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 .
DETAILED ACTION
Response to Amendment
In the Amendment dated 20 January 2026, the following occurred:
Claims 1, 11, and 21 were amended.
Claims 1-7, 9, 11-15, and 17-21 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 1-7, 9, 11-15, and 17-21 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.
Claims 1, 11, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite a method, device, and computer-readable medium for managing feedback for a radiologist to improve diagnostic accuracy, and therefore meet step 1.
Step 2A1
The limitations of Claim 1 recite receiving […] a feedback request […], the feedback request being associated with feedback for an exam performed on a patient, the feedback being obtained from a subsequent exam for the patient and comprising one or more of exam data, request identification data, or information indicating a reason for the feedback request; storing the feedback request […]; monitoring […information…] to identify […] a subsequent patient exam for the patient, wherein the subsequent patient exam corresponds to the feedback request based on one or more of the exam data and the request identification data; automatically generating a subsequent exam alert message in response to identifying the subsequent patient exam; transmitting […] the subsequent exam alert message […]; and receiving an acceptance or a rejection of feedback related to the feedback request and updating […] based on the acceptance or rejection, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
The limitations of Claims 11 and 21 recite (Claim 11 being representative) receiv[ing] […] a plurality of digital or digitized medical reports; automatically process[ing] the plurality of medical reports to produce a processed report that extracts patient medical information; receiv[ing] a feedback request […], the feedback request being related to feedback for a radiology report performed relative to a patient, and the feedback being obtained from a subsequent exam for the patient; automatically identify[ing] […] a medical report for the patient from among the plurality of processed medical reports, wherein the medical report is related to the feedback request; provid[ing] an electronic notification to […] the radiologist regarding the identified medical report related to the feedback request; and updat[ing] […] based on an acceptance or rejection response to the electronic notification, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
That is, other than reciting a method and device implemented by a memory and a processor (a general-purpose computing device), the claimed invention amounts to managing personal behavior or interaction between people. 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 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a computer having a database (claim 1) and a memory/processor/database combination (claims 11 and 21) that implement the identified abstract idea. The computing elements are not exclusively described by the applicant and 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 or components thereof. See MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims recite the additional elements of (1) one or more medical information systems including a Picture Archiving and Communication System (PACS), a Radiology Information System (RIS), a Laboratory Information System (LIS), or an Electronic Medical Record and (2) a device associated with the feedback request / radiologist having a requesting interface. Items (1) and (2) merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use is insufficient to provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
The claim further recites the additional element of using a machine learning model (which is undefined) to identify a subsequent patient exam and then updating the model. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). The Examiner notes that the machine learning models are described in the Specification at Para. 0049 as encompassing any type of machine learning classifier. Alternatively, or in addition, the implementation of the machine learning model to the clinical data merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claim. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
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 element of using a general-purpose computer to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component and cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of (1) one or more medical information systems including a Picture Archiving and Communication System (PACS), a Radiology Information System (RIS), a Laboratory Information System (LIS), or an Electronic Medical Record and (2) a device associated with the feedback request / radiologist having a requesting interface were considered to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the machine learning model to monitor data and then update the model was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (a generic computer having an updatable ML model). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Claims 2-7, 9, 12-15, and 17-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claims 2, 3, 13, and 14 merely describe the feedback request. Claim 3 further recites a natural language processing model. Claim 14 further recites a language processing model. Utilization of a language processing model equates 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.
Claims 4 and 15 merely describe determining relevance and identifying a medical report. Claim 5 merely describes determining relevance. Claims 6 and 20 merely describe the plurality of medical reports. Claims 7 and 19 merely describe processing the plurality of medical reports.
Claim 9 merely describes deleting the feedback request. Claim 12 merely describes visualizing the identified medical report and the radiology report. Claim 17 merely describes identifying a medical report. Claim 18 merely describes estimating, clustering, and inferring.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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, 9, 11-14, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Halsted (U.S. 2008/0046286) in view of Sevenster et al. (U.S. 2019/0131011) and Sragow et al. (U.S. 2024/0203546), referred to hereinafter as Sevenster and Sragow, respectively.
