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 .
Remarks
In response to communications sent March 24, 2023, claim(s) 1-20 are pending in this application; of these claims 1, 8, and 16 are in independent form.
Priority
The priority date of the instant claims is March 25, 2022, the date of the provisional patent application. This is based on a cursory comparison of the specification and drawings between the provisional patent application and the instant application.
Drawings
The drawing(s) filed on July 19, 2024 are accepted by the Examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
See the last sentence of Para [0333].
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: August 17, 2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-8, 16, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process, a type of abstract idea. This judicial exception is not integrated into a practical application because the additional element of accessing time series data is necessary pre-solution activity to carry out the mental process. Other elements including “applying it” on a general purpose computer. Note that the mental process may require a pen and paper, and broadest reasonable interpretation of the claims include small amounts of data that have little computational complexity. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because accessing data from memory is well-understood, routine, and conventional. See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Additional elements either limit the mental process or add training. However, training a neural network of the type claimed is well-understood, routine, and convention according to the following references:
Huang, Neng, et al. "An attention-based neural network basecaller for Oxford Nanopore sequencing data." 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019.
Baid, Gunjan, et al. "DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer." Nature biotechnology 41.2 (2023): 232-238.
Gravila, Felix, and Miroslav Pakanec. "Improving Basecalling Accuracy With Transformers." (2020) ).
No rejection is made for the claims involving concurrency, because concurrency is difficult to perform in the human mind. No rejection is made for claims involving multi-head attention mechanisms, because the judicial exception is being carried out by virtue of a particular data structure.
For further details of the rejections, see below:
1. A system comprising:
at least one processor (applying the judicial exception on a general purpose computer); and
a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor (applying the judicial exception on a general purpose computer), cause the system to:
access a time series sequence of a read, wherein respective time series elements in the time series sequence represent respective bases in the read (additional element beyond the abstract idea which is necessary pre-solution activity to carry out the judicial exception; accessing, storing, and retrieving data in computer memory is well-understood, routine, and conventional; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93);
generate a composite sequence for the read based on respective aggregate transformations of time series elements in the time series sequence, wherein a subject composite element in the composite sequence is generated based on an aggregate transformation of a corresponding group of time series elements in the time series sequence (mental process of generating composite sequences using pen and paper and applying simple aggregate transformation of small amounts of time series sequence data); and
process the composite sequence as an aggregate and generating a base call sequence having respective base calls for the respective bases in the read (mental process of generating base calls using pen and paper).
2. The system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to generate the composite sequence for the read based on the respective aggregate transformations of respective sliding windows of the time series elements in the time series sequence (mental process of generating composite sequences using pen and paper and applying simple aggregate transformation of small amounts of time series sequence data).
3. The system of claim 2, wherein the respective sliding windows have overlapping time series elements (limitations to the mental process is still a mental process).
4. The system of claim 2, wherein the respective sliding windows are non-overlapping (limitations to the mental process is still a mental process.
6. The system of claim 1, wherein a linear projection layer is trained to learn weights that apply the respective aggregate transformations and generate the composite sequence (an additional element beyond the abstract idea of training; but the claim as a whole is not integrated into practical application; because training a neural network is well-understood routine and conventional at the time of filing of the claimed invention; as evidence, see:
Huang, Neng, et al. "An attention-based neural network basecaller for Oxford Nanopore sequencing data." 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019.
Baid, Gunjan, et al. "DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer." Nature biotechnology 41.2 (2023): 232-238.
Gravila, Felix, and Miroslav Pakanec. "Improving Basecalling Accuracy With Transformers." (2020) ).
7. The system of claim 6, wherein the linear projection layer is trained to learn the weights that apply the respective aggregate transformations on respective sliding windows of the time series elements in the time series sequence and generate the composite sequence (an additional element beyond the abstract idea of training; but the claim as a whole is not integrated into practical application; because training a neural network is well-understood routine and conventional at the time of filing of the claimed invention; as evidence, see:
Huang, Neng, et al. "An attention-based neural network basecaller for Oxford Nanopore sequencing data." 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019.
