DETAILED ACTION
Claim Status
Claims 1-15 are rejected.
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 .
Priority
This application claims Foreign Priority to application # EP20208656.7, filed 11/19/2020. Foreign Priority is acknowledged. Therefore, the effective filing date of claims 1-15 is 11/19/2020.
This application is a 371 of PCT/EP2021/066541. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The Information Disclosure Statement filed on 07/24/2023 is in compliance with the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of list of references cited from each IDS is included with this Office Action.
Drawings
The drawings submitted on 05/16/2023 are accepted.
Claim Objections
Claim 2 objected to because of the following informalities: the claim should read “samples of the sequencing data include” rather than includes for noun-verb plurality agreement. Appropriate correction is required.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1 : YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1: A computer-implemented method for quantifying cellular activity from high throughput sequencing data, the method comprising: generating a multimodal knowledge graph by combining a gene regulatory network;(GRN); with gene annotations from domain knowledge, wherein the nodes of the multimodal knowledge graph are genes and wherein the gene annotations enrich the relations among the genes; creating a number of gene modules; GMs); by clustering embeddings of the genes of the GRN; embedding samples of sequencing data into the multimodal knowledge graph; and generating, for each sample of the sequencing data, an activation vector in which the respective sample is expressed as the distances between the embedding and centroids of each of the number of GMs,
Claim 2: The method according to claim 1, wherein the samples of the sequencing data includes single RNA-cell sequencing data, such that each sample of the sequencing data is a single cell,
Claim 3: The method according to claim 1, wherein embedding the samples of the sequencing data into the multimodal knowledge graph includes linearly combining the samples of the sequencing data with the embeddings of the genes,
Claim 4: clustering the embeddings and providing the clusters as the GMs,
Claim 5: The method according to any of claim 1, further comprising, prior to generating the activation vectors: applying a collaboration filtering algorithm to remove dropout values and other sources of noise from the high throughput sequencing data,
Claim 6: The method according to any of claim 1, further comprising: using the activation vectors as input for training a machine learning algorithm to predict a response of individual cells to an applied drug,
Claim 7: The method according to claim 6, further comprising: using the trained machine learning algorithm to predict a label 'responder' or 'non-responder' for each cell of the sequencing data,
Claim 8: The method according to claim 7, further comprising: a voting process that establishes a total response of a patient to a specific drug with a confidence score, wherein the fraction of cells predicted to respond to the drug represents the confidence that the patient will respond to the drug,
Claim 9: The method according to claim 8, further comprising: providing as output one or more of the most important activation vectors used in the prediction as an explanation for the obtained results,
Claim 14: The processing system according to claim 13, further comprising a treatment scheduler as a front-end application configured to be used by a clinician to load the prediction outcome and to select a treatment,
The limitations for “generating,” “creating,” and “clustering” are recited so broadly that there are embodiments which could be performed by a human being using a pen and paper, falling under the “mental process” grouping of abstract ideas. Generating vectors is a mathematical process of concatenating numbers together. Interpreting “generating centroids” in its broadest reasonable interpretation based on the specification (page 17 line 30-35), this is a mathematical process of finding the center of a cluster of points. As such, claims 1-15 recites an abstract idea ( Step 2A, Prong 1 : YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to effect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
Claim 4: using a graph convolutional neural network (GCNN) to create the embeddings of each gene based on the GRN with the gene annotations,
Claim 10: the system comprising one or more processors,
Claim 11: The processing system according to claim 10, further comprising a database containing high-throughput samples of different tumor cells, treated with different drugs, together with the multimodal knowledge graph,
Claim 12: The processing system according to claim 11, further comprising a server configured to use multimodal knowledge graph from the database for training a machine learning algorithm based on the activation vectors to predict a response of individual cells to an applied drug,
Claim 13: The processing system according to claim 12, further comprising a client configured to be used by a clinician to upload high throughput sequencing data obtained from a patient to the server, wherein the server is configured use the trained machine learning algorithm to predict a label 'responder' or 'non- responder' for each cell of the sequencing data, and return the prediction outcome including the drug(s) that yield(s) a response to the client,
Claim 15: A non-transitory computer-readable medium comprising code for causing one or more processors of a processing system,
There are no limitations that indicate that the claimed “graph convolutional neural network,” “system,” “a non-transitory computer readable medium” or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. The limitation for a “server” of which the “client” is a part, and the limitation for a “database” are forms of mere data gathering, similar to presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-15 are directed to an abstract idea ( Step 2A, Prong 2 : NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite additional elements enumerated above, in the section on step 2A.
Yue et al. (Bioinformatics, 36(4), 2020, 1241–1251) contains a review of graph embedding methods, and lists GCNN among them, providing evidence that GCNN is well-understood, routine and conventional (MPEP 2106.05(d)). The database are a form of Storing and retrieving information in memory, 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. The limitation for the server is a form of receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362. As discussed above, there are no additional limitations to indicate that the claimed “GCNN”, “system” or “non-transitory computer readable medium” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The limitation for treating the tumor cells equate to mere instructions to apply the judicial exception in a generic way because the treating step is so generically recited. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No). As such, claims 1-15 are not patent eligible.
Claim Rejections - 35 USC § 103
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, 10, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Aibar et al. (Nat Methods. 2017 November ; 14(11): 1083–1086.) and Saul et al. (Journal of Machine Learning Research, 2001).
