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 .
Status of Claims
This action is in reply to Applicant’s communication filed on June 26, 2025.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 27, 2023 was filed before the mailing date of the first office action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of Applicant’s claim for priority under 35 U.S.C. § 371 of International Application No. PCT/EP2021/074567 filed on September 7, 2021, which claims the benefit of Application No. DE10 2020 212 379.9 filed in the Germany on September 30, 2020.
Claim Rejections - 35 USC § 112(b)
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.
Claim 9 is 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 9 recites the following limitations:
“the first patient ontology” in line 3
“the second patient ontology” in line 3
“the common patient ontology” in line 5
There is insufficient antecedent basis for each of these limitations in the claim. Appropriate correction is required by Applicant.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
Claims 1 and 16 are directed to a method and a system respectively and therefore all fall into one of the four statutory categories. (Step 1: Yes, the claims fall into one of the four statutory categories).
Step 2A analysis - Prong one:
The substantially similar independent method and system claims, taking claim 1 as exemplary, recite the following limitations: A computer-implemented method for determining a similarity measure, wherein the similarity measure describes a similarity between a first patient and a second patient, comprising: receiving a first patient data record, wherein the first patient data record is assigned to the first patient; receiving a second patient data record, wherein the second patient data record is assigned to the second patient; receiving or determining a medical ontology, wherein the medical ontology is independent of the first patient data record and the second patient data record; determining a patient ontology based on the medical ontology and at least one of the first patient data record or the second patient data record; and determining the similarity measure based on the patient ontology.
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The remaining un-bolded limitations above, 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 method implemented by a computer, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the additional elements identified/bolded above, this claim encompasses a person collecting data for two different patients, obtaining a medical ontology, determining an ontology for the patient(s), and then determining a measure of similarity between the two patients 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 2A – Prong 1: Yes, the claims are abstract).
Step 2A analysis - Prong two:
Claims 1 and 16 recite additional elements beyond the abstract idea. Claim 1 recites a computer to implement the method. Claim 16 recites a determination system, an interface, and a computing unit.
This judicial exception is not integrated into a practical application. In particular, the claims recite a computer, a determination system, an interface and a computing unit which are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification explains that the determination system may be a computer, microcontroller or an integrated circuit that contains a memory to store programs, a computing unit which executes the programs and an interface to receive inputs and display outputs (see Applicant’s specification page 27-28, 49). The identified additional elements equate to saying “apply it.” MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application.
Accordingly, this/these additional element(s), when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because it/they does/do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 1 and 16 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional claimed elements are not integrated into a practical application).
Step 2B analysis:
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 elements of using a computer, a determination system, an interface and a computing unit to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The collective functions appear to be implemented using conventional computer systemization. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of well-understood, routine, and conventional activities previously known to the industry. Further, the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention. See MPEP 2106.05(d).
Applicant’s specification discloses the following:
Applicant describes embodiments of the disclosure at a very high level to include the use of a wide variety of processors, memories, interfaces, computing units, etc. (see pages 27-28, 49 and FIG. 10).
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
In summary, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). The claims do not provide an inventive concept significantly more than the abstract idea. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2B: No, the claims do not provide significantly more).
Dependent Claims 2-15 and 17-20 further define the abstract idea that is presented in independent Claim 1, and are further grouped as certain methods of organizing human activity and are abstract for the same reasons and basis as presented above. Further, Claims 9 and 17-19 recite additional elements beyond the abstract idea. Claim 17 recites a non-transitory computer program product, program sections and a determination system. Claim 18 recites a non-transitory computer-readable storage medium, program sections and a determination system. Claims 9 and 19 recite an application of a trained function. This/these additional element(s) is/are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. For example, as noted above, the Applicant’s specification indicates the use of known storage mediums.
Accordingly, this/these additional element(s), when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not recite additional elements that integrate the judicial exception into a practical application when considered both individually and as an ordered combination. Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5 and 9-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Martin et al. (US 20080201280).
