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 .
Status of Claims
Applicant’s submission filed 2/26/26 has been entered. Claims 2, 5, 8, 12, 14-20, 23, 25 are cancelled. Claims 32-36 are new. Claims 1, 3, 4, 6, 7, 9, 10, 11, 13, 21, 22, 24, 26-36 are presented for examination.
Claim Objections
Claim 33 objected to because of the following informalities: The claim refers back to itself. 33. (see e.g. “(New) The computer-implemented system of claim 33”), Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 33 rejected under 35 U.S.C. 112(a), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 33 recites “wherein the neural network is configured to extract feature vectors representing the distinguishable structural features from the image data and classify the grain variety based on the extracted feature vectors.” There appears to be no support for this limitation in the Applicant original disclosure.
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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1, 3, 4, 6, 7, 9, 10, 11, 13, 21, 22, 24, 26-31, 34-38 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending U.S. application # 18/891,898, in view of Thomas (2011/0191879 A1).
Although the claims at issue are not identical, they are not patentably distinct from each other because The current invention and the invention of Application 18/891,898 are drawn to identical subject matter. Whether or not the exchange of value and information takes place between vehicles and/or mobile devices does not change the scope of the claims.
This is a provisional obviousness-type double patenting because the conflicting claims have not in fact been patented.
Please see claims 1-20 of U.S. application # 18/891,898 below as an example.
App. # 19/277,126 Application 18/891,898 in view of Thomas, in further view of Kokko et al.
1. A method for tracking a variety of a grain through a supply chain comprising:
1. A method for tracking a variety of a grain through a supply chain comprising:
obtaining the variety of grain from a seed producer.
wherein the variety of the grain has a distinguishable characteristic that differentiates the grain variety from other varieties of a grain; and
Thomas (see e.g. [[0169] The present invention comprises a soybean plant characterized by molecular and physiological data obtained from the representative sample of said variety deposited with the American Type Culture Collection (ATCC). )
Thomas --(see e.g. [0167] Particular markers used for these purposes are not limited to any particular set of markers, but are envisioned to include any type of marker and marker profile which provides a means of distinguishing varieties.
(a) the distinguishable characteristic is phenotypic;
wherein: (a) the distinguishable characteristic is phenotypic;
(b) the distinguishable characteristic is
(i) a seed characteristic of a plurality of seed in the grain sample;
and (b) the distinguishable characteristic is (i) a seed characteristic of a plurality of seed in the grain sample
(ii) the seed characteristic is a visual characteristic; and
Thomas --(see e.g. [0060] Phenotypic Score. The Phenotypic Score is a visual rating of general appearance of the variety. All visual traits are considered in the score including healthiness, standability, appearance, and freedom of disease)
(iii) the seed characteristic is a unique seed marking; and
Thomas--(see e.g. [0009] The goal of soybean plant breeding is to develop new, unique and superior soybean cultivars and hybrids. –[0176] While determining the SSR genetic marker profile of the plants described supra, several unique SSR profiles may also be identified which did not appear in either parent of such plant. Such unique SSR profiles may arise during the breeding process from recombination or mutation. A combination of several unique alleles provides a means of identifying a plant variety, an F.sub.1 progeny produced from such variety, and progeny produced from such variety.)
wherein the distinguishable characteristic is uniform, stable, and heritable.
Claim 2-wherein the distinguishable characteristic is uniform, stable, and heritable.
capturing, by an imaging device, image data of the grain sample to identify seed characteristics including at least one of seed color or seed shape;
Kokko et al. (see e.g. [0145], [00051], [0157] ).
Identifying, by a computer system, the variety of the grain in the supply chain by executing an image-recognition algorithm comprising a neural network trained on labeled images of grain varieties having known characteristics corresponding to a plurality of grain varieties defined by the distinguishable characteristic,
Kokko et al. ([0132], [0206], [0010], [0012] ).
wherein the neural network is configured to analyze the newly received image data, compare attributes of the images to known grain varieties, identify correlations between characteristics detected in the images and output an identification of the variety of the grain based on the seed coat color or seed shape and, optionally, one or more of hilum color, plant seed luster;
Kokko et al. ( [0204-0208], [0256] , [0203]).
identifying the variety of the grain in the supply chain;
identifying the variety of the grain based on a distinguishable characteristic of a grain sample
wherein the identifying is performed by at least one of:
a seed producer to track the grain within the seed breeding program or tracking the grain variety end-to-end agriculture commodity supply chains,
Claim 8. The method of claim 1, wherein identifying is performed by a farmer, grain elevator, grain processor, distributor, retailer, or consumer.
a farmer to identify the purchase of a proper seed variety, to verify the authenticity of the grain variety in a purchased grain, or for crop management of the grain variety,
See claim 8.
a grain elevator to assure the authenticity of a grain variety, or segregate and store the grain variety from other grain varieties,
See claim 8.
a distributor to produce properly identified end products and market authenticated consumer-packaged goods to a retailer or to verify the authenticity of the grain variety in a purchased grain.”.
See claim 8.
Claim Rejections - 35 USC § 101
After further review, and in view of the amendments, the rejection under 101 is withdrawn. The combination of steps including “capturing, by an imaging device, image data of the grain sample to identify seed characteristics including at least one of seed color or seed shape; and a neural network trained on labeled images of grain varieties having known characteristics corresponding to a plurality of grain varieties defined by the distinguishable characteristic, wherein the neural network is configured to analyze the newly received image data, compare attributes of the images to known grain varieties, identify correlations between characteristics detected in the images and output an identification of the variety of the grain based on the seed coat color or seed shape and, optionally, one or more of hilum color, plant seed luster;” renders the claims eligible.
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.
Claims 1-7, 9-11, 13, 21-31 are rejected under 35 U.S.C. 103 as being unpatentable over Thomas (2011/0191879 A1), in view of PENNER et al. (WO 02064803 A2), in further view of Kokko et al. (US 20030072484 A1)
Re-claim 1, Thomas teaches -- A method for tracking a variety of a grain through a supply chain (se e.g. [0207) providing a means of tracking genetic profiles through crosses.) comprising:
-obtaining the variety of grain …,
(see e.g. [0169] The present invention comprises a soybean plant characterized by molecular and physiological data obtained from the representative sample of said variety deposited with the American Type Culture Collection (ATCC). )
- wherein the variety of the grain has a distinguishable characteristic that differentiates the grain variety from other varieties of a grain; and
(see e.g. [0167] Particular markers used for these purposes are not limited to any particular set of markers, but are envisioned to include any type of marker and marker profile which provides a means of distinguishing varieties.
(a) the distinguishable characteristic is phenotypic;
(see e.g. [0182] The gene for herbicide resistance may be used as a selectable marker and/or as a phenotypic trait.
