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 .
Claims 1-17,23,34-35 are pending. Claims 1-17,23,34-35 are rejected herein. Claims 18-22,24-33 are cancelled, either through this action or a previous action.
Priority
This application claims priority to applications PCT/US22/20590 and provisional application #63/161,660 and has an effective filing date equivalent to 16 March 2021.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17,23 and 34-35 are 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.
Claims 1,12 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method, a system, and a computer product for clinical decision support, which are within a statutory category (or are interpreted to be within a statutory category for subject matter eligibility analysis purposes).
Step 2A1
The limitations of (claim 1 being representative) reading genomic information of a patient from a datastore […]; determining one or more variant of the genomic information, the one or more variant being associated with a disease state; identifying each of the one or more variant as a medically actionable variant or as a gene of uncertain significance, generating a list of one or more phenotypic features related to other one or more variant of the genomic information; reading an evaluation of the patient for a presence of the one or more phenotypic features on the list; and providing a diagnosis and patient management information associated with the one or more phenotypic features and the disease state to a healthcare provider as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a system comprising a datastore, a computing node, a non-transitory computer readable storage medium and a computer program product and a processor the claimed invention amounts to managing personal behavior or interaction between people. For example, but for these generic computer parts, this claim encompasses a person reading genomic information of a patient from a datastore, determining one or more variant of the genomic information, the variant being associated with a disease state, identifying each of the one or more variant as a medically actionable variant or as a gene of uncertain significance, generating a list of one or more phenotypic features related to the one or more variant of the genomic information; reading an evaluation of the patient for a presence of the one or more phenotypic features on the list; and providing a diagnosis and patient management information associated with the one or more phenotypic features and the disease state to the healthcare provider in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a system comprising a datastore, a computing node, a non-transitory computer readable storage medium and a computer program product and a processor that implements the identified abstract idea. The system comprising a datastore, a computing node, a non-transitory computer readable storage medium and a computer program product and a processor are not described by the applicant and is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The claim does not include additional elements that are sufficient to 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 element of using a system comprising a datastore, a computing node, a non-transitory computer readable storage medium and a computer program product and a processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Claims 2-11,13-17 and 34-35 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 merely describe(s) that reading the genomic information of the patient comprises accessing a certain type of record. Claim 3 merely describes reading the genomic information of the patient comprises accessing a certain provider. Claim 4 merely describes determining the one or more variant comprises accessing a datastore containing associations between variants and disease states. Claim 5 merely describes generating the list of the one or more phenotypic features comprises accessing a datastore containing associations between variants and phenotypes. Claim 6 merely describes generating the list of the one or more phenotypic features comprises accessing a datastore containing certain associations. Claim 7 merely describes generating the list of the one or more phenotypic features. Claim 8 merely describes providing a diagnosis and patient management information comprising displaying the one or more phenotypic features. Claim 9 merely describes receiving the healthcare provider an evaluation of the one or more phenotypic features in the patient. Claim 10 merely describes training the learning system. Claim 11 merely describes storing phenotypic features. Claim 13 merely describes wherein reading the genomic information of the patient comprises accessing a certain record. Claim 14 merely describes wherein reading the genomic information of the patient comprises accessing a certain provider. Claim 15 merely generating the list of the one or more variant comprises comparing the genomic information of the patient to a certain sequence. Claim 16 merely describes generating the list of the one or more variant comprises accessing a certain datastore containing association between variants and disease states. Claim 17 merely describes generating the list of the one or more phenotypic features comprises accessing a datastore containing associations between variants and phenotypes. Claim 34 merely describes prompting the healthcare provider to request a report about the one or more variant. Claim 35 merely describes the evaluation does not include any evidence for the one or more phenotypic features present in the patient, and further comprising storing the evaluation.
Claim(s) 7 also includes the limitation “generating the list of the one or more phenotypic features comprises providing the one or more variant to a trained learning systems, and obtaining therefrom the one or more phenotypic features.” This is being analyzed as part of the abstract idea, with the abstract idea being: wherein generating the list of the one or more phenotypic features comprises providing the one or more variant to a trained learning systems, and obtaining therefrom the one or more phenotypic features. This use of the trained systems merely confines the use of the abstract idea to a particular technological environment or field of use and thus fails to add an inventive concept to the claims. The use of the term “trained learning systems” as recited here doesn’t, given broadest reasonable interpretation, confine the limitation to necessarily be beyond the abstract idea.
