Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of Claims
This action is in reply to the Request for Continued Examination filed on 04/27/2026.
Claims 1, 10 and 27 have been amended.
Claims 4-9 and 13-17 have been cancelled.
Claims 1-3, 10-11 and 18-30 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/27/2026 has been entered.
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 1-3, 10-11 and 18-30 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-3, 10-11 and 18-30 are directed to a method (i.e., a process). Accordingly, claims 1-3, 10-11 and 18-30 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 8 includes limitations that recite an abstract idea. Note that independent claims 1 and 10 cover method claims.
Specifically, independent claim 1 recites:
A method of generating variable entries in a clinical statement for a patient health record comprising:
preparing a customized user interface, the customized user interface tailored for a clinician role and a patient encounter type;
based on the clinician role and the patient encounter type, conducting a first series of randomization algorithms to randomly select, from stored sentence content structure data within a clinical statement content structure module a sentence structure of the clinical statement to increase variability and compliance with regulatory requirements and generating a randomized sentence structure;
conducting a second series of randomization algorithms to randomly select, from stored clinical finding value structure data within a clinical finding value structure module a structure of clinical finding value to increase variability and compliance with regulatory requirements and generating a randomized clinical finding value structure;
conducting a third series of randomization algorithms to randomly select, from stored patient name structure data within a patient name structure module, a structure of a patient name to increase variability and compliance with regulatory requirements and generating a randomized patient name structure,
wherein the first, second, and third series of randomization algorithms are conducted by a system comprising one or more processors in communication with the customized user interface, the one or more processors having at least one memory storage device capable of storing protocols executable by the one or more processors, the protocols causing the one or more processors to conduct the first, second, and third series of randomization algorithms;
receiving, via the customized user interface, a first input corresponding to a clinical finding value, the clinical finding value input via the customized user interface as a discrete selection;
receiving, via the customized user interface, a second input corresponding to a patient name;
generating, by concatenating outputs of the randomized sentence structure, the randomized clinical finding value structure, and the randomized patient name structure, the clinical statement based on the received first input and the received second input, the clinical statement including at least one variable entry generated using and based on the randomized sentence structure, randomized clinical finding value structure, and randomized patient name structure;
receiving, via the customized user interface, a third input corresponding to an accuracy of the generated clinical statement; and
generating a final clinical statement based on the received third input, the final clinical statement generated in a format compliant with regulatory requirements prohibiting duplication of clinical notes, thereby improving the accuracy and integrity of electronic health records.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because generating variable entries in a clinical statement and generating a final clinical statement based on the received input are a part of a medical workflow, which is managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “mathematical concepts” because generating the clinical statement based on the received first input and the received second input, the clinical statement including at least one variable entry that corresponds to selected and stored randomized sentence structure, randomized clinical finding value structure, and randomized patient name structure is using math. The foregoing underlined limitations also relate to claim 1 (similarly to claim 10).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 2-3, 11 and 18-30, these claims merely recite determining steps such as: claim 2 - generating the clinical statement comprises concatenating results from the first series of randomization algorithms, claim 3 - conducting a fourth series of randomization algorithms for randomizing a structure of a clinician name and generating a randomized clinician name structure, claim 11 – generating an encounter statement comprises generating more than one encounter statement, each generated encounter statement based on the received first input, claim 18 - a clinical finding value comprises receiving, through the customized user interface, a risk factor impacting a care plan for a patient, claim 19 - a clinical finding value comprises receiving, through the customized user interface, a level of engagement for a patient, claim 20 - a clinical finding value comprises receiving, through the customized user interface, a drug profile for a patient, claim 21 - the set of predictor parameters includes daily progress notes per day, long notes per day, orders per day, a length of stay, and an attending parameter, claim 22 – outputting a first variable entry corresponding to the clinical finding value received as the first input and the randomized clinical finding value structure, outputting a second variable entry corresponding to the patient name received as the second input and the randomized patient name structure and aggregating the first variable entry and the second variable entry into the clinical statemen, claim 23 – outputting a third variable entry corresponding to a clinical finding value received as a fourth input, the fourth input different from the first input, each of the first, second, and third variable entries unique from each other; and aggregating the third variable entry into the clinical statement, claim 24 - generating a second final encounter