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 .
Status of Claims
This office action for the 18/797409 application is in response to the communications filed January 22, 2026.
Claims 1, 4, 11, 13, and 14 were amended January 22, 2026.
Claim 15 was added as new January 22, 2026.
Claims 1-15 are currently pending and considered below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “by the Natural Language Processing (NLP) module”, “an application recommendation module”, “an application module” and “feedback processing module” in claim 1 and “an input document scanning module”, “a Natural Language Processing (NLP) module”, “an application recommendation module”, “an application module” and “feedback processing module” in claim 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 1,
The claim recites the limitations of “the Natural Language Processing (NLP) module”, “the attributes”, “the one or more output”, “the application module”, “the application specific one or more secondary knowledge graphs”, and “the one or more outputs”. Each of these limitations lack antecedent basis and the claim is considered to be indefinite for these reasons. For the purpose of examination, the Examiner will interpret these limitations as “a Natural Language Processing (NLP) module”, “attributes”, “one or more outputs”, “an application module”, “an application specific one or more secondary knowledge graphs”, and “the one or more outputs” respectively.
As per claim 11,
The claim recites the limitation of “the coder”. This limitation lacks antecedent basis and is therefore considered to be indefinite. For the purposes of examination, the Examiner will interpret this limitation as “a coder”
As per claim 13,
The claim recites the limitations of “The system”, “the method” and “the primary knowledge graph”. These limitations lack antecedent basis and the claim is considered to be indefinite for these reasons. For the purpose of examination, the Examiner will interpret these limitations as “A system”, “a method” and “a primary knowledge graph” respectively.
As per claim 14,
The claim recites the limitation of “The system as claimed in claim 11”. Claim 11 is not a system claim, but rather it is a method claim. Accordingly, this claim is indefinite. For the purposes of examination, the Examiner will interpret this limitation as “The method as claimed in claim 11”.
As per claims 2-12,
These claims are dependent from base claims that have been found to be indefinite. These claims are also found to be indefinite for this reason.
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 1-14 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.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method for optimizing chart review and audit workflows in accurate risk adjustment, comprising: a method for optimizing chart review and audit workflows in accurate risk adjustment, comprising: receiving one or more inputs, including a structured input and an unstructured input, from a user, wherein the structured input having a number of formats and the unstructured input having one or more input documents; pre-processing the one or more input documents to identify pages containing non-machine readable text, receiving data, from a primary knowledge graph, processes free text to build a patient-specific concept graph, expands the patient-specific concept graph with background knowledge from the primary knowledge graph, and obtains one or more outputs including identification of entities, types, attributes, assertion status, negation detection, temporal expression recognition, and location offset; providing one or more recommendations on diagnosis codes, wherein the diagnosis codes are used as a tool to group and identify diseases, disorders, symptoms, poisonings, adverse effects of drugs and chemicals, injuries and other reasons for patient encounters, queries application-specific secondary knowledge graphs using Subject-Predicate-Object (SPO) triples to traverse entity relationships, retrieves disease-related details including symptoms, medicines, and laboratory data, cross-references extracted information against derived lists from the knowledge graphs, and combined the retrieved information with one or more outputs to provide one or more suggestions; scrutinizing and validating the one or more suggestions by the user, wherein the user involved in development of a plurality of instructions and perform a plurality of curation tasks; collecting a plurality of feedback, during execution of the plurality of curation tasks, by the user. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. The identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“computer-implemented”, “that reduces Optical Character Recognition (OCR) processing time and errors”, “wherein only the identified pages are passed to an OCR engine for conversion to text format, thereby reducing processing time and errors associated with OCR processing”, “by a Natural Language Processing module implementing a deep learning pipeline, wherein the NLP module”, “passing the one or more outputs though an application recommendation module for”, “passing the one or more outputs to an application module, wherein the application module” which corresponds to merely using a computer as a tool to perform an abstract idea.
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“transmitting the plurality of feedback to a feedback processing module for making decisions for updating the application module, based on the collected plurality of feedback, including addition of new rules, fine-tuning of models.” which corresponds to mere data gathering and/or output.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the courts as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d)(II), such as:
“transmitting the plurality of feedback to a feedback processing module for making decisions for updating the application module, based on the collected plurality of feedback, including addition of new rules, fine-tuning of models.” which corresponds to receiving or transmitting data over a network.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 2,
Claim 2 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 2 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein said one or more input documents include but not limited to medical charts.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein said diagnosis codes include but not limited to ICD-10-CM codes.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the one or more suggestions include but not limited to ICD-10-CM codes, descriptions, diagnosis evidence, MEAT evidence, cross-walk to HCC codes, and RAF scores of HCC codes.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein said number of formats include but not limited to PDF files, scanned files, faxed files, HL7, FHIR, and CCDA.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 6 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein said one or more recommendations include but not limited to identify diseases, disorders, symptoms, poisonings, adverse effects of drugs and chemicals, injuries.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 7,
Claim 7 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein said one or more suggestions include but not limited to ICD-10-CM codes, descriptions, diagnosis evidence, MEAT evidence, cross-walk to HCC codes, and RAF scores of HCC codes.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 8 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the process of scrutinizing and the validating of the one or more suggestions include but not limited to accepting and rejecting the one or more suggestions, making updates and adding new instructions with supporting evidence from said medical charts.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 9,
Claim 9 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 9 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the primary knowledge graph includes but not limited to core knowledge graph.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 10,
Claim 10 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 10 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the secondary knowledge graph includes but not limited to ICD10-CM knowledge graph, MEAT knowledge graph.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 11,
Claim 11 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 11 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the plurality of feedback, including: an implicit feedback, based on the curation tasks including accepting, updating, and adding of actions, … collects the feedback; and an explicit feedback, based on the curation task including deletion of an action, requires confirmation from a coder.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“automatically” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 12,
Claim 12 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 12 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the knowledge graph including: i. a core knowledge graph having a source of information and facilitating assessments and evaluation; ii. a ICD-10-CM knowledge graph facilitating classification of medical conditions and supports precise documentation and reporting; and iii. MEAT (Monitor, Evaluate, Assess, and Treat) Knowledge Graph facilitating evidence-based insights to drive informed decisions and promote effective care management.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 13,
Claim 13 is substantially similar to claim 1. Accordingly, claim 13 is rejected for the same reasons as claim 1.
