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 .
Information Disclosure Statement
The Information Disclosure Statements filed February 26, 2024; February 26, 2024; August 29, 2024; August 29, 2024; and September 18, 2024 have been considered.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code (see Page 38, [00126]). 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.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions (i.e., product of nature, a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Every claimed invention must be examined to determine whether the claimed invention complies with 35 U.S.C. 101, particularly whether the claimed invention falls within a 35 U.S.C. 101 judicial exception of non-patentable subject matter (e.g., an abstract idea, law of nature, natural phenomenon, natural product etc.). Phenomena of nature, though just discovered, natural products, mental processes, and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work. See MPEP 2106. As per the “2019 Revised Subject Matter Eligibility Guidance” (Federal Register Vol. 84, No. 4, available 01-07-2019), claims drawn to a process, machine, manufacture or composition of matter are further analyzed according to a two-part process to determine if A) the claim(s) is/are “directed to” a judicial exception because the claims(s) recite(s) a judicial exception (i.e. prong one) that is not integrated into a practical application (i.e. prong two) and, if so, if B) the claim(s) provide(s) an inventive concept, i.e. recite(s) additional elements that amount to significantly more than the judicial exception.
Subject Matter Eligibility Test for Products and Processes
Step 1 - Is the Claim to a Process, Machine, Manufacture or Composition of Matter? YES
Claims 1-20 are directed to one of the statutory classes. Claims 1-20 are directed to a method comprising receiving genetic data (Process).
Step 2A, Prong One — Does the Claim Recite an Abstract Idea, Law of Nature, or Natural Phenomenon? YES
Claims 1-20 recite the abstract idea of receiving and processing data using mental steps and mathematical concepts. Claims directed to nothing more than abstract ideas, natural phenomena, and laws of nature are not eligible for patent protection (see MPEP 2106.04). Abstract ideas include certain methods of organizing human activity, and mental processes (including procedures for collecting, observing, determining, evaluating, and organizing information (See MPEP 2106.04(a)(2)). In particular, these abstract ideas include:
• Receiving genetic data (mental process, human mind is capable of receiving/collecting data, observing/evaluating, organizing information).
• At least one transcription start site (TSS) having an associated expression score (mental process, human mind is capable of receiving/ collecting data, observing/evaluating/determining, organizing information and mathematical concepts)
• Determining core promotors (mental process, human mind is capable of receiving/ collecting data, observing/evaluating, organizing information).
• Determining, based on the associated expression scores satisfying a threshold, a third plurality of nucleotide sequences from the second plurality of nucleotide sequences (mental process, human mind is capable of receiving/ collecting data, observing/evaluating/determining, organizing information and mathematical concepts).
• Determining, based on the third plurality of nucleotide sequences, a fourth plurality of nucleotide sequences labeled as not core promoters (mental process, human mind is capable of receiving/ collecting data, observing/evaluating, organizing information).
• Generating training data set based on core and non-core promotors (mental process, human mind is capable of receiving/ collecting data, observing/evaluating, organizing information).
• Training a generative model based on the training data set (mental process, human mind is capable of receiving/ collecting data, observing/evaluating, organizing information and mathematical relationships and/or algorithms).
• Determining a plurality of summit nucleotides and surrounding bases (mental process, human mind is capable of receiving/ collecting data, observing/evaluating/determining and organizing information).
• Determining shifted bases (mental process, human mind is capable of receiving/ collecting data, observing/evaluating/determining and organizing information).
Therefore, the claims recite elements that constitute one or more judicial exceptions.
Step 2A, Prong Two — Does the Claim Recite an Additional Elements that Integrate the Judicial Exception into a Practical Application? NO.
The Supreme Court has long distinguished between principles themselves, which are not patent eligible, and the integration of those principles into practical applications, which are patent eligible. However, absent are any additional elements recited in the claim beyond the judicial exceptions which integrate the exception into a practical application of the exception. The “integration into a practical application” requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The claim analysis continues with identifying additional elements beyond the judicial exceptions that might evidence integration of the judicial exceptions into a practical application. The steps or elements in addition to the judicial exceptions are: “each nucleotide sequence of the first plurality of nucleotide sequences comprises at least one transcription start site (TSS)”, “labeling as core and non-core promoters” and “outputting the generative model”, which is not indicative of integration into practical application. These steps, recited at a high level of generality, comprise routine data gathering, which is considered an insignificant extra-solution activity. This data gathering is required for using the judicial exceptions. (See MPEP 2106.05(g)). There are no further/additional steps which applies either the identified judicial exception into practical application. Thus, a careful evaluation of the claim as a whole fails to reveal the practical application of the judicial exception to, e.g., effect an improvement to the functioning of a computer or other technology/technical field, effect a particular treatment or prophylaxis for a disease or medical treatment, implement a particular machine that is integral to the claim, or effect a transformation or reduction of a particular article to a different state or thing, or to apply the judicial exception in another meaningful way beyond generally linking its use to a particular technological environment. Accordingly, the claims do not integrate the judicial exception(s) into a practical application and is therefore directed to a judicial exception.
