DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to a response filed 26 August 2026, on an application filed 27 November, which claims priority from a chain of applications claiming priority to 17 December 2015.
Claims 11 and 19 have been amended.
Claims 1-20 are currently pending and have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of U.S. Patent No. 10,546,654 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the present claims are contained within the patented claims, as shown herein:
Present claims Patented claims
1,10,19. 1,11,20.
2,11. 1,11,20.
3,12. 1,11,20.
4,13. 2,12.
5,14. 3,13.
6,15. 7,17.
7,16. 8,18.
8,17. 6,16.
9,18,20. 9,19,21.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-9 are drawn to a system, which is within the four statutory categories (i.e. machine). Claims 10-18 are drawn to a method, which is within the four statutory categories (i.e. process). Claims 19 and 20 are drawn to a non-transitory medium, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 1 recites: A system comprising:
at least one processor; and
memory storing instructions that, when executed by the at least one processor, causes the system to perform operations including:
analyzing medical transaction data in a large general population of patients to generate and dynamically update a data map, the data map pairing each of a plurality of medical suggestions with one or more analytic influence dimensions, each medical suggestion representing a medical transaction recommended by a medical professional and each analytic influence dimension in the data map specifying an attribute of a patient or an attribute of a physician associated with the medical transaction data, and each dimension-medical suggestion pair in the data map having a respective confidence score indicative of a degree of match between the medical suggestion and the analytic influence dimension in the medical transaction data over the large general population of patients, wherein dynamically updating the data map occurs via analysis of new medical transaction data for the large general population of patients;
receiving a medical record of a patient, wherein the medical record is associated with a set of components;
identifying one or more analytic influence dimensions for the medical record, each of the one or more analytic influence dimensions specifying an attribute of the patient or an attribute of a physician associated with the medical record;
obtaining, from the data map, relevant dimension-medical suggestion pairs, the relevant dimension-medical suggestion pairs having an analytic influence dimension in the data map that matches one of the one or more analytic influence dimensions for the medical record;
select a set of highest-ranked relevant dimension-medical suggestion pairs based on the respective confidence scores in the data map;
determining whether a component with a discrepancy exists by comparing a value of each of the set of components with a value of a corresponding component from the plurality of highest-ranked relevant dimension-medical suggestion pairs to determine a discrepancy; and
responsive to identifying a component with a discrepancy and to determining that the discrepancy satisfies a threshold: receiving a corrected value for the component with the discrepancy, and updating the medical record with the corrected value.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this analyzing data to identify relevant connections), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea(s) are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for claims 9 and 19 are identical as the abstract idea for claims 1, because the only difference between claims 1, 9 and 19 is that claim 1 recites a system, whereas claim 9 recites a method and claim 19 recites a non-transitory computer-readable media.
Dependent claims 2-9, 11-18 and 20 include other limitations, for example claims 2, 7, 11 and 16 recite use of models, claims 3-6 and 12-15 recite normalization operations, and claims 8, 9, 17, 18 and 20 further describe previously described elements, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent claims 2-9, 11-18 and 20 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claims 2-9, 11-18 and 20 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 9 and 19.
Prong 2 of Step 2A
Claims 1-20 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the structural components of the computer, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs 72-75 of the present Specification, see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language limiting the data to patient data, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application (e.g. see MPEP 2106.05(g)).
Additionally, dependent claims 2-9, 11-18 and 20 include other limitations, but these limitations also amount to no more than mere instructions to apply the exception (e.g. the use of models of claims 2, 7, 11 and 16), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data disclosed in dependent claims 2-9, 11-18 and 20), and/or do not include any additional elements beyond those already recited in independent claims 1, 9 and 19, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the structural components of the computer), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
Paragraphs 72-74 of the Specification discloses that the additional elements (i.e. the structural components of the computer) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data ) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Dependent claims 2-9, 11-18 and 20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 9 and 19, and/or the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. the use of models of claims 2, 7, 11 and 16), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data disclosed in dependent claims 2-9, 11-18 and 20), and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-6, 8-15 and 17-20 are rejected under 35 U.S.C. 103 as being obvious over Sheffer (U.S. PG-Pub 2015/0066539 A1), hereinafter Sheffer, further in view of Heinze et al. (U.S. Patent 6,915,254 B1), hereinafter Heinze, and Morris et al. (U.S. PG-Pub 2011/0105852 A1), hereinafter Morris.
