DETAILED ACTION
The present office action represents a final action 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 .
Priority
This application claims the priority date of September 30, 2022.
Status of Claims
Claims 1 and 15 are amended and Claims 1-13 and 15-20 are pending.
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 15-20 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.
Claim 15 recites the limitation “the data propagation reporting system” in line 6. There is insufficient antecedent basis for these limitations in the claim because claim 15 is an independent claim and the terms are not previously referenced therein. Further, claims 16-20 depend on claim 15 and are therefore rejected due to their dependency on claim 15. Examiner is interpreting “the data propagation reporting system” as “a data propagation reporting system”. Appropriate correction is requested.
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 1-13 and 15-20 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.
Claims 1-13 are drawn to a data propagation reporting system, which is within the four statutory categories (i.e., machine). Claims 15-20 are drawn to a method for reporting data propagation, which is within the four statutory categories (i.e., process).
Claims 1-13 recite a data propagation reporting system comprising:
a processor configured to:
receive patient consent data associated with a patient;
determine a first annotation associated with health data associated with the patient and a second annotation associated with the health data associated with the patient;
determine, based on the first annotation, first utilization information associated with use of the health data by a first system external to the data propagation reporting system;
determine, based on the second annotation, second utilization information associated with use of the health data by a second system external to the data propagation reporting system;
identify, based on the patient consent data, at least a portion of at least one of the first utilization information or the second utilization information associated with the patient;
receive an indication of a machine learning model;
determine whether at least a portion of the health data is included in training data of the machine learning model;
generate a patient data utilization report comprising the at least a portion of at least one of the first utilization information or the second utilization information associated with the patient, and an indication of whether the at least a portion of the health data is included in the training data of the machine learning model; and
output the patient data utilization report to the patient.
Claims 15-20 recite a method for reporting data propagation, the method comprising:
receiving patient consent data associated with a patient;
determining a first annotation associated with health data associated with a patient and a second annotation associated with the health data associated with the patient;
determining, based on the first annotation, first utilization information associated with use of the health data by a first system external to the data propagation reporting system;
determining, based on the second annotation, second utilization information associated with use of the health data by a second system external to the data propagation reporting system;
identifying, based on the patient consent data, at least a portion of at least one of the first utilization information or the second utilization information associated with the patient;
receiving an indication of a machine learning model;
determining whether at least a portion of the health data is included in training data of the machine learning model;
generating a patient data utilization report comprising at least a portion of at least one of the first utilization information or the second utilization information associated with the patient and an indication of whether the at least a portion of the health data is included in the training data of the machine learning model; and
output the patient data utilization report to the patient.
The bolded limitations, given the broadest reasonable interpretation, cover mathematical concepts or a certain method of organizing human activity but for the recitation of generic computer components (e.g., obtain an annotation associated with health data and determine utilization information associated with the health data). See MPEP 2106.04(a)(2)I and MPEP 2106.04(a)(2)II. The underlined limitations are not part of the identified abstract idea (the method of organizing human activity) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-13 and 16-20 are include similar limitations and 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 as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
The additional elements from claims 1 and 15 include:
data propagation reporting system (apply it, MPEP 2106.05(f)).
a processor configured to (apply it, MPEP 2106.05(f)).
by a first system (apply it, MPEP 2106.05(f)).
by a second system (apply it, MPEP 2106.05(f)).
The additional elements from claim 2 include:
a system (apply it, MPEP 2106.05(f)).
These additional elements, in the independent claims 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 “data propagation reporting system”, “a first system”, “a second system”, and “processor”, e.g. see Specification [0067] and [0143]. (see MPEP 2106.05(f)).
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 discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., Paragraphs [0067] and [0143], of the Specification discloses that the additional elements (i.e., data propagation reporting system, a first system, a second system, and processor) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare, patient data utilization reporting);
Dependent claims 2-13 and 16-20 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than those 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 patient data utilization reporting (obtain an annotation associated with health data and determine utilization information associated with the health data) or improves any other technology, and their collective functions merely provide conventional computer implementation.
