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 .
Specification
The disclosure is objected to because of the following informalities:
In ¶ 0042, “the databases 112” seems to be a grammatical error. Examiner recommends amending it to read -- the databases 122 --.
In ¶ 0049, “the databases 112” seems to be a grammatical error. Examiner recommends amending it to read -- the databases 122 --.
In ¶ 0130, “LPG 812” seems to be a grammatical error. Examiner recommends amending all instances to read -- LPG 810 --.
In ¶ 0135, “LPG 812” seems to be a grammatical error. Examiner recommends amending it to read -- LPG 810 --.
In ¶ 0142, “(e.g., the LPG 812 of FIG. 8)” seems to be a grammatical error. Examiner recommends amending it to read -- (e.g., the LPG 810 of FIG. 8) --.
In ¶ 0150, “LPG 1012” seems to be a grammatical error. Examiner recommends amending all instances to read -- LPG 1010 --.
In ¶ 0150, “(e.g., the LPG 812 of FIG. 8)” seems to be a grammatical error. Examiner recommends amending it to read -- (e.g., the LPG 810 of FIG. 8) --.
In ¶ 0158, “An LPG 1012 may be established” seems to be a grammatical error. Examiner recommends amending it to read -- An LPG 1010 may be established --.
In ¶ 0159, “LPG 1112” seems to be a grammatical error. Examiner recommends amending all instances to read -- LPG 1110 --.
In ¶ 0159, “(e.g., the LPG 812 of FIG. 8)” seems to be a grammatical error. Examiner recommends amending it to read -- (e.g., the LPG 810 of FIG. 8) --.
Appropriate correction is required.
Subject Matter Free of Prior Art
Claim(s) 1-20 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: “for each respective training example of the set of training examples... in response to determining that the quality level of the respective training example does not satisfy the predetermined quality threshold, using a first machine- learning model prompt to generate an updated version of the training SOAP note of the respective training example, using a second machine-learning model prompt to determine whether the updated version of the training SOAP note of the respective training example corresponds to the training transcript of the respective training example, in response to determining that the updated version of the training SOAP note of the respective training example corresponds to the training transcript of the respective training example, replacing the training SOAP note in the respective training example with the updated version of the training SOAP note” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 8, 15, claims 1, 8, 15are hereby deemed to be allowable over prior art. Originally numbered dependent claims 2-7, 9-14, 16-20 incorporate the allowable features of originally numbered independent claims 1, 8, 15, through dependency, respectively.
However, the claims are still rejected under 101.
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(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to a method which is within the four statutory categories (i.e., method). Claim 8 is drawn to a system which is within the four statutory categories (i.e., machine). Claim 15 is drawn to one or more non-transitory computer-readable media storing instructions which is within the four statutory categories (i.e., manufacture).
Independent claim 8 (which is representative of independent claims 1, 15) recites… accessing training data comprising a set of training examples, each training example of the set of training examples comprising a training transcript and a training Subjective, Objective, Assessment and Plan (SOAP) note corresponding to the training transcript; performing an evaluation process on the training data to result in evaluated training data, wherein performing the evaluation process on the training data comprises: for each respective training example of the set of training examples: determining whether a quality level of the respective training example satisfies a predetermined quality threshold, in response to determining that the quality level of the respective training example does not satisfy the predetermined quality threshold, using a first…model prompt to generate an updated version of the training SOAP note of the respective training example, using a second…model prompt to determine whether the updated version of the training SOAP note of the respective training example corresponds to the training transcript of the respective training example, in response to determining that the updated version of the training SOAP note of the respective training example corresponds to the training transcript of the respective training example, replacing the training SOAP note in the respective training example with the updated version of the training SOAP note; and generating a fine-tuned…model using the evaluated training data, wherein the fine-tuned…model is configured to perform a task associated with generating a SOAP note.
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. That is, other than reciting a “computer” to implement the method (claim 1), computing “system” (claims 8, 15), the claim encompasses rules or instructions to collect data, analyze the collected data, and output data based on the analysis. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships 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 claims recite an abstract idea.
Claim 1 recites additional elements (i.e., computer to implement the method; a first machine- learning model prompt; a second machine-learning model prompt; a fine-tuned machine-learning model). Claim 8 recites additional elements (i.e., A system comprising: one or more processing systems; and one or more computer-readable media storing instructions; a first machine- learning model prompt; a second machine-learning model prompt; a fine-tuned machine-learning model). Claim 15 recites additional elements (i.e., One or more non-transitory computer-readable media storing instructions; one or more processors; a system; a first machine- learning model prompt; a second machine-learning model prompt; a fine-tuned machine-learning model). Looking to the specifications, a computer having one or more processing systems, one or more computer-readable media storing instructions is described at a high level of generality (¶ 0038; ¶ 0089; ¶ 0104; ¶ 0129; ¶ 0168-0171; ¶ 0178-0182; ¶ 0188), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, “a first machine- learning model prompt,” “a second machine-learning model prompt,” “a fine-tuned machine-learning model” is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using machine learning prompts and models amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The 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. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., a computer having one or more processing systems, one or more computer-readable media storing instructions) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, “a first machine- learning model prompt,” “a second machine-learning model prompt,” “a fine-tuned machine-learning model” is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using machine learning prompts and models amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 2-7, 9-14, 16-20 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 2-7, 9-14, 16-20 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0375605 A1 teaches training a model to automatically generate a SOAP note based on historical data.
WO 2022/072785 A1 teaches training a machine learning model based on SOAP-structured EHR notes.
“Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations” teaches evaluating datasets of transcriptions and corresponding machine learning SOAP notes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily Huynh whose telephone number is (571)272-8317. The examiner can normally be reached on M-Th 8-5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683