Step 2B - Does the Claim Recite Additional Elements that Amount to Significantly More than the Judicial Exception? NO.
The Supreme Court has identified a number of considerations for determining whether a claim with additional elements amounts to “significantly more” than the judicial exception(s) itself. The claims as a whole are analyzed to determine whether any additional element/step, or combination of additional elements/steps, in addition to the identified judicial exception(s) is sufficient to ensure that the claim amounts to “significantly more” than the exception(s).
The eligibility analysis proceeds with identifying any additional elements or limitations, separate from the judicial exceptions, that might potentially render the claims directed to a judicial exception patent eligible. To render the claims patent- eligible, these elements must comprise meaningful limitations that add to or transform the judicial exception to the effect that it amounts to significantly more than the natural correlation or abstract idea itself - i.e. provide an “inventive concept’. The elements that are in addition to the judicial exception comprise: each nucleotide sequence of the first plurality of nucleotide sequences comprises at least one transcription start site (TSS), labeling as core and non-core promoters and outputting the generative model. When considered separately and in combination, these elements do not add significantly more to the judicial exception. These steps are well-understood, routine and conventional activities in the field. For example, Abeel et al. “ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles”, Bioinformatics, Volume 24, Issue 13, published July 01, 2008, cited on the IDS filed February 26, 2024, as well as Georgakilas et al. “Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data”. Sci Rep 10, Article 877, published January 21, 2020, cited on the IDS filed February 26, 2024, disclose each nucleotide sequence of the first plurality of nucleotide sequences comprises at least one transcription start site (TSS), labeling as core and non-core promoters and outputting the generative model. The claims recite an abstract idea with additional elements. Because these elements are not inventive concepts, the claims do not integrate the abstract idea into a practical application. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). The claims therefore do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Accordingly, the claims do not qualify as patent-eligible subject matter.
For further information, please see the latest revision of MPEP 2104-2106 {Patent Subject Matter Eligibility Under 35 U.S.C. 101}, including MPEP 2106.04 {Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception} and 2106.05 {Eligibility Step 2B: Whether a Claim Amounts to Significantly More}, as well as the guidance on Subject Matter Eligibility, including the 2019 Guidance issued Jan. 7, 2019, and the October 2019 Update, provided on the USPTO website at https:/Awww.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter- eligibility.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Abeel et al. “ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles”, Bioinformatics, Volume 24, Issue 13, published July 01, 2008, cited on the IDS filed February 26, 2024, in view of Georgakilas et al. “Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data”. Sci Rep 10, Article 877, published January 21, 2020, cited on the IDS filed February 26, 2024.
Regarding claim 1, Abeel teaches a method comprising receiving genetic data and the genetic data comprises a first plurality of nucleotide sequences (Page i25, Left Column, Fifth Paragraph—Right Column, Fourth Paragraph). Abeel teaches each nucleotide sequence of the first plurality of nucleotide sequences comprises at least one transcription start site (TSS) (Page i25, Right Column, Fourth Paragraph). Abeel teaches determining, based on the first plurality of nucleotide sequences, a second plurality of nucleotide sequences labeled as core promoters (Page i27, Right Column, Third Paragraph and Figures 3 and 4). Abeel teaches determining, based on a scores satisfying a threshold, a third plurality of nucleotide sequences from the second plurality of nucleotide sequences (Page i27, Right Column, Fourth-Fifth Paragraph, Page i28, Left Column, Last Paragraph, Table 1 and Figure 4). Abeel teaches determining, based on the third plurality of nucleotide sequences, a fourth plurality of nucleotide sequences labeled as not core promoters (Page i27, Right Column, Third Paragraph and Figures 3 and 4). Abeel teaches generating, based on the third plurality of nucleotide sequences labeled as core promoters and the fourth plurality of nucleotide sequences labeled as not core promoters, a training data set (Page i25, Right Column, Fourth Paragraph, Page i26, Right Column, Third paragraph and Figures 3 and 4). Abeel teaches training, based on the training data set, a generative model and outputting the generative model (Page i25, Right Column, Fourth and Sixth Paragraphs, Page i28, Right Column, First-Second Paragraphs, Page i30, Right Column, Second-Third Paragraphs and Figures 1, 3).
