DETAILED ACTION
This action is in response to the response to election/restriction filed on April 10, 2026. Invention I (claims 1-16) has been elected with traverse. Invention II (claims 17-19) and Invention III (claim 20) are pending. Claims 1-20 have been examined and are currently pending.
Response to Restriction
Applicant's election with traverse of I in the reply filed on April 10, 2026 is acknowledged. The traversal is found persuasive, therefore, the examiner has withdrawn the restriction.
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 .
Inventorship
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
ALICE/ MAYO: TWO-PART ANALYSIS
2A. First, a determination whether the claim is directed to a judicial exception (i.e., abstract idea).
Prong 1: A determination whether the claim recites a judicial exception (i.e., abstract idea).
Groupings of abstract ideas enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Mathematical concepts- mathematical relationships, mathematical formulas or equations, mathematical calculations.
Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Mental processes- concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
Prong 2: A determination whether the judicial exception (i.e., abstract idea) is integrated into a practical application.
Considerations indicative of integration into a practical application enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Improvement to the functioning of a computer, or an improvement to any other technology or technical field
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
Applying the judicial exception with, or by use of a particular machine.
Effecting a transformation or reduction of a particular article to a different state or thing
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception
Considerations that are not indicative of integration into a practical application enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea.
Adding insignificant extra-solution activity to the judicial exception.
Generally linking the use of the judicial exception to a particular technological environment or field of use.
2B. Second, a determination whether the claim provides an inventive concept (i.e., Whether the claim(s) include additional elements, or combinations of elements, that are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea)).
Considerations indicative of an inventive concept (aka “significantly more”) enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Improvement to the functioning of a computer, or an improvement to any other technology or technical field
Applying the judicial exception with, or by use of a particular machine.
Effecting a transformation or reduction of a particular article to a different state or thing
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception NOTE: The only consideration that does not overlap with the considerations indicative of integration into a practical application associated with step 2A: Prong 2.
Considerations that are not indicative of an inventive concept (aka “significantly more”) enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea.
Adding insignificant extra-solution activity to the judicial exception.
Generally linking the use of the judicial exception to a particular technological environment or field of use.
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. NOTE: The only consideration that does not overlap with the considerations that are not indicative of integration into a practical application associated with step 2A: Prong 2.
See also, 2019 Revised Patent Subject Matter Eligibility Guidance; Federal Register; Vol. 84, No. 4; Monday, January 7, 2019
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
1: Statutory Category
Applicant’s claimed invention, as described in independent claim 1 is directed to a method, independent claim 17 is directed to a method, and independent claim 20 is directed to a method.
2(A): The claim(s) are directed to a judicial exception (i.e., an abstract idea).
PRONG 1: The claim(s) recite a judicial exception (i.e., an abstract idea).
Mental Processes
Independent claim 1 recites:
“for a first encounter, receiving identification of a patient associated with the first encounter from the provider via a provider portal executing on a computing device accessed by the provider;
accessing a health record, in a population of health records, corresponding to the patient, the health record comprising a corpus of patient data associated with the patient;
accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses;
for a first diagnosis, in the population of diagnoses, accessing a first set of target indicators defined for the first diagnosis in a first module, in the population of modules, and supporting the first diagnosis;
extracting a first subset of patient indicators, from the corpus of patient data, corresponding to a first subset of target indicators in the first set of target indicators;”
Independent claim 17 recites:
“for an encounter, receiving identification of a patient associated with the encounter from the provider via a provider portal executing on a computing device accessed by the provider;
accessing a health record, in a population of health records, corresponding to the patient, the health record comprising a corpus of patient data and a list of current medications implemented by the patient;
accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses;
for a first module, in the population of modules, corresponding to a first diagnosis, accessing:
a first set of target indicators supporting the first diagnosis;
and a medication blacklist comprising a set of blacklisted medications predicted to exacerbate the first diagnosis;
extracting a first subset of patient indicators, from the corpus of patient data, corresponding to a first subset of target indicators in the first set of target indicators;"
Independent claim 20 recites:
“for an encounter, receiving identification of a patient associated with the encounter from the provider via a provider portal executing on a computing device accessed by the provider;
accessing a health record, in a population of health records, corresponding to the patient, the health record comprising a corpus of patient data associated with the patient;
accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses;
for a diagnosis, in the population of diagnoses, accessing a set of target indicators defined in a module, in the population of modules, corresponding to the diagnosis, the set of target indicators comprising:
a subset of primary target indicators supporting the diagnosis and required for predicting the diagnosis; and a subset of secondary target indicators supporting the diagnosis;
extracting a subset of primary patient indicators, from the corpus of patient data, corresponding to the subset of primary target indicators;
and in response to the subset of primary patient indicators corresponding to the subset of primary target indicators:
extracting a subset of secondary patient indicators, from the corpus of patient data, corresponding to the subset of secondary target indicators;”
Independent claims 1, 17, and 20 are directed to the abstract idea of mental processes. In particular, the limitations recited above are directed to concepts performed in the human mind through observation, evaluation, and judgment. Specifically, a person can receive an identification from a patient, access a patient health records, access a diagnostic model identifying indicators associated with a diagnosis, and extract data associated with patient records to generate or identify a diagnosis based on the accessed indicators. This can be performed using pen and paper. Additionally, the steps can be performed by a human with an aid of computer in order for the human to evaluate and make a judgment based on the available information or data.
Mathematical Concepts
Independent claim 1 recites:
“deriving a first confidence score for the first diagnosis for the patient based on the first subset of patient indicators and the first subset of target indicators;
and in response to the first confidence score exceeding a threshold score:
appending a list of predicted diagnoses with the first diagnosis;
generating a first notification comprising the list of predicted diagnoses and a first prompt to review the list of predicted diagnoses;
populating the first notification with the first subset of patient indicators linked to the first diagnosis;
and via the provider portal, transmitting the first notification to the provider for review.”
Independent claim 17 recites:
“deriving a first confidence score for the first diagnosis for the patient based on the first subset of patient indicators and the first subset of target indicators;
and in response to the first confidence score exceeding a threshold score:
predicting the first diagnosis for the patient for the encounter:
in response to predicting the first diagnosis for the patient:
appending a list of predicted diagnoses with the first diagnosis;
generating a first notification comprising the list of predicted diagnoses and a first prompt to review the list of predicted diagnoses;
and in response to the list of current medications comprising a first medication, in the set of blacklisted medications:
flagging the first medication for review by the provider;
and populating the first notification with a first alert indicating implementation of the first medication by the patient;
and via the provider portal, transmitting the first notification to the provider for review.”
Independent claim 20 recites:
“deriving a confidence score for the diagnosis for the patient on the subset of primary patient indicators and the subset of secondary patient indicators;
and in response to the confidence score exceeding a threshold score:
appending a list of predicted diagnoses with the diagnosis;
generating a notification comprising the list of predicted diagnoses and a prompt to review the list of predicted diagnoses;
populating the notification with the subset of primary patient indicators and the subset of secondary patient indicators linked to the diagnosis;
and via the provider portal, transmitting the notification to the provider for review.”
Independent claims 1, 17, and 20 are directed to the abstract idea mathematical concepts under mathematical calculations and mathematical relationships. The claims are directed to mathematical calculations by determining a confidence score for a diagnosis based on the relationship between patient indicators and target indicators. Additionally, the claims are directed to mathematical relationships by comparing if the determined confidence score is greater than the threshold score. If the determined confidence score is greater than a threshold score a series of steps are performed (e.g., appending a list of predicted diagnoses, generating a notification, and transmitting a notification).
Managing Personal Behavior or Relationships or Interactions Between People
Independent claim 1 recites:
“for a first encounter, receiving identification of a patient associated with the first encounter from the provider via a provider portal executing on a computing device accessed by the provider;
accessing a health record, in a population of health records, corresponding to the patient, the health record comprising a corpus of patient data associated with the patient;
accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses;
for a first diagnosis, in the population of diagnoses, accessing a first set of target indicators defined for the first diagnosis in a first module, in the population of modules, and supporting the first diagnosis;
extracting a first subset of patient indicators, from the corpus of patient data, corresponding to a first subset of target indicators in the first set of target indicators;”
Independent claim 17 recites:
“for an encounter, receiving identification of a patient associated with the encounter from the provider via a provider portal executing on a computing device accessed by the provider;
accessing a health record, in a population of health records, corresponding to the patient, the health record comprising a corpus of patient data and a list of current medications implemented by the patient;
accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses;
for a first module, in the population of modules, corresponding to a first diagnosis, accessing:
a first set of target indicators supporting the first diagnosis;
and a medication blacklist comprising a set of blacklisted medications predicted to exacerbate the first diagnosis;
extracting a first subset of patient indicators, from the corpus of patient data, corresponding to a first subset of target indicators in the first set of target indicators;"
Independent claim 20 recites:
“for an encounter, receiving identification of a patient associated with the encounter from the provider via a provider portal executing on a computing device accessed by the provider;
accessing a health record, in a population of health records, corresponding to the patient, the health record comprising a corpus of patient data associated with the patient;
accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses;
for a diagnosis, in the population of diagnoses, accessing a set of target indicators defined in a module, in the population of modules, corresponding to the diagnosis, the set of target indicators comprising:
a subset of primary target indicators supporting the diagnosis and required for predicting the diagnosis; and a subset of secondary target indicators supporting the diagnosis;
extracting a subset of primary patient indicators, from the corpus of patient data, corresponding to the subset of primary target indicators;
and in response to the subset of primary patient indicators corresponding to the subset of primary target indicators:
extracting a subset of secondary patient indicators, from the corpus of patient data, corresponding to the subset of secondary target indicators;”
Independent claims 1, 17, and 20 are directed to the abstract idea of managing personal behavior or relationships or interactions between people. According to MPEP 2106.04 (a)(2) II. C, managing personal behavior includes filtering content. The claims are directed to filtering content by accessing health records, accessing diagnostic model, and extracting patient indicators from the health records corresponding to the target indicators associated with the diagnostic model.
PRONG 2: The judicial exception (i.e., an abstract idea) is not integrated into a practical application.
