DETAILED ACTION
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 .
Information Disclosure Statement
The IDS filed 8/17/2023, 12/22/2022, 3/3/2022 have been considered by the Examiner.
Priority
Priority documents have not been filed in the instant application. The instant filing date of 3/3/2022 will be used for search and consideration.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-3 are drawn to a method, so a process.
Claims 4-6 are drawn to a system comprising an acquirer, retriever, creators, masking processor, and discriminator. The systems reads on a system of program modules. A transitory signal such as a carrier wave, or a program, per se, is not a statutory category of invention, as set forth in In re Nuijten. A review of the specification does not show a definition of computer readable media that excludes an embodiment that is information in a signal. As such, an embodiment of the claims read on non-statutory subject matter (In re Nuijten 84 USPQ2d 1495 (2007)). The applicants may overcome the rejection by 1) amendment of the claims to be limited to physical forms of computer readable storage media described in the specification or 2) by amending the claimed subject matter to be limited to “non-transitory”, see the notice regarding Computer Readable Media (1351 OG 212 (23 February 2010)).
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s):
1. retrieving an m/z list describing m/z values of marker-candidate proteins each of which is supposed to vary in mass among different subspecies, different strains or different types in a group of microorganisms belonging to the same species as the known microorganisms.
This step can be performed by the human mind by considering or thinking about data which is a list of m/z values representing mass spectra data for proteins. The step is therefore an abstract idea.
2. creating a mask which gives non-zero values only within a predetermined m/z range including each m/z value described in the m/z list; masking each of the plurality of mass spectra with the mask.
This step can be performed by the human mind by organizing information which are the m/z peak values considered important or by filtering out or sampling out values deemed unimportant. The step is therefore an abstract idea.
3. creating a plurality of wavelet images by performing continuous wavelet transform on each of the plurality of mass spectra after the masking;
This step of performing continuous wavelet transformation on mass spectral data reads on mathematics. A continuous wavelet transform applies a set of functions to signal-type data and can be performed using a Fast Fourier Transform. The result is wave-like data that can be displayed in a graph, i.e. an image. The step is entirely mathematical and therefore an abstract idea.
4. creating a discriminant model by machine leaning using, as training data, the plurality of wavelet images and information of the subspecies, strain or type of each of the known microorganisms.
This step reads on organizing information into a mathematical expression. The generically recited “machine learning” reads on a calculation performed with the aid of a computer and a discriminant model is a statistical model which is mathematics per se. Organizing data or features from images with machine learning to create a discriminant model reads on organizing information with mathematics. The step is therefore an abstract idea.
4. discriminating the subspecies, strain or type of an unknown microorganism belonging to the same species as the known microorganisms, by applying, to the discriminant model, a mass spectrum acquired by performing a mass spectrometric analysis on the unknown microorganism whose subspecies, strain or type is unknown.
The step reads on comparing mass spectrum data to the created mathematical model to perform a classification and thereby determine a subspecies, strain or type. The step can be performed by the human mind or with mathematics. The step is therefore an abstract idea.
Dependent claims 2, 3, 5 and 6-7 are further drawn to mathematical and mental process steps and are therefore also drawn to an abstract idea.
Step 2A Prong Two: Consideration of Practical Application
The instant claims result in determining information which is the subspecies, strain or type of microorganism. The claims do not recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
1. Acquire a plurality of mass spectra obtained by performing a mass spectrometric analysis on each of a plurality of known microorganisms which belong to a same species and whose subspecies, strains or types are known, as in claim 1.
This step reads on routine, conventional and well understood extra solution activity of performing mass spectrometry on microorganism samples to collect spectral data. The step is the extra solution activity of data gathering as described in MPEP 2106.05(g).
2. . Acquire a plurality of mass spectra obtained by performing a mass spectrometric analysis on each of a plurality of known microorganisms, as in claim 4.
This limitation reads on the transmission of data which is also deemed to be an extra solution activity as described in MPEP 2106.05(g).
3. creating a plurality of wavelet images, as in claims 1 and 4.
This step reads on extra solution activity of outputting data after continuous wave transform functions are applied. Outputting resulting data to a graph or image is routine extra solution activity as discussed in MPEP 2106.05(g).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because acquiring mass spectral data of microorganisms, transmitting spectral data and displaying results of mathematical analysis or transformations is routine, conventional and well understood such that these limitations do not add “significantly more” to the abstract idea.