The application, is an attempt to organize human activity or relates to mathematical concepts, using a system to obtain information associated with patient data and generate a report regarding utilization of patient data. The inventive concept is the system for determining patient data utilization and reporting, which is not patentable. Therefore, whether taken individually or as an ordered combination, claims 1-13 and 15-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 1-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Carlson (U.S. Pub. No. 2021/0407632 A1) in view of Krasnov (U.S. Pub. No. 2020/0097910 A1), Boussios (U.S. Pub. 2022/0148695 A1) and D’Ambrosia (U.S. Pub. No. 2010/0063841 A1).
Regarding claim 1, Carlson discloses a data propagation reporting system comprising (Paragraph [0005] discusses a system for identifying, analyzing, displaying health records.):
a processor configured to (Paragraph [0006] discusses a processor configured to.):
determine a first annotation (Examiner is interpreting annotation to include any change/access/use/metric of the data. Further Examiner notes that the prior art does not state “first” or “second”, however, it includes one or a combination of several parameters accessed.) associated with health data associated with the patient and a second annotation associated with the health data associated with the patient (Paragraphs [0028] and [0033] discuss identifying and displaying electronic health records and metrics may be used to identify which portions of a patient record should be displayed and the system tracks which records are consulted.);
determine, based on the first annotation, first utilization information associated with use of the health data by a first system external to the data propagation reporting system (Paragraphs [0006], [0028] and [0041]-[0042] discuss the system tracks users of the interface and identifies which records are consulted and accessed for which patients and the patient information/parameter accessed and one or more patients in the cohort and/or one or more identified records can be ranked, filtered, added, removed, or otherwise modified using clinical databases or other sources of relevant information such as Medscape, PubMed, Wikipedia, medical journals, other knowledge-based databases, clinician-curated data, and many more sources and the system associates the identified types of records with the patient cohort in a cohort database configured to store information.);
determine, based on the second annotation, second utilization information associated with use of the health data by a second system external to the data propagation reporting system (Paragraphs [0038] and [0041]-[0042] discuss the system identifies one or more patient parameters associated with the patient in order to create the patient cohort of related patients. For example, patients may be related based on a clinical context, such as illness, symptoms, treatment, medical history, and/or other clinical contexts. Patients may be related based on patient demographics such as sex, age, background, and/or other patient demographics. The patients may be related based on which record or records a user most commonly accesses or reviews for a patient. Patients may be identified as being related based on a combination of several of these and/or other parameters and one or more patients in the cohort and/or one or more identified records can be ranked, filtered, added, removed, or otherwise modified using clinical databases or other sources of relevant information such as Medscape, PubMed, Wikipedia, medical journals, other knowledge-based databases, clinician-curated data, and many more sources and the system associates the identified types of records with the patient cohort in a cohort database configured to store information.);
generate a patient data utilization report comprising the at least a portion of at least one of the first utilization information or the second utilization information associated with the patient (Paragraphs [0021], [0036]-[0037], and [0040] discuss the medical record system identifies the records accessed or reviewed by the user via tracking and the system identifies based on an analysis of the tracked activity, patient information accessed and the system may log the information or record sources accessed, and identify which of the logged sources are utilized most frequently.); and
output the patient data utilization information (Paragraphs [0040], [0049], and [0054] discuss the system identifies the types of information most commonly accessed for the particular patient cohort and may log the information or record sources accessed by one or more users and identify which of the logged sources are utilized most frequently and the system preferentially displays identified portions or snippets of the identified records based on the identified record type, information about the patient, identification of preferred snippets or portions based on user analysis described above, or snippets of the text (or image or other data) near the location that the ontological concept matching the ranked list was extracted and display the information.).
Carlson does not explicitly disclose:
receive patient consent data associated with a patient;
identify, based on the patient consent data, at least a portion of at least one of the first utilization information or the second utilization information associated with the patient;
receive an indication of a machine learning model;
determine whether at least a portion of the health data is included in training data of the machine learning model;
an indication of whether the at least a portion of the health data is included in the training data of the machine learning model.
output the patient data utilization report to the patient.
