DETAILED ACTION
Claims 1-6, 8-17 and 19-20 are pending in this application and have been examined under the priority date of 05/05/2023 in accordance with applicant’s prior filed provisional application. Claims 1 and 11 are amended and claims 7 and 18 have been canceled.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim to the benefit of prior filed provisional application 63/499,978 is acknowledged, accordingly claims 1-20 have been examined under the priority date of 05/03/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/30/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
35 U.S.C. 112(b)
Applicant’s arguments (see Remarks filed 07/08/2026) have been fully considered by the examiner and are persuasive. The examiner agrees that the claim amendments to claims 1 and 13 distinctly claim the subject matter intended. Therefore, for at least these reasons, the rejections made to claims 1 and 13 under 35 U.S.C. 112(b) have been withdrawn by the examiner.
35 U.S.C. 102(a)(1)
Applicant’s arguments (see Remarks filed 07/08/2026) have been fully considered by the examiner and are not persuasive. Applicant argues (see Remarks filed 07/08/2026, page 12 and 13) that Brauer fails to teach clustering vegetation index data, assigning labels to clusters or mapping the clusters to crop growth stages, therefore the “unsupervised mode” of Brauer is not analogous to that of claims 1 and 13. The examiner respectfully disagrees, Brauer teaches in paragraphs [0064]-[0067] that the model is configure to obtain satellite images of the crops and compile the collected data into combined or normalized vegetation indices, which would reduce the dimensionality. This would be understood by one of ordinary skill in the art as being analogous to collecting telemetric vegetation indices derived from the telemetry data obtained by a satellite and transforming them into reduced dimension indices.
Further, Brauer teaches in paragraph [0067] that the combined vegetation index metrics may be aggregated based on phenotype information, or physical characteristics, such that the indices of the data would be matched to the corresponding physical information. One of ordinary skill in the art would understand that this is analogous to clustering the vegetation indices based on physical characteristics or phenotypic characteristics. Further Brauer teaches in [0085] and [0088] that the vegetation index values are values which correspond to color analysis of the fields and the crop characteristics and yields associated with the fields. One of ordinary skill in the art would understand this as being analogous to the labels corresponding to the same crop growth stage since the values of the indices are clustered based on phenotype information, and the phenotype information corresponds directly to the determined crop growth stage. For at least these reasons, the examiner respectfully maintains the rejections under 35 U.S.C. 102 over Brauer.
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35 U.S.C. 103
Applicant’s arguments (see Remarks filed 07/08/2026) have been fully considered by the examiner and are not persuasive. The applicant argues that the combination of Brauer and Shankar would not be obvious because the combination fails to teach all the limitations of the independent claims from which the claims rejected using this combination depend. The examiner respectfully disagrees and notes that Brauer teaches the limitations of the independent claims as fully discussed above. For at least these reasons, the examiner respectfully maintains the rejections under 35 U.S.C. 103.
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 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 limitations are:
Machine learning step of claim 13
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.
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.
Further, this application includes the claim limitations of an “input module” and a “machine learning module” of claims 1 and 13, however these limitations are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claims recite sufficient structure, materials, or acts to entirely perform the recited function. The above limitations are disclosed as being operated on at least one processor, which the examiner is interpreting as the processor being capable of functioning as the modules, and therefore provides sufficient structure to perform the claimed actions of the modules as disclosed.
Because these claim limitations are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitations do not recite sufficient structure, materials, or acts to perform the claimed function.
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.
Claims 1-3, 5-6, 8-14, 16-17 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Brauer (US 20230108422 A1).
