DETAILED ACTION
Notice of 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 the Application
Claims 1-12 were pending and were rejected in the previous office action. Claims 1, 3-5, 7-9, and 11-12 were amended. Claims 1-12 remain pending and are examined in this office action.
Priority
As previously acknowledged, this U.S. patent application claims foreign priority to Indian Patent Application No. 202321019091, filed on March 21,2023.
Response to Arguments
Claim Objections:
Claims 1, 3-5, 7-9, and 10-11 were previously objected to for various informalities. Claims 1, 5, and 9 are amended to recite “Normalized Difference Vegetation Index (NDVI)” and “machine learning (ML)” to improve the clarity of the claims. Claims 3, 7, and 11 are amended to recite “a vegetation index.” Claims 4, 8 and 12 are amended to recite “a value of 100.” Therefore, the previous objections are overcome, and are withdrawn.
However, see the new objections to claims 1, 5, and 9 responsive to applicant’s amendments.
35 USC § 101:
Applicant’s arguments regarding the § 101 rejection of claims 1-12 (pgs. 9-13, remarks filed 10/15/2025) have been fully considered but they are not persuasive.
Step 2A: Applicant first argues that the claims are not directed to an abstract idea because the claims integrate the abstract idea into a practical application (pgs. 9-11, remarks). However, the examiner respectfully disagrees, as further detailed below.
In response to applicant’s argument that “the claimed subject matter integrates a judicial exception in terms of implementing a judicial exception in conjunction with, a particular machine that is integral to the claim, as discussed in MPEP § 2106.05(b) i.e., onsite sensors deployed on the field or land of interest communicate information about the field to a cloud server directly” (pgs. 9-10, remarks) – the examiner respectfully disagrees. The claim recites the limitation “wherein the land of interest is deployed with onsite sensors communicating with a cloud server directly” in a manner that is nominally recited and otherwise disjointed to the rest of the claims and its functions. At best, this additional element merely links the performance of the abstract idea to a particular technological environment (indicating the presence of onsite sensors that communicate with a cloud server are located on the land of interest), but it does not integrate the abstract idea into a practical application. See MPEP 2106.05(h). Also see MPEP 2106.05(b), showing “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 197 (1978)), and CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690 (Fed. Cir. 2011).”
In response to applicant’s argument that “the claimed subject matter integrates a judicial exception in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(1) and 2106.05(a)) i.e., Applicant submits that the claimed subject matter integrates a judicial exception into a practical application in terms of eliminating time series data of the main crop thereby reducing false positives being introduced when the main crop and the cover crop are same or when the window of the main crop exceeds regular duration” (pg. 10, remarks) – the examiner respectfully disagrees. Eliminating certain time series data to reduce false positives may amount to an improvement to the underlying abstract idea to provide a benefit (reducing false positives) derived from improving the abstract idea, but it does not provide an improvement to the functioning of a computer itself (e.g. a technical improvement to how a computer stores, processes, or receives/transmits data). See MPEP 2106.05(a)(II), “[I]t is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” The claims do not improve computer capabilities, but instead invoke computers merely as a tool to apply the abstract idea. See MPEP 2106.05(a)(I).
In response to applicant’s argument that “the judicial exception is integrated into a practical application in terms of multi-class model with classes of main crops and cover crops varying from region to region” (pg. 10, remarks) and “the integration into a practical application requirement is achieved in terms of applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e) i.e., fine-tuning the multi-class model (first and second ML classification model) by phenology based threshold to improve accuracy of classification” (pgs. 10-11) – the examiner respectfully disagrees. There is no indication that these limitations in applicant’s claims improve machine-learning technology or recite improved machine learning model(s). The cited portions of the claims and specification do not describe, in any level of detail, tuning specific parameters of the machine learning models themselves to achieve an improved machine learning model (as in, e.g. Ex Parte Desjardins), but merely describe fine-tuning the output received from the machine learning models using phenology-based thresholds after the fact (see spec. at ¶ 0027 “This output of ML/DL models are further fine-tuned by phenology based threshold…”). Regardless, this is the only mention of tuning in the entire specification - the tuning is described at a high level of generality and does not describe specific mechanisms for how tuning machine models themselves is performed. Instead, the claims appear to use existing machine learning models/technology to apply the abstract idea. See spec. at ¶ 0027, showing “As well known in the art, the ML models such Support Vector Machines (SVMs), Random Forest and the like can be used and built over training dataset for crop classification problem.” Further see spec. at ¶ 0041, showing “Any well-known ML based regression models can be used which takes input as NDVI, LAI and provides density of the crop (cover crop) present in the field.” See Recentive Analytics, Inc. v. Fox. Corp., showing “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” and "Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved.” Therefore, the examiner maintains that the use of the claimed machine learning models as current recited, provide no more than mere instructions to apply the abstract idea using generic computer implementation.