Regarding Claim 1, Martin discloses the following limitations:
A computer-implemented method for determining a similarity measure, wherein the similarity measure describes a similarity between a first patient and a second patient, comprising: (Martin discloses computer assisted clinical decision support where one or more similar patients are identified (a similarity between a first patient and a second patient) based on a similarity (determining a similarity measure). – abstract; paras 24, 48)
receiving a first patient data record, wherein the first patient data record is assigned to the first patient; receiving a second patient data record, wherein the second patient data record is assigned to the second patient; (Martin discloses that a patient record is input to the algorithm and distances or similarities for each feature from the patient record (receiving a first patient data record) to other patient records (receiving a second patient data record) are determined. A processor learns to identify at least one similar patient record from a set of patient records. – paras 9-10, 48)
receiving or determining a medical ontology, wherein the medical ontology is independent of the first patient data record and the second patient data record; (Martin discloses that a medical ontology may be used (receiving or determining a medical ontology) to provide information associated with one or more diseases and numerous medically relevant concepts, where the medical ontology is derived from sources such as the Medical Subject Headings (MeSH) and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and may further be derived from different ontologies (the medical ontology is independent of the first patient data record and the second patient data record – paras 32-40, 43). – abstract; paras 2, 4, 9-10, 32-40, 43; FIGs. 2, 4)
determining a patient ontology based on the medical ontology and at least one of the first patient data record or the second patient data record; (Martin discloses that the processor uses the machine-learnt algorithm and resulting incorporation of the medical ontology (based on the medical ontology) to extract and/or aggregate relevant similarity information from a patient record (determining a patient ontology based on the first patient data record or the second patient data record). The processor applies the machine-learnt algorithm to search for or identify one or more similar patient records from a set of patient records. Alternatively or additionally, the matrices or other representation of the algorithm are applied to data of a patient record to indicate a probability of membership in the class defined by the ontology (e.g., probability of a specific disease). – paras 45, 50)
and determining the similarity measure based on the patient ontology. (Martin discloses that, for similarity determination (determining the similarity measure), the processor applies the machine-learnt algorithm to a medical record of a patient. Data for features identified in the medical ontology are extracted from the patient record or input into the trained algorithm. This data is used as inputs to the trained algorithm. The trained algorithm aggregates the contributions of these features based on the incorporated medical ontology. For example, distances or similarities for each feature from the patient record to other patient records are determined (determining the similarity measure based on the patient ontology). The aggregation determines an average or other representation of distance from each of the previous patient records. Alternatively or additionally, the aggregation identifies only patient records with sufficient similarity for each of the features and/or nodes included in a given semantic grouping. – paras 45-50)
Regarding Claim 2, Martin discloses all the limitations above and further discloses the following limitations:
The method of claim 1, wherein the patient ontology is a common patient ontology, and the common patient ontology is based on the medical ontology, the first patient data record and the second patient data record. (Martin discloses determining distances or similarities for each extracted feature from the patient record to other patient records (the patient ontology is a common patient ontology). The aggregation determines an average or other representation of distance from each of the previous patient records (the common patient ontology is based on the first patient data record and the second patient data record). A final similarity to each stored record or groups of records is determined from a final aggregate. The most or sufficiently similar previous medical records are identified by the machine-learnt algorithm as a function of the multi-level medical ontology and/or semantic groupings (the common patient ontology is based on the medical ontology). – paras 48-50)
Regarding Claim 5, Martin discloses all the limitations above and further discloses the following limitations:
The method of claim 1, wherein the patient ontology is a first patient ontology and the first patient ontology is not based on the second patient data record, (Martin discloses that the matrices or other representation of the algorithm are applied to data of a patient record (a first patient ontology not based on the second patient data record) to indicate a probability of membership in the class defined by the ontology (e.g., probability of a specific disease). – para 45)
the method further comprising: determining a second patient ontology based on the medical ontology and the second patient data record, (Martin discloses that the most or sufficiently similar previous medical records are identified by the machine-learnt algorithm as a function of the multi-level medical ontology and/or semantic groupings (determining a second patient ontology based on the medical ontology). The aggregation determines an average or other representation of distance from each of the previous patient records (the common patient ontology is based on the first patient data record and the second patient data record). – paras 48-50)