statement based on the received first input and second input, the second final encounter statement containing one or more variable narratives unique from the one or more variable narratives of the final encounter statement, claim 25 - corresponding to one or more clinical finding values, the third input different from the first input, claim 26 - displaying the final encounter statement on a customized user interface, claim 27 – receiving an additional variable narrative to the generated encounter statement a modification to the at least one variable narrative receiving, a deletion of a variable narrative and receiving, approval of the generated encounter statement, claim 28 - variable narratives contained in the final encounter statement are unique, claim 29 - a risk factor implementing care planning for a patient, a level of engagement for the patient, and a drug profile for the patient and claim 30 - variable narrative in the final encounter statement includes a unique variable sentence structure generated by a randomized sentence structure algorithm.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1 and 10, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations mathematically but for the recitation of generic computer components. That is, other than reciting a customized user interface to perform the limitations, nothing in the claim elements precludes the steps from practically being performed mathematically. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment performed using math but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the customized user interface is recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “conducting a first series of randomization algorithms for randomizing a sentence structure of the clinical statement and generating a randomized sentence structure,” “conducting a second series of randomization algorithms for randomizing a structure of clinical finding value and generating a randomized clinical finding value structure” and “conducting a third series of randomization algorithms for randomizing a structure of a patient name and generating a randomized patient name structure” the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation “receiving a first input corresponding to a clinical finding value, the clinical finding value input …,” and “receiving a second input corresponding to a patient name” the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 1, regarding the additional limitations of the customized user interface, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 8 and analogous independent claims 1 and 10 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-3, 10-11 and 18-30 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Imam (US 2013/0132119 A1) in view of Hamiter (US 7,949,542 B1) further in view of Loscutoff (US 2020/0126650 A1).
Claim 1:
Imam discloses A method of generating variable entries in a clinical statement for a patient health record (See Fig. 5, P0092 generating reports.) comprising:
preparing a customized user interface, the customized user interface tailored for a clinician role and a patient encounter type (See specialist in P0016, P0037 tailored to the clinician role (radiologist) and the patient encounter type (CT, MRI, etc.));
based on the clinician role and the patient encounter type, conducting a first series of randomization algorithms to randomly select, from stored sentence content structure data within a clinical statement content structure module, for randomizing a sentence structure of the clinical statement to increase variability and compliance with regulatory requirements and generating a randomized sentence structure (See P0074-P0075 random synonym generator include sentence content, and report tuned to meet local, state, and federal standards and regulations in P0070.);
receiving, via the customized user interface, a first input corresponding to a clinical finding value, the clinical finding value input via the customized user interface as a discrete selection (See P0073 where code is the discrete selection);
receiving, via the customized user interface, a second input corresponding to a patient name (See P0066 where associated medical case file and report have patient name);
receiving, via the customized user interface, a third input corresponding to an accuracy of the generated clinical statement (See information entered into reports in P0066 include customized report.); and
generating a final clinical statement based on the received third input, the final clinical statement generated in a format compliant with regulatory requirements prohibiting duplication of clinical notes, thereby improving the accuracy and integrity of electronic health records (See finalized report in P0020,P0103-P0104, see report generator comply with local, state, and federal standards and regulations (e.g., following the American College of Radiology (ACR) standards) in P0070.).
Although Imam discloses a method for generating a final clinical statement using customized user interface, compliant with regulatory requirements, receiving clinical finding value input, a patient name for customizing and randomly selecting sentence content structure data mentioned above, Imam does not explicitly teach randomizing a structure of clinical finding value. Hamiter teaches:
conducting a second series of randomization algorithms to randomly select, from stored clinical finding value structure data within a clinical finding value structure module, for randomizing a structure of clinical finding value to increase variability and compliance with regulatory requirements and generating a randomized clinical finding value structure (See describing aortic cusp and caliber measurement of blood vessel branch in column 20, lines 34-56 by randomly inserting options of phrases with the same meaning to mimic natural language).
Therefore, it would have been obvious to one of ordinary skill in the art of generating text-based medical reports before the effective filing date of the claimed invention to modify the method of Lucas to include randomizing a structure of clinical finding value as taught by Hamiter to capture more accurate and detailed depictions, using more natural language report as mentioned in Hamiter’s column 3, lines 10-28.
