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 .
Information Disclosure Statement
1. The information disclosure statements (IDS) submitted on 10/06/2021 and 06/18/2021 has been considered by the examiner.
Claim Objections
2. Claim 25 is objected to because of the following informalities:
In claim 25, each option starts with a period (.), for better clarity of claims, examiner proposes using alphabets or number in parenthesis such as (a), (b).
Appropriate correction is required.
Specification
3. The disclosure is objected to because of the following informalities:
Paragraphs [44 and [109] recites a nonlinear relation f (xt, yt, dt) — (Ximg, Yimg) defined for xt € [0..XMAX [, yt € [0..YMAX [and at e [0..DMAX[, which recites non-ending parenthesis/brackets (as cited in BOLD here). The parenthesis/brackets after XMAX, YMAX and DMAX are non-closing/non-ending. Appropriate correction is required.
Claim Rejections - 35 USC § 112
4. Claims 2, 13 and 25 is 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 2 recites a nonlinear relation f (xt, yt, dt) — (Ximg, Yimg) defined for xt € [0..XMAX [, yt € [0..YMAX [and at e [0..DMAX[, which recites non-ending parenthesis/brackets (as cited in BOLD here). The parenthesis/brackets after XMAX, YMAX and DMAX are non-closing/non-ending, which makes the limitation indefinite, and therefore makes the claims indefinite.
Claim 13 recites the limitation “a collimator configured to capture the rays passing through said opening and to transmit these rays on a diffraction grating”. There is insufficient antecedent basis for “said opening” in the claim. The word “opening” is not mentioned anywhere before the limitation in question.
Claim 25 recites one of the options as “M=7”, there is nothing in the claim that tells what M stands for and fails to particularly point out and distinctly claim the subject matter which the inventor regards as the invention, therefore the claim limitation is indefinite, and therefore makes the claim indefinite.
Claim Rejections - 35 USC § 101
5. 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.
The USPTO “Interim Guidelines for Examination of Patent Applications for Patent Subject Matter Eligibility” (Official Gazette notice of 22 November 2005), Annex IV, reads as follows:
Descriptive material can be characterized as either "functional descriptive material" or "nonfunctional descriptive material." In this context, "functional descriptive material" consists of data structures and computer programs which impart functionality when employed as a computer component. (The definition of "data structure" is "a physical or logical relationship among data elements, designed to support specific data manipulation functions." The New IEEE Standard Dictionary of Electrical and Electronics Terms 308 (5th ed. 1993).) "Nonfunctional descriptive material" includes but is not limited to music, literary works and a compilation or mere arrangement of data.
When functional descriptive material is recorded on some computer-readable medium it becomes structurally and functionally interrelated to the medium and will be statutory in most cases since use of technology permits the function of the descriptive material to be realized. Compare In re Lowry, 32 F.3d 1579, 1583-84, 32 USPQ2d 1031, 1035 (Fed. Cir. 1994) (claim to data structure stored on a computer readable medium that increases computer efficiency held statutory) and Warmerdam, 33 F.3d at 1360-61, 31 USPQ2d at 1759 (claim to computer having a specific data structure stored in memory held statutory product-by-process claim) with Warmerdam, 33 F.3d at 1361, 31 USPQ2d at 1760 (claim to a data structure per se held nonstatutory).
In contrast, a claimed computer-readable medium encoded with a computer program is a computer element which defines structural and functional interrelationships between the computer program and the rest of the computer which permit the computer program's functionality to be realized, and is thus statutory. See Lowry, 32 F.3d at 1583-84, 32 USPQ2d at 1035.
6. Claim 27 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows.
Claim 27 defines a computer program embodying functional descriptive material (i.e., a computer program or computer executable code). However, the claim does not define a “computer-readable medium or computer-readable memory” and is thus non-statutory for that reason (i.e., “When functional descriptive material is recorded on some computer-readable medium it becomes structurally and functionally interrelated to the medium and will be statutory in most cases since use of technology permits the function of the descriptive material to be realized” – Guidelines Annex IV). The scope of the presently claimed invention encompasses products that are not necessarily computer readable, and thus NOT able to impart any functionality of the recited program. The examiner suggests amending the claim(s) to embody the program on “computer-readable medium” or equivalent; assuming the specification does NOT define the computer readable medium as a “signal”, “carrier wave”, or “transmission medium” which are deemed non-statutory (refer to “note” below). Any amendment to the claim should be commensurate with its corresponding disclosure.
