DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/31/2026 has been entered.
Claims 1-19 are currently pending in U.S. Patent Application No. 18/036,842 and an Office action on the merits follows. Examiner additionally notes that the claim set filed 03/31/2026 in conjunction with the Request for Continued Examination, features claims identical to/matching that set of claims filed 03/12/2026 (not entered – see Advisory Action mailed 03/16/2026).
Response to 35 USC § 101 Rejections
Applicant’s remarks at pages 10-16 (filed 03/31/2026) regarding Subject Matter Eligibility Analysis have been considered and determined non-persuasive. As identified in Advisory Action, remarks model those previously presented, and reference may be made to corresponding analysis/rebuttal provided in Final Office Action mailed 12/31/2025. To compliment the remarks at page 5 of the Final Office Action, and concerning Desjardins and the associated memo referenced in Applicant’s remarks at page 10 (of the remarks filed 03/31/2026 – since Desjardins memo is intervening, not addressed in Applicant’s November 2025 remarks (those remarks instead referencing guidance resulting in the three explicitly identified Abstract Idea groupings of the 2019 changes to Eligibility Analysis, with Examiner’s remarks of the Final also pre-emptively addressing considerations with respect to Desjardins)), the ARP’s Eligibility Analysis references an improvement explicitly disclosed in the associated Specification, and suggests that the Board’s sua sponte analysis (in finding the associated ‘adjusting parameters’ at large was not an ‘additional element’ and subsumed under/within an Abstract Idea exception falling under the mathematical concepts/operations grouping) may have overlooked the claim language “to optimize an objective function that depends in part on a penalty term that is based on the determined measures of importance of the plurality of parameters to the first machine learning task”. As identified in the associated memo (and the ARP’s 9/26/2025 decision itself – which did not disturb the Board’s Prong One determination), the ARP’s analysis found this an ‘additional element’ that served to realize Applicant’s disclosed improvement, addressing the known/recognized problem of “catastrophic forgetting”. The instant application however does not concern any improvement to e.g. a technical field that is machine learning model training, and as suggested in the PTO-303, it is still not clear to the Examiner what the improvement is for the case of the claimed invention (and which explicitly recited limitations, that are ‘additional elements’ realize it). Presumably, since this case was routed to USPC 382 Image Analysis, it concerns some improvement to an image based analysis itself. The eligibility analysis provided by the Examiner does not categorically reject the claims in view of a ‘use’ of a ML model to perform the broadly recited analysis – but instead asserts that the ‘analy[zing]’, as recited/ amended, may be performed mentally, and the ‘use’ of a ML model to otherwise perform such an analysis serves neither for integration at Prong Two of 2A, nor an ‘inventive concept’/ ‘significantly more’ (MPEP 2106.05(f), (h)) – see page 9 of the previously linked 2024 guidance, Example 47 claim 2, analysis for steps(s) (d) and (e). It is also asserted by the Examiner that the ‘analysis’ cannot be that which realizes the improvement if it is of a breadth that can be drawn under the exception – since that would constitute a situation similar to Alice and other precedents. In terms of newly presented arguments, Applicant merely asserts:
PNG
media_image1.png
624
932
media_image1.png
Greyscale
For Applicant’s competing eligibility analysis, how does the fact that the claim requires acquiring satellite imagery and ground data impact the eligibility analysis? The Examiner’s analysis does not draw these to the exception, but draws them to ‘additional elements’ that do not outweigh those drawn to the exception (i.e. the analyzing), and fail to serve for integration and/or ‘significantly more’ in view of 2106.05(g), (h), and/or (f) accordingly. The Examiner doesn’t argue that a person can mentally acquire satellite imagery (‘ground data’ broadly, perhaps), but instead asserts that just because the imagery is satellite imagery, does not preclude it from being visually/mentally analyzed by a human being. Can a person not mentally/visually analyze such imagery and ‘ground data’ broadly, so as to “generate an analysis result” (e.g. detect that a change/event has occurred, e.g. forest fire, red tide bloom, traffic jam, new construction, double cropping, among other non-limiting examples)? This appears to be Applicant’s assertion, in submitting that the analysis as recited is “incapable of being performed mentally” – however Applicant provides no explanation of why or how, particularly with reference to any explicitly recited claim language. Examiner argues that “a degree of coincidence” between ‘condition(s)’ characterizing associated ground data and a capture of satellite imagery, can be determined mentally (primarily because the recited ‘conditions’ are recited at a high level of generality and include e.g. sun-light and weather conditions – and a ‘degree of coincidence’ may be e.g. spatial, temporal (or any other domain given the ‘conditions’) proximity). Additionally, there are no constraints on ‘how’ the analysis otherwise occurs (except by ‘use’ of a ML model), and the ‘analysis result’ itself is recited at a high level of generality. A person can visually/mentally review/evaluate acquired satellite imagery (e.g. looking at imagery acquired by a consumer satellite rental platform equivalent/’space photography experience service’, e.g. the “Eye Connect” web app associated with Sony’s “STAR SPHERE” https://www.sony.com/en/SonyInfo/design/gallery/STARSPHERE/
with knowledge of and/or otherwise discerning an associated location, time of capture, etc., and mentally compare such information and information observed/deduced from the image itself with acquired/known/recognized (personally obtained or otherwise) “ground data”. Alternatively, a person can navigate via web browser to NASA Worldview, https://worldview.earthdata.nasa.gov/ manipulate the date based slider so as to visually/mentally detect changes between related imagery, and validate/corroborate observed/ detected information with e.g. ground based radar information as accessible at https://www.ncei.noaa.gov/maps/radar/. Subsequent to mentally evaluating/comparing a degree of coincidence/correlation/overlap (spatial, temporal, etc.,) between ‘conditions’ associated with each modality, a human/user may render/generate any number of ‘analysis results’. This comparison may be performed so as to mentally evaluate/decide that additional acquisitions are desired (for any of a broad array of subsequent uses), that a previous and/or predicted time/location of capture may be insufficient, or sufficient, for intended processing/ information collection purposes, and/or that any number of acquisitions may be validated (or invalidated) by potentially corroborating ground data, etc.. In essence, the analyzing in question, particularly in view of the manner in which it is recited at a high level of generality, involves no more than detecting/recognizing any of a broad array of events/changes, between satellite imagery acquisitions based on ‘a difference’, and considering potentially corroborating ground data for e.g. validation purposes among others. Such an analysis, even considering that it involves satellite imagery and ground data, falls squarely within subject matter directed to an exception. Similar to the notable case law previously identified at page 6 of the Final Office Action, recently and in the realm of image analysis (USPC 382), see also Dental Monitoring SAS, v Align Technology, Inc., Appeal No. 2024-2270 (Fed. Cir. July 07, 2026), available at - https://www.cafc.uscourts.gov/opinions-orders/24-2270.OPINION.7-7-2026_2719362.pdf finding the associated claims directed to the Abstract Idea of “collecting information, analyzing it, and displaying certain results of the collection and analysis”.
See also MPEP § 2106.04(a)(2) III. Mental Processes sub-section C. C. A Claim That Requires a Computer May Still Recite a Mental Process … 3. Using a computer as a tool to perform a mental process.