Although Imam and Hamiter teach the method for generating a final clinical statement using customized user interface, compliant with regulatory requirements, randomly selecting sentence content structure data and randomizing a structure of clinical finding value mentioned above, Imam and Hamiter do not explicitly teach randomizing a patient name. Loscutoff teaches:
conducting a third series of randomization algorithms to randomly select, from stored patient name structure data within a patient name structure module, for randomizing a structure of a patient name to increase variability and compliance with regulatory requirements and generating a randomized patient name structure (See P0079 different identifier types include name, surname, address, user identifier (ID), SSN, when formatting medical documents.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifier for health-related management before the effective filing date of the claimed invention to modify the method of Imam and Hamiter to include randomizing a patient name as taught by Loscutoff because it is common for names or identifying information to be in different formats as it prevents accidentally assigning tests to inappropriate members as mentioned in Loscutoff’s P0011.
Combination of Imam, Hamiter, and Loscutoff teach:
wherein the first, second, and third series of randomization algorithms are conducted by a system comprising one or more processors in communication with the customized user interface, the one or more processors having at least one memory storage device capable of storing protocols executable by the one or more processors, the protocols causing the one or more processors to conduct the first, second, and third series of randomization algorithms; and
generating, by concatenating outputs of the randomized sentence structure, the randomized clinical finding value structure, and the randomized patient name structure, the clinical statement based on the received first input and the received second input, the clinical statement including at least one variable entry generated using and based on the randomized sentence structure, randomized clinical finding value structure, and randomized patient name structure (at least Imam’s P0073-P0074, along with Hamiter, and Loscutoff);
Therefore, it would have been obvious to one of ordinary skill in the art of identifier for health-related management before the effective filing date of the claimed invention to modify the method of Imam in view of Hamiter to include the randomizing a structure of clinical finding value as taught by Hamiter because to capture more accurate and detailed depictions, using more natural language report, and further in view of Loscutoff to include randomizing a patient name as taught by Loscutoff, because it is common for names or identifying information to be in different formats as it prevents accidentally assigning tests to inappropriate members.
Regarding claim 2, Imam and Hamiter teach the method of claim 1 mentioned above, and Imam teaches wherein generating the clinical statement comprises concatenating results from the first series of randomization algorithms (See random generator in P0074, P0076 when stating conclusions in P0077.).
Regarding claim 3, although Imam and Hamiter teach the method of claim 1 mentioned above, Imam and Hamiter do not explicitly teach randomizing names. Loscutoff teaches:
further comprising conducting a fourth series of randomization algorithms for randomizing a structure of a clinician name and generating a randomized clinician name structure (See P0079 different identifier types include name, surname, address, user identifier (ID), SSN, when formatting medical documents.).
Therefore, it would have been obvious to one of ordinary skill in the art of identifier for health-related management before the effective filing date of the claimed invention to modify the method of Imam and Hamiter to include randomizing names as taught by Loscutoff because it is common for names or identifying information to be in different formats as it prevents accidentally assigning tests to inappropriate members as mentioned in Loscutoff’s P0011.
Regarding claim 21, Imam, Hamiter and Loscutoff teach the method of claim 1 mentioned above and Imam teaches further comprising generating, via the customized user interface, a patient assessment form based on one or more of: the clinician role, the patient encounter type, and a type of assessment to be conducted ((See specialist in P0016, P0037 tailored to the clinician role (radiologist) and the patient encounter type (CT, MRI, etc.).)
Claims 10-11 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Imam (US 2013/0132119 A1) in view of Hamiter (US 7,949,542 B1) further in view of Lucas (US 2021/0210184 A1).
Claim 10:
Imam discloses A method of providing narrative variations in a clinical statement such that the clinical statement is in compliance with regulatory requirements prohibiting copy-paste and cloning in electronic health records (EHRs) (See read-only memory in P0111 serve as prohibiting copy-paste and cloning. See P0074-P0075 random synonym generator include sentence content, and report tuned to meet local, state, and federal standards and regulations in P0070.), the method comprising:
conducting a first randomization algorithm, to randomly select, from stored sentence content structure data within a clinical statement content structure module a clinical statement template (See specialist in P0016, P0037 tailored to the clinician role (radiologist) and the patient encounter type (CT, MRI, etc.));
receiving via a customized user interface tailored for a clinician role and encounter type, a first input corresponding to one or more clinical finding values as discrete selections ((See P0037, specialist dictates evaluation in P0070-P0073 include where the code is the discrete selection).);
presenting the generated encounter statement to a clinician for review and modification via the customized user interface (See information entered into reports in P0066 include customized report.); and
generating a final encounter statement based on clinician review and modification, the final encounter statement including one or more variable narratives (See finalized report in P0020, P0103-P0104, see report generator comply with local, state, and federal standards and regulations (e.g., following the American College of Radiology (ACR) standards) in P0070.).
