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 .
Response to Arguments
Applicant’s arguments, see pg. 1, filed 5/25/2026, with respect to the objection to claim 16 have been fully considered and are persuasive. The objection of claim 16 has been withdrawn.
Applicant's arguments filed 5/25/2026 have been fully considered but they are not persuasive.
Regarding the 112B rejection of claim 16, Examiner agrees that the antecedent basis portion of the rejection has been resolved. However, Applicant has not addressed the second portion of the 112B rejection directed at claims 2 and 16. Therefore, the 112B rejection of claims 2 and 16 is maintained.
Applicant’s remaining arguments are directed at the previous 103 rejection of claim 8 (now amended to be included in claim 1). However, these arguments appear to be mere allegations that the combination of references would destroy functionality, without further explanation or reasoning as to why. Examiner agrees that Machireddy’s equation 8 clearly does not depend on any random seed or parameters intrinsic to the devices. However, examiner would like to point out that the generator matrix provided in Machireddy is merely an example and Machireddy is not relied on for any teaching of a random seed. There is no obvious reason to the examiner why using a random seed to generate the parameters of the parity section of the generator matrix (as disclosed by Ming), would result in any lost functionality or utility for Machireddy’s neural network matrix determination.
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.
Claims 2 and 16 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. Based on the amended limitation to claims 1 and 15: “the parameter of the first device and the parameter of the second device are respectively intrinsic to the first and second device,” it is unclear how exactly the third device has access to these parameters. Further, the Examiner is unsure why the third device would transmit an intrinsic parameter of the first device to the first device, as in claim 2, or why the third device would transmit an intrinsic parameter of the second device to the second device, as in claim 16. Appropriate correction and/or clarification is required.
Claims 1, 15, and 17 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.
Claims 1, 15, and 17 recite the limitation "the at least some parameters" in line 3 (claim 1), line 3 (claim 15), and line 6 (claim 17). There is insufficient antecedent basis for this limitation in the claim.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-5, 10-15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ming (WO 2021139751) in view of Guha (WO 2021181343) and Machireddy (“Guessing the Code: Learning Encoding Mappings Using the Back Propagation Algorithm”).
Regarding claim 1, Ming teaches:
A communication method, comprising:
determining a generator matrix, wherein … (para. 129: It can be learned from the foregoing content that the N coding sub-blocks are determined based on the original data block P and the coding matrix M. Therefore, during specific implementation, the target layer of the first communication device may first generate the first data block P and the coding matrix M according to the network coding parameter, and then generate the N coding sub-blocks of the first data block by using the first data block P and the coding matrix M.)
at least some parameters in the generator matrix are determined based on a random seed, (para. 175: The pseudo-random code seed may be used to generate N column vectors. In this way, the first communication device may generate the coding matrix M based on the generated N column vectors.)
…
performing, by the first device, channel encoding on first data based on the generator matrix to obtain second data; (para. 129: It can be learned from the foregoing content that the N coding sub-blocks are determined based on the original data block P and the coding matrix M.)
and sending, by the first device, the second data to the second device. (para. 12: Sending the N coding sub-blocks to a second communication device, where N is a positive integer.)
However, Ming does not explicitly teach wherein:
The generator matrix is a K*N matrix, K and N are positive integers, K is less than N, and a quantity of the at least some parameters is K*(N–K), K2 parameters in the K*N parameters of the generator matrix other than the at least some parameters form an identity matrix, and any two rows in the generator matrix are different.
the random seed is determined based on one or more of the following: the parameter of the first device or the parameter of the second device;
or the generator matrix is determined based on one or more of the following: the parameter of the first device or the parameter of the second device;
wherein the parameter of the first device and the parameter of the second device are respectively intrinsic to the first and second device.
In the analogous art of digital communication and pseudorandom seed generation, Guha teaches:
the random seed is determined based on one or more of the following: the parameter of the first device or the parameter of the second device; (para. 31 and fig. 3: Fig. 3 is a schematic diagram of generation of a random value by a system. The device 102a receives the application type selection and input data from a user. The coding module 104 receives random seeds 1 through 6, each from a different device, to generate a combined random seed. The combined random seed is used to generate a random number X, which the coding module provides to device 102a.) Based on fig. 3, it is clear that device 102a provides partial seed 6.
or the generator matrix is determined based on one or more of the following: the parameter of the first device or the parameter of the second device;
wherein the parameter of the first device and the parameter of the second device are respectively intrinsic to the first and second device. (para. 18: a partial seed may be generated using one or more parameters… the parameters can include, but is not limited to, the following: 1. ISA (Instruction Set Architecture) of the underlying device hardware (e.g. processor type). 2. Type of operating system running on the device (e.g. iOS, Android, Windows, Mac, etc.)…)
It would have been obvious to one of ordinary skill in the art, having the teachings of Ming and Guha before them before the effective filing date of the instant application to incorporate the pseudorandom seed generation taught by Guha into the system for encoding and transmitting data taught by Ming, to allow for benefits such as reliable, high-grade random number generation and protection against cyber-attacks (Guha, para. 15).
