DETAILED ACTION
Claim Status
claims 1, 4-5, 7, 12, 15-25, 40, 41 and 44 are rejected.
Claims 2-3, 8-11, 13-14, 26-39, 42-43, 45-47, 49-56 are canceled.
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, filed on 05/16/2023, makes no claim of priority. Therefore, the effective filing date of claims 1, 4-5, 7, 12, 15-25, 40, 41 and 44 is 05/16/2023.
Drawings
The drawings filed on 05/16/2023 are accepted.
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 05/23/2024 are incompliance with the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of list of references cited from each IDS is included with this Office Action.
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, 4-5, 7, 12, 15-25, 40, 41 and 44 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 (Claims 1, 4-7, 12, 15-25 (system), Claims 40-41 (method), Claim 44 (method), Claim 48 (system)) ( 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:
1. (b), that outputs a classification for an experimental parameter, among a plurality of measurement types, that was used to generate the mass spectrum.
4. (Currently Amended) The neural network of claim 1, wherein the experimental parameter comprises a surface type, a sample type, a liquid chromatography (LC) column type, an LC system pressure, a mass ionizer type, a buffer type, a pH, a temperature, a contamination, a subject characteristic, or any combination thereof.
5. (Currently Amended) The neural network of claim 4, wherein the mass spectrum is generated from at least a part of a biological sample, wherein the experimental parameter comprises the subject characteristic, and wherein the subject characteristic comprises a characteristic associated with a subject from which the sample is derived.
7. (Currently Amended) The neural network of claim 4, wherein the experimental parameter comprises the surface type, and wherein the surface type comprises a particle type.
12. (Currently Amended) The neural network of claim 1, wherein the mass spectrum is generated from biomolecules enriched using surface-adsorption.
15. The neural network of claim 4, wherein the biological sample comprises plasma or serum
16. (Currently Amended) The neural network of claim 15, wherein the biological sample comprises proteins.
17. (Currently Amended) The neural network of claim 1, wherein the mass spectrum is generated from tandem liquid chromatography-mass spectrometry (LC- MS/MS).
18. (Currently Amended) The neural network of claim 17, wherein the mass spectrum comprises an MS1 spectrum of the LC-MS/MS.
19. (Currently Amended) The neural network of claim 18, wherein the mass spectrum comprises an MS2 spectrum of the LC-MS/MS.
20. (Currently Amended) The neural network of claim 1, wherein the mass spectrum comprises a mass spectrum from sequential mass spectrometry (MS").
21. (Original) The neural network of claim 20, wherein the sequential mass spectrometry is tandem liquid chromatography-sequential mass spectrometry (LC-MS").
22. (Currently Amended) The neural network of claim 20 or 21, wherein n equals at least 3, 4,5, 6, 7, 8, 9, or 10.
23. (Currently Amended) The neural network of claim 1, wherein the mass spectrum is provided to the first layer as an image map.
24. (Original) The neural network of claim 23, wherein the image map is subjected to one or more image processing operations.
25. (Original) The neural network of claim 24, wherein the image processing operation comprises an image compression operation, an image filtering operation, an object detection operation, an image concatenation operation, an image segmentation operation, an image downsampling operation, or any combination thereof.
41. (Original) The method of claim 40, further comprising repeating (d) with one or more additional neural networks to provide a plurality of determinations and determining the potential operational error exists based on the plurality of determinations.
44. (b) training a neural network, on a training subset of the dataset, to distinguish between the first subset and the second subset; and
44. (c) testing the neural network on a holdout subset of the dataset to relabel a third subset of mass spectra in the plurality of mass spectra, thereby recategorizing a portion of (i) the first subset as non-anomalous, (ii) the second subset as anomalous, or (iii) both.
48. (iv) analyzing the mass spectrometry data using the serverless cloud computing instance, wherein the analyzing comprises associating, with the aid of a neural network, the mass spectrometry data with one or more experimental parameters; and
48. (v) identifying samples with the experimental parameter data inconsistent with a neural network association.
