DETAILED ACTION
This office action is in response to the preliminary amendments filed on 10/23/2021.
Claims 14 and 15 are cancelled.
Claims 1-13, and 16-22 are pending.
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
The information disclosure statements (IDS) submitted on 10/23/2021, 03/01/2024, and 04/05/2024 are in compliance with the provisions of 37 CFR 1.97 and have been considered by the examiner.
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.
Claims 5 and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 for omission of essential elements.
Regarding claims 5 and 16, the present claims are rejected under 35 U.S.C. 112(b), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted element is the supporting information in the present claim to reflect the details noted in the specification regarding the use of the claimed “syntax” for applicant’s invention. Examiner notes that the search for the present claims has been performed under the interpretation of the claims as to the best of the examiners ability in their current state. Said interpretation by examiner of the present claims used for search purposes is: “said information is included as syntax for decoding said parameters and said vector information in said bitstream”.
Claims 9-11 and 20-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 for omission of structural relationship.
Regarding claims 9-11 and 20-22, the present claims are rejected under 35 U.S.C. 112(b), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01. The omitted structural cooperative relationships are the relationship between the claimed statements of the “wherein Toeplitz(/Hankel-like/Vandermonde) operators are used” and their corresponding use in the respective independents “obtaining parameters characterizing a matrix operator”. The claim language leaves the relationship unclear between obtaining parameters characterizing said operators in the respective independent claims, and then using said operators in the present.
Examiner notes the claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors. It is examiner’s hope that, upon review of an updated set of claims, examiner can provide a more complete search of the above claims.
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 1-2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Amir (US 20160275395 A1), hereafter Amir, in view of Luo (US 20150055783 A1), hereafter Luo, further in view of Thiagarajan (US 20180084279 A1), hereafter Thiagarajan.
Regarding claim 1, Amir teaches:
A method, comprising: obtaining information representative of a displacement rank of a deep neural network (Amir [0119] teaches a method and system for neural network structure assessment, which, in one embodiment, a metadata unit analyzes the metadata of neural network component matrices to assess their association with Toeplitz patterns. Examiner notes that the displacement rank of a matrix corresponds to the matrix’s displacement from a Toeplitz pattern ).
Obtaining vector information representative of weights of matrices for the deep neural network (Amir [0107] teaches the metadata can include parameters such as properties of the neural network matrices, which in Amir [0034] are described to comprise synaptic weights).
Obtaining parameters characterizing a matrix operator for the deep neural network (Amir [0107-0108] teaches the metadata obtained for the neural network (as taught by Amir [0119] above) can comprise matrix mapping (i.e. a matrix operation) method information for a neural network’s sub-matrices).
Including in a dataset said information representative of the displacement rank and information of parameters characterizing a matrix operator; and transmitting said dataset (Amir [0119] teaches a library indexing process for the above described metadata, which comprises obtaining information about displacement rank and parameters characterizing matrix mapping, and then receiving (i.e. transmitting from the library) the metadata datasets).
Amir does not explicitly teach obtaining vector information representative of weights and non-linearities of matrices for the deep neural network; including in a bitstream said information representative of the displacement rank, vector information of non-linearities, and information of parameters characterizing a matrix operator; and transmitting said bitstream. Amir only goes so far as to teach obtaining vector information representative of weights of matrices for the deep neural network, lacking explicit mention of non-linearities.
However, Luo teaches obtaining vector information representative of non-linearities of matrices for the deep neural network (Luo [0078-0079] teaches a method of autoencoder usage in which non-linear feature spaces (i.e. non-linear matrices) are identified and learned. Examiner notes that vectors can be considered a type of matrix, and thus examiner equates “matrix” as taught to “vector” as claimed).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir and Luo before them, to include Luo’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as non-linearities in matrices are an important aspect for optimizing network compression operations (see Luo [0078-0079]).
Amir in view of Luo does not explicitly teach including in a bitstream said information representative of the displacement rank, vector information of non-linearities, and information of parameters characterizing a matrix operator; and transmitting said bitstream. Amir in view of Luo only goes so far as to teach including in a dataset said information representative of the displacement rank, vector information of non-linearities, and information of parameters characterizing a matrix operator; and transmitting said dataset, lacking explicit mention of bitstreams, rather only mentioning datasets.
