DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: image analysis apparatus configured to: receive…; driver assistance system to automatically control…; attachment configured to collect harvested material; working units configured to process…; optical recording apparatus configured to generate…; image analysis apparatus configured to determine…; driver assistance system configured to automatically control…; automatic processing unit is configured to optimize… in claims 12-19.
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. Applicant’s specification includes at least [0037]: secondary crushing device and the driver assistance system may form an automatic processing unit, [0043] optical recording apparatus 16 has at least one camera; [0046] driver assistance system 17 may include at least one processor 36 and at least one memory 37. In one or some embodiments, the processor 36 may comprise a microprocessor, controller.
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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-19 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of copending Application No. 18751515 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claim recite similar subject matter.
Regarding claims 1, 12, 15, claims 1-18 of copending Application No. 18751515 disclose
An image analysis method for a computer-implemented determination of a degree of grain cracking of grains within a flow of harvested material processed by at least one working unit of a forage harvester, the flow comprises whole grains and crushed grains as grain components and non-grain components, wherein the at least one working unit is automatically controlled depending on the determined degree of grain cracking (see at least claim 1: An image analysis method for a computer-implemented determination of a degree of grain cracking of grains within a flow of harvested material processed by at least one working unit of a forage harvester, the flow comprising whole grains and crushed grains as grain components and non-grain components, claim 15: A self-propelled forage harvester comprising: an attachment configured to pick up harvested material; one or more working units configured to process a flow of the harvested material, the one or more working units comprising a secondary crushing device), the method comprising:
obtaining, using at least one optical recording device, one or more images of the flow of harvested material (see at least claim 1: recording, using a camera system, one or more images of the flow of harvested material, claim 15);
in a first stage of the image analysis method, classifying image pixels contained in the one or more images into grain components and non-grain components (see at least claim 1: determining, by an image analysis apparatus, the degree of grain cracking by: classifying image pixels in the one or more images into grain components and non-grain components; classifying, using a segmentation model, whole grains and crushed grains within the image pixels of the one or more images classified as grain components, claim 6: pixels, claim 15);
in a second stage of the image analysis method, performing, using a length-width comparison, a length determination of a long main axis and a short main axis of one or more of the classified grain components, wherein at least one neural network performs the first stage and the second stage;
determining, using the length determination, the degree of grain cracking (see at least claim 11: wherein, to classify whole grains and crushed grains, a length determination of a long main axis and a short main axis of each classified grain component is performed using a length-width comparison; and to calculate the degree of grain cracking, quotient is formed from a sum of an area of classified grain components which fall below an adaptive limit value for length of the short main axes, and a sum of the area of all classified grain components, claims 12-14); and
automatically controlling the at least one working unit based on the degree of grain cracking (see at least claim 1: automatically controlling the at least one working unit based on the degree of grain cracking, claim 15: A self-propelled forage harvester comprising: an attachment configured to pick up harvested material; one or more working units configured to process a flow of the harvested material, the one or more working units comprising a secondary crushing device).
This is a provisional nonstatutory double patenting rejection.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 8-13, 15-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20220061216 (Heitmann).
As per claim(s) 1, 12, Heitmann discloses an image analysis method for a computer-implemented determination of a degree of grain cracking of grains within a flow of harvested material processed by at least one working unit of a forage harvester, the flow comprises whole grains and crushed grains as grain components and non-grain components, wherein the at least one working unit is automatically controlled depending on the determined degree of grain cracking, the method comprising:
obtaining, using at least one optical recording device, one or more images of the flow of harvested material (see at least [0012]: optical measuring system includes a camera for recording image data of the harvested material of the harvested material flow, claim 1: determine, using an image recognition algorithm, one or more image regions assigned to a comminuted grain component in the image data);
in a first stage of the image analysis method, classifying image pixels contained in the one or more images into grain components and non-grain components (see at least [0013]: Grain components and non-grain components may be differentiated with varying effectiveness in an optical analysis with different wavelengths of the recorded light, [0023]: harvested material 4 comprises grain components 5 and non-grain components 6, claim 1: determine, based on the one or more image regions, one or more geometric properties of the assigned comminuted grain components; and determine, based on the one or more geometric properties, an indicator of a processing quality of the comminuted grain components, [0026]: camera 11 therefore has at least a sufficient number of pixels to enable the disclosed image recognition as explained further below. The camera 11 may be arranged along the harvested material transport path 7 after the corn cracker 8 so that it records the already comminuted grain components 5);
in a second stage of the image analysis method, performing, using a length-width comparison, a length determination of a long main axis and a short main axis of one or more of the classified grain components (see at least [0047]: geometric properties may include geometric dimensions of the grain components 5. The geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area. Accordingly, in one or some embodiments, grain components 5 with a maximum cross-sectional area may be considered comminuted and may be included in the indicator of the processing quality),
wherein at least one neural network performs the first stage and the second stage (see at least [0016]: image recognition algorithm may be based on machine learning, such as deep learning);
determining, using the length determination, the degree of grain cracking (see at least [0031]: control assembly 9 is configured to determine geometric properties of the assigned grain components 5 in the image recognition routine based on the particular image regions 12, [0047]: geometric properties may include geometric dimensions of the grain components 5. The geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area, [0020]: optimization goal may comprise a predetermined percentage of comminuted grain components with predetermined geometric properties…predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0021], [0048]: indicator of the processing quality may depict a percentage of grain components 5 with predetermined geometric properties of the harvested material 4 or the grain components 5, and the calculation instructions may include a formation of the percentage…the percentage is that of grain components 5 with a predetermined maximum and/or minimum cross-sectional area); and
automatically controlling the at least one working unit based on the degree of grain cracking (see at least claim 13: wherein the corn cracker has two rollers configured to rotate during operation with an adjustable rotational speed of the two rollers, [0020]: optimization goal may comprise a predetermined percentage of comminuted grain components with predetermined geometric properties…predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0021]: control assembly may automatically regulate the machine parameter to reach the optimization goal, such as the control assembly adjusting different settings of the machine parameter successively in an optimization routine in order to determine a dependency between the machine parameter and the indicator of the processing quality).