Baid, Gunjan, et al. "DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer." Nature biotechnology 41.2 (2023): 232-238.
Gravila, Felix, and Miroslav Pakanec. "Improving Basecalling Accuracy With Transformers." (2020) ).
8. A non-transitory computer readable medium comprising instructions that, when executed by at least one processor (applying the judicial exception on a general purpose computer), cause a system to:
access a time series sequence of a read, wherein respective time series elements in the time series sequence represent respective bases in the read (additional element beyond the abstract idea which is necessary pre-solution activity to carry out the judicial exception; accessing, storing, and retrieving data in computer memory is well-understood, routine, and conventional; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93);
generate a composite sequence for the read based on respective aggregate transformations of time series elements in the time series sequence, wherein a subject composite element in the composite sequence is generated based on an aggregate transformation of a corresponding group of time series elements in the time series sequence; (mental process of generating composite sequences using pen and paper and applying simple aggregate transformation of small amounts of time series sequence data) and
process the composite sequence as an aggregate and generating a base call sequence having respective base calls for the respective bases in the read (mental process of generating base calls using pen and paper).
16. A computer-implemented method of base calling (applying the judicial exception on a general purpose computer), including:
accessing a time series sequence of a read, wherein respective time series elements in the time series sequence represent respective bases in the read (additional element beyond the abstract idea which is necessary pre-solution activity to carry out the judicial exception; accessing, storing, and retrieving data in computer memory is well-understood, routine, and conventional; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93);
generating a composite sequence for the read based on respective aggregate transformations of time series elements in the time series sequence, wherein a subject composite element in the composite sequence is generated based on an aggregate transformation of a corresponding group of time series elements in the time series sequence (mental process of generating composite sequences using pen and paper and applying simple aggregate transformation of small amounts of time series sequence data); and
processing the composite sequence as an aggregate and generating a base call sequence having respective base calls for the respective bases in the read (mental process of generating base calls using pen and paper).
20. The computer-implemented method of claim 16, wherein the respective time series elements are respective intensity values for respective sequencing cycles of a sequencing run (limitations to the additional element that is necessary pre-solution activity for carrying out the judicial exception).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 11,580,641 B1 (“Chen”).
As to claim 1, Chen teaches a system comprising:
at least one processor (Figure 17 and Col 29, lines 16-27); and
a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor (Figure 17 and Col 30 lines 5-37), cause the system to:
access a time series sequence of a read (Chen Figure 3 element 302: “Obtain images from multiple cycle” of sequencing, where each image is one of a series), wherein respective time series elements in the time series sequence represent respective bases in the read (Chen Figure 3 and Col 11 lines 29-56: “Determine sequences of nucleic acid molecules in a sequencing-by-synthesis process”);
generate a composite sequence for the read based on respective aggregate transformations of time series elements in the time series sequence (Chen Figure 3 elements 306, and 308 and Col 11 lines 29-56: use a convolutional neural network to detect center signals, which entails aggregating, to result in a time series of features), wherein a subject composite element in the composite sequence is generated based on an aggregate transformation of a corresponding group of time series elements in the time series sequence (Chen Figure 3 and Col 11 lines 29-56: aggregating the processed images that are in time series arrangement to result in a time series of features); and
process the composite sequence as an aggregate and generating a base call sequence having respective base calls for the respective bases in the read (Chen Figure 3 element 3010 and Col 11 lines 29-56: processes the sequential features for base calls that are derived from the original images of bases in the reads of the sequence-by-synthesis process).
As to claim 2, Chen teaches the system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to generate the composite sequence for the read based on the respective aggregate transformations of respective sliding windows of the time series elements in the time series sequence (Chen Figure 10 and col 18 lines 18-43: a sliding window of 5 cycle-elements in sequences of cycles to form a sequence of features for each sliding window of five times series-elements).