Response to 35 USC § 112(a) Rejections
PNG
media_image2.png
356
934
media_image2.png
Greyscale
Applicant’s remarks conflating scope of enablement with definiteness in view of threshold requirements for clarity and precision, have been considered and determined non-persuasive, because they still fail to substantively address those concerns/issues previously raised by the Examiner with respect to ‘undue experimentation’ and at least (A), (F) and (H) of the ‘Wands factors’. Applicant provides no rationale for why the claims as recited meet the threshold requirements for clarity and precision, but even if the claims were definite (which the Examiner does not concede), the associated breadth still raises scope of enablement issues (as a matter distinct from definiteness), because POSITA could not make and use the entire scope of the claimed invention without undue experimentation. The analyzing as recited includes all conceivable analysis based on two broad factors:
1) a ‘degree of coincidence’ (spatial proximity, temporal proximity, proximity between any of other domains that may be used to determine a degree of ‘coincidence’) in ‘conditions’ (sun-light, weather conditions, as non-limiting examples) associated with the two information modalities (satellite imagery and ‘ground data’); and
2) a ‘difference’ (any whatsoever) between satellite images obtained at different times.
So as to produce ‘an analysis result’ (which itself is problematically broad – since there are no limitations whatsoever characterizing the result – at least for the case of independent claim(s), e.g. perhaps new claim 19 seeks to further limit the result itself). Applicant’s Specification discloses a series of analysis processing embodiments/contexts to include “Agriculture”, “Ocean”, “City Planning/City Situation”, “Economic Indicator”, “Ship Monitoring”, “Grasping Traffic Conditions”, etc.. Even and particularly in view of this broad array of non-limiting exemplary disclosure, Applicant's Specification fails to provide a reasonable amount of guidance with respect to the direction in which experimentation should proceed – that is how a person of skill in the art can/should derive and/or obtain one or more model(s) that performs the analysis in question, any exemplary embodiments of such a model, and/or how specifically that model should operate. Instead, Applicant’s Specification at e.g. [0186] suggests “an AI engine using machine learning or the like” is the silver bullet that can detect some non-specific change/event, based on some difference, and some degree of coincidence (all in some latent space?), that a human might otherwise not perceive.
Response to 35 USC § 112(b) Rejections
Applicant’s remarks, filed 03/31/2026 at page 17, at best serve in general allegation that the claims as amended satisfy the requirements of the statute, but do not substantively address those concerns/issues previously raised. Applicant's remarks fail to identify how it is that a person of ordinary skill in the art would understand what is claimed (and what is not), even when the claim is read in light of the Specification. Amendment adding the additional basis that is 1) “a degree of coincidence” does not inherently/necessarily resolve clarity issues associated with the recited analyzing genus, for the same reason(s) that the basis of 2) “a difference” previously did not. Examiner does not recognize Applicant’s remarks to serve as any disclaimer of scope otherwise included in the recited language, as permissibly/ appropriately interpreted under BRI and in view of a plain meaning reading (MPEP §§ 2173.01 & 2111.01). Similarly, remarks reference no disavowal in Applicant’s Specification, serving to limit scope in a manner resolving clarity and precision issues (i.e. by excluding/narrowing a set of otherwise ‘reasonable interpretations’ with mutually exclusive characteristics, to any clear and precise subset thereof). With reference to MPEP §§ 2173.02 & 2173.04, the claim(s) remain directed to an analysis genus, that can be interpreted in various ways and such that it is not clear which analysis species are covered, and which are not. Put differently, what ‘conditions’ and associated domains (Spatial/Geometric? Temporal? Spectral? Radiometric? Semantic?) does/do the ‘degree of coincidence’ and ‘difference’ not cover? What ‘difference’ between the two images considered, would not be sufficient for detection/recognition of an event/change equivalent? Given the various potential differences in a detection of e.g. a red tide bloom vs. a traffic jam, in further view of what an ‘analysis result’ might comprise, and possible embodiments for ‘ground data’, it is at least fair to assert that the limitations in question involve a plurality of plausible constructions with reference to elements that are variable in nature, and an unreasonable degree of uncertainty as it relates to discerning which analysis species are covered and which are not. Examiner maintains the threshold requirements for clarity and precision remain unmet even if breadth is not equated with indefiniteness.
MPEP § 2173.02 (emphasis added):
If the language of the claim is such that a person of ordinary skill in the art could not interpret the metes and bounds of the claim so as to understand how to avoid infringement, a rejection of the claim under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, is appropriate. See IBSA Institut Biochimique, S.A. v. Teva Pharm. USA, Inc., 966 F.3d 1374, 1378-81, 2020 USPQ2d 10865 (Fed. Cir. 2020) (The court affirmed a district court’s finding of indefiniteness based upon a detailed analysis of the claim language itself as well as intrinsic and extrinsic evidence);
Corresponding rejections to the claims are maintained and reproduced below, with reference to the clarified rationale(s) identified herein.
Response to Arguments re. Prior Art as applied under 35 USC § 103
Applicant’s arguments/remarks asserting that the combination of Hirata (US 2020/0349585 A1) in view of Squires (US 2016/0034743 A1) and Freitag et al. (US 2018/0189564 A1) fails to fairly teach/suggest the analyzing as amended, so as to use/consider “a degree of coincidence of a condition of the acquired ground data and the corresponding condition for capturing the satellite image”, have been fully considered and determined non-persuasive. As previously identified in the Advisory Action, claim 14 as previously presented recites a “degree of coincidence of the condition of the acquired ground data”, for which using said degree in calculating/deriving the scaling parameters of Hirata [0068], was conceded as being absent from Hirata, as identified at page 30 of the Final Office Action mailed 12/31/2025. From pages 29-30 reproduced in part:
PNG
media_image3.png
645
971
media_image3.png
Greyscale
For the case of claim 1 as amended, the claim does not require utilizing said degree in such a capacity, but is instead directed to a determination of one more broadly. Applicant’s remarks do not substantively address Tran (US 2022/0111960 A1) (cited in the 07/15/2025 IDS) as previously applied in the rejection of claim 14. At page 20 of the remarks, Tran is referenced, but only in a manner asserting that the Advisory Action provides ‘additional characterization’ of the reference. Examiner would assert the point/argument from the Advisory Action stands unrebutted, and at least [0221] of Tran suggests the manner in which a ‘degree of coincidence’ between ground conditions and those associated with acquired satellite imagery (e.g. weather conditions as measured/measurable from the ground and impacting daylight spectrum), would be obvious if not routinely considered when seeking to calibrate/correct NDVI measures – applicable to the disclosure of Hirata, so as to ensure pixels classified as vegetation 63, are indeed vegetation, and not e.g. soil 62, water 61, (or alternatively between growth stages see Tran [0206]) etc., given their overlap in radiation intensity as illustrated in Hirata Fig. 4 (see the rationale as presented in page 31 of the Final Office Action). Reproduced from page 30 of the Final:
PNG
media_image4.png
812
968
media_image4.png
Greyscale
Assuming arguendo that Tran’s disclosure for taking into account weather conditions and a plurality of complementary data acquisitions does not constitute any determination of a ‘degree of coincidence’ between ground data and satellite imagery capture conditions (perhaps in view of Applicant’s assertion that the recited language requires an interpretation different than that/those relied upon by the Examiner in the application of Hirata for the case of claim 14, with which the Examiner would disagree – see also Tran’s cross-corelate disclosure e.g. Figs. 3, 2118, [0133], even if in the context of updating a navigation model, applicable to that illumination model of [0221]), Examiner previously provided three additional references with the Advisory Action. Reproduced below:
PNG
media_image5.png
494
1490
media_image5.png
Greyscale