[0019] In addition to phenotypic observations, the genotype of a plant can also be examined.
[0060] Phenotypic Score. The Phenotypic Score is a visual rating of general appearance of the variety. All visual traits are considered in the score including healthiness, standability, appearance, and freedom of disease
(b) the distinguishable characteristic is
(i) a seed characteristic of a plurality of seed in the grain sample;
(see e.g. [0016] harvesting a sample of one seed per plant, and using the one-seed sample to plant the next generation.
[0169] The present invention comprises a soybean plant characterized by molecular and physiological data obtained from the representative sample of said variety deposited with the American Type Culture Collection (ATCC).
Claim 18. A method of introducing a desired trait into soybean cultivar 3317361, wherein the method comprises: (a) crossing a 3317361 plant, wherein a representative sample of seed is deposited under ATCC Accession No. PTA-______, with a plant of another soybean cultivar that comprises a desired trait to produce progeny plants)
(ii) the seed characteristic is a visual characteristic; and
(see e.g. [0060] Phenotypic Score. The Phenotypic Score is a visual rating of general appearance of the variety. All visual traits are considered in the score including healthiness, standability, appearance, and freedom of disease)
(iii) the seed characteristic is a unique seed marking; and
(see e.g. [0009] The goal of soybean plant breeding is to develop new, unique and superior soybean cultivars and hybrids.
0176] While determining the SSR genetic marker profile of the plants described supra, several unique SSR profiles may also be identified which did not appear in either parent of such plant. Such unique SSR profiles may arise during the breeding process from recombination or mutation. A combination of several unique alleles provides a means of identifying a plant variety, an F.sub.1 progeny produced from such variety, and progeny produced from such variety.)
--wherein the distinguishable characteristic is uniform, stable, and heritable.
(see e.g. [0077], table 2 The cultivar has shown uniformity and stability, as described in the following variety description information.).
[0004] The complexity of inheritance influences choice of the breeding method. Backcross breeding is used to transfer one or a few favorable genes for a highly heritable trait into a desirable cultivar. This approach has been used extensively for breeding disease-resistant cultivars. Various recurrent selection techniques are used to improve quantitatively inherited traits controlled by numerous genes.
[0015] Backcross breeding has been used to transfer genes for a simply inherited, highly heritable trait into a desirable homozygous cultivar or inbred line which is the recurrent parent.)
And identifying the variety of the grain in the supply chain;
wherein the identifying is performed by at least one of:
a seed producer to track the grain within the seed breeding program or tracking the grain variety end-to-end agriculture commodity supply chains,
(see e.g. [0175] The SSR profile of soybean cultivar 3317361 can also be used to identify essentially derived varieties and other progeny varieties developed from the use of soybean cultivar 3317361, as well as cells and other plant parts thereof)
[0176] While determining the SSR genetic marker profile of the plants described supra, several unique SSR profiles may also be identified which did not appear in either parent of such plant. Such unique SSR profiles may arise during the breeding process from recombination or mutation. A combination of several unique alleles provides a means of identifying a plant variety, an F.sub.1 progeny produced from such variety, and progeny produced from such variety.
[0008] A most difficult task is the identification of individuals that are genetically superior, because for most traits the true genotypic value is masked by other confounding plant traits or environmental factors. One method of identifying a superior plant is to observe its performance relative to other experimental plants and to a widely grown standard cultivar.
0166] In addition to phenotypic observations, a plant can also be identified by its genotype. The genotype of a plant can be characterized through a genetic marker profile which can identify plants of the same variety, or a related variety, or be used to determine or validate a pedigree.
a farmer to identify the purchase of a proper seed variety, to verify the authenticity of the grain variety in a purchased grain, or for crop management of the grain variety,
a grain elevator to assure the authenticity of a grain variety, or segregate and store the grain variety from other grain varieties,
a grain processor to assure the authenticity of a grain variety, segregate and store the grain variety from other grain varieties, or more effectively market value-added end products, and
a distributor to produce properly identified end products and market authenticated consumer-packaged goods to a retailer or to verify the authenticity of the grain variety in a purchased grain.”.
Thomas anticipates a supply chain consisting of a seed producer, a grower, processor and consumer (see e.g. [0020] In addition to showing superior performance, there must be a demand for a new cultivar that is compatible with industry standards or which creates a new market. The introduction of a new cultivar will incur additional costs to the seed producer, the grower, processor and consumer for special advertising and marketing, altered seed and commercial production practices, and new product utilization. )
Although Thomas teach obtaining the variety of grain, Thomas does not explicitly teach obtaining the variety of grain from a seed producer.
However, in the same field of endeavor, PENNER et al. teach -- obtaining the variety of grain from a seed producer.
(see e.g. claim 19-- i) accepting harvested grain from the grower at a collection point
--;a delivery of grain by the grower to the elevator.
--The present invention is directed to a method of using phenotypic markers in proprietary seed or plant cultivars to facilitate (1) detection of harvested grain containing the proprietary trait and (2) determination of licensing fees.
PENNER et al. also teach ----identifying the variety of the grain in the supply chain;
(see e.g. --A mixture of yellow canola seeds with a black seeded canola line carrying a proprietary trait would enable trait fee collection and tracking of proprietary traits at the point of delivery in a manner identical to what is proposed for soybean.)
PENNER et al. also teach -- "variety" is a grouping of plants that are homogeneous and stable, and clearly distinguishable by at least one phenotypic characteristic from all other groupings of plants; "herbicidally culled seed" is seed which is selected as a result of herbicide application).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Thomas, and include the steps cited above, as taught by PENNER et al., in order to facilitate the identification of the harvested grain, and further allowing a collection of fees for the proprietary traits based on the presence of the marker in the harvested grain. (see e.g. PENNER et al.).
Thomas , in view of PENNER et al. do not teach the following limitations.
However, Kokko et al. teach
capturing, by an imaging device, image data of the grain sample to identify seed characteristics including at least one of seed color or seed shape; and
(see e.g. [0145] Any one of several image capture devices can be used to scan seeds or other objects. In one embodiment, seeds or other objects may be scanned using a CCD array (sensor), or optionally, a large flatbed scanner, digital camera, line scan camera, or CMOS arrays can be used.
[0051] In another embodiment, the invention provides a method for obtaining one or more image parameters of color for objects comprising the step of generating an outline of the pixels representing each of the objects.
[0157] Image analysis of the samples of known seeds or other objects then involves analyzing the pixels representing each of the known seeds or objects to generate data representative of one of more image parameters for each of the known seeds or objects, with the image parameters being dimension, shape, texture, and color. )
a neural network trained on labeled images of grain varieties having known characteristics corresponding to a plurality of grain varieties defined by the distinguishable characteristic,
[0132] The invention pertains to a method and apparatus for identifying or quantifying one or more characteristics of interest of seeds or other objects.