The applicant has not shown or demonstrated any of the requirements described above under "integration into a practical application" under step 2A. Specifically, the applicant's limitations are not "integrated into a practical application" because they are adding words "apply it" with the judicial exception, or mere instructions to implement an abstract idea merely as a tool to perform an abstract idea (see MPEP 2106.05(f)). Additionally, improvements to the functioning of a computer or any other technology or technical field has not been shown or disclosed (see MPEP 2106.05(a)). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the applicant’s limitations are not “significantly more” because they are adding words “apply it” with the judicial exception, or mere instructions to implement an abstract idea merely as a tool to perform an abstract idea (see MPEP 2106.05(f)). The applicant’s claimed limitations do not demonstrate an improvement to another technology or technical field, an improvement to the functioning of the computer itself, effecting a transformation or reduction of particular article to a different state or thing. The current application does not amount to 'significantly more' than the abstract idea as described above. The claim does not include additional elements or limitations individually or in combination that are sufficient to amount to significantly more than the judicial exception. Specifically, the individual elements of provider portal, computing device, diagnostic model, and modules amount to no more than implementing an idea with a computerized system and they are adding words “apply it” with the judicial exception, or mere instructions to implement an abstract idea merely as a tool to perform an abstract idea. In addition, the claims add insignificant extra solution activity to the judicial exception such as selecting a particular data source or type of data to be manipulated (see MPEP 2106.05(g)). In particular, the extraction of patient indications corresponding to the target indicators and a notification to the provider are insignificant extra solution activities. The additional elements taken in combination add nothing more than what is present when the elements are considered individually. Therefore, based on the two-part Alice Corp. analysis, there are no meaningful limitations in the claims that transform the exception (i.e., abstract idea) into a patent eligible application.
Dependent claims 2-16 and 18-19 are rejected as ineligible subject matter under 35 U.S.C. 101 based on a rationale similar to the claims from which they depend. The following dependent claims recite additional elements that are not part of the independent claim. Dependent claim 4 recites “provider portal” and “computing device”. Dependent claim 7 recites “a second module”, “population of modules”. Dependent claim 9 recites, “a first module” and “a second module”. Dependent claim 10 recites “a provider portal”. Dependent claim 11 recites “data packet”. Dependent claim 12 recites “diagnostic model”, “critical range database”, and “provider portal”. Dependent claim 13-14 recite “provider portal”. Dependent claim 15 recites “first module” and “provider portal”. Dependent claim 16 recites “first submodule”, “first module”, “second submodule”, “second module”, and “provider portal”. Dependent claim 18 recites “first module” and “provider portal”. Therefore, dependent claims 4, 7, and 9-16 do not recite additional elements that amount to significantly more than the judicial exception.
Since the claim(s) recite a judicial exception and fails to integrate the judicial exception into a practical application, the claim(s) is/are “directed to” the judicial exception. Thus, the claim(s) must be reviewed under the second step of the Alice/ Mayo analysis to determine whether the abstract idea has been applied in an eligible manner.
2(B): The claims do not provide an inventive concept (i.e., The claim(s) do not include additional elements, or combinations of elements, that are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea)).
As discussed with respect to Step 2A Prong Two, the additional element(s) in the claim amounts to no more than mere instructions to apply the exception using a generic computer component and adding insignificant extra-solution activity. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
For these reasons, there is no invention concept in the claim, and thus the claim is ineligible.
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.
Claim(s) 1-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dandala et al. US Publication 20180032678 A1 in view of Cave et al. US Publication 20170053079 A1 further in view of Lee et al. US Publication 20230126896 A1.
Claim 1:
As per claim 1, Dandala teaches a method comprising:
for a first encounter, receiving identification of a patient associated with the first encounter from the provider via a provider portal executing on a computing device accessed by the provider (paragraph 0046 “For example, the user 402 may input the patient identifier via the user interface 500. The patient identifier may be a unique identifier associated with the patient 405, a name, an address, a telephone number, or any other type of identifier of the patient 405.”);
accessing a health record, in a population of health records, corresponding to the patient, the health record comprising a corpus of patient data associated with the patient (paragraphs 0046-0047 and Figure 5 “For example, the user 402 may input the patient identifier via the user interface 500. The patient identifier may be a unique identifier associated with the patient 405, a name, an address, a telephone number, or any other type of identifier of the patient 405. The EMR analysis system 410 retrieves the patient EMR 425 from the EMR repository 420 based on the patient identifier, as shown at block 614. In one or more examples, the EMR repository 420 may include more than one EMRs associated with the patient 405. For example, the EMR repository 420 may include EMRs from one or more medical providers, such as hospitals, laboratories, dentists, eye-doctors, and other types of medical service providers. The EMR analysis system 410 may retrieve the specific type of EMRs from the EMR repository 420, such as EMRs from similar type of medical service provider as the user 402.”);
and in response to the first confidence score exceeding a threshold score (paragraphs 0049 and 0065 “In one or more examples, the EMR analysis system 410 may compare an entity-relation-score between the medical entities identified as related with a predetermined threshold, as shown at block 626. If the entity-relation-score crosses the predetermined threshold, that is if the entity-relation-score is greater (or lesser) than the predetermined threshold, the EMR analysis system 410 proceeds to highlight the related medical entity, as shown at block 628.”):
Dandala does not teach accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “…One preferred embodiment of the invention provides a means to differentiate and diagnose between two or more diseases through qualitative and quantitative analysis from multiple input sources, for example, differentiation and diagnosis between inflammatory bowel disease and irritable bowel disease or between gastroesophageal reflux disease and functional dyspepsia. The invention provides a scientific and accurate diagnostic system and methods that is reliable, simplified and cost efficient.” (paragraph 0002) and “According to the invention, a diagnosis score may be used to differentiate between different conditions, for example, irritable bowel syndrome, irritable bowel disorder, gastroesophageal reflux disease, dyspepsia, multiple sclerosis, systemic lupus erythematous, rheumatoid arthritis, acute coronary syndrome, pericarditis, and the likewise. Furthermore, the diagnosis score may be validated based on a retrospective analysis of medical records of patients.” (paragraph 0021). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandale to include accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses as taught by Cave in order to determine one or more potential disease impacting a patient’s health.
Dandala does not teach for a first diagnosis, in the population of diagnoses, accessing a first set of target indicators defined for the first diagnosis in a first module, in the population of modules, and supporting the first diagnosis. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “With a diagnosis score used to differentiate between IBD and IBSd, diagnosis parameters obtained in patients being evaluated for the diseases may include, but is not limited to, radiological findings consistent with IBD, endoscopic findings consistent with IBD, biopsy findings consistent with IBD, elevated inflammatory markers (such as C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin, and fecal lactoferrin) and signs and symptoms such as history of weight loss, history of hematochezia, extra-intestinal sign/symptoms, palpable mass on exam and/or perianal disease.” (paragraph 0023) and “With a diagnosis score used to differentiate between GERD and functional dyspepsia, diagnosis parameters obtained in patients being evaluated for the diseases may include, but is not limited to, signs and symptoms such as intermittent or continuous symptoms, nocturnal waking, nausea, ascending pain, bloating, etc.” (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include for a first diagnosis, in the population of diagnoses, accessing a first set of target indicators defined for the first diagnosis in a first module, in the population of modules, and supporting the first diagnosis as taught by Cave in order to identify parameters that suggest the patient has one or more diseases.
Dandala does not teach extracting a first subset of patient indicators, from the corpus of patient data, corresponding to a first subset of target indicators in the first set of target indicators. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “FIG. 1 illustrates a preferred embodiment of the diagnosis or scoring system 50 according to the invention. To utilize the scoring system 50 for differentiating different medical diagnosis according to the invention, a number of different databases or data sources 100 are utilized. Data sources 100 may produce any data resultant from laboratory tests, procedures, experienced signs and symptoms and includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0039), “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130…” (paragraph 0041), and “A diagnosis of a gastrointestinal disorder is conducted according to a scoring method using data from a variety of data sources. At step 504, data is received from a variety of data sources. Data includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include extracting a first subset of patient indicators, from the corpus of patient data, corresponding to a first subset of target indicators in the first set of target indicators as taught by Cave in order to determine whether one or more parameters exists in the patient’s medical records associated with particular predicted disease(s).
Dandala does not teach deriving a first confidence score for the first diagnosis for the patient based on the first subset of patient indicators and the first subset of target indicators. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “At step 506, values are determined from a review of the data. In one embodiment according to the invention, when radiology data is consistent with a first disorder such as IBD or GERD, a first value is assigned to the patient file. The first value may be a numerical value or other designation such as one point. In another embodiment, the patient file is allocated one point when inflammatory marker data is present. Inflammatory marker data includes, for example, one or more selected from the group comprising: C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin, and fecal lactoferrin.” (paragraph 0062), “In yet another embodiment, one point is apportioned to the patient file for each sign and symptom data. In certain embodiments of the scoring method, the maximum number of points for sign and symptom data is a five point value. Sign and symptom data may include for example, one or more selected from the group comprising: weight loss, history of hematochezia, extra-intestinal sign/symptom, palpable mass on exam and/or perianal disease. Furthermore, sign and symptom data may include intermittent or continuous symptoms, nocturnal waking, nausea, ascending pain, bloating, etc.” (paragraph 0063), “In certain other embodiments of the invention, endoscopy data and biopsy data is collected from different data sources. One point is designated to the patient file for each endoscopy data with a maximum point value possible such as two points. Endoscopy data may be directed to one or more of inflammation and ulceration. One point is assigned when biopsy data is consistent with a particular disorder such as IBD or GERD.” (paragraph 0064), and “At step 508, all the values are added to obtain a final point value of the patient file. The final value is compared to a spectrum of scores. The spectrum of scores includes a first range identifying the first disorder and a second range identifying a second disorder. As an example, the spectrum of scores is 0 to 10 with a first range of 2 to 10 identifying inflammatory bowel disease (IBD) and a second range of 0 to 2 identifying irritable bowel syndrome (IBSd). The spectrum of scores and ranges are merely exemplary, any value spectrum of scores and ranges is contemplated. However any range is contemplated such as a first range of 1 to 4 and a second range of 0 to 1.” (paragraph 0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include deriving a first confidence score for the first diagnosis for the patient based on the first subset of patient indicators and the first subset of target indicators as taught by Cave in order to determine the likelihood the patient has a particular disease.