Krasnov teaches:
receive patient consent data associated with a patient (Paragraphs [0032]-[0033], [0128], and [0189] discuss for example the Family Portal History shows the chronological record of aspects of the patient record that have been authorized by the patient for the family members to view.); and
identify, based on the patient consent data, at least a portion of the utilization information associated with the patient (Paragraphs [0181]-[0182] and [0203] discuss the system manages data flow and a user may only be permitted access to a subset of the data in another user’s data store, which is useful in protecting the privacy of a patient, for example, PatientSerivceIds provides control over individuals accessing a patient’s record, which identifies their approved activities or reason for access.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Carlson to include receive patient consent data associated with a patient and identify, based on the patient consent data, at least a portion of the utilization information associated with the patient, as taught by Krasnov, in order to provide consistent patent data in a centralized repository and meet the requirements of patient data privacy concerns in the field of healthcare and patient data collection. (Krasnov Paragraphs [0005]-[0006] and [0009].).
Boussios teaches:
receive an indication of a machine learning model (Paragraphs [0048] discuss a computer generates a model);
determine whether at least a portion of the health data is included in training data of the machine learning model (Paragraphs [0048], [0056], [0089] discuss a computer generates a model based on examples provided in a training set that includes data for patients that are identified as representative of a particular category and process patient data using a variety of techniques, such as deep learning models, to identify pertinent features in the patient data, the results of such processing can be used to define features for training and applying classifiers, whether for categorizing patients.);
an indication of whether the at least a portion of the health data is included in the training data of the machine learning model (Paragraph [0089] discusses a training set of patient data is specified for each category for which a classifier is to be built, the training set includes data for patients that are identified as representative of a particular category.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Carlson to include, receive an indication of a machine learning model, determine whether at least a portion of the health data is included in training data of the machine learning model, and an indication of whether the at least a portion of the health data is included in the training data of the machine learning model, as taught by Boussios, in order that different factors may be identified as having the most impact on the result of a model. (Boussios Paragraph [0011].).
D’Ambrosia teaches:
output the patient data utilization report to the patient (Paragraphs [0033]-[0034] discuss send notification of access to the personal medical record by the accessing entity to the patient.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Carlson to include output the patient data utilization report to the patient, as taught by D’Ambrosia, in order to provide subscribers with notification of who is accessing their medical records so they may police access and better protect the information. (D’Ambrosia Paragraph [0005].).
Regarding claim 2, Carlson discloses wherein the utilization information indicates at least one of:
a system in which the health data associated with the patient has been used (Paragraphs [0025] and [0028] and FIG. 1 discuss a system for identifying and displaying patient’s electronic health records.);
when the health data associated with the patient has been accessed (Paragraphs [0028] and [0032] discuss the system tracks users of the interface and identifies which records are commonly accessed for patients.); or
a purpose for which the health data associated with the patient has been used (Paragraphs [0032]-[0034] discuss the system tracks the activity of one or more users (clinician or other specialist) of the system, and the interface tools are utilized to monitor the user interaction with the system and identifies which records or record types are accessed, for example use clinician may be an expert for cardiology patients and relevant information identified.).
Regarding claim 3, Carlson discloses wherein the processor is further configured to:
obtain an indication that the health data associated with the patient has been accessed (Paragraph [0035] discusses the system utilizes eye-tracking software or algorithms to track or identify patient information accessed by the users of the medical record system.);
based on the indication, generate an access annotation configured to indicate that the health data associated with the patient was accessed (Paragraphs [0034]-[0037] discuss the system identifies, based on the analysis of the tracked activity, patient information accessed by one or more users through the electronic medical record interface, for example, the system may log the information.); and
add the access annotation to the health data, wherein the annotation comprises the access annotation (Paragraph [0037] discusses based on an analysis of the tracked activity, the system identifies patient information accessed and the system may log the information accessed.).