REGARDING CLAIM 1
Halsted teaches the claimed computer-implemented method for providing automated feedback for a radiologist to improve diagnostic accuracy, comprising:
receiving, by a processor coupled to a medical information system comprising one or more of a Picture Archiving and Communication System (PACS), a Radiology Information System (RIS), a Laboratory Information System (LIS), or an Electronic Medical Record (EMR)… [Para. 0005, 0008, 0063 teaches a system including a computer server having access to an electronic medical record (EMR), and includes an automated device coupled for communication.]
storing the feedback request in a database; [Para. 0065 teaches logging order requisitions in a database.]
monitoring, by the processor, the one or more medical information systems to identify […] a subsequent patient exam for the patient, wherein the subsequent patient exam corresponds to the feedback request based on one or more of the exam data and the request identification data; automatically generating a subsequent exam alert message in response to identifying the subsequent patient exam; [Para. 0054, 0063 teaches monitoring health information systems for a new event (subsequent patient exam), and when a new event occurs, the system generates a message alerting the user.]
Halsted may not explicitly teach
…a feedback request from a requesting interface, the feedback request being associated with feedback for an exam performed on a patient, the feedback being obtained from a subsequent exam for the patient and comprising one or more of exam data, request identification data, or information indicating a reason for the feedback request;
However, Sevenster teaches the following:
…a feedback request from a requesting interface, the feedback request being associated with feedback for an exam performed on a patient, the feedback being obtained from a subsequent exam for the patient and comprising one or more of exam data, request identification data, or information indicating a reason for the feedback request; [Para. 0013 teaches a follow-up recommendation (feedback request) is entered through a user interface. Para. 0038 teaches associating the follow-up recommendation with a patient identifier. The Examiner notes that the requesting interface has not been defined in the claim.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Halsted to receive a feedback request as taught by Sevenster, with the motivation of improving compliance (see Sevenster at Para. 0007).
Halsted in view of Sevenster may not explicitly teach
…using a machine learning model…
transmitting, by the processor, the subsequent exam alert message electronically to a device associated with the feedback request; and
receiving an acceptance or a rejection of feedback related to the feedback request.
However, Sragow teaches the following:
…using a machine learning model… [Para. 0297 teaches utilizing a machine learning model.]
transmitting, by the processor, the subsequent exam alert message electronically to a device associated with the feedback request; and receiving an acceptance or a rejection of feedback related to the feedback request. [Para. 0176 teaches a person providing an affirmation or denial (feedback data) via a patient interface device.]
updating said machine learning model based on the acceptance or rejection. [Para. 0005 teaches repeatedly training artificial neurons based on the feedback data.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Halsted in view of Sevenster to prompt a person to provide feedback data and use the data to update a machine learning model as taught by Sragow, with the motivation of improving accuracy (see Sragow at Para. 0033).
REGARDING CLAIM 2
Halsted in view of Sevenster and Sragow teaches the claimed computer-implemented method of claim 1.
Halsted further teaches
wherein the feedback request is a request from the radiologist to provide feedback related to a radiology report for the exam. [Para. 0089 teaches a radiologist requesting feedback. For example, a radiologist interpreting a CT scan of the chest may want to know what the patient’s pulmonologist thought of the case. A pathology result from a lesion seen on a prior CT scan of the chest may be of interest to the radiologist.]
REGARDING CLAIM 9
Halsted in view of Sevenster and Sragow teaches the claimed computer-implemented method of claim 1.
Sevenster further teaches
deleting the feedback request after receiving the acceptance response. [Para. 0048 teaches deleting an unauthorized order in response to a radiological interpretation.]