However, Thiagarajan teaches including in a bitstream said matrix information; and transmitting said bitstream (Thiagarajan [0086] teaches the inclusion of quantization matrices in a bitstream for transmission).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, and Thiagarajan before them, to include Thiagarajan’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as bitstream style compressions are a standard compression format in the art (see Thiagarajan [0021]).
Regarding claim 2, Amir teaches:
An apparatus, comprising: a processor, configured to perform: obtaining information representative of a displacement rank of a deep neural network (Amir [0119] teaches a method and system for neural network structure assessment, which, in one embodiment, a metadata unit analyzes the metadata of neural network component matrices to assess their association with Toeplitz patterns. Amir [0120] teaches a system with a processor. Examiner notes that the displacement rank of a matrix corresponds to the matrix’s displacement from a Toeplitz pattern).
Obtaining vector information representative of weights of matrices for the deep neural network (Amir [0107] teaches the metadata can include parameters such as properties of the neural network matrices, which in Amir [0034] are described to comprise synaptic weights).
Obtaining parameters characterizing a matrix operator for the deep neural network (Amir [0107-0108] teaches the metadata obtained for the neural network (as taught by Amir [0119] above) can comprise matrix mapping (i.e. a matrix operation) method information for a neural network’s sub-matrices).
Including in a dataset said information representative of the displacement rank and information of parameters characterizing a matrix operator; and transmitting said dataset (Amir [0119] teaches a library indexing process for the above described metadata, which comprises obtaining information about displacement rank and parameters characterizing matrix mapping, and then receiving (i.e. transmitting from the library) the metadata datasets).
Amir does not explicitly teach obtaining vector information representative of weights and non-linearities of matrices for the deep neural network; including in a bitstream said information representative of the displacement rank, vector information of non-linearities, and information of parameters characterizing a matrix operator; and transmitting said bitstream. Amir only goes so far as to teach obtaining vector information representative of weights of matrices for the deep neural network, lacking explicit mention of non-linearities.
However, Luo teaches obtaining vector information representative of non-linearities of matrices for the deep neural network (Luo [0078-0079] teaches a method of autoencoder usage in which non-linear feature spaces (i.e. non-linear matrices) are identified and learned).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir and Luo before them, to include Luo’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as non-linearities in matrices are an important aspect for optimizing network compression operations (see Luo [0078-0079]).
Amir in view of Luo does not explicitly teach including in a bitstream said information representative of the displacement rank, vector information of non-linearities, and information of parameters characterizing a matrix operator; and transmitting said bitstream. Amir in view of Luo only goes so far as to teach including in a dataset said information representative of the displacement rank, vector information of non-linearities, and information of parameters characterizing a matrix operator; and transmitting said dataset, lacking explicit mention of bitstreams, rather only mentioning datasets.
However, Thiagarajan teaches including in a bitstream said matrix information; and transmitting said bitstream (Thiagarajan [0086] teaches the inclusion of quantization matrices in a bitstream for transmission).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, and Thiagarajan before them, to include Thiagarajan’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as bitstream style compressions are a standard compression format in the art (see Thiagarajan [0021]).
Regarding claim 13, Amir in view of Luo, further in view of Thiagarajan teaches the elements of claim 1 as outlined above. Amir in view of Luo, further in view of Thiagarajan also teaches:
A non-transitory computer readable medium containing data content generated according to the method of claim 1 for playback using a processor (Amir [0120] teaches a processor, and Amir [0127] teaches a computer readable medium including physical storage devices).
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Amir in view of Luo, further in view of Thiagarajan and Pool (US 20190081637 A1), hereafter Pool.
Regarding claim 3, Amir teaches:
Using said information to generate rank vectors representative of weights of said deep neural network (Amir [0107] teaches the metadata can include parameters such as properties of the neural network matrices, which in Amir [0034] are described to comprise synaptic weights. Examiner notes that these parameters are in matrix format, which can be equated to vectors).
Obtaining said rank vectors to obtain weights information for said deep neural network (As discussed above, Amir [0107] teaches the metadata can include parameters such as properties of the neural network matrices).
Amir does not explicitly teach a method, comprising: parsing a bitstream for information representative of a layer of a deep neural network; using said information to generate rank vectors representative of weights and non-linearities of said deep neural network; Decoding said rank vectors to obtain weights and non-linearities information for said deep neural network. Amir only goes so far as to teach using said information to generate rank vectors representative of weights of said deep neural network; Obtaining said rank vectors to obtain weights and non-linearities information for said deep neural network, lacking explicit mention of non-linearities.