Other elements of the method include a non-transitory computer readable medium of claim 7 which is a recitation of a generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112-1st paragraph
The following is a quotation of the first paragraph of 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 4-7 are rejected under 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 4-7 are drawn to a system comprising (claim 4) a “known-sample data acquirer,” to acquire mass spectra, “list retriever” to retrieve an m/z list, “mask creator” to create a mas, “masking processor” to mas the plurality of mass spectra, “wavelet image creator” to create wavelet images by continuous wavelet transform, “model creator” to create a discriminant model, “discriminator,” to discriminate subspecies, strains and types of an unknown microorganism and “calibrator” (claim 5) to compare m/z values of peaks. The claims recite “means for” type language which includes recitation of “means” that are nonce terms and are examined under 35 USC 112(f). However, a review of the specification does not show a description or definition of these recited “means.” A review of the specification shows that aspects of the invention may be implemented using a program on computer (par. 0009 and 0016). However, a written description of what each of the system components or "means for” recited in claims 4 and 5 has not been provided.
Claim Rejections - 35 USC § 112-2nd paragraph
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 1-7 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 pre-AIA the applicant regards as the invention.
Claims 1 and 4 recite the step of retrieving an m/z list describing values of marker-candidate proteins each of which is supposed to vary in mass among different subspecies, strains and types of microorganisms. This limitation is unclear because it is unrelated to any of the previous of subsequent steps of the claim. The recited retrieved list is never used in any of the subsequent steps. It is therefore unclear why the step of retrieving a list of m/z values is performed and the method as a whole becomes unclear.
Furthermore in claims 1 and 4, the limitation reciting that each of the proteins “supposed to vary in mass,” is unclear. It is not clear if the masses in the list do or do not vary, i.e. the metes and bounds of “supposed to” are unclear. Clarification of the language is needed.
Claim 7 recites a computer readable medium recording a program “to make a computer function as components” of the system. This limitation is unclear and may be grammatically incorrect. It is not clear what is intended by “a computer function as components.” Perhaps the claim is intended to recite “a computer function as a component” of the system. Clarification of the language is needed.
Considerations under 112(f)
Claims 4-7 are drawn to a system comprising (claim 4) a “known-sample data acquirer,” to acquire mass spectra, “list retriever” to retrieve an m/z list, “mask creator” to create a mas, “masking processor” to mas the plurality of mass spectra, “wavelet image creator” to create wavelet images by continuous wavelet transform, “model creator” to create a discriminant model, “discriminator,” to discriminate subspecies, strains and types of an unknown microorganism and “calibrator” (claim 5) to compare m/z values of peaks. The claims recite “means for” type language which includes recitation of “means” that are nonce terms and are examined under 35 USC 112(f). As set forth in the 35 USC 112(a) rejection above, the specification does not provide a description of the “means for” carrying the claimed steps. One would not know what is needed to be a “means for” carrying out the claimed steps because it is unclear what structure corresponds to each of the recited means. The metes and bounds of the claim are therefore unclear.
Applicant is required to:
(a) Amend the claim so that the claim limitation will no longer be a means (or step) plus function limitation under 35 U.S.C. 112, sixth paragraph; or
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the claimed function 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 so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant is required to 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 1-7 are rejected under 35 U.S.C. 103(a) as being unpatentable over Teramoto et al. (Proc. Jpn. Acad. Serr. B vol. 95 (2019) pages 612-623 and Supplementary Materials) as applied to in view of Noda et al. (US 2016/0224830) and further in view of Norris et al. (US 2021/0389325).
Teramoto et al. teach a MALDI-MS method for distinguishing C. acnes phylotypes (Abstract); performing MALDI-MS on C. acnes is taught (page 615, col. 1 and Table 1)(i.e. acquiring a plurality of mass spectra by performing mass spectrometry on a plurality of known microorganisms which belong to the same species and whose subspecies, strains and types are known), as in claims 1 and 4.
Teramoto et al. teach creating a list of candidate marker proteins for distinguishing phylotypes of C. acnes (page 615, col. 2, par. 2, Table 2 and Figure 5)(i.e. retrieving an m/z list describing maker-candidate proteins which vary among different types in a group of microorganisms), as in claims 1 and 4.
Teramoto et al. teach assigning a “1” or “0” to biomarker mass peak values to indicate their presence or absence for a phylotype (Table 4)(i.e. creating a mas which gives a non-zero value only withing a predetermined m/z range including each m/z value described in the m/z list).