[0206] The extracted data from step (iii) of the unknown seed samples is then provided to the task-specific neural network, which has been previously trained with a training set of known seeds as described previously in Section A. The object analysis program activates the neural network decision engine, such that the neural network conducts an analysis of the extracted data from step (iii), reaches decisions regarding the extracted data, and generates a summary output of results, wherein the values of the one or more characteristics or parameters of interest for the unknown seeds are provided.
[0010] A training of a single neural network model with a first and a second training set of known objects having known values for the one or more characteristics of interest;
0012] C analyzing unknown objects having unknown values of the one or more characteristics of interest, )
0203] iii. Obtaining Spectral Data for the Samples of Unknown Seeds or Other Objects).
wherein the neural network is configured to analyze the newly received image data, compare attributes of the images to known grain varieties, identify correlations between characteristics detected in the images and output an identification of the variety of the grain based on the seed coat color or seed shape and, optionally, one or more of hilum color, plant seed luster;
(see e.g. [0204] During the prior image processing and analysis steps, an outline file containing the outlines of each individual object/seed in the image is generated by delineating the outline of the seeds (outer layer of pixels).
[0206] The extracted data from step (iii) of the unknown seed samples is then provided to the task-specific neural network, which has been previously trained with a training set of known seeds as described previously in Section A. The object analysis program activates the neural network decision engine, such that the neural network conducts an analysis of the extracted data from step (iii), reaches decisions regarding the extracted data, and generates a summary output of results, wherein the values of the one or more characteristics or parameters of interest for the unknown seeds are provided.
[0208] For seeds, identification and quantification of significant environmental or physiological seed conditions can be conducted, with the neural network being trained to identify and quantify economically significant environmental or physiological seed conditions (e.g. green seeds, piebald, frost or freezing damaged seeds, weathering, or Hard Vitreous Kernels).
[0256] The training process for the capability to undertake seed characterization tasks, involving detection, identification and quantification of seed sample characteristics (e.g., dockage, milling characteristics, color characterization), is similar to that described in Example 1 (seed diseases).
Kokko et al. also teach -(iii) the seed characteristic is a unique seed marking; and
(see e.g. [0242] The invention is applicable to the following classification tasks:
[0248] classification functions in barley (e.g., for distinguishing between and 2 row types; feed and malt types; for distinguishing between various malt varieties).
0252] Other strategies can include, but are not limited to, physically employing a dye or stain to aid GMO detection; genetically inserting a fluorescing marker on the seed coat that would be optically detected in the case of a GMO being analyzed; or breeding in an other type characteristic marker that readily identifies the sample as a GMO, preserving its identity (e.g., shape).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Thomas, in view of PENNER et al., and include the steps cited above, as taught by Kokko et al., for accuracy and repeatability (see e.g. [0161] )
Re-claim 3, Thomas teaches -- The method of claim 1, wherein the seed characteristic is selected from at least two of seed shape, seed coat color, hilum color, plant seed luster, and combinations thereof.
(see e.g. [0078] Soybean cultivar 3317361 has the following morphologic and other characteristics
Table 1 -Variety Description Information --Seed Coat Color, Seed Coat Luster, Hilum Color. --Determinate Flower Color:
[0185] The modified soybean cultivar 3317361 may be further characterized as having the physiological and morphological characteristics of soybean variety 3317361 listed in Table 1 )
[0159] Other genes and transcription factors that affect plant growth and agronomic traits, such as yield, flowering, plant growth, and/or plant structure, can be introduced or introgressed into plants.)
Re-claim 4, Thomas teaches -- The method of claim 3, wherein the seed shape is selected from the group consisting of spherical rounded, spherical flattened, elongate and elongate flattened or the seed coat color is selected from the group consisting of yellow, clack saddle, clack, brown, black spots, black rings, dark brown, red-brown, buff, dichromate and green, or the hilum color is selected from the group consisting of black, imperfect black, grey, dark brown, brown, light brown, imperfect yellow, and yellow, or the plant seed luster is selected from the group consisting of dull, mudfilm, mud-free-film and lustrous.
(see e.g. Table 1 - Seed Shape: Spherical flattened; Seed Coat Color: Yellow; Hilum Color: Black; Seed Coat Luster: Dull.).
Re-claim 6, Thomas teaches -- The method of claim 1, wherein the seed producer produces the distinguishable characteristic is obtained through plant breeding or plant biotechnology.
(see e.g. [0074] Single gene converted (conversion) plants refers to plants which are developed by a plant breeding technique called backcrossing wherein essentially all of the desired morphological and physiological characteristics of a variety are recovered in addition to the single gene transferred into the variety via the backcrossing technique or via genetic engineering.).
Re-claim 7, Thomas teaches -- The method of claim 1, wherein the distinguishable characteristic is obtained by a method comprising identifying Quantitative Trait Loci (QTLs) and incorporating the QTLs through plant breeding or plant biotechnology to establish a uniform, stable, and heritable distinguishable characteristic.
(see e.g. [0206] One use of molecular markers is Quantitative Trait Loci (QTL) mapping. QTL mapping is the use of markers, which are known to be closely linked to alleles that have measurable effects on a quantitative trait. Selection in the breeding process is based upon the accumulation of markers linked to the positive effecting alleles and/or the elimination of the markers linked to the negative effecting alleles from the plant's genome.)
(see e.g. [0077], table 2 The cultivar has shown uniformity and stability, as described in the following variety description information.).
[0004] The complexity of inheritance influences choice of the breeding method. Backcross breeding is used to transfer one or a few favorable genes for a highly heritable trait into a desirable cultivar. This approach has been used extensively for breeding disease-resistant cultivars. Various recurrent selection techniques are used to improve quantitatively inherited traits controlled by numerous genes.
[0015] Backcross breeding has been used to transfer genes for a simply inherited, highly heritable trait into a desirable homozygous cultivar or inbred line which is the recurrent parent.)
Re-claims 9, 10, Thomas teaches -- The method of claim 1, wherein the variety of the grain exhibits a value differentiating characteristic.
-- The method of claim 9, wherein the value differentiating characteristic is selected from seed source, a utilization characteristic, a crop management characteristic, disease resistance, drought resistance, pest resistance, herbicide resistance, antioxidant content, nutrient content, improved yield, improved growth, a processing characteristic, an environmental adaptation ability, and combinations thereof; and
wherein the utilization characteristic is selected from the group consisting of human consumption, animal consumption, oil source, solid food source, genetically modified origin, non-genetically modified origin, geographic origin, and combinations thereof and/or the crop management characteristic is selected from the group consisting of agronomic management, disease resistance, antioxidant content, nutrient content, improved yield, improved growth, a processing characteristic, a utilization characteristic, an environmental adaptation ability, and combinations thereof.