Regarding claim 1 Brauer discloses; A system for predicting crop growth stage, including (Brauer, [0007] the system pertains to processing and monitoring image data associated with crop bearing fields):
an input module (Brauer, [0025] a computing device accesses a data set), (Brauer, [0025] the system has a computer which receives multiple input images which have been captured from a satellite, multiple index values are derived from the images captured, applicant defines in paragraph [0024] that the telemetric indices are extracted from images such as a visible light image, therefore this is analogous to what is taught in Brauer as cited above),
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collecting of the vegetation corresponding to telemetric vegetation indices (Brauer, [0025] vegetation index information is derived from images received, and then characteristics of the crops are predicted from this data, where in [0068] the characteristics determined from the data may be growth stages for the crops),
wherein the telemetric vegetation indices are derived from telemetry data obtained from a satellite with a plurality of time period windows (Brauer, [0029] satellites may be configured to capture the data over a set period of time, or set time interval of a few days or weeks, the time intervals are tied to the image resolution for which altitude is a determining factor [0039] satellites may capture data of the same location at approximately the same time each day (time period window for sensing));
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and a machine learning module (Brauer, [0086] the computing device (machine learning module) may using different machine learning models, therefore it is analogous to a machine learning module), including a supervised mode and an unsupervised mode in operating a long short-term memory model (LSTM) (Brauer, [0049] the computing device has a supervised training stage (supervised mode), [0083] the system is trained using two different data sets, one with low resolution data without a ground truth which is used to generate output data (unsupervised learning, denoted as satellite images) images with ground truth data or input/output pairs (supervised, denoted as UAV-images, or known images) [0086] the computing device may use suitable techniques/models to generate merged index values where one example is an LSTM [0087] the computing device may forecast predictions about the crops using this generated data),
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(Brauer, [0086]- [0087] emphasis added)
wherein in the supervised mode, the machine learning module generates a first prediction model based on the correlation between the telemetric vegetation indices and the collected crop growth stage information (Brauer, [0046] computing device feeds in pairs of images to a discriminator where the images are known to be true corresponding pairs (supervised mode, supervised learning requires known inputs and output pairs), [0065] once the paired data is fed into the model, the model correlates the acquired image with the field that it came from, [0066] this telemetric vegetation data is then used to obtain the vegetation index values and then determine measures of crop growth and growth stages based on this),
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and wherein in the unsupervised mode (Brauer, [0064] after training the system collects satellite data at a set time interval (e.g. weekly or daily) and pre-processes it to acquire the index data, this occurs post training, therefore there are no paired inputs and outputs, therefore this is unsupervised and is being interpreted as unsupervised mode), the machine learning module collects the telemetric vegetation indices derived from telemetry data obtained from the satellite in the same time period window into the same group (Brauer, [0064] after training the system collects satellite data at a set time interval (e.g. weekly or daily) and pre-processes it to acquire the index data, [0066] the system compiles the vegetation indices for each field image, which have been captured at set time intervals, [0067] the index values are aggregated into groups by field, which are captured at set time intervals, therefore they indices would be aggregated to the same field at the same time period),
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and transforms the telemetric vegetation indices respectively into a plurality of dimension-reduced vegetation indices (Brauer, [0067] the system may generate a combined index (dimension reduced index) for each field from the index metrics),
to cluster the groups with the same implicit physical characteristics of the dimension-reduced vegetation indices into the same cluster (Brauer, [0067] the combined index metric for each field (dimension-reduced vegetation indices) can be used to aggregate index values with the phenotypic information for that index of values, where the vegetation index value would be the group member and each value would correspond to at least one feature/characteristic such that the index values which have the same feature would be aggregated/grouped),
and to label the telemetric vegetation indices in the same cluster with the same label (Brauer, [0067] the combined index metric for each field (dimension-reduced vegetation indices) can be used to aggregate index values with the phenotypic information for that index of values, where the vegetation index value would be the group member and each value would correspond to at least one feature/characteristic such that the index values which have the same feature would be aggregated/grouped, where the identified feature is analogous to a label);
and where at least one portion of the same labels of the telemetric vegetation indices, corresponds to the same growth stage in the crop growth stage information (Brauer, [0085] index values are derived based on the images where the index values may be values corresponding to color analysis of the fields, [0088] index values correspond to crop characteristics/yield prediction and can be assessed over time, indicating indices correspond to specific growth stages/crop characteristics where the same index value or range of values matches the a specific characteristic);
wherein the input module and the machine learning module are operated in at least one operating processor (Brauer, [0071] the computing device has a memory and processor which executes the processes described, this is analogous to an input module and the machine learning modules),
and a data transmission channel between the input module and the machine learning module is formed during the operation of the input module and the machine learning module (Brauer, [0075] the system includes a network interface which can include the internet, wireless or wired transmission channels for transmitting the data using the processor).
Regarding claim 2 Brauer discloses; The system for predicting crop growth stage according to claim 1, wherein the satellite conducts remote sensing of an area, to generate the telemetric vegetation indices corresponding to the area (Brauer, [0025] the system has a computer which receives multiple input images which have been captured from a satellite, multiple index values are derived from the images captured, [0067] the system may generate a combined index for each field from the index metrics), wherein the telemetric vegetation indices include: biomass, water content, or temperature (Brauer, [0067] the environmental factors included in the vegetation indices may include precipitation (water content) or temperature ranges or soil metrics (biomass)).