As per claim(s) 2, 13, Heitmann discloses wherein determining, using the length determination, the degree of grain cracking comprises: determining an area of classified grain components that fall below an adaptive limit value for the length of the short main axis (see at least [0017]: geometric properties may include geometric dimensions of the comminuted grain components, such as the geometric dimensions including any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area, [0020]: user may specify via an input a value of the indicator for a processing quality to be achieved, and/or a minimum value, and/or a maximum value…user may transmit his/her target specifications to the forage harvester and have the forage harvester modify operation in order to adjust the processing quality (e.g., to optimize of the processing quality), [0021]: predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0047]: geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area. Accordingly, in one or some embodiments, grain components 5 with a maximum cross-sectional area may be considered comminuted and may be included in the indicator of the processing quality).
As per claim(s) 3, Heitmann discloses wherein determining, using the length determination, the degree of grain cracking comprises: calculating a quotient of a sum of the area of classified grain components that fall below the adaptive limit value for the length of the short main axis and a sum of an area of all classified grain components; and calculating, based on the quotient, the degree of grain cracking (see at least [0021]: predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0047]: geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area. Accordingly, in one or some embodiments, grain components 5 with a maximum cross-sectional area may be considered comminuted and may be included in the indicator of the processing quality).
As per claim(s) 8, Heitmann discloses manually adapting the adaptive limit value (see at least [0020]: user may specify via an input a value of the indicator for a processing quality to be achieved, and/or a minimum value, and/or a maximum value…user may transmit his/her target specifications to the forage harvester and have the forage harvester modify operation in order to adjust the processing quality (e.g., to optimize of the processing quality)).
As per claim(s) 9, Heitmann discloses wherein manually adapting the adaptive limit value is performed by one or both of: a selection from a predefined or predefinable range of values for values of an average grain size; or by an input of at least one value of an average grain size (see at least [0020]: user may specify via an input a value of the indicator for a processing quality to be achieved, and/or a minimum value, and/or a maximum value…user may transmit his/her target specifications to the forage harvester and have the forage harvester modify operation in order to adjust the processing quality (e.g., to optimize of the processing quality)).
As per claim(s) 10, Heitmann discloses wherein the one or more images are cyclically recorded and transmitted to an image analysis apparatus for evaluation; and wherein the image pixels contained in the one or more images are classified using at least one of semantic image segmentation, object recognition or instance segmentation (see at least claim 1: determine, using an image recognition algorithm, one or more image regions assigned to a comminuted grain component in the image data).
As per claim(s) 11, Heitmann discloses wherein determining the long main axis and the short main axis of each classified grain component, in order to determine the area of the classified grain components that fall below the adaptive limit value for the length of the short main axes, is performed at time-spaced intervals (see at least [0017]: a percentage of comminuted grain components, with predetermined maximum and/or minimum geometric dimensions of the grain components, [0021]: predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0047]: geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area. Accordingly, in one or some embodiments, grain components 5 with a maximum cross-sectional area may be considered comminuted and may be included in the indicator of the processing quality.).