(see e.g. [0108] Likewise, by means of the present invention, plants can be genetically engineered to express various phenotypes of agronomic interest. Through the transformation of soybean, the expression of genes can be altered to enhance disease resistance, insect resistance, herbicide resistance, agronomic, grain quality, and other traits. )
Re-claim 11, Thomas teaches -- The method of claim 1, further comprising managing grain production for the variety of the grain; wherein managing grain production is selected from one or more of agronomic management, varying plant density, applying fertilizer, applying herbicide, applying pesticide, varying moisture conditions, varying crop location, selecting cultivation methods, and selecting harvesting methods.
(see e.g. [0083] In one embodiment the desired trait may be one or more of herbicide resistance, insect resistance, disease resistance, decreased phytate, or modified fatty acid or carbohydrate metabolism.)
[0186] In addition, the above process and other similar processes described herein may be used to produce first generation progeny soybean seed by adding a step at the end of the process that comprises crossing soybean cultivar 3317361 with the introgressed trait or locus with a different soybean plant and harvesting the resultant first generation progeny soybean seed.).
Re-claim 13, Thomas teaches -- The method of claim 1, wherein the grain is selected from the group consisting of soybeans, corn, rye, rice, wheat, buckwheat, oats, millet, and barley.
(see e.g. [0190] This invention is directed to methods for producing a soybean plant by crossing a first parent soybean plant with a second parent soybean plant wherein either the first or second parent soybean plant is variety 3317361.
[0188] Using Soybean Cultivar 3317361 to Develop Other Soybean Varieties
[0189] Soybean varieties such as soybean cultivar 3317361 are typically developed for use in seed and grain production. However, soybean varieties such as soybean cultivar 3317361 also provide a source of breeding material that may be used to develop new soybean varieties.)
Re-claim 21, Thomas anticipates -- The method of claim 1, wherein the identifying is performed by at least one of a farmer to verify the authenticity of the grain variety, a grain elevator to segregate the grain variety from other grain varieties in storage to maintain identity preservation and sell and deliver the identity preserved grain to customers, a grain processor to authenticate the identity of the grain variety, segregate the grain variety in storage to maintain identify preservation, perform value added grain processing on the grain variety, segregate end products of the grain processing, market end products identifies as the grain variety, a distributor to produce consumer-packaged goods using the grain variety purchased from the grain processor.
(see e.g. [0020] Proper testing should detect any major faults and establish the level of superiority or improvement over current cultivars. In addition to showing superior performance, there must be a demand for a new cultivar that is compatible with industry standards or which creates a new market. The introduction of a new cultivar will incur additional costs to the seed producer, the grower, processor and consumer for special advertising and marketing, altered seed and commercial production practices, and new product utilization. )
Note: Thomas anticipates the seed producer, a grower and processor and consumer as part of the supply chain which will at least identify the grain in the supply chain.
Re-claim 22, Thomas teaches -- A method for tracking a variety of a grain through a supply chain (see e.g. [0207) providing a means of tracking genetic profiles through crosses.) comprising:
obtaining the variety of grain [..]
(see e.g. [[0169] The present invention comprises a soybean plant characterized by molecular and physiological data obtained from the representative sample of said variety deposited with the American Type Culture Collection (ATCC). )
wherein the variety of the grain has a distinguishable characteristic that differentiates the grain variety from other varieties of a grain; and
(see e.g. [0167] Particular markers used for these purposes are not limited to any particular set of markers, but are envisioned to include any type of marker and marker profile which provides a means of distinguishing varieties.
the distinguishable characteristic is phenotypic;
(see e.g. [0182] T he gene for herbicide resistance may be used as a selectable marker and/or as a phenotypic trait.
[0019] In addition to phenotypic observations, the genotype of a plant can also be examined.
[0060] Phenotypic Score. The Phenotypic Score is a visual rating of general appearance of the variety. All visual traits are considered in the score including healthiness, standability, appearance, and freedom of disease)
(b) the distinguishable characteristic is a secondary characteristic of plants grown from the variety of the grain in the grain sample; and
(see e.g. [0079] This invention is also directed to methods for producing a soybean plant by crossing a first parent soybean plant with a second parent soybean plant, wherein the first or second soybean plant is the soybean plant from cultivar 3317361. Further, both first and second parent soybean plants may be from cultivar 3317361. )
wherein the secondary characteristic is selected from flower color, leaf shape, pubescence, pod appearance, plant shape, stem termination, bloom habit, growth habitat, leaf habitat, and combinations thereof.
(see e.g. [0078] Soybean cultivar 3317361 has the following morphologic and other characteristics
Table 1 -Variety Description Information --Seed Coat Color, Seed Coat Luster, Hilum Color. --Determinate Flower Color:
[0159] Other genes and transcription factors that affect plant growth and agronomic traits, such as yield, flowering, plant growth, and/or plant structure, can be introduced or introgressed into plants.
[0011] The development of new soybean cultivars requires the development and selection of soybean varieties, the crossing of these varieties and selection of superior hybrid crosses. The hybrid seed is produced by manual crosses between selected male-fertile parents or by using male sterility systems. These hybrids are selected for certain single gene traits such as pod color, flower color, pubescence color or herbicide resistance which indicate that the seed is truly a hybrid. Additional data on parental lines, as well as the phenotype of the hybrid, influence the breeder's decision whether to continue with the specific hybrid cross.)
--wherein the distinguishable characteristic is uniform, stable, and heritable.
(see e.g. [0077], table 2 The cultivar has shown uniformity and stability, as described in the following variety description information.).
[0004] The complexity of inheritance influences choice of the breeding method. Backcross breeding is used to transfer one or a few favorable genes for a highly heritable trait into a desirable cultivar. This approach has been used extensively for breeding disease-resistant cultivars. Various recurrent selection techniques are used to improve quantitatively inherited traits controlled by numerous genes.
[0015] Backcross breeding has been used to transfer genes for a simply inherited, highly heritable trait into a desirable homozygous cultivar or inbred line which is the recurrent parent.)
-- identifying the variety of the grain in the supply chain; wherein the identifying is performed by at least one of a farmer or distributor to verify the authenticity of the grain variety in a purchased grain, a grain elevator or a grain processor to in assure the authenticity of a grain variety, and/or segregate and store the grain variety from other grain varieties.
(see e.g. [0175] The SSR profile of soybean cultivar 3317361 can also be used to identify essentially derived varieties and other progeny varieties developed from the use of soybean cultivar 3317361, as well as cells and other plant parts thereof)
[0176] While determining the SSR genetic marker profile of the plants described supra, several unique SSR profiles may also be identified which did not appear in either parent of such plant. Such unique SSR profiles may arise during the breeding process from recombination or mutation. A combination of several unique alleles provides a means of identifying a plant variety, an F.sub.1 progeny produced from such variety, and progeny produced from such variety.