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Regarding claim 3 Brauer discloses; The system for predicting crop growth stage according to claim 2, wherein the biomass includes: soil fertility, and soil metabolic profile (Brauer, [0040] the satellite may collect data from soil sensors to include in the index data, which includes soil temperature, soil pH, soil moisture, composition, density and electrical conductivity of the soil, which are all factors which determine soil fertility and soil metabolism, therefore these are analogous to soil fertility and soil metabolic profiles).
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Regarding claim 5 Brauer discloses; The system for predicting crop growth stage according to claim 1, wherein the satellite includes: a sun-synchronous satellite, a geostationary satellite, a satellite with an inclination orbit, or a Molniya orbit satellite (Brauer, [0030] the satellites may be satellites from the Planetscope fleet, which includes multiple types of satellites such as a Superdove satellite which is a type of sun-synchronous satellite designed to image a location at a set solar time each day).
Regarding claim 6 Brauer discloses; The system for predicting crop growth stage according to claim 1, wherein the long short-term memory model includes a coding layer and a decoding layer (Brauer, [0061] the model architecture includes a decoder (decoding layer) and an encoder (coding layer), [0086] the computing device may use suitable techniques/models to generate merged index values where one example is an LSTM, therefore the network may be an LSTM model),
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wherein the telemetric vegetation indices are correspondingly transformed into a plurality of dimension-reduced vegetation indices in the coding layer (Brauer, [0067] the system may generate a combined index (dimension reduced index) for each field from the index metrics, these indices are encoded, indicating they are done in the encoder/coding layer),
and the dimension-reduced vegetation indices are respectively regressed to generate a plurality of regressed telemetric vegetation indices in the decoding layer (Brauer, [0086] the metrics and index values are merged (merged/dimension reduced indices) through multiple methods, which may include a random forest regression, [0087] this data is used to fine tune the predicted crop characteristics to be used along with the image data to generate predicted data and fine-tuned images, [0061] the step of generating the output is done at the decoder or the decoding layer).
Regarding claim 8 Brauer discloses; The system for predicting crop growth stage according to claim 1, wherein after the operation in the unsupervised mode, a second prediction model is obtained in the supervised mode, based on the correlation between the telemetric vegetation indices and the labels (Brauer, [0044] the model has a generate which generates paired data to be fed into the discriminator, where in this case the pairs of data would not have known input-output pairs, therefore this would be “unsupervised mode”, this occurs before the paired data is fed to the discriminator [0046] computing device feeds in pairs of images to a discriminator where the images are known to be true corresponding pairs (supervised mode, supervised learning requires known inputs and output pairs), [0065] once the paired data is fed into the model, the model correlates the acquired image with the field that it came from, [0066] this telemetric vegetation data is then used to obtain the vegetation index values and then determine measures of crop growth and growth stages based on this, this is being interpreted as a correlation between the indices and the labels/characteristics).
Regarding claim 9 Brauer discloses; The system for predicting crop growth stage according to claim 1, wherein the labels or the crop growth stage information include: harvesting, flowering, bagging, pruning, insect damage, or fruiting (Brauer, [0068] the growth stage data includes predicted harvest date, and predicted maturity dates for the crops, which would be analogous to harvesting information).
Regarding claim 10 Brauer discloses; The system for predicting crop growth stage according to claim 9, wherein the harvesting includes harvesting stage and harvest yield status (Brauer, [0068] the growth stage data includes predicted harvest date, and predicted maturity dates for the crops, which are analogous to a harvest stage and data).
Regarding claim 11 Brauer discloses; The system for predicting crop growth stage according to claim 1, wherein the telemetric vegetation indices include a plurality of index values, and at least one of the index values is generated based on an arithmetic calculation or a logical operation based on some of the other index values (Brauer, [0066] the index values may include the normalized difference vegetation index values which are calculated based on a ratio of the color values of the image).
Regarding claim 12 Brauer discloses; The system for predicting crop growth stage according to claim 1, wherein the crop growth stage information includes crop health status (Brauer, [0066] the system may determine metrics of crop health such as greenness measures).