Regarding claim 2, Abeel teaches determining, based on the first plurality of nucleotide sequences, the second plurality of nucleotide sequences labeled as core promoters comprises determining, based on the plurality of TSSs, a plurality of summit nucleotide bases (e.g., identifying a peak of base-stacking energy at the TSS; summit nucleotides, Page i27, Left Column, Second Paragraph and Figure 3). Abeel teaches determining, for each summit nucleotide base of the plurality of summit nucleotide bases, an associated plurality of surrounding bases (e.g., identifying a stable region of bases around the TSS corresponding to the core promoter, Page i27, Left Column, Second Paragraph and Figure 3). Abeel teaches storing (mapping) each summit nucleotide base and the associated plurality of surrounding bases as the second plurality of nucleotide sequences labeled as core promoters (Page i26, Left Column, Third Paragraph, Page i27, Left Column, Second Paragraph and Figure 3).
Regarding claim 3, Abeel teaches determining, based on the plurality of TSSs, the plurality of summit nucleotide bases comprises determining, for each of the plurality of TSSs, a nucleotide base having a Cap Analysis of Gene Expression (CAGE) signal (Page i25, Right column, First Paragraph).
Regarding claim 4, Abeel teaches determining, for each summit nucleotide base of the plurality of summit nucleotide bases, the associated plurality of surrounding bases comprises determining, for each summit nucleotide base of the plurality of summit nucleotide bases, a first plurality of nucleotide bases in the 5’ direction and a second plurality of nucleotide bases in the 3’ direction (e.g., identifying sites 50 bases in each direction from the TSS, Page i26, Right Column, First Paragraph, Figure 2 and Table 2).
Regarding claim 5, Abeel teaches the first plurality of nucleotide bases in the 5’ direction comprises 49 nucleotide bases and the second plurality of nucleotide bases in the 3’ direction comprises 50 nucleotide bases (Page i26, Right Column, First and Fourth Paragraph and Page i29, Left Column, Second Paragraph).
Regarding claim 13, Abeel teaches generating, based on the generative model, a nucleotide sequence (Page i25, Right Column, Seventh and Eighth Paragraphs).
Abeel does not teach or suggest at least one transcription start site (TSS) having an associated expression score. Abeel does not teach or suggest a nucleotide base having a strongest Cap Analysis of Gene Expression (CAGE) signal.
Georgakilas teaches receiving genetic data comprising a plurality of nucleotide sequences (Page 2, Third Paragraph, Page 4, Last Paragraph, Page 7, Last Paragraph and Figure 1). Georgakilas teaches the plurality of nucleotide sequences comprise at least one transcription start site (TSS) having an associated expression score (e.g., CAGE score, Page 8, Last Four Paragraphs and Page 10, Third-Fifth Paragraph). Georgakilas teaches summit nucleotide bases and a nucleotide base having a strongest Cap Analysis of Gene Expression (CAGE) (Peak) signal (Page 2, First-Third Paragraph and Figure 1). Georgakilas teaches determining surrounding associated bases (Page 1, Second Paragraph, Page 2, Third Paragraph and Figure 1). Georgakilas teaches a training dataset and training a generative model as well as outputting the generative model (Page 8, Eighth Paragraph—Page 9, Fourth Paragraph, Page 9, Sixth Paragraph—Page 10, Second Paragraph and Figures 1 and 4). Georgakilas teaches using the disclosed methods allows for a more sensitive procedure to harnesses the power of Machine Learning on promoter-associated and structural DNA features to provide highly accurate predictions without removing the majority of candidate TSS’s (Page 8, Sixth Paragraph).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have modified the teachings of Abeel, with the teachings of Georgakilas, using at least one TSS having an associated expression score. Using these methods (associated CAGE expression scores) would allow for a more sensitive procedure to harnesses the power of Machine Learning on promoter-associated and structural DNA features to provide highly accurate predictions without removing the majority of candidate TSS’s, as taught by Georgakilas (Page 8, Sixth Paragraph).