Claims 1 and 4 recite masking each of the plurality of mass spectra with the mask. Resampling and signal filtering is very well known to those of ordinary skill in the art. Teramoto et al. already teach applying a binary code (i.e. mask) to protein masses. It would be obvious to one of ordinary skill to apply a mask to mass spectra based on the relevant m/z range for that type or species of microorganism to filter out unneeded m/z values.
Teramoto et al. teach a workflow (i.e. discriminant model) for rapid identification of phylotypes of C. acnes which include identifying the finger print proteins associated with each different phylotype (page 621, Figure 5)(i.e. discriminating subspecies, strain or type of an unknown microorganism by applying to the discriminant model a mass spectrum of the unknown microorganism), as in claims 1 and 4.
Teramoto et al. teach (Figure 5) a workflow model for rapid identification of phylotypes (i.e. creating a discriminant model using information about subspecies, strains and types of microorganism).
Teramoto et al. do not teach creating a plurality of wavelet images by performing continuous wavelet transform on each of the plurality of mass spectra after the masking, as in claims 1 and 4.
Teramoto et al. do not teach machine learning for creating a discriminant model, wherein the machine learning model uses the wavelet images for training, as in claims 1 and 4.
Noda et al. however teach (Abstract) a continuous wavelet transform performed on spectrum data or profile spectrum data obtained by a mass spectrometry which produces a visualized image (par. 0006); various types of signal wave forms can be processed (par. 0023).
Norris et al. teach assessing similarity of a subject sample to a known sample using mass spectrometry peak data with a machine learning algorithm (par. 0009); the similarity is determined with peaks displayed in a mass spectrometric profile (i.e. images)(par. 0014) and classification is performed (par. 0083-0085).
It would be obvious to one of ordinary skill in the art at the time the invention was made to have implemented the method of Teramoto et al. for protein mass spectral peak classification of bacteria to determine bacteria subtype with the method of performing a continuous wavelet transform on mass spectral data as taught by Noda et al. Noda et al. provide motivation by teaching that the three dimensional transformation achieved from the continuous wavelet transform allow for a more precise peak detection in the profile spectrum (par. 0006). One of skill in the art would have had a reasonable expectation of success at combining Teramoto et al. and Noda et al. because both are concerned with peak detection in a mass spectrum; Teramoto et al. uses peaks to classify bacteria subtypes and Noda et al. allows for precision in peak determination.
It would be obvious to one of ordinary skill in the art at the time the invention was made to have implemented the method of Teramoto et al. in view of Noda et al. which make obvious performing continuous wavelet transform on protein mass spectra from bacteria with the method of Norris et al. for using machine learning on mass spectral data to perform sample classification. The elements taught by Teramoto et al., Noda et al. and Norris et al. are obvious to combine because machine learning is well known for classification of data from samples and images. The machine learning of Norris et al. would be obvious to implement on the mass spectral continuous wavelet transform images of Teramoto et al. in view of Noda et al. As a result, the predictable result classifying mass spectral sample data of bacteria with machine learning trained on accurate peaks determined from continuous wavelet transformed images would be achieved. Such a combination is merely a "predictable use of prior art elements according to their established functions." KSR Int’l 7, 127 S. Ct. at 1740.
Regarding dependent claims 2, 3 and 5-7
Teramoto et al. teach comparing assigning protein peaks based on comparison with theoretical masses calculated from amino acids sequences for each protein and removing biomarkers with peak shift or peaks that are too close (page 617, col. 1-2, connecting par.)(i.e. comparing m/z value in mass spectra with the m/z list and performing a calibration), as in claims 2 and 5.
Teramoto et al. teach a final list with “0” and “1” assigned (Table 4)(i.e. performing masking after calibration), as in claims 2 and 5.
Teramoto et al. teach candidate biomarkers L30, L29, S15, S19, L23, S08, L15, L09, L13, L06 (Table 2 and Table 4) and discriminating Cutibacterium acnes, as in claims 3 and 6.
Teramoto et al. teach L21 and L07/L12 as biomarkers (page 621, col. 1, par. 2 Table S1-1 in Supplementary Materials), as in claims 3 and 6.
Teramoto et al. teach using a computer software package which suggests a computer and program (page 615, col. 2, par. 1), as in claim 7.
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
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/Anna Skibinsky/
Primary Examiner, AU 1635