The dependent claims also include the following additional elements: “electronic health record interface” or “electronic health record” and a “short messaging service text message.” These additional elements generally link the judicial exception to a particular technological environment. Additional elements that generally link the judicial exception to a particular technological environment or field of use cannot serve to integrate the exception into a practical application or provide significantly more. See MPEP 2106.04(d)(l), Relevant Consideration for Evaluating Whether Additional Elements Integrate A Judicial Exception Into A Practical Application, and MPEP 2106.05(h).
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 indicating obviousness or nonobviousness.
Claim(s) 1-17,23,34-35 is/are rejected under 35 U.S.C. 103 as being unpatentable over US-20220189581-A1 (hereafter Neville) in view of US 2015/0310163 A1 (hereafter Kingsmore).
Regarding Claim 1
Neville teaches:
A method comprising: reading genomic information of a patient from a datastore encoded in a non-transitory computer readable medium; [Neville teaches at para. [0153], under the title Input Data for a Bayesian Classification Network, a variant database containing a list of all possible single nucleotide variants (SNVs) in the human genome, of all possible single nucleotide insertions and deletions in the human genome and of all multi-nucleotide, insertion and deletion variants (<100 bp) previously observed in humans is used to generate training data sets for the hierarchical Bayesian network for variant classification. Neville teaches at the Abstract a method of classifying a genetic variant comprising receiving, at a first plurality of trained nodes of a hierarchical Bayesian Network, input data comprising data of a genetic variant of a patient…Nevilles teaches at para. [0329] all of the calculations are run on the local computer (e.g., PC) CPU and therefore computations performed by the local processor need access exclusively to the data stored in the local data storage facility. The local data storage facility of the local computer is interpreted to have non-transitory computer readable medium. Collectively, Neville teaches reading genomic information of a patient from a datastore encoded in a non-transitory computer readable medium.]
determining one or more variant of the genomic information, the one or more variant being associated with a disease state; [Neville teaches at para. [0046] in an embodiment, the first plurality of trained node is trained to determine the relationship of a genetic variant to other known disease-causing genetic variants in the reference data set. This is determining one or more variant of the genomic information, the one or more variant being associated with a disease state.]
identifying each of the one or more variants as a medically actionable variant or as a gene of uncertain significance, [Neville teaches at the Abstract a method of classifying a genetic variant comprising receiving, at a first plurality of trained nodes of a hierarchical Bayesian Network, input data comprising data of a genetic variant of a patient. Neville teaches at para. [0073] in one embodiment, a first classification algorithm is used in step 110 to classify genetic variants as “pathogenic” (i.e. disease-causing or otherwise clinically relevant) or “benign” (i.e., not clinically relevant). The pathogenic classification is labeled as clinically relevant which is interpreted as being medically actionable. The “benign” (i.e., not clinically relevant) classification associated with the genetic variant is interpreted as a gene of uncertain significance.]
generating a list of one or more phenotypic features related to the one or more variant of the genomic information; [Neville teaches at Fig. 4 listing genetic variants with annotations of relevant genetic, biological and clinical features. Neville teaches at Fig. 16 listing of clinical features represented by a set of coordinates in n-dimensional space (i.e., phenotype signature). Neville teaches at Fig. 16 listing genetic variants represented by a set of coordinates in n-dimensional space (i.e. list of genotype signatures). Neville teaches at Fig. 16 posterior probabilities of phenotype-genotype-disease association and raked posterior probability of phenotype-genotype-disease associations. This is generating a list of one or more phenotypic features related to the one or more variant of the genomic information.]
Neville may not explicitly teach:
reading and evaluation of the patient for a presence of the one or more phenotypic features on the list;
and providing a diagnosis and patient management information associated with the one or more phenotypic features and the disease state to a healthcare provider.
Kingsmore teaches:
reading an evaluation of the patient for a presence of the one or more phenotypic features on the list; [Kingsmore teaches at Figure 2 variant detection/genotyping and using SSAGA-delimited or non-delimited variant interpretation. Kingsmore teaches at Figure 2 entering clinical findings into SSAGA. Kingsmore teaches at para. [0035] symptom and sign-assisted genome analysis (“SSAGA”) is a new clinic-pathological correlation tool that maps the clinical features of 591 well-established, recessive genetic diseases with pediatric presentations to corresponding phenotypes and genes known to cause symptoms. This teaches reading an evaluation of the patient for a presence of the one or more phenotypic features associated with the one or more variant. Kingsmore also teaches at Claim 1 (c) comparing said first phenotype associated gene data sets with a database of individualized genomic variations identified in said individual by sequencing a genome, an exome or part of a genome of said individual. Kingsmore teaches at Claim 1 (d) creating a prioritized list of phenotype-associated variations based on said comparisons and at Claim 1(e) comparing said phenotype-associated variation of said individual with a database of genetic disease to produce a prioritized list of probable diseases. Kingsmore teaches at para. [0215] the system analyzes the collected individual phenotypic information of the individual with one, two or three different databases of mapped causative genes for genetic diseases and associated phenotypes which results in three separate and distinct phenotype-associated gene data sets and teaches that these data sets are then combined to use for analysis.]