As to claim 3, Chen teaches the system of claim 2, wherein the respective sliding windows have overlapping time series elements (Chen Figure 10 and col 18 lines 18-43: a sliding window of 5 cycle-elements in sequences of cycles to form a sequence of features for each sliding window of five times-series elements; Figure 10: sliding windows are not ALL overlapping, some are sufficiently spaced apart).
As to claim 4, Chen teaches the system of claim 2, wherein the respective sliding windows are non-overlapping (Chen Figure 10 and col 18 lines 18-43: a sliding window of 5 cycle-elements in sequences of cycles to form a sequence of features for each sliding window of five times series elements;).
As to claim 5, Chen teaches the system of claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to concurrently generate the respective base calls for the respective bases in the read (Chen Figure 13 and Col 22 lines 1-29: illustrates a transformer architecture, which is known for parallel calculations rather than sequential calculations).
As to claim 6, Chen teaches the system of claim 1, wherein a linear projection layer is trained to learn weights that apply the respective aggregate transformations and generate the composite sequence (Chen Figure 13 elements 1352 and Col 21 lines 57-67: a linear projection as part of a transformer that learns from the sequences of aggregate read-cycles; weights are learned, including for the linear layer).
As to claim 7, Chen teaches the system of claim 6, wherein the linear projection layer is trained to learn the weights that apply the respective aggregate transformations on respective sliding windows of the time series elements in the time series sequence and generate the composite sequence (Chen Figure 13 elements 1352 and Col 21 lines 57-67: a linear projection as part of a transformer that learns from the sequences of aggregate read-cycles; weights are learned, including for the linear layer; Chen Figure 10 and col 18 lines 18-43: a sliding window of 5 cycle-elements in sequences of cycles to form a sequence of features for each sliding window of five times-series elements).
As to claim 8, Chen teaches a non-transitory computer readable medium comprising instructions that (Figure 17 and Col 30 lines 5-37), when executed by at least one processor, cause a system to:
access a time series sequence of a read (Chen Figure 3 element 302: “Obtain images from multiple cycle” of sequencing, where each image is one of a series), wherein respective time series elements in the time series sequence represent respective bases in the read (Chen Figure 3 and Col 11 lines 29-56: “Determine sequences of nucleic acid molecules in a sequencing-by-synthesis process”);
generate a composite sequence for the read based on respective aggregate transformations of time series elements in the time series sequence (Chen Figure 3 elements 306, and 308 and Col 11 lines 29-56: use a convolutional neural network to detect center signals, which entails aggregating, to result in a time series of features), wherein a subject composite element in the composite sequence is generated based on an aggregate transformation of a corresponding group of time series elements in the time series sequence (Chen Figure 3 and Col 11 lines 29-56: aggregating the processed images that are in time series arrangement to result in a time series of features); and
process the composite sequence as an aggregate and generating a base call sequence having respective base calls for the respective bases in the read (Chen Figure 3 element 3010 and Col 11 lines 29-56: processes the sequential features for base calls that are derived from the original images of bases in the reads of the sequence-by-synthesis process).
As to claim 9, Chen teaches the non-transitory computer readable medium of claim 8, wherein a multi-headed attention encoder is trained to process the composite sequence as the aggregate and to generate an alternative representation of the composite sequence (Chen Figure 13 and Col 20 lines 7-16: multi-head attention layer applied to feature vectors at various positions).
As to claim 10, Chen teaches the non-transitory computer readable medium of claim 9, wherein an output layer is trained to process the alternative representation of the composite sequence and generate the base call sequence (Chen Figure 13 element 1360 and Col 22 lines 1-12 : “Output Probability of Bases for all n cycles for all clusters” based on the multi-head attention layer).
As to claim 11, Chen teaches the non-transitory computer readable medium of claim 10, wherein the output layer is trained to concurrently (Chen Figure 13 and Col 22 lines 1-29: illustrates a transformer architecture, which is known for concurrent calculations rather than sequential calculations) generate base-wise classification likelihoods for each composite element in the composite sequence (Chen Figure 13 element 1360 and Col 22 lines 1-12 : “Output Probability of Bases for all n cycles for all clusters”).