As per claim 15, Heitmann discloses a forage harvester comprising:
an attachment configured to collect harvested material (see at least [0018]: forage harvester may have an attachment as a work assembly that generates harvested material flow for collecting the crop);
one or more working units configured to process a stream of the harvested material produced from the harvested material that is collected, the one or more working units comprise a secondary crushing device (see at least [0018]: forage harvester may have an attachment as a work assembly that generates harvested material flow for collecting the crop; a pre-pressing roller as a work assembly arranged or positioned in the harvested material flow with a rotational speed through which a chaff length of the harvested material may be adjusted; a cutterhead as a work assembly that is arranged or positioned in the harvested material flow for chopping the harvested material; or a corn cracker as a work assembly arranged or positioned in the harvested material flow);
an optical recording apparatus configured to generate one or more images of flow of harvested material (see at least [0012]: optical measuring system includes a camera for recording image data of the harvested material of the harvested material flow, claim 1: determine, using an image recognition algorithm, one or more image regions assigned to a comminuted grain component in the image data);
an image analysis apparatus configured to determine a degree of grain cracking of grains in the flow of the harvested material by: in a first stage of, classifying image pixels contained in the one or more images into grain components and non-grain components (see at least [0013]: Grain components and non-grain components may be differentiated with varying effectiveness in an optical analysis with different wavelengths of the recorded light, [0023]: harvested material 4 comprises grain components 5 and non-grain components 6, claim 1: determine, based on the one or more image regions, one or more geometric properties of the assigned comminuted grain components; and determine, based on the one or more geometric properties, an indicator of a processing quality of the comminuted grain components, [0026]: camera 11 therefore has at least a sufficient number of pixels to enable the disclosed image recognition as explained further below. The camera 11 may be arranged along the harvested material transport path 7 after the corn cracker 8 so that it records the already comminuted grain components 5);
in a second stage, performing, using a length-width comparison, a length determination of a long main axis and a short main axis of one or more of the classified grain components (see at least [0047]: geometric properties may include geometric dimensions of the grain components 5. The geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area. Accordingly, in one or some embodiments, grain components 5 with a maximum cross-sectional area may be considered comminuted and may be included in the indicator of the processing quality),
wherein at least one neural network performs the first stage and the second stage (see at least [0016]: image recognition algorithm may be based on machine learning, such as deep learning); and
determining, using the length determination, the degree of grain cracking (see at least claim 13: wherein the corn cracker has two rollers configured to rotate during operation with an adjustable rotational speed of the two rollers, [0020]: optimization goal may comprise a predetermined percentage of comminuted grain components with predetermined geometric properties…predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0021]: control assembly may automatically regulate the machine parameter to reach the optimization goal, such as the control assembly adjusting different settings of the machine parameter successively in an optimization routine in order to determine a dependency between the machine parameter and the indicator of the processing quality); and
a driver assistance system configured to automatically control, based on the degree of grain cracking, the secondary crushing device (see at least [0018]: a corn cracker as a work assembly arranged or positioned in the harvested material flow, such as the corn cracker having two rollers that rotate during operation with an adjustable rotational speed as a machine parameter, with the harvested material flow running through a gap with a gap width between the rollers that is adjustable as a machine parameter, and the rollers having a differential rotational speed that may be adjusted as a machine parameter by which the rotational speed of the rollers differs, [0020]: optimization goal may comprise a predetermined percentage of comminuted grain components with predetermined geometric properties…predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0021]: control assembly may automatically regulate the machine parameter to reach the optimization goal, such as the control assembly adjusting different settings of the machine parameter successively in an optimization routine in order to determine a dependency between the machine parameter and the indicator of the processing quality).
As per claim(s) 16, Heitmann discloses wherein the secondary crushing device has at least two rollers for breaking up whole grains in the flow of harvested material (see at least [0018]: a corn cracker as a work assembly arranged or positioned in the harvested material flow, such as the corn cracker having two rollers that rotate during operation with an adjustable rotational speed as a machine parameter),
wherein the secondary crushing device is configured to receive one or more commands in order to set one or more of the following parameters: at least one parameter indicative of rotary speed, wherein each of the at least two rollers configured to rotate during operation at the rotary speed; at least one parameter indicative of a gap width, wherein the gap width is a gap between the at least two rollers; and at least one parameter indicative of a speed difference, wherein the at least two rollers have the speed difference by which the rotary speeds of the at least two rollers differ (see at least [0018]: a corn cracker as a work assembly arranged or positioned in the harvested material flow, such as the corn cracker having two rollers that rotate during operation with an adjustable rotational speed as a machine parameter, with the harvested material flow running through a gap with a gap width between the rollers that is adjustable as a machine parameter, and the rollers having a differential rotational speed that may be adjusted as a machine parameter by which the rotational speed of the rollers differs); and
wherein the driver assistance system is configured to automatically generate, based on a specific degree of grain cracking, and send to the secondary crushing device the one or more commands in order to automatically control the at least one parameter indicative of the rotary speed, the gap width, or the speed difference (see at least [0018], claim 13: wherein the control assembly, based on the indicator of the processing quality of the comminuted grain components, is configured to adjust one or more of the adjustable speed of the two rollers, the adjustable differential rotational speed at which the rotational speeds of the two rollers differ, or the adjustable gap width in order to modify a value of the indicator of the processing quality of the comminuted grain components).