and providing a diagnosis and patient management information associated with the one or more phenotypic features and the disease state to a healthcare provider. [Kingsmore teaches at Figure 5 Iterative Human-Computer Interaction, involves physician patient encounter wherein the physician enters an initial set of observed symptoms, signs, test values: SxO1-SxOn. Kingsmore teaches at Figure 5 iterative human computer interaction a rank ordered differential diagnosis. This is interpreted to mean displaying a rank ordered differential diagnosis in the interaction. This is interpreted as providing a diagnosis and patient management information associated with the one or more phenotypic features and the disease state to a healthcare provider. Kingsmore also teaches at para. [0019] this system uses the patient’s symptoms, signs and/or laboratory values (Sx), and/or suspected mode of inheritance, obtained by a physician or there healthcare provider (such as a nurse or genetic counselor) and the patient’s genomic variations as data inputs, with dynamic prompts by the system, which concomitantly performs comprehensive, multinomial probabilistic classification, assisted by comprehensive databases of known mappings of genome sequence variations and known associated genes and known associated genetic diseases and known associated symptoms to provide an integrated, computer assisted probabilistic classification (or interpretation) of the clinical picture and the corresponding genomic variants in order to reach a Dx that is the likely cause of the patients symptoms and signs and genetic disease. Kingsmore teaches at para. [0008] that after the information is processed by the system, the system can display the results and/or possible list of diseases in the web based portal.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the method and apparatus for classification and/or prioritization of genetic variants of Neville to the system for genome analysis and genetic disease diagnosis of Kingsmore with the motivation of addressing acutely ill neonates with genetic diseases who are often discharged or deceased before a diagnosis is made (Kingsmore at para. [0002]).
Regarding Claim 12 and 23
Due to their similarity to Claim 1, Claim 12 and 23 are similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Regarding Claim 2
Neville/Kingsmore teach the method of claim 1. Neville/Kingsmore further teach:
wherein reading the genomic information of the patient comprises accessing an electronic health record of the patient. [Kingsmore at Figure 3 teaches accessing an electronic health record of the patient.]
Regarding Claim 3
Neville/Kingsmore teach the method of claim 1. Neville/Kingsmore further teach:
wherein reading the genomic information of the patient comprises accessing a sequencing provider. [Kingsmore teaches at Figure 2 next gen. sequencing, interpreted to be a “sequencing provider” there being no criteria other than literally providing the sequence.]
Regarding Claim 4
Neville/Kingsmore teach the method of claim 1. Neville/Kingsmore further teach:
wherein determining the one or more variant comprises comparing the genomic information of the patient to a reference sequence. [Kingsmore teaches at para. [0144] variants are compared to the reference gene and transcript annotation to determine the transcript-specific effects of a variant. Kingsmore teaches at Figure 2 entering clinical findings into SSAGA. Kingsmore teaches at para. [0035] symptom and sign-assisted genome analysis (“SSAGA”) is a new clinic-pathological correlation tool that maps the clinical features of 591 well-established, recessive genetic diseases with pediatric presentations to corresponding phenotypes and genes known to cause symptoms. This teaches wherein determining the one or more variant comprises comparing the genomic information of the patient to a reference sequence.]
Regarding Claim 5
Neville/Kingsmore teach the method of claim 1. Neville/Kingsmore further teach:
wherein generating the list of the one or more variant comprises accessing a datastore containing associations between variants and disease states. [Kingsmore teaches at Figure 2 variant detection/genotyping and using SSAGA-delimited or non-delimited variant interpretation. Kingsmore teaches at Figure 2 entering clinical findings into SSAGA. Kingsmore teaches at para. [0035] symptom and sign-assisted genome analysis (“SSAGA”) is a new clinic-pathological correlation tool that maps the clinical features of 591 well-established, recessive genetic diseases with pediatric presentations to corresponding phenotypes and genes known to cause symptoms. Kingsmore teaches at Table S1 a table containing associations between variants and disease, which is interpreted to be the phenotype or disease states.]
