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 .
DETAILED ACTION
Status of Claims
The present Office Action is pursuant to Applicant’s communication on 07-05-2023; this application has provisional application No. 63/358,573 filed on 07-06-2022.
Examiner’s Note
The rejections below group claims that may not be identical, but whose language and scope are so substantively similar as to lend themselves to grouping, in the interests of clarity and conciseness.
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 1-14 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claim(s) 1-14 is/are within the four statutory categories. Claim(s) 1-14 is/are drawn to a method1, system 2 which means that said claims(s) is/are within the four statutory categories (i.e. process). However, as will be shown below, arguendo, Aforementioned claim(s) is/are nonetheless unpatentable under 35 U.S.C. 101.
Prong 1 of Step 2A
What is claimed:
1. A computer-implemented method for personalized identification of neoantigens from sample de nova peptides sequences obtained from a patient, the method comprising: obtaining a first dataset of HLA-1 binding de nova self peptides sequences of the patient; obtaining a second dataset of patient allele-matched T-cell epitope sequences; wherein the first and second datasets are for training an artificial neural network to classify the sample peptide sequences based on T-cell recognition; selecting sample peptide sequences that match with sequences of the second dataset; and excluding sample peptide sequences that match with the first dataset, wherein the remaining selected sample peptide sequences are identified as candidate neoantigens.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas of a mental process and/or a certain method of organizing human activity because they recite a process that is a manner of organizing human activity, comprising employing pattern recognition of signatures associated with cancer phenotypes directed to pancreatic cancer, but for the recitation of generic computer components (i.e. the computer), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract ideas are deemed “additional elements,” and will be discussed in further detail below.
Artificial neural networks and data sorting are mathematical concepts or mental processes – obtaining two datasets (positive and negative), training a neural network, and selecting/excluding matching sequences; this process is akin to managing a bouncer at a nightclub wherein the bouncer is taught how to let in a person(s) to the nightclub by handing the bouncer a VIP list (positive T-cell) and a banned list, selecting a first person(s) on a line to the nightclub who represent matches on the VIP list and allowing said person(s) entry to the nightclub and rejecting a person(s) if the person matches the banned list, excluding entry to the club.
The above process is also considered a set of rules for organizing human activity associated with comparing genomic lists associated with immunotherapy candidates. Even if the task is highly complex, fundamentally, comparing lists and sorting them is an abstract idea. Even if the bouncer has a massive VIP list of millions of names, a high-speed scanner and computer can perform the process substantially instantaneously – hence employing a computer with neural networks is simply an apply, doing it on said computer and scanner.
Dependent claim(s) 2-8 and 10-14, include other limitations, for example:
2. The method of claim 1, wherein obtaining the first dataset comprises: conducting a HLA-1 immunoprecipitation assay on a patient cell sample; and sequencing peptides from the immunoprecipitation assay using mass spectrometry. 3. The method of claim 2, comprising obtaining sequenced peptides that are between and including Sand 14 amino acids in length for the first dataset. 4. The method of claim 2, wherein the patient cell sample comprise a normal cell sample, or a combination of normal and tumor cells sample.
5. The method of claim 1, wherein obtaining the second dataset comprises: obtaining a database of epitopes that are T cell positive, and selecting epitopes from the database that match against the patient's HLA-1 alleles.
6. The method of claim 5, comprising selecting peptides that are between and including 8 and 14 amino acids in length for the second dataset. 7. The method of claim 1, comprising training a binary classification model to predict T cell response to the sample peptide sequences.
8. The method of claim 1, comprising outputting a score representing the likelihood that a candidate neoantigen will be recognized by CDS+ T cells of the patient.
10. The system of claim 9, wherein the processor is configured to output a score representing the likelihood that a candidate neoantigen will be recognized by CDS+ T cells of the patient.
11. The system of claim 9, wherein the first and second dataset comprise peptide sequences that are between and including Sand 14 amino acids in length.
12. The system of claim 9, wherein the artificial neural network is trained on a binary classification model to predict T cell response to the sample peptide sequences.
13. The system of claim 9, wherein the plurality oflayered nodes comprise one or more of: an embedding layer; a bi-directional LSTM layer; a fully-connected layer with L2 regularizer; and a sigmoid activation layer.
14. The system of claim 13, wherein the plurality of layered nodes comprise one or more of: an embedding layer of S neural units; a bi-directional LSTM layer of S units; a fully-connected layer with L2 regularizer; and a sigmoid activation layer.
However these dependent claims only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent claim(s) 2-8 and 10-14 are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claim(s) 2-8 and 10-14 are nonetheless directed towards fundamentally the same abstract idea as independent Claim(s) 1, 9.