As per claim(s) 17, Heitmann discloses wherein the secondary crushing device and the driver assistance system form an automatic processing unit;
wherein the automatic processing unit is configured to optimize the at least one parameter indicative of the rotary speed, the gap width, or the speed difference depending on the determined degree of grain cracking and to preset optimized parameters of the secondary crushing device (see at least [0018]: the rollers having a differential rotational speed that may be adjusted as a machine parameter by which the rotational speed of the rollers differs, claim 13: wherein the control assembly, based on the indicator of the processing quality of the comminuted grain components, is configured to adjust one or more of the adjustable speed of the two rollers, the adjustable differential rotational speed at which the rotational speeds of the two rollers differ, or the adjustable gap width in order to modify a value of the indicator of the processing quality of the comminuted grain components, claim 16: based on the indicator of the processing quality and an optimization goal, adjust one or more machine parameters that affect the processing quality, claim 17: machine parameters comprises one or more of: a rotational speed of a pre-pressing roller; a gap width of the corn cracker; a differential rotational speed of rollers of the corn cracker; or rotational speed of the rollers of the corn cracker, claim 18: optimization goal comprises one or both of a predetermined percentage of the comminuted grain components with predetermined geometric properties or a predetermined fuel consumption).
As per claim(s) 18, Heitmann discloses wherein the optical recording apparatus is positioned along a harvested material transport path behind the secondary crushing device (see at least claim 14: wherein the camera is positioned on a discharge chute of the forage harvester).
As per claim(s) 19, Heitmann discloses wherein the optical recording apparatus is positioned on a discharge chute of the forage harvester (see at least claim 14: wherein the camera is positioned on a discharge chute of the forage harvester).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 6, 8-13, 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220061216 (Heitmann) in view of Rasmussen CB, Moeslund TB. Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images. Sensors. 2019; 19(16):3506. (Year: 2019) (Rasmussen)
As per claim(s) 1, 12, Heitmann discloses an image analysis method for a computer-implemented determination of a degree of grain cracking of grains within a flow of harvested material processed by at least one working unit of a forage harvester, the flow comprises whole grains and crushed grains as grain components and non-grain components, wherein the at least one working unit is automatically controlled depending on the determined degree of grain cracking, the method comprising:
obtaining, using at least one optical recording device, one or more images of the flow of harvested material (see at least [0012]: optical measuring system includes a camera for recording image data of the harvested material of the harvested material flow, claim 1: determine, using an image recognition algorithm, one or more image regions assigned to a comminuted grain component in the image data);
in a first stage of the image analysis method, classifying image pixels contained in the one or more images into grain components and non-grain components (see at least [0013]: Grain components and non-grain components may be differentiated with varying effectiveness in an optical analysis with different wavelengths of the recorded light, [0023]: harvested material 4 comprises grain components 5 and non-grain components 6, claim 1: determine, based on the one or more image regions, one or more geometric properties of the assigned comminuted grain components; and determine, based on the one or more geometric properties, an indicator of a processing quality of the comminuted grain components, [0026]: camera 11 therefore has at least a sufficient number of pixels to enable the disclosed image recognition as explained further below. The camera 11 may be arranged along the harvested material transport path 7 after the corn cracker 8 so that it records the already comminuted grain components 5);
in a second stage of the image analysis method, performing, using a length-width comparison, a length determination of a long main axis and a short main axis of one or more of the classified grain components (see at least [0047]: geometric properties may include geometric dimensions of the grain components 5. The geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area. Accordingly, in one or some embodiments, grain components 5 with a maximum cross-sectional area may be considered comminuted and may be included in the indicator of the processing quality),
wherein at least one neural network performs the first stage and the second stage (see at least [0016]: image recognition algorithm may be based on machine learning, such as deep learning);
determining, using the length determination, the degree of grain cracking (see at least [0031]: control assembly 9 is configured to determine geometric properties of the assigned grain components 5 in the image recognition routine based on the particular image regions 12, [0047]: geometric properties may include geometric dimensions of the grain components 5. The geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area, [0020]: optimization goal may comprise a predetermined percentage of comminuted grain components with predetermined geometric properties…predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0021], [0048]: indicator of the processing quality may depict a percentage of grain components 5 with predetermined geometric properties of the harvested material 4 or the grain components 5, and the calculation instructions may include a formation of the percentage…the percentage is that of grain components 5 with a predetermined maximum and/or minimum cross-sectional area); and
automatically controlling the at least one working unit based on the degree of grain cracking (see at least claim 13: wherein the corn cracker has two rollers configured to rotate during operation with an adjustable rotational speed of the two rollers, [0020]: optimization goal may comprise a predetermined percentage of comminuted grain components with predetermined geometric properties…predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0021]: control assembly may automatically regulate the machine parameter to reach the optimization goal, such as the control assembly adjusting different settings of the machine parameter successively in an optimization routine in order to determine a dependency between the machine parameter and the indicator of the processing quality).
