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
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 13 September 2024 and 26 March 2025 were filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 14 (and dependent claim 17) recite “A method for extracting additional information from a data independent acquisition (DIA) mass spectrometry experiment, comprising: obtaining n product ion spectra in a processor; comparing the n product ion spectra to a library of product ion mass spectra for known compounds to identify an initial i compounds corresponding to l spectra of the sample using the processor, and performing a reinforcement learning algorithm (RLA) using the processor in which the processor a. acting as an agent of the RLA, performs an action A.sub.t that includes searching one or more compound databases for compounds related to the i compounds, producing j related compounds, and applying one or more deep learning prediction algorithms (DLPAs) to predict k product ion spectra for the i+j compounds, b. acting as an environment of the RLA, compares the k spectra to the n spectra, producing a state, S.sub.t, in which i+j compounds produce m matching compounds and a reward, R.sub.t, for the agent if m>i, and c. if the R.sub.t is produced, sets the i compounds to the m compounds and the l spectra to the k spectra, and repeats steps (a)-(c).”
Claims 14 and 17, in view of the claim limitations, recite the abstract idea of “obtaining n product ion spectra in a processor; comparing the n product ion spectra to a library of product ion mass spectra for known compounds to identify an initial i compounds corresponding to l spectra of the sample using the processor, and performing a reinforcement learning algorithm (RLA) using the processor in which the processor a. acting as an agent of the RLA, performs an action A.sub.t that includes searching one or more compound databases for compounds related to the i compounds, producing j related compounds, and applying one or more deep learning prediction algorithms (DLPAs) to predict k product ion spectra for the i+j compounds, b. acting as an environment of the RLA, compares the k spectra to the n spectra, producing a state, S.sub.t, in which i+j compounds produce m matching compounds and a reward, R.sub.t, for the agent if m>i, and c. if the R.sub.t is produced, sets the i compounds to the m compounds and the l spectra to the k spectra, and repeats steps (a)-(c).”
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited “obtaining n product ion spectra in a processor; comparing the n product ion spectra to a library of product ion mass spectra for known compounds to identify an initial i compounds corresponding to l spectra of the sample using the processor, and performing a reinforcement learning algorithm (RLA) using the processor in which the processor a. acting as an agent of the RLA, performs an action A.sub.t that includes searching one or more compound databases for compounds related to the i compounds, producing j related compounds, and applying one or more deep learning prediction algorithms (DLPAs) to predict k product ion spectra for the i+j compounds, b. acting as an environment of the RLA, compares the k spectra to the n spectra, producing a state, S.sub.t, in which i+j compounds produce m matching compounds and a reward, R.sub.t, for the agent if m>i, and c. if the R.sub.t is produced, sets the i compounds to the m compounds and the l spectra to the k spectra, and repeats steps (a)-(c).”; therefore, the claims recite mental processes and mathematical concepts. Accordingly, the claims recite a mental process and mathematical concept, and thus, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of“[a] computer- implemented method” and “the method is carried out by one or more physical processors configured by machine-readable instructions” as recited in claims 1 and 15, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-13, 16 and 17 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0140]-[0141] (describing that the disclosure is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-13, 16 and 17 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea. Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-17 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tate et al (US 11,456,164) disclose a method for identifying a compound from tandem mass spectrometry product ions without using any a priori precursor ion information. A sample is analyzed using a tandem mass spectrometer, producing at least one measured product ion spectrum from a precursor mass-to-charge ratio range. The at least one measured product ion spectrum are received from the tandem mass spectrometer using a processor. A subset of measured product ions is selected from the at least one measured product ion spectrum using the processor. A list of candidate compounds is created that includes a score for each of the candidate compounds by searching a dictionary of potential compounds that includes one or more predicted product ions for each of the potential compounds using the subset of measured product ions using the processor. Tautenhahn et al (US 10,636,636) disclose a system that includes a database server and a plurality of processing nodes. The plurality of processing nodes are configured to receive mass spectrometry data from a plurality of samples; align the mass spectrometry data to correct for changes in retention time to generate a reference alignment; cluster compounds across the plurality of samples; store the reference alignment and clustered compound data to the database server; receive additional mass spectrometry data from additional samples; align the additional mass spectrometry data to a reference alignment within the database; correlate the compounds from the additional samples with the clustered compound data; classify the compounds; perform statistical analysis on the classified compounds to identify compounds meeting threshold criteria; and provide an indication of compounds meeting the threshold criteria. Bonner et al (US 8,809,770) disclose a plurality of product ion scans are performed on a tandem mass spectrometer one or more times in a single sample analysis across a mass range using a plurality of mass selection windows. All sample product ion spectra of all detectable compounds for each mass selection window are produced. All sample product ion spectra for each mass selection window are received from the tandem mass spectrometer using a processor. All sample product ion spectra for each mass selection window are stored as an electronic record of all detectable compounds of the sample using the processor. The electronic record is used to characterize compounds known at the time the electronic record is stored or to characterize compounds that became known after the electronic record was stored.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AN H DO whose telephone number is (571)272-2143. The examiner can normally be reached on M-F 7:00am-4:00pm.
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/AN H DO/Primary Examiner, Art Unit 2853
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