031]-FRANZEN discloses the bale weight sensor 72 may include a force sensor positioned and arranged to detect a weight of the while in the baling chamber 34. Further in paragraph [0032]-FRANZEN discloses the moisture sensor 74 may be positioned to detect the moisture level of the crop material either in the baling chamber 34. Further in paragraph [0022]-FRANZEN discloses the round baler 20 includes a housing 32 forming a baling chamber 34 (wherein the baling machinery is the round baler).).
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over FRANZEN (US 20240130295 A1), hereinafter referenced as FRANZEN, in view of FRANZEN et al. (US 20210176918 A1), hereinafter referenced as FRANZEN2, and further in view of SOMAROWTHU et al. (US 20240013363 A1), hereinafter referenced as SOMAROWTHU.
Regarding claim 2, FRANZEN in view of FRANZEN2 explicitly teach the computer-implemented method of claim 1,
FRANZEN in view of FRANZEN2 fail to explicitly teach wherein the sensor data comprises image data.
However, SOMAROWTHU explicitly teaches wherein the sensor data comprises image data (Fig. 4, #410 called image(s). Paragraph [0019]-SOMAROWTHU discloses image data or sensor data (e.g. spectroscopic data) of a crop can be acquired. The data may be acquired by devices mounted to an agricultural vehicle in some examples. The image and/or sensor data can be processed, detection techniques can be performed, and a leaf-to-stem ratio can be determined based on the detection techniques. Further in paragraph [0039]-SOMAROWTHU discloses one or more images 410 are input to image processing application 400 and received by a pre-processing module 402 (wherein the images are image data).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of SOMAROWTHU of wherein the sensor data comprises image data.
Wherein having FRANZEN’s bale data analysis and prediction system wherein the sensor data comprises image data.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and SOMAROWTHU relate to the analyzing crop materials, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while SOMAROWTHU automated techniques to determine leaf-to-stem ratio are described herein. These techniques enable the leaf-to-stem ratio to be determined on-the-go, for example on an agricultural vehicle. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and SOMAROWTHU et al. (US 20240013363 A1), Paragraph [0018].
Regarding claim 3, FRANZEN in view of FRANZEN2 and further in view of SOMAROWTHU explicitly teach the computer-implemented method of claim 2,
FRANZEN in view of FRANZEN2 fail to explicitly teach wherein the property of the baled plant material comprises a ratio of stem to leaves in the baled plant material.
SOMAROWTHU explicitly teaches wherein the property of the baled plant material comprises a ratio of stem to leaves in the baled plant material (Fig. 5, #506 called Perform leaf-to-stem ratio calculation and #508 called Determine an aggregate ratio value. Paragraph [0022]-SOMAROWTHU discloses the crop analysis system 110, in one example, may determine the leaf-to-stem ratio on-board the agricultural vehicle 160 and communicate the result to other devices such as remote system 150 and/or agricultural machine 140 (e.g. baler, etc.). In another example, the crop analysis system 110 may jointly determine the leaf-to-stem ratio with assistance from the remote system 150. For instance, data acquisition may be performed by the crop analysis system 110 onboard the agricultural vehicle 160 and remote system 150 may perform image analysis. The leaf-to-stem ratio determination may be performed by either the crop analysis system 110 or the remote system 150. Further in paragraph [0045]-SOMAROWTHU discloses when a crop is baled, for example, the aggregated measurements for each portion may be associated with each bale.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of SOMAROWTHU of wherein the property of the baled plant material comprises a ratio of stem to leaves in the baled plant material.
Wherein having FRANZEN’s bale data analysis and prediction system wherein the property of the baled plant material comprises a ratio of stem to leaves in the baled plant material.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and SOMAROWTHU relate to the analyzing crop materials, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while SOMAROWTHU automated techniques to determine leaf-to-stem ratio are described herein. These techniques enable the leaf-to-stem ratio to be determined on-the-go, for example on an agricultural vehicle. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and SOMAROWTHU et al. (US 20240013363 A1), Paragraph [0018].