Although Imam discloses a method for providing narrative variations in a clinical statement mentioned above, Imam does not explicitly teach randomizing a structure of clinical finding value and aggregating outputs from randomization algorithm to generate an encounter statement based on the clinical statement template. Hamiter teaches:
conducting a second randomization algorithm to randomly select, from stored clinical finding value structure data within a clinical finding value structure module, one or more random clinical finding value structures (See describing aortic cusp and caliber measurement of blood vessel branch in column 20, lines 34-56 by randomly inserting options of phrases with the same meaning to mimic natural language).
aggregating outputs from the first randomization algorithm and the second randomization algorithm to generate an encounter statement based on the clinical statement template, the received first input and the generated one or more random clinical finding value structures, the encounter statement including at least one variable narrative (See column 2, lines 24-37 selecting a "best fit" static graphic representation from a template library of graphic representations and randomly inserting options of phrases in column 20, lines 34-56.),
Therefore, it would have been obvious to one of ordinary skill in the art of generating text-based medical reports before the effective filing date of the claimed invention to modify the method of Imam to include randomizing a structure of clinical finding value and aggregating outputs from randomization algorithm to generate an encounter statement based on the clinical statement template as taught by Hamiter to capture more accurate and detailed depictions, using more natural language report as mentioned in Hamiter’s column 3, lines 10-28.
Regarding claim 11, Imam and Hamiter teach the method of claim 10 mentioned above, and Imam teaches wherein generating an encounter statement comprises generating more than one encounter statement, each generated encounter statement based on the received first input (See report findings that states conclusions in P0077, P0094-P0095.).
Regarding claim 26, Imam and Hamiter teach the method of claim 10 mentioned above, and Imam teaches further comprising displaying the final encounter statement on a customized user interface (See specialist in P0016, P0037 tailored to the clinician role (radiologist) and the patient encounter type (CT, MRI, etc.)).
Claims 18-20 and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Imam (US 2013/0132119 A1) in view of Hamiter (US 7,949,542 B1) further in view of Loscutoff (US 2020/0126650 A1) and Lucas (US 2021/0210184 A1).
Regarding claim 18, Imam, Hamiter and Loscutoff teach the method of claim 1 mentioned above and Lucas teaches wherein receiving a first input corresponding to a clinical finding value comprises receiving, through the customized user interface, a risk factor impacting a care plan for a patient (See P0049-P0050 where target therapies remedy the growth of cancer cells as a risk. Also, see [P0061] the individual's genotype can be compared with the published literature to determine likelihood of trait expression and disease risk to enhance personalized medicine suggestions.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam, Hamiter and Loscutoff to include input corresponding to a clinical finding value when a risk factor is impacting a care plan for a patient as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Regarding claim 19, Imam, Hamiter and Loscutoff teach the method of claim 1 mentioned above and Lucas teaches wherein receiving a first input corresponding to a clinical finding value comprises receiving, through the customized user interface, a level of engagement for a patient (See symptoms that the patient brought to their physicians attention during a routine checkup in P0061 and P00246-P0247 where corresponding question and feedback serve as a level of engagement for a patient.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam, Hamiter and Loscutoff to receiving input corresponding to a clinical finding value as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Regarding claim 20, Imam, Hamiter and Loscutoff teach the method of claim 1 mentioned above and Lucas teaches wherein receiving a first input corresponding to a clinical finding value comprises receiving, through the customized user interface, a drug profile for a patient (See sources of patient history, treatments, medications, therapies, hospice, responses to treatments, laboratory and testing results in P0056 would allow the user to customize a drug profile for a patient.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam, Hamiter and Loscutoff to corresponding to a clinical finding value comprises receiving a drug profile for a patient as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Regarding claim 22, Imam, Hamiter and Loscutoff teach the method of claim 1 mentioned above and Lucas teaches wherein generating the clinical statement based on the received first input and the received second input comprises: outputting a first variable entry corresponding to the clinical finding value received as the first input and the randomized clinical finding value structure (See summarizing conclusion made from the sequencing results in P0108. Also, see Field with corresponding Value in Fig. 2 [P0239] structured entities relating to diagnosis may be summarized with the final normalized entity, information from the entity structuring, and any confidence values generated during the classification and/or ranking/filtering.);
outputting a second variable entry corresponding to the patient name received as the second input and the randomized patient name structure; and aggregating the first variable entry and the second variable entry into the clinical statement (See summarizing conclusion made from the sequencing results in P0108. Also, see Field with corresponding Value in Fig. 2 [P0239] structured entities relating to diagnosis may be summarized with the final normalized entity, information from the entity structuring, and any confidence values generated during the classification and/or ranking/filtering.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam, Hamiter and Loscutoff to include the randomized clinical finding value structure, outputting variable entry corresponding to the patient name received as the second input and the randomized patient name structure and aggregating the variable entry and the variable entry into the clinical statement as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Regarding claim 23, Imam, Hamiter and Loscutoff teach the method of claim 1 mentioned above and Lucas teaches outputting a third variable entry corresponding to a clinical finding value received as a fourth input, the fourth input different from the first input, each of the first, second, and third variable entries unique from each other; and aggregating the third variable entry into the clinical statement (See [P0027] the system may detect when a patient record has been received, either partially or in full, and begin processing the patient record in aggregate or as a whole to determine relevant medical concepts for entry into the EMR. Also, see P0258 aggregating textual context data regarding a patient with cancer.