DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The IDS filed 10/16/2023 has been considered by the Examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of REPUBLIC OF KOREA Application 10-2023-0016382 filed 2/7/2023 is acknowledged.
Claim Status
Claims 1-10 are pending and are examined on the merits.
Claim Objections
Claim 1 is objected to because of the following informalities: claim 1 has sub-
elements (ii)-(v), but not (i). Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims recite the following “unit”:
Claim 1 recites:
a clinical information generating unit;
(i-1) a genetic informatics pipeline unit;
a predetermined unit;
(i-2) a predicted candidate group screening AI unit;
(i-3) a dimensionality reduction AI unit; and
(i-4) a genetic informatics integration unit.
Claim 4 recites:
an external service unit.
Claim 7 recites:
a detailed disease informatics unit; and
a detailed drug informatics unit.
In all the above cases, the recited units are the equivalents of “means”. All the specific units are structureless nonce terms. Hence, they all invokes 112(f).
Claim Rejections - 35 USC § 112—Second Paragraph
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 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.
Claim 1 recites the limitation "the preprocessed DNA bio information" in step (i-2). There is insufficient antecedent basis for this limitation in the claim. To amend the claim, Applicant can just delete the “the”.
Claim 1 recites the limitation “the bio information trained by the predicted candidate group screening AI unit and the dimensionality reduction AI unit” in step (i-5). There is insufficient antecedent basis for the limitation “the bio information” in the claim.
Claim 1 is drawn to a system comprising different “unit” that predicts medical risks. The specification does not provide a description of the “unit” carrying the claimed steps. One would not know what is needed to be a unit for carrying out the claimed steps and therefor it is unclear what is intended by the “unit” in order to meet the mete and bounds of the claim. It is therefore unclear what structure corresponds to what units and one would not know what is needed to be a units for carrying out the claimed steps. It is unclear what is intended by the “unit” in order to meet the mete and bounds of the claim.
Applicants have disclosed different “unit” in claim 1:
a clinical information generating unit;
(i-1) a DNA preprocessing unit;
(i-2) a bioinformatics pipeline unit, a predetermined unit;
(i-3) a predicted candidate group screening AI unit;
(i-4) a dimensionality reduction AI unit; and
(i-5) a bioinformatics integration unit,
Claims can be amended by specifying that the above recited units are software modules (functional but not physical structure), and providing other structural language (such as a computer/processor, or non-transitory computer readable storage media) to further limit the “system”.
Claim 1 recites “bio information trained by…” in step (i-5). Bio information is not normally “trained.” The claim fails to clarify whether the AI model is trained or the data is processed.
Claims 5 recites “a highest disease onset possibility”. It is unclear according to what metric, how disease onset possibility are scaled, and how ties are handled.
Claim 7 recites “a highest drug side effect possibility”. It is unclear according to what metric, how drug side effect possibility are scaled, and how ties are handled.
Claim Rejections - 35 USC § 112—First Paragraph
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims recite the following “unit” without structure limitations:
Claim 1 recites:
a clinical information generating unit;
(i-1) a DNA preprocessing unit;
(i-2) a bioinformatics pipeline unit, a predetermined unit;
(i-3) a predicted candidate group screening AI unit;
(i-4) a dimensionality reduction AI unit; and
(i-5) a bioinformatics integration unit,
Claim 1 is drawn to a system comprising units for predicting medical risks, wherein a review of the specification does not show a description or definition of the “unit”. A review of the specification shows that aspects of the invention may be implemented using software (page 4, descriptions about Fig.3 (unit 600); Fig. 5 (unit 800); Fig. 6-8). However a written description of what the "unit“ means for or stands for has not been provided.
Claims can be amended by specifying that the above recited units are software modules (functional but not physical structure), and providing other structural language (such as a computer/processor, or non-transitory computer readable storage media) to limit the “system”.
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 non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-10 are to a gene-based digital twin system which predicts medical risks, the system comprising four information sub-units:
a clinical information generating unit;
a patient’s digital twin file;
a disease information table; and
a drug information table,
and a “controller” which predicts and outputs a medical risk of the patient.