Note:
“A transitory, propagating signal … is not a “process, machine, manufacture, or composition of matter.” Those four categories define the explicit scope and reach of subject matter patentable under 35 U.S.C. § 101; thus, such a signal cannot be patentable subject matter.” (In re Nuijten, 84 USPQ2d 1495 (Fed. Cir. 2007). Should the full scope of the claim as properly read in light of the disclosure encompass non-statutory subject matter such as a “signal”, the claim as a whole would be non-statutory. Should the applicant’s specification define or exemplify the computer readable medium or memory (or whatever language applicant chooses to recite a computer readable medium equivalent) as statutory tangible products such as a hard drive, ROM, RAM, etc, as well as a non-statutory entity such as a “signal”, “carrier wave”, or “transmission medium”, the examiner suggests amending the claim to include the disclosed tangible computer readable storage media, while at the same time excluding the intangible transitory media such as signals, carrier waves, etc.
Merely reciting functional descriptive material as residing on a tangible medium is not sufficient. If the scope of the claimed medium covers media other than “computer readable” media (e.g., “a tangible media”, a “machine-readable media”, etc.), the claim remains non-statutory. The full scope of the claimed media (regardless of what words applicant chooses) should not fall outside that of a computer readable medium.
Claim Rejections - 35 USC § 102
7. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
8. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
9. Claims 1, 9, 17 and 25-27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fotiadou et al., 2017, “Deep Convolutional Neural Networks for the Classification of Snapshot Mosaic Hyperspectral Imagery” (pp. 185-190).
Regarding claim 1, Fotiadou discloses “Device for detecting features in a hyperspectral scene in three-dimensions, wherein the device comprises a direct detection system of features in said hyperspectral scene integrating a deep convolutional neural network designed to detect the feature sought in said hyperspectral scene from at least one compressed two-dimensional image of the hyperspectral scene” (Fotidou – page 185 – left column – Topic – “Abstract” – Spectral information obtained by hyperspectral sensors enables better characterization, identification and classification of the objects in a scene of interest…..we propose a novel machine learning technique that addresses the hyperspectral image classification problem by employing the state-of-the-art scheme of Convolutional Neural Networks (CNNs)…We apply the proposed method on novel dataset acquired by a snapshot mosaic spectral camera and demonstrate the potential of the proposed approach for accurate classification; page 188 – left column – Topic – “Data Acquisition & Experimental Setup” – We explored the classification of hyperspectral image acquired using a Ximea camera, equipped with the IMEC Snapshot Mosaic sensor. These flexible sensors optically subsample the 3D spatio-spectral information on a two-dimensional CMOS detector array, where a layer of Faby-Perot spectral filters is deposited on top of the detector array. The hyperspectral data is initially acquired in the form of 2D mosaic images (where 2D image is compressed two-dimensional image). In order to generate the 3D hypercubes, the spectral components are properly rearranged into separate spectral bands. In our experiments, we utilize a 4x4 snapshot mosaic hyperspectral sensor resolving 16 bands in the spectrum range of 470—630nm, with a spatial dimension of 256 x 512 pixels; page 189 – right column – Topic – “Conclusions and Future Work” – In this work we propose a scheme which exploits a deep feature learning architecture for efficient feature extraction. The state-of-the-art method of CNNs is able to identify representative features, encoding both spatial and spectral variations of hyperspectral scenes, and successfully assign each hyper-cube to a predefined class).
Regarding claim 9, Fotiadou discloses “A device for capturing an image of a hyperspectral scene and for detecting features in this three-dimensional hyperspectral scene comprising a device according to claim 1 and further comprising an acquisition system of the at least one compressed image of the hyperspectral scene in three dimensions (see citations made in the rejection of claim 1, see page 188 – left column – Topic – “Data Acquisition & Experimental Setup” teaches acquisition system).
Regarding claim 17, Fotiadou discloses “Device according to claim 9, wherein at least one of said compressed images is obtained by a sensor of the acquisition system whose wavelength is between 300 nanometers and 2000 nanometers (page 188 – left column – Topic – “Data Acquisition & Experimental Setup” – We explored the classification of hyperspectral image acquired using a Ximea camera, equipped with the IMEC Snapshot Mosaic sensor. These flexible sensors optically subsample the 3D spatio-spectral information on a two-dimensional CMOS detector array, where a layer of Faby-Perot spectral filters is deposited on top of the detector array. The hyperspectral data is initially acquired in the form of 2D mosaic images (where 2D image is compressed two-dimensional image). In order to generate the 3D hypercubes, the spectral components are properly rearranged into separate spectral bands. In our experiments, we utilize a 4x4 snapshot mosaic hyperspectral sensor resolving 16 bands in the spectrum range of 470—630nm, and is between 300 nanometers and 2000 nanometers).