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam, Hamiter and Loscutoff to include outputting variable entry corresponding to a clinical finding inputs as variable entries unique from each other and aggregating the variable entry into the clinical statement as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Claims 24-25 and 27-30 are rejected under 35 U.S.C. 103 as being unpatentable over Imam (US 2013/0132119 A1) in view of Hamiter (US 7,949,542 B1) further in view of Lucas (US 2021/0210184 A1).
Regarding claim 24, Imam and Hamiter teach the method of claim 10 mentioned above, and Lucas teaches further comprising generating a second final encounter statement based on the received first input and second input, the second final encounter statement containing one or more variable narratives unique from the one or more variable narratives of the final encounter statement (Shown in Fig. 2 the Field with corresponding Value mentioned in P0040, P0105.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam, and Hamiter to include generating a final encounter statement based on received inputs, the final encounter statement containing variable narratives unique from the variable narratives as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Regarding claim 25, Imam and Hamiter teach the method of claim 10 mentioned above, and Lucas teaches further comprising receiving a third input corresponding to one or more clinical finding values, the third input different from the first input (Besides performance scores, lab tests, pathology results and prognostic indicators in P0043, see Fig. 4 sample scores 95%, 97% and 85% mentioned in P0161-P00162.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam, and Hamiter to include clinical finding values input different from the first input as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Regarding claim 27, Imam and Hamiter teach the method of claim 10 mentioned above, and Lucas teaches further comprising receiving a second input corresponding to an accuracy of the generated encounter statement (See P0024, P0042 where continuous collection training data including text extraction, text cleaning, natural language processing techniques to increase accuracy and exemplary prescription drug statements serve as input corresponding to an accuracy of the generated encounter statements.) and wherein receiving the second input corresponding to an accuracy of the generated encounter statement comprises one or more of: receiving, via a customized user interface, an additional variable narrative to the generated encounter statement; receiving, via the customized user interface, a modification to the at least one variable narrative (See [P0160-P0161] if a document has been determined to have a high incidence of accuracy because a table on page 3 of a document may always return the correct gender for the patient, then the algorithm may identify that high accuracy has been provided for the document based on the one sentence of that document and stop processing a gender classification at the sentence level vector for that patient.); receiving, via the customized user interface, a deletion of a variable narrative; and receiving, via the customized user interface, approval of the generated encounter statement (See [P0171-P0172] for each mismatch in character, operations may be performed to elicit a match. For example, a mismatching character may be deleted, and the next character considered for a hit, which would account for having an extraneous character in a word, a character may be inserted at the mismatching character.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam and Hamiter to include the randomized clinical finding value structure, outputting variable entry corresponding to the patient name received as the second input and the randomized patient name structure and aggregating the variable entry and the variable entry into the clinical statement as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Regarding claim 28, Imam and Hamiter teach the method of claim 10 mentioned above, and Lucas teaches wherein each of the one or more variable narratives contained in the final encounter statement are unique (See P0093-P0094, [P0173] Fuzzy matching is structured around the text concepts included in the above enumerated list or the UMLS, including metadata fields CUI (the UMLS unique ID) and AUI (dictionary-specific unique ID), so that an exhaustive search may be performed for all medical concepts.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam and Hamiter to include generating a final encounter statement based on received inputs, the final encounter statement containing variable narratives unique from the variable narratives as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Regarding claim 29, Imam and Hamiter teach the method of claim 10 mentioned above, and Lucas teaches wherein receiving a first input corresponding to one or more clinical finding values comprises receiving at least one of: a risk factor implementing care planning for a patient, a level of engagement for the patient, and a drug profile for the patient (See sources of patient history, treatments, medications, therapies, hospice, responses to treatments, laboratory and testing results in P0056 would allow the user to customize a drug profile for a patient.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam and Hamiter to corresponding to a clinical finding value comprises receiving a drug profile for a patient as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Regarding claim 30, Imam and Hamiter teach the method of claim 10 mentioned above, and Lucas teaches wherein each variable narrative in the final encounter statement includes a unique variable sentence structure generated by a randomized sentence structure algorithm (See [P0160-P0161] if a document has been determined to have a high incidence of accuracy because a table on page 3 of a document may always return the correct gender for the patient, then the algorithm may identify that high accuracy has been provided for the document based on the one sentence of that document and stop processing a gender classification at the sentence level vector for that patient.).