Under a broadest reasonable interpretation (BRI), “a clinical information generating unit” reads on a carrier wave or transient signal; “Digital twin file” and “information table” are about data and information; “a controller”; “a gene-based digital twin system” (claim 1 preamble) are about carrier waves, transient signals, and data and information. It does not belong to any one of the four statutory categories. Claims 1-10 are hence rejected under 35 U.S.C. 101.
Applicant can amend claim 1 so it recites a computerized method (hence a process); a non-transitory computer-readable medium (that store the instructions outlined in claims); or a computer system with computing hardware components (that upon executing the processors, cause the system to perform the outlined processes).
Step 2A Prong One: Identification of Abstract Ideas
The claims recite:
(i-1) A DNA preprocessing unit which processes genetic sequencing data of a patient in a predetermined format;
Under a broadest interpretation (BRI) this element (“processes genetic sequencing data of a patient in a predetermined format”) is interpreted as a sequence annotation process wherein the genetic information is added to the raw DNA sequence. This process may be practically performed in the human mind using observation, evaluation, judgment, and opinion regarding a piece of sequence. Therefore, this element equates to an abstract idea of mental processes.
(i-2) A bioinformatics pipeline unit which sequentially outputs the preprocessed DNA bio information in a predetermined unit;
Under a BRI this element is interpreted as sequentially annotating process wherein the bio information is added to the raw DNA sequence. This process may be practically performed in the human mind using observation, evaluation, judgment, and opinion regarding a piece of sequence. Therefore, this element equates to an abstract idea of mental processes.
(i-3) A predicted candidate group screening AI unit which screens a predicted candidate group for the disease and a predicted candidate group for the drug with respect to the DNA bio information output from the bioinformatics pipeline unit.
The “AI unit” is recited in a general way. Under a BRI, it encompasses the classic cross-referencing method for adding the predicted candidate group for the disease and a predicted candidate group for the drug from existing knowledge. This process may be practically performed in the human mind using observation, evaluation, judgment, and opinion regarding a piece of sequence. Therefore, this element equates to an abstract idea of mental processes.
(i-4) A dimensionality reduction AI unit which reduces a size of the preprocessed DNA bio information;
The “AI unit” is recited in a general way. Under a BRI, it encompasses the classic PCA (principal component analysis) method for dimension reduction. PCA is an algorithm. Therefore, this step equates to an abstract idea of mathematical concepts.
(i-5) A bioinformatics integration unit which receives and integrates the bio information trained by the predicted candidate group screening AI unit and the dimensionality reduction AI unit from the genetic informatics pipeline unit;
“Receives and integrates” in step (i-5) encompasses putting two sets of data into one. Such data manipulations fall within the “mental processes” grouping of abstract ideas.
(ii) a patient’s digital twin file which is virtually generated based on information about a body of the patient, and the predicted candidate group for the disease and the predicted candidate group for the drug received from the bioinformatics integration unit;
“Virtually generated” in step (ii) encompasses putting multiple sets of data into one, through correlation or association, or reference. Such data manipulations fall within the “mental processes” grouping of abstract ideas.
(iii) A disease information table which receives and stores information about a disease from the outside;
This step describe an information table, which encompasses data observation; A “table which receives and stores information” encompasses organizing information into a table, which can be performed by the human mind. Such data manipulations fall within the “mental processes” grouping of abstract ideas.
(iv) A drug information table which receives and stores information about a drug from the outside; and
A “table which receives and stores information” requires writing and organizing received data into the table (hence to “receives and stores information). Therefore this step encompasses data manipulation and data organization that can be achieved in the human mind. Such data manipulations and data organization fall within the “mental processes” grouping of abstract ideas.
(v) A controller which predicts and outputs a medical risk of the patient based on the digital twin file, the disease information table, and the drug information table.
“Predicts and outputs a medical risk of the patient based on the digital twin file, the disease information table, and the drug information table” encompasses mathematical operation to calculate “medical risk”. Therefore, this step equates to an abstract idea of mental processes and mathematical concepts, as discussed above over elements (i-1 to i-5).