Regarding Claim 6
Neville/Kingsmore teach the method of claim 1. Neville/Kingsmore further teach:
wherein generating the list of the one or more phenotypic features comprises accessing a datastore containing associations between variants and phenotypes. [Kingsmore teaches at Figure 2 variant detection/genotyping and using SSAGA-delimited or non-delimited variant interpretation. Kingsmore teaches at Figure 2 entering clinical findings into SSAGA. Kingsmore teaches at para. [0035] symptom and sign-assisted genome analysis (“SSAGA”) is a new clinic-pathological correlation tool that maps the clinical features of 591 well-established, recessive genetic diseases with pediatric presentations to corresponding phenotypes and genes known to cause symptoms. Kingsmore teaches at Table S1 a table containing associations between variants and disease, which is interpreted here to be the phenotype.]
Regarding Claim 7
Neville/Kingsmore teach the method of claim 1. Neville/Kingsmore further teach:
wherein generating the list of the one or more phenotypic features comprises providing the one or more variant to a trained learning systems, and obtaining therefrom the one or more phenotypic features. [Kingsmore teaches at para. [0182] furthermore, a feature of the disclosed system of the present invention is continuous self-learning, meaning that the data from each patient for whom the system is used is anonymously applied to further “train” or update the clinical feature to disease to gene to variant classifiers or mappings. Kingsmore teaches at Figure 2 entering clinical findings into SSAGA. Kingsmore teaches at Figure 2 variant detection/genotyping and using SSAGA-delimited or non-delimited variant interpretation Kingsmore teaches at para. [0035] symptom and sign-assisted genome analysis (“SSAGA”) is a new clinic-pathological correlation tool that maps the clinical features of 591 well-established, recessive genetic diseases with pediatric presentations to corresponding phenotypes and genes known to cause symptoms.]
Regarding Claim 8
Neville/Kingsmore teach the method of claim 1. Neville/Kingsmore further teach:
wherein providing a diagnosis and patient management information comprises displaying the one or more phenotypic features in an electronic health record interface. [Kingsmore teaches this system uses the patient’s symptoms, signs, and/or laboratory values (Sx), an/or suspected mode of inheritance, obtained by a physician or other healthcare provider (such as a nurse or genetic counselor) and the patient’s genomic variations and known associated genetic diseases and known associated symptoms to provide an integrated, computer-assisted probabilistic classification (or interpretation) of the clinical picture and the corresponding genomic variants in order to reach a Dx that is the likely cause of the patient’s symptoms and signs and genetic disease. Kingsmore teaches at Figure 2 an electronic medical record and producing a final report with primary and secondary findings. Kingsmore teaches at Figure 2 a web interface. These teach prompting the healthcare provider and displaying the one or more phenotypic features in an electronic health record interface. Kingsmore teaches at Figure 2 entering clinical findings into SSAGA.]
Regarding Claim 9
Neville/Kingsmore teach the method of claim 1. Neville/Kingsmore further teach:
further comprising: receiving from the healthcare provider an evaluation of the one or more phenotypic features in the patient. [Kingsmore teaches at Figure 5 a physician patient encounter wherein the physician enters an initial set of observed symptoms, signs and tests values. The observed symptoms and signs are interpreted to be phenotypic features in the patient. Kingsmore teaches this system uses the patient’s symptoms, signs, and/or laboratory values (Sx), an/or suspected mode of inheritance, obtained by a physician or other healthcare provider (such as a nurse or genetic counselor) and the patient’s genomic variations and known associated genetic diseases and known associated symptoms to provide an integrated, computer-assisted probabilistic classification (or interpretation) of the clinical picture and the corresponding genomic variants in order to reach a Dx that is the likely cause of the patient’s symptoms and signs and genetic disease.]
Regarding Claim 10
Neville/Kingsmore teach the method of claim 9. Neville/Kingsmore further teach:
further comprising: providing the one or more variant and the evaluation of the one or more phenotypic features to a learning system, thereby training the learning system to associate the one or more variant and the one or more phenotypic features. [Kingsmore teaches at para. [0182] furthermore, a feature of the disclosed system of the present invention is continuous self-learning, meaning that the data from each patient for whom the system is used is anonymously applied to further “train” or update the clinical feature to disease to gene to variant classifiers or mappings. Kingsmore teaches at Figure 2 entering clinical findings into SSAGA. Kingsmore teaches at para. [0035] symptom and sign-assisted genome analysis (“SSAGA”) is a new clinic-pathological correlation tool that maps the clinical features of 591 well-established, recessive genetic diseases with pediatric presentations to corresponding phenotypes and genes known to cause symptoms.]
