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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 6-9, 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jamali (US Pub 20240256864) in view of Soltani Bidgoli (herein after Sol) (US Pub 20230178199) and Ye (US Pub 20240104392).
Re claims 1 and 17, Jamali discloses a wireless communication device and the associated method, comprising:
a processing system (Par 47, 67) that includes one or more processors (Par 47, 67) and one or more memories (Par 47, 67) coupled with the one or more processors (Par 47, 67), the processing system configured to cause the wireless communication device (Par 47, 67) to:
obtain a message vector (Par 35, 40-length k sequence of information bits);
generate a set of message sub-vectors using the message vector (Fig 1 input to 102, Fig 2 el 108; Par 35-36, 40 – U reshaped into matrix, each row is a sub-vector);
generate a set of message embedding vectors corresponding to the set of message sub-vectors (Figures 1 and 2 el 102; Par 35, 40, 47, 50-51, the first linear layer of FCNN 102 generates a set of message embedding vectors);
generate, using a set of layers, a set of codeword embedding vectors corresponding to the set of message embedding vectors (Figures 1 and 2 el 102; Par 34-35, 47, using the second and more hidden layers, generate a set of codeword embedding vectors);
generate, using the set of codeword embedding vectors (Fig 1 input from 102 to 103; Par 34-35, 47), a set of codeword sub-vectors (Figures 1 and 2 el 103; Par 34-35, 47, the linear layer of FCNN 103 generates a set of codeword sub-vectors), wherein the set of codeword sub-vectors is relative to the set of message sub-vectors (Figures 1 and 2 el 103; Par 34-35, 47, the linear layer of FCNN 103 generates a set of codeword sub-vectors);
combine the set of codeword sub-vectors to generate a codeword vector (Fig 1 output of 103; Par 34-35, “The encoder FCNNs 102, 103 map the information bits u to a length-n sequence of coded symbols c=E(u), known as a codeword”); and
transmit a communication carrying the codeword vector (Figures 1 and 2 el 200- channel; Par 20, 34-36); however, Jamali fails to explicitly disclose (1) wherein the set of layers comprises a set of self-attention layers of an artificial neural network; and (2) wherein the layer output values are permutation equivariant relative other layer output values.
Regarding item (1) above, this design is however disclosed by Sol. Sol discloses wherein the set of layers (Par 86, 91) comprises a set of self-attention layers (Par 86, 91) of an artificial neural network (Par 86, 91).
Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify the disclosure of Jamali in order to incorporate the attention layers of Sol based on the rationale of the use of a known technique to improve similar designs in the same way, in this instance the use of self-attentions layers in the processing allow for the application and desired functionality expected with the known processing techniques, in this instance allowing for feature processing withing the given vector analysis, providing improved efficiency and end coding result that meets the designed requirements for processing speed and quality considerations.
Regarding item (2) above, this design is however disclosed by Ye. Ye discloses wherein the layer output values are permutation equivariant relative other layer output values (Par 70-71).
Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify the disclosure of Jamali in order to incorporate the permutation characteristics of Ye based on the rationale of the use of a known technique to improve similar designs in the same way, in this instance by providing consistency and uniformity in the permutation model processing allows for less variables in the data processing required to reverse the encoding processing and successfully and efficiently retrieve data.
