DETAILED ACTION
Status
This communication is in response to the application and preliminary amendment filed on 13 February 2025. No claims have been canceled, claims 9-20 have been amended, and no new claims have been added; therefore, claims 1-20 are pending and presented for examination.
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 .
Priority
This Application is a 371 National Stage Entry of PCT/EP2023/ 072564, filed on 16 August 2023, which claims benefit under 35 U.S.C. 119(e) to U.S. Provisional Application No. 63/403,034, filed on 1 September 2022. Applicant’s claim for benefit is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 13 February 2025 was filed after the mailing date of the application on 13 May 2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a method (claims 1-8), system (claims 9-15), and non-transitory computer readable medium (claims 16-20), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a method of performing performance analysis of a type of procedure performed in a medical facility, the method comprising: providing a procedure log for the medical facility, wherein the procedure log stores information logging steps of procedures of the type of procedure performed in the medical facility; providing a predefined event list for the type of procedure performed in the medical facility, wherein the predefined event list includes key events for the type of procedure; performing natural language processing (NLP) on the procedure log using an NLP algorithm to extract keywords respectively associated with the key events in the predefined event list; creating a final list of the extracted keywords respectively associated with the key events in the predefined event list; generating a process workflow for each of the procedures; performing performance analysis of efficiency of performing the type of procedure in the medical facility by calculating time-related matrices and key performance indicators (KPIs) based on the process workflow; and displaying the performance analysis at a display connected to a user interface.
Independent claims 9 and 16 are analyzed similarly to claim 1 since claim 9 is directed to a system for performing performance analysis of a type of procedure performed in a medical facility, the system comprising: a user interface connected with a display; at least one processor; at least one non-transitory memory storing information to perform the same or similar operations as at claim 1, and claim 16 is directed to a non-transitory computer readable medium storing instructions for performing performance analysis of a type of procedure performed in a medical facility that, when executed by at least one processor, cause the at least one processor to: perform the same or similar operations as at claim 1.
The dependent claims (claims 2-8, 10-15, and 17-20) appear to be encompassed by the abstract idea of the independent claims since they merely indicate receiving a keyword list and procedure log, associating timestamps with the log and list-extracted keywords, and ordering events (claims 2, 10, and 17), performing sentence segmentation and text normalization before the NLP (claims 3, 11, and 18), performing vector similarity and frequency scoring for sentences and text (claims 4, 12, and 19), analyzing the completeness and plausibility of finding keywords (claims 5, 13, and 20), displaying a workflow and operational performance visualization (claims 6 and 14), such as by Gantt chart (claims 7 and 15), and/or where the medical facility comprises an interventional lab (e.g., a catheterization lab or operating room; claim 8).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of providing a performance analysis related to medical facility procedures; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the following grouping(s) of subject matter:
Certain methods of organizing human activity (e.g. … commercial or legal interactions such as agreements, contracts, legal obligations, advertising, marketing or sales activities/behaviors, or business relations; and/or managing personal behavior or relationships between people such as social activities, teaching, and following rules or instructions) since a person can provide the analysis of provided information, keyword identification/extraction, generating a workflow, and displaying or providing the analysis; and
Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) since the analysis indicated is based on the observation, evaluation, judgment, and/or opinion of the data – noting that the display may not necessarily be able to be performed mentally, or by the use of pen/pencil and paper; however, that is the basic reason certain methods of organizing human activity is apparently the primary or favored grouping.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are using natural language processing (NLP) and/or an NLP algorithm as well as a display to perform and provide the claim activities (at claim 1) and a system for performing performance analysis of a type of procedure performed in a medical facility, the system comprising: a user interface connected with a display; at least one processor as well as at least one non-transitory memory storing a non-transitory computer readable medium storing instructions for performing performance analysis (as at claims 9 and 16 respectively). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. Applicant ¶¶ 0021-0022 and Fig. 1 describe the system and that the activities “may be implemented by one or more servers, general purpose computers, central processing units, processors, microprocessors or microcontrollers”, etc. (at 0022).