However, the combination of Ming and Guha does not explicitly teach wherein:
The generator matrix is a K*N matrix, K and N are positive integers, K is less than N, and a quantity of the at least some parameters is K*(N–K), K2 parameters in the K*N parameters of the generator matrix other than the at least some parameters form an identity matrix, and any two rows in the generator matrix are different.
However, Examiner would like to note that these limitations are taught by rewriting the encoding matrix in Ming (for example, the one in para. 164), in systematic form, which is exceedingly well known in the art and amounts to merely appending an identity matrix to the parity matrix. For the purpose of clarity of the record, a reference is provided showing how an encoding matrix can be rewritten in systematic form.
In the analogous art of encoding data Machireddy teaches:
The quantity of the at least some parameters is K*(N–K),
K2 parameters in the K*N parameters of the generator matrix other than the at least some parameters form an identity matrix,
and any two rows in the generator matrix are different.
(pg. 2, equations 5, 7, and 8. Equation 8 describes a generator matrix in systematic form. The rightmost 4 columns are an identity matrix, and so the leftmost 3 columns are the (12) free parameters. K=4, N=7, so K*(N-K) = 4(7-4) = 12. The K^2 (=16) cells in the rightmost 4 rows form an identity matrix. At least two rows in the generator matrix are different. Note that the matrix P in equation 7 corresponds to the form of the parity matrix in Ming, and the generator matrix G in equation 8 is the corresponding systematic matrix with an identity matrix appended.)
One of ordinary skill in the art would find it exceedingly clear that the parity check matrix in Ming can be rewritten as a generator matrix in systematic form (as shown by Machireddy), and that this would be an obvious alternative.
Regarding claim 3, Ming, Guha, and Machireddy teaches the method according to claim 1. Ming further teaches:
Wherein the at least some parameters in the generator matrix are determined based on the random seed. (para. 175: The pseudo-random code seed may be used to generate N column vectors. In this way, the first communication device may generate the coding matrix M based on the generated N column vectors.)
However, Ming does not explicitly teach:
Wherein the at least some parameters in the generator matrix are determined based on… a neural network.
In the analogous art of encoding data, Machireddy teaches:
Wherein the at least some parameters in the generator matrix are determined based on… a neural network. (pg. 7, left hand col., section C: The goal of this experiment is to form the parity check equations and in turn the generator matrix and parity check matrix based on the outputs of the [neural] network.)
Regarding claim 4, Ming, Guha, and Machireddy teaches the method according to claim 1. Ming further teaches:
wherein at least some parameters in the generator matrix are determined based on the random seed,
and the following relationship exists between the random seed and the parameter of the first device, a channel encoding parameter, and the parameter of the second device: seed=G[… pseudo-random seed ,f3(z)], wherein seed is the random seed, … z is the channel encoding parameter, G is a first function, … and f3 is a fourth function, and G varies with… f3 [and the pseudo-random seed]. (para. 154: That is, the pseudo-random code seed determines the column vector information corresponding to the coding sub-block based on the distribution of the degree of freedom d. And para. 132: The network coding parameter includes… a third parameter. And para. 137: the third parameter is used to determine a degree of freedom distribution.) The degree of freedom distribution is mapped to the channel encoding parameter of the claims. The column vector information is mapped to the random seed of the claims. This is because the column vector information is random (determined based on a pseudo-random seed) and further is a seed (is used to determine the generator matrix).
