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 .
Claims 1-20 are pending. Claims 1-20 are rejected herein.
Priority
The application claims benefit of applications 17/196976 and 62/987749 and has an effective priority date of 10 March 2020.
Information Disclosure Statement
The information disclosure statements received 15 August 2025 and 10 March 2026 have been received and reviewed.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1,7,12,16,19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,9,16 of U.S. Patent No. 12254975. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1,9,16 of U.S. patent No. 12254975 anticipates every limitation of claims 1,9 and 16 and 7,12,19 of the instant application.
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.
Step 1: Statutory Category
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1,9, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a computer processor, non-transitory computer readable storage medium, device, and method to receive and transform healthcare data and generate a workflow for a healthcare professional.
Step 2A Prong One: The Abstract Idea
The limitations of (claim 1 being representative) receiving […] healthcare data associated with a plurality of patients, the plurality of patients including a first patient and a second patient; transforming […] the healthcare data from a plurality of differently-structured data formats into a consistent computer data structure configured for inputting to a trained statistical model, wherein an operation to perform the transforming includes one or more of: an aggregation operation configured to identify a plurality of entities associated with the healthcare data of differently-structured data formats and compile a plurality of data feeds for the plurality of entities onto a common consolidated entity; a normalization operation configured to reduce data redundancy across the plurality of data feeds for the plurality of entities; or an enrichment operation configured to identify and add curated healthcare data of the plurality of differently-structured data formats from the plurality of data feeds for a plurality of entities, into a record formatted according to the consistent computer data structure; validating […] that the healthcare data conforms to the consistent computer data structure; inputting […] to the trained statistical model, the healthcare data with the consistent computer data structure; surfacing […] using the trained statistical model, at least one hidden relationship within the healthcare data with the consistent computer data structure and encoding the at least one hidden relationship as an explicit value; generating […] a healthcare workflow of a healthcare professional using an output of the trained statistical model, the output of the trained statistical model generated using the healthcare data prepared with the consistent computer data structure, the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure, wherein the healthcare workflow of the healthcare professional comprises: a plurality of healthcare events for the healthcare professional, the plurality of healthcare events for the healthcare professional comprising a first healthcare event specific to the first patient and a second healthcare event specific to the second patient; an indication of order of the first healthcare event specific to the first patient and the second healthcare event specific to the second patient, wherein the order is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure… as drafted, is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind, mathematical concepts and certain methods of organizing human activity but for recitation of generic computer components. That is, other than reciting a system implemented by a computer processor and non-transitory computer readable storage medium the claimed invention amounts to a process that covers performance of the limitation in the mind, a mathematical process and managing personal behavior or interaction between people. For example, but for the computer processor and non-transitory computer readable storage medium, this claim encompasses a person thinking about patient data received and converting the patient data into a consistent computer data and manipulating the data in the manner described in the identified abstract idea, encompasses mathematical concept(s) and encompasses a person generating a workflow for a healthcare professional to plan a first and second healthcare event specific to the first and second patient in the manner specifically recited, supra. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, mathematical concepts and covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the mental processes, mathematical concepts, and “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
MPEP 2106 indicates in other claims, multiple abstract ideas, which may fall in the same or different groupings, or multiple laws of nature may be recited. In these cases, examiners should not parse the claim. For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A Prong One to make the analysis clear on the record. However, if possible, the examiner should consider the limitations together as a single abstract idea for Step 2A Prong Two and Step 2B (if necessary) rather than as a plurality of separate abstract ideas to be analyzed individually. The types of identified abstract ideas were considered together as a single abstract idea for analysis purposes.
Step 2A Prong Two: Practical Application
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computer processor and non-transitory computer readable storage medium that implements the identified abstract idea. The computer processor and non-transitory computer readable storage medium are not described by the applicant and are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The use of the trained statistical model, as recited, was determined to be part of the abstract idea. For example, the implementation of the trained statistical model to surface a relationship among healthcare data in the manner specifically recited merely confines the use of the abstract idea (i.e., the trained statistical model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible).
Independent claim 16 further recites generating a graphical user interface using an output of the trained statistical model. As recited, the graphical user interface generally links the judicial exception to a particular technological environment or field of use. Additional elements that generally link the judicial exception to a particular technological environment or field of use cannot serve to integrate the exception into a practical application. See MPEP 2106.04(d)(l), Relevant Consideration for Evaluating Whether Additional Elements Integrate A Judicial Exception Into A Practical Application, and MPEP 2106.05(h).
Step 2B: Significantly More
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained statistical model to surface a hidden relationship amongst healthcare data was found to confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use. This limitation was re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Independent claim 16 further recites generating a graphical user interface using an output of the trained statistical model. The graphical user interface generally links the judicial exception to a particular technological environment or field of use. Additional elements that generally link the judicial exception to a particular technological environment or field of use cannot serve to integrate the exception into a practical application or provide significantly more. See MPEP 2106.04(d)(l), Relevant Consideration for Evaluating Whether Additional Elements Integrate A Judicial Exception Into A Practical Application, and MPEP 2106.05(h).
