DETAILED ACTION
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
In reply filed on 28 October 2025 the following changes have been made: amendments to claims 1-2, 15-16, and 20.
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the
invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 15, and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1, 15, and 20 contain the recitations “by a first filtering subsystem” and “by a second filtering subsystem.” Examiner points out [0027] in parallel with Figure 1 shows that one subsystem of care management platform called the real-time provider intelligence system of the care management platform that “performs a series of filtering and sorting of the previously extracted data.” Applicant’s recitation of two subsystems constituting both a first filtering subsystem and a second filtering subsystem of the care management platform that performs the filtering appears to constitute new matter. MPEP 2163 notes, “The proscription against the introduction of new matter in a patent application (35 U.S.C. 132 and 251) serves to prevent an applicant from adding information that goes beyond the subject matter originally filed. See In re Rasmussen, 650 F.2d 1212, 1214, 211 USPQ 323, 326 (CCPA 1981); see also MPEP §§ 2163.06 through 2163.07 for a more detailed discussion of the written description requirement and its relationship to new matter.” Accordingly, a rejection for addition of new matter is necessary. The remaining dependent claims incorporate the deficiency of the independent claims and are rejected for the same 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claims 1-14), article of manufacture (claims 15-19), and machine (claim 20).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
receiving, by the care management platform from the user device, a request for patient records relevant to a patient, the request comprising a patient identifier (ID), a care provider identifier (ID), and a care provider context;
and
in response to the request for patient records:
generating, by the care management platform, a query that comprises the
patient ID from the received request as a search criteria of the query;
executing, by a search engine of the care management platform, the
query causing the search engine to access and search a patient data store for patient records that comprise the patient ID from the query;
receiving, by a first filtering subsystem of the care management platform from
the search engine, patient records returned in response to the querying;
performing, by the first filtering subsystem of the care management platform, a first filtering of the patient records returned in response to the querying, wherein the first filtering removes one or more the returned patient records that are not related to the care provider context using a deterministic rule-based filter executed by the first filtering subsystem to generate a first reduced set of patient records;
performing, by a second filtering subsystem of the care management platform, a second filtering of the patient records in the first reduced set of patient records, the second filtering using the first reduced set of patient records as input to a machine learning (ML) model that scores relevance of each of the first reduced set of patient records to the care provider context, and removing one or more patient records from the first reduced set of patient records based on the scores generated by the ML model to generate a final reduced set of patient records; and
transmitting, by the care management platform to the user device, a response comprising at least a subset of the final reduced set of patient records.
Claims 15 and 20 recite similar limitations as claim 1 but for the recitation of generic computer components.
These steps for querying, searching, and providing patient records, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. That is, nothing in the claim element precludes the italicized portions from managing personal behavior or relationships or interactions between people by organizing the activity around querying, sorting/filtering, and conveying patient records. This could be analogized to manual file keeping operations. The italicized portion containing the recitation of using the first reduced set of patient records as input to a machine learning (ML) model that scores relevance of each of the first reduced set of patient records has been treated as part of the abstract idea, specifically as mathematical calculations (e.g., see [0029] disclosing ML methods such as decision trees that are known in the art to incorporate information theory and statistics like Entropy, Gini Impurity, and Information Gain) which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance. If a claim limitation, under its broadest reasonable interpretation, covers performance as organizing human activity and mathematical calculations but for the recitation of generic computer components, then it falls within the “Methods of Organizing Human Activity” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claim 1, 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 (such as
recitation of by the care management platform from the user device; by the care management platform; by a search engine of the care management platform; causing the search engine; by a first filtering subsystem of the care management platform from the search engine; by the first filtering subsystem of the care management platform; using a deterministic rule-based filter executed by the first filtering subsystem; by a second filtering subsystem of the care management platform; by the ML model; by the care management platform to the user device; a non-transitory computer readable storage medium including instructions that, when executed by a processor, cause the processor to perform operations for a care management platform and a user device; and, a memory; and a processor coupled with the memory amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of receiving, […], a request for patient records relevant to a patient; and, receiving, […], patient records returned in response to the querying amounts to mere data gathering since it does not add meaningful limitations to the receiving actions performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity, and also add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to 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 and also add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as by the care management platform from the user device; by the care management platform; by a search engine of the care management platform; causing the search engine; by a first filtering subsystem of the care management platform from the search engine; by the first filtering subsystem of the care management platform; using a deterministic rule-based filter executed by the first filtering subsystem; by a second filtering subsystem of the care management platform; by the care management platform to the user device; a non-transitory computer readable storage medium including instructions that, when executed by a processor, cause the processor to perform operations for a care management platform and a user device; and, a memory; and a processor coupled with the memory, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f); also, by the ML model, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), MPEP 2106.05(f);
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of receiving, […], a request for patient records relevant to a patient; and, receiving, […], patient records returned in response to the querying, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
Looking at the limitations 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 provide generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-14 and 16-19 reciting particular aspects for querying, searching, and providing patient records such as
[Claim 2 & 16] wherein the method further comprises:
scoring, by the ML model, a relevancy of each of the patient records in the first reduced set of patient records based at least in part on the care provider context and zero or more unified medical language system (UMLS) concept unique identifiers (CUIs) mapped to one or more features of said each of the patient records; and
generating the final reduced set of patient records based on the scores associated with said each of the patient records generated by the ML model;
[Claim 3] wherein the final reduced set of patient records comprises a subset of the first reduced set of patient records, and each member selected for the subset is associated with a relevancy score satisfying a relevancy threshold;
[Claim 4] wherein the final reduced set of patient records comprises a subset of the first reduced set of patient records selected for having highest scores in the scoring of said each of the patient records;
[Claim 5 & 17] receiving, by the care management platform from the user device, user feedback regarding a patient record from the final reduced set of patient records, the feedback comprising positive or negative feedback of the relevancy of the patient record to the care provider context;
adding, by the care management platform, the feedback for the patient with the patient record and the provider context to an ML retraining data set for the ML model;
periodically retraining, by the care management platform, the ML model to generate a tuned ML model; and
using, by the care management platform, the tuned ML model when scoring a relevancy of patient records associated with a second request for patient records;
[Claim 6] wherein the user feedback is received by the care management platform from a graphical user interface rendered on the user device in response to a user selecting the patient record from within the graphical user interface, and wherein an application rendering the graphical user interface transmits the user feedback to the care management platform;
[Claim 7 & 18] receiving, at the care management platform from the user device, a user query for additional patient records, the query comprising: a keyword or natural language query, a patient ID, a care provider ID, and a care provider context;
executing, by the care management platform, the query against the patient data store for the additional patient records based on the patient ID, provider ID, and provider context;
selecting, by the care management platform, a subset of the additional patient records; and
transmitting, by the care management platform to the user device, the selected subset of the additional patient records causing a user interface of the use device to display the selected subset of the additional patient records;
[Claim 8] extracting, from each of the additional patient records and the user query, a semantic meaning of one or more features within each of the additional patient records and the user query;
constructing a semantic meaning vector for each of the additional patient records and the user query, the semantic meaning vector comprising a set of semantic meaning features and wherein extracted values are added to corresponding ones of the set of semantic meaning features in the semantic meaning vector constructed for said each of the additional patient records and the user query;
determining, by one or more ML model(s), a relevance score of a semantic meaning vector constructed for the user query to each semantic meaning vector corresponding to each of the additional patient records; and
selecting the subset of additional patient records as the additional patient records having a determined relevance score that satisfies a relevancy threshold;
[Claim 9 & 19] obtaining, by the care management platform, a new patient record, the new patient record being associated with the patient ID;
performing, by the care management platform, text extraction to extract one or more portions of text from the new patient record;
mapping, by the care management platform, zero or more unified medical language system (UMLS) concept unique identifiers (CUIs) to each of the one or more portions of text extracted from the new patient record, wherein each mapping is based on a matching of terms in a CUI to one or more terms in a portion of extracted text; and
filtering, by the care management platform, one or more irrelevant portions of text; and
adding, by the care management platform, the new patient record and one or more retained portions of text as new entries to the patient data store, wherein each new entry comprises metadata for the patient ID and metadata for the zero or more UMLS CUIs mapped to said each new entry;
[Claim 10] wherein the new patient record is unstructured data and comprises an image of a patient record, and performing the text extraction comprises:
performing one or more image processing operations to extract text from the image of the patient record;
[Claim 11] wherein the new patient record is structure data and comprises an electronic medical record, and performing the text extraction comprise:
extracting text from one or more text fields of the electronic medical record;
[Claim 12] wherein filtering the one or more irrelevant portions of text, comprises:
applying a machine learning (ML) model trained to determine a relevancy of textual content and/or one or more UMLS CUIs associated with the textual content to a domain of care to be provided to the patient;
[Claim 13] wherein the request is received as an application programming interface (API) based message generated by the user device, and the request is received at an API endpoint of the care management platform;
[Claim 14] wherein the request and the response are transmitted using a secure protocol for the exchange of information over a communications network;
these italicized portions are methods of organizing human activity since they merely describe types of data and determinations that can be performed by humans. The recitation of retraining the ML model, construction of a semantic meaning vector, and determination of a relevance score at a high level of generality has been treated as part of the abstract idea, specifically as mathematical calculations which falls within the abstract idea of mathematical concepts (see 2024 USPTO AI Guidance).