As per claims 1, 10 and 19, Sheffer discloses a system, a method, and a non-transitory machine-readable medium having information recorded thereon for completing a medical record, wherein the information, when read by a machine, causes the machine to perform operations including (Sheffer, see Figs. 3 and 6.):
at least one processor; and memory storing instructions that, when executed by the at least one processor, causes the system to perform operations including (Sheffer, see Figs. 3 and 6.):
receiving a medical record of a patient, wherein the medical record is associated with a set of components (See Sheffer paragraph 83, storing medical records (step 72) is performed using a medical records database or other memory system in communication with a computer processor, which executes the NLP engine or NLP software module; and paragraph 84, the medical records themselves include documents and information describing or related to patient treatment, including medical history, admission, procedural, and progress notes, consultant and specialist notes, diagnostic testing.); and
identifying one or more analytic influence dimensions for the medical record, each of the one or more analytic influence dimensions specifying an attribute of the patient or an attribute of a physician associated with the medical record (See Sheffer paragraph 100: a query 84 can be used to request evidence missing from medical record such as missing diagnosis need another.).
Sheffer fails to explicitly disclose:
analyzing/tracking medical transaction data in a large general population of patients to generate and dynamically update a data map/model, the data map pairing each of a plurality of medical suggestions with one or more analytic influence dimensions, each medical suggestion representing a medical transaction recommended by a medical professional and each analytic influence dimension in the data map specifying an attribute of a patient or an attribute of a physician associated with the medical transaction data, and each dimension-medical suggestion pair in the data map having a respective confidence score indicative of a degree of match between the medical suggestion and the analytic influence dimension in the medical transaction data over the large general population of patients, wherein dynamically updating the data map occurs via analysis of new medical transaction data for the large general population of patients;
obtaining, from the data map, relevant dimension-medical suggestion pairs, the relevant dimension-medical suggestion pairs having an analytic influence dimension in the data map that matches one of the one or more analytic influence dimensions for the medical record;
select a set of highest-ranked relevant dimension-medical suggestion pairs based on the respective confidence scores in the data map;
determining whether a component with a discrepancy exists by comparing a value of each of the set of components with a value of a corresponding component from the plurality of highest-ranked relevant dimension-medical suggestion pairs to determine a discrepancy; and
responsive to identifying a component with a discrepancy and to determining that the discrepancy satisfies a threshold: receiving a corrected value for the component with the discrepancy, and updating the medical record with the corrected value.
Heinze teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide:
analyzing/tracking medical transaction data in a large general population of patients to generate and dynamically update a data map/model (Heinze, see Fig. 4: ICD codes can be used to track medical transaction data for large population of patients.),
the data map pairing each of a plurality of medical suggestions with one or more analytic influence dimensions, each medical suggestion representing a medical transaction recommended by a medical professional and each analytic influence dimension in the data map specifying an attribute of a patient or an attribute of a physician associated with the medical transaction data (Heinze, see C20L61-64: databases are organized by grouping together a set of vectors under a common descriptor, which can be a code number representing a diagnosis or procedure (dimension); Fig. 3: each diagnosis or procedure (dimension) relates to a lexer (attribute) of a patient associated with medical transaction data such as body temperature or blood pressure.),
and each dimension-medical suggestion pair in the data map having a respective confidence score indicative of a degree of match between the medical suggestion and the analytic influence dimension in the medical transaction data over the large general population of patients (Heinze, see C20L64-C21L2: code numbers are assigned to individual parse items by comparing vectors defined in the database with the set of words in a parse item and measuring the difference; the code numbers with the smallest difference and below a maximum threshold is assigned to the parse item.),
wherein dynamically updating the data map occurs via analysis of new medical transaction data for the large general population of patients (Heinze, see C26L50-65 and C27L35-37: status fields for records are updated with ICD coding; processing triggering occurs based on a status update.);
obtaining, from the data map, relevant dimension-medical suggestion pairs, the relevant dimension-medical suggestion pairs having an analytic influence dimension in the data map that matches one of the one or more analytic influence dimensions for the medical record (Heinze, see C26L50-65 and C27L35-37: status fields for records are updated with ICD coding; processing triggering occurs based on a status update);