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over FRANZEN (US 20240130295 A1), hereinafter referenced as FRANZEN, in view of FRANZEN et al. (US 20210176918 A1), hereinafter referenced as FRANZEN2, and further in view of NONA et al. (US 20240155977 A1), hereinafter referenced as NONA.
Regarding claim 5, FRANZEN in view of FRANZEN2 explicitly teach the computer-implemented method of claim 4,
FRANZEN in view of FRANZEN2 fail to explicitly teach further comprising constructing a model of the baled plant material using the predicted property of each portion of plant material.
However, NONA explicitly teaches further comprising constructing a model of the baled plant material using the predicted property of each portion of plant material (Fig. 2. Paragraph [0044]-NONA discloses the final weight prediction for the bale can be compared by the controller to the measured final weight and a difference therebetween can be calculated. The controller may then calculate a model offset, as shown at step 1100, in dependence on the value of the difference between the final weight prediction and the measured final weight of the bale. The model offset is then applied to the weight prediction model (wherein the model of the baled plant material is weight prediction model with the applied offset and wherein the model offset is using the predicted property of each portion of plant material). Further in paragraph [0045]-NONA discloses Since the weight prediction model will be updated based on an offset calculated after a bale (Bale X) has been weighed, and therefore significantly after Bale X has been completed due to the presence of other completed bales ahead of Bale X in the baling chamber 26, the update to the weight prediction model will occur while another bale (Bale Y) is being made (wherein Bale X and Bale Y are portions of plant material).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of NONA of further comprising constructing a model of the baled plant material using the predicted property of each portion of plant material.
Wherein having FRANZEN’s bale data analysis and prediction system further comprising constructing a model of the baled plant material using the predicted property of each portion of plant material.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and NONA relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while NONA it is often desirable for the bales made by the baler to be substantially identical in weight. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and NONA et al. (US 20240155977 A1), Paragraph [0006].
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over FRANZEN (US 20240130295 A1), hereinafter referenced as FRANZEN, in view of FRANZEN et al. (US 20210176918 A1), hereinafter referenced as FRANZEN2, and further in view of GORIVALE et al. (US 20240032475 A1), hereinafter referenced as GORIVALE.
Regarding claim 6, FRANZEN in view of FRANZEN2 explicitly teach the computer-implemented method of claim 1,
FRANZEN in view of FRANZEN2 fail to explicitly teach wherein the sensor data is obtained prior to the baling of the plant material present in the baling chamber.
However, GORIVALE explicitly teaches wherein the sensor data is obtained prior to the baling of the plant material present in the baling chamber (Fig. 1. Paragraph [0037]-GORIVALE discloses the constituent sensor 112 can be positioned at locations at which the constituent sensor 112 has access to, and/or can receive, one or more samplings of crop material 106 that will become part of a crop bale 108. For example, according to certain embodiments, the constituent sensor 112 can be positioned at or along the loading mechanism 236 such that the constituent sensor 112 can obtain readings of crop material 106 being feed into the baling or compression chamber 226.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of GORIVALE of wherein the sensor data comprises image data.
Wherein having FRANZEN’s bale data analysis and prediction system wherein the sensor data comprises image data.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and GORIVALE relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while GORIVALE systems and methods for analyzing and grouping crop bales categorizing based on selected classification goals remain areas of interest. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and GORIVALE et al. (US 20240032475 A1), Paragraph [0004].
Regarding claim 7, FRANZEN in view of FRANZEN2 explicitly teach the computer-implemented method of claim 1,
FRANZEN in view of FRANZEN2 fail to explicitly teach wherein the sensor data is obtained after the baling of the plant material present in the baling chamber.
However, GORIVALE explicitly teaches wherein the sensor data is obtained after the baling of the plant material present in the baling chamber (Fig. 1. Paragraph [0037]-GORIVALE discloses the constituent sensor 112 can be positioned at or around the tailgate or rear door 116 of the harvesting machine 100, including outside of the compression chamber 226. According to such an embodiment, the constituent sensor 112 can sense or otherwise obtain information regarding the crop bale 108 upon completion of the compression or shaping of the crop material 106; during or after the crop bale 108 is bound by binding material; and/or as the crop bale 108 is being released through the tailgate or rear door 116 of the harvesting machine 100.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of GORIVALE of wherein the sensor data is obtained after the baling of the plant material present in the baling chamber.