Despite this additional evidence, no reliance upon them appears necessary because Tran at the minimum suggests (Fig. 3A, 3D, 3E, etc.,) data acquisitions at e.g. 2092/2110 and 2096/2114 of various/differing sensor modalities (in view of Tran [0011], [0022], [0091], [0215], etc., (even for drone/helicopter 10 embodiment (optional), ‘local’ data embodiments may be considered ‘ground data’ (in addition to [0215]), for comparison with “data provided in the public realm through satellites”, in addition to arguably suggested ground based radar, such as NEXRAD (and ground-proximate light reference cards at [0197]) – the point being that Tran at the minimum suggests a degree of coincidence/ correlation/ overlap/agreement between two complementary modalities)), in addition to those disclosed ‘cross co-relate’ steps between 1st and 2nd detected data(s) (e.g. 2118) (nearly an instance wherein this action could be properly made Final – MPEP 706.07(a) with reference to 1207.03(a), since the statutory basis and the evidence relied upon arguably remains the same). See also e.g. Tran [0215] “Other in- or on-ground sensors can be deployed to detect crop conditions, weather data, and many other details, which can then be transmitted to decision analytics platforms via the Internet of Things (where computing devices embedded in everyday objects are connected to the Internet to enable analytics). A solar-powered in-ground sensor can gather data on crop stress, air pressure, humidity, temperature, chlorophyll, canopy biomass, rainfall, and other information, which can then be analyzed on its platform to improve precision farming”. Tran further suggests in e.g. [0209] validating image based detection based on a geo-fencing/ reference to known/previously established locations/plot perimeters (arguably a spatial coincidence between aerial and alternatively derived (ground based sensor – e.g. Tran [0215], but more to the proposed combination, S101 of Hirata) information). In Summary, Tran [0221] (similar to the additionally supplied literature) evidences the manner in which POSITA, in seeking to measure NDVI accurately distinguishing between Hirata’s 63 vs 62/61, and/or stages of growth for 63 as suggested by Tran, would desire/be motivated to ascertain a reference/ calibrating intensity model, known to be influenced by factors such as “time of day, latitude, weather, and/or other factors” (Tran [0221]), and Tran’s additional teachings of determining a data correlation/coincidence (e.g. Figs 3A-3E, 2118, etc.,), even if in the context of building/updating a navigation model, apply similarly to that intensity model disclosed therein. Additionally supplied literature establishes the same, since they disclose considering satellite vs. ground modality ‘degree of coincidence’ so as to resolve potential inaccuracies otherwise – and specifically in the context of NDVI calibration.
Furthermore, Hirata explicitly discloses, with reference to Fig. 9 S204 “linking ground data with satellite data” ([0097]), which at the minimum suggests a spatial degree of coincidence between the two modalities, even if for the purposes of generating aggregate evaluation data (see Fig. 10, superimposed A and B, reading on new claim 19), and not explicitly for the purposes of deriving those scaling values of Hirata [0068].
Lastly, Lindores (US 2014/0012732 A1) (cited in the 12/31/2025 PTO-892) appears to confirm/complement the disclosure identified in Tran above (and similarly Zhang, Tittebrand, and Loew NPL cited 03/16/2026), in view of at least Fig. 9 930, wherein the ground-based data at 920 is used for aerial data calibration – see [0048] “Wide-area agricultural monitoring and prediction encompasses systems and methods to generate calibrated estimates of plant growth and corresponding field prescriptions. Data from ground and satellite based sensors are combined to obtain absolute, calibrated plant metrics, such as NDVI, over wide areas”. See also e.g. Lindores at [0054-0055] “Ground measurements provide absolute NDVI at high accuracy while satellite measurements provide relative NDVI over wide areas. When ground and satellite data are available for a common area at times that are not too far apart, the ground data may be used to resolve the unknown bias or offset in the satellite data. As an example, if field 107 in FIG. 1 is measured by a GreenSeeker scan and fields 101 through 109 (including 107) are measured by satellite imaging, then overlapping ground and satellite data for field 107 can be used to calibrate the satellite data for all of the fields”.
Examiner maintains that none of the limitations recited, as permissibly/appropriately interpreted, individually and in combination, appear distinguished over the state of the art as evidenced by references of record.
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.
Claim(s) 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in particular an Abstract Idea falling under at least the (c) mental processes grouping (concepts performed in the human mind including an observation, evaluation, judgement, opinion), not ‘integrated into a practical application’ at Prong Two of Step 2A and without ‘significantly more’ at Step 2B.
Step 1: The claim(s) in question are directed to a computer implemented (hardware/ structural limitations considered under the ‘apply it’ provisions of MPEP 2106.05(f)) method/ process for “analyz[ing] the acquired satellite image to generate an analysis result…”. (Step 1: Yes).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. Representative claims 1/17/18 recite at a high level of generality – “analyz[ing] the acquired satellite image to generate an analysis result by using a degree of coincidence… and a difference …”, not precluded from being performed mentally/visually even in view of the associated ‘satellite image’, and falling under at least the mental processes Abstract Idea grouping. Reference may be made to the 2024 PEG, Example 47 claim 2, wherein using an ANN did not preclude that anomaly detection and analysis of step(s) (d) and (e) from being drawn under the mental processes grouping at Prong One. See pages 6-7 of: https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf
Even if the recited ‘analyzing’ step requires the use of a computer (e.g. a user navigating NASA Worldview referenced in the remarks above, in a web browser, ESA Copernicus Browser, or equivalent), limitations involving image acquisition step(s) prior to the analysis in question, do not preclude the analysis/prediction itself from being performed mentally, and the recited ‘difference’, ‘change’, ‘condition’, ‘degree of coincidence’, etc., are not of a level of complexity (as recited) that would render the prediction impossible/impractical for mental evaluation. See MPEP 2106.04(a)(2) subsection C. A Claim that Requires a Computer May Still Recite a Mental Process. Dependent claims are similarly analyzed at Prong One (e.g. claim(s) 11, 13, etc.,) as they further comprise one or more limitations that may similarly be drawn under the mental processes Abstract Idea grouping. For the case of e.g. claim 11, that determination as to the necessity of a second satellite image different from the first may also be a determination performed mentally, e.g. upon visually inspecting the satellite image of a first imaging instance, consulting/considering associated meteorological/ground data suggesting the imagery may be e.g. impacted by recent rainfall, flooding, smoke from a nearby forest fire, cloud coverage, artifacts resultant from illumination conditions, etc., and deciding that a second imaging instance is desired accordingly. (Step 2A, Prong One: Yes).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application (derived from Alice/Mayo step two and not to be conflated with an assessment of utility – MPEP 2103) of the exception. This evaluation is performed by (1) identifying whether there are any ‘additional elements’ recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Examiner notes for consideration at Prong Two of 2A that MPEP 2106.05(a), (b), (c), and (e) generally concern limitations that are indicative of integration, whereas 2106.05(f), (g), and (h) generally concern limitations that are not indicative of integration. As an additional note, ‘additional elements’ are generally limitations excluded from interpretation under the Abstract Idea groupings, and may comprise portions of limitations otherwise identified as falling under those Abstract Idea groupings of the 2019 PEG (e.g. any ‘determination’ that may be made mentally accompanied by the use of a neural network and/or generic computer hardware considered under the ‘apply it’ considerations of 2106.05(f)). Any ‘providing’/outputting broadly, and ‘collection’ of data (i.e. image acquisition(s)), be they images for training any learning model and/or data/images visually observable/ evaluated by a user/operator, also fail(s) to integrate at least in view of MPEP 2106.05(g) (extra-solution data gathering/output) and/or 2106.05(h) as ‘generally linking’ the exception to a field of use involving machine learning and/or imagery so acquired (e.g. the use of aerial vehicles for acquiring said imagery broadly). The same determination holds for dependent claims that serve to limit the collection of data/images (by means of what is collected based on recited conditions) and/or introduce limitations generally linking to a field of use. None of the instant claims appear to explicitly/clearly capture/recite any disclosed improvement in technology (see MPEP 2106.05(a), with note that ‘functioning of a computer’ concerns functions integral to the way a computer operates (e.g. memory read-write for Enfish and virus scanning for Finjan) and not ‘functions’ that a generic computer can be programmed/adapted to perform (see also 2106.05(f))) and any ‘additional elements’, even when considered in combination, fail to integrate at Prong Two of Step 2A accordingly. Integration in view of subsection (a) requires an identification of the manner in which the improvement is achieved, to be explicitly and specifically (not at a high level of generality i.e. the ‘use of’ complementary modalities/data broadly for any conceivable satellite image analysis not inconsistent with diverse example embodiments) recited in the claims, as ‘additional elements’ precluded from interpretation under any of the Abstract Idea groupings (since the improvement cannot be to the exception itself). With reference to MPEP 2106.05(a):
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981))
Even when viewed in combination, the ‘additional elements’ present (namely the use of generic computer components, use of a broadly recited ML model, and data acquisition prior to analysis) do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: No), and the claims are directed to the judicial exception. (Revised Step 2A: Yes [Wingdings font/0xE0] Step 2B).