REGARDING CLAIM 11
Halsted teaches the claimed automated device for providing feedback to a radiologist to improve diagnostic accuracy, comprising: a memory; a processor coupled to the memory and a medical information system comprising one or more of a Picture Archiving and Communication System (PACS), a Radiology Information System (RIS), a Laboratory Information System (LIS), or an Electronic Medical Record (EMR), wherein the processor is configured to: [Para. 0005, 0008, 0063 teaches a system including a computer server having access to an electronic medical record, and includes an automated device coupled for communication.]
receive from the one or more medical information systems a plurality of digital or digitized medical reports; [Para. 0074 teaches receiving digital medical reports.]
automatically process the plurality of medical reports to produce a processed report that extracts patient medical information; [Para. 0044 teaches processing the plurality of medical reports to extract diagnoses (patient medical information). Para. 0056 teaches automatically scanning healthcare information sources associated with the patient.]
automatically identify […] a medical report for the patient from among the plurality of processed medical reports, wherein the medical report is related to the feedback request; [Para. 0042 teaches identifying a medical report related to a discrepancy, or cause for a feedback request.]
provide an electronic notification to an electronic device associated with the radiologist regarding the identified medical report related to the feedback request; and [Para. 0042 teaches providing a (electronic) notification including a link to the radiologist regarding the identified pathology report related to the feedback request.]
Halsted may not explicitly teach
receive a feedback request from a requesting interface, the feedback request being related to feedback for a radiology report performed relative to a patient, and the feedback being obtained from a subsequent exam for the patient;
However, Sevenster teaches the following:
receive a feedback request from a requesting interface, the feedback request being related to feedback for a radiology report performed relative to a patient, and the feedback being obtained from a subsequent exam for the patient; [Para. 0013 teaches a follow-up recommendation (feedback request) is entered through a user interface. Para. 0038 teaches associating the follow-up recommendation with a patient identifier. The Examiner notes that the requesting interface has not been defined in the claim.]
Motivation to combine the teaching of Sevenster with the teaching of Halsted is the same as that used with respect to claim 1 and is therefore reiterated here.
Halsted in view of Sevenster may not explicitly teach
…using a machine learning model…
However, Sragow teaches the following:
…using a machine learning model… [Para. 0297 teaches utilizing a machine learning model.]
update the model used to identify a medical report related to the feedback request based on an acceptance or rejection response to the electronic notification. [Para. 0005 teaches repeatedly training artificial neurons based on the feedback data.]
Motivation to combine the teaching of Sragow with the teachings of Halsted and Sevenster is the same as that used with respect to claim 1 and is therefore reiterated here.
REGARDING CLAIM 12
Halsted in view of Sevenster and Sragow teaches the claimed automated device of claim 11.
Halsted further teaches
wherein the processor is further configured to visualize the identified medical report and the radiology report for the radiologist. [Para. 0042 teaches offering a link to the subsequent pathology report (identified medical report) and the original radiology report to the radiologist. Para. 0068 teaches displaying reports.]
REGARDING CLAIM 13
Halsted in view of Sevenster and Sragow teaches the claimed automated device of claim 11.
Halsted further teaches
wherein the feedback request is a request from the radiologist to provide feedback related to the radiology report. [Para. 0089 teaches a radiologist requesting feedback. For example, a radiologist interpreting a CT scan of the chest may want to know what the patient’s pulmonologist thought of the case. A pathology result from a lesion seen on a prior CT scan of the chest may be of interest to the radiologist.]
REGARDING CLAIM 14
Halsted in view of Sevenster and Sragow teaches the claimed automated device of claim 11.
Halsted further teaches
wherein the feedback request is extracted from the radiology report using a language processing model. [Para. 0042 teaches extracting diagnostic terms from the radiology report using a language processing model.]
REGARDING CLAIM 20
Halsted in view of Sevenster and Sragow teaches the claimed automated device of claim 11.
Halsted further teaches
wherein the plurality of medical reports includes radiology reports and pathology reports. [Para. 0063 teaches radiology reports and pathology reports.]
REGARDING CLAIM 21
Claim 21 is analogous to Claim 11, thus Claim 21 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 11.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Halsted in view of Sevenster, Sragow, and Yeluri et al. (U.S. 2014/0006926), referred to hereinafter as Yeluri.
REGARDING CLAIM 3
Halsted in view of Sevenster and Sragow teaches the claimed computer-implemented method of claim 2.