However, Ming does not explicitly disclose wherein:
[a random seed =]… f1(x), f2(y)… x is the parameter of the first device, y is the parameter of the second device… f1 is a second function, f2 is a third function, … and G varies with each of f1, f2…
Guha teaches:
[a random seed =]… f1(x), f2(y)… x is the parameter of the first device, y is the parameter of the second device… f1 is a second function, f2 is a third function, … and G varies with each of f1, f2… (para. 31 and fig. 3: Fig. 3 is a schematic diagram of generation of a random value by a system. The device 102a receives the application type selection and input data from a user. The coding module 104 receives random seeds 1 through 6, each from a different device, to generate a combined random seed. The combined random seed is used to generate a random number X, which the coding module provides to device 102a. And see para. 18: a partial seed may be generated using one or more parameters… the parameters can include, but is not limited to, the following: 1. ISA (Instruction Set Architecture) of the underlying device hardware (e.g. processor type). 2. Type of operating system running on the device (e.g. iOS, Android, Windows, Mac, etc.)… And see para. 25: A raw partial seed may be generated using one or more entropy sources available to the device 102. The raw partial seed may be modulated by one or more operator functions to generate the partial seed.) Ming teaches: a random seed (column vector information) is based on an initial random seed and a channel encoding parameter (third parameter, used to determine a degree of freedom distribution). Guha teaches: a random seed (the initial random seed of Ming) is generated based on a function applied to a parameter of first and second devices. Therefore the combination teaches generating column vector information (a random seed) based on a degree of freedom distribution (third channel encoding parameter), an intrinsic parameter of the first device, and an intrinsic parameter of the second device.
It would have been obvious to one of ordinary skill in the art, having the teachings of Ming and Guha before them before the effective filing date of the instant application to incorporate the pseudorandom seed generation taught by Guha into the system for encoding and transmitting data taught by Ming, to allow for benefits such as reliable, high-grade random number generation and protection against cyber-attacks (Guha, para. 15).
Regarding claim 5, Ming, Guha, and Machireddy teaches the method according to claim 1. Ming further teaches:
wherein the at least some parameters are determined by processing the random seed according to one or more of the following algorithms:
a square number algorithm
or a chaos algorithm (para. 159: During specific implementation, the first pseudo-random code seed may generate N values, and each value represents a value of a degree of freedom of a column vector; the second pseudo-random code seed may generate N groups of values, each group of values includes V values, the value of V is equal to the number of elements with a value of 1 in the column vector, and each of the V values represents a number of an element with a value of 1 in the column vector.)
Regarding claim 10, Ming, Guha, and Machireddy teaches the method according to claim 1. Guha teaches:
The parameter of the first device comprises one or more of the following:
An identifier of the first device (para. 31 and fig. 3: Fig. 3 is a schematic diagram of generation of a random value by a system. The device 102a receives the application type selection and input data from a user. The coding module 104 receives random seeds 1 through 6, each from a different device, to generate a combined random seed. The combined random seed is used to generate a random number X, which the coding module provides to device 102a. And see para. 18: a partial seed may be generated using one or more parameters… the parameters can include, but is not limited to, the following: 1. ISA (Instruction Set Architecture) of the underlying device hardware (e.g. processor type). 2. Type of operating system running on the device (e.g. iOS, Android, Windows, Mac, etc.)) Specific information about the device (type of OS and hardware) is considered to be a device identifier.
or an address of the first device
It would have been obvious to one of ordinary skill in the art, having the teachings of Ming and Guha before them before the effective filing date of the instant application to incorporate the pseudorandom seed generation taught by Guha into the system for encoding and transmitting data taught by Ming, to allow for benefits such as reliable, high-grade random number generation and protection against cyber-attacks (Guha, para. 15).
Regarding claim 11, Ming, Guha, and Machireddy teaches the method according to claim 1. Guha teaches:
The parameter of the second device comprises one or more of the following:
An identifier of the second device (para. 31 and fig. 3: Fig. 3 is a schematic diagram of generation of a random value by a system. The device 102a receives the application type selection and input data from a user. The coding module 104 receives random seeds 1 through 6, each from a different device, to generate a combined random seed. The combined random seed is used to generate a random number X, which the coding module provides to device 102a. And see para. 18: a partial seed may be generated using one or more parameters… the parameters can include, but is not limited to, the following: 1. ISA (Instruction Set Architecture) of the underlying device hardware (e.g. processor type). 2. Type of operating system running on the device (e.g. iOS, Android, Windows, Mac, etc.)) Specific information about the device (type of OS and hardware) is considered to be a device identifier.
or an address of the second device
It would have been obvious to one of ordinary skill in the art, having the teachings of Ming and Guha before them before the effective filing date of the instant application to incorporate the pseudorandom seed generation taught by Guha into the system for encoding and transmitting data taught by Ming, to allow for benefits such as reliable, high-grade random number generation and protection against cyber-attacks (Guha, para. 15).