Regarding claim 13 Brauer discloses; A method for predicting crop growth stage, including:
operating an input module and a machine learning module in at least one operating processor (Brauer, [0071] the computing device has a memory and processor which executes the processes described, this is analogous to an input module and the machine learning modules);
forming a data transmission channel between the input module and the machine learning module when operating the input module and the machine learning module (Brauer, [0075] the system includes a network interface which can include the internet, wireless or wired transmission channels for transmitting the data using the processor);
the input module an input module (Brauer, [0025] a computing device accesses a data set), (Brauer, [0025] the system has a computer which receives multiple input images which have been captured from a satellite, multiple index values are derived from the images captured, applicant defines in paragraph [0024] that the telemetric indices are extracted from images such as a visible light image, therefore this is analogous to what is taught in Brauer as cited above),
collecting of the vegetation corresponding to telemetric vegetation indices (Brauer, [0025] vegetation index information is derived from images received, and then characteristics of the crops are predicted from this data, where in [0068] the characteristics determined from the data may be growth stages for the crops),
wherein the telemetric vegetation indices are derived from telemetry data obtained from a satellite with a plurality of time period windows (Brauer, [0029] satellites may be configured to capture the data over a set period of time, or set time interval of a few days or weeks, the time intervals are tied to the image resolution for which altitude is a determining factor [0039] satellites may capture data of the same location at approximately the same time each day (time period window for sensing));
and the machine learning module (Brauer, [0086] the computing device (machine learning module) may using different machine learning models, therefore it is analogous to a machine learning moduleincluding a supervised mode and an unsupervised mode in operating a long short-term memory model (LSTM), wherein in the supervised mode (Brauer, [0049] the computing device has a supervised training stage (supervised mode), [0083] the system is trained using two different data sets, one with low resolution data without a ground truth which is used to generate output data (unsupervised learning, denoted as satellite images) images with ground truth data or input/output pairs (supervised, denoted as UAV-images, or known images) [0086] the computing device may use suitable techniques/models to generate merged index values where one example is an LSTM [0087] the computing device may forecast predictions about the crops using this generated data),
the machine learning module generating a first prediction model based on the correlation between the telemetric vegetation indices and the collected crop growth stage information (Brauer, [0046] computing device feeds in pairs of images to a discriminator where the images are known to be true corresponding pairs (supervised mode, supervised learning requires known inputs and output pairs), [0065] once the paired data is fed into the model, the model correlates the acquired image with the field that it came from, [0066] this telemetric vegetation data is then used to obtain the vegetation index values and then determine measures of crop growth and growth stages based on this),
and wherein in the unsupervised mode (Brauer, [0064] after training the system collects satellite data at a set time interval (e.g. weekly or daily) and pre-processes it to acquire the index data, this occurs post training, therefore there are no paired inputs and outputs, therefore this is unsupervised and is being interpreted as unsupervised mode), the machine learning module collecting the telemetric vegetation indicesderived from the telemetry data obtained from the satellite in the same time period window into the same group (Brauer, [0064] after training the system collects satellite data at a set time interval (e.g. weekly or daily) and pre-processes it to acquire the index data, [0066] the system compiles the vegetation indices for each field image, which have been captured at set time intervals, [0067] the index values are aggregated into groups by field, which are captured at set time intervals, therefore they indices would be aggregated to the same field at the same time period),
and transforms the telemetric vegetation indices respectively into a plurality of dimension-reduced vegetation indices (Brauer, [0067] the system may generate a combined index (dimension reduced index) for each field from the index metrics),
clustering the groups with the same implicit physical characteristics of the dimension-reduced vegetation indices into the same cluster (Brauer, [0067] the combined index metric for each field (dimension-reduced vegetation indices) can be used to aggregate index values with the phenotypic information for that index of values, where the vegetation index value would be the group member and each value would correspond to at least one feature/characteristic such that the index values which have the same feature would be aggregated/grouped),
and labeling the telemetric vegetation indices in the same cluster with the same label (Brauer, [0067] the combined index metric for each field (dimension-reduced vegetation indices) can be used to aggregate index values with the phenotypic information for that index of values, where the vegetation index value would be the group member and each value would correspond to at least one feature/characteristic such that the index values which have the same feature would be aggregated/grouped, where the identified feature is analogous to a label),
and where at least one portion of the same labels of the telemetric vegetation indices, corresponds to the same growth stage in the crop growth stage information (Brauer, [0085] index values are derived based on the images where the index values may be values corresponding to color analysis of the fields, [0088] index values correspond to crop characteristics/yield prediction and can be assessed over time, indicating indices correspond to specific growth stages/crop characteristics where the same index value or range of values matches the a specific characteristic).