Step 2B: Applicant further argues, at Step 2B, that “the claimed subject achieves significantly more in terms of distinct additional elements of optical satellites capture multispectral satellite data, synthetic aperture radar satellite, onsite sensors, mobile devices, and cloud server” (pgs. 11-13) – however, the examiner respectfully disagrees. The claimed limitation specifying that the received first time series data is “sourced from an optical multi-spectral satellite data captured by an optical satellite” is merely descriptive of the data that is received (not how optical satellites are operated in some improved manner to capture data), and does not change that the function being carried out simply requires receiving data by one or more hardware processors. At best, this generally links the performance of the abstract idea to a particular field of use. The same analysis applies to a second time series data acquired for the estimated cover crop duration from a synthetic aperture radar satellite data – the description of this data as synthetic aperture radar satellite data merely describes the type of data being used, and at best generally links the performance of the abstract idea to a particular field technological environment or field of use. Regarding “wherein the land of interest is deployed with onsite sensors communicating with a cloud server directly” – as mentioned above, this nominally recited limitation merely links the performance of the abstract idea to a particular technological environment. Even if “communicating with a cloud server directly” were a positively recited being performed in the claim (which it is not), it would not add anything more than the use of computers or other machinery in their ordinary capacity to receive and transmit data. The examiner also notes there are no “mobile devices” recited in the claim, but if merely used for data transmission or output, it is unlikely such a limitation would alter the analysis above in any significant manner. Considering these additional elements as an ordered combination does not provide significantly more, and all of the recited elements either generally link the performance of the abstract idea to a particular technological environment/field of use, or recite mere instructions to apply the abstract idea using generic computer implementation.
Therefore, applicant’s remarks that the amended claims integrate the abstract idea into a practical application or add significantly more are not persuasive.
Please see the current § 101 rejection over claims 1-12 below, which is updated as necessitated by applicant’s amendments.
Claim Objections
Claims 1, 5 and 9 are objected to because of the following informalities:
Claims 1, 5 and 9 “wherein the determination of the main crop…and the determination of the type of cover crop…are fined-tuned by phenology based threshold” but appear they should recite “wherein the determination of the main crop…and the determination of the type of cover crop…are fined-tuned by a phenology based threshold”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1:
Claims 1-4 recite “A processor implemented method…” (i.e. a process); claims 5-8 recite “A system…comprising: a memory storing instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors…” (i.e. a machine); and claims 9-12 recite “One or more non-transitory machine-readable information storage mediums comprising one or more instructions…” (i.e. an article of manufacture). These claims fall under one of the four categories of statutory subject matter and as a result, pass Step 1 of the subject matter eligibility test. However, “Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not end the eligibility analysis, because claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection.” See MPEP 2106.04. Accordingly, the examiner continues the subject matter eligibility analysis below.
Step 2A Prong One:
Independent claim 1 (and similar claims 5 and 9) recites limitations for estimating cover crop duration, comprising:
receiving…a first time series data across a crop year for a land of interest,
separating…the first time series data into a vegetation and fallow period based on a first Normalized Difference Vegetation Index (NDVI) threshold to select the first time series data associated with the vegetation period;
determining…a main crop cultivated in the land of interest…by analyzing the first time series data associated with the vegetation period,
wherein the main crop is determined by eliminating associated time series data of the main crop thereby reducing false positives being introduced when the main crop and the cover crop are same or when a window of the main crop exceeds a duration;
determining…the main crop duration within the crop year using a first phenology based model and local domain knowledge;
discarding…partial time series data associated with the main crop duration from the first time series data associated with the vegetation period to obtain a cover crop time series data within the crop year;
comparing…a maximum NDVI obtained for the cover crop time series data with a second NDVI threshold to segregate the cover crop time series data as a non-crop vegetation data if the maximum NDVI is equal to or lower than the second NDVI threshold and a cover crop vegetation data if the maximum NDVI is greater than the second NDVI threshold;
determining…a type of cover crop by processing the cover crop vegetation data…wherein the determination of the main crop cultivated in the land of interest…and the determination of the type of cover crop…are fined-tuned by phenology based threshold thereby improving accuracy of classification;
estimating…a cover crop duration for the type of cover crop in days by analyzing the cover crop vegetation data using a second phenology based model and the local domain knowledge, a plurality of weather factors indices used to identify and eliminate low growth-no growth time duration from the cover crop duration and identifying a snow duration to eliminate period of dormancy within the cover crop duration due to presence of snow;
determining…density, and height of the type of cover crop from a second time series data acquired for the estimated cover crop duration from a synthetic aperture radar satellite data; and
generating…an Integrated Cover Crop Index (ICCI) for the land of interest based on the type of the cover crop, the density and height of cover crop, the cover crop duration, and the type of cover crop, wherein the ICCI quantifies a cover crop effort put into the land of interest on a predefined ascending scale
The limitations of independent claims 1, 5, and 9 above are determined to recite an abstract idea (i.e. estimating a cover crop duration and generating an integrated cover crop index for a land of interest, based on receiving and analyzing first and second time series data for a land of interest) for the reasons discussed in the following continued Step 2A Prong One analysis. Note that “An abstract idea can generally be described at different levels of abstraction.” Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240 (Fed. Cir. 2016).