As per claim 14,
Claim 14 depends from claim 11 and inherits all the limitations of the claim from which it depends. Claim 14 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the Natural Language Processing (NLP) module implements deep learning and machine learning techniques to obtain the one or more outputs.” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 15,
Claim 15 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 15 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising detecting handwritten notes within the one or more input documents … and generating an indication to the user identifying locations of the handwritten notes for verification” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“by the OCR engine” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
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.
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.
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.
Claims 1-3, 6 and 8-15 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 2023/0377748; herein referred to as Yang) in view of Sacaleanu et al. (US 2019/0006027; herein referred to as Sacaleanu).
As per claim 1,
Yang teaches a computer-implemented method for optimizing chart review and audit workflows in accurate risk adjustment, comprising: receiving one or more inputs, including a structured input and an unstructured input, from a user, wherein the structured input having a number of formats and the unstructured input having one or more input documents:
(Paragraphs [0037] and [0162] of Yang. The teaching describes a new method for automated clinical assessment generation, which generates a patient assessment and plan using a knowledge graph. Key innovations of embodiments include knowledge inference using both structured data (e.g., ICD codes, medications, and lab results) and unstructured EHR notes. Embodiments infer rich clinical knowledge from notes with a SOAP structure. In this case, the chief complaint and subjective evidence lead to objective measurements. Assessments can be inferred from both subjective and objective evidence, all of which lead to specific plans.)
Yang further teaches receiving data, from a primary knowledge graph, by a Natural Language Processing module implementing a deep learning pipeline, wherein the NLP module processes free text to build a patient-specific concept graph, expands the patient-specific concept graph with background knowledge from the primary knowledge graph, and obtains one or more outputs including identification of entities, types, attributes, assertion status, negation detection, temporal expression recognition, and location offset:
(Paragraphs [0038]-[0040], [0178] and [0180] of Yang. The teaching describes a local or patient-specific concept graph by NLP-processing the free text of the subjective and objective sections. This patient-specific concept graph is then expanded with background knowledge extracted from an external and comprehensive knowledge resource, such as the Unified Medical Language System (UMLS) (Bodenreider, 2004). Once the concept-graph is built, the method generates the assessment and plan from the built concept graph. This assessment and plan text generation is performed by a model, e.g., a decoder, that is trained end-to-end as further detailed below. Embodiments are built on an innovative graph neural network, where rich clinical knowledge is incorporated into an end-to-end corpus-learning system. The system (embodiments) learn to generate assessment and plan text from objective and subjective sections of a medical graph. The system generates novel content for the assessment text including differential diagnoses or other important related discussions that do not appear in the input text. The new functionality described herein generates the medical support text, i.e., assessment and plan, using a knowledge graph. The teachings of all patents, published applications and references cited herein are incorporated by reference in their entirety. Marilisa Amoia, Frank Diehl, Jesus Gimenez, Joe Pinto, Raphael Schumann, Fabian Stemmer, Paul Vozila, and Yi Zhang. 2018. Scalable wide and deep learning for computer assisted coding. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 1-7, New Orleans-Louisiana. Association for Computational Linguistics.)
Yang further teaches passing the one or more outputs though an application recommendation module for providing one or more recommendations on diagnosis codes, wherein the diagnosis codes are used as a tool to group and identify diseases, disorders, symptoms, poisonings, adverse effects of drugs and chemicals, injuries and other reasons for patient encounters:
(Paragraph [0097] of Yang. The teaching describes that an embodiment can pretrain a large code model which has the ability to infer diagnosis and recommended drug code for future patient visits based on the codes from the patient's previous visits. These inferred, i.e., predicted, diagnosis and drug codes can then be used to augment the patient's concept graph, e.g., at step 223.)
