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
This action is in response to amendment and/or remarks filed on 2025-09-17. In the current amendments, claims 1, 4, 10, and 12 have been amended. The objections made in the previous office action have been withdrawn. The arguments made against the 35 U.S.C 112(b) rejections were not found to be persuasive. The arguments made against the 35 U.S.C 101 rejections were not found to be persuasive. The arguments made against the 35 U.S.C 102 rejections are moot in light of the statutory basis of rejection being changed to 35 U.S.C 103.
The Examiner cites particular sections in the references as applied to the claims
below for the convenience of the applicant(s). Although the specified citations are
representative of the teachings in the art and are applied to the specific limitations within
the individual claim, other passages and figures may apply as well. It is respectfully
requested that, in preparing responses, the applicant(s) fully consider the references in
their entirety as potentially teaching all or part of the claimed invention, as well as the
context of the passage as taught by the prior art or disclosed by the Examiner.
Response to Arguments
Applicant’s arguments and remarks filed 2025-09-17 have been fully considered but were not all found to persuasive.
35 U.S.C 112
Applicant asserts:
Applicant asserts “the peak prediction unit includes a determination unit that calculates possible fragmentation cases and predicts a peak profile with the highest probability. The specification provides sufficient algorithmic detail for a skilled artisan to recognize the corresponding structure”.
Examiner’s response:
The Examiner respectfully disagrees. As claimed, there is no corresponding structure disclosed in the specification (i.e., the limitation is only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm). As recited in the instant case’s spec at [0046] “the machine learning unit 100, peak prediction unit 200, and the data acquisition unit 300 may be implemented as an algorithm … or a memory … and a processor that performs the above-mentioned operation using data stored in the memory.” This shows that the determination unit and peak prediction unit could simply be an algorithm while the machine learning unit and data acquisition units could be implemented by processors. As claimed, it is not entirely clear on what corresponding structure the peak prediction unit and determination unit have that make it clear they are not only supported by software i.e., corresponding to an algorithm and the computer or microprocessor programmed with the algorithm.
35 U.S.C 101
Applicant asserts:
Applicant asserts that claim 1 “now expressly recites that the system addresses the problem of overlapping peptides having identical mass-to-charge ratios (m/z) and retention times (RT) during a multiple reaction monitoring (MRM) process, thereby clarifying that the claims are directed to a concrete technical application rather than an abstract concept”.
Applicant asserts “the amended claim specifies a system architecture comprising modular units such as a peptide information acquisition unit, a spectrum recognition unit, and a determination unit. These components demonstrate that the invention is not a generic use of a computer, but a technical system configured to perform specific functions.”
Applicant asserts “the amended claim does not merely recite conventional computer elements, but leverages specific peptide characteristics (e.g., the presence or absence of proline) in the prediction process. This constitutes "significantly more" than an abstract idea.”
Examiner’s Response:
Examiner notes that in regard to the preamble thereby clarifying the claims directed to a concrete technical application rather than an abstract concept, the preamble itself does not have any patentable weight as the purpose or intended use of the preamble is not limiting in structure, and thus cannot be considered an inventive concept for prong 2 (see MPEP 2111.02).
Additionally, Examiner notes that in regard to the arguments made to prong 2A, the system modular units of peptide information acquisition unit, a spectrum recognition unit, and a determination unit are all recited in a general manner and further, under broadest reasonable interpretation by a person having ordinary skill in the art can be understood as algorithms.
Furthermore, Examiner notes that in regard to the arguments made to prong 2B, the data gathering step of “the machine learning unit extracting a plurality of characteristic information of the plurality of learning peptides, including whether proline is contained in each peptide”, the added limitation of including whether proline is contained in each peptide amounts to a well-understood, routine, conventional activity claimed in a merely generic manner (see MPEP 2106.05(d)). The Courts have recognized detecting DNA or enzymes in a sample, as a well-understood, routine, conventional activity Sequenom, 788 F.3d at 1377-78, 115 USPQ2d at 1157); Cleveland Clinic Foundation 859 F.3d at 1362, 123 USPQ2d at 1088 (Fed. Cir. 2017).
35 U.S.C 102
Applicant asserts:
Applicant asserts “Tsou … does not disclose a data acquisition unit comprising both a peptide information acquisition unit and a spectrum recognition unit as expressly recited. Amended claim 1, however, expressly specifies distinct sub-units that structure the data acquisition process, which are absent from Tsou’s disclosure.”
Examiner’s response:
Applicant’s arguments in regard to Tsou not disclosing amended limitations of distinct sub-units that structure the data acquisition process, have been fully considered and were not found to be persuasive. The amendments to the claim have necessitated the new grounds of rejection and therefore a final rejection is proper.
As claimed, there is no corresponding structure disclosed in the Applicant’s specification (i.e., the limitation is only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm).