Step 2A Prong 2
Dependent claims 2, 5-10, 12-13, and 16-19 recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claims 2 & 16 (by the ML model); claims 5 & 17 (by the care management platform from the user device; by the care management platform; and, using, by the care management platform, the tuned ML model); claim 6 (by the care management platform from a graphical user interface rendered on the user device in response to a user selecting the patient record from within the graphical user interface, and wherein an application rendering the graphical user interface transmits the user feedback to the care management platform); claims 7 &18 (at the care management platform from the user device; by the care management platform; and, by the care management platform to the user device); claim 8 (by one or more ML model(s)); claims 9 & 19 (by the care management platform); claim 10 (performing one or more image processing operations to extract text from the image of the patient record); and, claim 12 (applying a machine learning (ML) model trained to determine a relevancy of textual content and/or one or more UMLS CUIs associated with the textual content to a domain of care to be provided to the patient) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f)); and add insignificant extra-solution activity to the abstract idea such as claim 13 (wherein the request is received as an application programming interface (API) based message generated by the user device, and the request is received at an API endpoint of the care management platform) amounts to mere data gathering since it does not add meaningful limitations to the receiving actions performed, see MPEP 2106.05(g))). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 2, 5-10, 12, and 16-19 recites additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), MPEP 2106.05(f). Also, see [0081] which provides examples of off-the-shelf processors and memory. Dependent claim 13 amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i). There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Therefore, in consideration of all the facts, the present invention is not a patent-eligible invention under USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-4, 7, 13-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Woodlief et al. (US20220076794A1) in view of Mathur at al. (WO2022221175A1) and further in view of Straub et al. (WO2022034420A1).
Regarding claim 1, Woodlief discloses receiving, by the care management platform from the user device, a request for patient records relevant to a patient ([0012] “Wherein the first local server generates an electronic record request using at least one of the patient demographic information and record request information, and wherein the first local server transmits the electronic record request to the second local server via the server.”)
the request comprising a patient identifier (ID), a care provider identifier (ID), and a care provider context ([0059] “the request includes a unique ID (UID) that is associated or correlated with a patient ID for the patient […] primary care physician information, universal identifier, accession number, medical record number,” [0060] “electronic record request can include procedure identifiers”)
executing, by a search engine of the care management platform, the
query causing the search engine to access and search a patient data store for patient records that comprise the patient ID from the query ([0056] “a web-based or mobile-based portal to search for prior medical records” [0077] “Upon entering the patient ID and selecting the search icon 304, the local server 114 can retrieve the patient demographic information using a patient-level demographic query against a DICOM archive, patient record archive, a medical records database, and the like associated with the requesting medical provider 100.”)
receiving, by a first filtering subsystem of the care management platform from the search engine, patient records returned in response to the querying ([0081] “In the event that a query using the patient ID yields multiple results with differing demographic information, the interface 300 can display the multiple patient records and prompt the requesting medical provider 100 to select the patient record which they would like to utilize for the electronic record request.”)
performing, by the first filtering subsystem of the care management platform, a first filtering of the patient records returned in response to the querying, wherein the first filtering removes one or more returned patient records that are not related to the care provider context using a deterministic rule-based filter executed by the first filtering subsystem to generate a first reduced set of patient records ([0053] “a hospital information system (HIS)” [0097] “The interface 1000 displays a list 1002 of electronic record requests which require attention. The list 1002 can be filtered by various criteria, including, but not limited to, priority code, appointment date, electronic record request receipt date, physician, facility name, patient name, requesting medical provider, and the like.”)
and transmitting, by the care management platform to the user device, a response comprising at least a subset of the final reduced set of patient records ([0105] “FIG. 14 is an exemplary interface 1400 to confirm a transmission of selected prior medical records 1402 to a requesting medical provider, according to an embodiment of the invention. After confirming that the selected prior medical records 1402 are to be transmitted, the selected prior medical records are sent to the requesting medical provider 100.”)
Woodlief does not explicitly disclose however Mathur teaches performing, by a second filtering subsystem of the care management platform, a second filtering of the patient records in the first reduced set of patient records, the second filtering using the first reduced set of patient records as input to a machine learning (ML) model that scores relevance of each of the first reduced set of patient records to the care provider context ([105] “Such characteristic or feature of interest may be derived from patient or subjects records, or the processing of such patient or subjects records using machine learning or artificial intelligence. In some cases, the list of patients or subjects may comprise a filtered or ranked subset of patients or subjects as described elsewhere herein.” [28] “In some embodiments, (b) comprises processing a set of features of the first set of subject records using the trained machine learning algorithm to determine a score for each of at least a subset of the first set of subject records” [31] “In some embodiments, the set of features is selected from the group consisting of […] clinical characteristics” [15] “In some embodiments, the clinical procedure is a diagnostic test for a clinical disease, disorder, or condition. In some embodiments, the clinical disease, disorder, or condition is cancer. In some embodiments, the cancer is breast cancer.”)