Wherein having FRANZEN’s bale data analysis and prediction system wherein the sensor data is obtained after the baling of the plant material present in the baling chamber.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and GORIVALE relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while GORIVALE systems and methods for analyzing and grouping crop bales categorizing based on selected classification goals remain areas of interest. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and GORIVALE et al. (US 20240032475 A1), Paragraph [0004].
Claims 8 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over FRANZEN (US 20240130295 A1), hereinafter referenced as FRANZEN, in view of FRANZEN et al. (US 20210176918 A1), hereinafter referenced as FRANZEN2, and further in view of LANG et al. (US 20170287303 A1), hereinafter referenced as LANG.
Regarding claim 8, FRANZEN in view of FRANZEN2 explicitly teach the computer-implemented method of claim 1,
FRANZEN in view of FRANZEN2 fail to explicitly teach further comprising tagging the baled plant material with a bale-specific tag for identifying the bale.
However, LANG explicitly teaches further comprising tagging the baled plant material with a bale-specific tag for identifying the bale (Fig. 8, #52 called a visual identifier (wherein a visual identifier is a bale-specific tag for identifying the dale). Paragraph [0063]-LANG discloses each bale tag may include a RFID chip and antenna for transmitting signals to a remote location. Other technology may be incorporated in the bale tag including, but not limited to, a visual identification such as a barcode, a quick response (QR) code, or any other visual identifier known to the skilled artisan. In any event, the bale tag includes a readable identifier that corresponds to the bale tag and may be detected by a bale tag reader).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of LANG of further comprising tagging the baled plant material with a bale-specific tag for identifying the bale.
Wherein having FRANZEN’s bale data analysis and prediction system further comprising tagging the baled plant material with a bale-specific tag for identifying the bale.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and LANG relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while LANG enables a user to collect or associate data related to the baled crop via use of the bale tag. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and LANG et al. (US 20170287303 A1), Paragraph [0064].
Regarding claim 10, FRANZEN in view of FRANZEN2 and further in view of LANG explicitly teach the computer-implemented method of claim 8,
FRANZEN in view of FRANZEN2 fail to explicitly teach wherein the bale-specific tag comprises a QR code.
However, LANG explicitly teaches wherein the bale-specific tag comprises a QR code (Fig. 8, illustrates a QR code with #52 called a visual identifier. Paragraph [0063]-LANG discloses each bale tag may include a RFID chip and antenna for transmitting signals to a remote location. Other technology may be incorporated in the bale tag including, but not limited to, a visual identification such as a barcode, a quick response (QR) code, or any other visual identifier known to the skilled artisan. In any event, the bale tag includes a readable identifier that corresponds to the bale tag and may be detected by a bale tag reader).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 and further in view of LANG of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of LANG of wherein the bale-specific tag comprises a QR code.
Wherein having FRANZEN’s bale data analysis and prediction system wherein the bale-specific tag comprises a QR code.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and LANG relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while LANG enables a user to collect or associate data related to the baled crop via use of the bale tag. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and LANG et al. (US 20170287303 A1), Paragraph [0064].
Regarding claim 11, FRANZEN in view of FRANZEN2 explicitly teach the computer-implemented method of claim 1,
FRANZEN in view of FRANZEN2 fail to explicitly teach further comprising displaying the predicted one or more values of the property.
However, LANG explicitly teaches further comprising displaying the predicted one or more values of the property (Fig. 4, #66 called a display device. Paragraph [0041]-LANG discloses the reading device 54, as shown in FIG. 4, may include a receiving device 60 for receiving sensor data transmitted by the transmitting device 44 of the sensor 46, a processor 62 for processing the sensor data, a memory 64, and a display device 66 for displaying the processed sensor data. The device 54 enables the display of sensor data on the display device 66, directly or after further processing (wherein displayed sensor data after further processing is displaying the predicted one or more values).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of LANG of further comprising displaying the predicted one or more values of the property.