Dandala does not teach appending a list of predicted diagnoses with the first diagnosis. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130. Upon a diagnosis of a patient, based on the processed data, the information will then be presented in one or more client computers. Each client computer allows for a user interface such as a display device in which data can be further analyzed and organized based on user feedback.” (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include appending a list of predicted diagnoses with the first diagnosis as taught by Cave in order to combine all the possible diseases impacting the patient.
Dandala does not teach generating a first notification comprising the list of predicted diagnoses and a first prompt to review the list of predicted diagnoses. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130. Upon a diagnosis of a patient, based on the processed data, the information will then be presented in one or more client computers. Each client computer allows for a user interface such as a display device in which data can be further analyzed and organized based on user feedback.” (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include generating a first notification comprising the list of predicted diagnoses and a first prompt to review the list of predicted diagnoses as taught by Cave in order to provide an alert to a physician or medical professional regarding the patient’s potential disease(s).
Dandala does not teach populating the first notification with the first subset of patient indicators linked to the first diagnosis. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130. Upon a diagnosis of a patient, based on the processed data, the information will then be presented in one or more client computers. Each client computer allows for a user interface such as a display device in which data can be further analyzed and organized based on user feedback.” (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include populating the first notification with the first subset of patient indicators linked to the first diagnosis as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
Dandala and Cave do not teach and via the provider portal, transmitting the first notification to the provider for review. However, Lee teaches a Method for Providing Basic Data for Diagnosis, and System Therefor and further teaches, “In step 320, the list of multiple symptoms including the selection result of the user and the predicted disease information may be transmitted to the EMR of the attending physician of the user.” (paragraph 0200) and “In step 320, the list of multiple symptoms including the selection result of the user and the predicted disease information may be transmitted to the EMR of the attending physician of the user.” (paragraph 0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include and via the provider portal, transmitting the first notification to the provider for review as taught by Lee in order to display an alert to a physician or medical professional regarding the patient’s potential disease(s).
Claim 20:
As per claim 20, Dandala teaches the method comprising:
for an encounter, receiving identification of a patient associated with the encounter from the provider via a provider portal executing on a computing device accessed by the provider (paragraph 0046 “For example, the user 402 may input the patient identifier via the user interface 500. The patient identifier may be a unique identifier associated with the patient 405, a name, an address, a telephone number, or any other type of identifier of the patient 405.”);
accessing a health record, in a population of health records, corresponding to the patient, the health record comprising a corpus of patient data associated with the patient (paragraphs 0046-0047 and Figure 5 “For example, the user 402 may input the patient identifier via the user interface 500. The patient identifier may be a unique identifier associated with the patient 405, a name, an address, a telephone number, or any other type of identifier of the patient 405. The EMR analysis system 410 retrieves the patient EMR 425 from the EMR repository 420 based on the patient identifier, as shown at block 614. In one or more examples, the EMR repository 420 may include more than one EMRs associated with the patient 405. For example, the EMR repository 420 may include EMRs from one or more medical providers, such as hospitals, laboratories, dentists, eye-doctors, and other types of medical service providers. The EMR analysis system 410 may retrieve the specific type of EMRs from the EMR repository 420, such as EMRs from similar type of medical service provider as the user 402.”);
and in response to the confidence score exceeding a threshold score (paragraphs 0049 and 0065 “In one or more examples, the EMR analysis system 410 may compare an entity-relation-score between the medical entities identified as related with a predetermined threshold, as shown at block 626. If the entity-relation-score crosses the predetermined threshold, that is if the entity-relation-score is greater (or lesser) than the predetermined threshold, the EMR analysis system 410 proceeds to highlight the related medical entity, as shown at block 628.”):
Dandala does not teach accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “…One preferred embodiment of the invention provides a means to differentiate and diagnose between two or more diseases through qualitative and quantitative analysis from multiple input sources, for example, differentiation and diagnosis between inflammatory bowel disease and irritable bowel disease or between gastroesophageal reflux disease and functional dyspepsia. The invention provides a scientific and accurate diagnostic system and methods that is reliable, simplified and cost efficient.” (paragraph 0002) and “According to the invention, a diagnosis score may be used to differentiate between different conditions, for example, irritable bowel syndrome, irritable bowel disorder, gastroesophageal reflux disease, dyspepsia, multiple sclerosis, systemic lupus erythematous, rheumatoid arthritis, acute coronary syndrome, pericarditis, and the likewise. Furthermore, the diagnosis score may be validated based on a retrospective analysis of medical records of patients.” (paragraph 0021). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandale to include accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses as taught by Cave in order to determine one or more potential disease impacting a patient’s health.
Dandala does not teach for a diagnosis, in the population of diagnoses, accessing a set of target indicators defined in a module, in the population of modules, corresponding to the diagnosis, the set of target indicators comprising: However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “With a diagnosis score used to differentiate between IBD and IBSd, diagnosis parameters obtained in patients being evaluated for the diseases may include, but is not limited to, radiological findings consistent with IBD, endoscopic findings consistent with IBD, biopsy findings consistent with IBD, elevated inflammatory markers (such as C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin, and fecal lactoferrin) and signs and symptoms such as history of weight loss, history of hematochezia, extra-intestinal sign/symptoms, palpable mass on exam and/or perianal disease.” and “With a diagnosis score used to differentiate between GERD and functional dyspepsia, diagnosis parameters obtained in patients being evaluated for the diseases may include, but is not limited to, signs and symptoms such as intermittent or continuous symptoms, nocturnal waking, nausea, ascending pain, bloating, etc.” (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include for a diagnosis, in the population of diagnoses, accessing a set of target indicators defined in a module, in the population of modules, corresponding to the diagnosis, the set of target indicators comprising as taught by Cave in order to receive parameters to determine one or more potential diseases associated with a patient.
Dandala does not teach a subset of primary target indicators supporting the diagnosis and required for predicting the diagnosis. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “With a diagnosis score used to differentiate between IBD and IBSd, diagnosis parameters obtained in patients being evaluated for the diseases may include, but is not limited to, radiological findings consistent with IBD, endoscopic findings consistent with IBD, biopsy findings consistent with IBD, elevated inflammatory markers (such as C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin, and fecal lactoferrin) and signs and symptoms such as history of weight loss, history of hematochezia, extra-intestinal sign/symptoms, palpable mass on exam and/or perianal disease.” and “With a diagnosis score used to differentiate between GERD and functional dyspepsia, diagnosis parameters obtained in patients being evaluated for the diseases may include, but is not limited to, signs and symptoms such as intermittent or continuous symptoms, nocturnal waking, nausea, ascending pain, bloating, etc.” (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include a subset of primary target indicators supporting the diagnosis and required for predicting the diagnosis as taught by Cave in order to identify parameters to determine one or more potential diseases associated with a patient.
Dandala does not teach and a subset of secondary target indicators supporting the diagnosis. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “With a diagnosis score used to differentiate between IBD and IBSd, diagnosis parameters obtained in patients being evaluated for the diseases may include, but is not limited to, radiological findings consistent with IBD, endoscopic findings consistent with IBD, biopsy findings consistent with IBD, elevated inflammatory markers (such as C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin, and fecal lactoferrin) and signs and symptoms such as history of weight loss, history of hematochezia, extra-intestinal sign/symptoms, palpable mass on exam and/or perianal disease.” and “With a diagnosis score used to differentiate between GERD and functional dyspepsia, diagnosis parameters obtained in patients being evaluated for the diseases may include, but is not limited to, signs and symptoms such as intermittent or continuous symptoms, nocturnal waking, nausea, ascending pain, bloating, etc.” (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include a subset of secondary target indicators supporting the diagnosis as taught by Cave in order to identify parameters to determine one or more potential diseases associated with a patient.
Dandala does not teach extracting a subset of primary patient indicators, from the corpus of patient data, corresponding to the subset of primary target indicators. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “FIG. 1 illustrates a preferred embodiment of the diagnosis or scoring system 50 according to the invention. To utilize the scoring system 50 for differentiating different medical diagnosis according to the invention, a number of different databases or data sources 100 are utilized. Data sources 100 may produce any data resultant from laboratory tests, procedures, experienced signs and symptoms and includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0039), “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130…” (paragraph 0041), and “A diagnosis of a gastrointestinal disorder is conducted according to a scoring method using data from a variety of data sources. At step 504, data is received from a variety of data sources. Data includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include extracting a subset of primary patient indicators, from the corpus of patient data, corresponding to the subset of primary target indicators as taught by Cave in order to determine whether one or more parameters that exist in the patient’s medical records associated with particular predicted disease(s).
Dandala does not teach and in response to the subset of primary patient indicators corresponding to the subset of primary target indicators: However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “FIG. 1 illustrates a preferred embodiment of the diagnosis or scoring system 50 according to the invention. To utilize the scoring system 50 for differentiating different medical diagnosis according to the invention, a number of different databases or data sources 100 are utilized. Data sources 100 may produce any data resultant from laboratory tests, procedures, experienced signs and symptoms and includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0039), “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130…” (paragraph 0041), and “A diagnosis of a gastrointestinal disorder is conducted according to a scoring method using data from a variety of data sources. At step 504, data is received from a variety of data sources. Data includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and in response to the subset of primary patient indicators corresponding to the subset of primary target indicators as taught by Cave in order to determine whether one or more parameters that exist in the patient’s medical records associated with particular predicted disease(s).
Dandala does not teach extracting a subset of secondary patient indicators, from the corpus of patient data, corresponding to the subset of secondary target indicators. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “FIG. 1 illustrates a preferred embodiment of the diagnosis or scoring system 50 according to the invention. To utilize the scoring system 50 for differentiating different medical diagnosis according to the invention, a number of different databases or data sources 100 are utilized. Data sources 100 may produce any data resultant from laboratory tests, procedures, experienced signs and symptoms and includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0039), “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130…” (paragraph 0041), and “A diagnosis of a gastrointestinal disorder is conducted according to a scoring method using data from a variety of data sources. At step 504, data is received from a variety of data sources. Data includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include extracting a subset of secondary patient indicators, from the corpus of patient data, corresponding to the subset of secondary target indicators as taught by Cave in order to determine whether one or more parameters that exist in the patient’s medical records associated with particular predicted disease(s).