Regarding claim 12, Ming, Guha, and Machireddy teaches the method according to claim 4. Ming further teaches:
wherein the channel encoding parameter comprises one or more of the following items for channel encoding:
a length of the first data,
an encoding length, (para. 132: The network coding parameter includes at least one of the following: … a second parameter. And para. 136: The second parameter is used to determine a value of N. And para. 129: The N coding sub-blocks are determined based on the original data block P and the coding matrix M. And para. 159: the first pseudo-random code seed may generate N values, and each value represents a value of a degree of freedom of a column vector.)
or an encoding rate.
Regarding claim 13, Ming, Guha, and Machireddy teaches the method according to claim 12. Ming further teaches:
wherein the encoding length is greater than or equal to 16 bits,
or the encoding length is less than or equal to 128 bits. (para. 129: The N coding sub-blocks are determined based on the original data block P and the coding matrix M.) While Ming does not disclose an explicit value of an encoding length, N is an encoding length. Any encoding length teaches the limitations of claim 13.
Regarding claim 14, Ming, Guha, and Machireddy teaches the method according to claim 12. Ming further teaches:
wherein the encoding rate is greater than or equal 1/5, or the encoding rate is less than or equal to 4/5. (para. 167: Shows the matrix multiplication to encode the data.) While Ming does not disclose an explicit value of an encoding rate, there must be some encoding rate associated with this operation. Any encoding rate teaches the limitations of claim 14.
Regarding claim 15, Ming teaches:
A communication method, comprising:
determining a generator matrix, wherein: … (para. 129: It can be learned from the foregoing content that the N coding sub-blocks are determined based on the original data block P and the coding matrix M. Therefore, during specific implementation, the target layer of the first communication device may first generate the first data block P and the coding matrix M according to the network coding parameter, and then generate the N coding sub-blocks of the first data block by using the first data block P and the coding matrix M.)
at least some parameters in the generator matrix are determined based on a random seed, (para. 175: The pseudo-random code seed may be used to generate N column vectors. In this way, the first communication device may generate the coding matrix M based on the generated N column vectors.)
…
Receiving, by a second device, second data from the first device (para. 12: Sending the N coding sub-blocks to a second communication device, where N is a positive integer.)
and decoding, by the second device, the second data based on the generator matrix to obtain first data. (claim 18: A data processing method is applied to a second communication device, and the method includes: in a case that M coding sub-blocks corresponding to a first data block are received, obtaining M column vectors corresponding to the M coding sub-blocks, where each column vector includes K elements, K is a number of segmentation parts of the first data block, and M is a positive integer less than or equal to a number N of coding sub-blocks generated based on the first data block; generating a first matrix, where the first matrix includes the M column vectors; and restoring the first data block according to the first matrix and the M coding sub-blocks in a case that the first matrix is a row full rank matrix.)
However, Ming does not explicitly teach wherein:
The generator matrix is a K*N matrix, K and N are positive integers, K is less than N, and a quantity of the at least some parameters is K*(N–K), K2 parameters in the K*N parameters of the generator matrix other than the at least some parameters form an identity matrix, and any two rows in the generator matrix are different.
the random seed is determined based on one or more of the following: the parameter of the first device or the parameter of the second device; (
or the generator matrix is determined based on one or more of the following: the parameter of the first device or the parameter of the second device;
wherein the parameter of the first device and the parameter of the second device are respectively intrinsic to the first and second device.
In the analogous art of digital communication and pseudorandom seed generation, Guha teaches:
the random seed is determined based on one or more of the following: the parameter of the first device or the parameter of the second device; (para. 31 and fig. 3: Fig. 3 is a schematic diagram of generation of a random value by a system. The device 102a receives the application type selection and input data from a user. The coding module 104 receives random seeds 1 through 6, each from a different device, to generate a combined random seed. The combined random seed is used to generate a random number X, which the coding module provides to device 102a.) Based on fig. 3, it is clear that device 102a provides partial seed 6.
or the generator matrix is determined based on one or more of the following: the parameter of the first device or the parameter of the second device;
wherein the parameter of the first device and the parameter of the second device are respectively intrinsic to the first and second device. (para. 18: a partial seed may be generated using one or more parameters… the parameters can include, but is not limited to, the following: 1. ISA (Instruction Set Architecture) of the underlying device hardware (e.g. processor type). 2. Type of operating system running on the device (e.g. iOS, Android, Windows, Mac, etc.)…)
It would have been obvious to one of ordinary skill in the art, having the teachings of Ming and Guha before them before the effective filing date of the instant application to incorporate the pseudorandom seed generation taught by Guha into the system for encoding and transmitting data taught by Ming, to allow for benefits such as reliable, high-grade random number generation and protection against cyber-attacks (Guha, para. 15).