wherein the determining the similarity measure is based on the first patient ontology and the second patient ontology. (Martin discloses identifying only patient records with sufficient similarity (determining the similarity measure) for each of the features and/or nodes included in a given semantic grouping (based on the first patient ontology and the second patient ontology). – para 48)
Regarding Claim 9, Martin discloses all the limitations above and further discloses the following limitations:
The method of claim 1, wherein the determining the similarity measure is based on an application of a trained function to at least one of the first patient ontology or the second patient ontology, or the determining the similarity measure is based on an application of a trained function to the common patient ontology. (Martin discloses that similar patient records are inferred based on training with ontology information (a trained function to the common patient ontology). The processor applies the machine-learnt algorithm (an application of a trained function) to search for or identify one or more similar patient records from a set of patient records (the determining the similarity measure). The memory 14 may store training data or data to be searched. The data is a collection of two or more previously acquired patient records (a trained function to at least one of the first patient ontology or the second patient ontology). For example, hundreds, thousands or tens of thousands of patient records are obtained and stored. – paras 45, 47, 53-54)
Regarding Claim 10, Martin discloses all the limitations above and further discloses the following limitations:
The method of claim 1, wherein the similarity measure includes a plurality of similarity measures for a plurality of second patient data records and the second patient data records are assigned to a plurality of second patients, (Martin discloses determining a similarity (the similarity measure) between one or more similar patients (assigned to a plurality of second patients) from a set of patient records (a plurality of similarity measures for a plurality of second patient data records). – abstract; paras 10, 45, 72)
the method further comprising: determining a set of comparable patients based on the determined similarity measures, wherein the set of comparable patients is a subset of the plurality of second patients, and each of the comparable patients is similar to the first patient. (Martin discloses determining a similarity (based on the determined similarity measures) by identifying at least one or more similar patient records (each of the comparable patients is similar to the first patient) from a set of patient records (the set of comparable patients is a subset of the plurality of second patients). – abstract; paras 10, 45, 72)
Regarding Claim 11, Martin discloses all the limitations above and further discloses the following limitations:
The method of claim 10, further comprising: determining a probability value for a side effect of medical treatment of the first patient based on the set of comparable patients. (Martin discloses that the machine learning may be used for probabilistic decision support. FIG. 4 is an example of a medical ontology created for Atrial Septal Defect (ASD) and shows probable outcomes or symptoms (a probability value for a side effect of medical treatment). The mining engine may further infer a patient state as a function of the matched associated terms and corresponding probabilities of the associated terms indicating the patient state. In the examples herein, the ontologies are used for disease specific decision support, but the ontologies may be used for symptom, cause, effect, signs, other concepts, or other features for analysis. – abstract; paras 21, 41, 43, 47, 58, 61, 65; FIG. 4)
Regarding Claim 12, Martin discloses all the limitations above and further discloses the following limitations:
The method of claim 10, further comprising: determining a probability value for an outcome of medical treatment of the first patient based on the set of comparable patients. (Martin discloses that the machine learning may be used for probabilistic decision support and outcomes are identified based on similarity (a probability value for an outcome of medical treatment). Data-driven decision support by identifying similar previous patients for outcome correlation may assist in making the appropriate clinical decision. In the examples herein, the ontologies are used for disease specific decision support, but the ontologies may be used for symptom, cause, effect, signs, other concepts, or other features for analysis. – abstract; paras 21, 41, 43, 47, 58, 61, 65; FIG. 4)
Regarding Claim 13, Martin discloses all the limitations above and further discloses the following limitations:
The method of claim 1, wherein the patient ontology is based on at least one of the following types of data: genomic data from at least one of the first patient data record or the second patient data record, epigenomic data from at least one of the first patient data record or the second patient data record, transcriptomic data from at least one of the first patient data record or the second patient data record, proteomic data from at least one of the first patient data record or the second patient data record, or metabolomic data from at least one of the first patient data record or the second patient data record. (Martin discloses that the medical ontology incorporated into the machine learning model may include data from functional genomics (genomic data) and proteomics (proteomic data) experiments, and metabolic pathway information (metabolomic data). The similarity of one or more patients is determined based on the incorporated medical ontology, indicating that the patient records from which the information is being extracted to determine similarities may include these types of data. For example, Martin discloses that the patient records may include information such as genomic or proteomic sample data (from at least one of the first patient data record or the second patient data record). – abstract; paras 38-39, 50)