Halsted further teaches
wherein the feedback request is extracted from the radiology report using a natural language processing model… [Para. 0042 teaches extracting diagnostic terms from the radiology report using a natural language processing model.]
Halsted in view of Sevenster and Sragow may not explicitly teach
…and fields of the feedback request are prefilled by the natural language processing model.
However, Yeluri teaches the following:
…and fields of the feedback request are prefilled by the natural language processing model. [Para. 0024 teaches providing links/references in a second opinion (feedback) request via natural language processing.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Halsted in view of Sevenster and Sragow to fill out feedback requests via natural language processing as taught by Yeluri, with the motivation of decreasing difficulty for radiologists to retrieve information referenced in a report (see Yeluri at Para. 0025).
Claims 4, 6, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Halsted in view of Sevenster, Sragow, and Paik et al. (WO 2022/212771), referred to hereinafter as Paik.
REGARDING CLAIM 4
Halsted in view of Sevenster and Sragow teaches the claimed computer-implemented method of claim 2.
Halsted in view of Sevenster and Sragow may not explicitly teach
determining relevance of a plurality of medical reports to one another based upon one or more of anatomy identified in the radiology report, reference to the radiology report in the identified subsequent examination, or an indication that the subsequent examination is a follow-up of a recommendation in the radiology report;
and identifying a medical report related to the feedback based upon the determined relevance of the plurality of medical reports to one another.
However, Paik teaches the following:
determining relevance of a plurality of medical reports to one another based upon one or more of anatomy identified in the radiology report, reference to the radiology report in the identified subsequent examination, or an indication that the subsequent examination is a follow-up of a recommendation in the radiology report; [Para. 00244 teaches defining relevance of cases based on anatomy. The presence of relevant text in the (radiology) report is used to inform the selection.]
and identifying a medical report related to the feedback based upon the determined relevance of the plurality of medical reports to one another. [Para. 00244 teaches choosing a related case based on the defined relevance of medical reports.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Halsted in view of Sevenster and Sragow to determine relevance and identify a report as taught by Paik, with the motivation of improving diagnostic quality (see Paik at Para. 00295).
REGARDING CLAIM 6
Halsted in view of Sevenster, Sragow, and Paik teaches the claimed computer-implemented method of claim 4.
Halsted further teaches
wherein the plurality of medical reports includes radiology reports and pathology reports. [Para. 0063 teaches radiology reports and pathology reports.]
REGARDING CLAIM 15
Halsted in view of Sevenster and Sragow teaches the claimed automated device of claim 11.
Halsted in view of Sevenster and Sragow may not explicitly teach
determine the relevance of the plurality of medical reports to one another
and wherein identifying a medical report related to the feedback request is based upon the determined relevance of the plurality of medical reports to one another.
However, Paik teaches the following:
determine the relevance of the plurality of medical reports to one another… [Para. 00244 teaches defining the relevance of cases based on anatomy. The presence of relevant text in the (radiology) report is used to inform the selection.]
… and wherein identifying a medical report related to the feedback request is based upon the determined relevance of the plurality of medical reports to one another. [Para. 00244 teaches choosing a related case based on the defined relevance of medical reports.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Halsted in view of Sevenster and Sragow to determine relevance and identify a report as taught by Paik, with the motivation of improving diagnostic quality (see Paik at Para. 00295).
Claims 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Halsted in view of Sevenster, Sragow, Paik, and Bendersky et al. (U.S. 2021/0166822), referred to hereinafter as Bendersky.
REGARDING CLAIM 5
Halsted in view of Sevenster, Sragow, and Paik teaches the claimed computer-implemented method of claim 4.
Halsted in view of Sevenster, Sragow, and Paik may not explicitly teach
wherein determining relevance of the plurality of medical reports is further based upon extracted patient medical information, the method further comprising: clustering similar medical reports;
and inferring a group type for the clustered medical reports and labeling the clustered medical reports with the inferred group type.