Note: cancer suggests the care provider context.
and removing one or more patient records from the first reduced set of patient records based on the scores generated by the ML model to generate a final reduced set of patient records ([105] “In some cases, the targeted campaign or search may produce a list of patients or subjects having a particular characteristic or feature of interest. Such characteristic or feature of interest may be derived from patient or subjects records, or the processing of such patient or subjects records using machine learning or artificial intelligence. In some cases, the list of patients or subjects may comprise a filtered or ranked subset of patients or subjects as described elsewhere herein”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief performing a second filtering as taught by Mathur since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Woodlief in view of Mathur does not explicitly disclose however Straub teaches and in response to the request for patient records: generating, by the care management platform, a query that comprises the patient ID from the received request as a search criteria of the query ([0017] Aspects of the present disclosure relate to using a patient data molecule […] to retrieve a plurality of medical records from an input noun phrase.” [0030] “The system may be used to generate a query.” [0031] “the patient data molecule can
correspond to a plurality of medical records for an individual patient” [0032] “extraction may be performed, for example, by comparing the terms appearing in the document against a predetermined collection of terms that are eligible for searching” [0033] “associating the extracted term with a category that is semantically related with the extracted term”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief and Mathur generating a query that comprises the patient ID from the received request as a search criteria of the query as taught by Straub since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 2, Woodlief does not explicitly disclose however Mathur teaches scoring, by the ML model, a relevancy of each of the patient records in the first reduced set of patient records based at least in part on the care provider context and zero or more unified medical language system (UMLS) concept unique identifiers (CUIs) mapped to one or more features of said each of the patient records ([28] “In some embodiments, (b) comprises processing a set of features of the first set of subject records using the trained machine learning algorithm to determine a score for each of at least a subset of the first set of subject records” [31] “In some embodiments, the set of features is selected from the group consisting of demographic characteristics, clinical characteristics, clinical history, and history of past outreach.” [15] “In some embodiments, the clinical procedure is a diagnostic test for a clinical disease, disorder, or condition. In some embodiments, the clinical disease, disorder, or condition is cancer. In some embodiments, the cancer is breast cancer.”)
Note: cancer suggests the care provider context
and generating the final reduced set of patient records based on the scores associated with said each of the patient records generated by the ML model ([78] “In such cases, the subjects in the second set of subjects may comprise a listing, an arrangement, or a grouping of the first set of subjects according to a rank or a score. In some cases, the subjects in the second set of subjects may comprise a subset of the subjects in the first set of subjects.” [105] “In some cases, the targeted campaign or search may produce a list of patients or subjects having a particular characteristic or feature of interest. Such characteristic or feature of interest may be derived from patient or subjects records, or the processing of such patient or subjects records using machine learning or artificial intelligence. In some cases, the list of patients or subjects may comprise a filtered or ranked subset of patients or subjects as described elsewhere herein.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief scoring, by the ML model, a relevancy of each of the patient records in the first reduced set of patient records; and, generating the final reduced set of patient records as taught by Mathur since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 3, Woodlief does not explicitly disclose however Mathur teaches wherein the final reduced set of patient records comprises a subset of the first reduced set of patient records ([104] “In any of the embodiments described herein, […] the second set of subjects or subject records may be ranked or filtered based on one or more characteristics or features of the first […] set of subjects/ subject records.”)
and each member selected for the subset is associated with a relevancy score satisfying a relevancy threshold ([37] “generating the second set of subject records based at least in part on the scores for the at least the subset of the first set of subject records” [10] “In some embodiments, a given subject record is selected for inclusion in the second set of subject records when the given subject record (i) has a score that is greater than a first pre determined threshold.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief a subset of the first reduced set of patient records, and each member selected for the subset is associated with a relevancy score satisfying a relevancy threshold as taught by Mathur since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 4, Woodlief does not explicitly disclose however Mathur teaches discloses wherein the final reduced set of patient records comprises a subset of the first reduced set of patient records selected for having highest scores in the scoring of said each of the patient records ([30] “In some embodiments, a given subject record is selected for inclusion in the second set of subject records when the given subject record (i) has a score that is greater than a first pre determined threshold.” [34] “wherein the second set of subjects is a ranked subset of the first set of subjects, and wherein the second set of subject records is a subset of the first set of subject records”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief the final reduced set of patient records comprises a subset of the first reduced set of patient records selected for having highest scores in the scoring of said each of the patient records as taught by Mathur since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 7, Woodlief discloses receiving, at the care management platform from the user device, a user query for additional patient records, the query comprising: a keyword or natural language query, a patient ID, a care provider ID, and a care provider context ([0059] “the request includes a unique ID (UID) that is associated or correlated with a patient ID for the patient […] primary care physician information, universal identifier, accession number, medical record number,” [0060] “electronic record request can include procedure identifiers”)
executing, by the care management platform, the query against the patient data store for the additional patient records based on the patient ID, provider ID, and provider context ([0072] “For a selected electronic record request, the system can display matching patient records, and allows the external medical provider 102 to select all or specific patient records to transmit to the requesting medical provider 100, in response to the electronic record request.”)
selecting, by the care management platform, a subset of the additional patient records ([0098] “In addition, the external medical provider 102 can enter text in text box 1004 to query the list 1002 based on keywords, names, identification numbers, codes, etc. The list 1002 can automatically update based on the text entered, or being entered as the user is typing, in the text box 1004.”)
transmitting, by the care management platform to the user device, the selected subset of the additional patient records causing a user interface of the use device to display the selected subset of the additional patient records ([0105] “FIG. 14 is an exemplary interface 1400 to confirm a transmission of selected prior medical records 1402 to a requesting medical provider, according to an embodiment of the invention. After confirming that the selected prior medical records 1402 are to be transmitted, the selected prior medical records are sent to the requesting medical provider 100.” [0081] “the interface 300 can display the multiple patient records and prompt the requesting medical provider 100 to select the patient record which they would like to utilize”)
Regarding claim 13, Woodlief discloses wherein the request is received as an application programming interface (API) based message generated by the user device, and the request is received at an API endpoint of the care management platform ([0033] “As used herein, the term “data exchange interface” can refer to, for example, an interface, standard, specification and/or protocol that allows for disparate systems to share data […] [0045] “In an embodiment, the requesting medical provider 100 can enter a patient appointment into a patient scheduling system 118 that is integrated with the EHR system via a portal (i.e., such as in a FHIR interface compatible EHR or in an EHR with API access).”)
Regarding claim 14, Woodlief discloses wherein the request and the response are transmitted using a secure protocol for the exchange of information over a communications network ([0051] “In an embodiment, while the medical network 104 facilitates communication of an electronic record request between the medical providers 100, 102, the actual transmission of medical records can occur via encrypted, secure peer-to-peer communication channels 124”)
Regarding claim 15, Woodlief discloses receiving, by the care management platform from the user device, a request for patient records relevant to a patient ([0012] “Wherein the first local server generates an electronic record request using at least one of the patient demographic information and record request information, and wherein the first local server transmits the electronic record request to the second local server via the server.”)
the request comprising a patient identifier (ID), a care provider identifier (ID), and a care provider context ([0059] “the request includes a unique ID (UID) that is associated or correlated with a patient ID for the patient […] primary care physician information, universal identifier, accession number, medical record number,” [0060] “electronic record request can include procedure identifiers”)
executing, by a search engine of the care management platform, the
query causing the search engine to access and search a patient data store for patient records that comprise the patient ID from the query ([0056] “a web-based or mobile-based portal to search for prior medical records” [0077] “Upon entering the patient ID and selecting the search icon 304, the local server 114 can retrieve the patient demographic information using a patient-level demographic query against a DICOM archive, patient record archive, a medical records database, and the like associated with the requesting medical provider 100.”)
receiving, by a first filtering subsystem of the care management platform from the search engine, patient records returned in response to the querying ([0081] “In the event that a query using the patient ID yields multiple results with differing demographic information, the interface 300 can display the multiple patient records and prompt the requesting medical provider 100 to select the patient record which they would like to utilize for the electronic record request.”)
performing, by the first filtering subsystem of the care management platform, a first filtering of the patient records returned in response to the querying, wherein the first filtering removes one or more returned patient records that are not related to the care provider context using a deterministic rule-based filter executed by the first filtering subsystem to generate a first reduced set of patient records ([0053] “a hospital information system (HIS)” [0097] “The interface 1000 displays a list 1002 of electronic record requests which require attention. The list 1002 can be filtered by various criteria, including, but not limited to, priority code, appointment date, electronic record request receipt date, physician, facility name, patient name, requesting medical provider, and the like.”)
and transmitting, by the care management platform to the user device, a response comprising at least a subset of the final reduced set of patient records ([0105] “FIG. 14 is an exemplary interface 1400 to confirm a transmission of selected prior medical records 1402 to a requesting medical provider, according to an embodiment of the invention. After confirming that the selected prior medical records 1402 are to be transmitted, the selected prior medical records are sent to the requesting medical provider 100.”)