Regarding Claim 14, Martin discloses all the limitations above and further discloses the following limitations:
The method of claim 1, wherein the medical ontology maps at least one of the following influences: influence of a human genome on at least one of a human genome, an epigenome, a transcriptome, a proteome or a metabolome, influence of a human epigenome on at least one of a human genome, an epigenome, a transcriptome, a proteome or a metabolome, influence of a human transcriptome on at least one of a human genome, an epigenome, a transcriptome, a proteome or a metabolome, influence of a human proteome on at least one of a human genome, an epigenome, a transcriptome, a proteome or a metabolome, or influence of a human metabolome on at least one of a human genome, an epigenome, a transcriptome, a proteome or a metabolome. (Martin discloses that the medical ontology may be based on various sources of medical data including the Medical Subject Headings (MeSH); a thesaurus used for indexing and annotating journal articles and books in the PubMed database of biomedical literature. It is known that MeSH provides extensive information about the human genome and epigenome and their influence on other biological layers as documented in biomedical literature. Further, the medical ontology may be based on the Biological Pathway Exchange (BioPAX) project which is also known to provide information of the influence of the human genome, epigenome, transcriptome, proteome and metabolome. – paras 35, 39)
Regarding Claim 15, Martin discloses all the limitations above and further discloses the following limitations:
The method of claim 1, wherein at least one of the patient ontology is based on one of the following types of data, at least one of a genome sequence, germline mutations in the genome sequence or somatic mutations in the genome sequence of at least one of the first patient or the second patient, at least one of pre-existing conditions or comorbidities of at least one of the first patient or the second patient, symptoms occurring in at least one of the first patient or the second patient, lifestyle of at least one of the first patient or the second patient, the lifestyle including at least one of alcohol consumption, tobacco consumption or drug consumption of at least one of the first patient or the second patient, or physiological characteristics of at least one of the first patient or the second patient, the physiological characteristics including at least one of a height, a weight, an age, a gender or an ethnicity of at least one of the first patient or the second patient; (Martin discloses that the medical ontologies provide information associated with diseases, symptoms, medical findings, etc. The ontology may be used for identifying information such as diseases (pre-existing conditions of at least one of the first patient or the second patient – paras 4, 24, 34-36, 41, 45) and patient symptoms (symptoms occurring in at least one of the first patient or the second patient - paras 41, 43, 69). Alternatively or additionally, the matrices or other representation of the algorithm are applied to data of a patient record to indicate a probability of membership in the class defined by the ontology (e.g., probability of a specific disease) (pre-existing conditions of at least one of the first patient or the second patient - para 45). The patient records may include medications (lifestyle of at least one of the first patient or the second patient including drug - para 55) and the patients age (physiological characteristics of at least one of the first patient or the second patient including an age - para 55) and the medical ontology used to determine what terms to extract from the patient records may include an age of the patient (para 65).– paras 4, 24, 34-36, 41, 43, 55, 65, 69)
or the medical ontology is based on one of the following types of data, a gene expression in a human organism, at least one of transcription factor binding sites, enhancer sites or splice sites with respect to at least one of a human genome or a transcriptome, at least one of amino acid sequences or protein domains with respect to a human proteome, a spatial positional relationship of elements of a human genome or a proteome, clinical annotations with respect to elements of a human a genome, an epigenome, a transcriptome, a proteome or a metabolome, biological interaction pathways of a human genome, an epigenome, a transcriptome, a proteome or a metabolome, the biological interaction pathways including at least one of gene regulatory networks, metabolic pathways or signal transduction pathways, interaction between conditions and symptoms in humans, or interaction between pharmaceutical products, treatable diseases and side effects. (Martin discloses utilizing the Microarray Gene Expression Data (MGED) ontology (a gene expression in a human organism – para 38). The Biological Pathway Exchange (BioPAX) project (biological interaction pathways of a human genome, an epigenome, a transcriptome, a proteome or a metabolome) provides a common exchange format for biological pathway data, capturing the key elements of data models from a wide range of popular pathway databases. The established BioPax ontology covers metabolic pathway information (the biological interaction pathways including metabolic pathways – para 39). Medical ontologies provide information associated with one or more diseases and numerous medically relevant concepts such as symptoms (interaction between conditions and symptoms in humans – paras 4, 24). – paras 4, 24, 38-39)