Regarding claim 4, Carlson discloses wherein the health data comprises a plurality of datasets associated with the patient, and wherein the processor is further configured to (Paragraph [0042] discuss one or more patients and one or more identified records can be ranked, filtered, added, removed or modified using clinical databases or other sources of relevant information.):
obtain an indication of an algorithm to which a dataset of the plurality of datasets associated with the patient has contributed (Paragraphs [0040]-[0042] discuss the system identifies one or more types of information most commonly accessed based on a machine learning mechanism, for example, clinical concepts linked to a patient cohort for diagnostic or therapy decisions can be identified using the additional information and can thus be preferentially reported.);
based on the indication, generate a contribution annotation configured to indicate that the dataset associated with the patient has contributed to the algorithm (Paragraphs [0047]-[0048] and [0053]-[0055] discuss the system identifies, highlights, or provides portions of the identified one or more patient records and the generated patient cohort will further comprise an identification of one or more types of information, such as medical records, that are most commonly accessed, reviewed, or otherwise utilized by users of the medical record system in regard to the patients in the patient cohort.); and
add the contribution annotation to the health data associated with the patient, wherein the annotation comprises the contribution annotation (Paragraph [0037] discusses identifies, based on an analysis of the tracked activity, patient information accessed through the electronic medical record interface, for example, the system may log the information or record patient sources accessed by one or more users and store the information in a database.).
Regarding claim 5, Carlson discloses wherein the health data comprises a plurality of datasets associated with the patient, and wherein the processor is further configured to (Paragraphs [0056]-[0060] discuss a record identifier, which may be a processor that receives a request for information about a patient and identifies one or more types of records and sends the information to an interface.):
obtain an indication of a data collection in which a dataset of the plurality of datasets associated with the patient is included (Paragraphs [0058]-[0060] discuss the record identifier identifies the records commonly accessed based on patient parameters.);
based on the indication, generate an inclusion annotation configured to indicate that the dataset associated with the patient is included in the data collection (Paragraph [0060] discusses the record identifier identifies, based on the patient cohort with which the patient is associated, the records associated with the cohort most commonly accessed and the record identifier may send the patient records to the user interface.); and
add the inclusion annotation to the health data associated with the patient, wherein the annotation comprises the inclusion annotation (Paragraphs [0037] and [0059]-[0060] discuss the record identifier may analyze additional information about the patient and identify based on the patient cohort with which the patient is associated, the type of records most commonly accessed or utilized and the record identifier may identify or provide specific portions of the identified records and the system may log the information or record patient sources accessed by one or more users and store the information in a database.).
Regarding claim 6, Carlson discloses wherein the health data comprises a plurality of datasets associated with the patient, and wherein the processor is further configured to (Paragraphs [0056]-[0060] discuss a record identifier, which may be a processor that receives a request for information about a patient and identifies one or more types of records and sends the information to an interface.):
obtain an indication of an algorithm (Paragraph [0053] discuss a patient cohort generator that comprise patients that are related based on one or more parameters including based on which records a user most commonly accesses for a patient.); and
determine, based on the annotation, whether at least one dataset of the plurality of datasets associated with the patient has been used in contribution to the algorithm, wherein based on a determination that at least one dataset of the plurality of datasets associated with the patient has been used in contribution to the algorithm, the processor is further configured to (Paragraph [0046] discusses the system identifies, based on the patient cohort with which the patient is associated, one or more records of the patient that include information matching the one or more identified types of information associated with the patient cohort. For example, starting with the ranked list of ontological concepts, use NLP to extract any ontological concepts from this patient's records and then compare to the ranked list of concepts for the cohort to determine which documents include concepts that match the list.):
identify the at least one dataset of the plurality of datasets associated with the patient that has been used in contribution to the algorithm to (Paragraph [0046] discusses the system identifies, based on the patient cohort with which the patient is associated, one or more records of the patient that include information matching the one or more identified types of information associated with the patient cohort, for example, the system may select the documents matching the highest ranked concepts in the ranked list.); and
include an indication of the algorithm and an indication of the identified at least one dataset in the utilization information (Paragraphs [0035]-[0038] and [0046] discuss the record system utilizes eye-tracking algorithms to track information commonly accessed by the users of the medical system or natural language processing to access records and the system identifies, based on the patient cohort with which the patient is associated, one or more records of the patient that include information matching the one or more identified types of information associated with the patient cohort.).