Regarding Claim 11
Neville/Kingsmore teach the method of claim 1. Neville/Kingsmore further teaches:
further comprising: storing the phenotypic features of the patient in an electronic health record of the patient. [Kingsmore teaches at para. [0091] “The Variant Warehouse” is a relational database and accompanying lightweight web application that stores characterization results and makes them available through a simple query and display interface.]
Regarding Claim 13
Neville/Kingsmore teach the system of claim 12. Neville/Kingsmore further teach:
wherein reading the genomic information of the patient comprises accessing an electronic health record of the patient. [Kingsmore teaches at Figure 2 on the left hand side accessing the electronic medical record for an ill patient.]
Regarding Claim 14
Neville/Kingsmore teaches the system of claim 12. Neville/Kingsmore further teach:
wherein reading the genomic information of the patient comprises accessing a sequencing provider. [Kingsmore teaches at Figure 2 next gen. sequencing, interpreted to be a “sequencing provider” there being no criteria other than literally providing the sequence.]
Regarding Claim 15
Neville/Kingsmore teach the system of claim 12. Neville/Kingsmore further teach:
wherein generating the list of the one or more variants comprises comparing the genomic information of the patient to a reference sequence. [Kingsmore teaches at para. [0144] that variants are compared to the reference gene and transcript annotation to determine the transcript specific effects of a variant. This teaches determining the one or more variant comprises comparing the genomic information of the patient to a reference sequence. ]
Regarding Claim 16
Neville/Kingsmore teach the system of claim 12. Neville/Kingsmore further teach:
wherein generating the list of the one or more phenotypic features comprises accessing a datastore containing associations between variants and disease states. [Kingsmore teaches at Figure 2 variant detection/genotyping and using SSAGA-delimited or non-delimited variant interpretation. Kingsmore teaches at para. [0035] symptom and sign-assisted genome analysis (“SSAGA”) is a new clinic-pathological correlation tool that maps the clinical features of 591 well-established, recessive genetic diseases with pediatric presentations to corresponding phenotypes and genes known to cause symptoms. This teaches wherein determining the one or more phenotypic features comprises accessing a datastore containing associations between variants and disease states.]
Regarding Claim 17
Neville/Kingsmore teach the system of claim 12. Neville/Kingsmore further teach:
wherein generating the list of the one or more phenotypic features comprises accessing a datastore containing associations between variants and phenotypes. [Kingsmore teaches at Figure 2 variant detection/genotyping and using SSAGA-delimited or non-delimited variant interpretation. Kingsmore teaches at para. [0035] symptom and sign-assisted genome analysis (“SSAGA”) is a new clinic-pathological correlation tool that maps the clinical features of 591 well-established, recessive genetic diseases with pediatric presentations to corresponding phenotypes and genes known to cause symptoms. This teaches wherein determining the one or more phenotypic features comprises accessing a datastore containing associations between variants and phenotypic states.]
Regarding Claim 34
Neville/Kingsmore teach the method of claim 1. Neville/Kingsmore further teach:
further comprising: prompting the healthcare provider to request a report about the one or more variant. [Reid teaches at para. [0123] the method will further comprise receiving a request for a genetic profile of the one or more de-identified medical records, transmitting the request, wherein the request comprises an identifier for each of the one or more de-identified medical records, and receiving, the genetic profile from a remote computing device. The request for a genetic profile of the one or more de-identified medical records is prompting the healthcare provider to request a report about the one or more variant.]
Regarding Claim 35
Neville/Kingsmore teach the method of claim 34. Neville/Kingsmore further teach:
wherein the evaluation does not include any evidence for the one or more phenotypic features present in the patient, [Reid teaches at para. [0067] the medical information can comprise, for example, medical history, medical professional observations and remarks, laboratory reports, diagnoses, doctors’ orders, prescriptions, vital signs, fluid balance, respiratory function, blood parameters, electrocardiograms, x-rays, CT scans, MRI data, laboratory test results, diagnoses, prognoses, evaluations, admission and discharge notes, and patient registration information.]
and further comprising storing the evaluation. [Reid teaches at para. [0067 the one or more computing devices will be used to store, process, analyze, output and/or visualize biological data.]