Re claim 20; Jamali discloses a non-transitory computer-readable medium (Par 46-47, 67) storing a set of instructions for wireless communication (Par 46-47, 67), the set of instructions comprising:
one or more instructions (Par 46-47, 67) that, when executed by one or more processors (Par 46-47, 67) of a wireless communication device (Par 46-47, 67), cause the wireless communication device (Par 46-47, 67) to:
obtain a message vector (Par 35, 40-length k sequence of information bits);
generate a set of message sub-vectors using the message vector (Fig 1 input to 102, Fig 2 el 108; Par 35-36, 40 – U reshaped into matrix, each row is a sub-vector);
generate a set of message embedding vectors corresponding to the set of message sub-vectors (Figures 1 and 2 el 102; Par 35, 40, 47, 50-51, the first linear layer of FCNN 102 generates a set of message embedding vectors);
generate, using a set of layers, a set of codeword embedding vectors corresponding to the set of message embedding vectors (Figures 1 and 2 el 102; Par 34-35, 47, using the second and more hidden layers, generate a set of codeword embedding vectors);
generate, using the set of codeword embedding vectors (Fig 1 input from 102 to 103; Par 34-35, 47), a set of codeword sub-vectors (Figures 1 and 2 el 103; Par 34-35, 47, the linear layer of FCNN 103 generates a set of codeword sub-vectors), wherein the set of codeword sub-vectors is relative to the set of message sub-vectors (Figures 1 and 2 el 103; Par 34-35, 47, the linear layer of FCNN 103 generates a set of codeword sub-vectors);
combine the set of codeword sub-vectors to generate a codeword vector (Fig 1 output of 103; Par 34-35, “The encoder FCNNs 102, 103 map the information bits u to a length-n sequence of coded symbols c=E(u), known as a codeword”); and
transmit a communication carrying the codeword vector (Figures 1 and 2 el 200- channel; Par 20, 34-36); however, Jamali fails to explicitly disclose (1) wherein the set of layers comprises a set of self-attention layers of an artificial neural network; and (2) wherein the layer output values are permutation equivariant relative other layer output values.
Regarding item (1) above, this design is however disclosed by Sol. Sol discloses wherein the set of layers (Par 86, 91) comprises a set of self-attention layers (Par 86, 91) of an artificial neural network (Par 86, 91).
Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify the disclosure of Jamali in order to incorporate the attention layers of Sol based on the rationale of the use of a known technique to improve similar designs in the same way, in this instance the use of self-attentions layers in the processing allow for the application and desired functionality expected with the known processing techniques, in this instance allowing for feature processing withing the given vector analysis, providing improved efficiency and end coding result that meets the designed requirements for processing speed and quality considerations.
Regarding item (2) above, this design is however disclosed by Ye. Ye discloses wherein the layer output values are permutation equivariant relative other layer output values (Par 70-71).
Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify the disclosure of Jamali in order to incorporate the permutation characteristics of Ye based on the rationale of the use of a known technique to improve similar designs in the same way, in this instance by providing consistency and uniformity in the permutation model processing allows for less variables in the data processing required to reverse the encoding processing and successfully and efficiently retrieve data.
Re claims 4 and 18, the combined disclosure of Jamali, Sol and Ye as a whole discloses the wireless communication device of claim 1 and the associated method of claim 17, Sol further discloses wherein the artificial neural network consists of the set of self-attention layers (Par 86, 91).
Re claim 6, the combined disclosure of Jamali, Sol and Ye as a whole discloses the wireless communication device of claim 1; Jamali further discloses wherein the processing system is further configured to cause the wireless communication device to normalize the codeword vector (Par 54-55) in accordance with a target transmit power parameter (Par 54-55 – ‘thus the average power per coded symbol is equal to one’).
Re claim 7, the combined disclosure of Jamali, Sol and Ye as a whole discloses the wireless communication device of claim 1; Jamali further discloses wherein the processing system is further configured to cause the wireless communication device to scale a transmit power parameter of the codeword vector (Par 54-55 – ‘thus the average power per coded symbol is equal to one’, ‘and the coded sequence is normalized by the power normalization function’).
Re claim 8, the combined disclosure of Jamali, Sol and Ye as a whole discloses the wireless communication device of claim 1; Jamali further discloses wherein the processing system is further configured to cause the wireless communication device to receive, prior to obtaining the message vector (Fig 2, weights from 112 to 102/103; Par 34, 47, 49, 55- setting of weights and training design of model parameters), information indicating a set of model parameters for the artificial neural network (Fig 2, weights from 112 to 102/103; Par 34, 47, 49, 55- setting of weights and training design of model parameters), wherein the set of codeword embedding vectors is in accordance with the information indicating the set of model parameters (Fig 2, weights from 112 to 102/103; Par 34, 47, 49, 55- setting of weights and training design of model parameters).