Dependent Claims
Claims 2-8,10-15, and 17-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 3 merely describe(s) at least one prompt for the healthcare profession to provide care to the first patient via the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer structure, wherein the prompt comprises one or more visual indicators of priority for providing care to the first patient. Claim 4 merely describe(s) an ordered list of the plurality of healthcare events comprising a first healthcare event specific to the first patient and a second healthcare event specific to a second patient; and at least on prompt for the healthcare professional to provide care to the first patient prior to providing care to the second patient, wherein the at least one prompt for the healthcare professional to provide care to the first patient prior to providing care to the second patient is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure. Clam 5 merely describes updating, using an updated output of the trained statistical model generated using the healthcare data prepared with the consistent computer data structure, at least one indication of order of one or more healthcare events of the plurality of healthcare events within the healthcare workflow of the healthcare professional. Claim 6 and 18 merely describe transforming the healthcare data from a plurality of differently-structured data formats into the consistent computer data structure comprises transforming the healthcare data using natural language processing. Claim 7 merely describes the consistent computer data structure is configured for use with at least one of a foundational conception data operation, a knowledge data operation, a service data operation, a clinical decision support data operation, a documentation data operation, an analytics data operation, or an administrative data operation. Claim 8 merely describe wherein the at least one hidden relationship within the healthcare data with the consistent computer data structure comprises an indication of a clinical concept and/or value concept. Claim 10 merely describes a respective variable; and linked with the respective variable, a set of healthcare codes corresponding to each medical classification standards of the plurality of different medical classification standards, wherein each healthcare code of the set of healthcare codes corresponds to the diagnosis. Claim 11 merely describes at least one prompt to provide care to a first patient of the one or more patients, wherein the at least one prompt to provide care to the first patient is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure, wherein the at least one prompt comprises one or more visual indicators of priority for providing care to the first patient. Claim 12 and 19 merely describes wherein the consistent computer data structure is configured for use with at least one of a foundational concept data operation, a knowledge data operation, a service data operation, a clinical decision support data operation, a documentation data operation, an analytics data operation, or an administrative data operation. Claim 13 merely describes wherein the at least one hidden relationship within the healthcare data with the consistent computer data structure comprises an indication of a clinical concept and/or a value concept. Claim 14 and 20 merely describes the types of possible healthcare data. Claim 15 merely describes generating the first healthcare event using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure.
Claim(s) 2 and 17 merely describe(s) training the statistical model and linking healthcare codes to corresponding diagnosis. In addition, MPEP 2106.04(d)(I) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
The remaining additional elements (non-transitory computer readable storage medium) in the dependent claims were analyzed as were the generic computer part(s) in the independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0150650 A1 (hereafter Saunders) in view of US 11452652 B1 (hereafter McNair) in view of US 10483003 B1 (hereafter McNair2).
Regarding Claim 1
Saunders teaches:
[…]
transforming, by the at least one computer processor, the healthcare data from a plurality of differently-structured data formats into a consistent computer data structure configured for inputting to a trained statistical model, [Saunders teaches at para. [0134] collating data from disparate sources and creating a single database of this medical information allows for easy and rapid analysis of all relevant contextual data. Saunders teaches at para. [0135] converting data into a common structured format and a command data format. Saunders teaches at para. [0283] the data is structured so that it is put into a consistent computer record structure, with consistent fields, consistent units for values (e.g., grams), and so forth. The limitation “for inputting of the data into the trained statistical model” is intended use here.]
wherein an operation to perform the transforming includes one or more of: an aggregation operation configured to identify a plurality of entities associated with the healthcare data of differently structured data formats and compile a plurality of data feeds for the plurality of entities into a common consolidated entity; a normalization operation configured to reduce data redundancy across the plurality of data feeds for the plurality of entities; or an enrichment operation configured to identify and add curated healthcare data of the plurality of differently-structured data formats from the plurality of data feeds for the plurality of entities, into a record formatted according to the consistent computer data structure; [Saunders teaches at para. [0134] collating data from disparate sources and creating a single database of this medical information allows for easy and rapid analysis of all relevant contextual data. Saunders teaches at para. [0135] converting data into a common structured format and a command data format. Collectively, these teach identify a plurality of entities associated with the healthcare data of differently structured data formats and compile a plurality of data feeds for the plurality of entities into a common consolidated entity. The collated data from disparate sources are interpreted as the plurality of data feeds. Saunders teaches at para. [0163] it provides a pathway to the databases of storage, and functionality that formats data from the databases to make it suitable for consumption by these external clients, and likewise formats data from the external clients to make it suitable to send to the databases. Saunders teaches at para. [0178] the summary database will hold aggregated data over time for each diary category or module, for consumption by the timeline, and in the future, other systems as required. Saunders teaches at para. [0294] the output of the analysis process will also be sent to an aggregation process. Collectively, Saunders teaches wherein an operation to perform the transforming includes one or more of: an aggregation operation configured to identify a plurality of entities associated with the healthcare data of differently structured data formats and compile a plurality of data feeds for the plurality of entities into a common consolidated entity.]
validating, by the at least one computer processor, that the healthcare data conforms to the consistent computer data structure; [Saunders teaches at para. [0134] collating data from disparate sources and creating a single database of this medical information allows for easy and rapid analysis of all relevant contextual data. Saunders teaches at para. [0135] converting data into a common structured format and a command data format. Saunders teaches at para. [0163] it provides a pathway to the databases of storage, and functionality that formats data from the databases to make it suitable for consumption by these external clients, and likewise formats data from the external clients to make it suitable to send to the databases. Saunders teaches at para. [0283] the data is structured so that it is put into a consistent computer record structure, with consistent fields, consistent units for values (e.g., grams), and so forth. Collectively, this validating, by the at least one computer processor, that the healthcare data conforms to the consistent computer data structure. ]
Saunders may not explicitly teach:
A method comprising: receiving, by at least one computer processor, healthcare data associated with a plurality of patients, the plurality of patients including a first patient and a second patient;
[…]
inputting, by the at least one computer processor, to the trained statistical model, the healthcare data with consistent computer data structure;
surfacing, by the at least one computer processor, using the trained statistical model, at least one hidden relationship within the healthcare data with the consistent computer data structure and encoding the at least one hidden relationship as an explicit value;
generating, by the at least one computer processor, a healthcare workflow of a healthcare professional using an output of the trained statistical model, the output of the trained statistical model generated using the healthcare data prepared with consistent computer data structure,
the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure,
wherein the healthcare workflow of the healthcare professional comprises: a plurality of healthcare events for the healthcare professional, the plurality of healthcare events for the healthcare professional comprising a first healthcare event specific to the first patient and a second healthcare event specific to the second patient;
an indication of order of the first healthcare event specific to the first patient and the second healthcare event specific to the second patient,
wherein the order is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure.
McNair teaches:
surfacing, by the at least one computer processor, using the trained statistical model, at least one hidden relationship within the healthcare data with the consistent computer data structure and encoding the at least one hidden relationship as an explicit value; [McNair teaches at col. 14 line 20-23 likewise, censoring of cases (rows) in which an excessive proportion of explanatory variables’ values are missing also will be utilized for the model generation step in order to produce more reliable, accurate results. McNair teaches at col. 14, line 23-line 26 teaches alternatively, statistical multiple imputation methods will be utilized to produce plausible, unbiased estimates or substitute values for those explanatory variables whose values are missing. The production of explanatory variables values are interpreted as encoding the at least one hidden relationship as an explicit value. McNair teaches at col. 14 line 11-16 multivariable logistic regression, support vector machine (SVM), artificial neural network (ANN), Bayesian network, and other modeling methods are likewise capable of establishing statistical associations of explanatory variables with the “winner/loser” outcome variable, and thus these approaches will be used in some embodiments. McNair teaches at col. 14 line 20-23 teaches likewise, censoring of cases (rows) in which an excessive proportion of explanatory variables’ values are missing also will be utilized for the model generation step in order to produce more reliable, accurate results. These teach, via description, training the statistical model. Collectively, this teaches surfacing, by the at least one computer processor, using the trained statistical model, at least one hidden relationship within the healthcare data with the consistent computer data structure and encoding the at least one hidden relationship as an explicit value. The hidden relationship is the trained statistical model generating the absent values (interpreted as explicit values).]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the system and method for controlling permissions for selected recipients by owners of data of Saunders to the predicting and preventing caregiver and musculoskeletal injuries of McNair with the motivation of making transfer-lift-repositioning (TLR) activities substantially safer for both the caregiver and patient (McNair at col. 3 line 13-line 16).
Saunders/McNair may not explicitly teach:
A method comprising: receiving, by at least one computer processor, healthcare data associated with a plurality of patients, the plurality of patients including a first patient and a second patient;
inputting, by the at least one computer processor, to the trained statistical model, the healthcare data with consistent computer data structure;
generating, by the at least one computer processor, a healthcare workflow of a healthcare professional using an output of the trained statistical model, the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure,
wherein the healthcare workflow of the healthcare professional comprises: a plurality of healthcare events for the healthcare professional, the plurality of healthcare events for the healthcare professional comprising a first healthcare event specific to the first patient and a second healthcare event specific to the second patient;
[…]
generating, by the at least one computer processor, a healthcare workflow of a healthcare professional using an output of the trained statistical model, the output of the trained statistical model generated using the healthcare data prepared with the consistent computer data structure, the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure,
wherein the healthcare workflow of the healthcare professional comprises: a plurality of healthcare events for the healthcare professional, the plurality of healthcare events for the healthcare professional comprising a first healthcare event specific to the first patient and a second healthcare event specific to the second patient;
an indication of order of the first healthcare event specific to the first patient and the second healthcare event specific to the second patient,
wherein the order is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure.
McNair2 teaches:
A method comprising: receiving, by at least one computer processor, healthcare data associated with a plurality of patients, the plurality of patients including a first patient and a second patient; [McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. This is receiving healthcare data associated with a first patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. McNair2 teaches at Figure 4D Item 4380 presenting the clinical recommendation for the second patient in the second user interface. This is receiving (by the second user interface) healthcare data associated with a second patient. McNair2 teaches at col. 6 line 24-28 example operating environment includes a computerized system for compiling and/or running an embodiment of a method for providing decision support in accordance with embodiments of the present invention. McNair2 teaches at col. 8 line 14-18 computer system comprises one or more processors operable to receive instructions and process them accordingly, and may be embodied as a single computing device or multiple computing device communicatively coupled to each other. Collectively, this teaches receiving, by at least one computer processor, healthcare data associated with a plurality of patients, the plurality of patients including a first patient and a second patient.]
inputting, by the at least one computer processor, to the trained statistical model, the healthcare data with consistent computer data structure; [McNair2 teaches at col. 23 line 38-44 in some embodiment, the patient information is encoded into one or more clinical concepts, which will be translated or “mapped” to a standard or universal nomenclature, as described in connection to Figure 3A, thereby rendering the content consumable by other decision support services, applications, features, and agents described herein. This teaches the healthcare data with consistent computer data structure. McNair2 teaches at col. 26 line 22- line 25 in some embodiments, agent solvers 2010, from Fig. 2A, analyze years of de-identified health facts data to provide the health care agents with the knowledge needed to produce helpful conclusions. McNair2 teaches at col. 26 line 36 in some embodiments, the imputed information will be statistically imputed using information from similar patients, and statistical processes such as monte Carlo simulation. This teaches training the statistical model. Collectively, McNair2 teaches inputting, by the at least one computer processor, to the trained statistical model, the healthcare data with consistent computer data structure.]
generating, by the at least one computer processor, a healthcare workflow of a healthcare professional using an output of the trained statistical model, the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure, [McNair2 teaches at col. 23 line 38-44 in some embodiment, the patient information is encoded into one or more clinical concepts, which will be translated or “mapped” to a standard or universal nomenclature, as described in connection to Figure 3A, thereby rendering the content consumable by other decision support services, applications, features, and agents described herein. This teaches the healthcare data with consistent computer data structure. McNair2 teaches at col. 26 line 22- line 25 in some embodiments, agent solvers 2010, from Fig. 2A, analyze years of de-identified health facts data to provide the health care agents with the knowledge needed to produce helpful conclusions. McNair2 teaches at col. 26 line 36 in some embodiments, the imputed information will be statistically imputed using information from similar patients, and statistical processes such as monte Carlo simulation. This teaches the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure. McNair2 teaches at Figure 4D Item 4310 presenting a first clinical user interface for a first patient having a condition. McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. McNair2 teaches at Figure 4D Item 4330 associating the clinical decision support event with the condition. McNair2 teaches at Figure 4D Item 4340 determining a change in the condition of the first patient. McNair2 teaches at Figure 4D Item 4350 identifying a second patient having the condition of the first patient. McNair2 teaches at Figure 4D Item 4360 presenting a second clinical user interface for the second patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. This teaches the use of the output of the statistical model taught above. McNair2 teaches at Figure 4D Item 4380 presenting the clinical recommendation for the second patient in the second user interface. Collectively, this teaches generating, by the at least one computer processor, a healthcare workflow of a healthcare professional using an output of the trained statistical model, the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure.]
wherein the healthcare workflow of the healthcare professional comprises: a plurality of healthcare events for the healthcare professional, the plurality of healthcare events for the healthcare professional comprising a first healthcare event specific to the first patient and a second healthcare event specific to the second patient; [McNair2 teaches at Figure 4D Item 4310 presenting a first clinical user interface for a first patient having a condition. McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. McNair2 teaches at Figure 4D Item 4330 associating the clinical decision support event with the condition. This teaches a first healthcare event specific to the first patient. McNair2 teaches at Figure 4D Item 4340 determining a change in the condition of the first patient. McNair2 teaches at Figure 4D Item 4350 identifying a second patient having the condition of the first patient. McNair2 teaches at Figure 4D Item 4360 presenting a second clinical user interface for the second patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. McNair2 teaches at Figure 4D Item 4380 presenting the clinical recommendation for the second patient in the second user interface. The clinical recommendations teaches a second healthcare event specific to the second patient. Collectively, McNair2 teaches wherein the healthcare workflow of the healthcare professional comprises: a plurality of healthcare events for the healthcare professional, the plurality of healthcare events for the healthcare professional comprising a first healthcare event specific to the first patient and a second healthcare event specific to the second patient.]
[…]
generating, by the at least one computer processor, a healthcare workflow of a healthcare professional using an output of the trained statistical model, the output of the trained statistical model generated using the healthcare data prepared with the consistent computer data structure, the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure, [McNair2 teaches at col. 23 line 38-44 in some embodiment, the patient information is encoded into one or more clinical concepts, which will be translated or “mapped” to a standard or universal nomenclature, as described in connection to Figure 3A, thereby rendering the content consumable by other decision support services, applications, features, and agents described herein. This teaches the healthcare data with consistent computer data structure. McNair2 teaches at col. 26 line 22- line 25 in some embodiments, agent solvers 2010, from Fig. 2A, analyze years of de-identified health facts data to provide the health care agents with the knowledge needed to produce helpful conclusions. McNair2 teaches at col. 26 line 36 in some embodiments, the imputed information will be statistically imputed using information from similar patients, and statistical processes such as monte Carlo simulation. Collectively, this teaches the trained statistical model, the output comprising the at least one hidden relationship within the healthcare data. McNair2 teaches at Figure 4D Item 4310 presenting a first clinical user interface for a first patient having a condition. McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. McNair2 teaches at Figure 4D Item 4330 associating the clinical decision support event with the condition. McNair2 teaches at Figure 4D Item 4340 determining a change in the condition of the first patient. McNair2 teaches at Figure 4D Item 4350 identifying a second patient having the condition of the first patient. McNair2 teaches at Figure 4D Item 4360 presenting a second clinical user interface for the second patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. McNair2 teaches at Figure 4D Item 4380 presenting the clinical recommendation for the second patient in the second user interface. Collectively, McNair2 teaches generating, by the at least one computer processor, a healthcare workflow of a healthcare professional using an output of the trained statistical model, the output of the trained statistical model generated using the healthcare data prepared with the consistent computer data structure, the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure.]
wherein the healthcare workflow of the healthcare professional comprises: a plurality of healthcare events for the healthcare professional, the plurality of healthcare events for the healthcare professional comprising a first healthcare event specific to the first patient and a second healthcare event specific to the second patient; [McNair2 teaches at Figure 4D Item 4310 presenting a first clinical user interface for a first patient having a condition. McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. McNair2 teaches at Figure 4D Item 4330 associating the clinical decision support event with the condition. This teaches a first healthcare event specific to the first patient. McNair2 teaches at Figure 4D Item 4340 determining a change in the condition of the first patient. McNair2 teaches at Figure 4D Item 4350 identifying a second patient having the condition of the first patient. McNair2 teaches at Figure 4D Item 4360 presenting a second clinical user interface for the second patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. McNair2 teaches at Figure 4D Item 4380 presenting the clinical recommendation for the second patient in the second user interface. The presenting of the clinical recommendation for the second patient in the second user interface is a second healthcare event specific to the second patient. Collectively, McNair2 teaches a plurality of healthcare events for the healthcare professional, the plurality of healthcare events for the healthcare professional comprising a first healthcare event specific to the first patient and a second healthcare event specific to the second patient.]
an indication of order of the first healthcare event specific to the first patient and the second healthcare event specific to the second patient, [McNair2 teaches at Figure 4D Item 4310 presenting a first clinical user interface for a first patient having a condition. McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. McNair2 teaches at Figure 4D Item 4330 associating the clinical decision support event with the condition. McNair2 teaches at Figure 4D Item 4340 determining a change in the condition of the first patient. McNair2 teaches at Figure 4D Item 4350 identifying a second patient having the condition of the first patient. McNair2 teaches at Figure 4D Item 4360 presenting a second clinical user interface for the second patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. McNair2 teaches at Figure 4D Item 4380 presenting the clinical recommendation for the second patient in the second user interface. Collectively, McNair2 teaches an indication of order of the first healthcare event specific to the first patient and the second healthcare event specific to the second patient.]
wherein the order is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure. [McNair2 teaches at col. 23 line 38-44 in some embodiment, the patient information is encoded into one or more clinical concepts, which will be translated or “mapped” to a standard or universal nomenclature, as described in connection to Figure 3A, thereby rendering the content consumable by other decision support services, applications, features, and agents described herein. This teaches the healthcare data with consistent computer data structure. McNair2 teaches at col. 26 line 22- line 25 in some embodiments, agent solvers 2010, from Fig. 2A, analyze years of de-identified health facts data to provide the health care agents with the knowledge needed to produce helpful conclusions. McNair2 teaches at col. 26 line 36 in some embodiments, the imputed information will be statistically imputed using information from similar patients, and statistical processes such as monte Carlo simulation. Collectively, this teaches the trained statistical model, the output comprising the at least one hidden relationship within the healthcare data. McNair2 teaches at Figure 4D Item 4310 presenting a first clinical user interface for a first patient having a condition. McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. McNair2 teaches at Figure 4D Item 4330 associating the clinical decision support event with the condition. McNair2 teaches at Figure 4D Item 4340 determining a change in the condition of the first patient. McNair2 teaches at Figure 4D Item 4350 identifying a second patient having the condition of the first patient. McNair2 teaches at Figure 4D Item 4360 presenting a second clinical user interface for the second patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. McNair2 teaches at Figure 4D Item 4380 presenting the clinical recommendation for the second patient in the second user interface. Collectively, McNair2 teaches wherein the order is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the system and method for controlling permissions for selected recipients by owners of data of Saunders to the predicting and preventing caregiver musculoskeletal injuries of McNair to dynamically determining risk of clinical condition of McNair2 with the motivation of providing services for mapping clinical health information across health records systems and data sources; dynamically directing care processes triggered by findings at points in time to support specialists with contextualized decision support information for determining next actions, using information from care plans and pathways, in some cases; discovering and incorporating, into decision support service, new ontologies, and behavior generation, sensory perception, and world modeling to achieve adaptive goals (McNair2 col. 1, line 51-59).
Regarding Claim 9 and 16
Due to their similarity to Claim 1, Claim 9 and 16 are similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Regarding Claim 2
Saunders/McNair/McNair2 teach the method of claim 1. Saunders/McNair/McNair2 further teach:
wherein the trained statistical model is trained on training data comprising, for each diagnosis of a plurality of diagnoses; a respective variable; and linked with the respective variable, a set of healthcare codes corresponding to each medical classification standards of a plurality of different medical classification standards, wherein each healthcare code of the set of healthcare codes corresponds to the diagnosis. [McNair2 teaches at col. 23 line 38-44 in some embodiment, the patient information is encoded into one or more clinical concepts, which will be translated or “mapped” to a standard or universal nomenclature, as described in connection to Figure 3A, thereby rendering the content consumable by other decision support services, applications, features, and agents described herein. This teaches the healthcare data with consistent computer data structure. McNair2 teaches at col. 26 line 22- line 25 in some embodiments, agent solvers 2010, from Fig. 2A, analyze years of de-identified health facts data to provide the health care agents with the knowledge needed to produce helpful conclusions. McNair2 teaches at col. 26 line 36 in some embodiments, the imputed information will be statistically imputed using information from similar patients, and statistical processes such as monte Carlo simulation. Collectively, this teaches the trained statistical model, the output comprising the at least one hidden relationship within the healthcare data. McNair2 teaches at col. 4 in the Table with “Terms” that a clinical concept is a discretized health-related information capable of being encoded. McNair2 teaches at col. 4 in the Table with “Terms” a clinical concept will include clinical variable, clinical values, clinical information elements or combinations thereof. McNair2 teaches at col. 4 in the Table with “Terms” for example, a clinical variable having a clinical value will be encoded as a single code representing the clinical variable and clinical value. McNair2 teaches at col. 4 in the Table with “Terms” a clinical variable/attribute is a category or type of clinical information about a patient such as BP, respiratory rate, weight, blood glucose, sex, age, condition(s), diagnoses, or other type of clinical information. McNair2 teaches at figure 6D mapping a concept qualifier (i.e., has diagnosis) to an ICD9250.02 code and concept systolic bl… with value 125 mapped to an event code. This teaches wherein each healthcare code of the set of healthcare codes corresponds to the diagnosis. Collectively, McNair2 teaches the trained statistical model is trained on training data comprising, for each diagnosis of a plurality of diagnoses; a respective variable; and linked with the respective variable, a set of healthcare codes corresponding to each medical classification standards of a plurality of different medical classification standards, wherein each healthcare code of the set of healthcare codes corresponds to the diagnosis.]
Regarding Claim 10 and 17
Due to their similarity to Claim 2, Claim 10 and 17 are similarly analyzed and rejected in a manner consistent with the rejection of Claim 2.
Regarding Claim 3
Saunders/McNair/McNair2 teach the method of claim 1. Saunders/McNair/McNair2 further teach:
wherein the healthcare workflow of the healthcare professional comprises: at least one prompt for the healthcare professional to provide care to the first patient, wherein the at least one prompt for the healthcare professional to provide care to the first patient is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure, wherein the at least one prompt comprises one or more visual indicators of priority for providing care to the first patient. [McNair2 teaches at col. 23 line 38-44 in some embodiment, the patient information is encoded into one or more clinical concepts, which will be translated or “mapped” to a standard or universal nomenclature, as described in connection to Figure 3A, thereby rendering the content consumable by other decision support services, applications, features, and agents described herein. This teaches the healthcare data with consistent computer data structure. McNair2 teaches at col. 26 line 22- line 25 in some embodiments, agent solvers 2010, from Fig. 2A, analyze years of de-identified health facts data to provide the health care agents with the knowledge needed to produce helpful conclusions. McNair2 teaches at col. 26 line 36 in some embodiments, the imputed information will be statistically imputed using information from similar patients, and statistical processes such as monte Carlo simulation. Collectively, this teaches the trained statistical model, the output comprising the at least one hidden relationship within the healthcare data. McNair2 teaches at Figure 4D Item 4310 presenting a first clinical user interface for a first patient having a condition. McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. This teaches wherein the at least one prompt for the healthcare professional to provide care to the first patient is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure. McNair2 teaches at Figure 4D Item 4330 associating the clinical decision support event with the condition. McNair2 teaches at Figure 4D Item 4340 determining a change in the condition of the first patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. McNair2 at col. 41 line 40-42 teaches at a step 4640, based on the set of second sequences, determining a likelihood of a future health care event for a patient. McNair2 teaches at col. 41 line 43-45 at a step 4650, providing a recommendation of a health care action to take based on the likelihood of the future event. Collectively, McNair2 teaches the at least one prompt for the healthcare professional to provide care to the first patient prior to providing care to the second patient is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure.]
Regarding Claim 11
Due to its similarity to Claim 3, Claim 11 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 3.
Regarding Claim 4
Saunders/McNair/McNair2 teach the method of claim 1. Saunders/McNair/McNair2 further teach:
wherein the healthcare workflow of the healthcare professional comprises: an ordered list of the plurality of healthcare events comprising a first healthcare event specific to the first patient and a second healthcare event specific to the second patient; [McNair2 teaches at col. 23 line 38-44 in some embodiment, the patient information is encoded into one or more clinical concepts, which will be translated or “mapped” to a standard or universal nomenclature, as described in connection to Figure 3A, thereby rendering the content consumable by other decision support services, applications, features, and agents described herein. This teaches the healthcare data with consistent computer data structure. McNair2 teaches at col. 26 line 22- line 25 in some embodiments, agent solvers 2010, from Fig. 2A, analyze years of de-identified health facts data to provide the health care agents with the knowledge needed to produce helpful conclusions. McNair2 teaches at col. 26 line 36 in some embodiments, the imputed information will be statistically imputed using information from similar patients, and statistical processes such as monte Carlo simulation. Collectively, this teaches the trained statistical model, the output comprising the at least one hidden relationship within the healthcare data. McNair2 teaches at Figure 4D Item 4310 presenting a first clinical user interface for a first patient having a condition. McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. McNair2 teaches at Figure 4D Item 4330 associating the clinical decision support event with the condition. McNair2 teaches at Figure 4D Item 4340 determining a change in the condition of the first patient. McNair2 teaches at Figure 4D Item 4350 identifying a second patient having the condition of the first patient. McNair2 teaches at Figure 4D Item 4360 presenting a second clinical user interface for the second patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. McNair2 teaches at Figure 4D Item 4380 presenting the clinical recommendation for the second patient in the second user interface. Collectively, McNair2 teaches an ordered list of the plurality of healthcare events comprising a first healthcare event specific to the first patient and a second healthcare event specific to the second patient.]
and at least one prompt for the healthcare professional to provide care to the first patient prior to providing care to the second patient, [McNair2 teaches at Figure 4D Item 4310 presenting a first clinical user interface for a first patient having a condition. McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. This teaches the at least one prompt for the healthcare professional to provide care to the first patient prior to providing care to the second patient. McNair2 teaches at Figure 4D Item 4330 associating the clinical decision support event with the condition. McNair2 teaches at Figure 4D Item 4340 determining a change in the condition of the first patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. McNair2 teaches at Figure 4D Item 4380 presenting the clinical recommendation for the second patient in the second user interface.]
wherein the at least one prompt for the healthcare professional to provide care to the first patient prior to providing care to the second patient is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure. [McNair2 teaches at col. 23 line 38-44 in some embodiment, the patient information is encoded into one or more clinical concepts, which will be translated or “mapped” to a standard or universal nomenclature, as described in connection to Figure 3A, thereby rendering the content consumable by other decision support services, applications, features, and agents described herein. This teaches the healthcare data with consistent computer data structure. McNair2 teaches at col. 26 line 22- line 25 in some embodiments, agent solvers 2010, from Fig. 2A, analyze years of de-identified health facts data to provide the health care agents with the knowledge needed to produce helpful conclusions. McNair2 teaches at col. 26 line 36 in some embodiments, the imputed information will be statistically imputed using information from similar patients, and statistical processes such as monte Carlo simulation. Collectively, this teaches the trained statistical model, the output comprising the at least one hidden relationship within the healthcare data. McNair2 teaches at Figure 4D Item 4310 presenting a first clinical user interface for a first patient having a condition. McNair2 teaches at Figure 4D Item 4320 receiving a command to initiate a clinical decision support event associated with the patient. This teaches the at least one prompt for the healthcare professional to provide care to the first patient prior to providing care to the second patient. McNair2 teaches at Figure 4D Item 4330 associating the clinical decision support event with the condition. McNair2 teaches at Figure 4D Item 4340 determining a change in the condition of the first patient. McNair2 teaches at Figure 4D Item 437 determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient. Collectively, this teaches the at least one prompt for the healthcare professional to provide care to the first patient prior to providing care to the second patient is generated using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure.]
Regarding Claim 5
Saunders/McNair/McNair2 teach the method of claim 4. Saunders/McNair/McNair2 further explicitly teach:
further comprising: updating, using an updated output of the trained statistical model generated using the healthcare data prepared with the consistent computer data structure, at least one indication of order of one or more healthcare events of the plurality of healthcare events within the healthcare workflow of the healthcare professional. [It has been shown elsewhere that McNair2 teaches using an output of the trained statistical model generated using the healthcare data prepared with the consistent computer data structure. McNair2 teaches at col. 5 29-24 teaches upon opening a patient chart, the chart application connects with embodiments of the decision support services, which will operate in the cloud, to see if there are any new risks for a clinical condition (including a decision support event) to manage for a particular patient or set of patients. McNair2 teaches at col. 5 line 34-39 a dynamic menu or table of contents (TOC) indicates the risk for one or more clinical conditions, and in some embodiments, a newly identified risk is highlighted or colored. McNair2 teaches at col. 5 line 38-39 a caregiver will be notified of a newly identified risk by alarm, email or text message, icon or other technique. McNair2 teaches at col. 5 line 60-65 the decision support patient chart includes functionality for flexing or altering the information that is displayed (including what information is presented at the order, ranking, or priority that the information is presented) based on the caregiver specialty, condition(s), venue, or other attributes. Collectively, McNair2 teaches updating, using an updated output of the trained statistical model generated using the healthcare data prepared with the consistent computer data structure, at least one indication of order of one or more healthcare events of the plurality of healthcare events within the healthcare workflow of the healthcare professional.]
Regarding Claim 6
Saunders/McNair/McNair2 teach the method of claim 1. Saunders/McNair/McNair2 further teach:
wherein transforming the healthcare data from a plurality of differently-structured data formats into the consistent computer data structure comprises transforming the healthcare data using natural language processing. [McNair2 teaches at Figure 2C patient 1 data stream undergoing natural language processing. McNair2 teaches at col. 40 line 35-37 teaches method comprises, at step 4510, receiving unstructured health-related data associated with a first patient, the health related data including a discrete element. McNair2 teaches at col. 40 line 37-41 teaches in an embodiment, an NLP service, which will be embodied as a decoder program, software routine(s) or health care agent, is used in step 4510 to extract one or more discrete elements form the received unstructured health-related data. McNair2 teaches at col. 23 line 38-44 in some embodiment, the patient information is encoded into one or more clinical concepts, which will be translated or “mapped” to a standard or universal nomenclature, as described in connection to Figure 3A, thereby rendering the content consumable by other decision support services, applications, features, and agents described herein. This teaches the healthcare data with consistent computer data structure. Collectively, McNair2 teaches wherein transforming the healthcare data from a plurality of differently-structured data formats into the consistent computer data structure comprises transforming the healthcare data using natural language processing.]