Regarding Claim 16, Martin discloses the following limitations:
A determination system for determining a similarity measure, wherein the similarity measure describes a similarity between a first patient and a second patient, the determination system comprising: (Martin discloses computer assisted clinical decision support where one or more similar patients are identified (a similarity between a first patient and a second patient) based on a similarity (determining a similarity measure). – abstract; paras 24, 48)
an interface configured to, receive a first patient data record, wherein the first patient data record is assigned to the first patient, receive a second patient data record, wherein the second patient data record is assigned to the second patient; (Martin discloses that a patient record is input to the algorithm and distances or similarities for each feature from the patient record (receive a first patient data record) to other patient records (receive a second patient data record) are determined. A processor learns to identify at least one similar patient record from a set of patient records. The method is implemented using the system shown in FIG. 1, including a display that is part of a user interface (an interface). – paras 9-10, 48, 62-63; FIG. 1)
and a computing unit, wherein the interface or the computing unit is configured to receive or determine a medical ontology, and the medical ontology is independent of the first patient data record and the second patient data record, (Martin discloses that a medical ontology may be used (receive or determine a medical ontology) to provide information associated with one or more diseases and numerous medically relevant concepts, where the medical ontology is derived from sources such as the Medical Subject Headings (MeSH) and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and may further be derived from different ontologies (the medical ontology is independent of the first patient data record and the second patient data record – paras 32-40, 43). FIG. 1 shows the method is implemented using a computer (a computing unit). – abstract; paras 2, 4, 9-10, 15, 32-40, 43; FIGs. 1-2, 4)
the computing unit being configured to, determine a patient ontology based on the medical ontology and at least one of the first patient data record or the second patient data record, (Martin discloses that the processor uses the machine-learnt algorithm and resulting incorporation of the medical ontology (based on the medical ontology) to extract and/or aggregate relevant similarity information from a patient record (determine a patient ontology based on the first patient data record or the second patient data record). The processor applies the machine-learnt algorithm to search for or identify one or more similar patient records from a set of patient records. Alternatively or additionally, the matrices or other representation of the algorithm are applied to data of a patient record to indicate a probability of membership in the class defined by the ontology (e.g., probability of a specific disease). – paras 45, 50; FIG. 1)
and determine the similarity measure based on the patient ontology. (Martin discloses that, for similarity determination (determining the similarity measure), the processor applies the machine-learnt algorithm to a medical record of a patient. Data for features identified in the medical ontology are extracted from the patient record or input into the trained algorithm. This data is used as inputs to the trained algorithm. The trained algorithm aggregates the contributions of these features based on the incorporated medical ontology. For example, distances or similarities for each feature from the patient record to other patient records are determined (determine the similarity measure based on the patient ontology). The aggregation determines an average or other representation of distance from each of the previous patient records. Alternatively or additionally, the aggregation identifies only patient records with sufficient similarity for each of the features and/or nodes included in a given semantic grouping. – paras 45-50)
Regarding Claim 17, Martin discloses all the limitations above and further discloses the following limitations:
A non-transitory computer program product including program sections that, when executed by a determination system, cause the determination system to perform the method of claim 1. (Martin discloses a computer readable storage media has stored therein data representing instructions executable by a programmed processor (a determination system) for computer assisted clinical decision support with a medical ontology using machine learning. – paras 12, 51, 60)
Regarding Claim 18, Martin discloses all the limitations above and further discloses the following limitations:
A non-transitory computer-readable storage medium including program sections that, when executed by a determination system, cause the determination system to perform the method of claim 1. (Martin discloses a computer readable storage media has stored therein data representing instructions executable by a programmed processor (a determination system) for computer assisted clinical decision support with a medical ontology using machine learning. – paras 12, 51, 60)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Martin in view of Master et al. (US 20200075139).
