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 .
Priority
Receipt is acknowledged that application is a National Stage application of PCT/JP2022/001493. Receipt is acknowledged that application claims priority to foreign applications with application number JP2021-016153 dated 02/03/2021 and application number JP2021-203149 dated 12/15/2021. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Claim Objections
Claims 1 and 18 are objected to because of the following informalities: “decoding from the encoded compressed image” should read “decoding from the compressed image”. Appropriate correction is required.
Claim Interpretation
Regarding claim 3, “noise hindering read of the image information” is interpreted based on the definition in paragraph 26 of the specification: “Here, the "noise hindering read of the image information" indicates noise making it difficult for a person or a computer to recognize the original image information”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, 9-10, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Martino et al. (Di Martino, F., Loia, V., Perfilieva, I., & Sessa, S. (2008). An image coding/decoding method based on direct and inverse fuzzy transforms. International Journal of Approximate Reasoning, 48(1), 110-131.), hereinafter Martino, in view of Akhtman et al. (U.S. Patent No. 2017/0205337 A1), hereinafter Akhtman.
Regarding claim 1, Martino teaches a method, the method comprising:
obtaining matrix data representing an encoding matrix used to encode image data including image information in wavelength bands and to generate a compressed image and/or a decoding matrix used to generate the spectral data cube by decoding from the compressed image (Martino, encoding matrix to compress an image, pg. 116, 1st paragraph: “The original block RB is firstly compressed to a block FB of sizes 3 X 3 (hence p = 0,14063 = (3 X 3)/(8 X 8)) by using formula (12), in which the basic functions A1, A2, A3 and B1, B2, B3 are defined by formulas (13) and (14), respectively, and they form an uniform fuzzy partition of the interval”);
editing the matrix data in a way of causing the image information in at least one of the wavelength bands in the image data to be altered in the image data after being generated by decoding (Martino, A1, A2, A3 and B1, B2, B3 form a uniform fuzzy partition based on the characteristics of the image and alter the encoding values used for image compression, see equations 13-14 on pg. 114; pg. 116, last paragraph to pg. 117: “The block FB is decompressed to a block RFn(B)m(B) of sizes 8 X 8 via formula (15) by obtaining the following fuzzy relation…which corresponds to the successive image of Fig. 2”; see in Figures 1 and 2, attached below, how the image data after decoding is altered); and
outputting the matrix data after the editing (Martino, values are used during compression, see the Example on pg. 115-117 wherein Figure 2 is produced).
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However, Martino fails to teach wherein the image data is a spectral data cube, and further fails to explicitly teach wherein the method is executed by a computer. Akhtman teaches a method executed by a computer (Akhtman, para 45: “A corresponding hyperspectral image reconstruction module comprises a single- or multi-processor computing device, or a computer network”) wherein spectral data is compressed and reconstructed (Akhtman, Figure 1A, para 64: “producing a 2D set of spectral samples 8 and a computational method which is utilized to reconstruct a complete 3D spectral data cube from the aforementioned 2D set of optical samples”; para 99: “Variations of the aforementioned reconstruction model include changing the representation of x to a mathematical basis in which x becomes sparse. For instance, basis such as Fourier”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the computer and spectral data cube of Akhtman with the method of Martino in order utilize the processing power of a computing device to edit the matrix values and further alter hyperspectral image data in order to allow for a more detailed 3D representation of the objects imaged (Akhtman, para 43: “the invention provides a method for generating a spectral 3D-model of an imaged object and increasing the spectral reconstruction quality of an imaged scene”).
Regarding claim 5 (dependent on claim 1), Martino in view of Akhtman teaches wherein editing the matrix data includes rewriting the matrix data in a way of causing the image information in the wavelength bands in the spectral data cube to be altered in the spectral data cube after being generated by decoding (See combination in claim 1 – Martino teaches editing the matrix data to alter the decoded image, while Akhtman teaches processing of a spectral data cube.).