In addition, the instant case’s specification at [0046] mentions that “the data acquisition unit 300 may be implemented as an algorithm for controlling the operation of components in the system 1 … or a memory … and a processor that performs the above-mentioned operation using data stored in memory. In this case, the memory and the processor may be implemented as separate chips. Alternatively, the memory and the processor may also be implemented as a single chip.”. Using broadest reasonable interpretation in light of the specification, a person having ordinary skill in the art could interpret the sub-units of the data acquisition unit as software modules running on memory, or hardware components and for the processor to perform operations using data stored in memory, see fig. 1 items 300, 310, 320, 0043, 0044. Those at least one processors would be responsible for peptide information acquisition and spectrum recognition of which is now reflected in the new mapping below under section Claim Rejections – 35 U.S.C 103.
Applicant asserts:
Applicant asserts Tsou does not anticipate claim 1 by pointing to [0109] and [0012], which talk about various classifiers being employed which include deep learning systems in regard to the limitation in the instant case of “a machine learning unit comprising a plurality of predetermined models configured to apply predetermined weights … and acquiring peptide analysis learning data output from the plurality of learning models.”
Examiner’s response:
Applicant’s arguments in regard to Tsou failing to disclose the amended limitations of predetermined models and predetermined weights have been fully considered and were not found to be persuasive. The amendments to the claim have necessitated the new grounds of rejection and therefore a final rejection is proper.
Tsou discloses a deep learning algorithm being selected from several machine learning models, including pDeep, DeepMass, and PROSIT. Of these machine learning models, at least PROSIT is pretrained for purposes of the implementation of Tsou, see para [0076] and para [0101] of Tsou.
Applicant asserts:
Applicant asserts “Tsou generically discloses the use of various classifiers and peptide spectrum data (see [0109]), but does not teach or suggest extracting peptide-specific features which is to determine the presence or absence of proline. Incorporating this biochemical property is critical to improving fragmentation prediction and constitutes a distinguishing feature of the claimed invention that is entirely absent from Tsou.
Examiner’s response:
Applicant’s arguments in regard to the amended limitation of determining the presence or absence of proline considered have been fully considered but were not found to be persuasive. The amendments to the claim have necessitated the new grounds of rejection and therefore a final rejection is proper.
In addition, Examiner notes that the scope of claim 1 has been effectively narrowed as the originally claimed limitation in claims 4 and 12 of “detecting the presence or absence of proline in the unit peptide as an input value” was in reference to the second learning model of dependent claims 4 and 12 while the amended limitation “including whether proline is contained in each peptide” of claims 1 and 10 speaks to any of the learning models, which could be reasonably interpreted as relating to any learning model but the second, such as the first or third etc. However, upon further consideration, a new ground(s) of rejection is made in view of Tiwary et al. (“High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis” hereinafter referred to as Tiwary).
Applicant asserts:
Applicant asserts “Tsou describes generating a spectral library of predicted peptide fragment spectra using deep learning (see [0012]), but does not disclose a determination unit that predicts the spectral peptide under analysis. The claimed invention differs in both structure and function: amended Claim 1 recites a system configured to predict the spectral profile of a peptide to be confirmed in a biological sample, rather than merely producing a generalized spectral library”.
Examiner’s response:
Applicant’s arguments in regard to the amended limitation of a determination unit that predicts the spectral peptide under analysis have been fully considered but were not found to be persuasive. The amendments to the claim have necessitated the new grounds of rejection and therefore a final rejection is proper.
As claimed, there is no corresponding structure disclosed in the specification for the determination unit (i.e., the limitation is only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm).
The Examiner notes that in para [0232], Tsou recites that a prediction model determines a predicted peptide spectrum. The Applicant’s specification contains the determination unit within the peak prediction unit, however as there is no corresponding structure for the determination unit within the specification, the determination unit can be software modules running on memory, or hardware components for the processor to perform operations using data stored in memory, see fig. 1 items 210, para [0044].
35 U.S.C 103 in regard to claims 5 and 13
The Examiner finds the arguments made against the 35 U.S.C 103 moot because the new ground of rejection does not rely on references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant’s amendments have necessitated a change in the references applied.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
The following appears to be the closest portions of the specification corresponding to the 35 U.S.C 112(f) invocations:
peak prediction unit
[0044] Meanwhile, the peak prediction unit 200 may predict the spectral profile of the spectral data of the peptide to be confirmed using the peptide analysis learning data. The peptide to be confirmed may refer to a peptide that is an object of spectral profile prediction. The peak prediction unit may include a storage unit 220 for storing the above-described peptide analysis learning data and a determination unit 210 for performing peak prediction based on the peptide learning data. The peak prediction unit 200 may calculate the number of all cases in which fragmentation is possible from a peptide and predict a peak profile with the highest probability among them. A detailed operation of the peak prediction unit 200 predicting the peak of the peptide to be confirmed based on the data derived by the above-described machine learning unit will be described below.
determination unit
[0044] The peak prediction unit may include a storage unit 220 for storing the above-described peptide analysis learning data and a determination unit 210 for performing peak prediction based on the peptide learning data
a data acquisition unit including a peptide information acquisition unit and spectrum recognition unit
[0045] – [0046] Meanwhile, a data acquisition unit 300 may acquire the above-described plurality of learning peptide sequences and spectral data corresponding to the plurality of learning peptides.
