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 .
DETAILED ACTION
This action is responsive to the Application filed on 11/27/2023 which claims priority of provisions application 62/444955 filed on 1/11/2017. Claims 1-17 are pending in the case. Claim 1 is an independent claim.
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 5 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.
Regarding claim 5, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). To expedite prosecution, the Examiner will interpret the term as “at least one of”.
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 1-4 are rejected under 35 U.S.C. 103 as being obvious over “Sparse autoencoder”, Ng, 2001 in view of Havlik et al (US 20180246645 A1).
Referring to claim 1, Ng discloses an autoencoder comprising an artificial neural network for unsupervised learning of efficient encoding for a set of data, the autoencoder comprising:
an input layer having a first plurality of neurons for receiving input <first type of data>; (page 2 of Ng, input layer having a plurality of neurons for receiving data)
a compression layer adapted to represent the input <first type of data> in a <second form>, the compression layer having a second plurality of neurons, each connected to each neuron of the input layer, wherein the second plurality of neurons is smaller than a number of neurons in the first plurality of neurons; (page 13 of Ng, “The autoencoder tries to learn a function hW,b(x) ≈ x. In other words, it is trying to learn an approximation to the identity function, so as to output ˆ x that is similar to x. The identity function seems a particularly trivial function to be trying to learn; but by placing constraints on the network, such as by limiting the number of hidden units, we can discover interesting structure about the data. As a concrete example, suppose the inputs x are the pixel intensity values from a 10 × 10 image (100 pixels) so n = 100, and there are s2 = 50 hidden units in layer L2. Note that we also have y ∈ R100. Since there are only 50 hidden units, the network is forced to learn a compressed representation of the input. I.e., given only the vector of hidden unit activations a(2) ∈ R50, it must try to reconstruct the 100-pixel input x.”)
a decoding layer adapted to receive the <second form> from the compression layer, and to decode the <second form> to create a new <first type of data> closely resembling the input <first type of data>, the decoding layer having a third plurality of neurons each connected to each neuron of the compression layer, wherein the number of neurons in the third plurality of neurons is the same as the number of neurons in the first plurality of neurons, (page 13 of Ng, “The autoencoder tries to learn a function hW,b(x) ≈ x. In other words, it is trying to learn an approximation to the identity function, so as to output ˆ x that is similar to x. The identity function seems a particularly trivial function to be trying to learn; but by placing constraints on the network, such as by limiting the number of hidden units, we can discover interesting structure about the data. As a concrete example, suppose the inputs x are the pixel intensity values from a 10 × 10 image (100 pixels) so n = 100, and there are s2 = 50 hidden units in layer L2. Note that we also have y ∈ R100. Since there are only 50 hidden units, the network is forced to learn a compressed representation of the input. I.e., given only the vector of hidden unit activations a(2) ∈ R50, it must try to reconstruct the 100-pixel input x.”) each connection between the neurons in the input layer, the compression layer, and the decoding layer comprising a weight; (page 4 of Ng, “We will let nl denote the number of layers in our network; thus nl = 3 in our example. We label layer l as Ll, so layer L1 is the input layer, and layer Lnl the output layer. Our neural network has parameters (W,b) = (W(1),b(1), W(2),b(2)), where we write W(l) ij to denote the parameter (or weight) associated with the connection between unit j in layer l, and unit i in layer l+1. (Note the order of the indices.)”) and
a processor that calculates a difference between the new <first type of data> in the input <first type of data> and for updating at least one of the weights. (page 6 of Ng, squared-error cost function is to calculate the lost between the input and the output and including weight decay parameter).
Ng does not disclose <first type of data> being “uncompressed data”, <second form> being “compressed form”.
However, Havlik discloses compress uncompressed data. (Summary of Havlik).
Ng and Havlik are analogous art because both references concern compress raw input data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ng’s autodecoder of input data with compress uncompressed data as taught by Havlik. The motivation for doing so would have been to reduce the size of data files to conserve memory, storage space, and to more efficiently utilize transmission bandwidth.
Referring to claim 2, Ng in view of Havlik disclose the autoencoder of claim 1, wherein the weights are back propagated through the artificial neural network. (pages 6-7, section 2.2 back propagation)
Referring to claim 3, Ng in view of Havlik disclose the autoencoder of claim 1, wherein the weights are back propagated through the artificial neural network to generate an encoding for the input uncompressed data for dimensionality reduction. (page 13 of Ng, “The autoencoder tries to learn a function hW,b(x) ≈ x. In other words, it is trying to learn an approximation to the identity function, so as to output ˆ x that is similar to x. The identity function seems a particularly trivial function to be trying to learn; but by placing constraints on the network, such as by limiting the number of hidden units, we can discover interesting structure about the data. As a concrete example, suppose the inputs x are the pixel intensity values from a 10 × 10 image (100 pixels) so n = 100, and there are s2 = 50 hidden units in layer L2. Note that we also have y ∈ R100. Since there are only 50 hidden units, the network is forced to learn a compressed representation of the input. I.e., given only the vector of hidden unit activations a(2) ∈ R50, it must try to reconstruct the 100-pixel input x.”)