Regarding claim 9 (dependent on claim 1), Martino in view of Akhtman teaches wherein the compressed image is generated by an imaging device including a filter array (Akhtman, para 74: “spectral filter array 6”), the filter array includes multiple types of optical filters with transmission spectra different from one another (Akhtman, Figure 4, para 84: “A method for second degree cancellation consists of placing the SFA 6 over an existing color filter array 11 (such as a Bayer pattern filter array)”), the encoding matrix corresponds to a two-dimensional distribution of the transmission spectra of the filter array (Taught in the combination of Martino in view of Akhtman in claim 1; Martino teaches the corresponding encoding matrix based on image values, while Akhtman teaches the use of hyperspectral data), and generating the spectral data cube by decoding includes generating the spectral data cube by decoding from the compressed image with a compressed sensing process based on the decoding matrix (Akhtman teaches decoding of the spectral data cube. The decoding matrix of claim 1 referenced here is not a required limitation taught in claim 1 due to the “and/or” claim language, para 64: “employed for producing a 2D set of spectral samples 8 and a computational method which is utilized to reconstruct a complete 3D spectral data cube from the aforementioned 2D set of optical samples”).
Regarding claim 10 (dependent on claim 1), Martino in view of Akhtman teaches wherein the matrix data represents the encoding matrix (Martino, see claim 1 rejection).
Regarding claim 12 (dependent on claim 10), Martino in view of Akhtman teaches wherein outputting the matrix data after the editing includes storing the matrix data representing the encoding matrix in a storage medium (Akhtman further teaches a storage medium of the computing system that stores data used to compress spectral image data and reconstruct the 3D spectral image data, para 91: “Data from the imaging sensor 8 is then saved on the storage medium 9 and converted to spectral data by the reconstructor 3, composed of multiple processing units 10”; see combination statement below), and wherein the method further comprises:
obtaining the spectral data cube; and generating the compressed image by encoding the spectral data cube based on the matrix data after the editing (Taught by the combination of Martino in view of Akhtman in claim 1).
Martino fails to explicitly teach a storage medium. In the claim 1 rejection above, the method can be executed on the computer of Akhtman. Thus, the edited matrix data could be stored on the storage medium of the computer of Akhtman in order to facilitate use of the edited matrix to compress image data.
Regarding claim 18, all claim limitations are met and rendered obvious by Martino in view of Akhtman because the method steps of claim 1 are the same as claim 18. Akhtman further teaches a storage and a processing circuit of the computing system that stores data used to compress spectral image data and reconstruct the 3D spectral image data (Akhtman, para 91: “Data from the imaging sensor 8 is then saved on the storage medium 9 and converted to spectral data by the reconstructor 3, composed of multiple processing units 10”). Similar to the claim 1 rejection above, the method steps can be executed on the computer of Akhtman. Thus, the matrix data could be stored on the storage medium of the computer of Akhtman and the processing units of the computer of Akhtman could edit the matrix data in order to facilitate use of the edited matrix to compress image data.
Claims 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Martino in view of Akhtman, in further view of Isshiki (U.S. Patent No. 2020/0177879 A1).
Regarding claim 11 (dependent on claim 10), Martino in view of Akhtman fails to teach wherein outputting the matrix data includes transmitting the matrix data to a device that generates the compressed image by encoding the spectral data cube based on the matrix data representing the encoding matrix.
However, Isshiki teaches a similar method wherein different encoding matrices are outputted and transmitted, further disclosing: wherein outputting the matrix data (Isshiki, sampling matrix, para 92: “Hence, a sampling matrix is stored in the sampling matrix memory 260 as related with the identifier of each user”) includes transmitting the matrix data to a device that generates the compressed image by encoding the spectral data cube based on the matrix data representing the encoding matrix (Isshiki, para 95: “A sampling matrix used for the compressive sensing in the imaging apparatus 101 is related with the identifier of the user A and stored in the sampling matrix memory 260. When the user A transmits an image request of the user A from the terminal 301 to the server 200, the identifier of the user A is transmitted together”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the transmitting the matrix data, taught by Isshiki, with the method of Martino in order to enhance the security of the system by transmitting the matrix data after it is edited (Isshiki, para 90: “it is assumed that the server 200 is shared by a plurality of users, thereby enhancing a security between users”). Transmitting the matrix data before it is edited could risk the spectral data being encoded without the edited information.
