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 .
Applicants’ amendment
Applicants’ amendment filed 9/11/2025 has been received and entered. Claims 1 -20 have been amended.
Claims 1-20 are pending.
Election/Restriction
Applicant's election with traverse of Group II (claims 8-14) in the reply filed on 2/21/2019 was acknowledged. Upon initial review and search of the claim limitations the restriction requirement was withdrawn because claim amendments in the three groups now are consistent with the limitations required of each of the independent claims. The new amendments to the claims are consistent with the claimed invention previously examined.
Claims 1-20 are pending. Claims 1-20, drawn to a system of a memory and controller for genetic prediction, a method for choosing a model to predict characteristics of an individual and a computer readable medium for selecting a model to predict characteristics, are currently under examination.
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Priority
This application filed 7/12/2018 makes no claim for priority.
In prosecution, Applicants have not commented on the analysis of priority.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
An updated search of the inventors and Applicant Helix did not identify any new application filings.
In prosecution, a terminal disclaimer filed 6/4/2021 was APPROVED (see paper entered 6/4/2021) obviating the rejections over 10,217048, 9,922285, 10,438687 and 10,733509.
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 1-20 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 is withdrawn. Specifically, independent claims 1, 8 and 15 have been amended to delete limitations previously of issue, and now provide a more generic recitation of ‘machine learning polygenic models’ which have been trained to predict different characteristics which is generally supported and provides clarity to the metes and bounds required of the claims.
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.
Examiner comment
Applicants do not comment on the evaluation under 112 6th paragraph and for completeness of record the analysis and interpretation is provided. In prosecution it was indicated that the application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are:
1. a memory that stores polygenic models; and
2. a controller that provides for a series of steps that transmit, store, determines and predicts elements required of associating a genetic variant with a characteristic.
In view of the guidance of the specification, there does not appear to be a specialized memory or controller described by the claims, and in view of the art of record there is no physical memory source or controller that provides for the functions that follow these embodiments. Rather, the specification appears to support that the system and ‘genetic prediction server’ which comprises these limitations is a general purpose computer with the necessary programing that provides for the analysis or correlative models that are used to provide a user a characteristic consistent with the genetic data they provide. This interpretation appears consistent with claims 15-20 which is directed to a computer readable medium with programmed instructions and provide for the steps recited in the method and system claims 1-14.
Analysis provided in previous action
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are:
1. a memory that stores polygenic models; and
2. a controller that provides for a series of steps that transmit, store, determines and predicts elements required of associating a genetic variant with a characteristic.
In view of the guidance of the specification, there does not appear to be a specialized memory or controller described by the claims, and in view of the art of record there is no physical memory source or controller that provides for the functions that follow these embodiments. Rather, the specification appears to support that the system and ‘genetic prediction server’ which comprises these limitations is a general purpose computer with the necessary programing that provides for the analysis or correlative models that are used to provide a user a characteristic consistent with the genetic data they provide. This interpretation appears consistent with claims 15-20 which is directed to a computer readable medium with programmed instructions and provide for the steps recited in the method and system claims 1-14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
As noted above, Applicants have not commented in the instant remarks, and in view of the claim amendments the analysis appears to be consistent with the requirements of the claims for both the system and storage medium.
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 1-20 stand rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim analysis
The independent claims 1, 8 and 15 have been amended and still are generally directed to a method of providing trained machine learning models using known genotypes and known characteristics associated with demographic profiles, choosing a model based on the demographic profile of a patient, and make a prediction of a characteristic and transmitting the information to a user. Specifically, the claims have been amended to provide a new first step and to indicate the machine learning models are first trained, and wherein clauses describing the outcome of the training is that the models predict a characteristic based on a genetic loci, and in view as a whole appear to provide that the model provides a correlation with genetic variants and characteristics in various demographics. In view of the specification guidance, no specific machine learning is performed, nor are their any specific considerations or rules used for machine learning and as a whole appears to provide using machine learning to make correlations with genetic and observable characteristics. As amended, claims 1-7 provide a system, claims 8-14 are directed to a process, and claims 15-20 are directed to an article of manufacture that implements the process. For each dependent claims provide for how the model is trained or how many demographic elements are used to assess the model to be used, or that demographics are categorized and ranked for determining the appropriate model to apply to the genetic information provided by the user. The claims have been amended to indicate that the models are divided into sets and ‘calibrated’ using demographics, and in evaluation of the guidance of the specification the nature of the models and what they represent does not appear to have changed, and appears to require models that have genetic variants associated with a characteristic and if present relative demographic information the variant represents or from which the data was obtained. For example, the model could have BRCA1/2 alterations and the indication that it was identified to be correlated with breast cancer in females, and for ancestry that the data provides a family history of whether the family had the characteristic or carries the same alteration.