As to claim 12, Chen teaches the non-transitory computer readable medium of claim 11, wherein a base call for a subject base in the read is determined based on a maximum base-wise classification likelihood generated by the output layer for a corresponding composite element in the composite sequence (Chen Figure 13 element 1360 and Col 22 lines 1-12 : “Output Probability of Bases for all n cycles for all clusters”).
As to claim 13, Chen teaches the non-transitory computer readable medium of claim 9, wherein the multi-headed attention encoder is trained to correct for systematic errors in cluster amplification that are encoded in the read (Chen Col 12 lines 13-29: increasing accuracy using the self-attention architecture and training as an intended result).
As to claim 14, Chen teaches the non-transitory computer readable medium of claim 13, wherein the systematic errors include phasing and prephasing errors (Chen Col 1, lines 42 to Col 2 line 8: avoiding noise from phasing and prephasing as an intended result).
As to claim 15, Chen teaches the non-transitory computer readable medium of claim 14, wherein the systematic errors include context dependent intensity modulations (Chen Para 20 lines 7-16: capturing contextual relationships between elements of the position encoded vectors).
As to claim 16, Chen teaches a computer-implemented method of base calling (Figure 17 and Col 29, lines 16-27), including:
accessing a time series sequence of a read (Chen Figure 3 element 302: “Obtain images from multiple cycle” of sequencing, where each image is one of a series), wherein respective time series elements in the time series sequence represent respective bases in the read (Chen Figure 3 and Col 11 lines 29-56: “Determine sequences of nucleic acid molecules in a sequencing-by-synthesis process”);
generating a composite sequence for the read based on respective aggregate transformations of time series elements in the time series sequence (Chen Figure 3 elements 306, and 308 and Col 11 lines 29-56: use a convolutional neural network to detect center signals, which entails aggregating, to result in a time series of features), wherein a subject composite element in the composite sequence is generated based on an aggregate transformation of a corresponding group of time series elements in the time series sequence (Chen Figure 3 and Col 11 lines 29-56: aggregating the processed images that are in time series arrangement to result in a time series of features); and
processing the composite sequence as an aggregate and generating a base call sequence having respective base calls for the respective bases in the read (Chen Figure 3 element 3010 and Col 11 lines 29-56: processes the sequential features for base calls that are derived from the original images of bases in the reads of the sequence-by-synthesis process).
As to claim 17, Chen teaches the computer-implemented method of claim 16, wherein a multi-headed attention encoder is trained to process the composite sequence as the aggregate and to generate an alternative representation of the composite sequence (Chen Figure 13 and Col 20 lines 7-16: multi-head attention layer applied to feature vectors at various positions).
As to claim 18, Chen teaches the computer-implemented method of claim 17, wherein the multi-headed attention encoder is trained to analyze backward and forward flanking composite elements in conjunction with analyzing a subject composite element in the composite sequence (Chen Figure 13 element 1360 and Col 22 lines 1-12 : “Output Probability of Bases for all n cycles for all clusters” based on the multi-head attention layer).
As to claim 19, Chen teaches the computer-implemented method of claim 18, wherein a forward mask of the multi-headed attention encoder is deactivated to account for the forward flanking composite elements (Chen Col 20 lines 54- Col 21 line 10: masked multi-headed attention mechanism).
As to claim 20, Chen teaches the computer-implemented method of claim 16, wherein the respective time series elements are respective intensity values for respective sequencing cycles of a sequencing run (Chen Figure 3 element 302: “Obtain images from multiple cycle” of sequencing, where each image is one of a series).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huang, Neng, et al. "An attention-based neural network basecaller for Oxford Nanopore sequencing data." 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019.
Baid, Gunjan, et al. "DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer." Nature biotechnology 41.2 (2023): 232-238.
Gravila, Felix, and Miroslav Pakanec. "Improving Basecalling Accuracy With Transformers." (2020).
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/JESSE P FRUMKIN/ Primary Examiner, Art Unit 1685 August 7, 2026
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