DETAILED ACTION This action is in response to the original filing of 8-14-2023 . Claims 1- 2 2 are pending and have been considered below: Allowable Subject Matter Claim 21 is 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. 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 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. Claims 1 , 3-5, 9-11 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Single-Read Reconstruction for DNA Data Storage Using Transformers ; Yotam Nahum et al. (“Nahum”), pages 1-9, 10-10-2021 in view of Soft-Decision Decoding for DNA-Based Data Storage , Mu Zhang et al. (“Zhang”), Pages 1-5, © 2018 . Claim 1: Nahum discloses a method for estimating an information unit represented by DNA strands, the method comprises: sequencing the DNA strands to provide noisy copies of an encoded version of the information unit (Page 3; DNA Encoder-decoder transformer , Page 4: Stage 1; DNA sequence synthesized and Figure 3; DNA with noise injection for encoded version) ; wherein the information unit comprises information unit elements; neural network (NN) processing the multiple noisy copies by one or more NNs to provide a n estimate of the encoded information unit (Page 3; DNA Encoder-decoder transformer and Figure 3; noise injected versions provide ) ; wherein the estimate comprises estimated encoded information unit elements and an encoded information unit elements estimated confidence parameter; and decoding the encoded information unit to provide a prediction of the information unit (Page 4; Stage 2- 3 self-supervised learning prediction and Figure 3; the encoded sequence with codewords are divided based on error probability (GLGC class) for prediction and decoding ) . Nabum provides a prediction, however may not explicitly disclose a soft estimate . Zhang is provided because it discloses a soft decision decoding of DNA, from encoded sequence s ( abstract, and Introduction Column 2, Paragraphs 1-2 (soft decision of oligo) Page 2, Column 2, Paragraph 2 (encoded-oligo) ) . Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate soft decision capability with the noise injections of Nabum . One would have been motivated to provide the functionality because the method can provide significant performance gains (Zhang: Introduction) allowing efficient training. Claim 3 : Nahum and Zhang disclose a method according to claim 1, wherein the one or more NNs were trained using training simulated DNA strands (Nabum: Figure 3, Stage 1 ; synthesized ) . Claim 4 : Nahum and Zhang disclose a method according to claim 1, comprising training the one or more NNs using training simulated DNA strands (Nabum: Figure 3, Stage 1-2 ; synthesized ) . Claim 5 : Nahum and Zhang disclose a method according to claim 4, wherein the training simulated DNA strands are simulated by a generation process that comprises: obtaining training content; introducing errors to the training content to provide erroneous training content; and feeding to the erroneous training content to the at least one NNs (Nabum: Figure 3; Page 4, Stage 1-3; noise can provide error) . Claim 9 : Nahum and Zhang disclose a method according to claim 1, wherein encoded information unit comprises encoded segments, each encoded segment is represented by a cluster of simulated DNA strands that are noisy copies of the encoded segment, and wherein the soft estimate of the encoded information unit comprises soft estimates of the encoded segments (Nabum: Page 1, Column 2, Paragraph 3 and Page 7, Column 2, Paragraph 2; cluster DNA construction and Zhang: abstract, and Introduction Column 2, Paragraphs 1-2 (soft decision of oligo) Page 2, Column 2, Paragraph 2 (encoded-oligo)) . Claim 10 : Nahum and Zhang disclose a method according to claim 9 wherein at least some of the clusters are unknown (Nabum: Figures 1 and 3; encoding) . Claim 11: Nahum and Zhang disclose a method according to claim 9, wherein the encoded segments are without encoded segments inner-code (Nabum: Page 4, Stage 1 encoding removes inner cod ing ). Claim 22 is similar in scope to claim 1 and rejected under the same rationale. Regarding the non-transitory computer readable medium (Nabum: abstract and page 3: architecture; provides transformer system which have compute medium ) Claim 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Single-Read Reconstruction for DNA Data Storage Using Transformers ; Yotam Nahum et al. (“Nahum”), pages 1-9, 10-10-2021 and Soft-Decision Decoding for DNA-Based Data Storage , Mu Zhang et al. (“Zhang”), Pages 1-5, © 2018 in further view of Tsai et al. (“Tsai” 20220254450 A1). Claim 2: Nahum and Zhang disclose a method according to claim 1, wherein the one or more NNs comprise a first NN and a second NN, wherein the NN processing comprises (i) processing the noisy copies by the first NN (Nabum: Figure 3) , (ii) processing the noisy copies by the second NN, and (iii) determining the soft estimate based on an output of the first NN and an output of the second NN (Nabum: Figure 3; utilize multiple models and Zhang: abstract, and Introduction Column 2, Paragraphs 1-2 (soft decision of oligo) Page 2, Column 2, Paragraph 2 (encoded-oligo) ) . However Nabum may not explicitly disclose an inverse-ordered version . Tsai is provided because it disclose s a sequence which is reversed and used for training/processing (Paragraph 8). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate reverse sequences with the sequencing of Nabum . One would have been motivated to provide the functionality as a way to expand the noise injected samples for a more robust training. Claim 6 -8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Single-Read Reconstruction for DNA Data Storage Using Transformers ; Yotam Nahum et al. (“Nahum”), pages 1-9, 10-10-2021 and Soft-Decision Decoding for DNA-Based Data Storage , Mu Zhang et al. (“Zhang”), Pages 1-5, © 2018 in further view of Erhard et al. (“Erhard” 20210272651 A1). Claim 6 : Nahum and Zhang disclose a method according to claim 5, wherein the introducing of errors is executed based on error statistics of a combination of DNA strands synthesis and DNA strands sequencing (Nabum: Figure 3, Stage 1-3 ; Page 7, Paragraph 1; diverse error patterns can be statistical ) . Erhard is further provided because it discloses a sequence entity and further provides analyzing statistics for error determination, and further utilizing the error rate in synthesized data (abstract and Paragraph 8). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate error statistics with the patterned error of Nabum . One would have been motivated to provide the functionality as a way to expand the sample options utilizing analyzed data for a more robust training. Claim 7 : Nahum , Zhang and Erhard disclose a method according to claim 6 comprising modeling the error statistics (Nabum: Figure 3, Stage 1-3; Page 7, Paragraph 1; diverse error patterns can be statistical and Erhard: Paragraph 8; error stats) . Claim 8 : Nahum , Zhang and Erhard disclose a method according to claim 6 comprising generalizing the error statistics to provide expanded error statistics, wherein the introducing of the errors comprising applying the expanded error statistics (Nabum: Figure 3, Stage 1-3; Page 7, Paragraph 1; diverse error patterns can be statistical and Erhard: Paragraph 8; error stats) . Claim 12 -19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Single-Read Reconstruction for DNA Data Storage Using Transformers ; Yotam Nahum et al. (“Nahum”), pages 1-9, 10-10-2021 and Soft-Decision Decoding for DNA-Based Data Storage , Mu Zhang et al. (“Zhang”), Pages 1-5, © 2018 in further view of Filippova et al. (“ Filippova ” 20200294624 A1). Claim 12: Nahum and Zh an g disclose a method according to claim 9, but may not explicitly disclose wherein the decoding comprises classifying the encoded segments to different classes based on the estimated confidence parameter associated with elements of the encoded segments. Filippova is provided because it discloses a capability to determine classes of encoded sequence data based on a degree of confidence (Paragraph 1 8 3 ). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate classification of encoded sequences in Nabum . One would have been motivated to provide the functionality as a way to expand the sequence analysis for more robust evaluation of the training. Claim 13: Nahum , Zhang and Filippova disclose a method according to claim 12, comprising and applying different decoding steps on encoded segments that belong to at least two classes of the different classes (Nabum: Figure 3, Stage 3 ; each model will have its decode method (long/short) ; Filippova : Paragraph 183; classes- cancer/non-cancer ). Claim 14: Nahum, Zhang and Filippova disclose a method according to claim 12, comprising differently decoding encoded segments that belong to different classes (Nabum: Figure 3, Stage 3; each model will have its decode method (long/short) ). Claim 15: Nahum, Zhang and Filippova disclose a method according to claim 12, comprising ignoring encoded segments based on an estimated confidence parameter associated with the encoded segments (Page 4, Column 2, Paragraph 1; encoded segments discarded because probably (confidence) contains fewer errors ) . Claim 16: Nahum, Zhang and Filippova disclose a method according to claim 12, wherein the decoding comprises generating a binary version of the encoded segments (Nabum: Page 2, Column 2, Paragraph 2 and Figure 3, Stage 3(decoding); functionality for providing a binary output) . Claim 17: Nahum, Zhang and Filippova disclose a method according to claim 12, wherein the decoding comprises applying a DNA-flavor version of tensor-product decoding (Nabum: Figure 3: Stages 2-3 and Page4 Stage s 2- 3, model decoding provided based “flavor” such codeword and length ). Claim 18: Nahum, Zhang and Filippova disclose a method according to claim 17, wherein the decoding comprises constraint decoding (Nabum: Page 4, Stage 3, Constrained beam search; output based on certain length with valid codewords). Claim 19: Nahum, Zhang and Filippova disclose a method according to claim 17 wherein the applying of the DNA-flavor version of tensor-product decoding is a part of error correction decoding (Nabum: Figure 3: Stages 2-3 and Page4 Stages 2-3, model decoding provided based “flavor” such codeword and length). Claim 20: Nahum, Zhang and Filippova disclose a method according to claim 12, wherein the decoding comprises constraint decoding (Nabum: Page 4, Stage 3, Constrained beam search; output based on certain length with valid codewords) . Conclusion The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure: Parameter Estimation of a Convolutional Encoder from Noisy Observations; Janis Dingel et al. (“Dingel”), pages 1-5, ©2007 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. 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