determining whether a component with a discrepancy exists by comparing a value of each of the set of components with a value of a corresponding component from the plurality of highest-ranked relevant dimension-medical suggestion pairs to determine a discrepancy; and responsive to identifying a component with a discrepancy and to determining that the discrepancy satisfies a threshold: receiving a corrected value for the component with the discrepancy, and updating the medical record with the corrected value (Heinze, see C20L67-C21L2; the code number with the smallest difference and below a maximum threshold is assigned to the parse item.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the automated clinical recognition method of Sheffer to include:
analyzing/tracking medical transaction data in a large general population of patients to generate and dynamically update a data map/model, the data map pairing each of a plurality of medical suggestions with one or more analytic influence dimensions, each medical suggestion representing a medical transaction recommended by a medical professional and each analytic influence dimension in the data map specifying an attribute of a patient or an attribute of a physician associated with the medical transaction data, and each dimension-medical suggestion pair in the data map having a respective confidence score indicative of a degree of match between the medical suggestion and the analytic influence dimension in the medical transaction data over the large general population of patients, wherein dynamically updating the data map occurs via analysis of new medical transaction data for the large general population of patients;
obtaining, from the data map, relevant dimension-medical suggestion pairs, the relevant dimension-medical suggestion pairs having an analytic influence dimension in the data map that matches one of the one or more analytic influence dimensions for the medical record;
select a set of highest-ranked relevant dimension-medical suggestion pairs based on the respective confidence scores in the data map;
determining whether a component with a discrepancy exists by comparing a value of each of the set of components with a value of a corresponding component from the plurality of highest-ranked relevant dimension-medical suggestion pairs to determine a discrepancy; and
responsive to identifying a component with a discrepancy and to determining that the discrepancy satisfies a threshold: receiving a corrected value for the component with the discrepancy, and updating the medical record with the corrected value,
as taught by Heinze, in order to provide an automated clinical recognition method that realized a more reliable indicator of physician notes since such a modification is just a combination of known prior art elements that yields to the predictable result of normalization of medical records (Heinze, see C2L23-27: automation provides a more reliable indicator of physician notes.).
Neither Sheffer nor Heinze disclose select a set of highest-ranked relevant dimension-medical suggestion pairs based on the respective confidence scores in the data map. Additionally, although Heinze discloses validating a medical record using relevant dimension-medical suggestion pairs, it does not explicitly disclose the use of highest-ranked relevant dimension-medical suggestion pairs.
Morris teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide select a set of highest-ranked relevant dimension-medical suggestion pairs based on the respective confidence scores in the data map (See Morris, paragraph 85: data imputation is used to determine risk scores and rank risks of health outcomes; the process is configured to receive patient-level data as input, and to generate an indication of (a) risks of healthcare outcomes and (b) benefits of treatments as outputs; and paragraph 102: a ranked list is provided of different interventions or recommendations and their associated benefits, when the different interventions produce different benefits for the patient; see also paragraph 119.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the automated clinical recognition method of Sheffer/Heinze to include the means to select a set of highest-ranked relevant dimension-medical suggestion pairs based on the respective confidence scores in the data map, as taught by Morris, in order to provide an automated clinical recognition method that can assist physicians with ranking patient according to how much total benefit they are likely to receive from a full course of treatments since such a modification is just a combination of known prior art elements that yields to the predictable result of improved patient care. (Morris, see paragraph 119: Output from the total benefit calculator, for example, assists physicians or care managers who are looking at the population as a whole to rank patients according to how much total benefit they are likely to receive from a full course of treatments. The output can be used, for example for outreach to identify patients who need to be contacted and asked to see their doctors. The output also can be used to determine which patients should be provided with risk-benefits reporting or interactive use of the processes herein, because those patients are likely to benefit the most; see paragraph 0005: This reliance on the practitioner to be able to convey such details to the patient coupled with the possibility of misinterpretation by the patient exposes multiple degrees of human error capable of reducing the quality of life of the patient.).