Wherein having FRANZEN’s bale data analysis and prediction system further comprising displaying the predicted one or more values of the property.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and LANG relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while LANG enables a user to collect or associate data related to the baled crop via use of the bale tag. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and LANG et al. (US 20170287303 A1), Paragraph [0064].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over FRANZEN (US 20240130295 A1), hereinafter referenced as FRANZEN, in view of FRANZEN et al. (US 20210176918 A1), hereinafter referenced as FRANZEN2, and further in view of LANG et al. (US 20170287303 A1), hereinafter referenced as LANG, and further in view of GORIVALE et al. (US 20240032475 A1), hereinafter referenced as GORIVALE.
Regarding claim 9, FRANZEN in view of FRANZEN2 and further in view of LANG explicitly teach the computer-implemented method of claim 8,
FRANZEN in view of FRANZEN2 fail to explicitly teach wherein the bale-specific tag indicates a location of the uploaded predicted one or more values of the property of the baled plant material on the external server.
However, LANG explicitly teaches wherein the bale-specific tag indicates a location of the uploaded predicted one or more values of the property of the baled plant material on the external server (Fig. 12, illustrates an example external server with properties of the baled plant materials stored. Paragraph [0075]-LANG discloses a data matrix 1200 or spreadsheet is illustrated. As shown, the data collected or recorded may be stored in an organized format so that it may be retrieved at a later time. For example, a user of a mobile device 1130 may access the data wirelessly via Wi-Fi, cloud-based technology or any other known communication means by accessing the server 1110 or database where the information is stored. Further in paragraph [0076]-LANG discloses each bale may include one or more tags and therefore one or more tag identification numbers may be associated with any given bale. Even when more than one bale tag is coupled to a bale, the control unit 1108 is able to correlate the bale identification number and measured parameters associated with the bale to the one or more bale tag identification numbers (wherein the measured parameters are predicted one or more values and wherein the location indicated is the location where the tag identification number from the tag matches the stored identification number).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 and further in view of LANG of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of LANG of wherein the bale-specific tag indicates a location of the uploaded predicted one or more values of the property of the baled plant material on the external server.
Wherein having FRANZEN’s bale data analysis and prediction system wherein the bale-specific tag indicates a location of the uploaded predicted one or more values of the property of the baled plant material on the external server.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and LANG relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while LANG enables a user to collect or associate data related to the baled crop via use of the bale tag. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and LANG et al. (US 20170287303 A1), Paragraph [0064].
FRANZEN in view of FRANZEN2 and further in view of LANG fail to explicitly teach further comprising uploading the predicted one or more values of the property of the baled plant material to an external server.
However, GORIVALE explicitly teaches further comprising uploading the predicted one or more values of the property of the baled plant material to an external server (Fig. 5. Paragraph [0057]-GORIVALE discloses a database, such as, for example, the database 328 of the central system 314, can be built based on information pertaining to existing crop bales 108. According to certain embodiments, the database 328 can be based, at least in part, on particular properties the crop materials 106 measured for each of the different crop bales 108 that was communicated to the central system 314 at block 414 of FIG. 4. Thus, as information for additional crop bales 108 continues to be communicated to the central server at block 414, the size of the database 328 can increase (wherein information pertaining to existing crop bales is the predicted one or more values and wherein the central server is an external server).),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 and further in view of LANG of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of GORIVALE of further comprising uploading the predicted one or more values of the property of the baled plant material to an external server.
Wherein having FRANZEN’s bale data analysis and prediction system further comprising uploading the predicted one or more values of the property of the baled plant material to an external server.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and GORIVALE relate to the analyzing bales formed in a baler, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while GORIVALE systems and methods for analyzing and grouping crop bales categorizing based on selected classification goals remain areas of interest. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and GORIVALE et al. (US 20240032475 A1), Paragraph [0004].
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over FRANZEN (US 20240130295 A1), hereinafter referenced as FRANZEN, in view of FRANZEN et al. (US 20210176918 A1), hereinafter referenced as FRANZEN2, and further in view of YADAV et al. (US 20210314352 A1), hereinafter referenced as YADAV.