Regarding claim 14 Brauer discloses; The method for predicting crop growth stage according to claim 13, wherein the telemetric vegetation indices include (Brauer, [0025] the system has a computer which receives multiple input images which have been captured from a satellite, multiple index values are derived from the images captured, [0067] the system may generate a combined index for each field from the index metrics): biomass, water content, or temperature (Brauer, [0067] the environmental factors included in the vegetation indices may include precipitation (water content) or temperature ranges or soil metrics (biomass)).
Regarding claim 17 Brauer discloses; The method for predicting crop growth stage according to claim 13, further includes: after the operation in the unsupervised mode, generating a second prediction model in the supervised mode, based on the correlation between the telemetric vegetation indices and the labels (Brauer, [0044] the model has a generate which generates paired data to be fed into the discriminator, where in this case the pairs of data would not have known input-output pairs, therefore this would be “unsupervised mode”, this occurs before the paired data is fed to the discriminator [0046] computing device feeds in pairs of images to a discriminator where the images are known to be true corresponding pairs (supervised mode, supervised learning requires known inputs and output pairs), [0065] once the paired data is fed into the model, the model correlates the acquired image with the field that it came from, [0066] this telemetric vegetation data is then used to obtain the vegetation index values and then determine measures of crop growth and growth stages based on this, this is being interpreted as a correlation between the indices and the labels/characteristics).
Regarding claim 19 Brauer discloses; The system for predicting crop growth stage according to claim 18, wherein the harvesting includes harvesting stage and harvest yield status (Brauer, [0068] the growth stage data includes predicted harvest date, and predicted maturity dates for the crops, which are analogous to a harvest stage and data).
Regarding claim 20 Brauer discloses; The method for predicting crop growth stage according to claim 13, wherein the crop growth stage information includes crop health status (Brauer, [0066] the system may determine metrics of crop health such as greenness measures).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Brauer (US 20230108422 A1) in view of Shankar (US 20250104230 A1).
Regarding claim 4 Brauer fails to disclose; The system for predicting crop growth stage according to claim 1, wherein the telemetric vegetation indices include: normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI).
However, in the same field of endeavor of crop monitoring, Shankar teaches; wherein the telemetric vegetation indices include: normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) (Shankar, [0019] the vegetation index may be one or more of a normalized difference vegetation index (NVDI), a normalized difference red edge index (NDRE) and a normalized difference water index (NDWI)).
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Greyscale
(Shankar, [0019])
The combination of Brauer and Shankar would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The systems of Brauer and Shankar both teach methods of remote crop monitoring using satellites, however Brauer fails to teach the use of a normalized difference water index as a metric of crop monitoring. Shankar teaches this deficiency, as well as the use of a normalized difference vegetation index and a normalized red edge index. The motivation to use all of the normalized index values of Shankar with the system of Brauer lies in that the three disclosed values all are used to determine that the crops in the images are reflecting green light wavelengths, or a measure of “greenness” of the plant, which is a good indicator of crop health (Shankar, [0019] and Brauer, [0066] and [0085]).
Regarding claim 15 the combination of Brauer and Shankar teaches; The method for predicting crop growth stage according to claim 13, wherein the telemetric vegetation indices include: normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) (Shankar, [0019] the vegetation index may be one or more of a normalized difference vegetation index (NVDI), a normalized difference red edge index (NDRE) and a normalized difference water index (NDWI)).
The combination of Brauer and Shankar would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The systems of Brauer and Shankar both teach methods of remote crop monitoring using satellites, however Brauer fails to teach the use of a normalized difference water index as a metric of crop monitoring. Shankar teaches this deficiency, as well as the use of a normalized difference vegetation index and a normalized red edge index. The motivation to use all of the normalized index values of Shankar with the system of Brauer lies in that the three disclosed values all are used to determine that the crops in the images are reflecting green light wavelengths, or a measure of “greenness” of the plant, which is a good indicator of crop health (Shankar, [0019] and Brauer, [0066] and [0085]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ito, US 20220366668 A1 teaches a method of imaging fields and generating maps of field features to monitor crop health utilizing the same disclosed vegetation indices of claims 1-20.
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/J.M.E./Examiner, Art Unit 2666 /EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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