Sheffer/Heinze/Morris are all directed to the electronic processing of patient healthcare data. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
As per claims 2, 3, 9, 11, 12, 18 and 20, Sheffer/Heinze/Morris disclose claims 1 and 10, discussed above. Sheffer further discloses:
2,11. using a statistics model generated from the medical transaction data to estimate a value for a missing comp6, 8-15 and 17-20onent of the medical record based on values of other components in the medical record (Sheffer, see paragraph 100: the natural language processor analyzes medical records that identify clinical indicators (first component with populated value) in relation to rule based scenarios in an information model (first model); markers can be generated (second component with unpopulated value) according to clinical indicators; paragraph 29: CDI is applied to identify documentation with potential deficiencies concurrent with or immediately following patient stays. In this technique, requests or queries to the provider can be generated in near real time in order to help fill gaps in the clinical or treatment documentation at the point of care, or in short-term follow up. For example, documentation can be updated to fill gaps before discharge, during discharge, or soon after discharge. This type of CDI analysis can also be implemented to encompass CDI opportunities working on the floor, within or close to the patient care facility, and in teams of health information management (HIM) or care management specialists who can review records concurrently with the patient stay.);
3,12. wherein the medical transactions recommended by a medical professional include at least one of a medication drug prescription, a physical therapy referral, a diet recommendation, or a medical test (Records include prescriptions and tests, etc., see Sheffer paragraphs 26, 43, 49, 51, 57-64.); and
9,18,20. wherein an analytic influence dimension specifying an attribute of the patient includes at least one of: disease diagnosis; symptoms; or patient profile (Sheffer, see paragraph 84: medical records include medical history, admission, procedural and progress notes, diagnostic tests, and diagnoses statements.).
As per claims 4-6 and 13-15, Sheffer/Heinze/Morris disclose claims 1 and 10, discussed above. Sheffer further discloses:
6,15. wherein the medical record is normalized based on a model that is dynamically updated based on data related to medical transactions of the large general population of patients (See Sheffer, paragraph 101: clinical indicator comparison step (77b) may be executed during an office visit and the comparison may be in real time to allow the query to be generated; paragraph 29: CDI analysis can also be implemented to encompass CDI opportunities working on the floor, within or close to the patient care facility, and in teams of health information management (HIM) or care management specialists who can review records concurrently with the patient stay);
Sheffer fails to explicitly disclose, but Heinze teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide:
4,13. normalizing the medical record to generate a normalized medical record having the plurality of components (Heinze, see C2L51-60 - applying morphing, parsing and semantic analysis to the segments to generate a normalized file having a standardized form with parse items; identifying first type matches between parse items of the normalized file and a plurality of standard knowledge vectors, the first type matches being indicative of associations to a single code; generating associated codes at least on the basis of the first type matches and on the basis of natural language processing rules applied to the parse items; and outputting the generated associated codes - C5L33-38 - The generated codes and associations for justification, and various pieces of demographic information are placed in an output record that can be displayed to an interactive graphical user interface for review and/or modification, printed as a billing form, stored as an (archived) computer file, or sent as electronic input to a third-party billing software system); and
5,14. obtaining a medical record format associated with a user;
converting the normalized medical record into the medical record format; and
sending the converted normalized medical record to the user (Heinze, see C2L51-60 - applying morphing, parsing and semantic analysis to the segments to generate a normalized file having a standardized form with parse items; identifying first type matches between parse items of the normalized file and a plurality of standard knowledge vectors, the first type matches being indicative of associations to a single code; generating associated codes at least on the basis of the first type matches and on the basis of natural language processing rules applied to the parse items; and outputting the generated associated codes - C5L33-38 - The generated codes and associations for justification, and various pieces of demographic information are placed in an output record that can be displayed to an interactive graphical user interface for review and/or modification, printed as a billing form, stored as an (archived) computer file, or sent as electronic input to a third-party billing software system).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the automated clinical recognition method of Sheffer/Heinze/Morris to include normalizing a medical record in various methods, as taught by Heinze, in order to arrive at an automated clinical recognition method that provides a more reliable indicator of physician notes since such a modification is just a combination of known prior art elements that yields to the predictable result of normalizing electronic medical records (Heinze, see C2L23-27 – automation provides a more reliable indicator of physician notes.).
As per claims 8 and 17, Sheffer/Heinze/Morris disclose claims 1 and 10, discussed above. Sheffer fails to explicitly disclose, but Heinze teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide wherein the degree of match between the medical suggestion and the analytic influence dimension in the data map is based on occurrences of the dimension-medical suggestion pairs in the analyzed medical transaction data (See Heinze, Fig. 4: ICD codes can be used to track medical transaction data for large population of patients; C4L43-55: based on the bottom-up parse, phrases, clauses, and sentences are matched individually and in combination against knowledge-based vectors stored in a database. Millions of these vectors collectively represent a body of medical and coding knowledge. The vectors are the descriptions of diagnoses and procedures stored in one of three forms, i.e., “is-a”, “synonymy”, and “part/whole” type relations; The medical and coding knowledge represented by the vectors comprise the universe of types and relations, for example, between body parts and regions, infectious organisms, poisons, hazardous substances, drugs, and medicinals; C17L43-47: the anatomy database 150 provides synonymy and part whole relations and is secondary to the ICD and CPT databases 140, 145. Anatomy database 150 is used to determine codes that are Sensitive to the body part being affected.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the automated clinical recognition method of Sheffer/Heinze/Morris to include wherein the degree of match between the medical suggestion and the analytic influence dimension in the data map is based on occurrences of the dimension-medical suggestion pairs in the analyzed medical transaction data, as taught by Heinze, in order to arrive at an automated clinical recognition method that provides a more reliable indicator of physician notes since such a modification is just a combination of known prior art elements that yields to the predictable result of normalization of electronic medical records (Heinze, C2L23-27: automation provides a more reliable indicator of physician notes).