Yang further teaches passing the one or more outputs to an application module, wherein the application module queries application-specific secondary knowledge graphs using Subject-Predicate-Object (SPO) triples to traverse entity relationships, retrieves disease-related details including symptoms, medicines, and laboratory data, cross-references extracted information against derived lists from the knowledge graphs, and combined the retrieved information with one or more outputs to provide one or more suggestions:
(Paragraphs [0051] and [0110]-[0112] of Yang. The teaching describes the encoder 545 uses MIPS 558 to find similar subjective and objective sections 559 and similar patient diagnoses ICD codes 560. The data 559 and 560 may be combined into a graph and combined, e.g., via graph union 561 with the graphs 554 and 557 to form the patient graph 562. The encoder 545 also takes the patient's demographics information 563, lab, diagnosis and medication codes of the previous (n-1) visits 564 a and 564 n and uses a transformer encoder (not shown) to predict diagnosis and medication codes 565 for the next visit. The union graph 562 (which may include the graph 560 (graph of similar patient diagnoses ICD codes)) is processed with the GAT network 568 and the results matrix of this processing are concatenated 566 with the predicted data 565 matrix to form the graph 567 that can be processed by the downstream decoder to predict a next word in an assessment or plan. At step 222, natural language processing is performed on the soap note 100 and the patient information relations 331 shown in FIG. 3 are extracted. Specifically, the relations 331 a “patient→allergic→lantus insulin,” 331 b “patient→has→type 2 diabetes,” and 331 c “patient→want→weight loss.”)
Yang does not explicitly teach a risk adjustment that reduces Optical Character Recognition (OCR) processing time and errors, pre-processing the one or more input documents to identify pages containing non-machine readable text, wherein only the identified pages are passed to an OCR engine for conversion to text format, thereby reducing processing time and errors associated with OCR processing, scrutinizing and validating the one or more suggestions by the user, wherein the user involved in development of a plurality of instructions and perform a plurality of curation tasks; collecting a plurality of feedback, during execution of the plurality of curation tasks, by the user; or transmitting the plurality of feedback to a feedback processing module for making decisions for updating the application module, based on the collected plurality of feedback, including addition of new rules, fine-tuning of models.
However, Sacaleanu teaches a risk adjustment that reduces Optical Character Recognition (OCR) processing time and errors, and pre-processing the one or more input documents to identify pages containing non-machine readable text, wherein only the identified pages are passed to an OCR engine for conversion to text format, thereby reducing processing time and errors associated with OCR processing
(Paragraphs [0039]-[0042] of Sacaleanu. The teaching describes a data preparation module 204 which is configured to extract text from an unstructured electronic health record. For example, the data preparation module 204 may be configured to receive data representing an electronic health record, e.g., a PDF file. The data preparation module 204 may include one or more data processing engines, e.g., an optical character recognition (OCR) engine, that are configured to convert the received data into machine encoded text, e.g., in Hypertext Markup Language (HTML) format. The data preparation module 204 may parse the machine encoded text to extract a formatted text representation of the electronic health record. The data preparation module 204 may provide the formatted text representation of the electronic health record to the boundary detection module 206. This data preparation module is construed as an element that reduces OCR processing time and errors by preparing data for OCR analysis. Here all pages in the PDF file are understood to be identified as having non-machine readable text and are then sent for OCR processing.)
Sacaleanu further teaches input data for a knowledge graph that comes from a PDF using OCR:
(Paragraphs [0038] and [0039] of Sacaleanu. The teaching describes identify and extract entities from the text of each of the multiple documents the entity extraction and linking module 208 may apply natural language processing techniques. The entity extraction and linking module 208 may then apply reasoning techniques over multiple knowledge sources, e.g., including medical ontologies 212 and knowledge graphs or databases 214 to infer condition-evidence linking. In some implementations styling information, e.g., headings or text typeface, extracted from the EHR may be used to preserve the visual structure of the original EHR in the GUI, since styling information is often lost when extracting formatted text from a PDF document, e.g., using OCR techniques.)
Sacaleanu further teaches updating a knowledge base model with input from the user to provide feedback and changes to the model:
(Paragraphs [0071] and [0072] of Sacaleanu. The teaching describes that the system may apply a continuous learning loop to improve the accuracy of provided output data. For example, the system may further receive user input through the interactive GUI. A user may provide user input through the GUI indicating edits that should be made to the GUI, e.g., edits to the visualized document boundaries (separating the multiple documents) or edits to the linked supporting evidences and medical condition entities. The received user input may be processed and used by the system to update modules or databases included in the system. For example, the received user input may be used to update the knowledge base described above with reference to step 308, e.g., to remove a particular medication from a set of medications that is typically used to treat a particular disease. In this manner, future queries to the knowledge base reflect the user's feedback.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the teachings of Yang, the feedback teachings of Sacaleanu. Paragraph [0023] of Sacaleanu teaches that the provided methods of working with knowledge graphs to understand a patient’s condition result in an improved accuracy of identified medical conditions and would naturally result in improved healthcare services provided to the patients. One of ordinary skill in the art would have added to the teachings of Yang, the teachings of Sacaleanu based on this incentive without yielding unexpected results.
The combined teaching of Yang and Sacaleanu would have then taught scrutinizing and validating the one or more suggestions by the user, wherein the user involved in development of a plurality of instructions and perform a plurality of curation tasks; collecting a plurality of feedback, during execution of the plurality of curation tasks, by the user; and transmitting the plurality of feedback to a feedback processing module for making decisions for updating the application module, based on the collected plurality of feedback, including addition of new rules, fine-tuning of models:
(Paragraph [0097] of Yang. The teaching describes that an embodiment can pretrain a large code model which has the ability to infer diagnosis and recommended drug code for future patient visits based on the codes from the patient's previous visits. These inferred, i.e., predicted, diagnosis and drug codes can then be used to augment the patient's concept graph, e.g., at step 223.)