Dependent claims further describe tables, files and information, and data manipulations. They are also classified into abstract ideas.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a process of predicting and outputting a medical risk of the patient based on the digital twin file, the disease information table, and the drug information table, which is classified into an abstract idea of mental processes mathematical concepts. The claims do not recite any additional elements that integrate the abstract idea/judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claims recite the following structural components:
Claim 1 recites:
a clinical information generating unit;
(i-1) a DNA process unit;
(i-2) a bioinformatics pipeline unit
(i-3) a predicted candidate group screening AI unit;
(i-4) a dimensionality reduction AI unit; and
(i-4) a bioinformatics integration unit.
Under a BRI, these units are interpreted as generic computers (or part of a generic computer). These units are recitations of generic computer structures that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
The claims do not include additional elements that are sufficient to amount of significantly more than the judicial exception. Viewed as a whole, the claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 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.
Claims 1-3 and 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Zimmerman et. al.: (“Methods and systems for generating a patient digital twin”, US20190005200A1, published Jan. 3, 2019, priority June 28, 2017. Newly cited), O’Donnell et. al.: (“Genomic prescribing system and methods”, US20160239636A1, published Aug. 18, 2016, priority Oct. 7, 2013. Newly cited) and further in view of Wu et al.: (“Deep Learning Methods for Predicting Disease Status Using Genomic Data”, J. Biom. Biostat., 2018; PMCID: PMC6530791. Newly cited).
Claim 1 is interpreted as a gene-based digital twin system which predicts a medical risk.
Regarding claim 1, Zimmerman provides (section “Abstract”) “patient digital twin are disclosed. An example apparatus includes a processor and a memory. The example processor is to configure the memory according to a patient digital twin of a first patient. The example patient digital twin is to include a data structure created from a combination of patient medical record data, image data, genetic information, and historical information, the combination extracted from one or more information systems and arranged in the data structure to form a digital representation of the first patient”. Zimmerman teaches (instant preamble) the digital-twin patient information system with clinical information generating unit because the “patient medical record data” anticipates “clinical information” and “apparatus includes a processor and a memory” anticipates a generating unit.
Zimmerman does not teach “gene-based” digital twin or “a DNA preprocessing unit” (or “a DNA preprocessing pipeline”), or “a bioinformatics pipeline”. O’Donnell provides (claim 1, 2nd step) “obtaining by the processor pharmacogenomic data from one or more sources including research studies, peer reviewed articles, or reports related to the one or more genes and a medication”, which suggests a gene-based digital twin system.
O’Donnell provides (claim 1, 1st step) “receiving by processor patient data including genotyping analysis data of one or more patients, wherein the genotyping analysis data includes genetic information of a patient related to one or more genetic markers.” O’Donnell teaches receiving genetic/genotyping data of a patient. The “pipeline” and “predetermined unit” are implementation details of processing genetic data in a computer system. Under a BRI “predetermined unit” means chunks/markers/ variants/genes. Hence, O’Donnell’s genetic marker anticipates (i-1) “genetic sequencing data”; and (i-2) a “predetermined unit”. O’Donnell also teaches (i-1) a DNA preprocessing unit, because under a BRI, the genotyping analyzed data contains DNA sequences regarding individual genetic marker (according to specification at [009-010]). “Preprocessing” is not defined in the disclosure. Under a BRI, “preprocessing” usually means brief quality assurance of data before serious CPU commitment, which is usually part of data analysis pipeline. Therefore, O’Donnell’s “genotyping analysis data of one or more patients” suggests a DNA preprocessing unit. In summary, the two “unit” in (i-2) have two different meanings. The first one (pipeline unit) is interpreted as a software module, or a function software block; and the second one (predetermined unit) is interpreted as a information processing unit. Here a genetic marker.
Since “bioinformatics” is broad, and under a BRI DNA related information is part of bioinformatics. O’Donnell’s “DNA preprocessing unit” also anticipates (i-2) a bioinformatics pipeline unit (species anticipate genus). “Preprocessing” under a BRI is interpreted as a brief quality assurance of data before serious CPU commitment for expensive computing. Both “DNA preprocessing unit” and “DNA processing unit” anticipates a bioinformatics pipeline unit.