Woodlief does not explicitly disclose however Mathur teaches a non-transitory computer readable storage medium including instructions that, when executed by a processor, cause the processor to perform operations for a care management platform and a user device interacting during the providing of real-time provider intelligence information ([24] “In another aspect, the present disclosure provides a non-transitory computer readable medium comprising machine-executable code that, upon execution by one or more computer processors”)
performing, by a second filtering subsystem of the care management platform, a second filtering of the patient records in the first reduced set of patient records, the second filtering using the first reduced set of patient records as input to a machine learning (ML) model that scores relevance of each of the first reduced set of patient records to the care provider context ([105] “Such characteristic or feature of interest may be derived from patient or subjects records, or the processing of such patient or subjects records using machine learning or artificial intelligence. In some cases, the list of patients or subjects may comprise a filtered or ranked subset of patients or subjects as described elsewhere herein.” [28] “In some embodiments, (b) comprises processing a set of features of the first set of subject records using the trained machine learning algorithm to determine a score for each of at least a subset of the first set of subject records” [31] “In some embodiments, the set of features is selected from the group consisting of […] clinical characteristics” [15] “In some embodiments, the clinical procedure is a diagnostic test for a clinical disease, disorder, or condition. In some embodiments, the clinical disease, disorder, or condition is cancer. In some embodiments, the cancer is breast cancer.”)
Note: cancer suggests the care provider context.
and removing one or more patient records from the first reduced set of patient records based on the scores generated by the ML model to generate a final reduced set of patient records ([105] “In some cases, the targeted campaign or search may produce a list of patients or subjects having a particular characteristic or feature of interest. Such characteristic or feature of interest may be derived from patient or subjects records, or the processing of such patient or subjects records using machine learning or artificial intelligence. In some cases, the list of patients or subjects may comprise a filtered or ranked subset of patients or subjects as described elsewhere herein”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief a non-transitory computer readable medium and performing a second filtering as taught by Mathur since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Woodlief in view of Mathur does not explicitly disclose however Straub teaches and in response to the request for patient records: generating, by the care management platform, a query that comprises the patient ID from the received request as a search criteria of the query ([0017] Aspects of the present disclosure relate to using a patient data molecule […] to retrieve a plurality of medical records from an input noun phrase.” [0030] “The system may be used to generate a query.” [0031] “the patient data molecule can
correspond to a plurality of medical records for an individual patient” [0032] “extraction may be performed, for example, by comparing the terms appearing in the document against a predetermined collection of terms that are eligible for searching” [0033] “associating the extracted term with a category that is semantically related with the extracted term”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief and Mathur generating a query that comprises the patient ID from the received request as a search criteria of the query as taught by Straub since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 16, the limitations are rejected for the same reasons as stated above for claim 2.
Regarding claim 18, Woodlief discloses receiving, at the care management platform from the user device, a user query for additional patient records, the query comprising: a keyword or natural language query, a patient ID, a care provider ID, and a care provider context ([0059] “the request includes a unique ID (UID) that is associated or correlated with a patient ID for the patient […] primary care physician information, universal identifier, accession number, medical record number,” [0060] “electronic record request can include procedure identifiers”)
executing, by the care management platform, the query against the patient data store for the additional patient records based on the patient ID, provider ID, and provider context ([0072] “For a selected electronic record request, the system can display matching patient records, and allows the external medical provider 102 to select all or specific patient records to transmit to the requesting medical provider 100, in response to the electronic record request.”)
selecting, by the care management platform, a subset of the additional patient records ([0098] “In addition, the external medical provider 102 can enter text in text box 1004 to query the list 1002 based on keywords, names, identification numbers, codes, etc. The list 1002 can automatically update based on the text entered, or being entered as the user is typing, in the text box 1004.”)
transmitting, by the care management platform to the user device, the selected subset of the additional patient records causing a user interface of the use device to display the selected subset of the additional patient records ([0105] “FIG. 14 is an exemplary interface 1400 to confirm a transmission of selected prior medical records 1402 to a requesting medical provider, according to an embodiment of the invention. After confirming that the selected prior medical records 1402 are to be transmitted, the selected prior medical records are sent to the requesting medical provider 100.” [0081] “the interface 300 can display the multiple patient records and prompt the requesting medical provider 100 to select the patient record which they would like to utilize”)
Regarding claim 20, Woodlief discloses receive, from a user device, a request for patient records relevant to a patient ([0012] “Wherein the first local server generates an electronic record request using at least one of the patient demographic information and record request information, and wherein the first local server transmits the electronic record request to the second local server via the server.”)
the request comprising a patient identifier (ID), a care provider identifier (ID), and a care provider context ([0059] “the request includes a unique ID (UID) that is associated or correlated with a patient ID for the patient […] primary care physician information, universal identifier, accession number, medical record number,” [0060] “electronic record request can include procedure identifiers”)
execute, by a search engine of the care management platform, the
query causing the search engine to access and search a patient data store for patient records that comprise the patient ID from the query ([0056] “a web-based or mobile-based portal to search for prior medical records” [0077] “Upon entering the patient ID and selecting the search icon 304, the local server 114 can retrieve the patient demographic information using a patient-level demographic query against a DICOM archive, patient record archive, a medical records database, and the like associated with the requesting medical provider 100.”)
receive, by a first filtering subsystem of the care management platform from the search engine, patient records returned in response to the querying ([0081] “In the event that a query using the patient ID yields multiple results with differing demographic information, the interface 300 can display the multiple patient records and prompt the requesting medical provider 100 to select the patient record which they would like to utilize for the electronic record request.”)
perform, by the first filtering subsystem of the care management platform, a first filtering of the patient records returned in response to the querying, wherein the first filtering removes one or more returned patient records that are not related to the care provider context using a deterministic rule-based filter executed by the first filtering subsystem to generate a first reduced set of patient records ([0053] “a hospital information system (HIS)” [0097] “The interface 1000 displays a list 1002 of electronic record requests which require attention. The list 1002 can be filtered by various criteria, including, but not limited to, priority code, appointment date, electronic record request receipt date, physician, facility name, patient name, requesting medical provider, and the like.”)
and transmit, to the user device, a response comprising at least a subset of the final reduced set of patient records ([0105] “FIG. 14 is an exemplary interface 1400 to confirm a transmission of selected prior medical records 1402 to a requesting medical provider, according to an embodiment of the invention. After confirming that the selected prior medical records 1402 are to be transmitted, the selected prior medical records are sent to the requesting medical provider 100.”)
Woodlief does not explicitly disclose however Mathur teaches a memory; and
a processor coupled with the memory configured ([050] “Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto.”)
perform, by a second filtering subsystem of the care management platform, a second filtering of the patient records in the first reduced set of patient records, the second filtering using the first reduced set of patient records as input to a machine learning (ML) model that scores relevance of each of the first reduced set of patient records to the care provider context ([105] “Such characteristic or feature of interest may be derived from patient or subjects records, or the processing of such patient or subjects records using machine learning or artificial intelligence. In some cases, the list of patients or subjects may comprise a filtered or ranked subset of patients or subjects as described elsewhere herein.” [28] “In some embodiments, (b) comprises processing a set of features of the first set of subject records using the trained machine learning algorithm to determine a score for each of at least a subset of the first set of subject records” [31] “In some embodiments, the set of features is selected from the group consisting of […] clinical characteristics” [15] “In some embodiments, the clinical procedure is a diagnostic test for a clinical disease, disorder, or condition. In some embodiments, the clinical disease, disorder, or condition is cancer. In some embodiments, the cancer is breast cancer.”)