Regarding claim 12, FRANZEN in view of FRANZEN2 explicitly teach the computer-implemented method of claim 1,
FRANZEN in view of FRANZEN2 fail to explicitly teach further comprising: checking an external update server for any updates for the machine-learning algorithm; and responsive to the presence of an update for the machine-learning algorithm, downloading the update from the external server and updating the machine-learning algorithm using the update.
However, YADAV explicitly teaches checking an external update server for any updates for the machine-learning algorithm (Fig. 1A. Paragraph [003]-YADAV discloses these machine learning modules may be updated periodically or in response to certain events (e.g., a user of device 130 visiting a new website). For example, browser plugin module 160 may periodically download new or updated machine learning modules from server computer system 110 (wherein server computer system is an external update server).); and
responsive to the presence of an update for the machine-learning algorithm, downloading the update from the external server and updating the machine-learning algorithm using the update (Fig. 1A. Paragraph [0030]-YADAV discloses browser plugin module 160, in the illustrated embodiment, includes URL determination module 170 and one or more machine learning modules 122. Browser plugin module 160 is one example of application 180 and may be a plugin that is installed by a web browser of a user device. This web browser plugin may then be used to download one or more machine learning modules from server computer system 110. These machine learning modules may be updated periodically or in response to certain events (e.g., a user of device 130 visiting a new website). For example, browser plugin module 160 may periodically download new or updated machine learning modules from server computer system 110.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of FRANZEN in view of FRANZEN2 of a computer-implemented method for predicting a property of baled plant material produced by baling machinery configured to bale plant material present in a baling chamber, the computer-implemented method comprising: processing the sensor data, using a machine-learning algorithm, to predict one or more values for the property of the baled plant material produced by baling the plant material present in the baling chamber with the teachings of YADAV of checking an external update server for any updates for the machine-learning algorithm; and responsive to the presence of an update for the machine-learning algorithm, downloading the update from the external server and updating the machine-learning algorithm using the update.
Wherein having FRANZEN’s bale data analysis and prediction system checking an external update server for any updates for the machine-learning algorithm; and responsive to the presence of an update for the machine-learning algorithm, downloading the update from the external server and updating the machine-learning algorithm using the update.
The motivation behind the modification would have been to obtain a bale analysis and prediction system that enhances the accuracy of predicted characteristic measures of bales. Since both FRANZEN and YADAV relate to and utilize machine learning models to analyze data, wherein FRANZEN the controller may estimate the yield of each respective radial layer based on the moisture content of that respective layer, the volume of that respective layer, and the density of one or more previously completed bales. By doing so, the detail and granularity of the yield data is greatly increased, while YADAV the disclosed techniques may advantageously reduce latency and page load times while detecting and reporting on suspicious webpages. Please see FRANZEN (US 20240130295 A1), Paragraph [0008], and YADAV et al. (US 20210314352 A1), Paragraph [0024].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
ROELL et al. (US 20170086381 A1) – Systems and methods for optimizing the collection of a windrowed crop are described. In an exemplary implementation, conditions data is accessed and used to estimate the moisture content of a windrowed crop. The estimated moisture content is used to create an optimal collection prescription for the operation of baling equipment to collect the crop. During the collection of the crop, the moisture content of the crop is measured and compared to the estimated moisture value. The system may then revise the optimal collection prescription based on the measured moisture value. This process can then be repeated until all of the windrowed crop is collected…Abstract, Fig. 3.
NONA et al. (US 20230032085 A1) – A method for determining a quality of an agricultural bale produced by an agricultural baler, the method comprising the steps of: receiving a first bale parameter signal indicative of a first bale parameter of the agricultural bale; receiving a second bale parameter signal indicative of a second bale parameter of the agricultural bale, wherein the first bale parameter and the second bale parameter each represents a different physical property of the agricultural bale; determining, based on the respective first bale parameter signal and the second bale parameter signal, a bale quality parameter indicative of a perceived quality of the agricultural bale; and providing an electronic signal representative of the bale quality parameter…Abstract, Fig. 1.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN N WOLFSON whose telephone number is (571)272-1898. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ETHAN N WOLFSON/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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