Should it be found that Heitmann does not explicitly disclose determining, using the length determination, the degree of grain cracking,
Rasmussen teaches determining, using the length determination, the degree of grain cracking (see at least section 3.2: To evaluate the viability of the two CNN methods for kernel fragment recognition, we adopt the commonly used KPS score from the CSPS [3]. For each detected instance for either method the length of smallest axis from a rotated fitted bounding-box is found. This length gives an indication of the detected kernel instance that would pass through the 4.75 mm sieve screen used in CSPS. The smallest axis length is used as a quality indicator due to the three-dimensional shaking present in the Ro-Tap separators used in CSPS, therefore, particles are separated based upon the shortest diameter.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Heitmann by incorporating the teachings of Rasmussen with a reasonable expectation of success in order to use the short axis determining the process quality of the kernels based on the number of kernels not exceeding 4.75mm.
As per claim(s) 2, 13, Heitmann discloses wherein determining, using the length determination, the degree of grain cracking comprises: determining an area of classified grain components that fall below an adaptive limit value for the length of the short main axis (see at least [0017]: geometric properties may include geometric dimensions of the comminuted grain components, such as the geometric dimensions including any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area, [0020]: user may specify via an input a value of the indicator for a processing quality to be achieved, and/or a minimum value, and/or a maximum value…user may transmit his/her target specifications to the forage harvester and have the forage harvester modify operation in order to adjust the processing quality (e.g., to optimize of the processing quality), [0021]: predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0047]: geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area. Accordingly, in one or some embodiments, grain components 5 with a maximum cross-sectional area may be considered comminuted and may be included in the indicator of the processing quality).
Should it be found the Heitmann does not explicitly disclose wherein determining, using the length determination, the degree of grain cracking comprises: determining an area of classified grain components that fall below an adaptive limit value for the length of the short main axis,
Rasmussen teaches wherein determining, using the length determination, the degree of grain cracking comprises: determining an area of classified grain components that fall below an adaptive limit value for the length of the short main axis (see at least section 3.2: To evaluate the viability of the two CNN methods for kernel fragment recognition, we adopt the commonly used KPS score from the CSPS [3]. For each detected instance for either method the length of smallest axis from a rotated fitted bounding-box is found. This length gives an indication of the detected kernel instance that would pass through the 4.75 mm sieve screen used in CSPS. The smallest axis length is used as a quality indicator due to the three-dimensional shaking present in the Ro-Tap separators used in CSPS, therefore, particles are separated based upon the shortest diameter.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Heitmann by incorporating the teachings of Rasmussen with a reasonable expectation of success in order to use the short axis determining the process quality of the kernels based on the number of kernels not exceeding 4.75mm.
As per claim(s) 3, Heitmann discloses wherein determining, using the length determination, the degree of grain cracking comprises: calculating a quotient of a sum of the area of classified grain components that fall below the adaptive limit value for the length of the short main axis and a sum of an area of all classified grain components; and calculating, based on the quotient, the degree of grain cracking (see at least [0017]: a percentage of comminuted grain components, with predetermined maximum and/or minimum geometric dimensions of the grain components, [0021]: predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0047]: geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area. Accordingly, in one or some embodiments, grain components 5 with a maximum cross-sectional area may be considered comminuted and may be included in the indicator of the processing quality).
Should it be found that Heitmann does not explicitly disclose wherein determining, using the length determination, the degree of grain cracking comprises: calculating a quotient of a sum of the area of classified grain components that fall below the adaptive limit value for the length of the short main axis and a sum of an area of all classified grain components; and calculating, based on the quotient, the degree of grain cracking,
Rasmussen teaches wherein determining, using the length determination, the degree of grain cracking comprises: calculating a quotient of a sum of the area of classified grain components that fall below the adaptive limit value for the length of the short main axis and a sum of an area of all classified grain components; and calculating, based on the quotient, the degree of grain cracking (see at least section 3.2: To evaluate the viability of the two CNN methods for kernel fragment recognition, we adopt the commonly used KPS score from the CSPS [3]. For each detected instance for either method the length of smallest axis from a rotated fitted bounding-box is found. This length gives an indication of the detected kernel instance that would pass through the 4.75 mm sieve screen used in CSPS. The smallest axis length is used as a quality indicator due to the three-dimensional shaking present in the Ro-Tap separators used in CSPS, therefore, particles are separated based upon the shortest diameter.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Heitmann by incorporating the teachings of Rasmussen with a reasonable expectation of success in order to use the short axis determining the process quality of the kernels based on the number of kernels not exceeding 4.75mm.