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being obvious over Sheffer/Heinze/Morris, further in view of Hasan et al. (U.S. Patent 8,473,310 B2), hereinafter Hasan.
As per claims 7 and 16, Sheffer/Heinze/Morris disclose claims 6 and 15, discussed above. Sheffer fails to explicitly disclose, but Heinze teaches wherein the model includes a mapping from a plurality of permutations to a term (Hasan, see C17L25-33: a plurality of fields are listed representing data that can exist anywhere on the database sets and be in any format or language; C26L55-67: rules engine also remodels the data, if necessary to a structure or appearance predefined by the normalized format).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the automated clinical recognition method of Sheffer/Heinze/Morris to include wherein the model includes a mapping from a plurality of permutations to a term, as taught by Hasan, in order to arrive at an automated clinical recognition method that provides homogenized data readable by a variety of computers since such a modification is just a combination of known prior art elements that yields to the predictable result of normalizing electronic medical records (Hasan, see C2L35-39: the consequence of having different databases of different formats is that it is not possible to provide a central repository of homogenized data readable by any variety of computers.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Sheffer and Hasan are both directed to the electronic processing of patient healthcare data.
Response to Arguments
Applicant’s arguments filed 26 August 2026 concerning the rejection of all claims under 35 U.S.C. 101 and 103(a) have been fully considered but they are not persuasive.
With regard to the rejection of the claims under 35 USC 101, Applicant argues on pages 8-9 that the claims were incorrectly rejected as not being statutory because the claims were incorrectly mischaracterized as being directed to managing personal behavior or relationships or interactions between people because there are no people in the claims.
The Office respectfully disagrees. Please see the statutory rejection above, wherein the claims are shown to be directed to an abstract idea without significantly more.
Multiple CAFC decisions that the Office has characterized as Certain Method of Organizing Human Activity did not actively recite a person or persons performing the steps of the claims (see, e.g., EPG, TU communications, Ultramercial). Because whether a human is required to perform the step of the claim is not a requirement for claims to encompass certain method of organizing human activity, this argument is not persuasive.
Accordingly, the rejection is upheld.
With regard to the rejection of the claims under 35 USC 103, Applicant argues on pages 9-11 that the claims were incorrectly rejected over the cited prior art because the indication that Heinze mapping a “procedure” to dimensions “improperly conflates the medical suggestions and analytical dimensions”; and “Heinze contains no concept of extracted medical suggested components with values that are compared to clinical errors.”
The Office respectfully disagrees.
The applicant is attacking the application of one piece of prior art when the rejection is made over a combination of references. The primary reference, Shaeffer, is used to disclose the claimed dimensions, while Heinze and Morris provides missing elements. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). Here the combination of the cited references disclose the contested limitation.
Further, in response to applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., “concept of extracted medical suggested components with values that are compared to clinical errors”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant is invited to amend the claims to clarify the invention.
The remainder of Applicant's arguments have been fully considered but are moot because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Accordingly, the rejection is upheld.
In conclusion, all of the limitations which Applicant disputes as missing in the applied references, including the features newly added by amendment, have been fully addressed by the Office as either being fully disclosed or obvious in view of the collective teachings of Sheffer, Heinze, Morris and Hasan, based on the logic and sound scientific reasoning of one ordinarily skilled in the art at the time of the invention, as detailed in the remarks and explanations given in the preceding sections of the present Office Action and in the prior Office Action (29 April 2026), and incorporated herein.
Conclusion
Unused but cited relevant prior art includes:
Krishnan et al. (U.S. PG-Pub 2006/0184475 A1) discloses a method for missing data approaches in medical decision support systems.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
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/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
16 September 2026
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