[0008] A most difficult task is the identification of individuals that are genetically superior, because for most traits the true genotypic value is masked by other confounding plant traits or environmental factors. One method of identifying a superior plant is to observe its performance relative to other experimental plants and to a widely grown standard cultivar.
[0166] In addition to phenotypic observations, a plant can also be identified by its genotype. The genotype of a plant can be characterized through a genetic marker profile which can identify plants of the same variety, or a related variety, or be used to determine or validate a pedigree.
Although Thomas teach obtaining the variety of grain, Thomas does not explicitly teach obtaining the variety of grain from a seed producer.
However, in the same field of endeavor, PENNER et al. teach -- obtaining the variety of grain from a seed producer.
(see e.g. claim 19-- i) accepting harvested grain from the grower at a collection point
--;a delivery of grain by the grower to the elevator.
--The present invention is directed to a method of using phenotypic markers in proprietary seed or plant cultivars to facilitate (1) detection of harvested grain containing the proprietary trait and (2) determination of licensing fees.
PENNER et al. also teach ----identifying the variety of the grain in the supply chain;
(see e.g. --A mixture of yellow canola seeds with a black seeded canola line carrying a proprietary trait would enable trait fee collection and tracking of proprietary traits at the point of delivery in a manner identical to what is proposed for soybean.)
(b) the distinguishable characteristic is a secondary characteristic of plants grown from the variety of the grain in the grain sample; and
(see e.g. A plant seed mixture useful in the present invention may contain primary colored seeds and secondary colored seeds from the plant species of soybean, canola, or wheat. If soybeans are used, the secondary seed coat colors may be black, brown, heterozygous yellow, or determined by measuring the total light reflectance with a spectrophotometer for wavelengths from 550 to 650 nanometers.
claim 1. A plant seed mixture for identifying seed with a proprietary trait using a phenotypic marker comprising: about 90% to about 99.9% by weight of primary seeds for a plant variety with a genetically modified trait, said primary seeds having the dominant seed coat color of said plant varietal seeds; and
about 0.1% to about 10% by weight of secondary seeds of the same or different plant variety, said secondary seeds with or without a genetically modified trait, said secondary seeds having a seed coat color with at least one phenotypical difference from the primary seed coat color.
2. The plant seed mixture of claim 1 wherein the secondary seeds are homozygous for the phenotypical difference of seed coat color.)
PENNER et al. also teach -- "variety" is a grouping of plants that are homogeneous and stable, and clearly distinguishable by at least one phenotypic characteristic from all other groupings of plants; "herbicidally culled seed" is seed which is selected as a result of herbicide application).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Thomas, and include the steps cited above, as taught by PENNER et al., in order to facilitate the identification of the harvested grain, and further allowing a collection of fees for the proprietary traits based on the presence of the marker in the harvested grain. (see e.g. PENNER et al.).
Thomas , in view of PENNER et al. do not teach the following limitations.
However, Kokko et al. teach
wherein the identifying comprises: capturing, by an imaging device, image data of plants grown from the variety of the grain
(see e. g. [0145] Any one of several image capture devices can be used to scan seeds or other objects. In one embodiment, seeds or other objects may be scanned using a CCD array (sensor), or optionally, a large flatbed scanner, digital camera, line scan camera, or CMOS arrays can be used.
[0051] In another embodiment, the invention provides a method for obtaining one or more image parameters of color for objects comprising the step of generating an outline of the pixels representing each of the objects.
[0157] Image analysis of the samples of known seeds or other objects then involves analyzing the pixels representing each of the known seeds or objects to generate data representative of one of more image parameters for each of the known seeds or objects, with the image parameters being dimension, shape, texture, and color.
[0133] The "object" or "objects" thus refer to seeds or non-seed and non-grain articles, including plants and plant parts, food articles, biological materials, and industrial articles.)
And identifying, by a computer system, the variety of the grain in the supply chain by executing an image-recognition algorithm comprising a neural network trained on labeled images of grain varieties having known secondary characteristics corresponding to a plurality of grain varieties defined by the distinguishable characteristic,
(see e.g. [0132] The invention pertains to a method and apparatus for identifying or quantifying one or more characteristics of interest of seeds or other objects.
[0206] The extracted data from step (iii) of the unknown seed samples is then provided to the task-specific neural network, which has been previously trained with a training set of known seeds as described previously in Section A. The object analysis program activates the neural network decision engine, such that the neural network conducts an analysis of the extracted data from step (iii), reaches decisions regarding the extracted data, and generates a summary output of results, wherein the values of the one or more characteristics or parameters of interest for the unknown seeds are provided.)
--wherein the neural network is configured to analyze the newly received image data, compare attributes of the images to known grain varieties, identify correlations between characteristics detected in the images and output an identification of the variety of the based on the secondary characteristic detected in the image data;
(see e.g. [0204] During the prior image processing and analysis steps, an outline file containing the outlines of each individual object/seed in the image is generated by delineating the outline of the seeds (outer layer of pixels).
[0206] The extracted data from step (iii) of the unknown seed samples is then provided to the task-specific neural network, which has been previously trained with a training set of known seeds as described previously in Section A. The object analysis program activates the neural network decision engine, such that the neural network conducts an analysis of the extracted data from step (iii), reaches decisions regarding the extracted data, and generates a summary output of results, wherein the values of the one or more characteristics or parameters of interest for the unknown seeds are provided.
[0208] For seeds, identification and quantification of significant environmental or physiological seed conditions can be conducted, with the neural network being trained to identify and quantify economically significant environmental or physiological seed conditions (e.g. green seeds, piebald, frost or freezing damaged seeds, weathering, or Hard Vitreous Kernels).
[0256] The training process for the capability to undertake seed characterization tasks, involving detection, identification and quantification of seed sample characteristics (e.g., dockage, milling characteristics, color characterization), is similar to that described in Example 1 (seed diseases).
Kokko et al. also teach wherein the variety of the grain has a distinguishable characteristic that differentiates the grain variety from other varieties of a grain; and
(see e.g. [0242] The invention is applicable to the following classification tasks:
[0248] classification functions in barley (e.g., for distinguishing between and 2 row types; feed and malt types; for distinguishing between various malt varieties).
0252] Other strategies can include, but are not limited to, physically employing a dye or stain to aid GMO detection; genetically inserting a fluorescing marker on the seed coat that would be optically detected in the case of a GMO being analyzed; or breeding in an other type characteristic marker that readily identifies the sample as a GMO, preserving its identity (e.g., shape).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Thomas, in view of PENNER et al., and include the steps cited above, as taught by Kokko et al., for accuracy and repeatability (see e.g. [0161] ).