As per claim(s) 6, Heitmann does not explicitly disclose wherein a differentiation between whole grains and crushed grains is performed within the image pixels classified as grain components of a recorded image using semantic image segmentation.
However, Rasmussen teaches wherein a differentiation between whole grains and crushed grains is performed within the image pixels classified as grain components of a recorded image using semantic image segmentation (see at least section 1: identify whole or broken fragments in grains…train CNNs in both a bounding-box detector and instance segmentation form to automatically detect and localise kernel fragments in the challenging images).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Heitmann by incorporating the teachings of Rasmussen with a reasonable expectation of success in order to automatically detect and localise kernel fragments in the challenging images.
As per claim(s) 8, Heitmann discloses manually adapting the adaptive limit value (see at least [0020]: user may specify via an input a value of the indicator for a processing quality to be achieved, and/or a minimum value, and/or a maximum value…user may transmit his/her target specifications to the forage harvester and have the forage harvester modify operation in order to adjust the processing quality (e.g., to optimize of the processing quality)).
As per claim(s) 9, Heitmann discloses wherein manually adapting the adaptive limit value is performed by one or both of: a selection from a predefined or predefinable range of values for values of an average grain size; or by an input of at least one value of an average grain size (see at least [0020]: user may specify via an input a value of the indicator for a processing quality to be achieved, and/or a minimum value, and/or a maximum value…user may transmit his/her target specifications to the forage harvester and have the forage harvester modify operation in order to adjust the processing quality (e.g., to optimize of the processing quality)).
As per claim(s) 10, Heitmann discloses wherein the one or more images are cyclically recorded and transmitted to an image analysis apparatus for evaluation; and wherein the image pixels contained in the one or more images are classified using at least one of semantic image segmentation, object recognition or instance segmentation (see at least claim 1: determine, using an image recognition algorithm, one or more image regions assigned to a comminuted grain component in the image data).
As per claim(s) 11, Heitmann discloses wherein determining the long main axis and the short main axis of each classified grain component, in order to determine the area of the classified grain components that fall below the adaptive limit value for the length of the short main axes, is performed at time-spaced intervals (see at least [0017]: a percentage of comminuted grain components, with predetermined maximum and/or minimum geometric dimensions of the grain components, [0021]: predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0047]: geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area. Accordingly, in one or some embodiments, grain components 5 with a maximum cross-sectional area may be considered comminuted and may be included in the indicator of the processing quality),
Should it be found that Heitmann does not explicitly disclose wherein determining the long main axis and the short main axis of each classified grain component, in order to determine the area of the classified grain components that fall below the adaptive limit value for the length of the short main axes, is performed at time-spaced intervals.
However, Rasmussen teaches wherein determining the long main axis and the short main axis of each classified grain component, in order to determine the area of the classified grain components that fall below the adaptive limit value for the length of the short main axes, is performed at time-spaced intervals (see at least section 2.3: process is continuously performed, section 3.2: To evaluate the viability of the two CNN methods for kernel fragment recognition, we adopt the commonly used KPS score from the CSPS [3]. For each detected instance for either method the length of smallest axis from a rotated fitted bounding-box is found. This length gives an indication of the detected kernel instance that would pass through the 4.75 mm sieve screen used in CSPS. The smallest axis length is used as a quality indicator due to the three-dimensional shaking present in the Ro-Tap separators used in CSPS, therefore, particles are separated based upon the shortest diameter.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Heitmann by incorporating the teachings of Rasmussen with a reasonable expectation of success in order for improved performance and to use the short axis determining the process quality of the kernels based on the number of kernels not exceeding 4.75mm.
