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 .
Claims 1-20 are pending.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-10 are drawn to a system for generating clinical notes which is within the four statutory categories (i.e. machine). Claims 11-20 are drawn to a method for generating clinical notes which is within the four statutory categories (i.e. process).
Step 2A | Prong One
Claim 1 of Claims 1-10 (Group I) recite a system comprising:
one or more computer processors (MPEP § 2106.05(f), apply it); and
a non-transitory computer-readable storage having stored thereon instructions that when executed by the one or more processors cause the one or more processors to (MPEP § 2106.05(f), apply it):
train a machine learning model (MPEP § 2106.05(f), apply it) to generate clinical notes by repeatedly:
receiving a plurality of clinical notes associated with a patient;
selecting a final clinical note from the plurality of clinical notes;
selecting a predecessor clinical note from the plurality of clinical notes, wherein the predecessor clinical note chronologically precedes the final clinical note;
determining overlapping data between the final clinical note and the predecessor clinical note;
identifying the overlapping data from the final clinical note;
performing an action on the identified overlapping data from the final clinical note to generate a synthesized input clinical note, such that the synthesized input clinical note is devoid of the overlapping data;
providing the synthesized input clinical note and the predecessor clinical note to the machine learning model (MPEP § 2106.05(f), apply it);
receiving a predicted final clinical note from the machine learning model (MPEP § 2106.05(f), apply it), wherein the machine learning model (MPEP § 2106.05(f), apply it) generates the predicted final clinical note based on the synthesized input clinical note and the predecessor clinical note;
updating the machine learning model (MPEP § 2106.05(f), apply it) based on the predicted final clinical note and the final clinical note; and
output the machine learning model to at least one of a storage device or the non- transitory computer-readable storage (MPEP § 2106.05(g), insignificant extra-solution activity).
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity because it recites fundamental economic practices, commercial or legal interactions, and/or managing personal behavior or relationships or interactions between people. Any limitations not identified above as part of abstract idea are underlined and are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 11-20 (Group II) is identical as the abstract idea for Claims 1-10 (Group I), because the only difference is they are directed towards a different statutory category.
Dependent Claims 2-10 and 12-20 include other limitations, for example Claims 2 and 12 recite wherein each of the plurality of clinical notes is embedded with clinical metadata prior to training the machine learning model (MPEP § 2106.05(h), generally linking), Claims 3 and 13 recite wherein the clinical metadata comprises one or more of a document type and a creation date. Claims 4 and 16 recite performing the action on the identified overlapping data comprises removing the identified overlapping data from the final clinical note, Claims 5 and 14 recite wherein the determination of the overlapping data between the final clinical note and the predecessor clinical note is performed without access to historical electronic health record (EHR) data associated with the patient, Claims 6 and 15 recite wherein a model determines the overlapping data between the final clinical note and the predecessor clinical note, Claims 7 and 17 recite wherein the instructions when executed by the one or more processors further cause the one or more processors to (MPEP § 2106.05(f), apply it): receive a current clinical note associated with the patient after training of the machine learning model; provide the current clinical note to the machine learning model; receive an augmented clinical note from the machine learning model, wherein the machine learning model (MPEP § 2106.05(f), apply it) generates the augmented clinical note based on the current clinical note, and wherein the augmented clinical note comprises current data from the current clinical note and historical data associated with the patient that is not present in the current clinical note; and output the augmented clinical note to a user, Claims 8 and 18 recite wherein the machine learning model (MPEP § 2106.05(f), apply it) modifies at least a portion of the current data, and wherein the machine learning model further flags the portion in the augmented clinical note for review by the user, Claims 9 and 19 recite wherein the machine learning model (MPEP § 2106.05(f), apply it) further flags the historical data in the augmented clinical note for review by the user, and Claims 10 and 20 recite wherein the current clinical note further comprises a transcription of a conversation between the patient and the user, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 11.
Step 2A | Prong Two
Furthermore, Claims 1-20 are not integrated into a practical application because the additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of one or more computer processors; and a non-transitory computer-readable storage having stored thereon instructions that when executed by the one or more processors cause the one or more processors, training a machine learning model, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see page 4, lines 15-25, page 5: lines 26- page 6: line 2, and page 6: lines 23-27 of the present Specification, see MPEP 2106.05(f);
add insignificant extra-solution activity to the abstract idea – for example, the recitation of storing data, which amounts to an insignificant application, see MPEP 2106.05(g); and
generally link the abstract idea to a particular technological environment or field of use – for example, the recitation of embedding data, which amounts to limiting the abstract idea to the field of computers, see MPEP 2106.05(h)).
Step 2B
Furthermore, the Claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e. the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
page 4, lines 15-25, page 5: lines 26- page 6: line 2, and page 6: lines 23-27 of the Specification discloses that the additional elements (i.e. processors, non-transitory computer-readable medium, and machine learning) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. storing data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare); and
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites storing instruction and output data in a database and/or electronic memory, and retrieving the instructions data from storage in order to execute the program.
Dependent Claims 2-10 and 12-20 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the dependent claims do not further recite any additional elements not already found in the independent claims.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above-identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 5 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 5 and 14 recite “wherein the determination of the overlapping data between the final clinical note and the predecessor clinical note is performed without access to historical electronic health record (EHR) data associated with the patient.” It is unclear how this possible when the clinical notes are from the EHR data so comparing the two clinical notes requires accessing the patient’s history (Specification, page 5).
Appropriate correction is required.
Subject Matter Free from Prior Art
The prior art of record, including Leurs (U.S. Pub. No. 2023/0065999 A1) and Petri (U.S. Pub. No. 2020/0303048 A1), do not disclose or render obvious the combination of limitations found in the independent claims 1 and 11.
Leurs discloses generating clinical notes, including deduplicating information already in clinical notes and training a machine learning algorithm (See at least paragraphs [0057], [0075] and [0084]).
Petri discloses using templates using machine learning model to generate clinical notes and calibrating the machine learning model (See at least paragraphs [0051] and [0058-0059]).
Specifically, the prior art of record does not disclose or render obvious the steps of “providing the synthesized input clinical note and the predecessor clinical note to the machine learning model; receiving a predicted final clinical note from the machine learning model, wherein the machine learning model generates the predicted final clinical note based on the synthesized input clinical note and the predecessor clinical note; updating the machine learning model based on the predicted final clinical note and the final clinical note” in combination with the remaining limitations. Claims 2-10 and 12-20 are free from prior art as they depend from claims 1 and 11, respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachelle Reichert whose telephone number is (303)297-4782. The examiner can normally be reached M-F 9-5 MT.
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/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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