Zimmerman provides ([078]) “As shown in the example flow 1300 of FIG. 13, the patient digital twin 130 can be used to generate a risk profile 1302 for the patient 110. For example, based on information stored and/or otherwise modeled in the digital twin 130, the patient's 110 risk for certain conditions, diseases, etc., can be modeled to generate the patient's risk profile 1302. The risk profile 1302 can enumerate potential disease(s) and/or other condition(s) for which the patient 110 is at risk based on the digital twin 130”, which suggests (i-3) a predicted candidate group screening AI unit which screens a predicted candidate group for the disease.
Zimmerman does not teach (i-3) predict a candidate group for the drug with respect to DNA bio information. O’Donnell provides ([026]) “Physicians and patients have become increasingly aware that people often react or respond to drugs very differently, even when a drug is being used to treat the same condition. Recent research shows that individual genetic makeup often explains why a subject tolerates or responds to a given drug” and (023]) “In one embodiment, the invention includes an interface that delivers information regarding drug-gene correlations, connections between the aforementioned correlations and published literature, and drug substitutions”, which teaches predicting candidate group for the drug with respect to the DNA bio information output from the bioinformatics pipeline unit.
Neither Zimmerman nor O’Donnell teaches (i-4, i-5) “ dimensionality reduction” for data. Wu provides (page 1, section “Abstract”) “All four articles first used auto-encoders to project high-dimensional genomic data to a low dimensional space and then applied the state-of-the-art machine learning algorithms to predict disease status based on the low-dimensional representations.” Wu expressly teaches (i-4) AI/deep-learning dimensionality reduction of genomic data for disease prediction.
Zimmerman provides ([062]) “machine- and human-based diagnosis is leveraged to improve the patient digital twin 130. For example, healthcare software applications, medical big data, neural networks, other machine learning and/or artificial intelligence, etc., can be leveraged to diagnose, identify issue(s), propose solution(s) (e.g., medication, diagnosis, treatment, etc.) with respect to the digital twin”, which suggests (i-5) integrating bioinformatics related to the candidate group (favorable group for diagnosis and treatment) using AI tools. Because the digital twin 130 encompasses bio information (Zimmerman: ([044]) “the patient digital twin 130 includes electronic medical record (EMR) 210 information, images 220, genetic data 230”). dimensionality reduction is a tool and Wu teaches AI/deep-learning dimensionality reduction of genomic data in step (i-4).
Zimmerman provides ([035]) “three-dimensional (3D) modeling of the patient creates the digital twin”; ([070]) “imaging data can be used to form an avatar of the patient 110 for the patient digital twin 130 and/or used in combination with other patient data for simulation, diagnosis, etc.” and ([078]) “the patient digital twin 130 can be used to generate a risk profile 1302 for the patient” Zimmerman teaches (ii) a patient digital twin based on body/image/genetic/disease risk data. It would have been obvious to include disease/drug candidate information in Zimmerman’s patient digital twin to support personalized care.
Zimmerman does not teach acquiring disease information table. O’Donnell provides (claim 1, 2nd step) “obtaining … pharmacogenomic data from one or more sources including research studies, peer reviewed articles, or reports”. O’Donnell teaches (iii) obtaining external research/report information relevant to pharmacogenomics. Storing externally obtained disease information in a database/table is a routine implementation.
Zimmerman does not teach acquiring drug information table. O’Donnell provides (claim 1, 2nd step) “obtaining … pharmacogenomic data from one or more sources including research studies, peer reviewed articles, or reports related to the one or more genes and a medication.” O’Donnell directly teaches (iv) drug/medication information from outside sources and creation of a database of gene-medication correlations.
Zimmerman provides ([078]) “the patient digital twin 130 can be used to generate a risk profile 1302 for the patient” and ([004]) “the example patient digital twin is to be combinable with one or more rules to generate, using the processor, a recommendation for a patient health outcome based on modeling the patient digital twin as instructed by the one or more rules”. Zimmerman teaches (v) a processor/controller querying/modeling the digital twin to generate recommendations based on the digital twin, and the digital twin 130 contains genetic information (Zimmerman: [044]).
Zimmerman does not teach (ii, iv, and v) association of digital twin to drugs. O’Donnell provides (claim 1, 3rd step) “creating by the processor a database describing one or more correlations between the one or more genetic markers and one or more responses to the medication”. O’Donnell directly teaches correlation between genetic markers and medication/drug response.