The steps for “output”, “determining,” “training,” “testing, “analyzing” and “identifying” are all verbal equivalents for mathematical transformations made to numerical feature data. The steps are so broadly recited that they could be performed by a human being using a pen and paper. Therefore, these limitations fall under the “Mental process” grouping of abstract ideas. While claims 1, 40, 44, and 48 recite performing some aspects of the analysis with a “neural network” or “cloud computing instance”, there are no additional limitations that indicate that this neural network requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1, 4-5, 7, 12, 15-25, 40, 41 and 44 recite 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:
1. A neural network for identifying potential operational errors in mass spectrometry measurements
1. (a) a first layer that receives a mass spectrum
1. a second layer, in operable communication with the first layer
1. (a) contacting a plurality of biomolecules with a first surface and a second surface to adsorb the plurality of molecules thereon,
40. (b) desorbing the plurality of biomolecules from (i) the first surface to generate a first mass spectrum, and (ii) the second sample to generate a second mass spectrum
40. (c) performing mass spectrometry using (i) the first sample to generate a first mass spectrum, and (ii) the second sample to generate a second mass spectrum; and
40. (d) determining, using a neural network, whether the first mass spectrum is associated with signals from biomolecules desorbed from the first surface or the second surface, wherein a potential operational error exists when the first mass spectrum is not associated with signals from biomolecules desorbed from the first surface.
44. (a) providing a dataset comprising a plurality of mass spectra, wherein a first subset of the mass spectra is labeled with an anomaly indicator and the second subset of the mass spectra is not labeled with an anomaly indicator
48. (i) receiving experimental parameter data for a set of biological samples
48. (ii) receiving mass spectrometry data characterizing the set of biological samples
48. (iii) instantiating a serverless cloud computing instance;
The steps for receiving or providing mass spectrum data are “mere data gathering” limitations, 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. The steps for adsorbing and desorbing the biomolecules to obtain the mass spectra are also “mere data gathering,” similar to determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. There are no limitations that indicate that the claimed neural network, cloud computing instance 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. See also 573 U.S. at 224, 110 USPQ2d at 1984. Training and using the neural networks indicate a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). As such, claims 1, 4-5, 7, 12, 15-25, 40, 41 and 44 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.
Alzubadi et al. provides evidence that a “feature extraction layer” or input layer and a classification layer are well-understood, routine and conventional functions of neural networks (pg 14 ¶ 3). Serverless cloud computing instances are well-understood, routine and conventional, as explained by the abstract of Jinfeng et al. (arXiv:2206.12275, 2022). The abstract of evidentiary reference Rappold (Ann Lab Med. 2022 Sep 1;42(5):531–557) explains that the steps for adsorbing and desorbing molecules, as is done in LC-MS/MS, are well-understood, routine and conventional. As discussed above, there are no additional limitations to indicate that the claimed neural network or cloud computing instance 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 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, 4-5, 7, 12, 15-25, 40, 41 and 44 are not patent eligible.
Claim Rejections - 35 USC § 102
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.
Claims 1, 4-5, and 23-25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Abdelmoula et al. (Bioinformatics, 38(7), 2022, 2015–2021).
Regarding claim 1, fig. 2 of Abdelmoula shows a neural network with an input layer receiving a mass spectrum, and an output layer that provides a classification of tumor types.
Regarding claim 4, the neural network classifies a subject characteristic as its experimental parameter, namely whether the subject has cancer (abstract).
Regarding claim 5, the mass spectrum was generated from a biological sample (abstract). The subject characteristic of having cancer is associated with the subject.
Regarding claim 23, Abdelmoula’s method provides an image map of a mass spectrum to the first layer of the neural network (pg 2016 right col ¶ 4).
Regarding claim 24, Abdelmoula’s image is processed into a binary vector (pg 2016 right col ¶ 4).
Regarding claim 25, the vectorization of Abdelmoula’s image is an image compression operation (pg 2016 right col ¶ 4).