As described in MPEP 2106.04(a)(2)(III), “[T]he "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” and “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” The limitations recited by the representative independent claims 1, 5, and 9 above, under the broadest reasonable interpretation and but for the use of generic computer components, cover concepts (e.g. observation, evaluation, judgment, and opinion) that can reasonably be performed in the human mind or by the human mind with the aid of simple tools such as pen and paper. For example, the “receiving” step is an observation, while the “separating,” “determining,” “determining,” “discarding,” “comparing,” “determining,” “estimating,” “determining,” and “generating” steps are evaluations, judgments, or opinions. Therefore, as the processes above described by the representative independent claims 1, 5, and 9 can be characterized as mental processes (i.e. observation, evaluation, judgment, and opinion), but for the recitation of generic computer components in the claims, the claims fall under the “mental processes” category of judicial exceptions (i.e. abstract ideas).
Step 2A Prong Two:
The judicial exception (i.e. abstract idea) recited in claims 1, 5, and 9 is not integrated into a practical application because the claims recite mere instructions to apply the abstract idea (i.e. estimating a cover crop duration and generating an integrated cover crop index for a land of interest, based on receiving and analyzing first and second time series data for a land of interest) using generic computers/computer components (i.e. “processor implemented method,” “one or more hardware processors” of claim 1; “A system comprising…a memory storing instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to… ”of claim 5; “One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause…” of claim 9; and “using a trained first machine learning (ML) classification model,” “by the trained first ML classification model,” “using a second ML classification model” and “by the second ML classification model” of claims 1, 5, and 9). See MPEP 2106.05(f), showing “[C]laims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp.”
In further detail – The first and second “ML” classification models are no more than generically recited machine learning models that are used as tools to apply the abstract idea using generic computer implementation, and they do not include any technical features indicating an improvement to machine-learning technology or the machine learning models themselves (i.e. a technological improvement). See spec. at ¶ 0027, showing “As well known in the art, the ML models such Support Vector Machines (SVMs), Random Forest and the like can be used and built over training dataset for crop classification problem.” Further see spec. at ¶ 0041, showing “Any well-known ML based regression models can be used which takes input as NDVI, LAI and provides density of the crop (cover crop) present in the field.” See Recentive Analytics, Inc. v. Fox. Corp., showing “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” and "[T]he claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved.” Therefore, the examiner maintains that the use of the claimed machine learning models as current recited, provide no more than mere instructions to apply the abstract idea using generic computer implementation. Furthermore, the use the one or more hardware processors to receive time series data merely amount to the use of a generic computer in its ordinary capacity (e.g. to receive data). The use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more, but indicates that the claims recite mere instructions apply the abstract idea using a generic computer or computer components. See MPEP 2106.05(f). The received first time series data being “sourced from an optical multi-spectral satellite data captured by an optical satellite” and the second time series data being “from a synthetic aperture radar satellite data” of claims 1, 5, 9 is merely descriptive of the data that is received or acquired, and at best generally links the performance of the abstract idea to a particular technological environment or field of use. Finally, the limitation “wherein the land of interest is deployed with onsite sensors communicating with a cloud server directly” of claims 1, 5, 9 is nominally recited and otherwise disjointed to the rest of the claims and its functions. At best, this additional element merely links the performance of the abstract idea to a particular technological environment (indicating the presence of onsite sensors that communicate with a cloud server are located on the land of interest), but it does not integrate the abstract idea into a practical application. See MPEP 2106.05(h). Also see MPEP 2106.05(b), showing “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 197 (1978)), and CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690 (Fed. Cir. 2011).”
Therefore, because the claims, considered as a whole, do not recite anything that integrates the abstract idea into a practical application, the claims are directed to an abstract idea.