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Abeel et al. “ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles”, Bioinformatics, Volume 24, Issue 13, published July 01, 2008, cited on the IDS filed February 26, 2024, and Georgakilas et al. “Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data”. Sci Rep 10, Article 877, published January 21, 2020, cited on the IDS filed February 26, 2024, as applied to claims 1-5 and 13 above, in view of Kelley D. “Cross-species regulatory sequence activity prediction”, PLoS Comput Biol 16(7), published July 20, 2020, cited on the IDS filed February 26, 2024.
Regarding claim 6, Abeel teaches determining, based on the third plurality of nucleotide sequences, the fourth plurality of nucleotide sequences labeled as not core promoters (Page i27, Right Column, Third Paragraph and Figures 3 and 4).
Regarding claim 7, Abeel teaches determining, for each nucleotide sequence of the third plurality of nucleotide sequences as discussed above.
Abeel and Georgakilas do not teach or suggest determining, for each nucleotide sequence of the third plurality of nucleotide sequences, an associated plurality of shifted bases and storing each associated plurality of shifted bases as a fourth plurality of nucleotide sequences labeled as not core promoters. Abeel and Georgakilas do not teach or suggest determining, for each nucleotide sequence of the third plurality of nucleotide sequences, the associated plurality of shifted bases comprises shifting a quantity of nucleotide bases away from each nucleotide sequence of the third plurality of nucleotide sequences.
Kelley teaches a prediction model using CAGE gene expression signals from the TSS (Page 5, Fourth Paragraph). Kelley teaches a training data set identifying promoters (Page 17, Third Paragraph). Kelley teaches determining, for each nucleotide sequence of the third plurality of nucleotide sequences, an associated plurality of shifted bases and storing each associated plurality of shifted bases as a fourth plurality of nucleotide sequences labeled as not core promoters (Page 9, Third paragraph and Page 18, Fifth Paragraph). Kelley teaches determining, for each nucleotide sequence of the third plurality of nucleotide sequences, the associated plurality of shifted bases comprises shifting a quantity of nucleotide bases away from each nucleotide sequence of the third plurality of nucleotide sequences (Page 9, Third paragraph and Page 18, Fifth Paragraph). Kelley teaches using these methods allows for improved training data as well as improved test and prediction accuracy (Page 3, Last Paragraph—Page 4, First Paragraph and Figure 2).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Abeel and Georgakilas with the teachings of Kelley to determine, for each nucleotide sequence of the third plurality of nucleotide sequences, an associated plurality of shifted bases; and storing each associated plurality of shifted bases as well as shifting a quantity of nucleotide bases away from each nucleotide sequence. Using these methods would allow for improved training data as well as improved test and prediction accuracy as taught by Kelley (Page 3, Last Paragraph—Page 4, First Paragraph and Figure 2).
Claims 8-9, 11-12, 14-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Abeel et al. “ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles”, Bioinformatics, Volume 24, Issue 13, published July 01, 2008, cited on the IDS filed February 26, 2024, and Georgakilas et al. “Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data”. Sci Rep 10, Article 877, published January 21, 2020, cited on the IDS filed February 26, 2024, as applied to claims 1-5 and 13 above, in view of Linder et al. (“A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences”, Cell Systems, Vol. 11, Issue 1, published July 22, 2020.
Regarding claims 8-9 and 11, Abeel teaches training, based on the training data set, the generative model as discussed above.
Regarding claim 12, Abeel teaches the generative model as discussed above.
Regarding claims 14 and 15, Abeel teaches the generative model and core promoter sequence as discussed above .
Regarding claim 16, Abeel teaches engineering a promoter based on the core promoter sequence (Page i24, Right Column, Last Paragraph and Page i30, Right Column, Second Paragraph).
Regarding claim 17, Abeel teaches inserting the promoter into a nucleic acid construct (Page i24, Right Column, Last Paragraph—Page i25, Left Column, Second Paragraph).
Regarding claim 18, Abeel teaches inserting the promoter into the nucleic acid construct comprises inserting the promoter into the nucleic acid construct upstream of a transgene to drive expression of the transgene (Page i24, Right Column, First Paragraph).
Regarding claim 20, Abeel teaches providing, to a predictive model, the nucleotide sequence and determining, based on the predictive model, that the nucleotide sequence is a core promoter (Abstract and Figure 4).