However, Luo teaches using said information representative of weights and non-linearities (Luo [0078-0079] teaches a method of autoencoder usage in which non-linear feature spaces (i.e. non-linear matrices) are identified and learned).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir and Luo before them, to include Luo’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as non-linearities in matrices are an important aspect for optimizing network compression operations (see Luo [0078-0079]).
Amir in view of Luo does not explicitly teach a method, comprising: parsing a bitstream for information representative of a layer of a deep neural network; Decoding said rank vectors to obtain weights and non-linearities information for said deep neural network. Amir in view of Luo only goes so far as to teach a method, comprising: obtaining a dataset for information representative of a layer of a deep neural network; Decoding said rank vectors to obtain weights and non-linearities information for said deep neural network, lacking explicit mention of bitstreams, rather only mentioning datasets.
However, Thiagarajan teaches obtaining a bitstream (Thiagarajan [0086] teaches the inclusion of quantization matrices in a bitstream for transmission).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, and Thiagarajan before them, to include Thiagarajan’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as bitstream style compressions are a standard compression format in the art (see Thiagarajan [0021]).
Amir in view of Luo, further in view of Thiagarajan does not explicitly teach a method, comprising: parsing a bitstream for information representative of a layer of a deep neural network; Decoding said rank vectors to obtain weights and non-linearities information for said deep neural network. Amir in view of Luo only goes so far as to teach obtaining said rank vectors to obtain weights and non-linearities information for said deep neural network, lacking explicit mention of a parsing and decoding process.
However, Pool teaches a method, comprising: parsing a bitstream for information representative of a layer of a deep neural network; Decoding data to obtain weight information for said deep neural network.
A method, comprising: parsing a bitstream for information representative of a layer of a deep neural network (Pool [0071] teaches a method of parsing a dataset pertaining to a compressed neural network).
Decoding data to obtain weight information for said deep neural network (Pool [0134] teaches decoding a dataset corresponding to a compressed neural network).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, Thiagarajan, and Pool before them, to include Pool’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as the compression process standard to the art involves a decoding and parsing process (see Pool [0022]).
Regarding claim 4, Amir teaches:
Using said information to generate rank vectors representative of weights of said deep neural network (Amir [0107] teaches the metadata can include parameters such as properties of the neural network matrices, which in Amir [0034] are described to comprise synaptic weights. Examiner notes that these parameters are in matrix format, which can be equated to vectors).
Obtaining said rank vectors to obtain weights information for said deep neural network (As discussed above, Amir [0107] teaches the metadata can include parameters such as properties of the neural network matrices).
Amir does not explicitly teach a method, comprising: parsing a bitstream for information representative of a layer of a deep neural network; using said information to generate rank vectors representative of weights and non-linearities of said deep neural network; Decoding said rank vectors to obtain weights and non-linearities information for said deep neural network. Amir only goes so far as to teach using said information to generate rank vectors representative of weights of said deep neural network; Obtaining said rank vectors to obtain weights and non-linearities information for said deep neural network, lacking explicit mention of non-linearities.
However, Luo teaches using said information representative of weights and non-linearities (Luo [0078-0079] teaches a method of autoencoder usage in which non-linear feature spaces (i.e. non-linear matrices) are identified and learned).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir and Luo before them, to include Luo’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as non-linearities in matrices are an important aspect for optimizing network compression operations (see Luo [0078-0079]).
Amir in view of Luo does not explicitly teach a method, comprising: parsing a bitstream for information representative of a layer of a deep neural network; Decoding said rank vectors to obtain weights and non-linearities information for said deep neural network. Amir in view of Luo only goes so far as to teach a method, comprising: obtaining a dataset for information representative of a layer of a deep neural network; Decoding said rank vectors to obtain weights and non-linearities information for said deep neural network, lacking explicit mention of bitstreams, rather only mentioning datasets.
However, Thiagarajan teaches obtaining a bitstream (Thiagarajan [0086] teaches the inclusion of quantization matrices in a bitstream for transmission).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, and Thiagarajan before them, to include Thiagarajan’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as bitstream style compressions are a standard compression format in the art (see Thiagarajan [0021]).