Considerations with respect to the most recent SME Memo(s) available at: https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility and more specifically: https://www.uspto.gov/sites/default/files/documents/memo-desjardins.pdf have been addressed in the ‘Response to 35 USC § 101 Rejections’ section above and additionally in the Final Office Action at pages 3-7.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to ‘significantly more’ than the recited exception, i.e., whether any ‘additional element’, or combination of additional elements, adds an inventive concept to the claim. The considerations of Step 2A Prong 2 and Step 2B overlap, but differ in that 2B also requires considering whether the claims/limitations, particularly if drawn to extra-solution activity (2106.05(g)), feature any “specific limitation(s) other than what is well-understood, routine, conventional activity in the field” (WURC) (MPEP 2106.05(d)). Such a limitation if specifically recited however, must still be excluded from interpretation under any of the Abstract Idea groupings (else it is not an ‘additional element’ and extra-solution activity need not then be implicated). Limitations precluded from serving as indications of an inventive concept/ ‘significantly more’ include those that are not specifically recited (instead recited at a high level of generality), those that are established as WURC (e.g. by Applicant’s admission/disclosure, a plurality of cited references may serve to evidence the WURC nature of ‘analysis’ based at least in part on corroborating/ additional ground data – however Examiner’s analysis need not rely on this finding as a factual basis, because the analyzing in question falls squarely under the exception – it is purely a legal determination), those that are not ‘additional elements’ by nature of their analysis at Prong One (i.e. directed to the exception – see above re. deciding that a second acquisition may be advantageous/desired), and/or those that otherwise fail to serve for integration at Prong Two of 2A in view of 2106.05(f), (g) and/or (h). With reference to the linked July 2024 PEG document, at page 9:
PNG
media_image6.png
280
1148
media_image6.png
Greyscale
The claim(s) in question recite little beyond those limitations recited at a high level of generality and falling under the exception. (Step 2B: No).
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claim(s) 1-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while potentially being enabling for one or more satellite image analysis embodiments, does not reasonably provide enablement for the full scope of that broadly recited satellite image ‘analysis’ as recited in the claim(s) and afforded a plain meaning interpretation not inconsistent with the disclosure as a whole (see MPEP 2111.01). The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the invention commensurate in scope with these claims. See MPEP §§ 2164.06 and 2164.08. With reference to MPEP 2164.01(a), the Wands factors of particular note in this analysis are (A), (F) and (H). For a more specific articulation of the rationale supporting this rejection, see the bolded/ emphasized portions in the corresponding ‘Response 35 USC § 112(a) Rejections’ section above. Given the wide array of embodiments for “a difference” and associated “change” “extracted” by the model, Applicant’s Specification fails to provide a reasonable amount of guidance with respect to how a person of skill in the art can/should derive, obtain, and/or otherwise ‘use’ the model that performs the analysis in question, any exemplary embodiments of such a model, and/or how that model should/must operate specifically.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim(s) 1/17/18, lines 8-10, recites “analyze the acquired satellite image to generate an analysis result by using …. a degree of coincidence … and; … a difference …” which is unclear, even and particularly when viewed in light of the Specification, as it fails to apprise one of ordinary skill in the art what steps specifically are required for such an analysis/use and which analysis species may or may not be excluded. For a more specific articulation of the rationale supporting this rejection, see the bolded/ emphasized portions in the corresponding ‘Response 35 USC § 112(b) Rejections’ section above. As was previously identified, the fact that the claims in question may be amenable to at least one construction (as evidenced by the prior-art based grounds of rejection), does not mean the claim(s) provide clear notice of the metes and bounds sufficient to "inform those skilled in the art about the scope of the invention with reasonable certainty" Nautilus, Inc. v. Biosig Instruments, Inc., 572 U.S. 899, 110 USPQ2d at 1689 (2014). The Office does not necessarily interpret claims in the same manner as the courts, and interpretation during prosecution may effectively result in a lower threshold for ambiguity (beneficial for those reasons disclosed in MPEP 2173.02 subsection I). Claim(s) 17/18 correspond to claim 1 and are rejected accordingly.
Dependent claims 2-16 and 19 inherit and fail to cure that/those deficiencies identified for the case of independent claim(s) 1/17/18 above and are rejected accordingly.
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 of this title, 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.
1. Claims 1-10, 13, 15 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Hirata (US 2020/0349585 A1) in view of Squires (US 2016/0034743 A1), Freitag et al. (US 2018/0189564 A1), and Lindores (US 2014/0012732 A1).
