DETAILED ACTION
Claim Status
Claims 1-21 are rejected.
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 .
Priority
This application does not make a claim of priority. The effective filing date of claims 1-21 is 06/13/2023.
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 01/16/2024 is in
compliance with the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of list of
references cited from each IDS is included with this Office Action.
Drawings
The drawings filed on 06/13/2023 have been accepted.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 15-21 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 15 recites the limitation "the one or more processors" in claim 15. There is insufficient antecedent basis for this limitation in the claim. Claims 16-21 inherit this issue without resolving it, and are thus additionally rejected.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1 : YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
1, 8, 15. defining a set of patient groups from a subset of the EHR data that is representative of a subset of patients of the set of patients, the set of patient groups being defined using a set of criteria providing within a grouping strategy;
1, 8, 15. generating, for each patient group in the set of patient groups, a set of demographics triples and a set of medical triples, demographics triples in the set of demographics triples comprising one or more links between patient groups and one or more demographics entities, and medical triples in the set of medical triples comprising one or more links between patient groups and one or more medical entities;
1, 8, 15. providing a patients graph using the set of demographics triples and the set of medical triples;
2, 9, 16. The computer-implemented method of claim 1, further comprising extracting the subset of the EHR data from the EHR data using a cohort definition that defines at least one cohort criterion.
3, 10, 17. determining a set of risk factors that represent relative risk of co-occurrence of conditions within the subset of EHR data;
3, 10, 17. for each risk factor that meets a threshold risk factor, creating a medical triple representative of the conditions in the set of medical triples.
4, 11, 18. The computer-implemented method of claim 3, wherein a sampling strategy is used to determine the threshold risk factor.
5, 12, 19. The computer-implemented method of claim 1, wherein one or more medical triples represent at least one drug relative to one or more patient groups, the at least one drug being selected for inclusion in the medical triple based on a head-tail analysis of a drug statistics determined from the EHR data based on the patient groups.
6, 13, 20. The computer-implemented method of claim 1, wherein providing a patients graph using the set of demographics triples and the set of medical triples comprising concatenating demographics triples and medical triples.
7, 14, 21. The computer-implemented method of claim 1, wherein demographics triples in the set of demographics triples and medical triples in the set of medical triples are generated based on a target ontology.
The limitations for “defining,” “generating,” “providing,” and “training” are instructions for manipulating EHR data, which are a series of numbers, vectors, and variables. Though there is a limitation for training a KGE model, there are embodiments of creating embeddings of a knowledge graph that are performable by a human with a pen and paper (example: assigning numbers to the graph elements and adding/multiplying them for each triple), and embodiments of training a model from those embeddings that are also performable by a human with a pen and paper (example: regression). Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 1, 8 and 15 recite performing some aspects of the analysis with a “computer”, “system,” or “KGE,” there are no additional limitations that indicate that this computer requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-21 recite an abstract idea ( Step 2A, Prong 1 : YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to effect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
1. A computer-implemented method
8. A system comprising one or more processors; and a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for providing a knowledge graph embedding (KGE) model for predicting links between entities represented in a knowledge graph (KG)
15. Non-transitory computer-readable storage media coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for providing a knowledge graph embedding (KGE) model for predicting links between entities represented in a knowledge graph (KG)
1, 8, 15. receiving electronic health record (EHR) data comprising medical records for a set of patients;
1, 8, 15. training a KGE model using a KG and the patients graph to provide a trained KGE model;
1, 8, 15. providing the trained KGE model for inference to predict likelihood that a link between entities is factually correct.
Receiving EHR data and providing the trained KGE model is a type of mere data gathering and data output, respectively. The limitation that the trained KGE model is for inference is an intended use that does not affect the scope of the claims. These limitations are similar to presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93. There are no limitations that indicate that the claimed computer, system, “KGE”, or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-21 are directed to an abstract idea ( Step 2A, Prong 2 : NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite additional elements recited above, in the section on Step 2A.
The limitations for receiving the EHR data and outputting the KGE model are well understood, routine, and conventional as it is a form of storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). As discussed above, there are no additional limitations to indicate that the claimed computer, system, or “KGE” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No). As such, claims 1-21 are not patent eligible.
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.
Claims 1-2, 6-9, 13-16, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Kotnis and Duran (US 2020/0160215 A1, henceforth “Kotnis”) in view of Sloep et al. (Clinical and Translational Radiation Oncology 31(2021)93–96, henceforth “Sloep”), as well as Melo and Paulheim (K-CAP2017 Workshops and Tutorials Proceedings, 2017, henceforth “Melo”).
Regarding claim 1, Kotnis’s invention is a method for providing knowledge graph embedding to predict numerical values linked to entitites in a knowledge graph (¶ 10). Kotnis gives a suggestion about link prediction (¶ 8). Kotnis provides for computer implementation of the method (fig. 4, ¶ 89).
Kotnis is silent as to predicting the likelihood a link is factually correct.
Melo teaches link error correction for knowledge graphs (abstract).
Kotnis is silent as to grouping patients from EHR data. Sloep provides a suggestion about identifying patient groups from EHR data (pg 95 right col ¶ 3).
In the instant specification, a “triple” is a collection consisting of a patient variable, a linking verb, and either a demographic variable or a medical condition (¶48-49). Kotnis teaches generating demographics triples that link patients and demographics, and medical triples that link patients and diseases (fig. 3). Kotnis teaches providing a patients graph of these triples (fig. 3). The drawings show connections between variables and conditions or demographics, using linking verbs.
Kotnis teaches training a knowledge graph embedding model on the knowledge graph with added patient data (¶ 57, ¶ 66-67).
Kotnis gives a suggestion about link prediction (¶ 8).
Regarding claim 2, Kotnis’s method of extracting EHR data described in specification ¶ 65-67 and fig. 3 shows a cohort definition of “lupus,” which would have the cohort criterion of having lupus.
Regarding claim 6, the demographics triples (patient 1 -> born_in -> Tokyo) and medical triples (patient_1 -> had_disease -> Lupus) are concatenated into a single graph (fig. 3).
Regarding claim 7, applicant defines a “target ontology” as the system used by the EHR to define demographic and medical concepts. The EHR of Kotnis has a definition of demographic and medical concepts that is used by Kotnis to populate the knowledge and patient graphs (¶ 65-67).
Claims 8-9, 13-14, are restatements of claims 1-2 and 6-7, only differing in that they are directed to a system rather than a method. Likewise, claims 15-16 and 20-21 are restatements of claims 1-2 and 6-7 only differing in that they are directed to a non-transitory computer readable storage media rather than a method. The arguments against claims 1-2 and 6-7 apply to both of these other claim sets, mutatis mutandis.
Regarding claims 1-2, 6-9, 13-16, and 20-21, An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a suggestion to use identification of patient groups in the text of Sloep (pg 95 right col ¶ 3), in order to enable a personalized approach to prognosis (pg 95 right col ¶ 3). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as both are knowledge graph embedding methods and is the patient grouping method concept is thoroughly explained by Sloep (pg 95 right col ¶ 3). Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Kotnis by adding patient grouping, in order to enable personalized prognosis (Sloep pg 95 right col ¶ 3).
Regarding claims 1-2, 6-9, 13-16, and 20-21, An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a suggestion to use link error correction in the text of Melo (abstract), in order to correct errors in the graph (abstract). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as both are knowledge graph embedding methods and it is similar to the concept of link prediction that is thoroughly explained by Kotnis (¶ 8). Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Kotnis by adding error correction, in order to correct errors in the graph (Melo abstract).
Claims 3-4, 10-11, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kotnis in view of Sloep and Melo as applied to claim 1-2, 6-9, 13-16, and 20-21 above, and further in view of Monchka et al. (BMC Med Res Methodol. 2022 Jun 8;22:165, henceforth “Monchka”).
Regarding claim 3, Monchka teaches constructing a knowledge graph of medical triples by selecting variables based on their relative risk of co-occurrence: “Pairwise disease networks were separately constructed using each of seven co-occurrence measures: lift, relative risk…” (pg 1 abstract ¶ 2).
Monchka selected the top 50% of risk factor associations to create the medical triples (pg 7 left col ¶ 3). A threshold risk factor was selected based on taking a sample of the data and making a cut-off.
Regarding claim 4, Monchka sampled the top 50% of the associations to determine the threshold risk factor.
Claims 10-11 are restatements of claims 3-4, only differing in that they are directed to a system rather than a method. Likewise, claims 17-18 are restatements of claims 10-11 only differing in that they are directed to a non-transitory computer readable storage media rather than a method. The arguments against claims 3-4 apply to both of these other claim sets, mutatis mutandis.
Regarding claims 3-4, 10-11, and 17-18, An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use relative risk of co-occurrence to construct medical triples and to sample from a cutoff point for the top associations in the text of Monchka, in order to provide insights into co-occurring health conditions (pg 1 abstract ¶ 1). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as the method is explained in Monchka and they are both methods related to knowledge graph creation. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Kotnis by implementing the knowledge graph creation scheme of Monchka, in order to provide insights into co-occurring health conditions (pg 1 abstract ¶ 1).
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kotnis in view of Sloep and Melo as applied to claims 1-2, 6-9, 13-16, and 20-21 above, and further in view of Alshurafa et al. (Proc ACM Interact Mob Wearable Ubiquitous Technol. 2018 December ; 2(4)).
Alshurafa explains that head/tail analysis is useful for interaction amplitude data that is skewed towards few large values and many short values (section 3.2.1). In their case, the authors are measuring adherence to a health intervention for quitting smoking (fig. 4).
Claim 12 is a restatement of claim 5 only differing in that it is directed to a system rather than a method. Likewise, claim 19 is a restatement of claim 5 only differing in that it is directed to a non-transitory computer readable storage media rather than a method. The arguments against claim 5 apply to both of these other claim sets, mutatis mutandis.
Regarding claims 5, 12, and 19, An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use head/tail analysis to analyze health intervention data in the text of Alshurafa, because it is useful for health intervention engagement measurement due to it skewness (section 3.2.1). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as Kotnis is also measuring engagement with health interventions in the form of medications and treatments (fig. 3). Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Kotnis by employing the head/tail analysis of Alshurafa, in order to measure health intervention engagement (section 3.2.1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACELYN M HILL whose telephone number is (571)272-9871. The examiner can normally be reached Monday-Friday 8:30-5pm.
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/G.M.H./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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