The data acquisition unit 300 may include a peptide information acquisition unit 320 that acquires information such as charges, a length, and the presence or absence of amino acid proline, and a
spectrum recognition unit 310 that acquires spectrum information of the corresponding peptide. The spectrum recognition unit 310 may be implemented as a liquid chromatography apparatus, etc.
The peptide information acquisition unit 320 may be provided with a mass spectrometer and a protein electrophoresis device, etc., but there is no limitation in the device configuration corresponding to each configuration. Meanwhile, the machine learning unit 100, the peak prediction unit 200, and the data acquisition unit 300 may be implemented as an algorithm for controlling the operation of components in the system 1 for predicting a spectral profile of a peptide, or a memory (not shown).
a machine learning unit
[0008] The machine learning unit may include a first learning model performing learning using amino acid sequence type information included in the learning peptide as an input value. The first learning model may be implemented as a recurrent neural network (RNN). The machine learning unit may include a second learning model performing learning using charges, a mass, and a length of the unit peptide, and the presence or absence of proline in the unit peptide as an input value. The second learning model may be implemented as at least one fully connected layer. The machine learning unit may include a third learning model performing learning using fragmentation information corresponding to the two or more unit peptides as an input value. The third learning model may be implemented as a convolution neural network (CNN). The machine learning unit may predict a fragment sequence of the plurality of peptide product ions corresponding to each of a C direction and an N direction based on a position where the fragmentation of the unit peptide starts. The machine learning unit may acquire the peptide analysis learning data by giving a predetermined weight to each of the plurality of learning models.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding 35 U.S.C 112(f) invocations:
The following limitations invoke 35 U.S.C 112(f) or pre-AIA 35 U.S.C 112, sixth paragraph:
“a data acquisition unit including a peptide information acquisition unit and a spectrum recognition, the data acquisition acquiring characteristic information of a plurality of learning peptides and spectral data corresponding to the plurality of learning peptides” as recited in claims 1 and 10
“a machine learning unit including a plurality of predetermined learning configured to apply predetermined models, the machine learning unit extracting a plurality of characteristic information of the plurality of learning peptides, including whether proline is contained in each peptide, performing learning using the plurality of characteristic information and a spectrum corresponding to the plurality of learning peptides as respective input values of the plurality of learning models, and acquiring peptide analysis learning data output from the plurality of learning models …” as recited in claims 1 and 10
“and a peak prediction unit including a determination unit, the peak prediction unit predicting a spectral profile of spectral data corresponding to a peptide to be confirmed using the peptide analysis learning data when characteristic information of the peptide to be confirmed obtained from a biological sample is acquired” as recited in claims 1
“a peak prediction unit including a determination unit …” as recited in claim 1
However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. MPEP 2181 II. B. recites “However, if there is no corresponding structure disclosed in the specification (i.e., the limitation is only supported by software and does not correspond to an algorithm and the computer or microprocessor programmed with the algorithm), the limitation should be deemed indefinite as discussed above, and the claim should be rejected under 35 U.S.C 112(b) or pre-AIA 35 U.S.C 112, second paragraph.”. The portions of the specification identified above do not clearly link the claim language to a “computer or microprocessor programmed with the algorithm” for any of the data acquisition, machine learning, determination, or peak prediction units recited in the claims.
Therefore, the claims are indefinite and are rejected under 35 U.S.C 112(b) or pre-AIA 35 U.S.C 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Regarding dependent claims
Claims
2-9 are dependent on claim 1
11-13 are dependent on claim 10
are therefore similarly rejected for including the deficiencies of claim 1 and 10 respectively.
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 1-13 are rejected under 35 U.S.C 101 because the claimed invention is
directed to an abstract idea without significantly more. The analysis of the claims will
follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57
(January 7, 2019) (“2019 PEG).