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore, the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-6, 8-10, 13-14, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ng Tari et al. (U.S. Patent Application Publication No. 2016/0147954, hereinafter Ng Tari) in view of Grantsharov et al. (U.S. Patent Application Publication No. 2018/0122506, hereinafter Grantcharov).
Claim 1: Ng Tari discloses a method of performing performance analysis of a type of procedure performed in a medical facility, the method comprising:
providing a procedure log for the medical facility, wherein the procedure log stores information logging steps of procedures of the type of procedure performed in the medical facility (see Ng Tari at least at, e.g., ¶¶ 0067, “Clinical workflows are typically defined to include one or more steps, elements, and/or actions to be taken in response to one or more events and/or according to a schedule. Events may include receiving a healthcare message associated with one or more aspects of a clinical record, opening a record(s) for new patient(s), receiving a transferred patient, reviewing and reporting on an image, and/or any other instance and/or situation that requires or dictates responsive action or processing. The actions, elements, and/or steps of a clinical workflow may include placing an order for one or more clinical tests, scheduling a procedure, requesting certain information to supplement a received healthcare record, retrieving additional information associated with a patient, providing instructions to a patient and/or a healthcare practitioner associated with the treatment of the patient, radiology image reading, and/or any other action useful in processing healthcare information. The defined clinical workflows can include manual actions, elements, and/or steps to be taken by, for example, an administrator or practitioner, electronic actions, elements, and/or steps to be taken by a system or device, and/or a combination of manual and electronic action(s), element(s), and/or step(s)”, 0076, “Data source events (e.g., new documents, updated documents, lab results, exams for review, and/or other medical information, etc.) are pushed or pulled from the data source 402 to the IRCC processor 404 to trigger processing of the data from the data source”; citation hereafter by number only);
providing a predefined event list for the type of procedure performed in the medical facility, wherein the predefined event list includes key events for the type of procedure (0067, “Clinical workflows are typically defined to include one or more steps, elements, and/or actions to be taken in response to one or more events and/or according to a schedule. Events may include receiving a healthcare message associated with one or more aspects of a clinical record, opening a record(s) for new patient(s), receiving a transferred patient, reviewing and reporting on an image, and/or any other instance and/or situation that requires or dictates responsive action or processing. The actions, elements, and/or steps of a clinical workflow may include placing an order for one or more clinical tests, scheduling a procedure, requesting certain information to supplement a received healthcare record, retrieving additional information associated with a patient, providing instructions to a patient and/or a healthcare practitioner associated with the treatment of the patient, radiology image reading, and/or any other action useful in processing healthcare information. The defined clinical workflows can include manual actions, elements, and/or steps to be taken by, for example, an administrator or practitioner, electronic actions, elements, and/or steps to be taken by a system or device, and/or a combination of manual and electronic action(s), element(s), and/or step(s)”, 0076, “Data source events (e.g., new documents, updated documents, lab results, exams for review, and/or other medical information, etc.) are pushed or pulled from the data source 402 to the IRCC processor 404 to trigger processing of the data from the data source”);
performing natural language processing (NLP) on the procedure log using an NLP algorithm to extract keywords respectively associated with the key events in the predefined event list (0024, “The example intelligent recommendation system leverages natural language processing (NLP) to generate data from unstructured content”, 0078, “the natural language processor 406 parses and processes incoming data (e.g., document data) to create document meta data”, 0095, “A radiologist, for example, can be presented with summary information, trending, and extracted features made available so that the radiology does not have to search through a patient's prior radiology report history. The radiologist receives decision support including relevant clinical and diagnostic information to assist in a more definitive, efficient diagnosis”; 0102-0103, regarding feature extraction, analysis, summarization, and quantitative extraction as at items 808-816 of Fig. 8);
creating a final list of the extracted keywords respectively associated with the key events in the predefined event list (0024, 0078, 0095, 0102-0103 as above);
generating a process workflow for each of the procedures (0027, “A domain model filters global usage allowing only those points by users that are relevant to a clinical situation (e.g. only users specific to the current/selected workflow, etc.)”, 0028, “Data preference and relevancy can be determined with respect to a radiology workflow … the radiology desktop provides a diagnostic hub and facilitates a dynamic workflow and adaptive composition of a graphical user interface”, 0036, “Certain examples facilitate user-initiated launching of third-party applications directly from a desktop interface to help provide a seamless workflow by sharing user, patient, and/or other contexts”, 0067 as discussing workflows);
performing performance analysis of efficiency of performing the type of procedure in the medical facility (0108, “Semantic knowledge 928 can then be used with the learning from data 922 to perform context-driven analysis 924 (e.g., including a relevancy evaluation, supplemental information, best practices, workflow, etc.)“, 0109, “Results of the analysis 924 are provided via a user interface 930 to a user such as a clinician, other healthcare practitioner, healthcare application (e.g., image viewer, reporting tool, archive, data storage, etc.)”, 0141, “the systems and methods of this subject matter described herein can be configured to provide an ability to better understand large volumes of data generated by devices across diverse locations, in a manner that allows such data to be more easily exchanged, sorted, analyzed, acted upon, and learned from to achieve more strategic decision-making, more value from technology spend, improved quality and compliance in delivery of services, better customer or business outcomes, and optimization of operational efficiencies in productivity, maintenance and management of assets (e.g., devices and personnel) within complex workflow environments that may involve resource constraints across diverse location”); and displaying the performance analysis at a display connected to a user interface (0105, “As shown in the example of FIG. 8, the diagnostic hub 850 formulates and displays reporting information based on the features and associated information provided by the processor 805. Information provided via the diagnostic hub 850 includes trending and timeline information 852, and one or more reports 854. Upon selection of (e.g., clicking on, mouse over, etc.) a report, a summary 856 of that report can be provided, for example” and 0108-0109, 0141 as above).
Ng Tari, however, does not appear to explicitly disclose the analysis being by calculating time-related matrices and key performance indicators (KPIs) based on the process workflow. Grantcharov, though, teaches “Embodiments described herein may integrate multiple, clinically relevant feeds (audio/video/metadata) for a medical procedure, and allows a comprehensive analysis of human and technology performance for the medical procedure, organizational processes and links them to safety efficiency and outcomes as events” (Grantcharov at 0174), including where “a log file may be generated that indicates key time points, including start and end time of the recording session” (Grantcharov at 0137), “process operations may translate point cloud and/or depth mapping position, distance and change measurements into real-world distance measurements. These measurements may permit the creation of the key performance indicators (KPI's), in a semi-autonomous fashion. KPI's can be used to further analysis and/or provide recommendations on workflow and human factors impacting timeline and chain of events” (Grantcharov at 0280), and “To extract the data in a meaningful and structured way, time stamping may be used to provide temporal order among a set of events. Time-stamped events may be displayed on a timeline, and summarized in a matrix that indicates the duration and frequency of each event” (Grantcharov at 0295). Therefore, the Examiner understands and finds that to calculate time matrices and KPIs based on the workflow is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide recommendations to impact improvement.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the analysis of Ng Tari with the matrices and KPIs of Grantcharov in order to calculate time matrices and KPIs based on the workflow so as to provide recommendations so as to impact improvement.
The rationale for combining in this manner is that to calculate time matrices and KPIs based on the workflow is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide recommendations to impact improvement as explained above.
Claim 2: Ng Tari in view of Grantcharov discloses the method of claim 1, wherein generating the process workflow for each of the procedures comprises:
receiving the final list of the extracted keywords (Ng Tari at 0024, 0078, 0095, 0102-0103 as above);
receiving the procedure log formatted by a data interface using information extracted from a plurality of data sources (Ng Tari at 0078);
extracting timestamps from the formatted procedure log, and associating the extracted timestamps with the extracted keywords from the final list (Ng Tari at 0097, “A report summary, similarity score (sindex) for each document, a summary tag for a timeline display” – the timeline display indicating extracted timestamps used); and
ordering the key events using the extracted keywords and the associated timestamps (Ng Tari at 0097, “A report summary, similarity score (sindex) for each document, a summary tag for a timeline display”).
Claim 5: Ng Tari in view of Grantcharov discloses the method of claim 1, further comprising: performing a quality check on the extracted key words following the NLP, wherein the quality check comprises analyzing completeness and plausibility of finding the extracted keywords (Ng Tari at 0097, 0100, 0105, timeline display and information; Grantcharov at 0363, “FIG. 25 is a timeline chart 2500 illustrative of some results where the system was able to identify potential errors in proximity to actual technical errors and/or technical events, according to some embodiments. The chart may be generated as part of a user interface with interface indicator elements for clinical events detected in the session container file”, as combined above and using the rationale as at the combination above).
Claim 6: Ng Tari in view of Grantcharov discloses the method of claim 1, wherein displaying the performance analysis comprises: displaying a workflow visualization for each procedure of the type of procedure performed in the medical facility; and displaying an operational performance overview visually indicating a length of time of each procedure (Ng Tari at 0097, timeline display of results; Grantcharov at 0363, “FIG. 25 is a timeline chart 2500 illustrative of some results”, as combined above and using the rationale as at the combination above).
Claim 8: Ng Tari in view of Grantcharov discloses the method of claim 1, wherein the medical facility comprises an interventional lab (Grantcharov at 0051, “feeds from one or more data capture devices located in an operating room or other patient intervention area”, as combined above and using the rationale as at the combination above).
9-10, 13-14, 16-17, and 20 are rejected on the same basis as claims 1-2 and 5-6 above since Ng Tari in view of Grantcharov discloses a system for performing performance analysis of a type of procedure performed in a medical facility, the system comprising: a user interface connected with a display; at least one processor; at least one non-transitory memory storing: a procedure log for the medical facility (for claims 9-10 and 13-14) and a non-transitory computer readable medium storing instructions for performing performance analysis of a type of procedure performed in a medical facility that, when executed by at least one processor, cause the at least one processor to perform the same or similar operations as at claims 1-2 and 5-6 above (see Ng Tari at 0006-0006, Grantcharov at 0204-0205, 0257).
Claims 3, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ng Tari in view of Grantsharov in further view of Datla et al. (U.S. Patent Application Publication No. 2020/0168343, hereinafter Datla).
Claims 3, 11, and 18: Ng Tari in view of Grantcharov discloses the method, system, and medium of claims 1, 9, and 16 but does not appear to explicitly disclose further comprising: preprocessing description text of the information stored in the procedure log before performing the NLP, wherein the preprocessing includes sentence segmentation and text normalization to provide event sentences unique to the medical facility. Datla, though, teaches a “process flow 300 may include a process 305 in which text associated with evidence is received. Specifically, the evidence text that may be identified by the user and/or online sources may be curated using a semantic role labeling (SRL) operation as depicted as process 320. Prior to the SRL operation in process 320, the process flow 300 may include a process 310 in which a text cleaning operation is performed and a process 315 in which a sentence segmentation operation is performed. Accordingly, the text from the process 305 may be normalized for the process 320.... [so that] a plurality of relations may be established for the text from the process” (Datla at 0034), where “the blog engine 250 may utilize a natural language processing (NLP) functionality that analyzes the syntax and extracts semantic elements and keywords from the microblog” (Datla at 0038), Fig. 3 of Datla indicating the sentence segmentation and normalization is prior to the generation engine operation, where the information is regarding a “health-related topic” or “health-related topics” (Datla at 0025, 0026, 0028). Therefore, the Examiner understands and finds that to use sentence segmentation and text normalization in relation to NLP is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide recommendations to impact improvement.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the analysis of Ng Tari in view of Grantcharov with the text analysis of Datla in order to use sentence segmentation and text normalization in relation to NLP so as to provide recommendations to impact improvement.
The rationale for combining in this manner is that to use sentence segmentation and text normalization in relation to NLP is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide recommendations to impact improvement as explained above.
Claims 4, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ng Tari in view of Grantsharov in further view of Datla and in still further view of Chen et al. (U.S. Patent Application Publication No. 2020/0074242, hereinafter Chen).
Claims 4, 12 and 19: Ng Tari in view of Grantcharov in further view of Datla discloses the method of claims 3, 11, and 18, but does not appear to explicitly disclose wherein performing the NLP on the procedure log comprises: performing similarity scoring by converting the event sentences to vectors and calculating similarity scores between the vectors of the event sentences and event text in the predefined event list; and performing frequency scoring by multiplying a document frequency (DF) of the event sentences and an inverse of a term frequency difference (ITFD) of the event sentences and the event text, wherein the ITFD is a difference between an average number of times the event sentences appear in a case and an expected number of times the event text appears in the case. Where Ng Tari indicates similarity scoring (Ng Tari at 0078, “calculate similarity and/or dissimilarity to a clinical scenario, concept, and/or other criterion”), Chen teaches using NLP (Chen at 0046, 0078, 0085) with a similarity score based on clustering to separate sentences into groups where “Each group has sentences with similar word vector” (Chen at 0065), and using TFIDF (Chen at 0062, 0064, where TFIDF is term frequency inverse document frequency as the product of the term frequency in a document and the inverse of the term frequency) in order to denote and use word occurrence for more refined text analysis. Therefore, the Examiner understands and finds that to use vector similarity and frequency analysis is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to refine the text analysis.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the analysis of Ng Tari in view of Grantcharov and Datla with the analysis of Chen in order to c use vector similarity and frequency analysis so as to refine the text analysis.
The rationale for combining in this manner is that to use vector similarity and frequency analysis is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to refine the text analysis as explained above.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ng Tari in view of Grantsharov in further view of Schaeffer et al. (U.S. Patent Application Publication No. 2022/0044826, hereinafter Schaeffer).
Claim 7: Ng Tari in view of Grantcharov discloses the method of claim 6, but does not appear to explicitly disclose wherein the operation performance overview is displayed as a Gantt chart. Schaeffer, though, teaches that “natural language processing may permit a user to use the system to query for general insights directly” (Schaeffer at 0110) in relation to health, medical, laboratory, clinic, etc. procedures and efficiency (Schaeffer at 0332), where “an event timeline Gantt style chart is provided for a high-level overview, coupled with a tabular detail panel. The display may also enable the visualization and comparison of multiple patients concurrently on a normalized timeline, for the purposes of identifying both areas of overlap, and potential discontinuity across a patient subset” (Schaeffer at 0127). Therefore, the Examiner understands and finds that to use a Gantt chart is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to visualize and identify overlap and discontinuity.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the analysis of Ng Tari in view of Grantcharov with the Gantt charting of Schaeffer in order to use a Gantt chart so as to visualize and identify overlap and discontinuity.
The rationale for combining in this manner is that to use a Gantt chart is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to visualize and identify overlap and discontinuity as explained above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Solilov et al. (U.S. Patent Application Publication No. 2013/0132108, hereinafter Solilov) indicates “An example operation metrics collection and processing system is to mine a data set including patient and exam workflow data from information source(s) according to an operational metric for a workflow of interest. An example healthcare workflow performance monitoring system includes a contextual analysis engine to mine a data set to identify patterns based on current and historical healthcare data for a healthcare workflow and extract context information from the identified patterns and data mined information. The example system includes a statistical modeling engine to dynamically create contextual performance indicators based on the context and pattern information including a contextual ordering of events in the healthcare workflow. The example system includes a workflow decision engine to evaluate the contextual performance indicators based on a model and monitor measurements associated with the contextual performance indicators, the workflow decision engine to process feedback to update the context performance indicators” (at Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT D GARTLAND whose telephone number is (571)270-5501. The examiner can normally be reached M-F 8:30 AM - 5 PM.
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685