However, the combination of Ming and Guha does not explicitly teach wherein:
The generator matrix is a K*N matrix, K and N are positive integers, K is less than N, and a quantity of the at least some parameters is K*(N–K), K2 parameters in the K*N parameters of the generator matrix other than the at least some parameters form an identity matrix, and any two rows in the generator matrix are different.
However, Examiner would like to note that these limitations are taught by rewriting the encoding matrix in Ming (for example, the one in para. 164), in systematic form, which is exceedingly well known in the art and amounts to merely appending an identity matrix to the parity matrix. For the purpose of clarity of the record, a reference is provided showing how an encoding matrix can be rewritten in systematic form.
In the analogous art of encoding data Machireddy teaches:
The quantity of the at least some parameters is K*(N–K),
K2 parameters in the K*N parameters of the generator matrix other than the at least some parameters form an identity matrix,
and any two rows in the generator matrix are different.
(pg. 2, equations 5, 7, and 8. Equation 8 describes a generator matrix in systematic form. The rightmost 4 columns are an identity matrix, and so the leftmost 3 columns are the (12) free parameters. K=4, N=7, so K*(N-K) = 4(7-4) = 12. The K^2 (=16) cells in the rightmost 4 rows form an identity matrix. At least two rows in the generator matrix are different. Note that the matrix P in equation 7 corresponds to the form of the parity matrix in Ming, and the generator matrix G in equation 8 is the corresponding systematic matrix with an identity matrix appended.)
One of ordinary skill in the art would find it exceedingly clear that the parity check matrix in Ming can be rewritten as a generator matrix in systematic form (as shown by Machireddy), and that this would be an obvious alternative.
Claims 17, 19, and 20 correspond to claims 1, 3, and 4 (respectively), and are rejected accordingly.
Claims 2, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ming in view of Guha, Machireddy, and Matsumoto (US 20100211846).
Regarding claim 2, the combination of Ming, Guha, and Machireddy teaches the method according to claim 1. However, the combination of Ming, Guha, and Machireddy does not explicitly disclose wherein:
before the performing, by the first device, channel encoding on first data based on the generator matrix to obtain second data, the method further comprises:
receiving, by the first device, configuration information from a third device, wherein the configuration information comprises one or more of the following:
the generator matrix,
the parameter of the first device,
or the parameter of the second device.
In the analogous art of digital communications, Matsumoto teaches:
before the performing, by the first device, channel encoding on first data based on the generator matrix to obtain second data, the method further comprises:
receiving, by the first device, configuration information from a third device, wherein the configuration information comprises one or more of the following:
the generator matrix, (fig. 2, check matrix generating device 30 provides parity check matrix Hm to codeword generating unit 35, so that the message may be encoded.)
the parameter of the first device,
or the parameter of the second device.
It would have been obvious, to one of ordinary skill in the art, having the teachings of Ming, Guha, Machireddy, and Matsumoto before them before the effective filing date of the claimed invention, to incorporate a separate device for generating a generator matrix that provides the generator matrix to the device for encoding and transmission of data (Matsumoto) into the method of encoding taught by Ming and Guha, to allow for benefits such as: being able to easily generate a encoding matrix having good performance and regularity (Matsumoto, para. 32).
Regarding claim 16, the combination of Ming, Guha, and Machireddy teaches the method according to claim 15. However, the combination of Ming, Guha, and Machireddy does not explicitly disclose wherein:
before the decoding, receiving, by the second device, configuration information from the third device, wherein the configuration information comprises one or more of the following:
the generator matrix,
the parameter of the first device,
or the parameter of the second device.
In the analogous art of digital communications, Matsumoto teaches:
before the decoding, receiving, by the second device, configuration information from the third device, wherein the configuration information comprises one or more of the following:
the generator matrix, (fig. 3, check matrix generating device 30 provides parity check matrix Hm to message decoding unit 41, so that the message may be decoded.)
the parameter of the first device,
or the parameter of the second device.
It would have been obvious, to one of ordinary skill in the art, having the teachings of Ming, Guha, Machireddy, and Matsumoto before them before the effective filing date of the claimed invention, to incorporate a separate device for generating a generator matrix that provides the generator matrix to the device for encoding and transmission of data (Matsumoto) into the method of encoding taught by Ming, Guha, and Machireddy, to allow for benefits such as: being able to easily generate a encoding matrix having good performance and regularity (Matsumoto, para. 32).
Claim 18 corresponds to claim 2, and is rejected accordingly.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JACK KENSINGTON BARNETT/ Examiner, Art Unit 2111
/MARK D FEATHERSTONE/ Supervisory Patent Examiner, Art Unit 2111
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