Dandala does not teach deriving a confidence score for the diagnosis for the patient on the subset of primary patient indicators and the subset of secondary patient indicators. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “At step 506, values are determined from a review of the data. In one embodiment according to the invention, when radiology data is consistent with a first disorder such as IBD or GERD, a first value is assigned to the patient file. The first value may be a numerical value or other designation such as one point. In another embodiment, the patient file is allocated one point when inflammatory marker data is present. Inflammatory marker data includes, for example, one or more selected from the group comprising: C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin, and fecal lactoferrin.” (paragraph 0062), “In yet another embodiment, one point is apportioned to the patient file for each sign and symptom data. In certain embodiments of the scoring method, the maximum number of points for sign and symptom data is a five point value. Sign and symptom data may include for example, one or more selected from the group comprising: weight loss, history of hematochezia, extra-intestinal sign/symptom, palpable mass on exam and/or perianal disease. Furthermore, sign and symptom data may include intermittent or continuous symptoms, nocturnal waking, nausea, ascending pain, bloating, etc.” (paragraph 0063), “In certain other embodiments of the invention, endoscopy data and biopsy data is collected from different data sources. One point is designated to the patient file for each endoscopy data with a maximum point value possible such as two points. Endoscopy data may be directed to one or more of inflammation and ulceration. One point is assigned when biopsy data is consistent with a particular disorder such as IBD or GERD.” (paragraph 0064), and “At step 508, all the values are added to obtain a final point value of the patient file. The final value is compared to a spectrum of scores. The spectrum of scores includes a first range identifying the first disorder and a second range identifying a second disorder. As an example, the spectrum of scores is 0 to 10 with a first range of 2 to 10 identifying inflammatory bowel disease (IBD) and a second range of 0 to 2 identifying irritable bowel syndrome (IBSd). The spectrum of scores and ranges are merely exemplary, any value spectrum of scores and ranges is contemplated. However any range is contemplated such as a first range of 1 to 4 and a second range of 0 to 1.” (paragraph 0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include deriving a confidence score for the diagnosis for the patient on the subset of primary patient indicators and the subset of secondary patient indicators as taught by Cave in order to determine the likelihood the patient has a particular disease.
Dandala does not teach appending a list of predicted diagnoses with the diagnosis. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130. Upon a diagnosis of a patient, based on the processed data, the information will then be presented in one or more client computers. Each client computer allows for a user interface such as a display device in which data can be further analyzed and organized based on user feedback.” (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include appending a list of predicted diagnoses with the diagnosis as taught by Cave in order to combine all the possible diseases impacting the patient.
Dandala does not teach generating a notification comprising the list of predicted diagnoses and a prompt to review the list of predicted diagnoses. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130. Upon a diagnosis of a patient, based on the processed data, the information will then be presented in one or more client computers. Each client computer allows for a user interface such as a display device in which data can be further analyzed and organized based on user feedback.” (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include generating a notification comprising the list of predicted diagnoses and a prompt to review the list of predicted diagnoses as taught by Cave in order to provide an alert to a physician or medical professional regarding the patient’s potential disease(s).
Dandala does not teach populating the notification with the subset of primary patient indicators and the subset of secondary patient indicators linked to the diagnosis. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130. Upon a diagnosis of a patient, based on the processed data, the information will then be presented in one or more client computers. Each client computer allows for a user interface such as a display device in which data can be further analyzed and organized based on user feedback.” (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include populating the notification with the subset of primary patient indicators and the subset of secondary patient indicators linked to the diagnosis as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
Dandala and Cave do not teach and via the provider portal, transmitting the notification to the provider for review. However, Lee teaches a Method for Providing Basic Data for Diagnosis, and System Therefor and further teaches, “In step 320, the list of multiple symptoms including the selection result of the user and the predicted disease information may be transmitted to the EMR of the attending physician of the user.” (paragraph 0200) and “In step 320, the list of multiple symptoms including the selection result of the user and the predicted disease information may be transmitted to the EMR of the attending physician of the user.” (paragraph 0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Lee to include via the provider portal, transmitting the notification to the provider for review as taught by Lee in order to display an alert to a physician or medical professional regarding the patient’s potential disease(s).
Claim 2:
As per claim 2, Dandala, Cave, and Lee teach the method of claim 1 as described above and Cave further teaches wherein accessing the first set of target indicators comprises accessing:
a subset of primary target indicators supporting the first diagnosis and required for predicting the first diagnosis (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include a subset of primary target indicators supporting the first diagnosis and required for predicting the first diagnosis as taught by Cave in order to identify parameters to determine one or more potential diseases associated with a patient.
and a subset of secondary target indicators supporting the first diagnosis (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and a subset of secondary target indicators supporting the first diagnosis as taught by Cave in order to identify parameters to determine one or more potential diseases associated with a patient.
wherein extracting the first subset of patient indicators comprises:
extracting a subset of primary patient indicators, from the corpus of patient data, corresponding to the subset of primary target indicators (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include extracting a subset of primary patient indicators, from the corpus of patient data, corresponding to the subset of primary target indicators as taught by Cave in order to determine whether one or more parameters that exist in the patient’s medical records associated with particular predicted disease(s).
and in response to the subset of primary patient indicators, from the corpus of patient data, corresponding to the subset of primary target indicators, extracting a subset of secondary patient indicators, from the corpus of patient data, corresponding to the subset of secondary target indicators (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and in response to the subset of primary patient indicators, from the corpus of patient data, corresponding to the subset of primary target indicators, extracting a subset of secondary patient indicators, from the corpus of patient data, corresponding to the subset of secondary target indicators as taught by Cave in order to determine whether one or more parameters that exist in the patient’s medical records associated with particular predicted disease(s).
and wherein deriving the first confidence score for the first diagnosis comprises deriving the first confidence score based on the subset of primary patient indicators, the subset of primary target indicators, the subset of secondary patient indicators, and the subset of secondary target indicators (paragraphs 0062-0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and wherein deriving the first confidence score for the first diagnosis comprises deriving the first confidence score based on the subset of primary patient indicators, the subset of primary target indicators, the subset of secondary patient indicators, and the subset of secondary target indicators taught by Cave in order to determine the likelihood the patient has a particular disease.
Claim 3:
As per claim 3, Dandala, Cave, and Lee teach the method of claim 2 as described above and Cave further teaches wherein accessing the first set of target indicators comprises accessing:
the subset of primary target indicators comprising a primary target indicator required for predicting the first diagnosis (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include the subset of primary target indicators comprising a primary target indicator required for predicting the first diagnosis as taught by Cave in order to receive parameters to determine one or more potential diseases associated with a patient.
and the subset of secondary target indicators comprising a first secondary target indicator and a second secondary target indicator (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and the subset of secondary target indicators comprising a first secondary target indicator and a second secondary target indicator as taught by Cave in order to identify parameters associated with one or more potential diseases.
wherein extracting the first subset of patient indicators comprises:
extracting a primary patient indicator, from the corpus of patient data, corresponding to the primary target indicator (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include extracting a primary patient indicator, from the corpus of patient data, corresponding to the primary target indicator as taught by Cave in order to determine whether one or more parameters that exist in the patient’s medical records associated with particular predicted disease(s).
in response to the primary patient indicator corresponding to the primary target indicator, extracting a first secondary patient indicator, from the corpus of patient data, corresponding to the first secondary target indicator (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include in response to the primary patient indicator corresponding to the primary target indicator, extracting a first secondary patient indicator, from the corpus of patient data, corresponding to the first secondary target indicator as taught by Cave in order to determine whether one or more parameters that exist in the patient’s medical records associated with particular predicted disease(s).
and in response to the primary patient indicator corresponding to the primary target indicator, the first secondary patient indicator corresponding to the first secondary target indicator, extracting a second secondary patient indicator, from the corpus of patient data, corresponding to the second secondary target indicator (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and in response to the primary patient indicator corresponding to the primary target indicator, the first secondary patient indicator corresponding to the first secondary target indicator, extracting a second secondary patient indicator, from the corpus of patient data, corresponding to the second secondary target indicator taught by Cave in order to determine whether one or more parameters that exist in the patient’s medical records associated with particular predicted disease(s).
and wherein appending the list of predicted diagnoses with the first diagnosis comprises:
in response to the primary patient indicator corresponding to the primary target indicator, appending the list of predicted diagnoses with the first diagnosis labeled as a possible diagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include in response to the primary patient indicator corresponding to the primary target indicator, appending the list of predicted diagnoses with the first diagnosis labeled as a possible diagnosis as taught by Cave in order to combine all the possible diseases impacting the patient.
in response to the primary patient indicator corresponding to the primary target indicator, and the first secondary patient indicator corresponding to the first secondary target indicator, appending the list of predicted diagnoses with the first diagnosis labeled as a probable diagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include in response to the primary patient indicator corresponding to the primary target indicator, and the first secondary patient indicator corresponding to the first secondary target indicator, appending the list of predicted diagnoses with the first diagnosis labeled as a probable diagnosis as taught by Cave in order to identify possible disease(s) associated with a patient.
in response to the primary patient indicator corresponding to the primary target indicator, the first secondary patient indicator corresponding to the first secondary target indicator, and the second secondary patient indicator corresponding to the second secondary target indicator, appending the list of predicted diagnoses with the first diagnosis labeled as a positive diagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include in response to the primary patient indicator corresponding to the primary target indicator, the first secondary patient indicator corresponding to the first secondary target indicator, and the second secondary patient indicator corresponding to the second secondary target indicator, appending the list of predicted diagnoses with the first diagnosis labeled as a positive diagnosis as taught by Cave in order to confirm diseases impacting the patient.
and in response to absence of the primary target indicator in the subset of primary patient indicators, rejecting the first diagnosis for further investigation (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and in response to absence of the primary target indicator in the subset of primary patient indicators, rejecting the first diagnosis for further investigation as taught by Cave in order to indicate the patient does not have a disease.