Response to Arguments
35 U.S.C. 101
Applicant argues that the abstract idea should not be categorized as a certain method of organizing human activity. The claims do not instruct a health provider what to do; they provide an information output. Providing information is not the same as managing behavior. The Examiner relies on the limitation “providing a diagnosis and patient management information… to a healthcare provider” to argue the claims recite managing interactions. However, outputting the result of a computational process to a user is a standard feature of any computer implemented invention. Under the Examiner’s logic, virtually any computer system that displays results to a human user would be abstract, which is not the standard. Applicant cites the Federal Circuit in Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals (887 F. 3d 1117).
The abstract idea is categorized as both a mental process and a certain method of organizing human activity. Note that the MPEP discourages parsing the claim and indicates the abstract idea should be considered as a single abstract idea for subject matter eligibility purposes. The claim is not solely being categorized as a certain method of organizing human activity because of “…providing a diagnosis and patient management information associated with the one or more phenotypic features and the disease state to a healthcare provider.” To paraphrase, the independent claims recite reading genomic information of a patient from a datastore […]; determining one or more variant of the genomic information, the one or more variant being associated with a disease state; identifying each of the one or more variant as a medically actionable variant or as a gene of uncertain significance, generating a list of one or more phenotypic features related to other one or more variant of the genomic information; reading an evaluation of the patient for a presence of the one or more phenotypic features on the list; and providing a diagnosis and patient management information associated with the one or more phenotypic features and the disease state to a healthcare provider. The independent claim recites a way of using genomic information to identify medically actionable variants and generate a list of associated phenotypic features related to the variant to be used to screen a patient for the phenotypic features related to the medically actionable variant and ultimately to diagnose that patient. The Examiner agrees with Applicant’s assertion, the limitations fall within managing personal behavior and relationships or interactions between people regardless if the data is displayed to the healthcare provider in the manner specifically recited. The entire claim is directed towards managing personal behavior and relationships or interactions between people, specifically rules or instructions for a healthcare provider to follow to perform a form of reverse phenotyping to screen patients for disease. MPEP 2106.04(a)(2) indicates that the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.
Additionally, the Examiner maintains that independent claim 1 also represents a mental process. Respectfully, one point of disagreement concerning the use of the mental process categorization on independent claim 1 appears to be Applicant’s repeated assertion that the size, volume and complexity of information being processed is what is beyond can be practically performed in the human mind. The Examiner is asserting that there are embodiments of independent claim 1, for example, that could be practically completed during a timed examination in a classroom setting or during medical examination of a patient. For example, consider the limitation “reading genomic information of a patient from a datastore…”. The term “genomic” means of or relating to a genome or genomics, and the word “genomics” means a branch of biotechnology concerned with applying the techniques of genetics and molecular biology to the genetic mapping and DNA sequencing of sets of genes or the complete genomes of selected organisms, with results organized in a database. The size of the organism’s “genomic information” is not defined to be not practical via the broadest reasonable interpretation of the word genomic and achieving the abstract idea mentally for certain bacteriophages, for example, would be practical to perform. Additionally, largely because of the breadth of the broadest reasonable interpretation of the terms “genomic information” and “datastore,” a student or doctor reading the results of a restriction digest created via read the results of gel electrophoresis could follow the recited steps in independent claim 1 to achieve the abstract idea. Finally, though it does not appear to be the intended embodiment, given the breadth of these limitations, a medical student or doctor using a karyotype to diagnose down syndrome or some other medical conditions via the presence or absence of chromosomes and/or parts of chromosomes (also genomic information) thereby practically completing the abstract idea as a mental process. For example, there being no requirement for the specific form of the datastore or the genomic information, by reading the karyotype (interpreted to be the datastore) for the presence or absence of a chromosome or chromosome part the doctor could practically quickly read genomic information of patient from a datastore. Similarly, by determining the presence or absence of a chromosome or chromosome part the doctor could determine if the trisomy 21 had occurred in the manner specifically recited: determine one or more variant of the genomic information, the one or more variant being associated with a disease state. Then the doctor could generate a list of one or more phenotypic features related to one or more variant of the genomic information (joints that are loose and too flexible related to trisomy 21). In fact, a doctor could practically complete the entire independent claim 1 as a mental process to diagnose a patient with down syndrome.
While the Examiner cites MPEP 2106.04(a)(2)(II) for the proposition that person-computer interactions can fall within this grouping, that provision is not a blanket rule (Office Action, at 20). The rule applies where the claims actually recite the organization or management of human activity, not merely because a human receives output from a computer.