Re-claim 24, Thomas teaches –The method of claim 23, wherein the flower color is selected from the group consisting of white, purple, and other, or the leaf shape is selected from the group consisting of lanceolate, triangular, pointed ovate, rounded ovate and linear, or the pod appearance is selected from the group consisting of brown, dark brown and black, or the plant shape is selected from the group consisting of erect, semi-erect, indeterminate, semi-prostrate, prostrate, wild, semi-wild, and combinations thereof, and/or the stem termination is selected from the group consisting of determinate and indeterminate.
(see e.g. Table 1 ---Determinate Flower Color: Purple).
Claim 26 recites similar limitations as claim 6 and is therefore rejected under the same art and rationale.
Claim 27 recites similar limitations as claim 7 and is therefore rejected under the same art and rationale.
Claim 28 recites similar limitations as claims 9 and 10 and is therefore rejected under the same art and rationale.
Claim 29 recites similar limitations as claim 11 and is therefore rejected under the same art and rationale.
Re-claim 30, Thomas does not teach the limitation as claimed.
However PENNER et al. teach --The method of claim 22, wherein the secondary characteristic comprises a color or pigmentation detectable by multispectral imaging, hyperspectral imaging, or LiDAR.
(see e.g. Seed color may be measured using a Technicon near-infrared reflectance (NIR) spectrophotometer calibrated to determine total light reflectance (optical density) from 550 to 650 nanometers. This wavelength setting allows separation of yellow from brown from black seeds. Alternatively, optical scanning technology can be used to distinguish seeds on the basis of color. Both NIR and optical scanning can be set up for high-throughput analysis.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Thomas, and include the steps cited above, as taught by PENNER et al., in order to allow separation of yellow from brown from black seeds to distinguish seeds on the basis of color.(see e.g. PENNER et al. )
Claim 31 recites similar limitations as claim 13 and is therefore rejected under the same art and rationale.
Claims 32, 39 are rejected under 35 U.S.C. 103 as being unpatentable over Kokko et al. (US 20030072484 A1)
Re-claim 32, Kokko et al. teach--A computer-implemented system for identifying a grain variety comprising an imaging device configured to capture image data of a grain or a plant grown from the grain;
(see e.g. [0145] Any one of several image capture devices can be used to scan seeds or other objects. In one embodiment, seeds or other objects may be scanned using a CCD array (sensor), or optionally, a large flatbed scanner, digital camera, line scan camera, or CMOS arrays can be used.
[0051] In another embodiment, the invention provides a method for obtaining one or more image parameters of color for objects comprising the step of generating an outline of the pixels representing each of the objects.
[0157] Image analysis of the samples of known seeds or other objects then involves analyzing the pixels representing each of the known seeds or objects to generate data representative of one of more image parameters for each of the known seeds or objects, with the image parameters being dimension, shape, texture, and color. )
and a computer system configured to identify the grain variety based on a set of distinguishable structural features detected in the image data,
(see e.g. [0132] The invention pertains to a method and apparatus for identifying or quantifying one or more characteristics of interest of seeds or other objects.
[0206] The extracted data from step (iii) of the unknown seed samples is then provided to the task-specific neural network, which has been previously trained with a training set of known seeds as described previously in Section A. The object analysis program activates the neural network decision engine, such that the neural network conducts an analysis of the extracted data from step (iii), reaches decisions regarding the extracted data, and generates a summary output of results, wherein the values of the one or more characteristics or parameters of interest for the unknown seeds are provided.)
---wherein the distinguishable structural features are selected from the group consisting of seed shape, seed coat color, hilum color, plant seed luster, and combinations thereof,
(see e.g. [0166] For seeds, as an example, color data is particularly significant in distinguishing the type of seed (e.g., Red, White, and Durum wheats).
[0218] The digital image was then processed by initially extracting the seeds from the background using edge detection. The image of the seeds was first eroded with a 3.times.3 erode filter, 1 pass, and 10 strength, which eliminated a small area on the outer circumference of each seed. A single outer layer of pixels affected by scanned edge effects is then eliminated. A known filter algorithm (e.g., SOBEL or ROBERTS) was applied to detect the edge of the seed's image.
[0208] the invention is applicable to seed sample characterization tasks involving detection, identification and quantification of sample features (e.g. dockage, milling characteristics, color characterization.
[0193] Next, the seed analysis program opens an environment file including 34 dimensional, shape, textural, and densitometric parameters as shown in Table 2.).
--wherein the computer system executes an image-recognition algorithm configured to analyze the image data and output an identification of the grain variety.
(see e.g. [0157] Image analysis of the samples of known seeds or other objects then involves analyzing the pixels representing each of the known seeds or objects to generate data representative of one of more image parameters for each of the known seeds or objects, with the image parameters being dimension, shape, texture, and color.
0153] Processing begins by detecting an edge of each of the objects and distinguishing each of the objects from the background by applying an edge detection algorithm, and eliminating an outer layer of pixels on the outer circumference of each of the objects and any debris.
[0078] In a further embodiment, the invention provides a preferred apparatus to achieve such processing and analysis, comprising: an image capture device (e.g., scanner, camera); a seed or object presentation device (hardware presentation e.g., holder, tray, or belt); a monitor; and a computer having an executable seed or object analysis program which is written with programming software (e.g., Visual Basic.TM.); image analysis software (e.g., ImageProPlus.TM.); data processing software (e.g., Excel.TM.); and neural network modelling software (e.g., Predict.TM.). A)
Claim 39 recites similar limitations as claim 1 and 32 and is therefore rejected under the same art and rationale.
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Kokko et al. (US 20030072484 A1), in view of BAINBRIDGE et al. (WO 2021255458 A1).
Re-claim 33, Kokko et al. teach ---The computer-implemented system of claim 33, wherein the image- recognition algorithm comprises a neural network trained on labeled image data corresponding to a plurality of grain varieties, the labeled image data associating each grain variety with a unique combination of the distinguishable structural features,
(see e.g. [0132] The invention pertains to a method and apparatus for identifying or quantifying one or more characteristics of interest of seeds or other objects.
[0206] The extracted data from step (iii) of the unknown seed samples is then provided to the task-specific neural network, which has been previously trained with a training set of known seeds as described previously in Section A. The object analysis program activates the neural network decision engine, such that the neural network conducts an analysis of the extracted data from step (iii), reaches decisions regarding the extracted data, and generates a summary output of results, wherein the values of the one or more characteristics or parameters of interest for the unknown seeds are provided.)
[0233] The unknown seeds are scanned to obtain an image which is then processed and analyzed. The neural network then analyzes the extracted data of the unknown seeds to determine the value of the one or more environmental or physiological parameters of interest of the unknown seeds.)