Claim 4:
As per claim 4, Dandala, Cave, and Lee teach the method of claim 1 as described above and Dandala further teaches
and further comprising: for a second encounter, receiving identification of a second patient associated with the second encounter from the provider via the provider portal executing on the computing device accessed by the provider (paragraph 0046);
accessing a second health record, in the population of health records, corresponding to the second patient, the second health record comprising a corpus of patient data associated with the second patient (paragraphs 0046-0047 and Figure 5);
Cave further teaches wherein accessing the first set of target indicators comprises accessing a first target indicator assigned a first weight and a second target indicator assigned a second weight less than the first weight (paragraphs 0039 and 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include wherein accessing the first set of target indicators comprises accessing a first target indicator assigned a first weight and a second target indicator assigned a second weight less than the first weight as taught by Cave in order to assign or divide percentages to one or more parameters associated with one or more diseases.
Cave further teaches wherein extracting the first subset of patient indicators, from the corpus of patient data, comprises extracting a first patient indicator corresponding to the first target indicator in the first set of target indicators (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include wherein extracting the first subset of patient indicators, from the corpus of patient data, comprises extracting a first patient indicator corresponding to the first target indicator in the first set of target indicators as taught by Cave in order to determine whether one or more parameters exists in the patient’s medical records associated with particular predicted disease(s).
Cave further teaches wherein deriving the first confidence score for the first diagnosis comprises deriving the first confidence score based on the first patient indicator, the first target indicator, and the first weight (paragraphs 0062-0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include wherein deriving the first confidence score for the first diagnosis comprises deriving the first confidence score based on the first patient indicator, the first target indicator, and the first weight as taught by Cave in order to determine the likelihood the patient has a particular disease.
Cave further teaches for the first module, in the population of modules, corresponding to the first diagnosis, accessing the first set of target indicators supporting the first diagnosis (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include for the first module, in the population of modules, corresponding to the first diagnosis, accessing the first set of target indicators supporting the first diagnosis as taught by Cave in order to identify parameters that suggest the patient has one or more diseases.
Cave further teaches extracting a second patient indicator, from the corpus of patient data, corresponding to the second target indicator in the first set of target indicators (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include extracting a second patient indicator, from the corpus of patient data, corresponding to the second target indicator in the first set of target indicators as taught by Cave in order to determine whether one or more parameters exists in the patient’s medical records associated with particular predicted disease(s).
Cave further teaches and deriving a second confidence score for the first diagnosis for the second patient based on the second patient indicator, the second target indicator, and the second weight, the second confidence score less than the first confidence score (paragraphs 0062-0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include deriving a first confidence score for the first diagnosis for the patient based on the first subset of patient indicators and the first subset of target indicators as taught by Cave in order to determine the likelihood the patient has a particular disease.
Claim 5:
As per claim 5, Dandala, Cave, and Lee teach the method of claim 1 as described above and Cave further teaches wherein accessing the first set of target indicators comprises accessing the first set of target indicators comprising a first target indicator defining:
a first weight assigned to a first target sampling window corresponding to a first time difference between recordation of patient data corresponding to the first target indicator and the first encounter (paragraphs 0039 and 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include a first weight assigned to a first target sampling window corresponding to a first time difference between recordation of patient data corresponding to the first target indicator and the first encounter as taught by Cave in order to determine the impact on time on the parameters associated with a disease.
and a second weight assigned to a second target sampling window corresponding to a second time difference between recordation of patient data corresponding to the first target indicator and the first encounter (paragraphs 0039 and 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and a second weight assigned to a second target sampling window corresponding to a second time difference between recordation of patient data corresponding to the first target indicator and the first encounter as taught by Cave in order to determine the impact on time on the parameters associated with a disease.
wherein extracting the first subset of patient indicators from the corpus of patient data comprises extracting a first patient indicator from the corpus of patient data (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include extracting a first subset of patient indicators, from the corpus of patient data, corresponding to a first subset of target indicators in the first set of target indicators as taught by Cave in order to determine whether one or more parameters exists in the patient’s medical records associated with particular predicted disease(s).
and wherein deriving the first confidence score for the first diagnosis comprises:
in response to the first patient indicator corresponding to the first target sampling window, deriving the first confidence score based on the first patient indicator, the first target indicator, and the first weight (paragraphs 0062-0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include in response to the first patient indicator corresponding to the first target sampling window, deriving the first confidence score based on the first patient indicator, the first target indicator, and the first weight as taught by Cave in order to determine the likelihood the patient has a particular disease.
and in response to the first patient indicator corresponding to the second target sampling window, deriving the first confidence score based on the first patient indicator, the first target indicator, and the second weight (paragraphs 0062-0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and in response to the first patient indicator corresponding to the second target sampling window, deriving the first confidence score based on the first patient indicator, the first target indicator, and the second weight as taught by Cave in order to determine the likelihood the patient has a particular disease.
Claim 6:
As per claim 6, Dandala, Cave, and Lee teach the method of claim 1 as described above and Dandala further teaches further comprising, in response to the first confidence score falling below the threshold score, rejecting the first diagnosis for the patient for the first encounter (paragraph 0049).
Claim 7:
As per claim 7, Dandala, Cave, and Lee teach the method of claim 1 as described above and Cave further teaches further comprising:
accessing a second module, from the population of modules, corresponding to a second diagnosis and defining a second set of target indicators supporting the second diagnosis (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include accessing a second module, from the population of modules, corresponding to a second diagnosis and defining a second set of target indicators supporting the second diagnosis as taught by Cave in order to identify parameters that suggest the patient has one or more diseases.
extracting a second subset of patient indicators, from the corpus of patient data, corresponding to a second subset of target indicators in the second set of target indicators (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include extracting a second subset of patient indicators, from the corpus of patient data, corresponding to a second subset of target indicators in the second set of target indicators as taught by Cave in order to determine whether one or more parameters exists in the patient’s medical records associated with particular predicted disease(s).
deriving a second confidence score for the second diagnosis for the patient based on the second subset of patient indicators and the second subset of target indicators (paragraphs 0062-0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include deriving a second confidence score for the second diagnosis for the patient based on the second subset of patient indicators and the second subset of target indicators as taught by Cave in order to determine the likelihood the patient has a particular disease.
and in response to the second confidence score exceeding the threshold score, appending the list of predicted diagnoses with the second diagnosis (paragraphs 0041 and 0062-0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and in response to the second confidence score exceeding the threshold score, appending the list of predicted diagnoses with the second diagnosis as taught by Cave in order to combine all the possible diseases impacting the patient.
and wherein populating the first notification with the first subset of patient indicators linked to the first diagnosis comprises populating the first notification with:
the first subset of patient indicators, linked to the first diagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include the first subset of patient indicators, linked to the first diagnosis as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
and the second subset of patient indicators, linked to the second diagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and the second subset of patient indicators, linked to the second diagnosis as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
Claim 8:
As per claim 8, Dandala, Cave, and Lee teach the method of claim 7 as described above and Cave further teaches wherein appending the list of predicted diagnoses with the first diagnosis and the second diagnosis comprises, in response to the first confidence score exceeding the second confidence score:
appending the list of predicted diagnoses with the first diagnosis in a first slot in the list of predicted diagnoses (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include appending the list of predicted diagnoses with the first diagnosis in a first slot in the list of predicted diagnoses as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
and appending the list of predicted diagnoses with the second diagnosis in a second slot, below the first slot, in the list of predicted diagnoses (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and appending the list of predicted diagnoses with the second diagnosis in a second slot, below the first slot, in the list of predicted diagnoses as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
Claim 9:
As per claim 9, Dandala, Cave, and Lee teach the method of claim 7 as described above and Cave further teaches further comprising:
accessing a first urgency level assigned to the first diagnosis in the first module (paragraphs 0023-0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include accessing a first urgency level assigned to the first diagnosis in the first module as taught by Cave in order to rank the patient’s one or more diseases in terms of severity.
and accessing a second urgency level assigned to the second diagnosis in the second module (paragraphs 0023-0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and accessing a second urgency level assigned to the second diagnosis in the second module as taught by Cave in order to rank the patient’s one or more diseases in terms of severity.
and wherein appending the list of predicted diagnoses with the first diagnosis and the second diagnosis comprises, in response to the first urgency level exceeding the second urgency level:
appending the list of predicted diagnoses with the first diagnosis in a first slot in the list of predicted diagnoses (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include appending the list of predicted diagnoses with the first diagnosis in a first slot in the list of predicted diagnoses as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
and appending the list of predicted diagnoses with the second diagnosis in a second slot, below the first slot, in the list of predicted diagnoses (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and appending the list of predicted diagnoses with the second diagnosis in a second slot, below the first slot, in the list of predicted diagnoses as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
Claim 10:
As per claim 10, Dandala, Cave, and Lee teach the method of claim 1 as described above and Lee further teaches further comprising:
initializing a provider note for the first encounter with the patient (paragraph 0200). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandale and Cave to include initializing a provider note for the first encounter with the patient as taught by Lee in order to generate a summary of the patient’s condition or illness.
in response to receiving acceptance of the first diagnosis by the provider, appending the provider note with:
the first diagnosis (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandale and Cave to include initializing a provider note for the first encounter with the patient the first diagnosis as taught by Lee in order to generate a summary of the patient’s condition or illness.
the first subset of patient indicators supporting the first diagnosis (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandale and Cave to include the first subset of patient indicators supporting the first diagnosis as taught by Lee in order to generate a summary of the patient’s condition or illness.
and a treatment pathway, predicted to treat the first diagnosis, selected by the provider within the provider portal (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandale and Cave to include and a treatment pathway, predicted to treat the first diagnosis, selected by the provider within the provider portal as taught by Lee in order to generate a medical plant to address the patient’s illness.
generating a second notification comprising a second prompt to verify the provider note for transmitting to a health insurance agency associated with the patient (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandale and Cave to include generating a second notification comprising a second prompt to verify the provider note for transmitting to a health insurance agency associated with the patient as taught by Lee in order to notify the patient’s health insurance company.
and via the provider portal, transmitting the second notification to the provider for review (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandale and Cave to include and via the provider portal, transmitting the second notification to the provider for review as taught by Lee in order to display an alert to a physician or medical professional regarding the patient’s potential disease(s).