Regarding claim 7, Carlson discloses wherein the health data comprises a plurality of datasets associated with the patient, and wherein the processor is further configured to (Paragraphs [0056]-[0060] discuss a record identifier, which may be a processor that receives a request for information about a patient and identifies one or more types of records and sends the information to an interface.):
obtain an indication of a data collection (Paragraphs [0016] and [0037] discuss analyze obtained medical information about a patient and the system identifies, based on an analysis of the tracked activity, patient information accessed by a user through the electronic medical record interface.); and
determine, based on the annotation, whether at least one dataset of the plurality of datasets associated with the patient has been included in the data collection, wherein based on a determination that at least one dataset of the plurality of datasets associated with the patient has been included in the data collection, the processor is further configured to (Paragraph [0046] discusses the system identifies, based on the patient cohort with which the patient is associated, one or more records of the patient that include information matching the one or more identified types of information associated with the patient cohort. For example, starting with the ranked list of ontological concepts, use NLP to extract any ontological concepts from this patient's records and then compare to the ranked list of concepts for the cohort to determine which documents include concepts that match the list.):
identify the at least one dataset of the plurality of datasets associated with the patient that has been included in the data collection (Paragraph [0046] discusses the system identifies, based on the patient cohort with which the patient is associated, one or more records of the patient that include information matching the one or more identified types of information associated with the patient cohort, for example, the system may select the documents matching the highest ranked concepts in the ranked list.); and
include an indication of the data collection and an indication of the identified at least one dataset in the utilization information (Paragraph [0046] discusses the record system identifies, based on the patient cohort with which the patient is associated, one or more records of the patient that include information matching the one or more identified types of information associated with the patient cohort.).
Regarding claim 8, Carlson discloses wherein the health data comprises a plurality of datasets associated with the patient, and wherein the processor is further configured to (Paragraphs [0056]-[0060] discuss a record identifier, which may be a processor that receives a request for information about a patient and identifies one or more types of records and sends the information to an interface.):
obtain an indication of a dataset of the plurality of datasets associated with the patient (Paragraphs [0058]-[0060] discuss the record identifier identifies the patient records commonly accessed based on patient parameters.); and
identify, based on the annotation, a control program to which the dataset has contributed, wherein the utilization information comprises an indication of the identified control program (Paragraph [0031] discusses the generated patient cohort comprises an identification of one or more types of information, such as medical records, that are most commonly accessed, reviewed, or otherwise utilized by users of the medical record system in regard to the patients in the patient cohort. Thus, if a user of the system frequently accesses X-rays for patients in an orthopedic clinical context, a patient cohort comprising orthopedic patients may have X-rays as one of the types of information associated with that cohort. Accordingly, if a plurality of patient cohorts are generated, each cohort may be associated with unique and/or overlapping record types or types of information.).
Regarding claim 9, Carlson discloses wherein the health data comprises a plurality of datasets associated with the patient, and wherein the processor is further configured to (Paragraphs [0056]-0060] discuss a record identifier, which may be a processor that receives a request for information about a patient and identifies one or more types of records and sends the information to an interface.):
obtain an indication of a dataset of the plurality of datasets associated with the patient (Paragraphs [0058]-[0060] discuss the record identifier identifies the patient records commonly accessed based on patient parameters.); and
identify, based on the annotation, a data collection that the dataset has been included in, wherein the utilization information comprises an indication of the identified data collection (Paragraph [0031] discusses the generated patient cohort comprises an identification of one or more types of information, such as medical records, that are most commonly accessed, reviewed, or otherwise utilized by users of the medical record system in regard to the patients in the patient cohort. Thus, if a user of the system frequently accesses X-rays for patients in an orthopedic clinical context, a patient cohort comprising orthopedic patients may have X-rays as one of the types of information associated with that cohort.).