Regarding claim 1:
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a machine.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
a peak prediction unit including a determination unit, the peak prediction unit predicting a spectral profile of spectral data corresponding to a peptide to be confirmed using the peptide analysis learning data when characteristic information of the peptide to be confirmed obtained from a biological sample is acquired — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
A system for predicting a spectral profile of a peptide of a product ion of a peptide, in order distinguish overlapping peaks of different peptides having similar retention times (RT) and mass-to-charge (m/z) ratios during a multiple reaction monitoring (MRM) process, the system — amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
A data acquisition unit including a peptide information acquisition unit and a spectrum recognition unit, the data acquisition acquiring characteristic information of a plurality of learning peptides and spectral data corresponding to the plurality of learning peptides — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
a machine learning unit including a plurality of predetermined learning models configured to apply predetermined weights — amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
the machine learning unit extracting a plurality of characteristic information of the plurality of learning peptides, including whether proline is contained in each peptide — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
performing learning using the plurality of characteristic information and a spectrum corresponding to the plurality of learning peptides as respective input values of the plurality of learning models — amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
acquiring peptide analysis learning data output from the plurality of learning models — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activities in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “data acquisition unit acquiring characteristic information of a plurality of learning peptides and spectral data corresponding to the plurality of learning peptides”, “extracting a plurality of characteristic information of the plurality of learning peptides”, and “acquiring peptide analysis learning data output from the plurality of learning models” limitations were found to be insignificant extra-solution activities in claim 1. These limitations are recited at a high level of generality and amount to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). Additionally, MPEP 2106.05(f) cannot integrate the abstract ideas listed into a practical application.
Regarding claim 2:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the machine learning unit includes a first learning model performing learning using amino acid sequence type information included in the learning peptide as an input value — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely relates the machine learning unit to amino acid sequence type information.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply this exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept.
Regarding claim 3:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the first learning model is implemented as a recurrent neural network (RNN) — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely relates the machine learning model to a specific type of neural network, an RNN.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply this exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept.
Regarding claim 4:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the machine learning unit includes a second learning model performing learning using charges, a mass, and a length of a unit peptide — amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 5:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the second learning model is implemented as at least one fully connected layer — amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 6:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the machine learning unit includes a third learning model performing learning using fragmentation information corresponding to two or more unit peptides as an input value — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely relates the learning model to peptides via fragmentation information.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply this exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept.
Regarding claim 7:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the third learning model is implemented as a convolution neural network (CNN) — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely relates the learning model to a CNN.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply this exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept.
Regarding claim 8:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the machine learning unit predicts a fragment sequence of a plurality of peptide product ions corresponding to each of a C direction and an N direction based on a position where the fragmentation of the unit peptide starts — amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 9:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the machine learning unit acquires the peptide analysis learning data by giving a predetermined weight to each of the plurality of learning models — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activities in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “wherein the machine learning unit acquires the peptide analysis learning data by giving a predetermined weight to each of the plurality of learning models” limitation was found to be insignificant extra-solution activities in claim 9. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Regarding claim 10:
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a machine.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas.
wherein the machine learning unit additionally performs learning by comparing a predicted spectrum and an actually measured spectrum with each other — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
A system for predicting a spectral profile of a peptide, in order to distinguish overlapping peaks of different peptides having similar retention times (RT) and mass-to-charge (m/z) ratios during a multiple reaction monitoring (MRM) process — amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
A data acquisition unit including a peptide information acquisition unit and a spectrum recognition unit, the data acquisition unit acquiring characteristic information of a plurality of learning peptides and spectral data corresponding to the plurality of learning peptides — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
a machine learning unit including a plurality of predetermined learning models configured to apply predetermined weights — amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
extracting a plurality of characteristic information of the plurality of learning peptides, including whether proline is contained in each peptide — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
performing learning using the plurality of characteristic information and a spectrum corresponding to the plurality of learning peptides as respective input values of the plurality of learning models — amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
acquiring peptide analysis learning data output from the plurality of learning models — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activities in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “data acquisition unit acquiring characteristic information of a plurality of learning peptides and spectral data corresponding to the plurality of learning peptides”, “extracting a plurality of characteristic information of the plurality of learning peptides”, and “acquiring peptide analysis learning data output from the plurality of learning models” limitations were found to be insignificant extra-solution activities in claim 10. These limitations are recited at a high level of generality and amount to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). Additionally, MPEP 2106.05(f) cannot integrate the abstract ideas listed into a practical application.
Regarding claim 11:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 10, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the machine learning unit includes a first learning model performing learning using amino acid sequence type information included in the learning peptide as an input value — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely relates the machine learning unit to amino acid sequence type information.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply this exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept.
Regarding claim 12:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 10, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the machine learning unit includes a second learning model performing learning using charges, a mass, and a length of a unit peptide — amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 13:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 10, which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the machine learning unit includes a third learning model performing learning using fragmentation information corresponding to two or more unit peptides as an input value of a sliding window manner — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely relates the learning model to peptides via fragmentation information.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply this exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept.
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 indicati