Note: cancer suggests the care provider context.
and removing one or more patient records from the first reduced set of patient records based on the scores generated by the ML model to generate a final reduced set of patient records ([105] “In some cases, the targeted campaign or search may produce a list of patients or subjects having a particular characteristic or feature of interest. Such characteristic or feature of interest may be derived from patient or subjects records, or the processing of such patient or subjects records using machine learning or artificial intelligence. In some cases, the list of patients or subjects may comprise a filtered or ranked subset of patients or subjects as described elsewhere herein”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief a memory, a processor, and performing a second filtering as taught by Mathur since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Woodlief in view of Mathur does not explicitly disclose however Straub teaches and in response to the request for patient records: generate a query that comprises the patient ID from the received request as a search criteria of the query ([0017] Aspects of the present disclosure relate to using a patient data molecule […] to retrieve a plurality of medical records from an input noun phrase.” [0030] “The system may be used to generate a query.” [0031] “the patient data molecule can correspond to a plurality of medical records for an individual patient” [0032] “extraction may be performed, for example, by comparing the terms appearing in the document against a predetermined collection of terms that are eligible for searching” [0033] “associating the extracted term with a category that is semantically related with the extracted term”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief and Mathur generating a query that comprises the patient ID from the received request as a search criteria of the query as taught by Straub since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Claim(s) 5-6 and 17 rejected under 35 U.S.C. 103 as being unpatentable over Woodlief et al. (US20220076794A1) in view of Mathur at al. (WO2022221175A1), Straub et al. (WO2022034420A1), and further in view of Goyal et al. (US12094582B1).
Regarding claim 5, Woodlief in view of Mathur and Straub does not explicitly disclose however Goyal teaches receiving, by the care management platform from the user device, user feedback regarding a patient record from the final reduced set of patient records ([pg. 31] “obtaining a label corresponding to a group of care records by querying a user using an active learning framework.”)
the feedback comprising positive or negative feedback of the relevancy of the patient record to the care provider context ([pg. 21] “For each of the training samples, a label is further included that indicates whether or not the care records and episodes in the sample should be merged together (label=1, positive training sample) or not merged together (label=0, negative training sample.”)
adding, by the care management platform, the feedback for the patient with the patient record and the provider context to an ML retraining data set for the ML model ([pg. 21] “In some embodiments, the system uses active learning frameworks. […] In each round of active learning, the model 404 can assess informativeness of unlabeled data, select the most informative examples to be labelled by an oracle, and be retrained using the newly labelled data”)
periodically retraining, by the care management platform, the ML model to generate a tuned ML model ([pg. 9] “repeating steps (c)-(g) zero or more times using the new series to identify a final series of medical episodes, wherein each medical episode of the final series of medical episodes corresponds to one or more care records in the initial series of care records” [pg. 20] “After the first iteration, the system repeats the process for the current list of care records/episodes” [pg. 23] “Thus, the model iteratively improves and generates additional training data in the process by only querying for the “most difficult” and/or “most informative” examples.”)
and using, by the care management platform, the tuned ML model when scoring a relevancy of patient records associated with a second request for patient records ([pg. 20] “In some embodiments, a supervised machine learning model similar to M may be used to derive the similarity score. In other embodiments, the similarity score may be unsupervised (e.g. L-p norm of the difference between the feature vectors, arbitrarily high dimensional dot products between the feature vectors, etc).”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief, Mathur, and Straub receiving user feedback, adding feedback to the ML retraining data, and using the tuned ML model when scoring a relevancy of a patient as taught by Goyal since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 6, Woodlief discloses wherein the user feedback is received by the care management platform from a graphical user interface rendered on the user device in response to a user selecting the patient record from within the graphical user interface ([0020] “FIG. 6 is an exemplary interface for selecting recipients for an electronic record request” [0072] “For a selected electronic record request, the system can display matching patient records, and allows the external medical provider 102 to select all or specific patient records to transmit” [0102] “For a selected prior medical record, the external medical provider 102 can upload various […] notes.”)
and wherein an application rendering the graphical user interface transmits the user feedback to the care management platform ([0102] “For a selected prior medical record, the external medical provider 102 can upload various […] notes.” [0105] “The peer-to-peer communication channel 124 allows for the external medical record to be securely transmitted directly from the external medical provider 102 to the requesting medical provider 100”)
Regarding claim 17, the limitations are rejected for the same reasons as claim 5.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Woodlief et al. (US20220076794A1) in view of Mathur at al. (WO2022221175A1), Straub et al. (WO2022034420A1), and further in view of Eberholst et al. (US20090083231A1).
Regarding claim 8, Woodlief does not explicitly disclose however Mathur teaches and selecting the subset of additional patient records as the additional patient records having a determined relevance score that satisfies a relevancy threshold ([37] “generating the second set of subject records based at least in part on the scores for the at least the subset of the first set of subject records” [10] “In some embodiments, a given subject record is selected for inclusion in the second set of subject records when the given subject record (i) has a score that is greater than a first pre determined threshold.”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief selecting the subset of additional patient records having the relevance score as taught by Mathur since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Woodlief in view of Mathur and Straub does not explicitly disclose however Eberholst teaches extracting, from each of the additional patient records and the user query, a semantic meaning of one or more features within each of the additional patient records and the user query ([0049] “A similarity-based algorithm is understood as an algorithm that uses the similarity values of the edges for computing properties or characteristics of the similarity network. This may involve searches over the similarity network with respect to a query” [0052] “For example, the query might be issued by a user of the system and received via a user interface.” [0085] “The system 100 further includes a similarity processing unit 170 being operable to receive the similarity network 160 as input and to perform similarity based algorithms over the similarity network 160. This allows for computing semantic similarities between the electronic data records of the first set 115 and the second set 125.”)
constructing a semantic meaning vector for each of the additional patient records and the user query, the semantic meaning vector comprising a set of semantic meaning features and wherein extracted values are added to corresponding ones of the set of semantic meaning features in the semantic meaning vector constructed for said each of the additional patient records and the user query ([0049] “A similarity-based algorithm is understood as an algorithm that uses the similarity values of the edges for computing properties or characteristics of the similarity network. This may involve searches over the similarity network with respect to a query” [0074] “The first annotation unit 110 is operable to compute concept vectors 130 for the first set 115 of electronic data records. The coordinates of the concept vectors 130 represent scores of the concepts for the respective electronic data records of the first set 115. For each electronic data record of the first set 115, a concept vector 130 is computed. The concepts are part of an ontology 140 which might be, for example, the SNOMED-ontology.”)
determining, by one or more ML model(s), a relevance score of a semantic meaning vector constructed for the user query to each semantic meaning vector corresponding to each of the additional patient records ([0038] “The coordinates of the concept vectors represent the scores of the concepts of the respective electronic data record. The scores are values that indicate the relevance of the respective concept for the respective electronic data record according to a predefined measure.” [0082] “a linear annotator is a linear function f from Rd onto {−1,+1} represented as a vector (w,b)εRd+1 such that f(di)=sign(<w,di>+b), the output of f representing whether a given concept (for example, food) is mentioned in the document di (returning +1) or not (returning −1). Such function f is widely used in text mining and is generally built using machine learning or pattern recognition techniques such as Support Vector Machines.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief, Mathur, and Straub extracting semantic meaning, constructing a semantic meaning vector, and determining a relevance score as taught by Eberholst since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Claim(s) 9-12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Woodlief et al. (US20220076794A1) in view of Mathur at al. (WO2022221175A1), Straub et al. (WO2022034420A1), and further in view of Lucas at al. (US20200126663A1).