Regarding claim 10, Carlson discloses wherein the health data comprises a plurality of datasets associated with the patient, wherein the annotation indicates a characterization of a dataset of the plurality of datasets, and wherein the processor is further configured to (Paragraphs [0056]-0060] discuss a record identifier, which may be a processor that receives a request for information about a patient and identifies one or more types of records and sends the information to an interface.):
obtain an indication of the dataset (Paragraphs [0058]-[0060] discuss the record identifier identifies the patient records commonly accessed based on patient parameters.); and
include an indication of the determination in the patient data utilization report (Paragraph [0046] discusses the record system identifies, based on the patient cohort with which the patient is associated, one or more records of the patient that include information matching the one or more identified types of information associated with the patient cohort.).
Carlson does not explicitly disclose:
a privacy characterization; and
determine, based on the privacy characterization, whether the dataset comprises private data, public data, or both.
Krasnov teaches:
a privacy characterization (Paragraphs [0181] and [0203] discusses the system manages data flow and a user may only be permitted access to a subset of the data in another user’s data store, which is useful in protecting the privacy of a patient.); and
determine, based on the privacy characterization, whether the dataset comprises private data, public data, or both (Paragraphs [0181]-[0182] and [0203] discuss the system manages data flow and a user may only be permitted access to a subset of the data in another user’s data store, which is useful in protecting the privacy of a patient, for example, PatientSerivceIds provides control over individuals accessing a patient’s record, which identifies their approved activities or reason for access.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Carlson to include a privacy characterization and determine, based on the privacy characterization, whether the dataset comprises private data, public data, or both, as taught by Krasnov, in order to provide consistent patent data in a centralized repository and meet the requirements of patient data privacy concerns in the field of healthcare and patient data collection. (Krasnov Paragraphs [0005]-[0006] and [0009].).
Regarding claim 11, Carlson discloses wherein the processor is further configured to:
determine an identification of the health data, wherein the identification indicates the patient could be identified based on the health data (Paragraphs [0021] discuss the system tracks the types of information that are accessed and logs this information along with demographic, vital, diagnosis, etc. classifying information about the associated patient and the most important types of information can be listed and ranked across different patient cohorts.).
include information in the patient data utilization report (Paragraphs [0021], [0036]-[0037], and [0040] discuss the medical record system identifies the records accessed or reviewed by the user via tracking and the system identifies based on an analysis of the tracked activity, patient information accessed and the system may log the information or record sources accessed, and identify which of the logged sources are utilized most frequently.).
Carlson does not explicitly disclose:
an identification level; and
identification level of the health data, wherein the identification level indicates a level of ease with which the patient could be identified.
Krasnov teaches:
an identification level (Paragraphs [0181]-[0182] and [0203] discuss the system manages data flow and a user may only be permitted access to a subset of the data in another user’s data store, which is useful in protecting the privacy of a patient, for example, PatientSerivceIds provides control over individuals accessing a patient’s record, which identifies their approved activities or reason for access.); and
identification level of the health data, wherein the identification level indicates a level of ease with which the patient could be identified (Paragraphs [0072] and [0181] discuss the database contains a subset of data, such as anonymized data or the data may be encrypted and for example third parties may only be permitted access to a subset of the data in a patient care team’s data store (e.g., anonymized), which is useful in protecting the privacy of a patient.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Carlson to include an identification level and identification level of the health data, wherein the identification level indicates a level of ease with which the patient could be identified, as taught by Krasnov, in order to provide consistent patent data in a centralized repository and meet the requirements of patient data privacy concerns in the field of healthcare and patient data collection. (Krasnov Paragraphs [0005]-[0006] and [0009].).