Abeel does not teach or suggest generating, for each nucleotide sequence in the training data set, a plurality of seed sequence and target nucleotide pairs. Abeel does not teach or suggest vectorizing each seed sequence and target nucleotide pair of the plurality of seed sequence and target nucleotide pairs and training, based on the vectorized seed sequence and target nucleotide pairs, the generative model. Abeel does not teach or suggest each seed sequence and target nucleotide pair comprises a seed sequence having a defined length and a target nucleotide immediately following the seed sequence on a given nucleotide sequence. Abeel does not teach or suggest vectorizing each seed sequence and target nucleotide pair of the plurality of seed sequence and target nucleotide pairs comprises encoding each nucleotide as a respective number. Abeel does not teach or suggest the generative model comprises a long-short term memory (LSTM) recurrent neural network (RNN). Abeel does not teach or suggest based on the generative model, the nucleotide sequence comprises receiving a seed sequence. Abeel does not teach or suggest predicting, based on the seed sequence, a next nucleotide. Abeel does not teach or suggest appending the next nucleotide to the seed sequence and repeating until a desired length for the nucleotide sequence is reached. Abeel does not teach or suggest the desired length is from about 50 nucleotides to about 100 nucleotides.
Linder teaches deep exploration networks (DENs), generative and predictive models using promoters (Abstract/Summary and Page 60, Left Column, First Paragraph). Linder teaches a training dataset (Page 59, Right Column, First Paragraph). Linder teaches vectorizing each seed sequence and target nucleotide pair of the plurality of seed sequence and target nucleotide pairs (Page 50, Right Column, Second and Third Paragraphs and Page e10, Tenth Paragraph). Linder teaches training, based on the vectorized seed sequence and target nucleotide pairs, the generative model (Page e2, First Paragraph and Page e13, Sixth Paragraph). Linder teaches each seed sequence and target nucleotide pair comprises a seed sequence having a defined length and a target nucleotide immediately following the seed sequence on a given nucleotide sequence (Page 50, Right Column, Second Paragraph and Page e4, Seventh Paragraph). Linder teaches vectorizing each seed sequence and target nucleotide pair of the plurality of seed sequence and target nucleotide pairs comprises encoding each nucleotide as a respective number (Page 50, Right Column, Second Paragraph and Page e4, Seventh Paragraph). Linder teaches the generative model comprises a long-short term memory (LSTM) recurrent neural network (RNN) (Page 56, Right Column, First Paragraph and Page e14, Eighth Paragraph). Linder teaches based on the generative model, the nucleotide sequence comprises receiving a seed sequence (Page 50, Right Column, Second Paragraph). Linder teaches predicting, based on the seed sequence, a next nucleotide and appending the next nucleotide to the seed sequence and repeating until a desired length for the nucleotide sequence is reached (Page 59, Last Paragraph—Page 60, First Paragraph and Page e12, Fourth Paragraph). Linder teaches the desired length is from about 50 nucleotides to about 100 nucleotides (Page e10, Fifth-Sixth Paragraph and Figure 4). Linder teaches using the disclosed methods and training data allows for improved fitness and diversity of the generative model (Page 50, Left Column, Third Paragraph, Page 52, Right Column, Second Paragraph and Page).
As a common field of endeavor Abeel, Georgakilas and Linder all disclose methods identifying TSS and promoter regions.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Abeel and Georgakilas with the teachings of Linder, using vectorized seed and target sequences, and training the generative model with said vectorized see and target sequences. Using these methods and training data sets would allow for improved fitness and diversity of the generative model as taught by Linder (Page 50, Left Column, Third Paragraph, Page 52, Right Column, Second Paragraph and Page).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Abeel et al. “ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles”, Bioinformatics, Volume 24, Issue 13, published July 01, 2008, cited on the IDS filed February 26, 2024, and Georgakilas et al. “Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data”. Sci Rep 10, Article 877, published January 21, 2020, cited on the IDS filed February 26, 2024, and Linder et al. (“A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences”, Cell Systems, Vol. 11, Issue 1, published July 22, 2020, as applied to claims 8-9, 11-12, 14-18 and 20 above, in view of Zhao et al. (“Systematic clustering of transcription start site landscapes”. PLoS One, 6(8), published August 24, 2011.
Regarding claim 10, Abel, Georgakilas and Linder teach generating, for each nucleotide sequence in the training data set, the plurality of seed sequence and target nucleotide pairs as discussed above.