Kokko et al. do not teach the following limitations as claimed.
However, BAINBRIDGE et al. teach and wherein the neural network is configured to extract feature vectors representing the distinguishable structural features from the image data and classify the grain variety based on the extracted feature vectors.
(see e.g. --The method may further comprise generating or updating a spatially resolved model or map of the identified crop features in the AOI. Each crop feature in the model is associated or tagged with an attribute vector comprising its respective one or more crop feature attributes. The model may comprise a two or three- dimensional (X, Y, Z) point cloud, where each two/three-dimensional point represents the (X, Y, Z) location of an individual crop feature which is associated/tagged with its attribute vector.
-----The method may further comprise training a machine learning model using the field-camera specific training images and training data set. The method may comprise training a machine learning model to identify crop features and determine crop feature attributes of crop features in images of crops generated by a field camera using the field-camera specific training images and training data set. The machine learning model may be or comprise a deep or convolutional neural network. The trained machine learning model may be used in the crop monitoring method of the first aspect. Each hyperspectral image in the time series may be taken from substantially the same position relative to the crops. The method may further comprise generating the image data by taking a hyperspectral image, or a series or plurality of hyperspectral images over a period of time, using a hyperspectral camera. The hyperspectral images may be taken with the hyperspectral camera in substantially the same position relative to the crops.
---The method therefore generates practical quantitative data and outputs that a farmer can use to maintain the crops, predict yield, diagnose crop loss. Farmers can use the output of the method to take proactive intervention steps to protect yield through early detection and diagnosis of crop loss events such as disease, pests and weeds. In an embodiment, the crop features are identified, and certain (secondary) crop feature attributes are determined using computer vision and machine learning techniques applied to the images. By using a machine learning model trained using input from an experienced farmer or farm worker, all the crops in the AOI can be analysed in a consistent manner using the same expert insight. This approach leverages expert knowledge and takes away the person to person variability in traditional methods involving manual crop inspections. Identifying crop features may comprise detecting and classifying one or more crop features in each image. The one or more crop features in each image may be identified using a machine learning model trained on a dataset of crop images to identify the one or more crop features in the respective image based, at least in part, on one or more image features extracted from each respective image.).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Kokko et al. , and include the steps cited above, as taught by BAINBRIDGE et al., because by using a machine learning model trained using input from an experienced farmer or farm worker, all the crops in the AOI can be analysed in a consistent manner using the same expert insight (see e.g. BAINBRIDGE et al.,).
Claims 34, 37, 38 are rejected under 35 U.S.C. 103 as being unpatentable over Thomas (2011/0191879 A1), in view of PENNER et al. (WO 02064803 A2), in further view of Kokko et al. (US 20030072484 A1), in further view of Obropta et al. (US 20180042176 A1)
Re-claim 34, Although anticipated by Kokko et al. teach –[0165] To ensure accurate image analysis, images with optimum brightness, contrast, resolution and colour spectral characteristics are desired.
Thomas, in view of PENNER et al., in view of Kokko et al. do not teach the limitations as claimed.
However, Obropta et al. explicitly teach --The method of claim 1, wherein capturing the image data comprises capturing spectral image data at least partially outside a visible spectrum detectable by a human eye, including at least one of infrared or ultraviolet, and wherein the neural network is configured to identify the variety of the grain based on characteristics detected in the spectral image data that are not discernible by the human eye.
(see e. g. [0131] In some embodiments, the multispectral camera 137 may be used by sensor package 120 to capture images of the harvested specialty crops in one or more different spectra outside the visible spectrum. The spectral signature of the harvested specialty crops may be used to identify the specialty crops being harvested, for example, by comparing spectral data to values retrieved from a database. In some embodiments, the multispectral camera 137 may capture a broad range of wavelengths of light that is processed using one or more band-pass filters.
[0086] In some embodiments, the imaging sensor(s) 117 are configured to capture one or more electromagnetic spectra. For example, the imaging sensor(s) 117 may include one or more near infrared sensors configured to detect emissions in the 700 nm-2500 nm range.
[0055] In some embodiments, the system is further programmed to: (1) access a trained statistical model configured to output information indicative of harvested specialty crop quality (e.g., a USDA grade number or an indication of rot); (2) provide, as input to the trained statistical model, at least one feature selected from the group consisting of the image, the depth information, the length of the major axis, and the length of the minor axis; and (3) determine quality of crops in the set of harvested specialty crops based on output of the trained statistical model. For example, in some embodiments, an image depicting one or more individual harvested specialty crops may be provided as input to a neural network that is trained to detect rot (and/or any other factor indicative of quality examples of which are provided herein) on the surface of each of the one or more individual harvested specialty crops.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Thomas, in view of PENNER et al., in view of Kokko et al., and include the steps cited above, as taught by Obropta et al., in order to accurately determine yield, quality, individual shape and/or volume of a crop, and many other characteristics that may be useful for the commercialization and cultivation of specialty crops. (see e.g. [0041] ).
Re-claims 37, 38, Thomas, in view of PENNER et al., in view of Kokko et al. do not teach the limitations as claimed.
However, Obropta et al. teach --The method of claim 1, wherein identifying the variety of the grain comprises analyzing sensor-derived data representing wavelengths outside a visible spectrum and generating the identification based on the analyzed sensor-derived data, wherein the analyzing cannot be performed by visual assessment by a human.
(see e.g. [0131] In some embodiments, the multispectral camera 137 may capture a broad range of wavelengths of light that is processed using one or more band-pass filters.
0044] In some embodiments, the device includes a thermal imaging sensor. The thermal imaging sensor may be used for any suitable purpose. For example, the thermal imaging sensor may be used to detect thermal radiation from the harvested specialty crops. Such information may be used to determine whether the temperature of the specialty crops is suitable for harvesting. Harvesting crops whose temperature is too high may not be desirable, for example. Including a thermal imaging sensor in the sensor package allows for the sensor package, in some embodiments, to transmit a warning to an operator to stop harvesting crops that are too hot and/or to automatically stop harvesting.
38. The method of claim 1, wherein capturing the image data comprises at least one of thermal imaging, multispectral imaging, hyperspectral imaging, or LiDAR, and wherein identifying the variety of the grain is based on characteristics detected in the image data that are not discernible by the human eye.