Referring to claim 4, Ng in view of Havlik disclose the autoencoder of claim 1, wherein the compressed form comprises a file format including a header and a compressed data of the input uncompressed data. ([0020] of Havlik, compressed form includes header and compressed data)
Claims 5-11 are rejected under 35 U.S.C. 103 as being obvious over “Sparse autoencoder”, Ng, 2001 in view of Havlik et al (US 20180246645 A1) and in further view of Zuo et al (US 20100027686 A1).
Referring to claim 5, Ng in view of Havlik disclose the autoencoder of claim 4. Ng in view of Havlik do not specifically disclose wherein the header includes information about the compressed data such as a compression treatment applied to the uncompressed background, a number of identified objects in the compressed data, and object information for each of the identified objects.
However, Zuo discloses the header includes information about the compressed data such as a compression treatment applied to the uncompressed background, a number of identified objects in the compressed data, and object information for each of the identified objects (see 112 2nd rejection above, here, [0063] of Zuo, number of elements are found using cluster classifications)
Ng and Havlik and Zuo are analogous art because both references concern compress raw input data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ng’s autodecoder of input data with compress uncompressed data as taught by Havlik and classifying number of elements as taught by Zuo. The motivation for doing so would have been to easily locate elements based on classification of the image pixels.
Referring to claim 6, Ng in view of Havlik and Zuo disclose the autoencoder of claim 5, wherein contents of the object information comprise information regarding a type of object, a location of the compressed object, and the compression treatment applied to the object. ([0060]-[0063] of Zuo, number of elements are found using cluster classifications/types and location of the elements by pixel and [0076] of Zuo)
Referring to claim 7, Ng in view of Havlik and Zuo disclose the autoencoder of claim 6, wherein the location of the compressed object specifies the location of the compressed object in a resulting image. ([0060]-[0063] of Zuo, number of elements are found using cluster classifications/types and location of the elements by pixel and [0076] of Zuo)
Referring to claim 8, Ng in view of Havlik and Zuo disclose the autoencoder of claim 5, wherein the compression treatment specifies a type of decoder associated with a first compression treatment to be used to decompress the compressed object. ([0076] of Zuo, “For each cluster a set of coefficients may be selected that minimizes an averaged measure of difference between pixel values from stream input 10 and pixel values obtained by using the set of filter coefficients for post filtering the results of compression and decompression by compressor 12 and decompressor 14. The averaged measure of difference is taken averaged over pixel locations that are assigned to the same cluster. The mean square difference may be used for example, but any other measure may be used, such as a statistical measure of perceptual difference. Techniques for selecting sets of coefficients that minimize an averaged measure for a given set of pixels are known per se.”)
Referring to claim 9, Ng in view of Havlik and Zuo disclose the autoencoder of claim 5, wherein the compressed form includes a decoder associated with the compression treatment of the object contained in the object information and/or a decoder associated with the compression treatment applied to the uncompressed background. ([0078] of Havlik, “The general concept of the invention is based on_analyzing what kind of structure was present in the original image, and how the compressed version could be improved at the decoder side (e.g. typically looking more like the original; or in general have a higher image quality, e.g. sharpness) by applying a particular one of a set of image optimized/trained filters, the coefficients/parameters of said filters being added to the signal. Only limited extra data needs to be transmitted to specify those filters; and e.g. a couple of image locations on which some of them are applied.”)
Referring to claim 10, Ng in view of Havlik and Zuo disclose the autoencoder of claim 9, wherein the decoder appears before the header. ([0021] of Havlik, process decoding before header)
Referring to claim 11, Ng in view of Havlik and Zuo disclose the autoencoder of claim 9, wherein the decoder appears between the header and the compressed data. (Fig. 1A and [0022]-[0025] of Havlik, decoder in between header and compressed data)
Claims 12-17 are rejected under 35 U.S.C. 103 as being obvious over “Sparse autoencoder”, Ng, 2001 in view of Havlik et al (US 20180246645 A1) and in further view of Zuo et al (US 20100027686 A1) and in further view of Goddard (US 6430591 B1).
Referring to claim 12, Ng in view of Havlik and Zuo disclose the autoencoder of claim 9. Ng in view of Havlik and Zuo do not specifically disclose wherein the decoder is downloaded independent of the compressed form.