Amir in view of Luo, further in view of Thiagarajan does not explicitly teach an apparatus, comprising: a processor, configured to perform: parsing a bitstream for information representative of a layer of a deep neural network; Decoding said rank vectors to obtain weights and non-linearities information for said deep neural network. Amir in view of Luo only goes so far as to teach obtaining said rank vectors to obtain weights and non-linearities information for said deep neural network, lacking explicit mention of a parsing and decoding process.
However, Pool teaches An apparatus, comprising: a processor, configured to perform: parsing a bitstream for information representative of a layer of a deep neural network; Decoding data to obtain weight information for said deep neural network.
An apparatus, comprising: a processor, configured to perform: parsing a bitstream for information representative of a layer of a deep neural network (Pool [0071] teaches a method of parsing a dataset pertaining to a compressed neural network).
Decoding data to obtain weight information for said deep neural network (Pool [0134] teaches decoding a dataset corresponding to a compressed neural network).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, Thiagarajan, and Pool before them, to include Pool’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as the compression process standard to the art involves a decoding and parsing process (see Pool [0022]).
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Amir in view of Luo, further in view of Thiagarajan and Minezawa (US 20200184318 A1), hereafter Minezawa.
Regarding claim 5, Amir in view of Luo, further in view of Thiagarajan teaches the elements of claim 1 as outlined above. Amir in view of Luo, further in view of Thiagarajan does not explicitly teach:
Said information is included as syntax in said bitstream.
However, Minezawa teaches said information is included as syntax in said bitstream (Minezawa [0051] teaches a decompression process of compressed neural network data, and Minezawa [0131-0133, 0147] teaches the syntax used for the decompression process).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, Thiagarajan, and Minezawa before them, to include Minezawa’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as a coding and decoding syntax is a commonly used technique for compression techniques (see Minezawa [0028-0030, Fig 13-15]).
Regarding claim 16, the present claim recites similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Amir in view of Luo, further in view of Thiagarajan and Sainath (US 20170076196 A1), hereafter Sainath.
Regarding claim 6, Amir in view of Luo, further in view of Thiagarajan teaches the elements of claim 1 as outlined above. Amir in view of Luo, further in view of Thiagarajan does not explicitly teach:
Said matrix is limited to Low Displacement Rank matrices.
However, Sainath teaches said matrix is limited to Low Displacement Rank matrices (Sainath [0035] teaches discusses a recurrent neural network compression method which involves control of complexity of the neural network matrices using displacement rank, which can include tuning displacement rank to be low).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, Thiagarajan, and Sainath before them, to include Sainath’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as a low displacement rank matrices are highly structured, which can be useful in effective compression schemes (see Sainath [0035]).
Regarding claim 17, the present claim recites similar limitations as corresponding claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Claims 7-8 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Amir in view of Luo, further in view of Thiagarajan, Sainath, and Minezawa.
Regarding claim 7, Amir in view of Luo, further in view of Thiagarajan teaches the elements of claim 1 as outlined above. Amir in view of Luo, further in view of Thiagarajan also teaches:
Parameters indicative of circulant parameters in the bitstream and a Low Displacement Rank structure (Sainath [0035] teaches control of, and thereby examiner notes, control over indicative parameters for, both circulant matrices and low displacement rank matrices).
Amir in view of Luo, further in view of Thiagarajan and Sainath does not explicitly teach a flag in said bitstream indicates parameters.
However, Minezawa teaches a flag in said bitstream indicates parameters (Minezawa [0147] teaches a flag system for indication of neural network parameters in a compression/decompression process).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, Thiagarajan, Sainath, and Minezawa before them, to include Minezawa’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as incorporating indicative flags is a commonly used technique for compression and decompression methods (see Minezawa [0131-0134]).
Regarding claim 8, Amir in view of Luo, further in view of Thiagarajan teaches the elements of claim 7 as outlined above. Amir in view of Luo, further in view of Thiagarajan also teaches:
Particular values of said circulant parameters indicate use of low rank approximations (as discussed in the analysis of claim 7, Sainath [0035] teaches a method of controlling neural network compression based on circulant parameters that are indicative of low displacement rank matrices).
Regarding claim 18, the present claim recites similar limitations as corresponding claim 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale.
Regarding claim 19, the present claim recites similar limitations as corresponding claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale.
Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Amir in view of Luo, further in view of Thiagarajan and Brockway (US 20160022164 A1), hereafter Brockway.
Regarding claim 9, Amir in view of Luo, further in view of Thiagarajan teaches the elements of claim 1 as outlined above. Amir in view of Luo, further in view of Thiagarajan does not explicitly teach:
Toeplitz operators are used.
However, Brockway teaches Toeplitz operators are used (Brockway [0094] teaches the use of an operator matrix in the form of a Toeplitz matrix).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, Thiagarajan, and Brockway before them, to include Brockway’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as Toeplitz operators are useful in terms of transforming complex neural network matrices (See Brockway [0094-0095]).
Regarding claim 20, the present claim recites similar limitations as corresponding claim 9 and is rejected for similar reasons as claim 9 using similar teachings and rationale.
Claims 10 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Amir in view of Luo, further in view of Thiagarajan and Ye (US 20190371018 A1), hereafter Ye.
Regarding claim 10, Amir in view of Luo, further in view of Thiagarajan teaches the elements of claim 1 as outlined above. Amir in view of Luo, further in view of Thiagarajan does not explicitly teach:
Hankel-like operators are used.
However, Ye teaches Hankel-like operators are used (Ye [0039] teaches the use of Hankel matrix operators in convolutional operations of a neural network).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, Thiagarajan, and Ye before them, to include Ye’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as Hankel-like operators are useful in terms of transforming and denoising complex neural network matrices (See Ye [0059-0072]).
Regarding claim 21, the present claim recites similar limitations as corresponding claim 10 and is rejected for similar reasons as claim 10 using similar teachings and rationale.
Claims 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Amir in view of Luo, further in view of Thiagarajan and Ribeiro (US 20190066187 A1), hereafter Ribeiro.
Regarding claim 11, Amir in view of Luo, further in view of Thiagarajan teaches the elements of claim 1 as outlined above. Amir in view of Luo, further in view of Thiagarajan does not explicitly teach:
Vandermonde operators are used.
However, Ribeiro teaches Vandermonde operators are used (Ribeiro [0067-0068] teaches the use of the Vandermonde matrix in matrix operations).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, Thiagarajan, and Ribeiro before them, to include Ribeiro’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as Vandermonde operators are useful in terms of transforming complex neural network matrices (See Ribeiro [0067-0070]).
Regarding claim 22, the present claim recites similar limitations as corresponding claim 11 and is rejected for similar reasons as claim 11 using similar teachings and rationale.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Amir in view of Luo, further in view of Thiagarajan, Pool, Ulaganathan (US 10771807 B1), hereafter Ulaganathan.
Regarding claim 12, Amir in view of Luo, further in view of Thiagarajan teaches the elements of claim 4 as outlined above. Amir in view of Luo, further in view of Thiagarajan does not explicitly teach:
At least one of (i) an antenna configured to receive a signal, the signal including the video block, (ii) a band limiter configured to limit the received signal to a band of frequencies that includes the video block, and (iii) a display configured to display an output representative of a video block.
However, Ulaganathan teaches at least one of (i) an antenna configured to receive a signal, the signal including the video block, (ii) a band limiter configured to limit the received signal to a band of frequencies that includes the video block, and (iii) a display configured to display an output representative of a video block (Ulaganathan [C4 L20:37] teaches a video receiving device including antennas to receive signals, which in Ulaganathan [C4 L56:67, C5 L1-10] teaches the video is segmented into blocks. Examiner notes that, since ability of the invention of Ulaganathan to both receive video data as well as work with data blocks, it is feasible that the invention of Ulaganathan would be capable of receiving video blocks. Examiner also notes that the language used in the claim of “at least one of (i) an antenna configured to receive a signal, the signal including the video block, (ii) a band limiter configured to limit the received signal to a band of frequencies that includes the video block, and (iii) a display configured to display an output representative of a video block” implies optional or contingent limitations that are thus not all required for the scope of the claim).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Amir, Luo, Thiagarajan, Pool, and Ulaganathan before them, to include Ulaganathan’s specific feature/module in the system of Amir performing neural network matrices assessment and transformation. One would have been motivated to make such a combination, as sending and receiving of compressed video blocks is advantageous for memory management purposes (see Ulaganathan [C1 L17:37]).
Conclusion
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/M.G.C./Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144