Regarding claim 19, Martino in view of Akhtman fails to teach a device comprising a storage that stores the matrix data after the editing, the matrix data being output from the device according to claim 18, and a processing circuit that executes a process of encoding the spectral data cube based on the matrix data and generating the compressed image and/or a process of generating the spectral data cube by decoding from the compressed image based on the matrix data.
However, Isshiki teaches a similar method, disclosing: a storage that stores the matrix data after the editing (Isshiki, sampling matrix memory storing identifiable matrices, para 92: “Hence, a sampling matrix is stored in the sampling matrix memory 260 as related with the identifier of each user”), the matrix data being output from the device according to claim 18 (Taught in combination with Martino in view of Akhtman; Isshiki teaches receiving outputted matrix data, see next citation), and a processing circuit that executes a process (Isshiki, see system of devices in Figure 11, para 90: “This image transmission and reception system includes a plurality of imaging apparatuses 101 through 103, the server 200, a plurality of terminals 301 through 303, and the network 400 which are interconnected via the network 400”) of encoding the spectral data cube based on the matrix data and generating the compressed image (Isshiki, para 95: “A sampling matrix used for the compressive sensing in the imaging apparatus 101 is related with the identifier of the user A and stored in the sampling matrix memory 260. When the user A transmits an image request of the user A from the terminal 301 to the server 200, the identifier of the user A is transmitted together”) and/or a process of generating the spectral data cube by decoding from the compressed image based on the matrix data.
Isshiki teaches a method wherein different encoding matrices are outputted and transmitted to devices via a network and server (Isshiki, Figure 11). Martino in view of Akhtman teaches the matrix data being output from the device according to claim 18. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have combined the transmitting the matrix data, taught by Isshiki, with the method of Martino in order to enhance the security of the system by transmitting the matrix data after it is edited from a central device (Isshiki, para 90: “it is assumed that the server 200 is shared by a plurality of users, thereby enhancing a security between users”). Transmitting the matrix data before it is edited could risk the spectral data being encoded without the edited information. This combination also allows the communication between multiple imaging/computing devices, allowing flexibility in the management of computational resources and location of physical devices/cameras.
Allowable Subject Matter
Claims 2-4, 6-8, 13-17, and 20-21 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. The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 2, prior art such as Kim et al. (Kim, Y., Duric, Z., & Richards, D. (2006, July). Modified matrix encoding technique for minimal distortion steganography. In International Workshop on Information Hiding (pp. 314-327). Berlin, Heidelberg: Springer Berlin Heidelberg.) teaches wherein an encoding matrix is modified to implement steganography or fragile watermarking during image compression (Kim, abstract), but fails to teach wherein the spectral data cube is altered after being generated by decoding (emphasis added). Further, the prior art fails to teach wherein the matrix data is edited in a way of causing the image information to include a noise hindering read or gradation and resolution change, as claimed in claims 3-4 (emphasis added). Due to their dependence on claim 2, claims 13-14 are similarly objected to.
Regarding claim 6, the prior art fails to teach editing the decoding matrix data in a way of causing the image information in at least one of the wavelength bands in the image data to be altered in the image data after being generated by decoding, thus failing to teach wherein the matrix data represents the decoding matrix. Therefore, the prior art similarly fails to teach claims 15-16 and 20-21 as a whole. Due to their dependence on claim 6, claims 7-8 are similarly objected to.
Regarding claim 17, while Isshiki teaches encoding and decoding matrix data that is identifiable by a user identifier, the prior art fails to teach wherein this matrix data is edited in a way wherein the spectral data cube is altered after being generated by decoding (emphasis added), and is therefore inconsistent with the limitations of claim 1.
In view of the foregoing, the prior art references alone or in reasonable combination are insufficient to teach the invention as a whole, as claimed in claims 2-4, 6-8, 13-17, and 20-21.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Kim et al. (Kim, Y., Duric, Z., & Richards, D. (2006, July). Modified matrix encoding technique for minimal distortion steganography. In International Workshop on Information Hiding (pp. 314-327). Berlin, Heidelberg: Springer Berlin Heidelberg.), referenced in the above section.
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/EMMA E DRYDEN/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677