As to claim 1, Hirata discloses a data analysis apparatus (Figs. 1-2, Fig. 9, [0006] “The first aspect of the present invention is an evaluation information generating system that evaluates a ground area owned by a user, using satellite data observed by an artificial satellite”) comprising:
circuitry (Fig. 2, 311, 312, etc.,) configured to acquire a satellite image obtained by an artificial satellite imaging a predetermined place on a ground at a Fig. 1, data path 1, Fig. 3 data 40, [0009] “a satellite data acquisition unit configured to acquire the satellite data”, Fig. 9 S201, [0060] “The satellite data acquisition unit 311 is a functional unit that acquires satellite data observed by an artificial satellite from the satellite data storage 401. In this embodiment, the satellite data acquisition unit 311 acquires satellite data obtained by an optical satellite or a synthetic aperture radar (SAR) satellite specifically, among the satellite data recorded in the external storage device. The satellite data includes ultraviolet light, infrared light, microwaves and the like, with wavelengths other than those of visible light that is visible to the human eye”, [0061], etc.,), acquire ground data corresponding to a condition for capturing the satellite image from the ground data acquired on the ground (Fig. 3, Fig. 7, [0014], [0015], [0016] “In the present invention, the evaluation information generating system may include a meteorological data acquisition unit that acquires meteorological data, and the evaluation data generating unit generates evaluation data of the user or the ground area based on the meteorological data, the user information, the ground area information, and the situation of the ground area. This enables calculation of a degree of certainty of the information declared by the user as well as objective evaluation of the ground area”, [0073], [0074] “The meteorological data 50 is the meteorological data observed by a common meteorological observation system and includes temperatures 51, hours of daylight 52, precipitation 53, wind direction 54, wind speed 55, wind/flood damage history 56, and so on. The meteorological data 50 is not limited to the items listed above and may be any applicable information. Optionally, data acquired from sensors installed in a smartphone or farm equipment may be used as the meteorological data”, etc.,),
analyze the acquired satellite image to generate an analysis result (see results/ information as supplied to 333, accessible by financial institution/creditor via 300, in view of processing/analysis of farmland evaluation unit 331 in conjunction with farmer evaluation unit 332, Fig. 7 S105 in view of Fig. 10, etc.,) by using
a degree of coincidence of a condition of the acquired ground data and the corresponding condition for capturing the satellite image (Fig. 9 S204 “linking ground data with satellite data”, [0097] “At step S204, the farmland evaluation unit 331 links the maps with the agricultural field information (e.g., position and area) to identify the situation of the user's agricultural field (ground area). In this embodiment, the farmland evaluation unit 331 links the agricultural field information (e.g., position and area) shown in FIG. 10(A) with the situation of the ground area shown in FIG. 10(B) and generates a map to be used for evaluation shown in FIG. 10(C) to identify the situation of the user's agricultural field. This way, for example, the farmland evaluation unit 331 can identify the situation of the agricultural field shown in FIG. 10(C) as "soil", etc.,), and
a difference between the acquired satellite image obtained at a time and a satellite image obtained at a previous time ([0011-0012], [0017], Fig. 9 S205, [0078] “The farmland evaluation unit 331 is a functional unit that evaluates an agricultural field based on the satellite data and the ground data”, [0079] “the farmland evaluation unit 331 determines whether or not double cropping is being practiced from temporal change of image data based on the satellite data. For example, if the farmland evaluation unit 331 detects a transition from soil to vegetation in the agricultural field twice or more in one year based on the temporal change of image data, the farmland evaluation unit determines that double cropping is being practiced”, [0082] “The farmer evaluation unit 332 may generate evaluation data by any combination of information”, etc.,), and
control output of the analysis result of the analyzed satellite image (Fig. 1, 331/332 output analysis data to information storage 333 and accessible by financial institution/creditor via server device 300, [0083], etc.,),
wherein the difference between the acquired satellite image obtained at a time and the satellite image obtained at the previous time is determined using machine learning to extract (Hirata [0017] “In the present invention, the evaluation data generating unit may generate the evaluation data using an artificial intelligence algorithm (such as machine learning). For example, classifiers created through use of a machine learning algorithm may be used to determine whether or not the situation of the vegetation is normal or abnormal based on the image data in time series order generated from satellite data, and if it is abnormal, evaluation data indicating it as a risk at the agricultural field may be generated. The learning algorithm is not limited to a particular type and any suitable algorithm may be adopted. Deep learning, SVM, and other algorithms may be used in some embodiments”) a change corresponding to the difference used to generate the analysis result ([0011-0012], [0017], Fig. 9 S205, [0078-0079]).
While Hirata at the minimum suggests an image acquisition for a predetermined place, Hirata fails to explicitly disclose that artificial satellite imaging as being for a predetermined time.
Squires however evidences the obvious nature of a satellite image acquisition for a predetermined place at a predetermined time ([0015] “The customer may select simple inputs through web pages to place an order. To enable this, the invention may include many computer programs and databases through a central computer algorithm. One of the databases may contain the orbit definition of each satellite. The algorithm may match the satellite ground tracks to the customers image request, identifying times when images may be recorded, obtaining view angles, and expected lighting conditions at the time of imaging. A computer may also evaluate forecasted cloud cover. The imaging time options and associated data may be delivered to the customer through a web browser. The customer may select an imaging option which then may be committed to a database for satellite commanding. The customer's ground coordinates and time may be converted into pointing coordinates and time for the satellite. An automatic algorithm may determine when the identified satellite may be over a ground station and may send the imaging command to the satellite which may proceed with operations until it arrives at the correct orbit coordinates and time to record the image”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the system and method of Hirata such that the artificial satellite images therein are acquired at one or more predetermined times as taught/suggested by Squires (e.g. prior to the start of a growing season, after growing has commenced, etc.,), the motivation as similarly taught/suggested therein that such a predetermined time may ensure the area of interest/place and resultant imagery is obtained under one or more expected/desired lighting conditions, cloud coverage/ visibility, field/land changes in association various points within a growing cycle, etc., thereby ensuring that the imagery is of a quality useable and features information desired for subsequent/ downstream analysis.
Freitag further evidences the obvious nature of an ‘analysis’ involving a difference between satellite images acquired at different times (Abs, [0003] “Different crop types change color differently throughout their growth season. For example, com fields typically turns green earlier than soybean fields in late spring. Using a set of satellite images captured during different dates of a growing season, one may observe how the color of each of the pixels on the satellite images varies over the growing season. Using the color variation, one may determine the pixel corresponding to what type of crop being planted to perform crop type identification”, [0004], see also ground/weather data 208, etc. in view of a motivation as similarly taught/suggested therein, that such a difference/ change between image acquisitions may serve to more accurately distinguish e.g. one crop from another, [0018], etc.,). While Hirata’s disclosure at [0017] is sufficient for teaching/suggesting a machine learning model that extracts a change broadly, Freitag also discloses the obvious nature of using machine learning to extract a change corresponding to a difference used to generate an analysis result ([0018] “”, [0049] “The classifier 230 classifies the crop type (e.g., corn versus soy) within a given satellite pixel location and a growing season using the temporal sequence of the satellite measured vegetation index versus the weather data adjusted temporal variable. FIG. 4 shows an illustrative comparison of such temporal sequences in which NDVI is chosen as the vegetation index and the AGDU is the temporal variable. The classifier 230 classifies the two corn sequences (curves) and the two soy sequences (curves) using machine learning”, [0050-0055], [0053] “In addition, weather data may also be included as additional training features. Of value are the weather data impacting crop growth (e.g., expected to impact vegetation index values)”, etc.,).
Freitag additionally at least suggests a coincidence/correspondence/alignment between satellite imagery and additional data not of an image modality, useable in a generation of classifier 230, and also for the purposes of generating an output data format facilitating user understanding ([0018] “The present invention provides systems and methods for accurate crop type identification based on satellite observation (e.g., satellite imagery), weather data, and/or additional data relating to crop growth (such as soil information, irrigation, etc.) using machine learning. In some embodiments, the temporal axis of all satellite remote sensing measurements are reprojected onto a temporal variable that is more aligned with the actual crop growth compared to the calendar day-of-year. The temporal variable is determined using, for example, weather information. Thus, the satellite measured color variation of the same type of crop, as a function of the temporal variable in different years and locations, becomes more similar to each other, which simplifies the differentiation and identification of different crop types. In another embodiment, additional features beyond the satellite measure crop color variable are introduced to the machine learning based crop type classifier to improve accuracy of crop type identification”, [0051-0055]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the system and method of Hirata in view of Squires such that an analysis of the image acquired at the predetermined time further involves a difference between said image and one acquired at a previous/earlier time as taught/ suggested by Hirata and Freitag, and further one as derived ‘using’ machine learning to extract a change, similarly taught/suggested by Hirata and Freitag, as a consideration of such a difference in said analysis may serve to more accurately determine, among other information, which specific crops are being grown.