Regarding claim 12, Carlson discloses wherein the processor is further configured to:
detect that an event related to at least one of access, inclusion, or utilization of the health data associated with the patient has occurred (Paragraphs [0028]-[0030]) discuss the system tracks users of the interface and identifies which records are accessed for a patient.); and
generate the report in response to detecting the occurrence of the event (Paragraphs [0021], [0036]-[0037], [0040] and FIG. 2 discuss the medical record system identifies the records accessed or reviewed by the user via tracking and the system identifies based on an analysis of the tracked activity, patient information accessed and the system may log the information or record sources accessed, and identify which of the logged sources are utilized most frequently.).
Regarding claim 13, Carlson discloses wherein the processor is further configured to:
generate a visualization of the impact that the health data associated with the patient has had on at least one of (Paragraphs [0005], 0038], and [0048]-[0049] discuss the system identifies one or more patient parameters and it can display identified relevant patient medical records and the displayed records are displayed with links to clinical databases indicating the clinical value of the clinical issues for the patient treatment.):
an area of medicine (Paragraphs [0005] and [0031] discuss the system generates a patient cohort with similar parameters and the commonly utilized medical records are identified, for example if a user of the system frequently accesses X-rays for patients in an orthopedic clinical context, a patient cohort, a patient cohort comprising orthopedic patients may have X-rays as one of the types of information associated with that cohort.);
development of a treatment (Paragraph [0049] discusses patient treatment.); or
a geographical area (Paragraph [0038] discusses patients may be related based on a clinical context, such as illness, symptoms, treatment, medical history, and/or other clinical contexts, patient demographics such as sex, age, background, and/or other patient demographics, based on which record or records a user most commonly accesses or reviews for a patient.).
Regarding claim 15, Carlson discloses a method for reporting data propagation, the method comprising (Paragraphs [0029]-[0030] discuss a method for identifying and displaying electronic health records of a patient accessed based on various parameters.):
determining a first annotation associated with health data associated with a patient and a second annotation associated with the health data associated with the patient (Paragraphs [0028] and [0033] discuss identifying and displaying electronic health records and metrics may be used to identify which portions of a record should be displayed and the system tracks which records are accessed.);
determining, based on the first annotation, first utilization information associated with use of the health data by a first system external to the data propagation reporting system (Paragraphs [0006], [0028] and [0041]-[0042] discuss the system tracks users of the interface and identifies which records are consulted and accessed for which patients and the patient information/parameter accessed and one or more patients in the cohort and/or one or more identified records can be ranked, filtered, added, removed, or otherwise modified using clinical databases or other sources of relevant information such as Medscape, PubMed, Wikipedia, medical journals, other knowledge-based databases, clinician-curated data, and many more sources and the system associates the identified types of records with the patient cohort in a cohort database configured to store information.);
determining, based on the second annotation, second utilization information associated with use of the health data by a second system external to the data propagation reporting system (Paragraphs [0038] and [0041]-[0042] discuss the system identifies one or more patient parameters associated with the patient in order to create the patient cohort of related patients. For example, patients may be related based on a clinical context, such as illness, symptoms, treatment, medical history, and/or other clinical contexts. Patients may be related based on patient demographics such as sex, age, background, and/or other patient demographics. The patients may be related based on which record or records a user most commonly accesses or reviews for a patient. Patients may be identified as being related based on a combination of several of these and/or other parameters and one or more patients in the cohort and/or one or more identified records can be ranked, filtered, added, removed, or otherwise modified using clinical databases or other sources of relevant information such as Medscape, PubMed, Wikipedia, medical journals, other knowledge-based databases, clinician-curated data, and many more sources and the system associates the identified types of records with the patient cohort in a cohort database configured to store information.);
generating a patient data utilization report comprising at least a portion of at least one of the first utilization information or the second utilization information associated with the patient (Paragraphs [0021], [0036]-[0037], and [0040] discuss the medical record system identifies the records accessed or reviewed by the user via tracking and the system identifies based on an analysis of the tracked activity, patient information accessed and the system may log the information or record sources accessed, and identify which of the logged sources are utilized most frequently .); and
output the patient data utilization information (Paragraphs [0040], [0049], and [0054] discuss the system identifies the types of information most commonly accessed for the particular patient cohort and may log the information or record sources accessed by one or more users and identify which of the logged sources are utilized most frequently and the system preferentially displays identified portions or snippets of the identified records based on the identified record type, information about the patient, identification of preferred snippets or portions based on user analysis described above, or snippets of the text (or image or other data) near the location that the ontological concept matching the ranked list was extracted and display the information.).