(see e.g. [0034] Accordingly, some embodiments provide for a device for use in connection with monitoring and assessing characteristics of harvested specialty crops. In some embodiments, the device may include: (1) an imaging sensor (e.g., a color camera, a monochrome camera, a multi-spectral camera, etc.) configured to capture one or more image(s) of a set of harvested specialty crops; (2) a depth sensor (e.g., an ultrasonic sensor, a LIDAR (Light Detection and Ranging) sensor, another imaging sensor, etc.); (3) processing circuitry configured to generate depth information at least in part by using data obtained by the depth sensor (and, in some embodiments, by also using the image(s) obtained by the imaging sensor);
[0133] The sensor package 120 also includes the thermal imaging sensor 153 configured to capture thermal images of the harvested specialty crops. In some embodiments, the thermal imaging sensor 153 may be configured to capture data indicating the temperature of the harvested specialty crops. The temperature may be displayed to the operator of the harvester to indicate whether temperature conditions are suitable for harvesting. In some embodiments, the thermal imaging sensor 153 may include an infrared sensor. In some embodiments, operators at processing facilities may use information provided by the thermal imaging sensor (and/or one or more other sensors) to reject a load of crops, for example, because the temperature of the crops is too high and/or there is too much tare.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Thomas, in view of PENNER et al., in view of Kokko et al., and include the steps cited above, as taught by Obropta et al., in order to accurately determine yield, quality, individual shape and/or volume of a crop, and many other characteristics that may be useful for the commercialization and cultivation of specialty crops. (see e.g. [0041] ).
Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over Thomas (2011/0191879 A1), in view of PENNER et al. (WO 02064803 A2), in further view of Kokko et al. (US 20030072484 A1), in further view of Van Den Enden et al. (US 20190223402 A1).
Re-claim 35, Thomas, in view of PENNER et al., in view of Kokko et al. do not teach the limitations as claimed.
However Van Den Enden et al. teach The method of claim 1, wherein the distinguishable characteristic comprises pigmentation detectable using at least one of an infrared sensor, an ultraviolet sensor, or a fluorescence sensor, and wherein identifying the variety of the grain is based on the detected pigmentation that is not discernible by the human eye.
(see e.g. [0034] The novel seed color of seeds produced by plants carrying the QTL of the invention homozygously is defined as the seeds having a lighter color at the mature seed stage, as compared to the color of seeds produced by a plant not comprising the QTL homozygously. The seed coats of said seeds have a reduced amount of the brown pigments that are present in the seed coats of seeds at the mature seed stage produced by plants not comprising the QTL homozygously. In particular, the seed coats of said seeds have such a low amount of the brown pigments that are normally present in the seed coats of wild type seeds that the brown color in the seed coats of said seeds of plants of the invention is not detectable by the eye.
[0095] Selection of plants in the F2 can be done phenotypically as well as by using the said marker(s) which directly or indirectly detect(s) the QTL underlying the trait. Phenotypic selection can suitably be done by determining the color of the seeds using an RHS color chart for reference and/or by determining the color profile by colorimetry and/or image analysis of the eggplant seeds.
[0138] Photography was conducted in a standardized set-up in a darkened room using a Nikon D7000 camera with a Nikon AF-S 35 mm f/1.8G DX 35 mm lens with circular B+W polarisation filter. The standardized camera set up used daylight fluorescent lamps (4×36 watts, 5400 K, CRI 98, 40 kHz) with a polarisation filter.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Thomas, in view of PENNER et al., in view of Kokko et al., and include the steps cited above, as taught by Van Den Enden et al., because [0136] Environmental conditions, including light source, object size, color background, and angle of vision or illumination may affect how a colored object appears to a human observer. In order to overcome this subjectivity, color measurement systems and instruments have been developed for quantifying colors and expressing colors in terms of variables which describe the color. (see e.g. [0136]).
Response to Arguments
Applicant's arguments filed 2/26/26 have been fully considered but they are not persuasive.
Applicant’s remarks:
---Nothing in Thomas suggests any unique seed markings that is "a distinguishable characteristic that differentiates the grain variety from other varieties of a grain.
The Office Action's citations to genetic marker profiles and SSR profiles in Thomas (e.g., citations to "SSR profile") do not teach or suggest identifying based on "images of grain varieties defined by the distinguishable characteristic" and a computer system identifying grain based on the distinguishable characteristic detected in the image data.
Examiner’s response:
Thomas anticipate a distinguishable characteristic that differentiates the grain variety from other varieties of a grain (see e.g. [0167] Particular markers used for these purposes are not limited to any particular set of markers, but are envisioned to include any type of marker and marker profile which provides a means of distinguishing varieties. )
Furthermore, Kokko et al. teach --[[0248] classification functions in barley (e.g., for distinguishing between and 2 row types; feed and malt types; for distinguishing between various malt varieties).
0252] Other strategies can include, but are not limited to, physically employing a dye or stain to aid GMO detection; genetically inserting a fluorescing marker on the seed coat that would be optically detected in the case of a GMO being analyzed; or breeding in an other type characteristic marker that readily identifies the sample as a GMO, preserving its identity (e.g., shape).).
Applicant’s remarks:
The mixtures of Penner may be sufficient for identifying a grown grain at the elevator, but fail to disclose a "distinguishable characteristic is stable, uniform, and heritable" and thus capable of detection throughout the supply chain. T
Examiner’s response:
Penner teaches a secondary characteristic on a seed ( see e.g. Penner: A plant seed mixture useful in the present invention may contain primary colored seeds and secondary colored seeds from the plant species of soybean, canola, or wheat. If soybeans are used, the secondary seed coat colors may be black, brown, heterozygous yellow, or determined by measuring the total light reflectance with a spectrophotometer for wavelengths from 550 to 650 nanometers.
Furthermore, Penner’s secondary characteristic is not a characteristic of a second grain. It is a secondary seed coat color on the a single seed such as a soybean.
(see e.g. Penner: -If soybeans are used, the secondary seed coat colors may be black, brown, heterozygous yellow, or determined by measuring the total light reflectance with a spectrophotometer for wavelengths from 550 to 650 nanometers)
Applicant’s remarks:
Claims 1-7, 9-11, 13, 21-31 have been provisionally rejected on the ground of
nonstatutory double patenting as allegedly being unpatentable over claims 1-20 of copending U.S. Application No. 18/891,898, in view of Thomas (2011/0191879 A1). See Office Action, p.
3. Applicant respectfully traverses this provisional rejection, noting that no allowable subject matter has yet been identified. Applicant will address the double-patenting rejection should it remain after otherwise allowable subject matter is identified.
c) Examiner’s response:
Applicant’s remark is noted.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A) Olivier et al. (US 20140215653 A1) - IDENTIFICATION AND USE OF EARLY EMBRYO AND/OR EARLY ENDOSPERM SPECIFIC PROMOTERS FOR GENE EXPRESSION IN MAIZE.
B) Ludwig et al. (US 20030033224 A1) -Product Identity Preservation And Tracing
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/LUNA CHAMPAGNE/Primary Examiner, Art Unit 3627 March 16, 2026