(Paragraphs [0110]-[0112] of Yang. The teaching describes the encoder 545 uses MIPS 558 to find similar subjective and objective sections 559 and similar patient diagnoses ICD codes 560. The data 559 and 560 may be combined into a graph and combined, e.g., via graph union 561 with the graphs 554 and 557 to form the patient graph 562. The encoder 545 also takes the patient's demographics information 563, lab, diagnosis and medication codes of the previous (n-1) visits 564 a and 564 n and uses a transformer encoder (not shown) to predict diagnosis and medication codes 565 for the next visit. The union graph 562 (which may include the graph 560 (graph of similar patient diagnoses ICD codes)) is processed with the GAT network 568 and the results matrix of this processing are concatenated 566 with the predicted data 565 matrix to form the graph 567 that can be processed by the downstream decoder to predict a next word in an assessment or plan.)
(Paragraphs [0071] and [0072] of Sacaleanu. The teaching describes that the system may apply a continuous learning loop to improve the accuracy of provided output data. For example, the system may further receive user input through the interactive GUI. A user may provide user input through the GUI indicating edits that should be made to the GUI, e.g., edits to the visualized document boundaries (separating the multiple documents) or edits to the linked supporting evidences and medical condition entities. The received user input may be processed and used by the system to update modules or databases included in the system. For example, the received user input may be used to update the knowledge base described above with reference to step 308, e.g., to remove a particular medication from a set of medications that is typically used to treat a particular disease. In this manner, future queries to the knowledge base reflect the user's feedback.)
As per claim 2,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
The combined teaching of Yang and Sacaleanu further teaches wherein said one or more input documents include but not limited to medical charts:
(Paragraph [0162] of Yang. The teaching describes include knowledge inference using both structured data (e.g., ICD codes, medications, and lab results) and unstructured EHR notes. Embodiments infer rich clinical knowledge from notes with a SOAP structure. In this case, the chief complaint and subjective evidence lead to objective measurements. Assessments can be inferred from both subjective and objective evidence, all of which lead to specific plans.)
(Paragraph [0042] of Sacaleanu. The teaching describes that the data preparation module 204 may include one or more data processing engines, e.g., an optical character recognition (OCR) engine, that are configured to convert the received data into machine encoded text)
As per claim 3,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
Yang further teaches wherein said diagnosis codes include but not limited to ICD-10-CM codes:
(Paragraphs [0166] and [0201] of Yang. The teaching describes that machine learning based models have been developed to predict structured outputs, such as the International Classification of Diseases, Clinical Modification (ICDCM) codes. It is clear that these are ICD-10-CM codes.)
As per claim 6,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
Yang further teaches wherein said one or more recommendations include but not limited to identify diseases, disorders, symptoms, poisonings, adverse effects of drugs and chemicals, injuries:
(Paragraphs [0076] and [0097] of Yang. The teaching describes that an embodiment can pretrain a large code model which has the ability to infer diagnosis and recommended drug code for future patient visits based on the codes from the patient's previous visits. These inferred, i.e., predicted, diagnosis and drug codes can then be used to augment the patient's concept graph, e.g., at step 223. Medline Plus describes symptoms, causes, treatments, and prevention information of over 1000 diseases, illnesses, health conditions, and wellness issues. Embodiments can use Open IE (Stanovsky et al., 2018) to extract external medical knowledge from MedlinePlus to augment, i.e., expand, a patient's concept graph. This recommendation from the Medline Plus is construed to cover diseases, disorders, symptoms, poisonings, adverse effects of drugs and chemicals, injuries due to its extensive knowledge base.)
As per claim 8,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
The combined teaching of Yang and Sacaleanu further teaches wherein the process of scrutinizing and the validating of the one or more suggestions include but not limited to accepting and rejecting the one or more suggestions, making updates and adding new instructions with supporting evidence from said medical charts:
(Paragraph [0097] of Yang. The teaching describes that an embodiment can pretrain a large code model which has the ability to infer diagnosis and recommended drug code for future patient visits based on the codes from the patient's previous visits. These inferred, i.e., predicted, diagnosis and drug codes can then be used to augment the patient's concept graph, e.g., at step 223.)
(Paragraphs [0110]-[0112] of Yang. The teaching describes the encoder 545 uses MIPS 558 to find similar subjective and objective sections 559 and similar patient diagnoses ICD codes 560. The data 559 and 560 may be combined into a graph and combined, e.g., via graph union 561 with the graphs 554 and 557 to form the patient graph 562. The encoder 545 also takes the patient's demographics information 563, lab, diagnosis and medication codes of the previous (n-1) visits 564 a and 564 n and uses a transformer encoder (not shown) to predict diagnosis and medication codes 565 for the next visit. The union graph 562 (which may include the graph 560 (graph of similar patient diagnoses ICD codes)) is processed with the GAT network 568 and the results matrix of this processing are concatenated 566 with the predicted data 565 matrix to form the graph 567 that can be processed by the downstream decoder to predict a next word in an assessment or plan.)