Regarding Claim 3, Martin discloses all the limitations above and further discloses the following limitations:
and the similarity measure is based on a probability of an edge between the first node and the second node. (Martin discloses that for each term or relationship between two terms (an edge between the first node and the second node), a probability is provided (a probability). Medical ontologies are provided in a structured format, with different links (an edge) between different terms (the first node and the second node). – paras 42, 58)
Martin does not disclose the following limitations met by Master:
The method of claim 2, wherein the common patient ontology comprises a graph, wherein a subgraph relates to the medical ontology, the first patient data relates to at least one first node outside the subgraph and at least one edge between the first node and the subgraph, the second patient data relates to at least one second node outside the subgraph and at least one edge between the second node and the subgraph, (Master teaches transforming unstructured electronic health records of a patient into a patient graph, the patient graph representing a set of clinical concepts and corresponding semantic roles for the patient, wherein nodes in the graph represent clinical concepts and wherein edges in the graph represent semantic roles corresponding to the clinical concepts. In one implementation, the graph search and analysis engine identifies matching patient graphs in real time by indexing patient graphs within the universal graph (a subgraph relates to the medical ontology, the first patient data relates to at least one first node outside the subgraph and at least one edge between the first node and the subgraph, the second patient data relates to at least one second node outside the subgraph and at least one edge between the second node and the subgraph). For example, for each patient graph, the graph search and analysis engine can identify the patient graph's representation in the universal graph by performing a graph mining algorithm. – paras 7, 15, 24, 26)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified incorporating medical ontologies to determine similar patient records as disclosed by Martin to incorporate a patient graph for each patient and indexing each patient graph within a universal graph as taught by Master in order to quickly match patients with clinical studies that fit their medical needs (see Master para 8).
Regarding Claim 4, Martin discloses all the limitations above and further discloses the following limitations:
wherein the determining the similarity measure is based on the first patient ontology and the second patient ontology. (Martin discloses that, for similarity determination (determining the similarity measure), the processor applies the machine-learnt algorithm to a medical record of a patient. Data for features identified in the medical ontology are extracted from the patient record or input into the trained algorithm. This data is used as inputs to the trained algorithm. The trained algorithm aggregates the contributions of these features based on the incorporated medical ontology. For example, distances or similarities for each feature from the patient record to other patient records are determined (determining the similarity measure based on the first patient ontology and the second patient ontology). The aggregation determines an average or other representation of distance from each of the previous patient records. Alternatively or additionally, the aggregation identifies only patient records with sufficient similarity for each of the features and/or nodes included in a given semantic grouping. – paras 45-50)
Martin does not disclose the following limitations met by Master:
The method of claim 2, further comprising: determining a first patient ontology based on the common patient ontology; and determining a second patient ontology based on the common patient ontology, (Master teaches a computer system that uses natural language and semantics processing to ingest a corpus of medical data to curate a universal graph. The computer system then leverages the universal graph to process electronic health records of a population of patients. Each patient graph is indexed within the universal graph (the common patient ontology). For example, for each patient graph, the graph search and analysis engine can identify the patient graph's representation in the universal graph by performing a graph mining algorithm (determining a first/second patient ontology based on the common patient ontology). – paras 10, 24, 26)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified incorporating medical ontologies to determine similar patient records as disclosed by Martin to incorporate a patient graph for each patient and indexing each patient graph within a universal graph as taught by Master in order to quickly match patients with clinical studies that fit their medical needs (see Master para 8).
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Martin, in view of Master, further in view of Pedro et al. (US 20090024615).
Regarding Claim 6, Martin discloses all the limitations above, however, does not disclose the following limitations met by Master:
The method of claim 5, wherein the first patient ontology comprises a first graph, the second patient ontology comprises a second graph, (Master teaches that for each patient in the population of patients (the first/second patient), transforming the associated unstructured electronic health records of the patient into a patient graph, wherein nodes in the graph represent clinical concepts and wherein edges in the graph represent semantic roles corresponding to the clinical concepts (the first/second patient ontology comprises a first/second graph). – paras 7, 15)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified incorporating medical ontologies to determine similar patient records as disclosed by Martin to incorporate creating a patient graph for each patient as taught by Master in order to quickly match patients with clinical studies that fit their medical needs (see Master para 8).