Regarding claim 9, Woodlief in view of Mathur and Straub does not explicitly disclose however Lucas teaches obtaining, by the care management platform, a new patient record, the new patient record being associated with the patient ID ([0046] “The home screen 14 may provide access to patient records through a patient interface 18 that may provide, for one or more patients, patient identification information 20, such as the patient's name, diagnosis, and record identifiers”)
performing, by the care management platform, text extraction to extract one or more portions of text from the new patient record ([0053] “Once pre-processed, the document may be submitted for optical character recognition (OCR) on the document to convert the text into a machine-readable format, such as text, html, JSON, or XML using other document processing tools.”)
mapping, by the care management platform, zero or more unified medical language system (UMLS) concept unique identifiers (CUIs) to each of the one or more portions of text extracted from the new patient record, wherein each mapping is based on a matching of terms in a CUI to one or more terms in a portion of extracted text ([0180] “Fuzzy matching is structured around the text concepts included in the above enumerated list or the UMLS, including metadata fields CUI (the UMLS unique ID) and AUI (dictionary-specific unique ID), so that an exhaustive search may be performed for all medical concepts. […] At the end of text normalization, some of the extracted candidates may have zero matches but others may have many matches. For example, there are many versions of Tylenol throughout the UMLS database because of the number of dictionaries represented therein. Fortunately, the CUI (the UMLS uuid) provides a generalization to join similar concepts, which reduces the number of matches from one for each potential database to the number of unique CUIs represented.”)
and filtering, by the care management platform, one or more irrelevant portions of text ([0096] “Other normalized results may include each main cancer site, such as brain, lung, liver, ovary, or bone marrow, a predetermined catch-all for unknown sites, or known codes which are irrelevant and may be filtered.”)
and adding, by the care management platform, the new patient record and one or more retained portions of text as new entries to the patient data store ([0257] “Upon validation of the information, the application may also store a copy of the data (after deidentification and/or removal of protected health information) to include in future reports.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief, Mathur, and Straub obtaining a new patient record, extracting text, mapping zero or more UMLS CUIs, filtering irrelevant portions of text, and adding the patient record and its retained text to the patient data store as taught by Lucas since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 10, Woodlief in view of Mathur and Straub does not explicitly disclose however Lucas teaches wherein the new patient record is unstructured data ([0007] “parts of a patient's record which may include rich and meaningful data (such as diagnoses and treatments captured in progress or follow-up notes, flow sheets, pathology reports, radiology reports, etc.) remain isolated, unstructured”)
and comprises an image of a patient record ([0049] “Turning to FIG. 2, once a user has selected a patient, completed adding a new patient, or is adding a new patient from a report, an electronic document capture screen 24 may appear […] Exemplary electronic document captures may include a structured data form (such as JSON, XML, HTML, etc.), an image (such as JPEG, PNG, etc.), a PDF of a document, report, or file, or a typeface or handwritten copy of a document, report, or file.”)
and performing the text extraction comprises: performing one or more image processing operations to extract text from the image of the patient record ([0054] “In another embodiment, additional pre-processing may be performed after submitting an image to OCR to determine whether the detected text is “reasonable” before outputting final results.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief, Mathur, and Straub unstructured data, image of a patient record, and performing text extraction as taught by Lucas since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 11, Woodlief in view of Mathur and Straub does not explicitly disclose however Lucas teaches wherein the new patient record is structure data and comprises an electronic medical record ([0097] “The system accelerates a structuring of clinical data in a patient's record. The system may execute subroutines that highlight, suggest, and pre-populate an electronic medical record (“EHR” or “EMR”).”)and performing the text extraction comprise: extracting text from one or more text fields of the electronic medical record ([0097] “The system may provide other formats of structured clinical data, with relevant medical concepts extracted from the text and documents of record.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief, Mathur, and Straub structure data and extracting text from one or more text fields of the EMR as taught by Lucas since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 12, Woodlief in view of Mathur and Straub does not explicitly disclose however Lucas teaches applying a machine learning (ML) model trained to determine a relevancy of textual content and/or one or more UMLS CUIs associated with the textual content to a domain of care to be provided to the patient ([0100] “In the field of clinical abstraction from EHR and EMR documents, machine learning or deep learning may be combined with NLP techniques to abstract relevant medical concepts. […] For instance, the simple text “The patient was given Tylenol 50 mg at 10:35 am.” may be analyzed using a machine learning algorithm (MLA) trained on EHR and EMR documents” [0177] “Relevancy may also be determined based on proximity to a concept candidate hit. […] However, certain medical concepts, such as medications, may fall within an abstraction category such as treatment. A treatment may have fields such as treatment type (the medicine) and date and time of treatment (11:35 am). By considering proximity to other concept candidates, key information may be retained even if the concept may not exist in the database.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Woodlief, Mathur, and Straub applying ML to determine the relevancy of textual content as it applied to a domain of care to a patient as taught by Lucas since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 19, the limitations are rejected for the same reasons as stated above for claim 9.
Response to Arguments
The arguments filed on 28 October 2025 have been considered, but are not fully persuasive.
Regarding the drawings, applicant has submitted new drawings to replaces the duplicate 880 label with 890. Therefore, the drawing objection has been withdrawn.
Regarding the USC 101 rejection, the applicant first makes the case for practical application on pages 17 to 20 arguing that the claims as a whole recite a practical application. Applicant points to amended claim 1 where the system uses filtering, including ML based filtering, on large amounts of records returned to reduce the records returned. Applicant points to the problem in the specification [0001] which discloses the problem of the difficulty in sifting through structured and unstructured patient data; this creates a technical challenge of how to efficiently and accurately process a large amount of returned records. Applicant also cites the specification which states that experimental results have shown filtering enables improved accuracy and improved computational efficiency. Applicant asserts that the filtering is performed to address and improve technical challenges related to reducing computational load of a search system and improving accuracy to improve search results; thus, the amended claims addressing concrete technical problems in remote search - namely, over-inclusive and non-relevant result generation and excessive computational load experienced by such search systems. Applicant submits that the specification describes and the claim limitations address specific technical challenges related to remote search of patient records by reciting a specific combination and order of systems executed by the patient care platform, the use of specific data, and performs specific operation, where as a ordered combination, the claims provide a specific technical approach to address problems related to remote search accuracy computational load at such search systems.
Examiner disagrees with the applicant’s arguments. As will be further explained below, the applicant’s claims do not provide a practical application. The problem presented in [0001] of the specification is a non-technological healthcare business problem. There is no mention of technological issues (i.e., rooted in computer technology) with existing “remote search” systems in the specification. The amended claims also do not focus on a specific technical improvement to the underlying computer system itself. Simply using computers in place of humans to perform the abstract data task (i.e., improve upon the abstract idea) is not a practical application and certainly not patent-eligible (see MPEP 2106). Examiner points out that the applicant’s specification [0049] provides a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. The applicant also emphasizes the benefits of improved accuracy and improved computational efficiency in their arguments, but examiner points the specification lacks technical details on how such benefits are achieved by the computer processes. Instead, the specification supports the examiner’s analysis that the claims are applying existing computers/technology to a new data environment and calling it an improvement. For instance, examiner points to [0025] of the specification which discloses use of Lucene which is flexible and scalable. Indeed, one of ordinary skill in the art would recognize that Lucene is highly scalable and is specifically designed to handle large datasets and computational loads, including efficient data filtering (i.e., the benefits of the present invention). The specification simply discloses using Lucene & does not improve Lucene. Thus, this example illustrates that the applicant is using computers, as well as off-the-shelf machine learning, for their intended benefits. Going back to the previous statement of the specification providing “a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art”, examiner asserts it is also not clear how performing the first deterministic rules-based filtering followed by the second ML-based filtering really improves search results and computational efficiency.