Regarding claim 25, claim 25 recites “Device according claim 1 further comprising one and / or the other of the following characteristics:
. the acquisition system comprises means for acquiring at least one compressed image of a focal plane of the hyperspectral scene;
. the compressed image is non-homogeneous;
. the neural network is designed to generate an image for each sought feature where a value for each pixel at the coordinates (x; y) corresponds to the probability of presence of said feature at the same coordinates (x; y) of the hyperspectral scene;
. the obtained compressed image contains the image portion of the non-diffracted scene in the center;
. the direct detection system does not implement calculation of a hyperspectral cube of the scene for the detection of features;
._M=7”.
As cited in the rejections of claim 1 and 9, Fotiadou at least discloses the acquisition system comprises means for acquiring at least one compressed image of a focal plane of the hyperspectral scene.
Regarding claim 26, claim 26 has been similarly analyzed and rejected as per claim 1 rejection citations.
Regarding claim 27, claim 27 has been similarly analyzed and rejected as per claim 1 rejection citations.
10. Claims 10 and 11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fotiadou et al., 2017, “Deep Convolutional Neural Networks for the Classification of Snapshot Mosaic Hyperspectral Imagery” (pp. 185-190), and further in view of Coffey, October 2015, “Hyperspectral Imaging for Safety and Security” (pp. 28-33).
Regarding claim 10, claim 10 recites “Device according to claim 9 wherein the acquisition system comprises a compact mechanical design integrable in a portable and autonomous device, and where in the detection system is included in said portable and autonomous device”. Fotiadou as cited in the rejection of claims 1 and 9, teaches acquiring hyperspectral image using a Ximea camera, equipped with the IMEC Snapshot Mosaic sensor., where these flexible sensors optically subsample the 3D spatio-spectral information on a two-dimensional CMOS detector array. Ximea’s camera equipped with the IMEC Snapshot Mosaic sensor is very well known for its compact size and portability; and is further shown by Fotiadou in figure 2, where compact camera is connected to portable computer/system, and Ximea provides a compact mechanical design. Further adding, as per MPEP 2144.04, which clearly states that “making portable, making separable and making integral" would be an obvious design choice. Fotiadou as cited teaches a portable hyperspectral acquisition and detection system/device but does not explicitly teach system/device being integrated in an autonomous device. Examiner here asserts that it is very well known to use Hyperspectral imaging in an autonomous device such as UAVs (Unmanned Ariel Vehicles), and further cites Coffey to provide evidentiary teachings. Coffey teaches using Hyperspectral imaging in a UAV (page 33). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use the teachings of incorporating hyperspectral imaging in an autonomous device as taught by Coffey in the invention of Fotiadou. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use the teachings of incorporating hyperspectral imaging in an autonomous device as taught by Coffey in the invention of Fotiadou, in order to extend Fotiadou’s invention’s applicability in applications such as aerial surveillance, inspection of power lines, pipelines, and oil and gas flare stacks and also in precision agriculture, where human manual inspection is very limited, and making it easy.
Regarding claim 11, claim 11 recites “Device according to claim 9, wherein at least one of said compressed images is obtained by an infrared sensor of the acquisition system”. As cited in the rejection of claim 1 and 9, Fotiadou teaches “we utilize a 4x4 snapshot mosaic hyperspectral sensor resolving 16 bands in the spectrum range of 470-630nm, with a spatial dimension of 256x512 pixels”. Fotiadou does not teach using infrared range sensor, however, Coffey on page teaches “One new design features mosaic filters with one filter per pixel. The 4x4 sensor features 16 spectral bands between 470 and 630 nm of the visible range. The 5x5 provides 25 bands between 600 and 1000nm of the visible-IR range”. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use the teachings of simply using/replacing a larger array mosaic sensor over the smaller array that extends the range to IR range in order to cover infrared range features in the image as taught by Coffey in the invention of Fotiadou. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use the teachings of simply using/replacing a larger array mosaic sensor over the smaller array that extends the range to IR range in order to cover infrared range features in the image as taught by Coffey in the invention of Fotiadou, for extending the applicability to infrared imaging analysis.
11. Claims 3-8, 12, 14-16, 18-24 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Manav Seth whose telephone number is (571) 272-7456. The examiner can normally be reached on Monday to Friday from 8:30 am to 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Sumati Lefkowitz, can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MANAV SETH/Primary Examiner, Art Unit 2672 February 24, 2024