Therefore, it would have been obvious to one of ordinary skill in the art of predicting clinical concepts before the effective filing date of the claimed invention to modify the method of Imam and Hamiter to include generating a final encounter statement based on received inputs, the final encounter statement containing variable narratives unique from the variable narratives as taught by Lucas for providing optimized care when analyzing a robust amount of electronic health records as mentioned in Lucas’ P0003-P0004.
Response to Arguments
Applicant argues that amended claims 1 and 10 recite limitations that are integrated into a practical application and the amended claim limitations recite significantly more than generic computing, see pgs. 10-12 of Remarks – Examiner disagrees.
Executing protocols, randomly selecting a stored sentence, clinical value and patient name, interface tailored to clinician role and patient encounter type, discretely inputting and presenting generated narratives for clinician validation are operations any generic computer is merely using the computer as a tool to implement the abstract idea (saying “apply it”) and is merely using the computer in the manner in which it was designed to be used, i.e., performing generic computer functions. The recited improvements are nonetheless directed towards improving the abstract idea and not the computer itself – that is, the recited invention may improve generating the clinical statement based on the received first input and the received second input, the clinical statement including a variable entry that corresponds to the randomized sentence structure, randomly identifying clinical finding values, and randomized patient name structure using math (i.e. the abstract idea), but there is no evidence to show that it improves the structural or functional properties of the computer itself.
Applicant further argues that humans are fundamentally incapable of random selections, see pgs. 10-12 of Remarks – Examiner disagrees.
Perhaps, conducting a series of randomization algorithms is done to protect a patient’s identity and healthcare records? With no basis or significance for conducting the series of randomization algorithms by randomizing sentence structures of a clinical statement or clinical finding values, a human could certainly perform such randomizing tasks. Arguably, humans could randomly select an unnamed file, medical report and or medical image, speculatively that clinical statements and findings will be identified.
Applicant argues that the claims 1 and 10 recite significantly more than generic computing, e.g. see pgs. 12-13 of Remarks – Examiner disagrees.
Besides no technological implementations or improvements being claimed, the instant case is not solving a problem in assisting regulatory requirements in the electronic health records (EHR) field. Implementing randomization algorithms, prohibiting duplication, copy-paste and cloning are basic data processing tasks that a generic computer would be expected to do, especially using basic PDF file formatting. Also, no technological implementations or improvements to the functioning of the computer itself have been genuinely set forth and are nonetheless directed towards improving the abstract idea and not the computer itself – that is, the recited invention may improve variability and compliance with regulatory requirements (i.e. the abstract idea), but there is no evidence to show that it improves the structural or functional properties of the computer itself, outside of improving the computer specifically for implementing the abstract idea.
Regarding the prior art rejections, Applicant’s arguments have been fully considered, but are now moot in view of the new grounds of rejection. The Examiner has entered a new rejection under 35 USC § 103(a) and applied new art and art already of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kanada (US 2012/0054230 A1) & Sasidharan (US 2025/0118399 A1).
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/T.S.W./Examiner, Art Unit 3687 08/22/2026
/Anita Y Coupe/Supervisory Patent Examiner, Art Unit 3619
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