For step 1 of the 101 analysis, the claims are found to be directed to a statutory category of a product and a process.
For step 2A of the 101 analysis, the judicial exception of the claims are the instructional steps of applying models to sequence data based on demographics. The claims have been amended to indicate that the models have been trained in association with demographics, and in view of the guidance of the specification are general and broad relating to any characteristic and all possible demographics and any correlation with any genetic correlation. The specification does not give any detail to how any model is created only that machine learning is used, nor importantly any specific guidance on how a model is specifically trained, or how they are ranked relative to any of the elements beyond indicating that relevant demographics and variants would be chosen for model application. At the most simple level the model provide for a one to one correlation being encompassed by the claim, for example BRCA1/2 genetic loci which is informative for the risk of breast cancer as a characteristic. However, the claims as amended provide for not only what is known, but now for training, without any specification as to how, and correlating any possible loci with any characteristic without any weight as to whether the correlation is true, informative or how extensive to any demographic it might apply as there is no specific data required of the claims and broadly the model would be completely dependent and reliant on what is provided in any resulting model.
In review of the amendments, each of the steps broadly set forth the judicial exception as a set of instructions for analysis of sequence data at a loci for association to a characteristic and appears to be directed to a Mental Processes, that is concepts performed in the human mind (including an observation, evaluation, judgment, opinion). The specification provides prophetic examples, however in a review of the relevant art correlations between genetic mutations/variants are known to exist, and in some cases predominate in certain demographics; for example BRCA1 mutations present in Jewish woman, and the characteristic to predisposition to breast cancer. A review of the specification fails to provide any unique associations, and provides only the general guidance to evaluate know genetic data correlated to known characteristics and demographics and use this to make a prediction on a user, and broadly appear to encompass looking up the information in a table where the table contains the genetic variant, characteristic and any associated demographics in which it was studied. The method as claimed appears to be a process that one can perform in one’s mind and on paper.
Recent guidance from the office requires that the judicial exception be evaluated under a second prong to determine whether the judicial exception is practically applied. In the instant case, the claims do not have an additional element beyond the use of a server or that the instruction are stored on a media and the results of the analysis are transmitted in a final step. As discussed above, this judicial exception requires steps recited at high level of generality and are only stored on a non-transitory, and is not found to be a practical application of the judicial exception as broadly set forth. Moreover, as provided by the art of record such methods were also well known and used consistent with the claims. For example, Chatterjee et al. teach that genetic variants were known and associated with a variety of diseases, and which predisposes an individual to the associated disease. Chatterjee et al. teach that certain families fall into high risk groups providing for the limitation and concept of a demographic being associated with genetic variants and disease/characteristic, and the specific example of choosing a model for example determining the presence of a BRCA1 or BRACA2 variant in an individual to predict disease burden.
For step 2B of the 101 analysis, each of the independent claims recites additional elements and are found to be the steps of analyzing genetic data through modelling. In review of the specification, there is no evidence that there are special instructions for the modeling, only that different models are used for prediction purposes. As such, the claims do not provide for any additional element to consider under step 2B that amount to significantly more given the evidence of record and teachings of the specification. As explained in the Alice framework, the Court wrote that "[i]n cases involving software innovations, [the step one] inquiry often turns on whether the claims focus on the specific asserted improvement in computer capabilities or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool." The Court further noted that "[s]ince Alice, we have found software inventions to be patent-eligible where they have made non-abstract improvements to existing technological processes and computer technology." Moreover, these improvements must be specific -- "[a]n improved result, without more stated in the claim, is not enough to confer eligibility to an otherwise abstract idea . . . [t]o be patent-eligible, the claims must recite a specific means or method that solves a problem in an existing technological process."
As indicated in the summary of the judicial exception above and in view of the teachings of the specification, the steps are drawn to analysis of known sequence data which is correlated to known characteristics and demographics and associating known information with that provided by a user. The claims recite that the correlations are present as models, however the models do not appear to be complex databases but rather directed to specific correlations and similar to information provided in a table of genetic variant, characteristic of variant and any demographic. While the claims recite the method instruction are stored on a medium and could be implemented on a computer, together the steps do not appear to result in significantly more than a means to compare sequences. The judicial exception of the method as claimed can be performed by hand and in light of the previous claims to a computer medium and in light of the teaching of the specification on a computer. In review of the instant specification the methods do not appear to require a special type of processor and can be performed on a general purpose computer. As noted in prong 2, the art as demonstrated by Chatterjee et al. teach that genetic variants were known and associated with a variety of diseases, and which predisposes an individual to the associated disease and certain families fall into high risk groups providing for the limitation and concept of a demographic being associated with genetic variants and disease/characteristic, and the specific example of choosing a model that was previously determined in determining the presence of a BRCA1 or BRACA2 variant for example.