Regarding claims 1, 10 and 15, Aibar combines a gene regulatory network (GRN) of genes (pg 2 ¶ 2) with gene annotations from domain knowledge that enriches the relation between genes (pg 8 ¶ 1).
Aibar creates “regulons,” or modules/clusters of genes related by the GRN and annotation information (pg 2 ¶ 2).
Aibar generates a t-SNE plot of the samples where the sample is expressed as a distance from an embedding to the centroids of the regulon clusters (pg 14 fig. 1A and description).
Aibar is silent as to embedding the samples of the sequencing data into the knowledge graph.
Saul teaches using graph embedding to combine different types of information into a single graph (pg 1 abstract).
Regarding claim 2, the samples of Aibar are from sc-RNAseq (pg 1 abstract).
Regarding claim 3, Saul teaches locally linear embedding, which embeds different types of high dimensional data with each other through linear combination (pg 2 ¶ 2).
Regarding claims 1-3, 10 and 15, an invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use linear combination embedding in the text of Saul, to integrate different types of high dimensional data. There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as the method of Aibar is related to the type of high dimensional data that Saul’s method works with. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Aibar by embedding the cells into the gene graph with linear combination, in order to reap the analytical benefits of a combined graph (Saul abstract).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Aibar and Saul as applied to claims 1-3, 10, and 15 above, and further in view of Yue et al. (Bioinformatics, 36(4), 2020, 1241–1251).
Regarding claim 4, Yue teaches creating embeddings with a graph convolutional neural network (pg 1243 left col ¶ 5). Aibar teaches clustering the embeddings into gene modules (pg 2 ¶ 2).
Regarding claim 4, an invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use graph convolutional neural networks in the text of Yue to create embeddings from network data. There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as Aibar and Saul work with network data. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Aibar and Saul by incorporating the graph convolutional neural network of Yue, in order to better learn node embeddings (pg 1243 left col ¶ 5).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Aibar and Saul as applied to claims 1-3, 10 and 15 above, and further in view of Wang et al. (KDD 2015).
Regarding claim 5, Wang teaches using collaboration filtering as a method to reduce noise (abstract).
Regarding claim 5, an invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use collaboration filtering in the text of Wang in order to reduce noise (abstract). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as there are no features of collaborative filtering which would make it non-trivial to apply to the model of Aibar and Saul (abstract). Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Aibar and Saul by incorporating the collaborative filtering of Wang, in order to reduce noise (abstract).
Claims 6-7 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Aibar and Saul as applied to claims 1-3, 10 and 15 above, and further in view of Sakellaropoulos et al. (Cell Reports 29, 3367–3373, December 10, 2019).
Regarding claim 6, Sakellaropoulos teaches using the activation vectors to predict drug response and non-response (pg 3368, fig. 1B description).
Regarding claims 7 and 12, Sakellaropoulos teaches using machine learning to predict drug response and non-response (pg 3368, fig. 1B description).
Regarding claim 11, Sakellaropoulos teaches a database with tumor cells treated with different drugs (abstract). Aibar teaches the multimodal knowledge graph (abstract).
Regarding claims 6-7 and 11-12, An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use drug response prediction in the text of Sakellaropoulos to predict drug targets. There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as Aibar and Saul’s method is related to cell information that could change drug efficacy. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Aibar and Saul by using drug response prediction, in order to find new drug targets (Sakellaropoulos abstract).
Claim 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Aibar, Saul, and Sakellaropoulos as applied to claims 1-3, 10 and 15 above, and further in view of Sheng et al. (IEEE J Biomed Health Inform. 2015 Mar 13;19(4):1264–1270.).
Regarding claim 8, Sheng teaches using a voting scheme of cell data responses to create a “sensitivity signature” or confidence score to predict drug response (abstract).
Regarding claim 9, Sheng outputs the top ten most sensitive activation vectors (conclusion ¶ 2).
Regarding claim 8, an invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use a voting-based sensitivity signature and output in the text of Sheng (abstract), in order to find drug targets. There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as both methods are related to predicting outcomes from personal genomics data. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Aibar and Saul by incorporating the voting scheme of Sheng, in order to find drug targets (abstract).
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Aibar, Saul, Sheng and Sakellaropoulos as applied to claims 1-3, 10, 11-12 and 15 above, and further in view of Click2Drug (Swiss Institute of Bioinformatics, 2018, https://www.click2drug.org/).
Regarding claim 13, Sakellaropoulos teaches using machine learning to predict drug response and non-response (pg 3368, fig. 1B description). Sheng teaches selecting optimal drugs for a patient (conclusion ¶ 2). Click2drug describes web servers that allow clinicians to upload high throughput sequencing data and receive result information (Target Prediction, Web Services).
Regarding claim 14, Click2drug describes software tools that allow an individual to upload data and receive potential treatments (Target Prediction, Software).
Regarding claims 13 and 14, An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use client-and-server operational schemes in the text of Click2Drug (Target Prediction). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as both methods deal with the same data types. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Aibar and Saul by incorporating a client-and-server operational setup, in order to predict drug targets.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACELYN M HILL whose telephone number is (571)272-9871. The examiner can normally be reached Monday-Friday 8:30-5pm.
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/G.M.H./Examiner, Art Unit 1685
/Robert J. Kallal/Examiner, Art Unit 1685
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