First, one of ordinary skill in the art would recognize that the sequential application of two filters can introduce latency, particularly if the deterministic rules are computationally intensive or if the machine learning model is complex. In time-sensitive applications, this overhead might negate any potential improvements in result quality. The applicant’s specification does not explain/lacks detail on how performance overhead is managed that is necessary for an improvement to be apparent to a person of ordinary skill in the art. Thus, it is not evident from the specification how the amended claims improve search results and computational efficiency.
Second, one of ordinary skill in the art would recognize that introducing a two-stage filtering system adds complexity to the overall architecture. Maintaining and updating both the deterministic rules and the machine learning model can be more demanding than managing a single system, especially if the rules are numerous or intricate. The applicant’s specification does not explain/lacks detail on how increased complexity of the system is managed that is necessary for an improvement to be apparent to a person of ordinary skill in the art. Thus, it is not evident from the specification how the amended claims improve search results and computational efficiency.
Third, one of ordinary skill in the art would recognize that if the initial deterministic rules are too restrictive or inaccurate, they might filter out relevant results before the machine learning model even has a chance to evaluate them. This can lead to a reduction in recall, even if the precision of the remaining results is high or large. The applicant’s specification does not explain/lacks detail on how over-filtering is managed that is necessary for an improvement to be apparent to a person of ordinary skill in the art. Thus, it is not evident from the specification how the amended claims improve search results and computational efficiency.
Fourth, one of ordinary skill in the art would recognize that the deterministic rules and the machine learning model might be designed to address similar aspects of the search query. If the rules capture most of the easily identifiable patterns, the machine learning model might not offer significant added value or could even conflict with the rules, leading to inconsistent results. The applicant’s specification does not explain/lacks detail on how redundancy is managed that is necessary for an improvement to be apparent to a person of ordinary skill in the art. Thus, it is not evident from the specification how the amended claims improve search results and computational efficiency.
Fifth, one of ordinary skill in the art would recognize that if the deterministic rules are based on biased or incomplete understanding of the data, they could inadvertently amplify existing biases within the dataset, leading the machine learning model to learn and perpetuate these biases. The applicant’s specification does not explain/lacks detail on how data skew or bias amplification is managed that is necessary for an improvement to be apparent to a person of ordinary skill in the art. Thus, it is not evident from the specification how the amended claims improve search results and computational efficiency.
Based on the facts at hand, it is clear the claims do not provide a practical application and thus are not patent-eligible.
On pages 20 to 22 the applicant argues that under Step 2A Prong 1 that submits that the claims do not include subject matter related to mathematical concepts. Applicant submits that the presently amended claims, when considered as a whole, include more than methods of organizing human activity as the claims recite a series of operations that are not methods of organizing human activity. The applicant also asserts the claims do not recite a mental process. Citing the August 4, 2025 USPTO Memo the applicant, the applicant points to the limitations such as the ML model asserting that the operations are not related to mental processes that practically could be performed in the human mind.
Examiner disagrees with the applicant’s arguments. Examiner asserts the current claim amendments are still not helpful in overcoming the USC 101 rejection. The claims still recite an organizing human activity and also include math, hence also falling under mathematical concepts. The applicant seems to take a much broader interpretation of USC 101 and attempts to stretch the claim limitations to argue that the claims are not abstract. Examiner points to the USPTO October 2019 Guidance (also incorporated in MPEP 2106) which states that claims can recite an abstract idea even if they are claimed as being performed on a computer. The USPTO October 2019 Guidance is clear in that the courts have found claims requiring a generic computer or nominally reciting a generic
computer may still recite an abstract idea even though the limitations may not be entirely performed by humans. The concept of sorting/searching & filtering queries is conceptually analogous to manual file keeping operations similar to how a librarian searches or ranks results. The computers in the claims are not used in a specific, inventive way. The claims are very outcome-focused and do not detail how each of the outcomes are reached. For instance, the argued limitation of executing the query causing the search engine to access and search a patient data store simply states that a patient data store is accessed and searched without detailing how the computer is programmed to carry out the searching and accessing. Similarly, the present machine learning model claimed is a black box model with no clarity on the actual computer processing or how the computer is programmed to achieve the results in a non-abstract way different from how humans analyze/process data. Examiner asserts that the August 4, 2025 Memo from the USPTO does not apply to the present invention as that memo was in regard to inventions that demonstrated improvements to AI technology and thus were AI-focused inventions; this is clearly not the case for the present invention. One of ordinary skill in the art would understand that applicant’s invention is directed to judicial exception, as also confirmed by multiple subject matter experts at the USPTO. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to ‘implement[ing] the abstract idea of intermediated settlement on a generic computer’, it cannot save OIP's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted).
On pages 23 to 25 the applicant argues under Step 2A Prong 2 that the presently amended claims improve a specific and ordered combination of limitation including reciting specific limitations for performing accurate and efficient search in a distributed computing environment. Applicant asserts that the specific and ordered combination of operations are performed in a way that ensures accurate and efficient search is performed by using the claimed technical operations. Citing similarity to McRO v Bandai et al., 837 F.3d 1299 (2016), the applicant asserts that the presently claimed technique is directed to a computer-implemented technique of for performing remote search in a way that obtains stored data based on data identifiers by executing a search engine, performs a filtering of the returned data through execution of a deterministic rule-based filter, performs a subsequent filtering to further reduce data records by executing an ML based filtering, and then transmits a response from the second filtering to the requesting system, which are not abstract ideas. Applicant asserts that the claimed features are claimed with specificity to reflect an inventive concept beyond merely execution by generic computer hardware. Applicant points out that unlike Intellectual Ventures, the solution is achieved through specific technical operations beyond those that may be considered as merely data gathering, and recites specific technical features of the search, first filtering, second filtering, and data communication between remote systems. Applicant concludes by stating that the technical problem, the improvement, and how the
improvement is realized are discussed in the specification, and the realization of
the solution resulting in the improvement are actively claimed by a specific and ordered combination of systems and operations executing in concert. Applicant submits that the claims provide a practical application under Step 2A Prong 2.
Examiner disagrees with the applicant’s arguments. Further analyzing the additional elements under Step 2A Prong 2, as stated above, the specification provides a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. In McRO, Inc. v.
Bandai Namco Games America Inc., et al., 837 F.3d 1299 (Fed. Cir. 2016) the
technological improvement the Federal Circuit found was that the claimed
invention programmed a computer to perform tasks which could not be done previously in a computer; this is not the case for the present invention. The MPEP provides that improvements to the functioning of a computer or to any other technology or technical field can signal eligibility, see MPEP 2106.05(a), and provides examples of improvements to computer functionality, MPEP 2106.05(a)(I), and improvements to any other technology of technical field, MPEP 2106.05(a)(I). “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool”. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification' s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general-purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691. Unlike Enfish, the instant claimed invention appears to improve upon a judicial exception rather than a problem in the software arts. Rather than improving a computer's algorithm (i.e., solving a technically based problem), the claimed invention purports to solve the non-technological problem of the difficulty in sifting through structured and unstructured patient data ([0001] of the specification) through using computers as a tool for querying, searching, and providing patient records. The applicant’s main/glaring issue is that specification does not show or describe a deficiency in the technology. Applicant is merely using computers to improve upon the judicial exception. That is, all the applicant is doing is applying computers/generic machine learning for their intended benefit(s) to a new data environment and calling it an improvement (see Customedia Techs., LLC v. Dish Network Corp., Case No.18-2239 (Fed.