(Paragraphs [0071] and [0072] of Sacaleanu. The teaching describes that the system may apply a continuous learning loop to improve the accuracy of provided output data. For example, the system may further receive user input through the interactive GUI. A user may provide user input through the GUI indicating edits that should be made to the GUI, e.g., edits to the visualized document boundaries (separating the multiple documents) or edits to the linked supporting evidences and medical condition entities. The received user input may be processed and used by the system to update modules or databases included in the system. For example, the received user input may be used to update the knowledge base described above with reference to step 308, e.g., to remove a particular medication from a set of medications that is typically used to treat a particular disease. In this manner, future queries to the knowledge base reflect the user's feedback.)
As per claim 9,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
Yang further teaches wherein the primary knowledge graph includes but not limited to core knowledge graph:
(Paragraphs [0037] and [0162] of Yang. The teaching describes a new method for automated clinical assessment generation, which generates a patient assessment and plan using a knowledge graph. Key innovations of embodiments include knowledge inference using both structured data (e.g., ICD codes, medications, and lab results) and unstructured EHR notes. Embodiments infer rich clinical knowledge from notes with a SOAP structure. In this case, the chief complaint and subjective evidence lead to objective measurements. Assessments can be inferred from both subjective and objective evidence, all of which lead to specific plans. Here the medications and lab results would form the basis of the core knowledge graph, with the addition of ICD knowledge and SOAP knowledge. SOAP notes and MEAT information are construed to be functionally similar.)
As per claim 10,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
Yang further teaches wherein the secondary knowledge graph includes but not limited to ICD10-CM knowledge graph, MEAT knowledge graph:
(Paragraphs [0037] and [0162] of Yang. The teaching describes a new method for automated clinical assessment generation, which generates a patient assessment and plan using a knowledge graph. Key innovations of embodiments include knowledge inference using both structured data (e.g., ICD codes, medications, and lab results) and unstructured EHR notes. Embodiments infer rich clinical knowledge from notes with a SOAP structure. In this case, the chief complaint and subjective evidence lead to objective measurements. Assessments can be inferred from both subjective and objective evidence, all of which lead to specific plans. Here the medications and lab results would form the basis of the core knowledge graph, with the addition of ICD knowledge and SOAP knowledge. SOAP notes and MEAT information are construed to be functionally similar.)
(Paragraphs [0110]-[0112] of Yang. The teaching describes the encoder 545 uses MIPS 558 to find similar subjective and objective sections 559 and similar patient diagnoses ICD codes 560. The data 559 and 560 may be combined into a graph and combined, e.g., via graph union 561 with the graphs 554 and 557 to form the patient graph 562. The encoder 545 also takes the patient's demographics information 563, lab, diagnosis and medication codes of the previous (n-1) visits 564 a and 564 n and uses a transformer encoder (not shown) to predict diagnosis and medication codes 565 for the next visit. The union graph 562 (which may include the graph 560 (graph of similar patient diagnoses ICD codes)) is processed with the GAT network 568 and the results matrix of this processing are concatenated 566 with the predicted data 565 matrix to form the graph 567 that can be processed by the downstream decoder to predict a next word in an assessment or plan.)
As per claim 11,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
Sacaleanu further teaches wherein the plurality of feedback, including: an implicit feedback, based on the curation tasks including accepting, updating, and adding of actions, automatically collects the feedback; and an explicit feedback, based on the curation task including deletion of an action, requires confirmation from a coder:
Paragraphs [0071] and [0072] of Sacaleanu. The teaching describes that the system may apply a continuous learning loop to improve the accuracy of provided output data. For example, the system may further receive user input through the interactive GUI. A user may provide user input through the GUI indicating edits that should be made to the GUI, e.g., edits to the visualized document boundaries (separating the multiple documents) or edits to the linked supporting evidences and medical condition entities. The received user input may be processed and used by the system to update modules or databases included in the system. For example, the received user input may be used to update the knowledge base described above with reference to step 308, e.g., to remove a particular medication from a set of medications that is typically used to treat a particular disease. In this manner, future queries to the knowledge base reflect the user's feedback.)
As per claim 12,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
Yang further teaches wherein the knowledge graph including: i. a core knowledge graph having a source of information and facilitating assessments and evaluation; ii. a ICD-10-CM knowledge graph facilitating classification of medical conditions and supports precise documentation and reporting; and iii. MEAT (Monitor, Evaluate, Assess, and Treat) Knowledge Graph facilitating evidence-based insights to drive informed decisions and promote effective care management:
(Paragraphs [0037] and [0162] of Yang. The teaching describes a new method for automated clinical assessment generation, which generates a patient assessment and plan using a knowledge graph. Key innovations of embodiments include knowledge inference using both structured data (e.g., ICD codes, medications, and lab results) and unstructured EHR notes. Embodiments infer rich clinical knowledge from notes with a SOAP structure. In this case, the chief complaint and subjective evidence lead to objective measurements. Assessments can be inferred from both subjective and objective evidence, all of which lead to specific plans. Here the medications and lab results would form the basis of the core knowledge graph, with the addition of ICD knowledge and SOAP knowledge. SOAP notes and MEAT information are construed to be functionally similar.)
