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 .
Claim Status
Claims 1-10 are pending and under examination.
Claims 1, 5-6, and 10 are objected to.
Claims 1-10 are rejected.
Claims 1 and 6 are independent.
No claims are allowed, amended, canceled, new, or withdrawn.
Office Action Outline
Rejections applied
Abbreviations
x
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
x
102, 103
JE
Judicial Exception
x
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
Double Patenting
MM/DD/YYYY
date format
Priority
As detailed in the 09/06/2023 filing receipt, this application claims priority to as early as 06/08/2023, the filing date of Taiwanese Application TAIWAN 112121368. At this point in examination, all claims have been interpreted as being accorded the priority date of 06/08/2023.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. See paper entered 08/31/2023.
Claim Objections
Claims 1, 5, 6, and 10 are objected to because of the following informalities:
In the step beginning with "training a disease prediction"... , claims 1 and 6 recite "and (ii) the feature values of the selected features for the sample" which for consistent claim language should be amended to "and (ii) the feature values of the selected features for [[the]]each sample."
Claims 5 and 10 recite "selecting a specified number of the microbiota features are selected as the selected features based on the feature ranking" which appears to have an extra phrase. It is suggested to amend claims 5 and 10 to "selecting a specified number of the microbiota features as the selected features based on the feature ranking."
Claim 6 recites "a processing device, loading a program" which appears to be missing the word "for" and should be corrected to "a processing device, for loading a program"
Appropriate correction is required.
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 1-10 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. Claims depending from rejected claims are rejected similarly, unless otherwise noted, and any amendments in response to the following rejections should be applied throughout the claims, as appropriate.
The following bolded terms in the recitations below require but lack clear antecedent. If the bolded recitations refer to previously instantiated instances, then it is not clear which instances those are. If the bolded recitations instantiate the claim elements, this is not clear. (Bold and italic emphasis added by the examiner.)
• "selecting a portion of the extracted microbiota features" (Claims 1 and 6; note feature values, not features, were extracted earlier in the claims.)
• "(ii) the feature values of the selected features" (Claims 1 and 6; note, earlier in the claims, feature values were extracted for multiple microbiota, not for selected features.)
• "wherein the microbiota features" (Claims 1 and 6; it is not clear if this is referring to multiple microbiota features or to extracted microbiota features or selected features..)
• "the corresponding feature pool" (Claims 5 and 10; it is suggested to amend to "a corresponding feature pool.")
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 details the following framework to analyze Subject Matter Eligibility:
• Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? (see MPEP § 2106.03)
• Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. an abstract idea, a law of nature, or a natural phenomenon? (see MPEP §§ 2106.04(a), 2106.04(a)(2) & 2106.04(b)).
• Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application? (see MPEP § 2106.04(d))
• Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? (see MPEP § 2106.05)
Step 1:
Claims 1-5 are directed to a 101 process, here a method. Claims 6-10 are directed to a 101 machine or manufacture, here a system. As such, claims 1-10 are directed to a related method and system, which fall under categories of statutory subject matter. (See MPEP § 2106.03). (Step 1: Yes.)
Step 2A, Prong One:
The claims are found to recite judicial exceptions (JEs) of abstract ideas in the form of mental processes and mathematical concepts as follows:
Independent claims 1 and 6 recite mental processes of: "extracting feature values;" "selecting a portion of the extracted microbiota features;" and "training a disease prediction model (also considered a mathematical concept)."
Claims 3 and 8 recite a mathematical concept of: "a hierarchical ratio between two taxa on a taxonomic level."
Claims 4 and 9 recite a mathematical concept of: "a Beta diversity matrix."
Claims 5 and 10 recite mental processes of: "inputting the disease data into multiple feature selection models, each selection model selects microbiota feature(s) to form corresponding feature pools."
Claims 2 and 7 further limit the species level features respectively of claims 1 and 6.
Step 2A Prong One Summary: The claims recite mental processes and mathematical concepts. When considering the broadest reasonable interpretation (BRI) of the claims, the mental processes recited in independent claims 1 and 6 (e.g., "extracting feature values," "selecting a portion of the extracted microbiota features," and "training a disease prediction model," etc.) are directed to processes that may be performed in the human mind, or with pen and paper, as there are no particular limitations recited in claims 1and 6 which would prevent the mental processes from being performed in the human mind or with pen and paper. The claims recite inherent mathematical processes in e.g., training a model, a hierarchical ratio, and a Beta diversity matrix, while details of which are not explicitly shown, are discussed in Specification [0033-0038], [0052], etc. Although the method is executed on a computer and the system requires a processor, a claim that requires a computer may still recite a mental process [see MPEP 2106.04(a)(2)(III)(C)]. Further, although a general-purpose computer can perform the analysis at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea. Therefore, the claims recite elements that constitute a judicial exception in the form of abstract ideas.