Based upon an analysis with respect to the claim as a whole, claims 1-20 do not recite something significantly different than a judicial exception of establishing a correlation between genetic data for various demographics, and applying the models to new data sets submitted by a user. Claims 1-20 are directed towards a system and implemented method of receiving sequence data from a user and comparing the data to identify and/or predict characteristics based on the sequence and demographic information provided. Dependent claims set forth additional steps which are more specifically define the considerations and steps of calculating, and comparing, and do not add additional elements which result in significantly more to the claimed method for the analysis.
Response to Applicants arguments
Applicants note the amendments to claim 1 and submits that the claims as a whole integrate any possible judicial exception and is an improvement to the art as a whole and thus are patent eligible under 101.
In response, the amendments have been acknowledged and analyzed above.
Applicants provide an overview of the invention as described in the specification noting Fig 1 and argue that polygenic models, for example ML models based on demographics cannot be practiced in one’s mind and is not a mental process as assessed under Step 2A prong 1. Applicants argue that providing a prediction server and a genomics server provides for a ranking based on demographics such as sex, ancestry, age,… and the ML polygenic models are specific and can be applied for greater accuracy and represents an improvement.
In response, the claims are directed broadly and generically to ‘polygenic models’ which the specification does not specifically require or define what the model is. Further, encompassed by the claims as analyzed above is a simple correlation/model for a given loci and a given characteristic which could be provided in a table form for easy review by observation. For the breadth the specification provides in paragraph [0008] that ‘One embodiment is a genetic prediction server that includes a memory that stores polygenic models which predict characteristics of individuals based on genetic variants of the individuals (i.e., genetic variants that are included within the genetic makeup of the individuals), including a set of polygenic models for a characteristic that each perform a different analysis of genetic variants when making a prediction.’ which can comprise a table with genetic variants and associated characteristics with various studied BRCA gene variants, as provided in the analysis and comparison of the BRCA gene variants associated with predisposition to cancer. Importantly, even though the claims as amended indicate to train a model, the specification fails to provide any detailed guidance for identifying new variants or loci, beyond the ability of machine learning providing correlations within any data set it is provided. It is noted that the models are not machine learning models, but rather machine learning is one means to analyze and associate data generally, but it not a function of the model itself. With respect to the amount of data that might be present in a genome, it is acknowledged that this may be true, however the claims do not require creating any of the models, it simply requires that others have and in fact given the guidance of the specification requires for purposes of 112 that such exist since the specification provides no models, nor does it provide for any of the necessary guidance on making informative correlations to generate the models required of the instant claims.
The guidance of the specification is noted, and that Applicants provide an overview of how models are made and calibrated and argue that targeting a demographic makes the choice of polygenic model more accurate. However, the specification guidance and drawings have been evaluated for what the claims are directed to. For the products, as set forth in the 112 6th analysis, there does not appear to be any new or unique computer needed, there are no new models provided, nor any specific analysis required when associated a subjects variants with those in a model as required by the claims. In all, the recitation of use of a computer or implementing the method on a network does not appear to affect the network and is considered to be an environment in which the method of matching a model with variants and characteristics is used assess genetic data from a subject. The amendments to the claim have been analyzed and they appear to be high level indications of how the data is processed using a computer such as transmitting and storing. The steps of selecting and predicting are also generic, and still appear to be provide by accessing information about a patient and comparing to known data present in a table. The art of record demonstrates that different genetic variants are associated with different traits, for example that is the variants associated with BRAC1 and cancer are different from that for other cancers for example necessitating the need for multiple different models. Given the evidence of record, it appears that multiple models are necessary for the myriads of possible traits, and given the breadth of ‘traits’ no one model could exist to define all possible traits. Given the lack of specific guidance in the specification and limitations for the breadth of the claims to provide polygenic models and selecting the model to use based on a demographic, it does appear that the claims are directed to a general concept of matching known genotypes that are associated with characteristics such as BRCA, CF, PKU for example. It is noted that in review of the specification there is no specific guidance to any specific models, no specific rules in creating models, nor how to choose a model given a variety of possible demographics which could exist and the great number of possible traits that exist. The judicial exception and concept the claimed invention is directed to appears to be supported and enabled by the art for possible correlations that can be identified and the idea that the best correlation be applied for a given demographic for a given genotype. The 21page specification provides only the general guidance to create multiple models and to choose an optimal model for the analysis and transmitting a result without any guidance on how these specifically are to be accomplished.