As per claim 13,
Claim 13 is substantially similar to claim 1. Accordingly, claim 13 is rejected for the same reasons as claim 1.
As per claim 14,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 11.
Yang further teaches wherein the Natural Language Processing (NLP) module implements deep learning and machine learning techniques to obtain the one or more outputs:
(Paragraphs [0038]-[0040], [0178] and [0180] of Yang. The teaching describes a local or patient-specific concept graph by NLP-processing the free text of the subjective and objective sections. This patient-specific concept graph is then expanded with background knowledge extracted from an external and comprehensive knowledge resource, such as the Unified Medical Language System (UMLS) (Bodenreider, 2004). Once the concept-graph is built, the method generates the assessment and plan from the built concept graph. This assessment and plan text generation is performed by a model, e.g., a decoder, that is trained end-to-end as further detailed below. Embodiments are built on an innovative graph neural network, where rich clinical knowledge is incorporated into an end-to-end corpus-learning system. The system (embodiments) learn to generate assessment and plan text from objective and subjective sections of a medical graph. The system generates novel content for the assessment text including differential diagnoses or other important related discussions that do not appear in the input text. The new functionality described herein generates the medical support text, i.e., assessment and plan, using a knowledge graph. The teachings of all patents, published applications and references cited herein are incorporated by reference in their entirety. Marilisa Amoia, Frank Diehl, Jesus Gimenez, Joe Pinto, Raphael Schumann, Fabian Stemmer, Paul Vozila, and Yi Zhang. 2018. Scalable wide and deep learning for computer assisted coding. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 1-7, New Orleans-Louisiana. Association for Computational Linguistics.)
As per claim 15,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
Sacaleanu further teaches further comprising detecting handwritten notes within the one or more input documents by the OCR engine and generating an indication to the user identifying locations of the handwritten notes for verification:
(Paragraphs [0036] and [0071]-[0079] of Sacaleanu. The teaching describes that the system may apply a continuous learning loop to improve the accuracy of provided output data. For example, the system may further receive user input through the interactive GUI. A user may provide user input through the GUI indicating edits that should be made to the GUI, e.g., edits to the visualized document boundaries (separating the multiple documents) or edits to the linked supporting evidences and medical condition entities. The received user input may be processed and used by the system to update modules or databases included in the system. For example, the received user input may be used to update the knowledge base described above with reference to step 308, e.g., to remove a particular medication from a set of medications that is typically used to treat a particular disease. In this manner, future queries to the knowledge base reflect the user's feedback. During the segmentation stage 104, the boundary detection module 206 receives formatted text extracted from the EHR and segments the formatted text into multiple documents, each document including a portion of the text extracted from the EHR. The boundary detection module 206 segments the received formatted text into multiple documents based on document type. For example, the data preparation module may separate the received formatted text into respective documents representing physician notes, prescriptions, laboratory results, admission or discharge notes, letters of referral, procedure notes or radiology images using machine learning techniques and/or business rules that detect boundaries between different encounters in the received data. For each portion of text, the system determines, based on the output from the first classifier, whether the portion of text is a boundary page or not (step 408). In response to determining that a portion of text is not a boundary page, the system determines, based on the output from the second classifier, whether the portion of text is relevant or not (step 410 a). In response to determining that the portion of text is not relevant, the system removes the portion of text from the formatted text representations of the electronic health record (step 412). In response to determining that the portion of text is relevant, the system provides the portion of text as output (step 416).)
Claims 4 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Yang and Sacaleanu in further view of Austin et al. (US 2018/0122499; herein referred to as Austin).
As per claim 4,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
Yang further teaches wherein the one or more suggestions include but not limited to ICD-10-CM codes, descriptions, diagnosis evidence, MEAT evidence:
(Paragraphs [0037] and [0162] of Yang. The teaching describes a new method for automated clinical assessment generation, which generates a patient assessment and plan using a knowledge graph. Key innovations of embodiments include knowledge inference using both structured data (e.g., ICD codes, medications, and lab results) and unstructured EHR notes. Embodiments infer rich clinical knowledge from notes with a SOAP structure. In this case, the chief complaint and subjective evidence lead to objective measurements. Assessments can be inferred from both subjective and objective evidence, all of which lead to specific plans. Here the medications and lab results would form the basis of the core knowledge graph, with the addition of ICD knowledge and SOAP knowledge. SOAP notes and MEAT information are construed to be functionally similar.)
The combined teaching of Yang and Sacaleanu does not explicitly teach cross-walk to HCC codes, and RAF scores of HCC codes.
However, Austin teaches cross-walking ICD-10 codes to their corresponding HCC codes and presenting RAF scores of the HCC codes:
(Paragraphs [0047] and [0048] of Austin. The teaching describes that Health information system (HIS) rules engine 23 of server 22 may link conditions and diagnosis codes (ICD9 or ICD10) with corresponding HCCs. In turn, server 22 implements one or more techniques of this disclosure to detect possible errors or omissions in the ICD codes. In various use cases, certain conditions may trigger server 22 to generate suggestions for medical staff members to examine or reexamine at the language of the diagnosis, or to look for and document other co-morbidities that could lead to a more accurate HCC risk score. For instance, “diabetes with peripheral vascular disease manifestations” may trigger a different HCC risk score than “diabetes without complications.”)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the combined teaching of Yang and Sacaleanu, the HCC code teachings of Austin. Paragraph [0047] teaches that the usage of the HCC risk methods disclosed allow for finding missing data and errors in the ICD codes. One of ordinary skill in the art would have added to the combined teaching of Yang and Sacaleanu based on this incentive without yielding unexpected results.