However, Goddard discloses wherein the decoder is downloaded independent of the compressed form. (col. 9, line 63-col. 10, line 2, decode a downloading GIF style image)
Ng and Havlik and Zuo and Goddard are analogous art because both references concern compress raw input data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ng’s autodecoder of input data with compress uncompressed data as taught by Havlik and classifying number of elements as taught by Zuo and downloadable compressed data as taught by Goddard. The motivation for doing so would have been to easily locate elements based on classification of the image pixels.
Referring to claim 13, Ng in view of Havlik and Zuo disclose the autoencoder of claim 9. Ng in view of Havlik and Zuo do not specifically disclose wherein the decoder is distributed with a decoder API.
However, Goddard discloses wherein the decoder is distributed with a decoder API. (col. 8, lines 20-31, decoder is an API such as Microsoft Window 95 URL Moniker API)
Ng and Havlik and Zuo and Goddard are analogous art because both references concern compress raw input data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ng’s autodecoder of input data with compress uncompressed data as taught by Havlik and classifying number of elements as taught by Zuo and downloadable compressed data as taught by Goddard. The motivation for doing so would have been to easily locate elements based on classification of the image pixels.
Referring to claim 14, Ng in view of Havlik and Zuo disclose the autoencoder of claim 9. Ng in view of Havlik and Zuo do not specifically disclose wherein the decoder is downloadable as a browser extension.
However, Goddard discloses wherein the decoder is downloadable as a browser extension. (col. 9, line 63-col. 10, line 2, decode a downloading GIF style image)
Ng and Havlik and Zuo and Goddard are analogous art because both references concern compress raw input data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ng’s autodecoder of input data with compress uncompressed data as taught by Havlik and classifying number of elements as taught by Zuo and downloadable compressed data as taught by Goddard. The motivation for doing so would have been to easily locate elements based on classification of the image pixels.
Referring to claim 15, Ng in view of Havlik and Zuo disclose the autoencoder of claim 9. Ng in view of Havlik and Zuo do not specifically disclose wherein the decoder is downloadable as part of an operating system.
However, Goddard discloses wherein the decoder is downloadable as part of an operating system. (col. 8, lines 20-31, decoder is an API such as Microsoft Window 95 URL Moniker API)
Ng and Havlik and Zuo and Goddard are analogous art because both references concern compress raw input data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ng’s autodecoder of input data with compress uncompressed data as taught by Havlik and classifying number of elements as taught by Zuo and downloadable compressed data as taught by Goddard. The motivation for doing so would have been to easily locate elements based on classification of the image pixels.
Referring to claim 16, Ng in view of Havlik and Zuo disclose the autoencoder of claim 9. Ng in view of Havlik and Zuo do not specifically disclose wherein the decoder is downloadable as a user application.
However, Goddard discloses wherein the decoder is downloadable as a user application. (col. 8, lines 20-31, decoder is an API such as Microsoft Window 95 URL Moniker API)
Ng and Havlik and Zuo and Goddard are analogous art because both references concern compress raw input data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ng’s autodecoder of input data with compress uncompressed data as taught by Havlik and classifying number of elements as taught by Zuo and downloadable compressed data as taught by Goddard. The motivation for doing so would have been to easily locate elements based on classification of the image pixels.
Referring to claim 17, Ng in view of Havlik and Zuo disclose the autoencoder of claim 9. Ng in view of Havlik and Zuo do not specifically disclose wherein the decoder includes a unique ID.
However, Goddard discloses wherein the decoder includes a unique ID. (col. 9, line 63-col. 10, line 29)
Ng and Havlik and Zuo and Goddard are analogous art because both references concern compress raw input data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Ng’s autodecoder of input data with compress uncompressed data as taught by Havlik and classifying number of elements as taught by Zuo and downloadable compressed data as taught by Goddard. The motivation for doing so would have been to easily locate elements based on classification of the image pixels.
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Klevin et al (US 5095374 A): A method and apparatus featuring the compression and decompression of the video image data on a single application specific integrated circuit (ASIC) which utilizes typical Huffman coding techniques, but which defines a run count generally as a plurality of zeros between each pair of ones and which further uses a prediction window having substantially no directional biasing errors associated therewith. Additionally, the compression/decompression apparatus of this invention utilizes a reordering technique whereby all of the "high confidence" predictions are placed in one end of the buffer and all the "low confidence" predictions are placed in another end of the same buffer. This reordering, together with the unbiased window configuration and associated run length count definition, allows for high efficiency lossless compression and decompression of associated image data.
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://;www.uspto.gov/patent/laws-and-regulations/interview-practice.
Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e- mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAIMEI JIANG whose telephone number is (571)270-1590. The examiner can normally be reached M-F 9-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela D Reyes can be reached at 571-270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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