Prong 2 of Step 2A
Claim(s) 1, 9 is/are not integrated into a practical application because the additional elements (i.e. comprising non-underlined limitations above – in this case a processor and at least one memory providing a plurality of layered nodes configured to form an artificial neural more neoantigen candidates, the artificial neural network trained on obtained data) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a computer, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see ¶¶1-59 of the present Specification, see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g).
Additionally, dependent claim(s) 2-8 and 10-14 include other limitations, but these limitations also amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not include any additional elements beyond those already recited in independent Claim(s) 1, 9, hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claim(s) 1, 9 do/does not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. comprising non-underlined limitations above – in this case a processor and at least one memory providing a plurality of layered nodes configured to form an artificial neural more neoantigen candidates, the artificial neural network trained on obtained data), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
¶¶1-59 of the Specification discloses that the additional elements (i.e. the computer) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites storing or uploading media;
Dependent claim(s) 2-8 and 10-14 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite any additional elements not already recited in independent Claim(s) 1, 9 hence does not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claim(s) 1-14 is/are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
Claim(s) 1 and 5-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shan3 in view of Smith4.Regarding claim(s) 1, 9, Shan discloses: A computer-implemented method for personalized identification of neoantigens from sample de nova peptides sequences obtained from a patient, A computer implemented system for personalized identification of neoantigens from sample peptides sequences obtained from a patient using neural networks, a processor and at least one memory providing a plurality of layered nodes configured to form an artificial neural more neoantigen candidates, the artificial neural network trained on, the computer implemented system comprising the method comprising5:
obtaining a first dataset of HLA-1 binding de nova self peptides sequences of the patient; [“DeepNovo is used to identify novel [de novo] peptides from human leukocyte antigen (HLA) dataset”6, wherein “[t]o generate the training data for DeepNovo, an in-house database search tool was built for DIA data. First, from each precursor feature and its associated MS/MS spectra, a pseudo-spectrum was generated. In particular, the Pearson correlation coefficient was calculated between the LC eluting profiles of the precursor and MS/MS fragment ions. Then, fragment ions were selected that have Pearson correlation coefficient above 0.6 and used up to 500 most correlated ones to form the pseudo-spectrum”7]
Regarding [b], Shan discloses obtaining a second dataset; [“The pseudo-spectra and the corresponding precursor information like rn/z, charge were then searched against the UniProt/Swiss-Prot human database. This step can be done with any conventional DDA database search engines, such as PEAKS DB [13] was used in this case. Common parameter settings were used, such as: trypsin digestion, fixed modification C(Carbamidomethylation), precursor mass tolerance 30 ppm, fragment mass tolerance 0.02 Da for the plasma dataset. For HLA datasets, non-enzyme digestion, no modification, 20 ppm and 0.05 Da were used. The peptides were identified at 1% FDR cut-off were then assigned to the corresponding precursor features and were used as labels for training”8]
Shan does not explicitly disclose as disclosed by Smith:
obtaining a second dataset of patient allele-matched T-cell epitope sequences; [“CD8+ T cell recognition of peptide epitopes plays a central role in immune responses against pathogens and tumors... we capitalize on recent (neo-)epitope data to train a predictor of immunogenic epitopes (PRIME)”9 obtained “from healthy donors and cancer patients … [validated] using naïve CD8+ T cells … simulated in vitro with each peptide separately”10]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Shan, including mechanism(s) [b] as taught by Smith. One of ordinary skill would have been so motivated to employ said mechanism(s) since PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition11.
Regarding [c], Shan discloses employing a neural network for classification; [“The confidence score of a peptide sequence is the sum of its amino acids' scores. The score of each amino acid is the log of the output probability distribution, i.e., the final softmax layer of the neural network model, at each sequencing iteration. The score was trained using only the training dataset. When applied to a new specific dataset, in some cases cut-off was selected to filter the de nova results of that dataset. This is similar to the case of database search, when setting 1% FDR, the cut-off score changes from one dataset to another. However, there is no such target-decoy method to estimate FDR for de nova sequencing. Hence, database search and de nova results were compared on their overlap- ping features, calculated de nova accuracy from above, and plotted the distribution of de nova confidence score with respect to de nova accuracy (FIG. 7B). Then, from the distribution, a cut-off of de nova confidence score was selected so that the de nova accuracy is 90% at amino acid level on the overlapping features. Finally, that cut-off was applied to de nova results of all features”12; see also ¶¶17, 124]
Shan does not explicitly disclose as disclosed by Smith:
wherein the first and second datasets are for training an artificial neural network to classify the sample peptide sequences based on T-cell recognition; [“CD8+ T cell recognition of peptide epitopes” 13]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Shan, including mechanism(s) [c] as taught by Smith. One of ordinary skill would have been so motivated to employ said mechanism(s) since PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition14.