As per claim 15, Heitmann discloses a forage harvester comprising:
an attachment configured to collect harvested material (see at least [0018]: forage harvester may have an attachment as a work assembly that generates harvested material flow for collecting the crop);
one or more working units configured to process a stream of the harvested material produced from the harvested material that is collected, the one or more working units comprise a secondary crushing device (see at least [0018]: forage harvester may have an attachment as a work assembly that generates harvested material flow for collecting the crop; a pre-pressing roller as a work assembly arranged or positioned in the harvested material flow with a rotational speed through which a chaff length of the harvested material may be adjusted; a cutterhead as a work assembly that is arranged or positioned in the harvested material flow for chopping the harvested material; or a corn cracker as a work assembly arranged or positioned in the harvested material flow);
an optical recording apparatus configured to generate one or more images of flow of harvested material (see at least [0012]: optical measuring system includes a camera for recording image data of the harvested material of the harvested material flow, claim 1: determine, using an image recognition algorithm, one or more image regions assigned to a comminuted grain component in the image data);
an image analysis apparatus configured to determine a degree of grain cracking of grains in the flow of the harvested material by: in a first stage of, classifying image pixels contained in the one or more images into grain components and non-grain components (see at least [0013]: Grain components and non-grain components may be differentiated with varying effectiveness in an optical analysis with different wavelengths of the recorded light, [0023]: harvested material 4 comprises grain components 5 and non-grain components 6, claim 1: determine, based on the one or more image regions, one or more geometric properties of the assigned comminuted grain components; and determine, based on the one or more geometric properties, an indicator of a processing quality of the comminuted grain components, [0026]: camera 11 therefore has at least a sufficient number of pixels to enable the disclosed image recognition as explained further below. The camera 11 may be arranged along the harvested material transport path 7 after the corn cracker 8 so that it records the already comminuted grain components 5);
in a second stage, performing, using a length-width comparison, a length determination of a long main axis and a short main axis of one or more of the classified grain components (see at least [0047]: geometric properties may include geometric dimensions of the grain components 5. The geometric dimensions may include any one, any combination, or all of: a shortest side length; a greatest side length; or a cross-sectional area. Accordingly, in one or some embodiments, grain components 5 with a maximum cross-sectional area may be considered comminuted and may be included in the indicator of the processing quality),
wherein at least one neural network performs the first stage and the second stage (see at least [0016]: image recognition algorithm may be based on machine learning, such as deep learning); and
determining, using the length determination, the degree of grain cracking (see at least claim 13: wherein the corn cracker has two rollers configured to rotate during operation with an adjustable rotational speed of the two rollers, [0020]: optimization goal may comprise a predetermined percentage of comminuted grain components with predetermined geometric properties…predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0021]: control assembly may automatically regulate the machine parameter to reach the optimization goal, such as the control assembly adjusting different settings of the machine parameter successively in an optimization routine in order to determine a dependency between the machine parameter and the indicator of the processing quality); and
a driver assistance system configured to automatically control, based on the degree of grain cracking, the secondary crushing device (see at least [0018]: a corn cracker as a work assembly arranged or positioned in the harvested material flow, such as the corn cracker having two rollers that rotate during operation with an adjustable rotational speed as a machine parameter, with the harvested material flow running through a gap with a gap width between the rollers that is adjustable as a machine parameter, and the rollers having a differential rotational speed that may be adjusted as a machine parameter by which the rotational speed of the rollers differs, [0020]: optimization goal may comprise a predetermined percentage of comminuted grain components with predetermined geometric properties…predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm, [0021]: control assembly may automatically regulate the machine parameter to reach the optimization goal, such as the control assembly adjusting different settings of the machine parameter successively in an optimization routine in order to determine a dependency between the machine parameter and the indicator of the processing quality).
Should it be found that Heitmann does not explicitly disclose determining, using the length determination, the degree of grain cracking,
Rasmussen teaches determining, using the length determination, the degree of grain cracking (see at least section 3.2: To evaluate the viability of the two CNN methods for kernel fragment recognition, we adopt the commonly used KPS score from the CSPS [3]. For each detected instance for either method the length of smallest axis from a rotated fitted bounding-box is found. This length gives an indication of the detected kernel instance that would pass through the 4.75 mm sieve screen used in CSPS. The smallest axis length is used as a quality indicator due to the three-dimensional shaking present in the Ro-Tap separators used in CSPS, therefore, particles are separated based upon the shortest diameter.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Heitmann by incorporating the teachings of Rasmussen with a reasonable expectation of success in order to use the short axis determining the process quality of the kernels based on the number of kernels not exceeding 4.75mm.
As per claim(s) 16, Heitmann discloses wherein the secondary crushing device has at least two rollers for breaking up whole grains in the flow of harvested material (see at least [0018]: a corn cracker as a work assembly arranged or positioned in the harvested material flow, such as the corn cracker having two rollers that rotate during operation with an adjustable rotational speed as a machine parameter),
wherein the secondary crushing device is configured to receive one or more commands in order to set one or more of the following parameters: at least one parameter indicative of rotary speed, wherein each of the at least two rollers configured to rotate during operation at the rotary speed; at least one parameter indicative of a gap width, wherein the gap width is a gap between the at least two rollers; and at least one parameter indicative of a speed difference, wherein the at least two rollers have the speed difference by which the rotary speeds of the at least two rollers differ (see at least [0018]: a corn cracker as a work assembly arranged or positioned in the harvested material flow, such as the corn cracker having two rollers that rotate during operation with an adjustable rotational speed as a machine parameter, with the harvested material flow running through a gap with a gap width between the rollers that is adjustable as a machine parameter, and the rollers having a differential rotational speed that may be adjusted as a machine parameter by which the rotational speed of the rollers differs); and
wherein the driver assistance system is configured to automatically generate, based on a specific degree of grain cracking, and send to the secondary crushing device the one or more commands in order to automatically control the at least one parameter indicative of the rotary speed, the gap width, or the speed difference (see at least [0018], claim 13: wherein the control assembly, based on the indicator of the processing quality of the comminuted grain components, is configured to adjust one or more of the adjustable speed of the two rollers, the adjustable differential rotational speed at which the rotational speeds of the two rollers differ, or the adjustable gap width in order to modify a value of the indicator of the processing quality of the comminuted grain components).