(Step 2A, Prong One: Yes.)
Step 2A, Prong Two:
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs). Here at Step 2A, Prong Two, any remaining steps and/or elements not identified as JEs are therefore in addition to the identified JE(s), and are considered additional elements. Because the claims have been interpreted as being directed to judicial exceptions (abstract ideas in this instance) then Step 2A, Prong Two provides that the claims be examined further to determine whether the judicial exception is integrated into a practical application [see MPEP § 2106.04(d)]. A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
24. MPEP § 2106.04(d)(I) lists the following five example considerations for evaluating whether a judicial exception is integrated into a practical application:
(1) An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
(2) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2).
(3) Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b).
(4) Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c).
(5) Applying or using 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, as discussed in MPEP § 2106.05(e).
The claims recite additional elements as follows:
Additional elements of data gathering, inputting, and/or outputting steps: Claim 6 recites the additional element of storing data. Data gathering steps are additional elements which perform functions of inputting, collecting, and outputting the data needed to carry out the abstract idea. These steps are considered insignificant extra-solution activity, and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed, nor do they provide an improvement to technology (see MPEP § 2106.04(d)(I)).
Additional elements of computer components: Claim 1 recites a computer. Claim 6 recites a system, storage device, processing device, and a program. The claims require only generic computer components, which do not improve computer technology, and do not integrate the recited judicial exception into a practical application (see MPEP § 2106.04(d)(1) and MPEP § 2106.05(f)).
Step 2A Prong Two summary: The claims have been further analyzed with respect to Step 2A, Prong Two, and no additional elements have been found, alone or in combination, that would integrate the judicial exception into a practical application. At this point in examination, it is not yet the case that any of the Step 2A Prong Two considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of: (1) an improvement, (2) a treatment, (3) a particular machine, or (4) a transformation is clear in the record. For example, regarding the first consideration for improvement at MPEP 2106.04(d)(1), the record, including the Specification, does not yet clearly disclose an explanation of improvement over the previous state of the technology field, and the claims do not yet clearly result in such an improvement. (Step 2A, Prong Two: No).
Step 2B analysis:
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept. An inventive concept is furnished by an element or combination of elements that is recited in the claim in addition to the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself (see MPEP § 2106.05).
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 are well-understood, routine, and conventional. Those additional elements are as follows:
Additional elements of data gathering, inputting, and outputting steps: The additional element of storing data (claim 6) does not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network and storing and retrieving information in memory [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as extra-solution activity.
Additional elements of computer components: The additional elements of a computer (claim 1) and a system, storage device, processing device, and program (claim 6) do not cause the claims to rise to the level of significantly more than the judicial exception, and as such do not provide an inventive concept; these are conventional computer components.
All limitations of claims 1-10 have been analyzed with respect to Step 2B, and none provides a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception, and thus do not transform the judicial exception into a patent eligible application of the exceptions. Step2B: NO.
Therefore, the claims, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non patent-eligible subject matter.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Oh (Scientific reports, vol. 10(1):6026, p 1-9 (2020); cited on the attached form PTO-892), in view of Sharma (Bioinformatics, vol. 36(17), pp.4544-4550 (2020); cited on the attached form PTO-892).
Regarding claim 1, the method for establishing a disease prediction model, executed by a computer system, reads on "DeepMicro: deep representation learning for disease prediction based on microbiome data." (Oh, p.1, title and entire article).
Regarding claim 1, the recited extracting feature values for microbiota features from data of each of a plurality of samples reads on "human gut metagenomic samples of six different disease cohorts: inflammatory bowel disease (IBD), type 2 diabetes in European women (EW-T2D), type 2 diabetes in Chinese (C-T2D) cohortobesity, (Obesity), liver cirrhosis (Cirrhosis), and colorectal cancer (Colorectal)." (Oh, p. 2, ¶ 3).
Regarding claim 1, the recited selecting a portion of the extracted microbiota features as selected features reads on "Two types of microbiome profiles were extracted from the metagenomic samples: 1) strain-level marker profile and 2) species-level relative abundance profile... utilized MetAML to preprocess the abundance profile by selecting species-level features." (Oh, p. 2, ¶ 4).