As per claim 7,
Claim 7 is substantially similar to claim 4. Accordingly, claim 7 is rejected for the same reasons as claim 4.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Yang and Sacaleanu in further view of Ton et al et al. (US 11,783,922; herein referred to as Tong).
As per claim 5,
The combined teaching of Yang and Sacaleanu teaches the limitations of claim 1.
Sacaleanu further teaches wherein said number of formats include but not limited to PDF files and faxed files:
(Paragraphs [0038], [0039] and [0049] of Sacaleanu. The teaching describes identify and extract entities from the text of each of the multiple documents the entity extraction and linking module 208 may apply natural language processing techniques. The entity extraction and linking module 208 may then apply reasoning techniques over multiple knowledge sources, e.g., including medical ontologies 212 and knowledge graphs or databases 214 to infer condition-evidence linking. In some implementations styling information, e.g., headings or text typeface, extracted from the EHR may be used to preserve the visual structure of the original EHR in the GUI, since styling information is often lost when extracting formatted text from a PDF document, e.g., using OCR techniques. the machine models and rules database 216 may include a second classifier that has been configured through training to receive, as input, feature vectors representing a portion of formatted text and to process the received input to generate, as output, a score indicating a likelihood that the portion of formatted text includes irrelevant text or information. Examples of irrelevant text or information include patient contact information, fax cover sheets, blank pages, pages with junk characters, domain specific non relevant pages such as hospital brochure information, laboratory procedure information. Because the system identifies a fax cover sheet as irrelevant information, it necessarily means that fax information other than the coversheet is relevant information)
Sacaleanu does not explicitly teach wherein said number of formats include scanned files, HL7, FHIR, and CCDA.
However, Tong teaches receiving medical information in formats including scanned files, HL7, FHIR, and CCDA:
(Column 14 Lines 57-67, Column 15 Lines 1-32 and Column 17 Lines 51-63. The teaching describes that data is received by the repository 416 from a plurality of different sources. A first source is the registry 1202. A registry could be an immunization registry or controlled substances registries. A second source is the EHR report 1204. A third source is the Unified Consolidated Clinical Document Architecture (CCDA) format 1206. The CCDA for Meaningful Use Stage 2 is a flexible markup standard developed by HL7 that defines structure of medical records to facilitate interchange between providers & patients. A fourth source is the EHR/Practice Management (PM) screen. A fifth source is through an application programming interface (API) 1210 for exchanging health records like a web service. A sixth source is HL7 Fast Healthcare Interoperability Resources (FHIR)(which is pronounced “fire”) 1211 which is a draft standard describing data formats and elements (also known as “resources” or “nodes”). The standard was created by the Health Level Seven International (HL7) health-care standards organization. FHIR builds on previous data format standards from HL7, like HL7 version 2.x and HL7 version 3.x. But it is easier to implement because it uses a modern web-based suite of API technology, including a HTTP-based representational state transfer (RESTful) protocol, hypertext markup language (HTML) and Cascading Style Sheets for user interface integration, a choice of Javascript Object Notation (JSON), Extensible Markup Language (XML) or resource description framework (RDF) for data representation, and Atom for results. One of its goals is to facilitate interoperation between legacy health care systems, to make it easy to provide health care information to health care providers and individuals on a wide variety of devices from computers to tablets to cell phones, and to allow third-party application developers to provide medical applications which can be easily integrated into existing systems. FHIR provides an alternative to document-centric approaches by directly exposing discrete data elements as services. For example, basic elements of healthcare like patients, admissions, diagnostic reports and medications can each be retrieved and manipulated via their own resource uniform resource locators (URLs). FHIR was supported at an American Medical Informatics Association meeting by companies like Cerner which value its open and extensible nature. he images and scanned documents are attached to patients chart in step 2110 and forwarded to repository 416.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the combined teaching of Yang and Sacaleanu, the formats disclosed by Tong. Column 4 Lines 47-67 and Column 5 Lines 1-24 of Tong teach that the disclosed methods, including the formats used by the CIN result in the improvement of patient outcomes that provide a reduced cost to managing clinical information. One of ordinary skill in the art would have added to the combined teaching of Yang and Sacaleanu, the teaching of Tong based on this incentive without yielding unexpected results.
Response to Arguments
Applicant's arguments filed January 22, 2026 have been fully considered.
Applicant arguments pertaining to claim objections are persuasive. The Applicant’s amendments obviate the Examiner’s previous objections. Accordingly they are hereby removed.
Applicant’s arguments pertaining to claim interpretation under 35 U.S.C. 112(f) are not persuasive.
The Applicant argues that the amendments to the pending claims provide enough structure to avoid interpretation under 35 U.S.C. 112(f).