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 7, 12, 15-22, 40, and 44 are rejected under 35 U.S.C. 103 as being unpatentable over Abdelmoula as applied to claims 1, 4-5, and 23-25 under 35 USC 102(a)(1) above, and further in view of Ouassil et al. (Sci. Adv. 8, eabm0898 (2022)).
Regarding claim 7, Ouassil teaches testing binding of proteins to carbon nanotubes (abstract).
Regarding claim 12, Ouassil’s method uses surface adsorption (pg 2 right col ¶ 1).
Regarding claim 15, Ouassil’s method used blood plasma as biological samples (pg 2 right col ¶ 1).
Regarding claim 16, Ouassil’s biological sample comprises proteins (abstract).
Regarding claim 17, Ouassil’s method uses LC-MS/MS (pg 2 right col ¶ 1).
Regarding claims 18-19, Abdelmoula (fig. 1) shows both m/z features (ms1) and spectra features (ms2) being included in the neural network training.
Regarding claims 20-21, Ouassil specifies that their method uses tandem liquid chromatography-mass spectrometry and is sequential (pg 2 right col ¶ 1).
Regarding claim 22, Ouassil’s system used LC-MS^8 for 8 analytes (fig. 4).
Regarding claim 40, Ouassil’s proteins are adsorbed to (GT)15-SWCNTs (pg 2 right col ¶ 1) and to (GT)6-SWCNTs (pg 6 left col ¶ 2), then desorbed to generate two sets of samples. Mass spectrometry was implemented to generate two mass spectra (pg 2 right col ¶ 1), which were distinguished using an XGBoost classifier (pg 3 left col ¶ 2).
Ouassil is silent as to a neural network.
Abdelmoula teaches the use of a neural network (abstract).
Regarding claim 44, fig. 1 of Abdelmoula shows a training and testing set and workflow where a first subset is labeled with an anomaly indicator.
Regarding claims 7, 12, 15-22, 40, 44, 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 neural network in the text of Abdelmoula, because it demonstrates higher accuracy and speed than classical machine learning methods (abstract). 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 LC-MS/MS analysis. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Ouassil by incorporating the neural network of Abdelmoula, in order to take advantage of the speed and accuracy increases (abstract).
Claim 41 is rejected under 35 U.S.C. 103 as being unpatentable over Abdelmoula and Ouassil as applied to claims 1, 4-5, 7, 12, 15-25, 40, and 44 above, and further in view of Kantz et al. (Anal. Chem. 2019 October 01; 91(19): 12407–12413).
Regarding claim 41, Kantz uses an image based deep neural network and a peak group parameter neural network to identify operational error based on determinations (pg 4 ¶ 2-3).
Regarding claim 41, 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 multiple neural networks in the text of Kantz to identify errors (pg 4 ¶ 2-3). 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 LC-MS/MS analysis. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Abdelmoula and Ouassil by implementing the multiple neural networks of Kantz, in order to better identify errors (pg 4 ¶ 2-3).
Claim 48 is rejected under 35 U.S.C. 103 as being unpatentable over Abdelmoula, Ouassil and Kantz as applied to claims 1, 4-5, 7, 12, 15-25, 40-41 and 44 above, and further in view of Jinfeng et al. (arXiv:2206.12275, 2022).
Claim 48’s steps i-iv are similar to steps a-d of claim 40, and step v is similar to claim 41, but implemented on a serverless cloud computing instance. The arguments against claims 40-41 apply, mutatis mutandis. Jinfeng et al teaches a serverless cloud computing instance (abstract).
Regarding claim 48, 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 serverless computing in the text of Jinfeng, as it frees developers from tedious infrastructure management (abstract). 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 already computer implemented. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Abdelmoula, Ouassil and Kantz by implementing it with serverless computing as in Jinfeng, in order to in order to free developers from tedious infrastructure management (abstract).
Conclusion
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/G.M.H./Examiner, Art Unit 1685
/Robert J. Kallal/Examiner, Art Unit 1685
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