Claim 11:
As per claim 11, Dandala, Cave, and Lee teach the method of claim 1 as described above and Dandala further teaches further comprising:
in response to the first confidence score exceeding the threshold score:
generating a first data packet comprising the first diagnosis and the first subset of patient indicators (paragraphs 0045 and 0048-0049);
in response to receiving acceptance of the first diagnosis by the provider, populating the first data packet with a first value indicating acceptance of the first diagnosis (paragraphs 0045 and 0048-0049);
in response to receiving rejection of the first diagnosis by the provider, populating the first data packet with a second value indicating rejection of the first diagnosis (paragraphs 0045 and 0048-0049);
and storing the first data packet in a diagnosis record, in a population of diagnosis records, associated with the patient and comprising a series of data packets generated for a series of encounters with the patient (paragraphs 0045 and 0048-0049).
Claim 12:
As per claim 12, Dandala, Cave, and Lee teach the method of claim 1 as described above and Cave further teaches further comprising:
for each indicator, in a population of indicators defined in the diagnostic model, accessing a critical range defined for the indicator in a critical range database (paragraphs 0002 and 0021). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandale to include for each indicator, in a population of indicators defined in the diagnostic model, accessing a critical range defined for the indicator in a critical range database as taught by Cave in order to assess one or more potential disease impacting a patient’s health.
for a first indicator, in the population of indicators, defining a first critical range, extracting a first patient indicator, from the corpus of patient data, corresponding to the first indicator (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include for a first indicator, in the population of indicators, defining a first critical range, extracting a first patient indicator, from the corpus of patient data, corresponding to the first indicator taught by Cave in order to determine whether one or more parameters exists in the patient’s medical records associated with particular predicted disease(s).
and in response to the first patient indicator falling within the first critical range defined for the first indicator:
generating an alert comprising the first patient indicator and the first critical range defined for the first indicator (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include generating an alert comprising the first patient indicator and the first critical range defined for the first indicator as taught by Cave in order to provide an alert to a physician or medical professional regarding the patient’s potential disease(s).
populating the alert with a second prompt to review the first patient indicator (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include populating the alert with a second prompt to review the first patient indicator as taught by Cave in order to provide an alert to a physician or medical professional regarding the patient’s potential disease(s).
Lee further teaches and via the provider portal, transmitting the alert to the provider for review (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include and via the provider portal, transmitting the alert to the provider for review as taught by Lee in order to display an alert to a physician or medical professional regarding the patient’s potential disease(s).
Claim 13:
As per claim 13, Dandala, Cave, and Lee teach the method of claim 1 as described above and Dandala further teaches further comprising:
generating a diagnosis record for the first encounter with the patient (paragraphs 0045-0046);
in response to the first confidence score falling below the threshold score and exceeding a minimum threshold score, less than the threshold score, defined for the first diagnosis, storing the first diagnosis as a possible diagnosis within the diagnosis record (paragraph 0049);
for a second encounter with the patient, accessing the health record comprising a new set of patient data captured for the patient during a time period succeeding the first encounter and preceding the second encounter (paragraphs 0045-0046 and 0049-0050);
accessing the diagnosis record indicating the first diagnosis as the possible diagnosis (paragraphs 0045-0046 and 0049-0050);
extracting a second subset of patient indicators, from the new set of patient data, corresponding to a second subset of target indicators in the first set of target indicators (paragraphs 0045-0046 and 0049-0050);
Cave further teaches deriving a second confidence score for the first diagnosis for the patient based on the second subset of patient indicators and the second subset of target indicators (paragraphs 0062-0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include deriving a second confidence score for the first diagnosis for the patient based on the second subset of patient indicators and the second subset of target indicators as taught by Cave in order to determine the likelihood the patient has a particular disease.
and in response to the second confidence score exceeding the threshold score:
Cave further teaches appending the list of predicted diagnoses with the first diagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include appending the list of predicted diagnoses with the first diagnosis as taught by Cave in order to combine all the possible diseases impacting the patient.
Cave further teaches generating a second notification comprising the list of predicted diagnoses and a second prompt to review the list of predicted diagnoses (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include generating a second notification comprising the list of predicted diagnoses and a second prompt to review the list of predicted diagnoses as taught by Cave in order to provide an alert to a physician or medical professional regarding the patient’s potential disease(s).
Cave further teaches populating the second notification with the second subset of patient indicators, linked to the first diagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include populating the second notification with the second subset of patient indicators, linked to the first diagnosis as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
Lee further teaches and via the provider portal, transmitting the second notification to the provider for review (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include and via the provider portal, transmitting the second notification to the provider for review as taught by Lee in order to display an alert to a physician or medical professional regarding the patient’s potential disease(s).
Claim 14:
As per claim 14, Dandala, Cave, and Lee teach the method of claim 1 as described above and Dandala further teaches further comprising, in response to the first confidence score falling below the threshold score and exceeding a minimum threshold score defined for the first diagnosis:
accessing a target test method defined for the first set of target indicators, defined within the first module, the target test method configured to yield a patient indicator corresponding to the first set of target indicators (paragraphs 0045-0046 and 0049-0050);
generating a second notification indicating insufficient evidence for the first diagnosis and comprising a suggestion to execute the target test method with the patient (paragraphs 0045-0046 and 0049-0050);
Lee further teaches and via the provider portal, transmitting the second notification to the provider for review (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include a and via the provider portal, transmitting the second notification to the provider for review as taught by Lee in order to display an alert to a physician or medical professional data regarding a patient’s health.
Claim 15:
As per claim 15, Dandala, Cave, and Lee teach the method of claim 14 as described above and Dandala further teaches further comprising:
in response to execution of the target test method with the patient:
for a second encounter with the patient, accessing the health record comprising a new set of patient data corresponding to the target test method and captured for the patient during a time period succeeding the first encounter and preceding the second encounter (paragraphs 0045-0046 and 0049-0050);
extracting a second subset of patient indicators, from the new set of patient data, corresponding to a second subset set of target indicators in the first set of target indicators defined for the first diagnosis in the first module (paragraphs 0045-0046 and 0049-0050);
Cave further teaches deriving a second confidence score for the first diagnosis for the patient based on the second subset of patient indicators and the second subset of target indicators (paragraphs 0062-0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include deriving a second confidence score for the first diagnosis for the patient based on the second subset of patient indicators and the second subset of target indicators as taught by Cave in order to determine the likelihood the patient has a particular disease.
and in response to the second confidence score exceeding the threshold score:
Cave further teaches appending a second list of predicted diagnoses with the first diagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include appending a second list of predicted diagnoses with the first diagnosis as taught by Cave in order to combine all the possible diseases impacting the patient.
Cave further teaches generating a second notification comprising the second list of predicted diagnoses and a second prompt to review the second list of predicted diagnoses (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include generating a second notification comprising the second list of predicted diagnoses and a second prompt to review the second list of predicted diagnoses as taught by Cave in order to provide an alert to a physician or medical professional regarding the patient’s potential disease(s).
Cave further teaches populating the second notification with the second subset of patient indicators linked to the first diagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include populating the second notification with the second subset of patient indicators linked to the first diagnosis as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
Lee further teaches and via the provider portal, transmitting the second notification to the provider for review (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include via the provider portal, transmitting the second notification to the provider for review as taught by Lee in order to display an alert to a physician or medical professional regarding the patient’s potential disease(s).
Claim 16:
As per claim 16, Dandale, Cave, and Lee teach the method of claim 1 as described above and Cave further teaches wherein accessing the first set of target indicators comprises accessing the first module comprising:
a first submodule corresponding to a first subdiagnosis of the first diagnosis, the first submodule defining a first subset of secondary target indicators, of the first set of target indicators, supporting the first subdiagnosis (paragraphs 0023-0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include a first submodule corresponding to a first subdiagnosis of the first diagnosis, the first submodule defining a first subset of secondary target indicators, of the first set of target indicators, supporting the first subdiagnosis as taught by Cave in order to identify parameters that suggest the patient has one or more diseases.
and a second submodule corresponding to a second subdiagnosis of the first diagnosis, the second submodule defining a second subset of secondary target indicators, of the first set of target indicators, supporting the second subdiagnosis (paragraphs 0023-0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and a second submodule corresponding to a second subdiagnosis of the first diagnosis, the second submodule defining a second subset of secondary target indicators, of the first set of target indicators, supporting the second subdiagnosis as taught by Cave in order to identify parameters that suggest the patient has one or more diseases.
wherein extracting the first subset of patient indicators comprises extracting:
a subset of primary patient indicators corresponding to a subset of primary target indicators and supporting the primary diagnosis (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include extracting a first subset of patient indicators, from the corpus of patient data, corresponding to a first subset of target indicators in the first set of target indicators a subset of primary patient indicators corresponding to a subset of primary target indicators and supporting the primary diagnosis as taught by Cave in order to determine whether one or more parameters exists in the patient’s medical records associated with particular predicted disease(s).
a first subset of secondary patient indicators corresponding to the first subset of secondary target indicators and supporting the first subdiagnosis of the first diagnosis (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include a first subset of secondary patient indicators corresponding to the first subset of secondary target indicators and supporting the first subdiagnosis of the first diagnosis as taught by Cave in order to determine whether one or more parameters exists in the patient’s medical records associated with particular predicted disease(s).
and a second subset of secondary patient indicators corresponding to the second subset of secondary target indicators and supporting the second subdiagnosis of the first diagnosis (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include and a second subset of secondary patient indicators corresponding to the second subset of secondary target indicators and supporting the second subdiagnosis of the first diagnosis as taught by Cave in order to determine whether one or more parameters exists in the patient’s medical records associated with particular predicted disease(s).