Agreed.
The claims here recite a technical process; the human interaction is incidental to receiving the result. Applicant argues that the 101 should be withdrawn on these grounds.
Agreed that the claims recite a technical process. Disagree that the rejection made under 35 U.S.C. 101 should be withdrawn for the reasons given throughout this document.
During the telephonic Examiner’s Interview, held on March 26, 2026, the Examiner and SPE suggested that the claims will be better characterized as a mental process, which is also suggested in the Office Action (Office Action, at 20). Applicant respectfully disagrees.
This document outlines the position that the claims represent both a mental process and a certain method of organizing human activity. Both assertions are supported as required in the relevant responses above.
The MPEP states that if a claim “requires a computer, it may still recite a mental process” only if the underlying operations “can practically be performed in the mind.” (MPEP 2106.04(a)(2)(III). In the instant application, the claimed steps involve reading genomic information (comprising tens of thousands of protein-coding genes and millions of variants), computationally determining variants, comparing them against disease-state databases, and generating phenotypic feature lists. The Applicant previously argued, and it remains true, that a person cannot practically read, process, and analyze entire genomic datasets in any useful timeframe. The volume and complexity of the data make mental performance impracticable.
The Examiner would take this opportunity to point out this is an area of active disagreement, and, as pointed out in the relevant answer above, it appears to be in part related to the use of the word genomic to attempt to implicate volume and complexity to the data being processed. As explained throughout this document, broadest reasonable interpretation of the claim limitations currently do not require the claim to deal with large amounts of genomic data or unusually complex data. For example, the presence or absence of a chromosome may be read, processed and ascertained almost immediately via mental process by merely looking at some entire genomic datastores.
The Examiner dismissed the impracticality argument by stating “whether the process is practical or not is beside the point.” This contradicts the USPTO’s own guidance. The October 2019 Update to Subject Matter Eligibility guidance (2019 PEG”) explicitly states: “If a claim recites a limitation that can practically be performed in the human mind, the limitation falls within the mental process grouping. Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind. The sheer scale of genomic data processing (analyzing potentially millions of variants against phenotype databases) cannot practically be performed mentally. This is not merely a speed improvement; it is a categorical impossibility for the human mind to hold and process this volume of data.
The Examiner agrees with applicant’s quotation of the 2019 PEG, which is definitive, and respectfully apologizes for any confusion their statement may have caused. The statement does not replicate the terminology put forth in the PEG but reflects the fact that, if the limitation eluded the abstract idea net via becoming unpractical to complete in the human mind, the limitation in question could possibly be addressed in a rejection under 35 U.S.C. 101 anyway via analysis as an additional element. Regardless, the Examiner maintains the independent claim is practically performable in the human mind as explained in the relevant responses above. The Examiner points out that “reading genomic information from a patient from a datastore,” as an example, could involve reading vastly different amounts of information. Reciting “genomic” currently isn’t limiting the claim to situations that deal exclusively with amounts of information that are not practical to perform mentally. The abstract idea could be practically executed mentally during a timed examination or during a medical examination of patient. The Examiner is bound to broadest reasonable interpretation.
Further, the claims require reading from a “datastore encoded in a non-transitory computer readable medium,” accessing variant-disease association databases, and using “trained learning systems” (Claim 7). These are not incidental computer implementations of otherwise mental tasks; they are integral to the claimed process. A trained machine learning model has no mental analog, but instead it is inherently a computer-performed function.
“Accessing variant-disease association databases” could or could not require the use of a computer. Moreover, the MPEP indicates that a claim can still recite a mental process and require the use of a computer or computer(s) and/or even pen and paper. Finally, Claim 10, (The method of claim 9, further comprising: providing the one or more variant and the evaluation of the one or more phenotypic features to a learning system, thereby training the learning system to associate the one or more variant and the one or more phenotypic features.) as example, further refines and extends the abstract idea. The claim doesn’t recite how the learning will occur, just the input and that it will associate the one or more variant and the one or more phenotypic features.
Additionally, the claimed process employs a specific technical approach: starting from genomic variants, generating predicted phenotypic features, and then evaluating the patient for those features (reverse phenotyping). This is a defined computational methodology with specific directionality (genotype [Wingdings font/0xE0] predicted phenotype [Wingdings font/0xE0] clinical evaluation), not an open-ended mental evaluation.