Under any assertion that an equivalent degree of coincidence of a condition of the acquired ground data and the corresponding condition for capturing the satellite image is not fairly taught/suggested by that ground and satellite modality ‘linking’/ registration/ alignment of Hirata in view of a spatial proximity/overlap (Fig. 9 S204, [0097]) because it is not clear that Hirata uses this degree of spatial overlap/coincidence to determine those correction/scaling factors of [0068], but instead for generating a registered/aligned aggregate data set more broadly, Lindores among other references (Tran [0221] in view of [0215], [0197], etc.,) evidences the obvious nature of analyzing an acquired satellite image by using a degree of coincidence of a condition of the acquired ground data and the corresponding condition for capturing the satellite image (ideal interpretation as guided by Applicant’s pgpub at [0171], and Fig. 12 S7; see equivalent in Lindores Fig. 9 930 in view of 920, see also 950/960, Fig. 3, Fig. 5, [0048] “Wide-area agricultural monitoring and prediction encompasses systems and methods to generate calibrated estimates of plant growth and corresponding field prescriptions. Data from ground and satellite based sensors are combined to obtain absolute, calibrated plant metrics, such as NDVI, over wide areas. Further inputs, such as soil, crop characteristics and climate data, are stored in a database. A processor uses the measured plant metrics and database information to create customized field prescription maps that show where, when and how much fertilizer, pesticide or other treatment should be applied to a field to maximize crop yield”, [0054] “Actual satellite images, however, do not provide absolute NDVI with the high accuracy available using ground-based sensors. Variations in lighting (i.e., position of the sun), atmospheric effects (e.g., clouds, haze, dust, rain, etc.), and satellite position all introduce biases and offsets that are difficult to quantify”, [0055] “Ground measurements provide absolute NDVI at high accuracy while satellite measurements provide relative NDVI over wide areas. When ground and satellite data are available for a common area at times that are not too far apart, the ground data may be used to resolve the unknown bias or offset in the satellite data. As an example, if field 107 in FIG. 1 is measured by a GreenSeeker scan and fields 101 through 109 (including 107) are measured by satellite imaging, then overlapping ground and satellite data for field 107 can be used to calibrate the satellite data for all of the fields” etc.,).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the combination of Hirata as proposed, so as to implement the teachings of Lindores when seeking to determine those scaling parameters of Hirata [0068], under a rationale similar/matching that previously presented in the rejection of claim 14 – that such a calibration/correction may ensure pixels classified as vegetation 63 vs soil 62 and/or water 61, vegetation at various growth stages, various crop types, etc., are indeed/most accurately members of those determined classes based on a higher accuracy/absolute NDVI corrected for offset/bias attributable to lighting conditions and/or weather/atmospheric effects.
As to claim 2, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 1.
Hirata further discloses the apparatus wherein the circuitry acquires, as the ground data corresponding to the condition for capturing the satellite image, ground data at a time within a predetermined period of the predetermined time at which the artificial satellite performs imaging ([0072-0076] in further view of e.g. [0079], excluding those historical data embodiments of e.g. Fig. 3; a predetermined period met by that data which is considered pertinent/relevant to the time at which corresponding imagery was acquired – see also Squires as applied for the case of claim 7 below).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the proposed combination such that a predetermined period of the predetermined time is considered as readily apparent to POSITA, since POSITA would similarly recognize that such a temporal threshold would serve to minimize inaccurate analysis resultant from non-pertinent data.
As to claim 3, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 1.
Hirata further discloses the apparatus wherein the circuitry acquires, as the ground data corresponding to the condition for capturing the satellite image, ground data at a place within a predetermined distance of the predetermined place at which the artificial satellite performs imaging (Figs. 7-8, Fig. 10, [0099], etc., requiring that ground data 20/50 at a place close enough to enable that ‘linking’ of Hirata S204).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the proposed combination such that a predetermined distance of the predetermined place is considered as readily apparent to POSITA, since POSITA would similarly recognize that such a distance threshold would serve to minimize inaccurate analysis resultant from non-pertinent data.
As to claim 4, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 1.
Hirata further discloses the apparatus wherein the circuitry acquires, as the ground data corresponding to the condition for capturing the satellite image, ground data satisfying an environmental condition when the artificial satellite performs imaging ([0074] “The meteorological data 50 is the meteorological data observed by a common meteorological observation system and includes temperatures 51, hours of daylight 52, precipitation 53, wind direction 54, wind speed 55, wind/flood damage history 56, and so on. The meteorological data 50 is not limited to the items listed above and may be any applicable information”).
As to claim 5, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 4.
Hirata further discloses the apparatus wherein the environmental condition includes a weather condition ([0074] “meteorological data 50 is the meteorological data observed by a common meteorological observation system and includes temperatures 51, hours of daylight 52, precipitation 53, wind direction 54, wind speed 55, wind/flood damage history 56, and so on”).
As to claim 6, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 4.
Hirata further discloses the apparatus wherein the environmental condition includes an incident condition of sun light ([0074] hours of daylight 52).
As to claim 7, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 1.
Hirata suggests the apparatus wherein the circuitry is further configured to estimate, from the acquired ground data, ground data satellite performs imaging, and wherein the circuitry analyzes the satellite image based on of the estimated ground data and the satellite image (Hirata meteorological data 50, flood risk 25, estimated data as per e.g. [0082] and [0098], etc., see claim 1 above).
Squires further evidences the obvious nature of estimating ground data at the predetermined time at which the artificial satellite performs imaging (Squires [0015] “expected lighting conditions at the time of imaging” and “forecasted cloud cover”, etc.,). A person having ordinary skill in the art would also recognize that e.g. hyperspectral imagery (and/or select bands/frequencies) may be significantly impacted by rainfall, fire/smoke, etc., as e.g. increased soil moisture/water content (in the ground/field and even on canopy/vegetation surfaces), the presence of smoke, etc., may change reflectance/ absorption characteristics and resultant imagery accordingly (pertinent to disclosure of Hirata Fig. 6).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the proposed combination to comprise estimating ground data at the predetermined time at which the artificial satellite performs imaging as taught/suggested by Squires, the motivation as similarly taught/suggested therein and readily recognized by a person having ordinary skill in the art that such estimated data may serve in the same/similar capacity, further characterized by a reasonable expectation of success, as that disclosed in Hirata.
As to claims 8-9, these claims comprise limitations corresponding to those of claim 7 (at the predetermined time) but for the case that the estimated ground data is “at the predetermined place” (claim 8) and “under an environmental condition” (claim 9), and are rejected accordingly in view of that disclosure as identified for the case of claim(s) 1/7 and claim(s) 3 and 4-6 respectively.
As to claim 10, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 1.
Hirata suggests the apparatus wherein the circuitry acquires a plurality of pieces of the ground data (Hirata Fig. 3), and
wherein the circuitry analyzes the satellite image based on the acquired ground data obtained by performing data processing on the plurality of pieces of the acquired ground data and the satellite image ([0011-0012], [0017], Fig. 9 S205, [0078] “The farmland evaluation unit 331 is a functional unit that evaluates an agricultural field based on the satellite data and the ground data”, [0079], [0082] “The farmer evaluation unit 332 may generate evaluation data by any combination of information”, etc.,).
As to claim 13, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 1.
Hirata further suggests the apparatus wherein the circuitry corrects the analysis result of the satellite image by using a position of at least one sensor that acquires the acquired ground data (Hirata position information associated with 201 and information collector terminal 200 (see Fig. 2), and similarly that of terminal 100, Fig. 7 S101, [0051] “The geographic information acquisition unit 201 acquires, for example, information obtained by the information collector through positioning using a global positioning system installed on the information collector terminal 200, or information input by the information collector using an application”, etc., as relied upon for subsequent analysis/processing to include [0081] “The weight to each piece of the information can be changed depending on the financial institution or the like that consults the credit information. Meteorological data or other information may be used as items for correcting the agricultural field score obtained by formula (3) above when calculating the agricultural field score. The agricultural field score based on formula (3) above is just one example and the farmland evaluation unit 331 may generate evaluation data by any combination of information”; see also Lindores as applied for the case of claim 1 above).