and further comprising:
detecting a first quantity of patient indicators in the first subset of secondary patient indicators, and a second quantity of patient indicators in the second subset of secondary patient indicators (paragraphs 0039, 0041, and 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include detecting a first quantity of patient indicators in the first subset of secondary patient indicators, and a second quantity of patient indicators in the second subset of secondary patient indicators as taught by Cave in order to determine whether one or more parameters exists in the patient’s medical records associated with particular predicted disease(s).
in response to the first quantity of patient indicators, in the first subset of secondary patient indicators, exceeding a first threshold quantity:
appending the list of predicted diagnoses with the first subdiagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include appending the list of predicted diagnoses with the first subdiagnosis as taught by Cave in order to combine all the possible diseases impacting the patient.
generating a second notification comprising the list of predicted diagnoses and a second prompt to review the list of predicted diagnoses (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include generating a second notification comprising the list of predicted diagnoses and a second prompt to review the list of predicted diagnoses as taught by Cave in order to provide an alert to a physician or medical professional regarding the patient’s potential disease(s).
populating the second notification with the subset of primary patient indicators, and the first subset of secondary patient indicators, linked to the first subdiagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include populating the second notification with the subset of primary patient indicators, and the first subset of secondary patient indicators, linked to the first subdiagnosis as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
and in response to the second quantity of patient indicators, in the second subset of secondary patient indicators, exceeding a second threshold quantity:
appending the list of predicted diagnoses with the second subdiagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include appending the list of predicted diagnoses with the second subdiagnosis as taught by Cave in order to combine all the possible diseases impacting the patient.
generating a third notification comprising the list of predicted diagnoses and a third prompt to review the list of predicted diagnoses (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include generating a third notification comprising the list of predicted diagnoses and a third prompt to review the list of predicted diagnoses as taught by Cave in order to provide an alert to a physician or medical professional regarding the patient’s potential disease(s).
populating the third notification with the subset of primary patient indicators and the second subset of secondary patient indicators, linked to the second subdiagnosis (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include populating the third notification with the subset of primary patient indicators and the second subset of secondary patient indicators, linked to the second subdiagnosis as taught by Cave in order to provide relevant data to the physician or medical professional regarding the patient’s potential disease(s).
Lee further teaches and via the provider portal, transmitting the second notification to the provider for review (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include and via the provider portal, transmitting the second notification to the provider for review as taught by Lee in order to display an alert to a physician or medical professional regarding the patient’s potential disease(s).
Lee further teaches and via the provider portal, transmitting the third notification to the provider for review (paragraphs 0200-0201). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include and via the provider portal, transmitting the third notification to the provider for review as taught by Lee in order to display an alert to a physician or medical professional regarding the patient’s potential disease(s).
Claim(s) 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dandala et al. US Publication 20180032678 A1 in view of Cave et al. US Publication 20170053079 A1 further in view of Fuchs et al. US Publication 20140088992 A1.
Claim 17:
As per claim 17, Dandala teaches the method comprising:
for an encounter, receiving identification of a patient associated with the encounter from the provider via a provider portal executing on a computing device accessed by the provider (paragraph 0046 “For example, the user 402 may input the patient identifier via the user interface 500. The patient identifier may be a unique identifier associated with the patient 405, a name, an address, a telephone number, or any other type of identifier of the patient 405.”);
accessing a health record, in a population of health records, corresponding to the patient, the health record comprising a corpus of patient data and a list of current medications implemented by the patient (paragraphs 0045-0047 and Figure 5 “FIG. 5 illustrates example lists of medical entities such as lists of medical problems 510, medications 520, medical procedures 530, laboratory procedures 540, vitals 560, social history 570, and allergies 580.”, “For example, the user 402 may input the patient identifier via the user interface 500. The patient identifier may be a unique identifier associated with the patient 405, a name, an address, a telephone number, or any other type of identifier of the patient 405. The EMR analysis system 410 retrieves the patient EMR 425 from the EMR repository 420 based on the patient identifier, as shown at block 614. In one or more examples, the EMR repository 420 may include more than one EMRs associated with the patient 405. For example, the EMR repository 420 may include EMRs from one or more medical providers, such as hospitals, laboratories, dentists, eye-doctors, and other types of medical service providers. The EMR analysis system 410 may retrieve the specific type of EMRs from the EMR repository 420, such as EMRs from similar type of medical service provider as the user 402.”);
and in response to the first confidence score exceeding a threshold score (paragraphs 0049 and 0065 “In one or more examples, the EMR analysis system 410 may compare an entity-relation-score between the medical entities identified as related with a predetermined threshold, as shown at block 626. If the entity-relation-score crosses the predetermined threshold, that is if the entity-relation-score is greater (or lesser) than the predetermined threshold, the EMR analysis system 410 proceeds to highlight the related medical entity, as shown at block 628.”):
Dandala does not teach accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “…One preferred embodiment of the invention provides a means to differentiate and diagnose between two or more diseases through qualitative and quantitative analysis from multiple input sources, for example, differentiation and diagnosis between inflammatory bowel disease and irritable bowel disease or between gastroesophageal reflux disease and functional dyspepsia. The invention provides a scientific and accurate diagnostic system and methods that is reliable, simplified and cost efficient.” (paragraph 0002) and “According to the invention, a diagnosis score may be used to differentiate between different conditions, for example, irritable bowel syndrome, irritable bowel disorder, gastroesophageal reflux disease, dyspepsia, multiple sclerosis, systemic lupus erythematous, rheumatoid arthritis, acute coronary syndrome, pericarditis, and the likewise. Furthermore, the diagnosis score may be validated based on a retrospective analysis of medical records of patients.” (paragraph 0021). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandale to include accessing a diagnostic model comprising a population of modules, corresponding to a population of diagnoses, each module, in the population of modules, defining a set of target indicators supporting a corresponding diagnosis in the population of diagnoses as taught by Cave in order to determine one or more potential disease impacting a patient’s health.
Dandala does not teach for a first module, in the population of modules, corresponding to a first diagnosis, accessing: a first set of target indicators supporting the first diagnosis. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “With a diagnosis score used to differentiate between IBD and IBSd, diagnosis parameters obtained in patients being evaluated for the diseases may include, but is not limited to, radiological findings consistent with IBD, endoscopic findings consistent with IBD, biopsy findings consistent with IBD, elevated inflammatory markers (such as C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin, and fecal lactoferrin) and signs and symptoms such as history of weight loss, history of hematochezia, extra-intestinal sign/symptoms, palpable mass on exam and/or perianal disease.” and “With a diagnosis score used to differentiate between GERD and functional dyspepsia, diagnosis parameters obtained in patients being evaluated for the diseases may include, but is not limited to, signs and symptoms such as intermittent or continuous symptoms, nocturnal waking, nausea, ascending pain, bloating, etc.” (paragraph 0024). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include a first module, in the population of modules, corresponding to a first diagnosis, accessing: a first set of target indicators supporting the first diagnosis as taught by Cave in order to receive parameters to determine one or more potential diseases associated with a patient.
Dandala does not teach extracting a first subset of patient indicators, from the corpus of patient data, corresponding to a first subset of target indicators in the first set of target indicators. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “FIG. 1 illustrates a preferred embodiment of the diagnosis or scoring system 50 according to the invention. To utilize the scoring system 50 for differentiating different medical diagnosis according to the invention, a number of different databases or data sources 100 are utilized. Data sources 100 may produce any data resultant from laboratory tests, procedures, experienced signs and symptoms and includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0039), “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130…” (paragraph 0041), and “A diagnosis of a gastrointestinal disorder is conducted according to a scoring method using data from a variety of data sources. At step 504, data is received from a variety of data sources. Data includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include extracting a first subset of patient indicators, from the corpus of patient data, corresponding to a first subset of target indicators in the first set of target indicators as taught by Cave in order to determine whether one or more parameters that exist in the patient’s medical records associated with particular predicted disease(s).
Dandala does not teach deriving a first confidence score for the first diagnosis for the patient based on the first subset of patient indicators and the first subset of target indicators. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “At step 506, values are determined from a review of the data. In one embodiment according to the invention, when radiology data is consistent with a first disorder such as IBD or GERD, a first value is assigned to the patient file. The first value may be a numerical value or other designation such as one point. In another embodiment, the patient file is allocated one point when inflammatory marker data is present. Inflammatory marker data includes, for example, one or more selected from the group comprising: C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin, and fecal lactoferrin.” (paragraph 0062), “In yet another embodiment, one point is apportioned to the patient file for each sign and symptom data. In certain embodiments of the scoring method, the maximum number of points for sign and symptom data is a five point value. Sign and symptom data may include for example, one or more selected from the group comprising: weight loss, history of hematochezia, extra-intestinal sign/symptom, palpable mass on exam and/or perianal disease. Furthermore, sign and symptom data may include intermittent or continuous symptoms, nocturnal waking, nausea, ascending pain, bloating, etc.” (paragraph 0063), “In certain other embodiments of the invention, endoscopy data and biopsy data is collected from different data sources. One point is designated to the patient file for each endoscopy data with a maximum point value possible such as two points. Endoscopy data may be directed to one or more of inflammation and ulceration. One point is assigned when biopsy data is consistent with a particular disorder such as IBD or GERD.” (paragraph 0064), and “At step 508, all the values are added to obtain a final point value of the patient file. The final value is compared to a spectrum of scores. The spectrum of scores includes a first range identifying the first disorder and a second range identifying a second disorder. As an example, the spectrum of scores is 0 to 10 with a first range of 2 to 10 identifying inflammatory bowel disease (IBD) and a second range of 0 to 2 identifying irritable bowel syndrome (IBSd). The spectrum of scores and ranges are merely exemplary, any value spectrum of scores and ranges is contemplated. However any range is contemplated such as a first range of 1 to 4 and a second range of 0 to 1.” (paragraph 0065). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include deriving a first confidence score for the first diagnosis for the patient based on the first subset of patient indicators and the first subset of target indicators as taught by Cave in order to determine the likelihood the patient has a particular disease.