Carlson does not explicitly disclose:
receiving patient consent data associated with a patient;
identifying, based on the patient consent data, at least a portion of at least one of the first utilization information or the second utilization information associated with the patient;
receiving an indication of a machine learning model;
determining whether at least a portion of the health data is included in training data of the machine learning model;
and an indication of whether the at least a portion of the health data is included in the training data of the machine learning model;
output the patient data utilization report to the patient.
Krasnov teaches:
receiving patient consent data associated with a patient (Paragraphs [0032]-[0033], [0128], and [0189] discuss for example the Family Portal History shows the chronological record of aspects of the patient record that have been authorized by the patient for the family members to view.); and
identifying, based on the patient consent data, at least a portion of the utilization information associated with the patient (Paragraphs [0181]-[0182] and [0203] discuss the system manages data flow and a user may only be permitted access to a subset of the data in another user’s data store, which is useful in protecting the privacy of a patient, for example, PatientSerivceIds provides control over individuals accessing a patient’s record, which identifies their approved activities or reason for access.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Carlson to include receiving patient consent data associated with a patient and identifying, based on the patient consent data, at least a portion of the utilization information associated with the patient, as taught by Krasnov, in order to provide consistent patent data in a centralized repository and meet the requirements of patient data privacy concerns in the field of healthcare and patient data collection. (Krasnov Paragraphs [0005]-[0006] and [0009].).
Boussios teaches:
receiving an indication of a machine learning model (Paragraphs [0048] discuss a computer generates a model);
determining whether at least a portion of the health data is included in training data of the machine learning model (Paragraphs [0048], [0056], [0089] discuss a computer generates a model based on examples provided in a training set that includes data for patients that are identified as representative of a particular category and process patient data using a variety of techniques, such as deep learning models, to identify pertinent features in the patient data, the results of such processing can be used to define features for training and applying classifiers, whether for categorizing patients.);
an indication of whether the at least a portion of the health data is included in the training data of the machine learning model (Paragraph [0089] discusses a training set of patient data is specified for each category for which a classifier is to be built, the training set includes data for patients that are identified as representative of a particular category.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Carlson to include, receiving an indication of a machine learning model, determining whether at least a portion of the health data is included in training data of the machine learning model, and an indication of whether the at least a portion of the health data is included in the training data of the machine learning model, as taught by Boussios, in order that different factors may be identified as having the most impact on the result of a model. (Boussios Paragraph [0011].).
D’Ambrosia teaches:
output the patient data utilization report to the patient (Paragraphs [0033]-[0034] discuss send notification of access to the personal medical record by the accessing entity to the patient.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Carlson to include output the patient data utilization report to the patient, as taught by D’Ambrosia, in order to provide subscribers with notification of who is accessing their medical records so they may police access and better protect the information. (D’Ambrosia Paragraph [0005].).
Regarding claim 16, Carlson discloses wherein the utilization information indicates at least one of:
a system in which the health data associated with the patient has been used (Paragraphs [0025] and [0028] and FIG. 1 discuss a system for identifying and displaying patient’s electronic health records.);
when the health data associated with the patient has been accessed (Paragraphs [0028] and [0032] discuss the system tracks users of the interface and identifies which records are commonly accessed for patients.); or
a purpose for which the health data associated with the patient has been used (Paragraphs [0032]-[0034] discuss the system tracks the activity of one or m