Regarding claim 2, Zimmerman provides ([078]) “the patient digital twin 130 can be used to generate a risk profile 1302 for the patient” and the digital twin 130 contains genetic information (Zimmerman: [044]). However, Zimmerman does not teach gene variants impact on drug candidates.
O’Donnell provides [048] “Genes are analyzed using a panel of gene variants that may be a custom panel including, for example, variants selected because of compelling scientific evidence. It is also contemplated the panel may be an existing panel, such as the Sequenom ADME commercial panel, consisting of variants typically implicated in drug metabolism. As an example, gene variants may include those that have compelling scientific evidence in favor of their potential pharmacogenomic role in treatment strategies based on published research”. Hence, O’Donnell teaches gene variants impact on drug candidates.
Regarding claim 3, Zimmerman does not teach a gene variant list. O’Donnell provides ([048-049]) a variant list with name of the gene, variant type, variant IDs. O’Donnell does not teaches a zygosity type, a chromosome number, a position, and a genotype, but it would be obvious to have these information available in a regular SNP database.
Regarding claims 6-8, Zimmerman is not explicit in multi-level risk. O’Donnell provides ([066]) “the pharmacogenomic signal element 506 is an alert icon 514, shown in the form of a traffic light signal, but any form is contemplated. A “green” traffic light signal communicates a favorable relationship between the genotype and the drug, i.e., the patient has a genotype which suggests an improved chance of benefit or a decreased risk of toxicity with the drug. A “yellow” traffic light signal communicates a cautionary relationship between the genotype and the drug, i.e., the patient has a genotype associated with possible undesirable outcomes when using the medication. A “red” traffic light signal communicates a warning relationship between the genotype and the drug, i.e., the patient has a genotype that confers a significant increase in risk with use of the drug” and ([024]) “The invention facilitates physicians to pre-identify patients who are most likely to experience severe side effects from medications, or to predict which of their patients might require alternative dosing. It could also prove beneficial by identifying those patients who are most likely to respond to a given medication.” O’Donnell teaches dividing candidate group for the drug into a plurality of levels on side effects.
Regarding claims 9-10, Zimmerman provides ([035]) “Three-dimensional (3D) modeling of the patient creates the digital twin”. and ([077]) “the patient digital twin 130 can be visualized to a user as an avatar or other visual representation (e.g., two-dimensional, three-dimensional, four-dimensional (e.g., including a time component to simulate, navigate, etc., backward and/or forward in time), etc.) including patient information overlaid on human anatomy visualization, made available upon drilling down into a particular anatomy, etc.” Zimmerman expressly teaches 3D body avatar modeling and visualization of the patient information. “Disease or drug” would be an obvious part of the patient digital twin.
It would have been prima facie obvious to incorporate O’Donnell’s pharmacogenomic correlation database into Zimmerman’s patient digital twin because Zimmerman expressly contemplates genetic data, prescriptions, patient modeling, and health recommendations. O’Donnell supplies known disease/drug pharmacogenomic logic that improves the usefulness of Zimmerman’s digital twin for personalized medicine.
One would reasonably expect success because Zimmerman and O’Donnell are closely related in field and purpose. Zimmerman teaches a patient digital twin integrating genetic and medical data for patient modeling/recommendation. O’Donnell teaches a pharmacogenomic system correlating genetic markers with medication response and providing prescribing recommendations. Both systems are computer-implemented healthcare informatics systems using patient genetic/medical data, databases, processors, and user interfaces.
It would have been prima facie obvious to apply Wu’s AI dimensionality reduction to Zimmerman/O’Donnell’s genetic data pipeline to improve computational efficiency and prediction performance. Because genomic datasets are high-dimensional and Wu teaches that dimensionality reduction improves computational handling and disease-prediction modeling of genomic data.
One would reasonably expect success because Wu specifically teaches successful use of autoencoders/deep learning to project genomic data to lower-dimensional representations for disease prediction. The combination will reduce computational burden, improve feature representation, and enable more efficient disease/drug association processing within the digital twin system.
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).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/298,614 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the digital twin based on genetic information encompasses the gene-based digital twin system.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
No claims are allowed.
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/GL/
Patent Examiner
Art Unit 1686
/Anna Skibinsky/
Primary Examiner, AU 1635
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