Step 2B:
Claims 1, 5 and 9 do not include additional elements that are sufficient to amount to significantly more than the judicial exception (i.e. abstract idea), whether considered alone or as an ordered combination, because as mentioned above, the claims recite mere instructions to apply the abstract idea (i.e. estimating a cover crop duration and generating an integrated cover crop index for a land of interest, based on receiving and analyzing first and second time series data for a land of interest) using generic computers/computer components (i.e. “processor implemented method,” “one or more hardware processors” of claim 1; “A system comprising…a memory storing instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to… ”of claim 5; “One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause…” of claim 9; and “using a trained first machine learning (ML) classification model,” “by the trained first ML classification model,” “using a second ML classification model” and “by the second ML classification model” of claims 1, 5, and 9).
As above, the first and second “ML” classification models are no more than generically recited machine learning models that are used as tools to apply the abstract idea using generic computer implementation, and they do not include any technical features indicating an improvement to machine-learning technology or the machine learning models themselves (i.e. a technological improvement). Using the one or more hardware processors to receive time series data merely amounts to using a generic computer in its ordinary capacity (e.g. to receive data) and as a tool to apply the abstract idea, which does not add significantly more. The received time series data being “sourced from an optical multi-spectral satellite data captured by an optical satellite” and the second time series data being “from a synthetic aperture radar satellite data” of claims 1, 5, 9 is merely descriptive of the data that is received or acquired (and subsequently used in the performance of the abstract idea), and at best generally links the performance of the abstract idea to a particular technological environment or field of use. Finally, the limitation “wherein the land of interest is deployed with onsite sensors communicating with a cloud server directly” of claims 1, 5, 9 is nominally recited and otherwise disjointed to the rest of the claims and its functions, which at best generally links the performance of the abstract idea to a particular technological environment (indicating the presence of onsite sensors that communicate with a cloud server are located on the land of interest). Considered the additional elements as an ordered combination does not alter the analysis above, or add significantly more than the abstract idea.
Dependent Claims 2-4, 6-8, and 10-12:
Dependent claims 2-4, 6-8, and 10-12 are directed to the same abstract idea as independent claims 1, 5, and 9 above as they do not recite anything that integrates the abstract idea into a practical application or amounts to significantly more than the abstract idea. In further detail:
Claims 2, 6, and 10 specify “wherein the presence of snow and snow duration is derived from Normalized Difference Snow Index (NDSI),” which does not add any additional elements but merely further describes the abstract idea above.
Claims 3, 7, and 11 recite “wherein the land of interest corresponds to a Geo-tagged field boundary, located via applications running on field devices and wherein the local domain knowledge, weather indices, snow period data and a vegetation index associated with the land of interest is obtained prior to determining the type of cover crop, obtaining the cover crop duration and the ICC,” which describes the “land of interest” and when data associated with the land of interest was obtained. Specifying a “Geo-tagged field boundary, located via applications running on field devices” generally links the performance of the abstract idea to a particular technological environment, as it merely describes the land of interest, and is not a positively recited function. However, even if it was, the specification describes these functions in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) (¶ 0022: “The system 102, with techniques known in the art can geo tag the fields to identify boundaries of each field or the land of interest and segregate the satellite data in accordance with the fields to be consumed by the system for estimation of cover crop duration”).
Claims 4, 8, and 12 recite “wherein a score for the ICCI has values between 0 and 100, wherein a value of 0 indicates the cover crop has not been grown in the land of interest and a value of 100 indicates the cover crop has optimal density and height and has been grown for the entire duration between two main crop seasons in the land of interest,” which does not add any additional elements but merely further describes the abstract idea above.
Therefore, claims 1-12 are ineligible under § 101.
Novelty/Non-Obviousness
Note: Claims 1-12 remain novel and nonobvious over the prior art for the same reasons discussed in the 7/30/2025 non-final rejection (see pgs. 10-14 of the 7/30/2025 non-final rejection).
An updated search by the examiner did not change the previous determination, and the claims have been even further narrowed by the current amendments.
WO 2023034386 A1 to Campbell et al. (Campbell) is newly cited as relevant to the claimed invention, and generally teaches the detection of cover crops using satellite remote sensing/satellite images as evidential data for the purpose of awarding ecosystem credits (Campbell: ¶ 0036, ¶ 0269, ¶ 0298-0301; see ¶ 0010-0015 for context), but would not cure the deficiencies discussed in the previous office action.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hunter Molnar whose telephone number is (571)272-8271. The examiner can normally be reached Monday - Friday, 7:30 - 4:00 EST.
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/HUNTER MOLNAR/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628