Regarding claim 1, the recited training a disease prediction model, wherein each piece of training data comprises (i) disease data for each of the samples and (ii) the feature values of the selected features for the sample reads on "To train deep representation models, we split each dataset into a training set, a validation set, and a test set" (Oh, p.4, ¶ 2), "human gut metagenomic samples of six different disease cohorts" (Oh, p. 2, ¶ 3), and "Two types of microbiome profiles were extracted from the metagenomic samples: 1) strain-level marker profile and 2) species-level relative abundance profile... We utilized MetAML to preprocess the abundance profile by selecting species-level features." (Oh, p. 2, ¶ 4)
Regarding claim 1, the recited microbiota features comprise species-level features, microbiota interaction features, and community-level features reads on " We utilized MetAML to preprocess the abundance profile by selecting species-level features." (Oh, p. 2, ¶ 4).
Regarding claim 2, the recited species-level features comprise relative abundance data and presence/absence data of each of a plurality of species reads on "Two types of microbiome profiles were extracted from the metagenomic samples: 1) strain-level marker profile and 2) species-level relative abundance profile... We utilized MetAML to preprocess the abundance profile by selecting species-level features." (Oh, p. 2, ¶ 4)
Oh does not show the recited microbiota features comprise...microbiota interaction features, and community-level features of claim 1 (shown by Sharma).
Oh does not show the recited microbiota interaction features comprise a hierarchical ratio between two taxa on a taxonomic level of claim 3 (shown by Sharma).
Oh does not show the recited community-level features comprise a Beta diversity matrix of claim 4 (shown by Sharma).
Oh does not show the recited inputting the disease data and the microbiota features into multiple feature selection models to obtain multiple feature pools, wherein each feature selection models selects a microbiota feature(s) to form a corresponding feature pool of claim 5 (shown by Sharma).
Oh does not show the recited ranking the microbiota features based on frequency of selection into the feature pools to obtain a feature ranking of claim 5 (shown by Sharma).
Oh does not show the recited selecting a specified number of the microbiota features as the selected features based on the feature ranking of claim 5 (shown by Sharma).
Regarding claim 1, the recited microbiota features comprise...microbiota interaction features, and community-level features reads on " Thirty two Operational Taxonomic Units (OTUs), potentially associated with risk of disease were randomly selected and interactions between three OTUs were used to introduce non-linearity" (Sharma, p.4544, Abstract), and "features from each cluster were combined" (Sharma, p.4546, ¶ 2),
Regarding claim 3, the recited microbiota interaction features comprise a hierarchical ratio between two taxa on a taxonomic level reads on "there is an inherent correlation due to hierarchical taxonomy of microbial Operational Taxonomic Units (OTUs)" (Sharma, p.4544, abstract), and "...a sample taxonomy tree containing various taxonomic levels and illustrates that hierarchy in OTU data is complex and clusters corresponding to the different phyla can contain a varied number of OTUs. (Sharma, p.4545, col.1).
Regarding claim 4, the recited community-level features comprise a Beta diversity matrix reads on " a) Example heatmap obtained by plotting Spearman rank coefficients between positively correlated OTUs in a cluster. (b) Cumulative coefficient obtained with respect to each row of the heatmap matrix" (Sharma, p.4546, fig.1).
Regarding claim 5, the recited inputting the disease data and the microbiota features into multiple feature selection models to obtain multiple feature pools, wherein each feature selection models selects a microbiota feature(s) to form a corresponding feature pool reads on "We experimented with using three types of CNN models " (Sharma, p.4545, col.1), and "pooling Layer (Conv2 and Pool2) were used to extract features" (Sharma, p.4546, col.1).
Regarding claim 5, the recited ranking the microbiota features based on frequency of selection into the feature pools to obtain a feature ranking reads on " Ordering based on correlation: The second approach that we used was to order the OTUs based on their correlation with each other using Spearman rank." (Sharma, p.4545, col.2).
Regarding claim 5, the recited selecting a specified number of the microbiota features as the selected features based on the feature ranking reads on "selected 32 OTUs randomly as the OTUs that were potentially associated with risk of disease, also ensuring that all clusters contribute to these OTUs." (Sharma, p.4547, col.1).
Regarding claim 6, the system, storage device, and processing device reads on "the NVIDIA Tesla P100 GPU with 16GB of RAM" (Sharma, p.4548, col.1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of deep learning for disease prediction based on microbiome data of Oh, with the ensemble of neural networks on stratified microbiome data for disease prediction of Sharma. This is because Sharma provides motivation to modify by showing a two-step approach aided in capturing the relationships between OTUs sharing a phylum efficiently. One would have had a reasonable expectation of success in doing so because Oh and Sharma are generally drawn to related teaching, and one of ordinary skill in the art would have understood how to and would have been motivated to apply the teaching of Sharma to the related teachings of Oh, and as such, the combination would have been obvious.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
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/M.A.V./Examiner, Art Unit 1687
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686
Prosecution Insights