As per claim(s) 17, Heitmann discloses wherein the secondary crushing device and the driver assistance system form an automatic processing unit;
wherein the automatic processing unit is configured to optimize the at least one parameter indicative of the rotary speed, the gap width, or the speed difference depending on the determined degree of grain cracking and to preset optimized parameters of the secondary crushing device (see at least [0018]: the rollers having a differential rotational speed that may be adjusted as a machine parameter by which the rotational speed of the rollers differs, claim 13: wherein the control assembly, based on the indicator of the processing quality of the comminuted grain components, is configured to adjust one or more of the adjustable speed of the two rollers, the adjustable differential rotational speed at which the rotational speeds of the two rollers differ, or the adjustable gap width in order to modify a value of the indicator of the processing quality of the comminuted grain components, claim 16: based on the indicator of the processing quality and an optimization goal, adjust one or more machine parameters that affect the processing quality, claim 17: machine parameters comprises one or more of: a rotational speed of a pre-pressing roller; a gap width of the corn cracker; a differential rotational speed of rollers of the corn cracker; or rotational speed of the rollers of the corn cracker, claim 18: optimization goal comprises one or both of a predetermined percentage of the comminuted grain components with predetermined geometric properties or a predetermined fuel consumption).
As per claim(s) 18, Heitmann discloses wherein the optical recording apparatus is positioned along a harvested material transport path behind the secondary crushing device (see at least claim 14: wherein the camera is positioned on a discharge chute of the forage harvester).
As per claim(s) 19, Heitmann discloses wherein the optical recording apparatus is positioned on a discharge chute of the forage harvester (see at least claim 14: wherein the camera is positioned on a discharge chute of the forage harvester).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heitmann in view of Rasmussen, and further in view of US 20160029561 (Fischer).
As per claim(s) 7, Heitmann discloses wherein an average value representing a mean size of the grains is formed for a visible area from the sum of the area of whole grains determined within an interval, from which the adaptive limit value is dynamically derived as a fractional value of one or both of the long main axis or the short main axis (see at least [0021]: predetermined percentage of comminuted grain components may be a percentage of at least 70% of grain components with a non-strainable cross-section of at most 4.75 mm, and/or the optimization goal may be an average size of the comminuted grain components between 118 mm and 4.75 mm).
Should it be found that Heitmann does not explicitly disclose the adaptive limit value is dynamically derived as a fractional value of one or both of the long main axis or the short main axis,
Fischer teaches the adaptive limit value is dynamically derived as a fractional value of one or both of the long main axis or the short main axis (see at least [0040]: A limit value also can be defined as the upper limit of the fine-grain fraction, which is lower than the lower limit of the coarse-grain fraction. It can therefore be ensured that kernel-type particles that, due to the size thereof, cannot, with certainty, be identified as an intact kernel or as a kernel fragment, are not assigned to the fine-grain fraction or to the coarse-grain fraction. Such particles also can be distributed to one or more fractions having an intermediary grain size, [0041]: Steps S3 to S5 are repeated until all the kernel particles that can be identified in the image have been evaluated, and then the procedure branches off to step S6, in order to evaluate the cardinality of the fractions that was obtained).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Heitmann by incorporating the teachings of Fischer with a reasonable expectation of success in order to ensure that kernel-type particles that, due to the size thereof, cannot, with certainty, be identified as an intact kernel or as a kernel fragment, are not assigned to the fine-grain fraction or to the coarse-grain fraction.
Allowable Subject Matter
Claims 4-5, 14 would be allowable if the Double Patenting rejection is overcome and if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The prior art taken either individually or in combination with other prior art of record fails to disclose, suggest, teach, or render obvious the invention as a whole:
Regarding claim 4, automatically adapting the adaptive limit value.
Regarding claim 5, wherein the adaptive limit value is automatically adapted cyclically at intervals based on one or both of the long main axis or the short main axis.
Regarding claim 14, wherein the image analysis apparatus is further configured to automatically adapt the adaptive limit value based on one or both of the long main axis or the short main axis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELINA M SHUDY whose telephone number is (571)272-6757. The examiner can normally be reached M - F 10am - 6pm.
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Angelina Shudy
Primary Examiner
Art Unit 3668
/Angelina M Shudy/Primary Examiner, Art Unit 3668
Prosecution Insights