The Examiner respectfully disagrees. Each of the argued amendments that allegedly provide adequate structure amount to nothing more than software configurations or processes. Software per se is incapable of providing structure as it requires hardware to execute the steps of the software program. Software per se is otherwise intangible. Limitations like pipelines, graphs and engines amount to nothing more than software per se. Given that the only structural limitations pertaining to these functions is “module” and that “module” is insufficient in providing structural detail in a claim, claim interpretation under 35 U.S.C. 112(f) is maintained. The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008); Mass. Inst. of Tech. v. Abacus Software, 462 F.3d 1344, 1354, 80 USPQ2d 1225, 1228 (Fed. Cir. 2006); Personalized Media, 161 F.3d at 704, 48 USPQ2d at 1886–87; Mas-Hamilton Group v. LaGard, Inc., 156 F.3d 1206, 1214-1215, 48 USPQ2d 1010, 1017 (Fed. Cir. 1998). See MPEP 2181 (I)(A).
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive.
The Applicant argues that the amended claims provide a specific technical improvement that transform any alleged abstract idea into a practical application. Specifically, the claimed invention provides improved Optical Character Recognition processing efficiency by selectively processing only pages that require OCR conversion, enhances data processing accuracy through specialized deep learning, provides accurate entity relationship mapping by using SPO triples and enables precise clinical entity extraction through assertion status detection, negation detection, and temporal expression recognition.
The Examiner respectfully disagrees. First with regard to OCR improvements, it is true that this asserted improvement is stated in paragraph [0020] of the as-filed specification. However, this assertion is merely a bare assertion of improvement. Only processing pages in OCR that are non-machine readable does not effect the actual processing speed of the OCR process. If a document requires all pages to be processed through OCR, it would actually take longer with more processing power to process the document than it would have taken by just indiscriminately processing each page in OCR. Even if a document was only partially processed through OCR, this is only reducing the load of the OCR processing artificially as opposed to changing the processing itself. Say for example, it takes 2 seconds per page to analyze each page in a document in conventional OCR processing and there are 30 pages in the document. It would take 60 seconds to process this document. The argued improvement here is that if only every other page in the document needed to be processed in OCR, the document would only take 30 seconds to process. However, this processing rate is still 1 page per 2 seconds. The state of the OCR processing did not change, rather only the processing load changed. This does not constitute an improvement to processing efficiency. The Applicant has merely implemented OCR in a generic manner, in its ordinary capacity, and asserted a processing efficiency based on a reduced load. Second with regard to enhancing data processing accuracy through specialized deep learning, there is no evidence in the specification that the Applicant is implementing deep learning techniques beyond their ordinary capacity. There is no discussion about the technical details about how the invention implements deep learning beyond merely applying deep learning to the invention. Accordingly, this is a bare assertion of improvement. Third with regard to provides accurate entity relationship mapping by using SPO triples, this element is not technical. This limitation exists within the abstract idea and is therefore not capable of providing a practical application of the abstract idea. An abstract idea cannot be, at the same time, an additional element to that same abstract idea. Only additional elements to an abstract idea can qualify for a practical application. Fourth with regard to enables precise clinical entity extraction through assertion status detection, negation detection, and temporal expression recognition, this consideration again falls within the abstract idea. This limitation exists within the abstract idea and is therefore not capable of providing a practical application of the abstract idea. An abstract idea cannot be, at the same time, an additional element to that same abstract idea. Only additional elements to an abstract idea can qualify for a practical application.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 103 are not persuasive.
The Applicant argues that the cited prior art does not teach the limitations of claim 1. Specifically, the cited prior art, alone or in combination are deficient in teaching a patient specific concept graph through a deep learning pipeline, expanding the concept graph with background knowledge from a primary knowledge graph and querying secondary knowledge graphs using SPO triples to traverse entity relationships and retrieve disease related details.
The Examiner respectfully disagrees. The prior art, in the updated rejection above, teaches each of these elements. Please refer to the above.
The Applicant further argues that Yang does not teach SPO triples.
The Examiner respectfully disagrees. Yang, in the updated rejection above, teaches each of these elements. Please refer to the above.
The Applicant further argues that the NPL outputs include “assertion status, negation detection, temporal expression recognition” which goes beyond Yang’s general implementation of NLP.
The Examiner respectfully disagrees. The NLP outputs as claimed are not “assertion status, negation detection, temporal expression recognition”. Rather the outputs are “one or more outputs including identification of entities, types, attributes, assertion status, negation detection, temporal expression recognition, and location offset”. This limitation requires only 1 of these types of outputs to be met. Accordingly, the Applicant has relied on features that are not claimed. Yang’s generation of assessment and plan text from objective and subjective sections of a medical graph satisfies at least one of these types of outputs given how broad identification of entities, types, and attributes can be construed.
Applicant’s remaining arguments pertaining to rejections of claims 4, 5 and 7 are rendered moot in light of the foregoing.
The Applicant appears to argue features of the Figures into the claimed amendments with their reference to Figures 1-5. The specific detail of these figures are not claimed by the listing of claims and the Figure’s respective details cannot be imputed to the claims that are written. In the case that the Applicant was arguing that the prior art does not teach the elements of the Figures, this argument is not relevant as these features are not claimed. Otherwise these arguments are rendered moot in light of the forgoing.
Conclusion
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 concerning this communication or earlier communications from the examiner should be directed to CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
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