Martin and Master do not teach the following limitations met by Pedro:
and the similarity measure is based on a similarity between the first graph and the second graph. (Pedro teaches that an application of medical ontologies include a more effective search of patient records. The ontologies comprise graph nodes that represent concepts and graph edges that represent links between concepts. A similarity of each pair of graphs is calculated (a similarity between the first graph and the second graph) and those graphs whose similarity is greater than a predetermined threshold are retained. – paras 3, 20)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified incorporating medical ontologies to determine similar patient records as disclosed by Martin to incorporate calculating the similarity between each pair of graphs as taught by Pedro in order to speed up the process of knowledge acquisition (see Pedro para 12).
Regarding Claim 7, Martin, Master and Pedro disclose all the limitations above and further disclose the following limitations:
The method of claim 6, wherein the similarity measure comprises at least one of the following measures: a graph edit distance of the first graph and the second graph, or a maximum common subgraph distance of the first graph and the second graph. (Pedro teaches, given two graphs g.sub.1 and g.sub.2, the edit distance (a graph edit distance of the first graph and the second graph) between g.sub.1 and g.sub.2 is the minimum number of edit operations necessary to transform g.sub.1 into g.sub.2. Further, maximum common subgraph (a maximum common subgraph distance of the first graph and the second graph) is determined in order to calculate graph similarity distance. – paras 69, 71-72, 74-75)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified incorporating medical ontologies to determine similar patient records as disclosed by Martin to incorporate calculating the graph edit distance and the maximum common subgraph distance as taught by Pedro in order to speed up the process of knowledge acquisition (see Pedro para 12).
Claims 8 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Martin, in view of Master, in view of Pedro, further in view of Hu (EP 3333770 A1).
Regarding Claim 8, Martin, Master and Pedro disclose all the limitations above, however, they do not disclose the following limitations met by Hu:
The method of claim 6, wherein the similarity measure is based on at least one of vertex embedding or graph embedding of the first graph and the second graph. (Hu teaches A method comprises carrying out a graph entity matching process between first and second graphs in which a first image representing the first graph, and a second image representing an arrangement of graph entities of the second graph, are obtained, a measure of similarity (the similarity measure) between the first and second images is computed using an image-based graph embedding process. Graph matching finds the best alignment of vertices from two different graphs. Graph matching takes two graphs as input and generates a list of vertex pairs where the first vertex comes from the first graph and the second vertex comes from the second graph (based on vertex embedding). – paras abstract; 4-6, 12-13, 70)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified incorporating medical ontologies to determine similar patient records as disclosed by Martin to incorporate calculating a measure of similarity between two graphs via vertex embedding as taught by Hu in order to overcome a high run-time cost and a rise in errors (see Hu paras 7, 9).
Regarding Claim 19, Martin, Master, Pedro and Hu disclose all the limitations above and further disclose the following limitations:
The method of claim 8, wherein the determining the similarity measure is based on an application of a trained function to at least one of the first patient ontology or the second patient ontology, or the determining the similarity measure is based on an application of a trained function to the common patient ontology. (Martin discloses that similar patient records are inferred based on training with ontology information (a trained function to the common patient ontology). The processor applies the machine-learnt algorithm (an application of a trained function) to search for or identify one or more similar patient records from a set of patient records (the determining the similarity measure). The memory 14 may store training data or data to be searched. The data is a collection of two or more previously acquired patient records (a trained function to at least one of the first patient ontology or the second patient ontology). For example, hundreds, thousands or tens of thousands of patient records are obtained and stored. – paras 45, 47, 53-54)
Regarding Claim 20, Martin, Master, Pedro and Hu disclose all the limitations above and further disclose the following limitations:
The method of claim 19, wherein the similarity measure includes a plurality of similarity measures for a plurality of second patient data records and the second patient data records are assigned to a plurality of second patients, (Martin discloses determining a similarity (the similarity measure) between one or more similar patients (assigned to a plurality of second patients) from a set of patient records (a plurality of similarity measures for a plurality of second patient data records). – abstract; paras 10, 45, 72)
the method further comprising: determining a set of comparable patients based on the determined similarity measures, wherein the set of comparable patients is a subset of the plurality of second patients, and each of the comparable patients is similar to the first patient. (Martin discloses determining a similarity (based on the determined similarity measures) by identifying at least one or more similar patient records (each of the comparable patients is similar to the first patient) from a set of patient records (the set of comparable patients is a subset of the plurality of second patients). – abstract; paras 10, 45, 72)
Conclusion
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/K.E.V./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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