However, Bendersky teaches the following:
wherein determining relevance of the plurality of medical reports is further based upon extracted patient medical information, the method further comprising: clustering similar medical reports; [Para. 0095 teaches clustering radiology reports.]
and inferring a group type for the clustered medical reports and labeling the clustered medical reports with the inferred group type. [Para. 0007 teaches inferring the imaging modality of a procedure and the body anatomy being imaged (the group type). Claim 1 teaches assigning labels to the reports.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Halsted in view of Sevenster, Sragow, and Paik to cluster and label medical reports as taught by Bendersky, with the motivation of improving clinical workflow (see Bendersky at Para. 0007).
REGARDING CLAIM 7
Halsted in view of Sevenster, Sragow, and Paik teaches the claimed computer-implemented method of claim 4.
Halsted in view of Sevenster, Sragow, and Paik may not explicitly teach
wherein determining relevance of the plurality of patient medical reports includes document structure processing, syntactic parsing of an output of the document structure processing, extracting entities from an output of the syntactic parsing, and determining an anatomy inference on the extracted entities.
However, Bendersky teaches the following:
wherein determining relevance of the plurality of patient medical reports includes document structure processing, syntactic parsing of an output of the document structure processing, extracting entities from an output of the syntactic parsing, and determining an anatomy inference on the extracted entities. [Para. 0007 teaches classifying radiology reports by inferring the anatomy. The Examiner notes that a person having skill in the art would understand the use of natural language processing to extract anatomical information necessarily includes syntactic parsing and extracting of entities.]
Motivation to combine the teaching of Bendersky with the teachings of Halsted, Sevenster, Sragow, and Paik is the same as that used with respect to claim 5 and is therefore reiterated here.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Halsted in view of Sevenster, Sragow, and Kulon (U.S. 2017/0109473).
REGARDING CLAIM 17
Halsted in view of Sevenster and Sragow teaches the claimed automated device of claim 11.
Halsted in view of Sevenster and Sragow may not explicitly teach
wherein identifying a medical report related to the feedback request includes calculating a score based upon one of anatomy identified in the radiology report, reference to the radiology report in a subsequent examination, and an indication that a subsequent examination is a follow-up of a recommendation in the radiology report.
However, Kulon teaches the following:
wherein identifying a medical report related to the feedback request includes calculating a score based upon one of anatomy identified in the radiology report, reference to the radiology report in a subsequent examination, and an indication that a subsequent examination is a follow-up of a recommendation in the radiology report. [Para. 0035 teaches checking that body part (anatomy identified) matches between the recommended examination and the subsequent examinations. Para. 0037 teaches scoring detected results based on metrics of relevance.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the device of Halsted in view of Sevenster and Sragow to score relevance based on identified anatomy as taught by Kulon, with the motivation of maximizing the relevance of results (see Kulon at Para. 0017).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Halsted in view of Sevenster, Sragow, Kulon, and Bendersky.
REGARDING CLAIM 18
Halsted in view of Sevenster and Sragow teaches the claimed automated device of claim 11.
Halsted in view of Sevenster and Sragow may not explicitly teach
estimate the similarity between the plurality of medical reports based upon the extracted patient medical information;
However, Kulon teaches the following:
estimate the similarity between the plurality of medical reports based upon the extracted patient medical information; [Claim 7 teaches comparing an extracted recommended exam to subsequent reports to determine the similarity.]
Motivation to combine the teaching of Kulon with the teaching of Halsted, Sevenster, and Sragow is the same as that used with respect to claim 17 and is therefore reiterated here.
Halsted in view of Sevenster, Sragow, and Kulon may not explicitly teach
cluster similar medical reports;
and infer a group type for the clustered medical reports and labeling the clustered medical reports with the inferred group type.
However, Bendersky teaches the following:
cluster similar medical reports; [Para. 0095 teaches clustering radiology reports.]
and infer a group type for the clustered medical reports and labeling the clustered medical reports with the inferred group type. [Para. 0007 teaches inferring the imaging modality of a procedure and the body anatomy being imaged (the group type). Claim 1 teaches assigning labels to the reports.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Halsted in view of Sevenster, Sragow, and Kulon to cluster and label medical reports as taught by Bendersky, with the motivation of improving clinical workflow (see Bendersky at Para. 0007).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Halsted in view of Sevenster, Sragow, and Bendersky.
REGARDING CLAIM 19
Halsted in view of Sevenster and Sragow teaches the claimed automated device of claim 11.
Halsted in view of Sevenster and Sragow may not explicitly teach
wherein processing the plurality of patient medical reports includes document structure processing, syntactic parsing of an output of the document structure processing, extracting entities from an output of the syntactic parsing, and determining an anatomy inference on the extracted entities.
However, Bendersky teaches the following:
wherein processing the plurality of patient medical reports includes document structure processing, syntactic parsing of an output of the document structure processing, extracting entities from an output of the syntactic parsing, and determining an anatomy inference on the extracted entities. [Para. 0007 teaches classifying radiology reports by inferring the anatomy. The Examiner notes that a person having skill in the art would understand the use of natural language processing to extract anatomical information necessarily includes syntactic parsing and extracting of entities.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, before the effective filling date of the invention, to modify the computer-implemented method of Halsted in view of Sevenster and Sragow to cluster and label medical reports as taught by Bendersky, with the motivation of improving clinical workflow (see Bendersky at Para. 0007).
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-7, 9, 11-15, and 17-21, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
Applicant asserts that the claims as a whole are not directed to a method or
system of managing personal behavior or relationships or interactions between people (i.e., following rules or instructions). …the present claims recite steps that are not instructions to a human being to follow when analyzing a medical report. For example, among other things, a human being does not "receiv[e], from a medical information system comprising one or more of a Picture Archiving and Communication System (PACS), a Radiology Information System (RIS), a Laboratory Information System (LIS), or an Electronic Medical Record (EMR), a feedback request from a requesting interface," or "stor[e] the feedback request in a database," or "automatically generat[e] a subsequent exam alert message in response to identifying the subsequent patient exam," or "transmit[] the subsequent exam alert message electronically to a device associated with the feedback request," or "update[e] said machine learning model based on the acceptance or rejection."
Regarding (a), the Examiner respectfully disagrees. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that, given the broadest reasonable interpretation in light of the Specification, the identified claim elements represent a series of rules or instructions for a person or persons, with the aid of a computer, to follow to provide feedback for a radiologist. The Examiner notes that Applicant’s Background describes sharing feedback and information (see Spec. Para. 0034) as a pathologist task. Furthermore, the Examiner submits that healthcare itself is inherently represents the organization of human activity.
Applicant has not pointed to anything in the claims that fall outside of this characterization. That the claim purportedly performs the step of generating a subsequent exam alert message “automatically” does not remove it from being directed to Certain Methods of Organizing Human Activity. Humans perform actions automatically all the time. When you get in your car to go somewhere you automatically turn the engine on. Even assuming that this is not true (which it is), performing the step “automatically” is a consequence of confining the abstract idea to a computer. This is supported by MPEP 2106.05(a)(I) which states: “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: […] iii. Mere automation of manual processes...” Because the claim elements fall under a series of rules or instructions for a person or persons to follow to provide feedback for a radiologist, the claimed invention is directed to an abstract idea.
Finally, each of the items listed by the Applicant that were not part of the identified abstraction (i.e., PACS, the ML model) were fully analyzed as additional elements and were found not to provide a practical application or significantly more for the reasons noted in the basis of rejection.
The claims are tied to a particular machine arrangement integral to performance,
namely "a processor coupled to a medical information system comprising one or more of a Picture Archiving and Communication System (PACS), a Radiology Information System (RIS), a Laboratory Information System (LIS), or an Electronic Medical Record (EMR)" and "a requesting interface," and "a database" storing feedback requests, and "a machine learning model," and "a device associated with the feedback request." This arrangement imposes a meaningful limit and requires use of specific medical imaging hardware and data infrastructure…
Regarding (b), the Examiner respectfully disagrees. MPEP 2106.04(d)(2) indicates that a practical application may be present where the judicial exception is implemented using or in conjunction with a particular machine or manufacture. The instant claims do not recite a particular machine and, instead, recite that the abstract idea is implemented by a general-purpose computer. MPEP 2106.05(b)(I) indicates that applying the judicial exception “by use of conventional computer functions does not qualify as a particular machine.” Because there is no particularity with respect to the computer that implements the abstract idea, thus requiring the Examiner to conclude that the abstract idea is implemented by a general-purpose computer, a practical application is not present.
