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 10-2023-0016383 filed 02/07/2023 is acknowledged.
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 “a predetermined unit” at the end of step (i-1). It is unclear whether this means base pairs, reads, variants, genes, chromosomes, files, batches, windows, sequence chunks, or something else. For similar reason, all the other “unit” that invoke 112(f) interpretation are also indefinite::
Claim 1 recites:
a clinical information generating unit;
(i-1) a genetic informatics pipeline 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.
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 “predicted candidate group for the disease” and “predicted candidate group for the drug” in step (i-2). It is unclear whether candidate group means disease candidates, drug candidates, genes, variants, patients, or recommendations.
Claim 1 recites “genetic information trained by…” Genetic information is not normally “trained.” The claim fails to clarify whether the AI model is trained or the data is processed.
Claim 9 recites “highest association”. It is unclear according to what metric, how disease and drug associations are scaled, and how ties are handled.
Claim 10 recites “reduces the genetic information in a range of 20000”. This is grammatically and technically ambiguous.
Claim Rejections - 35 USC § 112
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 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.
Claim 1 is to a “system”, which requires structural limitation. However, the disclosure provides no structural description to the above recited units.
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 disease or drug association providing system for a digital twin with genetic information screened by an artificial intelligence, the system comprising four generic sub-units:
a clinical information generating unit;
a patient’s digital twin file;
a disease information table; and
a drug information table.
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 disease or drug association providing system” (claim 1 preamble) are read on 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 genetic informatics pipeline unit which sequentially outputs genetic sequencing data of a patient in a predetermined unit;
This element (“sequentially outputs genetic sequencing data of a patient”) is interpreted as a sequence annotation process wherein the genetic information is added to the raw sequence. Under a BRI, this process which 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 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 genetic information output from the genetic informatics pipeline unit;
“Screens” in this step i-2d) encompasses observing two data set and performing an evaluation to identify “candidate group”. Such mental observations or evaluations fall within the “mental processes” grouping of abstract ideas.
(i-3) A dimensionality reduction AI unit which reduces a size of the genetic 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-4) A genetic informatics integration unit which receives and integrates the genetic 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-4) 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 genetic informatics 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. Such data manipulations and data organization fall within the “mental processes” grouping of abstract ideas.
(v) A controller which calculates and provides an association with the disease information table about the genetic information of the digital twin file and an association with the drug information table about the genetic information of the digital twin file.
“Calculates and provides an association with the disease information table” encompasses mathematical operation to calculate association. Therefore, this step equates to an abstract idea of mathematical concepts.
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 calculating and providing an association with the disease information table about the genetic information of the digital twin file and an association with the drug information table about the genetic information of the digital twin file, which is classified into an abstract idea of 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 genetic informatics pipeline 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.
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 1-10 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 disease or drug association providing system for a digital twin with genetic information screened by an artificial intelligence.
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 including genetic information. The “patient medical record data” encompasses drug/disease associated with the patient.
Zimmerman does not teach “a genetic information pipeline”. 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. If “predetermined unit” means chunks/markers/ variants/genes, O’Donnell’s genetic markers substantially overlap. Hence, O’Donnell suggests (i-1) a genetic informatics pipeline that receiving genetic/genotyping sequence data of a patient, because the genotyping data encompasses genotyping sequences.
Zimmerman provides ([062]) “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-2) using a candidate screening AI for candidate groups for disease and drug related to the genetic information of a patient.
Neither Zimmerman nor O’Donnell teaches dimensional 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-3) 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-4) integrating genetic information related to the candidate group (favorable group for diagnosis and treatment) using AI tools. Because the digital twin 130 encompasses genetic 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-3).
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 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]).
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]). Zimmerman teaches associating digital twin with disease information.
Regarding claim 3, Zimmerman does not teach 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. Using variant gene name and drug name as database keys/fields is a predictable implementation. Claim 3 is obvious and close to O’Donnell except for integration into the claimed digital twin environment.
Regarding claim 4: Zimmerman provides ([076]) “the overall digital twin 130. For example, different patient 110 body systems (e.g., vascular, neural, musculoskeletal, immune, etc.) can be structured and modeled as separate networks, data structures, etc. In certain examples, the digital twin 130 can be implemented as a nested series of learning networks, data structures, etc., including an umbrella construct and subsystem constructs formed within the umbrella”. Providing results through a networked service/application is a routine and predictable implementation of Zimmerman.
Regarding claim 5, Zimmerman provides ([076]) “the overall digital twin 130. For example, different patient 110 body systems (e.g., vascular, neural, musculoskeletal, immune, etc.) can be structured and modeled as separate networks, data structures, etc. In certain examples, the digital twin 130 can be implemented as a nested series of learning networks, data structures, etc., including an umbrella construct and subsystem constructs formed within the umbrella”. Providing results through a networked service/application is a routine and predictable implementation of Zimmerman.
Regarding claim 6, Zimmerman is not explicit in reporting. O’Donnell obtains data from (claim 1 2nd step) “research studies, peer reviewed articles, or reports” and (claim 1, 4th step) “displays pharmacogenomic elements including medication, pharmacogenomic signal, and evidence level”. Formatting the association as a report is routine. Claim 6 is obvious.
Regarding claim 7, Zimmerman is not explicit in reporting. O’Donnell provides (claim 1 4th step) “displaying on the user interface a plurality of elements, the plurality of elements including a medication element identifying the medication, a pharmacogenomic signal element identifying a relationship between the one or more genes and the medication, a level of evidence element identifying evidence from one or more studies performed on the medication and the one or more genetic markers”. Separating disease informatics and drug informatics into report sections is an obvious report-organization choice.
Regarding claim 8, Zimmerman provides ([035]) “Three-dimensional (3D) modeling of the patient creates the digital twin”. Zimmerman expressly teaches 3D modeling of the patient digital twin.
Regarding claim 9: Zimmerman provides ([045]) “the patient digital twin 130 can serve as an overall model or avatar of the patient 110 and can also model particular aspects of the patient 110 corresponding to particular data source(s) 210-260” 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 teaches overlaying patient information on anatomy in a 3D/avatar digital twin. “Human anatomy” anticipates different body parts. Displaying the body part with the highest association is a predictable visualization of the calculated association result.
Regarding claim 10, Wu provides (section “Abstract”) “auto-encoders to project high-dimensional genomic data to a low dimensional space”. Wu teaches AI dimensionality reduction of genomic data. If “range of 20000” means a reduced feature count, selecting a feature dimension is likely an optimization/result-effective variable. However, this phrase is also indefinite under 35 USC §112(b).
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.
Conclusion
No claims are allowed.
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/GL/
Patent Examiner
Art Unit 1686
/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights