DETAILED ACTION
This communication is in response to application filed on July 24, 2024.
Claim 1 is pending and presented for examination on the merits.
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 .
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter). (MPEP 2106.03)
Claim 1 recites a series of steps, thus falling within one of the four statutory classes; i.e., a process.
Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. (MPEP 2106.04).
Claim 1 recites a method of phenotyping performed at a computing system, comprising:
receiving a request to identify a target population having a phenotype;
obtaining one or more predefined characteristics associated with the phenotype;
identifying, using a retriever model, a set of subjects as potential members of the target population by searching a first set of one or more databases for patient records;
obtaining, for each respective subject in the set of subjects, medical information by searching a second set of one or more databases;
providing, for each respective subject in the set of subjects, corresponding medical information of the medical information to an artificial intelligence (AI) component;
providing a set of natural language instructions to the AI component, wherein the set of natural language instructions provide a context to the AI component for natural language processing of the corresponding medical information to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics; and
obtaining, from the AI component, identification of a subset of subjects from the set of subjects determined by the AI component to be, or to have a high likelihood of being, a member of the target population through a determination by the AI component that each subject in the subset of subjects has at least one of the one or more predefined characteristics, wherein, for each respective subject in the subset of subjects, the determination for at least one of the one or more predefined characteristics is made through natural language processing of corresponding medical information using the set of natural language instructions.
The limitations of receiving a request to identify a target population having a phenotype; obtaining one or more predefined characteristics associated with the phenotype; identifying a set of subjects as potential members of the target population by searching a first set of one or more databases for patient records; obtaining, for each respective subject in the set of subjects, medical information by searching a second set of one or more databases; providing, for each respective subject in the set of subjects, corresponding medical information of the medical information; providing a set of natural language instructions, wherein the set of natural language instructions provide a context for natural language processing of the corresponding medical information to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics; and obtaining identification of a subset of subjects from the set of subjects determined to be, or to have a high likelihood of being, a member of the target population through a determination that each subject in the subset of subjects has at least one of the one or more predefined characteristics, wherein, for each respective subject in the subset of subjects, the determination for at least one of the one or more predefined characteristics is made through natural language processing of corresponding medical information using the set of natural language instructions, as drafted, are processes that, under their broadest reasonable interpretations, exemplify interactions between people (including following rules or instructions).
That is, other than reciting “at a computing system”, “using a retriever model” and “an artificial intelligence (AI) component”, nothing in the claim disqualifies the steps from being interactions between people. For example, but for the “at a computing system”, “using a retriever model” and “an artificial intelligence (AI) component” language, the steps of “receiving”, “obtaining”, “identifying” and “providing” in the context of this claim encompasses steps people can perform (e.g., a researcher receives a request to identify clinical trial participants meeting a phenotype; the researcher obtains characteristics of the phenotype; the researcher identifies a set of subjects as potential participants by searching database(s) for patient records; the researcher obtains, for each potential participant, medical information by searching other database(s); the researcher provides, for each potential participant, their medical information to another researcher; provides verbal or written instructions to the other researcher, wherein the verbal or written instructions provide a context to the other researcher for processing the medical information to determine whether a respective potential participant meets the phenotype; and obtaining, from the other researcher, identification of a subset of potential participants, by the other researcher to be, or to have a high likelihood of being, a clinical trial participant meeting a phenotype, through a determination by the other researcher that each subject in the subset meets the phenotype, wherein, for each respective subject, the determination for the characteristics is made through language processing of corresponding medical information using the set of instructions).
If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas 1 .
Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and then evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Prong Two distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception. (MPEP 2106.04).
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
“computing system”
“retriever model”
“an artificial intelligence (AI) component”
The limitations of “at a computing system”, “using a retriever model” and “an artificial intelligence (AI) component”, are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(f) Mere Instructions To Apply An Exception).
Therefore, under Step 2A, Prong Two, the claims are directed to an abstract idea.
Step 2B: Identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). (MPEP 2106.05)
The claims do 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 “at a computing system”, “using a retriever model” and “an artificial intelligence (AI) component”, alone and in combination amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Therefore, claim 1 is 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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 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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Peissig et. al. (“Importance of multi-modal approaches to effectively identify cataract cases from electronic health records”, hereinafter Peissig), in view of Wang et al. (“Query bot for retrieving patients’ clinical history: A COVID-19 use-case”, hereinafter Wang)
As per claim 1, Peissig discloses a method of phenotyping performed at a computing system, comprising:
obtaining one or more predefined characteristics associated with the phenotype (“A multi-modal phenotyping strategy was applied to the EHR data and documents to identify information pertaining to nuclear sclerotic, posterior sub-capsular, and cortical (N-P-C) cataract subtypes, severity (numeric grading scale), and eye.”);
identifying, using a retriever model, a set of subjects as potential members of the target population by searching a first set of one or more databases for patient records (“Over 3.5 million documents for the PMRP cohort were preprocessed using a pattern search mechanism for the term ‘cataract.’”);
obtaining, for each respective subject in the set of subjects, medical information by searching a second set of one or more databases (Figure 2, EHR Text Documents);
providing, for each respective subject in the set of subjects, corresponding medical information of the medical information to an artificial intelligence (AI) component (Figure 2, MedLEE NLP);
obtaining, from the AI component, identification of a subset of subjects from the set of subjects determined by the AI component to be, or to have a high likelihood of being, a member of the target population through a determination by the AI component that each subject in the subset of subjects has at least one of the one or more predefined characteristics, wherein, for each respective subject in the subset of subjects, the determination for at least one of the one or more predefined characteristics is made through natural language processing of corresponding medical information (Figure 2 “Subjects with N-P-C subtypes (619)”).
Peissig does not explicitly disclose, but Wang teaches:
receiving a request to identify a target population having a phenotype (Figure 5);
providing a set of natural language instructions to an AI component, wherein the set of natural language instructions provide a context to the AI component for natural language processing of corresponding medical information to determine whether a respective subject in a set of subjects has at least one of one or more predefined characteristics, using the set of natural language instructions (Figure 5).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include Wang’s teachings in the system of Peissig, in order to provide a more intuitive user interface, and since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAJIME ROJAS whose telephone number is (571)270-5491. The examiner can normally be reached Monday - Friday 8:00 AM-4:00 PM.
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/HAJIME ROJAS/Primary Patent Examiner, Art Unit 3682
1 Certain Methods Of Organizing Human Activity
fundamental economic principles or practices (including hedging, insurance, mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
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