As to claim 15, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 1.
Hirata further teaches/suggests the apparatus wherein the ground data includes data acquired by a sensor device on the ground (Hirata Fig. 1 and 2 terminal(s) 100/200, meteorological data 402, [0014], [0072-0074] “Optionally, data acquired from sensors installed in a smartphone or farm equipment may be used as the meteorological data”, etc., see also Lindores GreenSeeker, etc.,).
As to claim 17, this claim is the method claim corresponding to the apparatus of claim 1 and is rejected accordingly.
As to claim 18, this claim is the non-transitory CRM claim corresponding to the apparatus of claim 1 and is rejected accordingly.
As to claim 19, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 1.
Hirata further teaches/suggests the apparatus wherein the analysis result is output by superimposing the acquired ground data on the acquired satellite image obtained at the predetermined time (Hirata Fig. 10) according to the degree of coincidence (a degree of spatial overlap/co-location) of the condition of the ground data with respect to at least one of the predetermined place (met in Hirata, see Fig. 9 S204 “linking ground data with satellite data”, [0097] “At step S204, the farmland evaluation unit 331 links the maps with the agricultural field information (e.g., position and area) to identify the situation of the user's agricultural field (ground area). In this embodiment, the farmland evaluation unit 331 links the agricultural field information (e.g., position and area) shown in FIG. 10(A) with the situation of the ground area shown in FIG. 10(B) and generates a map to be used for evaluation shown in FIG. 10(C) to identify the situation of the user's agricultural field. This way, for example, the farmland evaluation unit 331 can identify the situation of the agricultural field shown in FIG. 10(C) as "soil", etc.,) or the predetermined (see claim 1) time at which the acquired satellite image is obtained (suggested in Hirata as the linking/ registration/alignment between ground-data and satellite imagery, even and particularly if each are not acquired concurrently, involves only linking that data concerning a shared/common temporal proximity/range as would be required to render aggregate map data that is useful/ relevant/accurate – i.e. concerning a same growing season, or, a same grower/farmer, etc.; see also Lindores Fig. 3 in view of Fig 9, [0054-0055], etc.,).
2. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Hirata (US 2020/0349585 A1) in view of Squires (US 2016/0034743 A1), Freitag et al. (US 2018/0189564 A1), Lindores (US 2014/0012732 A1) and League (US 2017/0124116 A1).
As to claim 11, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 1.
Hirata fails to explicitly disclose the apparatus wherein the circuitry is further configured to determine whether or not to perform imaging with a second artificial satellite different from the artificial satellite which has performed imaging first, based on the analysis.
League however evidences the obvious nature of an apparatus wherein an analysis processing unit determines whether or not to perform imaging with a second artificial satellite different from a first (satellite imagery source 24, [0006] PCM disclosure incorporated by reference, [0032], [0034], etc., [0011] “What is needed is a system and method to improve analyst and intelligence asset efficiency through harvesting, compiling, and distilling open source data according to self-generated and/or user-specified criteria to qualify the data that is most likely to be relevant to directing further intelligence collection and analysis, and more particularly useful for directing satellites to acquire images of geographic areas having higher probability of yielding useful intelligence information”, [0012] “and identifying a target geographic location for future satellite imagery acquisition based on the predicted geographic progression. The overall system to aggregate and process data from multiple sources to produce analyst-ready information for targeting of intelligence acquisition may be generally referred to herein as MARI”, [0023], [0045] “Analytics engine 40 may correlate any number of flagged and/or correlated ADPs with one another or with one or more historical events, and may flag correlationships as a potentially important emerging event. Analytics engine 40 may then predict a geographic path of an event, useful for directing further satellite imagery acquisition or other intelligence efforts”, [0046], etc.,), on a basis of an analysis result (that is also obtained by using acquired ground data) (Asynchronous Data 22 comprising weather, cell tower, earthquake feed, News, OSM, other, etc.,, [0007], [0025], [0026-0029], etc.,).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the proposed combination so as to comprise determining whether or not imaging with a second artificial satellite different from the artificial satellite which has performed imaging first is necessary/desired/advantageous based on an analysis result obtained by using acquired ground data and previously acquired satellite imagery as taught/suggested by League, the motivation as similarly taught/suggested therein that such an analysis and subsequent acquisition based thereon would serve to facilitate intelligence/imagery gathering on the basis of e.g. asynchronous data suggesting one or more e.g. predicted geographic progression(s) and/or events of interest (analogous to a ‘double cropping’ event of Hirata).
3. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Hirata (US 2020/0349585 A1) in view of Squires (US 2016/0034743 A1), Freitag et al. (US 2018/0189564 A1), Lindores (US 2014/0012732 A1), League (US 2017/0124116 A1) and Okazaki (US 2020/0143212 A1).
As to claim 12, Hirata in view of Squires, Freitag, Lindores and League teaches/suggests the apparatus of claim 11.
Hirata in view of Squires, Freitag, Lindores and League further suggests the apparatus wherein, in a case where circuitry determines to perform imaging using the second artificial satellite, the circuitry is further configured to control the second artificial satellite to perform the imaging League [0011], [0012], [0023], [0045], etc., in view of that proposed modification/ combination as presented above for the case of intervening claim 11).
Freitag discloses measuring NDVI via high resolution AVHRR ([0033]), Lindores further discloses in e.g. [0053] that the resolution of today’s satellite images are suitable for the agricultural metric determining purposes disclosed therein, and Hirata at [0071] discloses the manner in which generated maps may be impacted by/modified to account for the resolution of satellite data, while falling silent regarding a second acquisition being higher resolution than a first.
Squires however discloses satellite imaging resolution as being an image quality attribute for user consideration in satellite imagery requisition (Fig. 2 in view of Step L, [0010]).
Okazaki further evidences the obvious nature of selecting/opting for higher resolution imagery so at to enable subsequent processing tasks/analytics based thereon ([0040] “In some embodiments, the system may be configured to query multiple remote database systems to obtain at least two aerial images of a given property location 102b. The aerial images available at the various databases, for example, may differ in resolution and recency of capture. Through collecting two or more images of a particular property, for example, the system may analyze each image to determine a best quality image for use in condition analysis. The condition analysis can include balancing of multiple factors such as, in some examples, clarity, completeness, and recency”, [0073] “In some implementations, if the image quality analysis and preparation engine 326 determines that the acquired image is insufficient, the image quality analysis and preparation engine 326 may request a replacement image from the image acquisition engine 336. For example, the image acquisition engine 336 may obtain images based upon a variety of factors including, in some examples, recency of capture, resolution, cost, and/or applicability to a particular property characteristic analysis. Upon determination by the image quality analysis and preparation engine 326 that the first obtained image is insufficient, for example, the image acquisition engine 336 may determine a next best source for obtaining an image of the property”, etc.,).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the proposed combination as presented in the rejection of claim 11, such that subsequently acquired imagery is characterized by a resolution higher than that of a first acquisition, as taught/suggested by Okazaki, the motivation as similarly taught/suggested therein and readily recognized by POSITA that such a higher resolution acquisition may be better suited for the analysis requirements/ desires of the user (in the case of the proposed combination, the party/vendor collecting the farm/farmer information and/or the financial/insurance/etc., institutions to which that service/information is provided – analogous to the disclosure of Okazaki (see e.g. MPEP 2141.01(a) regarding the tests/basis “relevant field of endeavor” and/or “reasonably pertinent” upon which ‘analogous art’ may be established as such)).
4. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Hirata (US 2020/0349585 A1) in view of Squires (US 2016/0034743 A1), Freitag et al. (US 2018/0189564 A1), Lindores (US 2014/0012732 A1) and Tran (US 2022/0111960 A1).
As to claim 14, Hirata in view of Squires, Freitag and Lindores teaches/suggests the apparatus of claim 13.
Hirata further suggests the apparatus wherein the circuitry is further configured to change weighting of a correction degree of the analysis result according to the degree of coincidence of the condition of the acquired ground data with respect to the position of the at least one sensor ([0081], in view of [0079], see also [0068] and scaling parameters/weights for adjusting shades of respective colors associated with water 61, soil 62 and vegetation 63 of Hirata; in further view of that updated mapping for the case of claim 13, and that location data acquisition for terminal(s) 200/100). Examiner further notes the recited language does not specify any resolution/granularity associated with the various ‘degree’(s) recited, and as such a degree that is essentially binary in nature appears to read.
While the instant claim language under Broadest Reasonable Interpretation does not require a ‘correction’ to e.g. a NDVI (see Applicant’s PGPub at [0171] with reference to S7), Hirata falls silent regarding any NDVI value correction/modification according to a degree of coincidence of the condition of the acquired ground data. In other words, Hirata falls silent regarding means for determining the scaling parameters of [0068] specifically. As identified in the Response to Remarks above, various references of record and to include Lindores as presented in the rejection of claim 1 above, evidence the obvious nature of satellite imagery NDVI correction on the basis of complementary ground data.
Tran further evidences the obvious nature of calibrating/correcting NDVI for crop imagery analysis according to a degree of coincidence of the condition of the acquired ground data ([0221] “The most convenient illumination source is daylight from the sun. The spectrum of daylight must be taken into account, however. The intensity of daylight at 780 nm is about 75% of that at 660 nm, for instance. Calibrating the spectrum of daylight illumination may be done in any of several ways. The simplest method is to use an assumed average spectrum shape. Alternatively, one may take a photograph of the sky or of a white reference reflector and measure the intensity of light at wavelengths of interest. Still another possibility is to use a model of the daylight spectrum versus time of day, latitude, weather, and/or other factors. Such a model may account for enhanced red and reduced blue intensity when the sun is low in the sky, for example”, [0197], etc.; Tran evidences the manner in which various conditions such as time of day, location/latitude, weather, and other factors – similar to that corresponding disclosure of Lindores, impact(s) NDVI readings and further suggests failure to calibrate for incident light/illumination conditions may produce e.g. measurements/indices falsely suggesting vigorous growth/plant health (or otherwise, false indicators/measures of poor crop health); see also the teachings of Tran as identified above, as they relate to a degree of coincidence between differing modalities in the context of building a navigation model).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the proposed combination to comprise calibrating/ correcting NDVI for crop imagery according to a degree of coincidence of the condition of the acquired ground data as taught/suggested by Hirata, Tran, Lindores (Zhang not relied upon, etc.,), the motivation as similarly taught/suggested therein that such a correction ensures more accurately distinguishing between at least water sources 61, soil 62, and vegetation 63 if not otherwise more accurate crop type 23, stage of progression, and/or health/disease related thereto.
5. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Hirata (US 2020/0349585 A1) in view of Squires (US 2016/0034743 A1), Freitag et al. (US 2018/0189564 A1), Lindores (US 2014/0012732 A1) and Wakayama (US 2018/0294872 A1).
As to claim 16, Hirata in view of Squires and Freitag teaches/suggests the apparatus of claim 1.
Hirata fails to explicitly disclose the apparatus wherein the ground data includes data collected through a store-and-forward scheme.
Wakayama evidences the obvious nature of ground data collected through a store-and-forward scheme ([0004], [0006], [0017] “FIG . 1B illustrates a store-and-forward communication system employing nanosats in which energy-cognizant scheduling may be implemented according to illustrative embodiments”, [0029] “a nanosat acting as a message ferry is illustrated in FIG. 1A. As shown in FIG. 1A, a nanosat 110 orbits the earth in a path 120. The nanosat 110 has a footprint 130 which represents the radio signal transmit / receive range of the nanosat at a given time. As the nanosat 110 orbits the Earth, it passes over different ground stations 140 and remote user devices 150 at different destinations. The nanosat receives messages of different sizes and different priorities from and transmits such messages to the ground stations 140 and the remote user devices 150 within its footprint. Thus, the nanosat 110 acts as a message ferry between the ground stations 140 and the remote user devices 150”, [0031] “Thus, messages may be relayed from the remote user device 150c to the ground station 140b via the nanosat 110c”, [0033], etc.,).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the proposed combination such that one or more data are collected through a store-and-forward scheme as taught/suggested by Wakayama, the motivation as similarly taught/suggested therein that such a scheme may utilize satellites as “ferries” to relay information between devices/sensors in remote locations and ground stations (and vice versa), that may ensure the collection of information/data even during instances characterized by intermittent/limited connectivity and/or for locations remote from ground stations in a decentralized manner.
Additional References
Prior art made of record and not relied upon that is considered pertinent to applicant's disclosure:
Additionally cited references (see attached PTO-892) otherwise not relied upon above have been made of record in view of the manner in which they evidence the general state of the art. As an additional consideration for Applicant, the prosecution history and resultant claims for the case of related Application No. 18/036,845 (amended in that claim set filed 04/07/2026 to include “wherein the degree of coincidence is determined using at least one a temporal closeness or place closeness between the conditions”) continue to be monitored with respect to Double Patenting. Given the distinct filings, and the manner in which Double Patenting rejections are generally not made between processes/embodiments that may be properly restricted, there is perhaps a rebuttable presumption that the two applications involve non-obvious variants. The instant application and that of reference read potentially as processes useable together/overlapping, wherein the instant app may concern more a ‘difference’ detection prior to correction, and the ‘845 application may concern a ‘correct[ion]’ afterwards (overlapping given instant claim 14 if it sufficiently corresponds to that ‘correcting’), but omitting the ML model based ‘difference’ detection. New claim 20 of the co-pending ‘845 Application however, given new claim 19 of the instant application, may serve to affirm the Double Patenting rejection presented in the associated Application’s prosecution history, because 19 reads similarly, and almost so as to suggest that the recited “analysis result” might comprise “corrected information”.
PNG
media_image7.png
220
642
media_image7.png
Greyscale
From ‘845:
PNG
media_image8.png
186
630
media_image8.png
Greyscale
In that instance, it may be appropriate to argue that modifying the claims of reference (the ‘845 application in this instance), so as to additionally perform/consider a ‘difference’ detection broadly between two satellite images (e.g. for the purposes of detecting double cropping as disclosed in Hirata [0079]) ‘using machine learning to extract a change corresponding to the difference’ (Hirata [0017], Freitag [0018], [0051-0055]), under at least one plausible construction, would require nothing outside of the resolved level of ordinary skill in the art as evidenced by the references of record (MPEP 2141 in further view of MPEP 2143 Rationale A and/or G).
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IAN L LEMIEUX whose telephone number is (571)270-5796. The examiner can normally be reached Mon - Fri 9:00 - 6:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached on 571-272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IAN L LEMIEUX/Primary Examiner, Art Unit 2669
Prosecution Insights