Re claim 9, the combined disclosure of Jamali, Sol and Ye as a whole discloses the wireless communication device of claim 8; Jamali further discloses wherein the set of model parameters indicates at least one of a set of weights (Fig 2, weights from 112 to 102/103; Par 34, 47, 49, 55- setting of weights and training design of model parameters) or a set of biases (Fig 2, weights from 112 to 102/103; Par 34, 47, 49, 55- setting of biases and training design of model parameters).
Re claim 15, the combined disclosure of Jamali, Sol and Ye as a whole discloses the wireless communication device of claim 1; Ye further discloses wherein the set of codeword sub-vectors is permutation equivariant relative to the set of message sub-vectors such that, for a given permutation of the set of message sub-vectors, the set of codeword sub- vectors is arranged in accordance with the given permutation (Par 70-71 – discloses the FFN and normalization associated as maintaining this characteristic; details of the vector associated to codeword/message as shown in Jamali).
Re claim 16, the combined disclosure of Jamali, Sol and Ye as a whole discloses the wireless communication device of claim 15; Ye further discloses wherein the set of codeword sub-vectors is permutation equivariant relative to the set of message sub-vectors for all permutations of the set of message sub-vectors (Par 70-71 – discloses the FFN and normalization associated as maintaining this characteristic throughout processing; details of the vector associated to codeword/message as shown in Jamali).
Claims 5 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Jamali, Sol and Ye as applied to claims 1 and 17 above, and further in view of Onoro-Rubio (herein after Ono)(US Pub 20230077692).
Re claim 5 and 19, the combined disclosure of Jamali, Sol and Ye as a whole discloses the wireless communication device of claim 1 and the associated method of claim 17, but fails however to explicitly disclose wherein the artificial neural network is a permutation-equivariant artificial neural network.
This design is however disclosed by Ono. Ono discloses wherein the artificial neural network is a permutation-equivariant artificial neural network (Par 38).
Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify the disclosure of Jamali in order to incorporate the permutation characteristics neural network design of Ono based on the rationale of the use of a known technique to improve similar designs in the same way, in this instance by providing consistency and uniformity in the permutation characteristics of the neural network processing allows for less variables in the data processing required to reverse the encoding processing and successfully and efficiently retrieve data.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Jamali, Sol and Ye as applied to claim 1, and further in view of Vannucci (herein after Van)(US Pub 20100011041).
Re claim 13, the combined disclosure of Jamali, Sol and Ye as a whole discloses the wireless communication device of claim 1, but fails however to explicitly disclose wherein, to cause the wireless communication device to generate the set of message sub-vectors, the processing system is configured to cause the wireless communication device to add a padding value to one or more message sub-vectors of the set of message sub-vectors.
This design is however disclosed by Van. Van discloses wherein; to cause the wireless communication device to generate the set of message sub-vectors (Par 54, 58, 76), the processing system is configured to cause the wireless communication device to add a padding value (Par 54, 58, 76) to one or more message sub-vectors of the set of message sub-vectors (Par 54, 58, 76).
Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the invention to modify the disclosure of Jamali in order to incorporate the data padding processing of Van based on the rationale of the use of a known technique to improve similar designs in the same way, in this instance by performing the data padding on specified sub-vectors provides consistency and uniformity in the vector size under processing allowing for a more efficient and effective data processing and transmission of the resultant codewords produced.
Allowable Subject Matter
Claims 2-3, 10-12, 14 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: The prior art of record fails to anticipate or render obvious the limitations of the above cited claims. Re claims 2 and 3 the prior art fails to explicitly disclose the claimed common input/output linear layer design. Re claim 10 the prior art fails to explicitly disclose wherein the model parameters are associated with the number of bits in the message payload as claimed. Re claim 14 the prior art fails to explicitly disclose the conversion processing as specifically claimed.
Conclusion
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/MICHAEL R NEFF/ Primary Examiner, Art Unit 2631
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