Shan discloses:
selecting sample peptide sequences that match with sequences of the second dataset (i.e., the processor configured to perform selecting); [“The remaining 130 are de nova peptides... filtered [excluded] using the augmented-database search with 1% FDR”15] and
excluding sample peptide sequences that match with the first dataset, wherein the remaining selected sample peptide sequences are identified as candidate neoantigens. [“The remaining 130 are de nova peptides... filtered [excluded] using the augmented-database search with 1% FDR”16; see also FIGs 3-4]
Regarding claim(s) 5, Shan-Smith as a combination disclose: The method of claim 1, Smith disclosing [a]: wherein obtaining the second dataset comprises: obtaining a database of epitopes that are T cell positive, and selecting epitopes from the database that match against the patient's HLA-1 alleles. [“immunogenic peptides were defined as peptides recognized by some T cells in T-cell assays”17]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Shan, including mechanism(s) [a] as taught by Smith. One of ordinary skill would have been so motivated to employ said mechanism(s) since PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition18.
Regarding claim(s) 6, Shan-Smith as a combination disclose: The method of claim 5, Smith disclosing [a]: comprising selecting peptides that are between and including 8 and 14 amino acids in length for the second dataset. [“AUC values (Figure S4D) were computed by taking as negatives for each sample 99-fold excess peptides randomly selected from the human proteome with length 8 to 14”19]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Shan, including mechanism(s) [a] as taught by Smith. One of ordinary skill would have been so motivated to employ said mechanism(s) since PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition20.
Regarding claim(s) 7, Shan-Smith as a combination disclose: The method of claim 1, Shan disclosing: comprising training a binary classification model to predict T cell response to the sample peptide sequences. [Wherein a “neural network [engaged in] end-end” “During training, the activation function of last layer was changed from softmax function to sigmoid function thus the model will give a probability between 0 and 1 for each of the 26 classes (note that here the sum of these 26 probabilities might not equal 1). Then for each class the binary classification focal loss could be computed for each class using the above formula and the average of those 26 losses was used as the final loss. At inference time the activation function was switched back to softmax as this was found to lead to better performance”21]
Regarding claim(s) 8, 10, Shan-Smith as a combination disclose: The method of claim 1, The system of claim 9, Smith disclosing [a]: comprising outputting a score representing the likelihood that a candidate neoantigen will be recognized by CD8+ T cells of the patient. [“CD8+ T cell recognition of peptide epitopes plays a central role in immune responses against pathogens and tumors... PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency”22]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Shan, including mechanism(s) [a] as taught by Smith. One of ordinary skill would have been so motivated to employ said mechanism(s) since PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition23.
Regarding claim(s) 11, Shan-Smith as a combination disclose: The system of claim 9, Smith disclosing [a]: comprising obtaining sequenced peptides that are between and including 8 and 14 amino acids in length for the first dataset and second dataset. [“AUC values (Figure S4D) were computed by taking as negatives for each sample 99-fold excess peptides randomly selected from the human proteome with length 8 to 14”24]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Shan, including mechanism(s) [a] as taught by Smith. One of ordinary skill would have been so motivated to employ said mechanism(s) since PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition25.
Regarding claim(s) 12, Shan-Smith as a combination disclose: The system of claim 9, Shan disclosing: wherein the artificial neural network is trained on a binary classification model to predict T cell response to the sample peptide sequences. [Wherein a “neural network [engaged in] end-end” “During training, the activation function of last layer was changed from softmax function to sigmoid function thus the model will give a probability between 0 and 1 for each of the 26 classes (note that here the sum of these 26 probabilities might not equal 1). Then for each class the binary classification focal loss could be computed for each class using the above formula and the average of those 26 losses was used as the final loss. At inference time the activation function was switched back to softmax as this was found to lead to better performance”26]
Regarding claim(s) 13, Shan-Smith as a combination disclose: The system of claim 9, Shan disclosing: wherein the plurality of layered nodes comprise one or more of:
an embedding layer;
a bi-directional LSTM layer;
a fully-connected layer with L2 regularizer; and
a sigmoid activation layer. [“During training, the activation function of last layer was changed from softmax function to sigmoid function thus the model will give a probability between O and 1 for each of the 26 classes (note that here the sum of these 26 probabilities might not equal 1). Then for each class the binary classification focal loss could be computed for each class using the above formula and the average of those 26 losses was used as the final loss. At inference time the activation function was switched back to softmax as this was found to lead to better performance”27]
Regarding claim(s) 14, Shan-Smith as a combination disclose: The system of claim 13, Shan disclosing: wherein the plurality of layered nodes comprise one or more of:
an embedding layer of 5 neural units;
a bi-directional LSTM layer of 5 units;
a fully-connected layer with L2 regularizer; and
a sigmoid activation layer. [“During training, the activation function of last layer was changed from softmax function to sigmoid function thus the model will give a probability between O and 1 for each of the 26 classes (note that here the sum of these 26 probabilities might not equal 1). Then for each class the binary classification focal loss could be computed for each class using the above formula and the average of those 26 losses was used as the final loss. At inference time the activation function was switched back to softmax as this was found to lead to better performance”28]
Claim(s) 2-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shan in view of Smith and further in view of Weinschenk29.