Cir. Mar. 6, 2020). The examiner asserts the following facts are evident: 1) the invention does NOT involve a novel algorithm or data structure that significantly improves the computer's functionality, 2) the invention does NOT involve a new hardware component or configuration that works with the computer to achieve a specific technical benefit, and 3) the computer is NOT used in a completely new way demonstrating a significant technical advancement. Improvement to the abstract idea is not an improvement to computer technology. Thus, examiner does not see how the present claims improve the functioning of a computer or provide improvements to any other technology or technical field. The claimed invention appears similar to the example of improvements that are insufficient to show an improvement in computer-functionality such as arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). See MPEP 2106.05(a)(I)(viii). The asserted benefits from the applicant’s invention seem to come as a result of the use of general-purpose computers. The broad claims are still lacking concrete limitations to integrate the abstract idea into a practical application. Examiner points out that the claimed limitations have no indication in the specification that the operations recited invoke any inventive programming, require any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is implemented using other than generic computer components to perform generic computer functions. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (fed Cir. 2014) (“[A]fter Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.”). Most importantly, in DDR Holdings & unlike the present claims, the claims at issue specified how interactions with the Internet were manipulated to yield a desired result—a result that overrode the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink. 773 F.3d at 1258; 113 USPQ2d at 1106. The examiner also points out that there is no indication in the specification that the claimed invention affects a transformation or reduction of a particular article to a different state or thing. Examiner points to the recitation of machine learning (ML) in the claim(s) as generic. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice Corp. v. CLS Banklnt'l, 573 U.S. 208 223 (2014). Applicant does not and cannot contend they invented the concept of machine learning, nor does the specification disclose any new machine learning technique. In fact, the applicant’s specification [0029] recognizes known machine learning models in the art. The alleged improvement of using the machine learning model lies in the abstract idea itself, not to any technological improvement nor to any improvement to the functioning of a computer. See BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88 (Fed. Cir. 2018). The fact pattern of the applicant’s claims is congruent to the Recentive Analytics, Inc. v. Fox Corp., 2025 U.S.P.Q.2d 628 (Fed. Cir. 2025) decision by the Federal Circuit. Just like in Recentive, the present claims do not delineate steps through which the machine learning technology achieves an improvement. See, e.g., IBM v. Zillow Grp., Inc., 50 F.4th 1371, 1381 (Fed. Cir. 2022) (holding abstract a claim that "d[id] not sufficiently describe how to achieve [its stated] results in a non-abstract way," because "[s]uch functional claim language, without more, is insufficient for patentability under our law." (quoting Two-Way Media Ltd v. Comcast Cable Commc'ns, LLC, 874 F.3d 1329, 1337 (Fed. Cir. 2017))); see also Intell. Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1342 (Fed. Cir. 2017) (similar); Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016) (similar). Claiming a mere concept or functional result without disclosing the implementation details does not overcome USC 101. Applying an established technique to a new field or data set is insufficient for patent eligibility. Examiner further points out that improving efficiency (pg. 19 and 23-24 of the applicant’s arguments) is not sufficient to show an improvement in computer
functionality as set forth by the courts in Intellectual Ventures I LLC v. Capital
One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir.
2015); claiming the improved speed or efficiency inherent with applying the
abstract idea on a computer does not integrate a judicial exception into a
practical application or provide an inventive concept. This further supports the examiner’s assertion that the claims do not integrate a judicial exception into a practical application. To show an involvement of a computer assists in improving technology, the claims must recite details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP 2106.05(a)(II)). In Finjan, Inc. v. Blue Coat Systems the courts found that the claims were “directed to a non-abstract improvement in computer functionality…” (MPEP 2106.04(d)). The present invention does not meet the condition set forth by the courts and thus does not integrate the judicial exception into a practical application.
On pages 25 to 27 the applicant argues that for Step 2B the claims recite significantly more than any abstract idea contained. Citing Bascom, the applicant submits that the arrangement of the amended claim 1 neither conventional nor generic and, like the filtering in Bascom, is an improvement to existing technological processes related to efficiently and accurately performing remote search. Applicant also states that the unconventional operations recited in claims 1-20 confine these claims to a particular and useful application which improves efficiency of filtering and thus determination of relevant search results through multi-stage filtering operations; that the claimed operations are rooted in computer technology and not merely “apply it.” Applicant submits that all the claims are patent-eligible under USC 101 and requests withdrawal of the SUC 101 rejection.
Examiner disagrees with the applicant’s arguments. With respect to Step 2B, the same “apply it” analysis as well as the well-understood, routine, conventional analysis was performed court case citations, which didn’t result in the claim being eligible under USC 101. Examiner is not persuaded by the applicant’s assertion of usefulness/useful application; nowhere in the MPEP is this listed as a consideration for patent eligibility. In comparison to Bascom, examiner points out that Bascom is not similar to the present application because Bascom claimed a technical improvement in the art i.e., a technology-based solution to filter content on the internet. On the other hand, the present claims are not presenting an improvement to computer technology (as indicated above). Furthermore, the use of a computer or other machinery in its ordinary capacity for economic or other tasks or simply adding a general-purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). The "significantly more" standard has not been satisfied. To conclude, the applicant has not demonstrated that their invention is inventive. Thus, the present invention is not patent-eligible under USC 101. Therefore, the USC 101 rejection is maintained.
Regarding the USC 102 rejection, applicant’s arguments have been considered but are moot since they do not apply to the newly cited references, Mathur & Straub, under USC 103. Therefore, the USC 102(a)(1) rejection has been withdrawn.
Regarding the USC 103 rejection, the applicant’s arguments have been considered but are moot since they do not apply to the newly cited reference of record, Straub, which has been cited the teach the generating a query limitation. while Gordon has still been cited to teach the machine learning limitations as well as the communicate limitation. The combination of Woodlief and Marthur have been cited to teach the remaining added limitations. With respect to BRI, examiner points the applicant’s construction of the claims do not yet recite what they want the interpretation to be. For example, the current construction of the claims would encompass a request & query for patient records for a patient identified as Brad Smith associated with the same care provider and having the same care provider context. Upon execution of the query, the search results would then display a list of every patient record associated with the identified Brad Smith (including noise i.e., other patient records with a similar name). As can be seen, the claim language would need to be adjusted to exclude this and other interpretations. The remaining claims are still rejected under USC 103.
Prior Art Cited but Not Relied Upon
Sengan, S., Kamalam, G. K., Vellingiri, J., Gopal, J., Velayutham, P., & Subramaniyaswamy, V. (2020). Medical information retrieval systems for e-Health care records using fuzzy based machine learning model. Microprocessors and Microsystems, 103344.
This reference is relevant because it discloses the applciant’s invention of querying, filtering, and transmitting medical records with relevance and machine learning being incorporated.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 WINSTON FURTADO whose telephone number is (571)272-5349. The examiner can normally be reached Monday-Friday 8:00 AM to 4:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WINSTON R FURTADO/Examiner, Art Unit 3687
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