As stated and supported throughout the document, the claims are categorizable as a mental process and a certain method of organizing human activity. The specific directionality indicated was incorporated into the abstract idea.
It is difficult to imagine that the phrase “integrated into the practical application” could have any substantive meaning if the above claim language were not considered to be integrated in the practical application.” The claims very clearly “use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” (2019 PEG, at 11). For example, the limitation of “providing a diagnosis and patient management information associated with the one or more phenotypic features and the disease state to a healthcare provider” is clearly providing a practical application of the claimed method.
Applicant claims herein that “providing a diagnosis and patient management information associated with the one or more phenotypic features and the disease state to a healthcare provider” is incidental to the solution and yet clearly provides a practical application. Regardless, the limitation quoted is part of the unified abstract idea as described at length and supported throughout this document. Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application.
Applicant argues that Neville only describes identifying “disease-causing genetic variants” that are pathogenic or not clinically relevant (Neville at para. [0073]). Neville is silent as to the difference between pathogenic and medically actionable.
The Examiner disagrees. Neville teaches at para. [0073] in one embodiment, a first classification algorithm is used in step 110 to classify genetic variants as “pathogenic” (i.e. disease-causing or otherwise clinically relevant) or “benign” (i.e., not clinically relevant). The pathogenic classification as disease-causing or otherwise clinically relevant was interpreted as being medically actionable.
Moreover, Neville uses this identification of pathogenic genes as performed by a laboratory or variant scientist, for molecular and clinical diagnoses (Neville, at Fig. 1A). In contrast, the step as claimed identifies the one or more variant, the uses this identification to generate a list of phenotypic features, which is then used to diagnose.
Neville teaches at para. [008] Fig. 1A illustrates a current/known process of genetic testing, including variant interpretation, which does appear to rely on a laboratory scientist. However, Neville’s methods disclosed throughout do not exclusively rely on a laboratory or variant scientist. For example, Neville invokes automated classification algorithms etc., for example see Fig. 16 in the variant prioritization process which require automation by hierarchical Bayesian network. Regardless, the claimed steps in question could be performed mentally by a doctor or healthcare provider, given broadest reasonable interpretation.
Neville instead uses phenotypic features to manually interpret the variant, and then only after the phenotypic features have been considered a diagnosis is made (Neville, at Fig. 1A and [0252]). The method as claimed identifies variants and then uses that information to generate a list of phenotypic features. This reverse phenotyping process is key, where instead of using phenotypic features to identify genetic variants, the variants are first identified and then phenotypic features of interest are generated (Specification at [0048]-[0049]). As such, Applicant submits that Neville does not teach at least the step of “identifying each of the one or more variant as a medically actionable variant or as a gene of uncertain significance.”
Neville teaches at the Abstract a method of classifying a genetic variant comprising receiving, at a first plurality of trained nodes of a hierarchical Bayesian Network, input data comprising data of a genetic variant of a patient. Neville teaches at para. [0028] Fig. 17 shows an example of input data for the variant prioritization network. Neville teaches at Figure 17 determining mathematically the contribution of each listed gene to a pathogenic classification (or no pathogenic classification, interpreted to be identifying the gene of uncertain significance). This teaches identifying each of the one or more variant as a medically actionable variant. Neville teaches at para. [0073] in one embodiment, a first classification algorithm is used in step 110 to classify genetic variants as “pathogenic” (i.e. disease-causing or otherwise clinically relevant) or “benign” (i.e., not clinically relevant). The pathogenic classification is labeled as clinically relevant which is interpreted as being medically actionable. The benign classification is labeled as not clinically relevant, which is interpreted as a gene of uncertain significance.
The Office Action is silent to whether Kingsmore teaches the step of “identifying each of the one or more variant as a medically actionable variant or as a gene of uncertain significance.” Applicant notes that the previous response argues against the use of Kingsmore to teach this step. As such, Applicant further submits that neither Neville nor Kingsmore, alone or in combination, teach, suggest, or disclose the step of “identifying each of the one or more variant as a medically actionable variant or as a gene of uncertain significance.”
See above, Neville teaches the limitation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2017/0286594 A1 (hereafter Reid) discusses methods and systems for generating and analyzing genetic variant-phenotype association results are disclosed.
Schulze, Thomas G., and Francis J. McMahon. "Defining the phenotype in human genetic studies: forward genetics and reverse phenotyping." Human heredity 58.3-4 (2005): 131-138. Schulze teaches general background on reverse phenotyping.
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.
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/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
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