Comparing the fact pattern to relevant case law cited in the MPEP, unlike Enfish and a unique database structure and DDR holding that was found to affect the function of the GUI, the present claims given the breadth are similar to providing a table of known genetic variants and traits/diseases/conditions with a listing of other demographic data that was observed, for example BRAC1 variants, associated with breast cancer, commonly observed in Jewish population, and choosing the row that best fits the input information. In review of the guidance and evidence of record there does not appear to be any evidence that the claims as broadly set forth provide for any improvement, or for what it improves over previous choosing of models. As discussed in the basis of the rejection and as evidenced by the art of record, the use of different models that correlate traits or characteristics with genetic information and demographics were known and used, and an active area of research. Again, there is no evidence of record for a single model that would be applied to every genetic variant combined with all possible traits as previously asserted in arguments. Clearly, the use and existence of many different models to provide for informative correlations for diagnosis, prognosis and general patient care provides evidence that the skilled artisan would use different models as necessary. As analyzed above, the claims as broadly set forth do appear to preempt others from choosing from a variety of models to apply for a given condition as provided in Alice. claims recite the use of polygenic servers and models which are applied to the analysis of the users genetic information and demographics, in review of the claim limitations, guidance of the specification and the art of record it appears that the process can be performed in one’s mind or by hand by comparing information in a table to that provided by a user. There is no specific requirement of the data provided to the model, and as clearly set forth in the claims the association of the genetic variation and characteristic are both known. Similar to the example of BRCA1, such information is known and the suggestion to use servers and models to make this association does not appear to provide significantly more than the direction to use known correlations of genetics, resulting characteristics and any associated demographics that may be related to the prevalence of a genetic variant or characteristic.
Given the lack of guidance on how new associations would be created, and only the broad guidance to use known associations, the claims appear to broadly be directed to the use of others work that establishes the association of known genetic variants and characteristics with the suggestion to apply this knowledge using those models to analyze information from an individual.
As indicated previously, one way to overcome a rejection for non-patent-eligible subject matter is to persuasively argue that the claimed subject matter is not directed to a judicial exception. Another way for the applicants to overcome the rejection is to persuasively argue that the claims contain elements in addition to the judicial exception that either individually or as an ordered combination are not well understood, routine, or conventional. Another way for the applicants to overcome the rejection is to persuasively argue that the claims as a whole result in an improvement to a technology. Persuasive evidence for an improvement to a technology could be a comparison of results of the claimed subject matter with results of the prior art, or arguments based on scientific reasoning that the claimed subject matter inherently results an improvement over the prior art. The applicants should show why the claims require the improvement in all embodiments.
Conclusion
No claim is allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
In prosecution, the closest art of record has been Chatterjee et al., Koo et al., Moons et al. and Hendriksen et al. which provide generally that using genetic data to predict a disease or a condition in an individual were known. More specifically, Chatterjee et al. teach that genetic variants were known and associated with a variety of diseases, and which predisposes an individual to the associated disease. Chatterjee et al. teach that certain families fall into high risk groups providing for the limitation and concept of a demographic being associated with genetic variants and disease/characteristic, and the specific example of choosing a model for example determining the presence of a BRCA1 or BRACA2 variant in an individual to predict disease burden. In addition, Chatterjee et al. teach that unlike genetic variants alone, environmental risk factors can change over the lifespan of individuals. Thus, repeated measurements of environmental risk factors or risk biomarkers will be needed to provide a risk assessment for diseases associated with both long-term average exposure levels and trends in exposure levels over time providing other demographic associations. Prospective cohort studies with longitudinally measured risk factor data will be needed for the development of dynamic models for risk prediction. Research is also needed on statistical methodology for the development, validation and application of risk models with time-dependent risk factors which provide more broadly for developing and associating the appropriate demographics to genetic variants for use in broader populations. More generally, Koo et al. provide a review teaching that there are many types of machine learning methods to identify susceptibility genes for application to characteristics of epidemiology providing an overview of neural networks (NNs), support vector machine (SVM), and random forests (RFs) as know common tools used to generate models for multifactorial diseases. Koo et al. gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Additionally, Koo et al. provide a listing of additional references in support of the successful application of the machine learning tools for a variety of genetic variants and diseases. Both Hendricksen et al. and Moons are provided for teaching the use of prediction models and for a more detailed discussion for the variety of demographics providing for the development, validation and impact assessment for the use of models for different groups of users. The use of machine learning models to determine associations of genetic variations and diseases or characteristics among different demographic groups was well known in the art. Given the art of record, there appears to be a detailed knowledge for training and using machine learning modelling to predict and associate genetics and disease.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph T Woitach whose telephone number is (571)272-0739. The examiner can normally be reached Mon-Fri; 8:00-4:00.
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/Joseph Woitach/ Primary Examiner, Art Unit 1687