Dandala does not teach predicting the first diagnosis for the patient for the encounter. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “FIG. 1 illustrates a preferred embodiment of the diagnosis or scoring system 50 according to the invention. To utilize the scoring system 50 for differentiating different medical diagnosis according to the invention, a number of different databases or data sources 100 are utilized. Data sources 100 may produce any data resultant from laboratory tests, procedures, experienced signs and symptoms and includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0039), “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130…” (paragraph 0041), and “A diagnosis of a gastrointestinal disorder is conducted according to a scoring method using data from a variety of data sources. At step 504, data is received from a variety of data sources. Data includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include predicting the first diagnosis for the patient for the encounter as taught by Cave in order to identify one or more potential diseases impacting the patient.
Dandala does not teach in response to predicting the first diagnosis for the patient. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “FIG. 1 illustrates a preferred embodiment of the diagnosis or scoring system 50 according to the invention. To utilize the scoring system 50 for differentiating different medical diagnosis according to the invention, a number of different databases or data sources 100 are utilized. Data sources 100 may produce any data resultant from laboratory tests, procedures, experienced signs and symptoms and includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0039), “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130…” (paragraph 0041), and “A diagnosis of a gastrointestinal disorder is conducted according to a scoring method using data from a variety of data sources. At step 504, data is received from a variety of data sources. Data includes, for example, radiology data, inflammatory marker data, and sign and symptom data, endoscopy data and the biopsy data.” (paragraph 0060). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include in response to predicting the first diagnosis for the patient as taught by Cave in order to identify one or more potential diseases impacting the patient.
Dandala does not teach appending a list of predicted diagnoses with the first diagnosis. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130. Upon a diagnosis of a patient, based on the processed data, the information will then be presented in one or more client computers. Each client computer allows for a user interface such as a display device in which data can be further analyzed and organized based on user feedback.” (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include appending a list of predicted diagnoses with the first diagnosis as taught by Cave in order to combine all the possible diseases impacting the patient.
Dandala does not teach generating a first notification comprising the list of predicted diagnoses and a first prompt to review the list of predicted diagnoses. However, Cave teaches a System and Methods for Scoring Data to Differentiate Between Disorders and further teaches, “Each data source 100 is input into the management system 150 to organize, assign, evaluate and diagnose a patient with a syndrome, disease, disorder or the likewise based on the summation of results from the compiled data sources 110, 120, 130. Upon a diagnosis of a patient, based on the processed data, the information will then be presented in one or more client computers. Each client computer allows for a user interface such as a display device in which data can be further analyzed and organized based on user feedback.” (paragraph 0041). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala to include generating a first notification comprising the list of predicted diagnoses and a first prompt to review the list of predicted diagnoses as taught by Cave in order to provide an alert to a physician or medical professional regarding the patient’s potential disease(s).
Dandala and Cave do not teach and a medication blacklist comprising a set of blacklisted medications predicted to exacerbate the first diagnosis. However, Fuchs teaches a Method and System for Detecting and Categorizing Disease and further teaches, “The integration of the disclosed system and methods into a management system can allow the care provider to view the alert in the context of the patient's medical records. For example, an alert indicating that a patient has AKI can be accompanied by a suggestion that the clinician should consider holding, stopping, or adjusting the dose of medications that can cause or worsen renal function (e.g., ACE-I and NSAIDs).” (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include a medication blacklist comprising a set of blacklisted medications predicted to exacerbate the first diagnosis as taught by Fuchs in order to identify medications that worsen the patient’s predicted disease(s).
Dandala and Cave do not teach and in response to the list of current medications comprising a first medication, in the set of blacklisted medications: However, Fuchs teaches a Method and System for Detecting and Categorizing Disease and further teaches, “The integration of the disclosed system and methods into a management system can allow the care provider to view the alert in the context of the patient's medical records. For example, an alert indicating that a patient has AKI can be accompanied by a suggestion that the clinician should consider holding, stopping, or adjusting the dose of medications that can cause or worsen renal function (e.g., ACE-I and NSAIDs).” (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include in response to the list of current medications comprising a first medication, in the set of blacklisted medications as taught by Fuchs in order to identify medications that worsen the patient’s predicted disease(s).
Dandala and Cave do not teach flagging the first medication for review by the provider. However, Fuchs teaches a Method and System for Detecting and Categorizing Disease and further teaches, “The integration of the disclosed system and methods into a management system can allow the care provider to view the alert in the context of the patient's medical records. For example, an alert indicating that a patient has AKI can be accompanied by a suggestion that the clinician should consider holding, stopping, or adjusting the dose of medications that can cause or worsen renal function (e.g., ACE-I and NSAIDs).” (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include flagging the first medication for review by the provider as taught by Fuchs in order to receive input from a physician or medical professional regarding a particular medication.
Dandala and Cave do not teach and populating the first notification with a first alert indicating implementation of the first medication by the patient. However, Fuchs teaches a Method and System for Detecting and Categorizing Disease and further teaches, “The integration of the disclosed system and methods into a management system can allow the care provider to view the alert in the context of the patient's medical records. For example, an alert indicating that a patient has AKI can be accompanied by a suggestion that the clinician should consider holding, stopping, or adjusting the dose of medications that can cause or worsen renal function (e.g., ACE-I and NSAIDs).” (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include populating the first notification with a first alert indicating implementation of the first medication by the patient as taught by Fuchs in order to create a warning message regarding the impacts of the medication to the patient.
Dandala and Cave do not teach and via the provider portal, transmitting the first notification to the provider for review. However, Fuchs teaches a Method and System for Detecting and Categorizing Disease and further teaches, “The integration of the disclosed system and methods into a management system can allow the care provider to view the alert in the context of the patient's medical records. For example, an alert indicating that a patient has AKI can be accompanied by a suggestion that the clinician should consider holding, stopping, or adjusting the dose of medications that can cause or worsen renal function (e.g., ACE-I and NSAIDs).” (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include via the provider portal, transmitting the first notification to the provider for review as taught by Fuchs in order to provide a warning message regarding the impacts of the medication to the patient to the physician or medical professional.
Claim 19:
As per claim 19, Dandala, Cave, and Fuchs teach the method of claim 17 as described above and Fuchs further teaches wherein accessing the medication blacklist comprising the set of blacklisted medications comprises accessing the medication blacklist comprising the set of blacklisted medications and a set of blacklisted compounds predicted to exacerbate the first diagnosis (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include wherein accessing the medication blacklist comprising the set of blacklisted medications comprises accessing the medication blacklist comprising the set of blacklisted medications and a set of blacklisted compounds predicted to exacerbate the first diagnosis as taught by Fuchs in order to identify medications that worsen the patient’s predicted disease(s).
and further comprising, in response to the first confidence score exceeding the threshold score and in response to the list of current medications comprising a second medication comprising a first compound in the set of blacklisted compounds:
flagging the second medication for review by the provider (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include flagging the second medication for review by the provider as taught by Fuchs in order to receive input from a physician or medical professional regarding a particular medication.
and populating the first notification with a second alert indicating implementation of the second medication, comprising the first compound predicted to exacerbate the first diagnosis, by the patient (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include and populating the first notification with a second alert indicating implementation of the second medication, comprising the first compound predicted to exacerbate the first diagnosis, by the patient as taught by Fuchs in order to provide a warning message regarding the impacts of the medication to the patient to the physician or medical professional.
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Dandala, Cave, and Fuchs as applied to claim 17 above, and further in view of Li et al. US Publication 20240000784 A1.
Claim 18:
As per claim 18, Dandala, Cave, and Fuchs teach the method of claim 17 as described above but do not teach further comprising:
for the first module, accessing a medication whitelist comprising a set of whitelisted medications configured to treat the first diagnosis. However, Li teaches Compositions and Methods for the Treatment and Diagnosis of Cancer and further teaches, “Another aspect of the invention includes methods of diagnosing cancer or predicting drug response or patient outcomes. This allows for the identification of subjects eligible for treatment with the methods described herein. As used herein, a “subject eligible for treatment” is a subject that would be expected to benefit from treatment with a method disclosed herein because they exhibit one or more indicia that allow for prediction of a positive drug response or patient outcome.” (paragraph 0109). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala, Cave, and Fuchs to include for the first module, accessing a medication whitelist comprising a set of whitelisted medications configured to treat the first diagnosis as taught by Li in order to determine medication that have a positive impact or address the patient’s illness.
and in response to the first confidence score exceeding the threshold score and in response to the list of current medications comprising a second medication, in the set of whitelisted medications:
Fuchs further teaches flagging the second medication for review by the provider (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include flagging the second medication for review by the provider as taught by Fuchs in order to receive input from a physician or medical professional regarding a particular medication.
Fuchs further teaches populating a second notification with a second alert indicating implementation of the second medication, predicted to treat the first diagnosis, by the patient (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include populating a second notification with a second alert indicating implementation of the second medication, predicted to treat the first diagnosis, by the patient as taught by Fuchs in order to provide a warning message regarding the impacts of the medication to the patient to the physician or medical professional.
Fuchs further teaches and via the provider portal, transmitting the second notification to the provider for review (paragraph 0049). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Dandala and Cave to include and via the provider portal, transmitting the second notification to the provider for review as taught by Fuchs in order to provide a warning message regarding the impacts of the medication to the patient to the physician or medical professional.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yang et al. US Publication 20230245735 A1 Electronic Medical Record Data Analysis and Electronic Medical Record Data Analysis Method
Yang discloses an electronic medical record data analysis system and an electronic medical record data analysis method are provided. The electronic medical record data analysis system includes a storage device and a processor. The storage device is configured to store an electronic medical record data analysis module and a post-processing module. The processor obtains electronic medical record data. The processor executes the electronic medical record data analysis module to analyze the electronic medical record data and generate a plurality of disease diagnosis codes and a plurality of correlation degree scores corresponding to the electronic medical record data. The processor sorts the plurality of disease diagnosis codes according to the plurality of correlation degree scores, to generate an initial list, and executes the post-processing module to post-process the initial list according to a preset coding rule. The processor generates a recommendation list according to the post-processed initial list.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW L HAMILTON whose telephone number is (571)270-1837. The examiner can normally be reached Monday-Thursday 9:30-5:30 pm EST.
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/MATTHEW L HAMILTON/Primary Examiner, Art Unit 3682
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