Abel does not teach or suggest clustering, based on the associated expression scores, the TSSs. Abeel does not teach or suggest determining, for each cluster of TSSs, an interquantile width. Abel does not teach or suggest labeling, based on the interquantile width, each TSS as a sharp TSS or a broad TSS. Abel does not teach or suggest dividing, based on sharp TSS or broad TSS labeling, the nucleotide sequences in the training data set into a sharp TSS group or a broad TSS group. Abel does not teach or suggest applying a sliding window of the defined length and having a defined step size to each nucleotide sequence and storing, at each step of the sliding window, a seed sequence and target nucleotide pair.
Zhao teaches identification of TSS and core promoter and classifying the TSS as broad or sharp (Abstract and Page 1, Right Column, Second Paragraph). Zhao teaches clustering, based on the associated expression scores, the TSSs (Page 4, Left Column, Last Two Paragraphs). Zhao teaches determining, for each cluster of TSSs, an interquantile width and labeling, based on the interquantile width, each TSS, as a sharp TSS or a broad TSS (Page 1, Right Column, Second Paragraph, Page 6, Left Column, Last Paragraph and Figure 2). Zhao teaches dividing, based on sharp TSS or broad TSS labeling, the nucleotide sequences in the training data set into a sharp TSS group or a broad TSS group (Page 1, Right Column, Second Paragraph and Page 4, Right Column, Fourth-Sixth Paragraph). Zhao teaches applying a sliding window of the defined length and having a defined step size to each nucleotide sequence and storing, at each step of the sliding window, a seed sequence and target nucleotide pair (Page 6, Left column, First Paragraph, Page 9, Right Column, Third-Fifth Paraphs and Figure S6). Zhao teaches using this clustering approach, labeling the TSSs as broad or sharp, allows for exploration and analysis of similarities and stabilities of TSSs (Abstract).
As a common field of endeavor Abeel, Georgakilas, Linder and Zhao all disclose methods identifying TSS and promoter regions.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the combined teachings of Abeel, Georgakilas and Linder, with the teachings of Zhao, clustering the TSS and labeling each TSS as sharp or broad. Using this clustering approach, labeling the TSSs as broad or sharp, allows for exploration and analysis of similarities and stabilities of TSSs as taught by Zhao (Abstract).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Abeel et al. “ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles”, Bioinformatics, Volume 24, Issue 13, published July 01, 2008, cited on the IDS filed February 26, 2024, and Georgakilas et al. “Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data”. Sci Rep 10, Article 877, published January 21, 2020, cited on the IDS filed February 26, 2024, and Linder et al. (“A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences”, Cell Systems, Vol. 11, Issue 1, published July 22, 2020, as applied to claims 8-9, 11-12, 14-18 and 20, in view of Engreitz et al. (WIPO International Application Publication WO 2018/064208 A1, published April 05, 2018).
Regarding claim 19, Abeel teaches the nucleic acid construct as discussed above.
Abeel does not teach or suggest producing an adeno associated virus or a lenti-virus comprising the nucleic acid construct.
Engreitz teaches using known promoters and TSS expression peaks, as well as surrounding nucleotide to identify gene regulatory elements (Page , [0029]). Engreitz teaches producing training data generated through mapping the regulatory elements to train a predictive model (Page 243, [00928] and Page 250, [00955]). Engreitz teaches producing an adeno associated virus or a lenti-virus comprising the nucleic acid construct (Pages 11-12, [0036] and Page 113, [00324]). Engreitz teaches it is advantageous to produce an adeno associated virus (AAV) vector because the AAV vector allows for an increased number of promoters which would allow for targeting up to about 50 genes (Pages 60-61, [00186]).
As a common field of endeavor Abeel, Georgakilas, Linder and Engreitz all disclose methods identifying TSS and promoter regions.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Abeel, Georgakilas and Linder, with the teachings of Engreitz, producing an AAV comprising the nucleic acid construct. It is advantageous to produce an adeno associated virus (AAV) vector when identifying regulatory elements because the AAV vector allows for an increased number of promoters which would allow for targeting up to about 50 genes as taught by Engreitz (Pages 60-61, [00186]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA DANIELLE PARISI whose telephone number is (571)272-8025. The examiner can normally be reached Mon - Friday 7:30-5:00 Eastern with alternate Fridays off.
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/JESSICA D PARISI/Examiner, Art Unit 1684
/HEATHER CALAMITA/Supervisory Patent Examiner, Art Unit 1684