Further, there is no claimed “arrangement” withing the meaning of the word. The locations of the noted items are not claimed at all.
…the claims effect a transformation of a particular article, namely a feedback request, into a different state and thing, namely an acceptance or a rejection of feedback which is used to update a machine learning model.
Regarding (c), the Examiner respectfully disagrees. MPEP 2106.04(d)(2) indicates that a practical application may be present where the claimed invention effects a transformation or reduction of a particular article to a different state or thing. MPEP 2106.05(c) thereafter describes that a transformation is present where a physical object or substance is transformed to a different state or thing. Notably, the mere manipulation of data has been deemed not to be a transformation within the meaning of the term “transformation.” See MPEP 2106.05(c): “mere manipulation of basic mathematical constructs i.e., the paradigmatic abstract idea, has not been deemed a transformation” (internal quotations omitted). Because no transformation is present in Applicant’s claimed invention, a practical application is not present.
…“Examiners are expected to consider existing precedent like Enfish…”
Regarding (d), the Examiner respectfully submits that Enfish was considered. MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves the functioning of a computer. See also MPEP 2106.05(a)(I). The technological environment of Applicant’s claim is a general-purpose computer. Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the computer that results from the implementation of Applicant’s claim. There is no indication that the computer is made to run faster, more efficiently, or utilize less power. In fact, the computer may be caused to operate slower and less efficiently through the implementation of Applicant’s claimed invention; we do not know. Because there is no improvement to the function of the computer, a practical application is not present. The claimed features are merely invoking a computer as a tool to implement an abstract idea. Applicant’s claimed invention is not even remotely like that in Enfish because Applicant’s claims are not directed to a new form of database structure.
…the claimed method is directly analogous to the Ex Parte Desjardins claims, and thus the reasoning and decision of Ex Parte Desjardins is directly applicable. Notably, for example, the claims include "updating said machine learning model based on the acceptance or rejection." The ability of the machine learning model to be updated with the specific output of the system/method, namely the acceptance or rejection of the feedback, is an enumerated improvement in training a model… the specification identifies the improvement to machine learning technology by explaining how improved training using better training data results in more accurate predictions by the trained machine learning model…
Regarding (e), the Examiner respectfully submits that there is no improvement to the claim machine learning as there is in Desjardins. As found by the Panel, the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" represents “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application- “improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel.
Applicant’s claims do not provide such an improvement. There is no indication in the cited portion of the Specification that the claimed invention provides an improvement as to how model is trained. Improving the accuracy of a machine learning model by supplying it with specific data is not an improvement to how the model is trained within the meaning of Desjardins (see quotations from Recentive, infra). This is how all machine learning models are optimized (i.e., select training data, train the model, compare the output to validation data, receive feedback, adjust the parameters of the training data according to the comparison/ feedback, and repeat until an accuracy threshold is met). Put another way, the particular way the machine learning model of applicant’s invention uses the data to train itself is not improved, which is the holding of Desjardins. Applicant is merely improving the accuracy of the model by optimizing the data selected/used by the model. Improving the accuracy of a model is not an improvement by any measure in MPEP 2106.
Examiner’s position is also supported by the decision in Recentive Analytics, Inc. v. Fox Corp. Recentive held that non-specifically claimed training of an ML algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12.
Rejection under 35 U.S.C. § 103
Regarding the rejection of Claims 1-7, 9, 11-15, and 17-21, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment.
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Von Reden (U.S. 2016/0147946) which discloses systems, methods, and apparatus for analysis, presentation, and comparison of clinical information.
Kumar et al. (U.S. 2021/0027883) which discloses methods and systems for workflow management.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAMRYN B LEWIS whose telephone number is (703)756-1807. The examiner can normally be reached Monday - Friday, 11:00 am - 8:00 pm EST.
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/CAMRYN B. LEWIS/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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