Regarding claim(s) 2, Shan-Smith as a combination disclose: The method of claim 1; however aforementioned combination does not disclose as disclosed by Weinschenk wherein obtaining the first dataset comprises:
conducting a HLA-1 immunoprecipitation assay on a patient cell sample; [“HLA peptide pools from shock-frozen tissue samples were obtained by immune precipitation from solid tissues according to a slightly modified protocol”]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Shan, including mechanism(s) [c] as taught by Weinschenk. One of ordinary skill would have been so motivated to employ said mechanism(s) to facilitate optimal acquisition of HLA peptides from samples directed to immunotherapy30.
Shan discloses:
sequencing peptides from the immunoprecipitation assay using mass spectrometry. [“The CNN can then be used to generate a probability vector of the original mass spectrometry image, portion thereof, or data representation of same for each of the other sequence locations. In this way, in some embodiments, the CNN is used to predict the amino acid sequence of a peptide based on mass spectrometry data of b-ions and y-ions or other peptide fragments”31]
Regarding claim(s) 3, Shan-Smith-Weinschenk as a combination disclose: The method of claim 2, Smith disclosing [a]: comprising obtaining sequenced peptides that are between and including 8 and 14 amino acids in length for the first dataset and second dataset. [“AUC values (Figure S4D) were computed by taking as negatives for each sample 99-fold excess peptides randomly selected from the human proteome with length 8 to 14”32]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Shan, including mechanism(s) [a] as taught by Smith. One of ordinary skill would have been so motivated to employ said mechanism(s) since PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition33.
Regarding claim(s) 4, Shan-Smith-Weinschenk as a combination disclose: The method of claim 2, Weinschenk disclosing: wherein the patient cell sample comprise a normal cell sample, or a combination of normal and tumor cells sample. [“allows the identification and selection of relevant over-presented peptide vaccine candidates based on direct relative quantitation of HLA-restricted peptide levels on cancer tissues in comparison to several different non-cancerous tissues and organ”34]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Shan, including mechanism(s) [a] as taught by Weinschenk. One of ordinary skill would have been so motivated to employ said mechanism(s) to facilitate optimal acquisition of HLA peptides from samples directed to immunotherapy35.
Conclusion
The prior art made of record36 and NOT relied upon is considered pertinent to applicant's disclosure:
Perreault37:
Acute myeloid leukemia (AML) has not benefited from innovative immunotherapies, mainly because of the lack of actionable immune targets. Novel tumor-specific antigens (TSAs) shared by a large proportion of AML cells are described herein. Most of the TSAs described herein derives from aberrantly expressed unmutated genomic sequences, such as intronic and intergenic sequences, which are not expressed in normal tissues. Nucleic acids, compositions, cells and vaccines derived from these TSAs are described. The use of the TSAs, nucleic acids, compositions, cells and vaccines for the treatment of leukemia such as AML is also described.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL EZEWOKO whose telephone number is 571 272 7850. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached on 571 270 5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-7850.
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/MICHAEL I EZEWOKO/Primary Examiner, Art Unit 3682
1 Claim(s) 1-8
2 Claim(s) 9-14
3 US 2019/0147983
4 See Form 892: Non-Patent Literature
5 ¶¶112, 125, 136: processors, memory instantiating neural networks
6 ¶190
7 ¶176
8 ¶177
9 Page 1
10 Page e5
11 Page 1
12 ¶179
13 Page 1
14 Page 1
15 ¶191
16 ¶191
17 Page 11
18 Page 1
19 Page e5
20 Page 1
21 ¶162
22 Page 1
23 Page 1
24 Page e5
25 Page 1
26 ¶162
27 ¶162
28 ¶162
29 US 11,939,401
30 71:1-67
31 ¶91
32 Page e5
33 Page 1
34 46:48-47:30
35 71:1-67
36Please see Form 892 for complete listing
37 US 2023/0287070
Prosecution Insights