Regarding Claim 18
Due to its similarity to Claim 6, Claim 18 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 6.
Regarding Claim 7
Saunders/McNair/McNair2 teach the method of claim 1. Saunders/McNair/McNair2 further teach:
wherein the consistent computer data structure is configured to use with at least one of a foundational concept data operation, a knowledge data operation, a service data operation, a clinical decision support data operation, a documentation data operation, an analytics data operation, or an administrative data operation. [Saunders teaches at para. [0134] collating data from disparate sources and creating a single database of this medical information allows for easy and rapid analysis of all relevant contextual data. Saunders teaches at para. [0135] converting data into a common structured format and a command data format. Saunders teaches at para. [0283] the data is structured so that it is put into a consistent computer record structure, with consistent fields, consistent units for values (e.g., grams), and so forth. Saunders teaches at para. [0285] after the medical information is structured, which will include normalizing the information into a consistent and standardized record format, the information is stored in a diary for the particular patient in a database. Saunders teaches at para. [0285] information from a patient’s diary will be accessed by an analysis process, which performs analysis by processing the complex relationships between data. Collectively, Saunders teaches wherein the consistent computer data structure is configured to use with at least one of an analytics data operation.]
Regarding Claim 19
Due to its similarity to Claim 7, Claim 19 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 7.
Regarding Claim 8
Saunders/McNair/McNair2 teach the method of claim 1. Saunders/McNair/McNair2 further teach:
wherein the at least one hidden relationship within the healthcare data with the consistent computer data structure comprises an indication of a clinical concept and/or value concept. [Saunders teaches at para. [0283] the data is structured so that it is put into a consistent computer record structure, with consistent fields, consistent units for values (e.g., grams), and so forth. Saunders teaches at para. [0285] after the medical information is structured, which will include normalizing the information into a consistent and standardized record format, the information is stored in a diary for the particular patient in a database. Saunders teaches at para. [0285] information from a patient’s diary will be accessed by an analysis process, which performs analysis by processing the complex relationships between data. Saunders teaches at para. [0286] this analysis of the patient background, treatment and/or medication information and other patient data will be rendered on the screen visually at a process including but not limited to the form of color, highlighter, arrows, or indicators. The treatment and/or medication information is the clinical concept and/or value concept. Collectively, this teaches determining the at least hidden relationship within the healthcare data with the consistent computer data structure comprises an indication of a clinical concept and/or value concept.]
Regarding Claim 13
Due to its similarity to Claim 8, Claim 13 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 8.
Regarding Claim 14
Saunders/McNair/McNair2 teach the system of claim 9. Saunders/McNair/McNair2 further teach:
wherein the healthcare data includes data from two or more of: electronic healthcare records; clinical data; socio-economic data; patient-reported data; home, clinic, and/or hospital device data; claim data from Centers for Medicare and Medicaid Services; claim data from commercial payors; financial data, administrative data; or data from a commercial data aggregator. [Saunders teaches at para. [0298] in certain embodiments, the patient historical data includes paper-based records and electronic records. This teaches wherein the healthcare data includes data from: electronic healthcare records and clinical data.]
Regarding Claim 20
Due to its similarity to Claim 14, Claim 20 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 14.
Regarding Claim 12
Saunders/McNair/McNair2 teach the system of claim 9. Saunders/McNair/McNair2 further teach:
wherein the consistent computer data structure is configured for use with at least one of a foundational concept data operation, a knowledge data operation, a service data operation, a clinical decision support data operation, a documentation data operation, an analytics data operation, or an administrative data operation. [Saunders teaches at para. [0283] the data is structured so that it is put into a consistent computer record structure, with consistent fields, consistent units for values (e.g., grams), and so forth. Saunders teaches at para. [0285] after the medical information is structured, which will include normalizing the information into a consistent and standardized record format, the information is stored in a diary for the particular patient in a database. Saunders teaches at para. [0285] information from a patient’s diary will be accessed by an analysis process, which performs analysis by processing the complex relationships between data. Saunders teaches at para. [0286] this analysis of the patient background, treatment and/or medication information and other patient data will be rendered on the screen visually at a process including but not limited to the form of color, highlighter, arrows, or indicators. This teaches determining the at least hidden relationship within the healthcare data with the consistent computer data structure comprises an indication of a clinical concept and/or value concept. Saunders teaches at para. [0286] the analysis will also be used by the system and method to suggest that a healthcare professional make changes to a drug or treatment, or to a clinical trial or experiment. Collectively, Saunders teaches wherein the consistent computer data structure is configured for use with at least an analytics data operation.]
Regarding Claim 19
Due to its similarity to Claim 12, Claim 19 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 12.
Regarding Claim 15
Saunders/McNair/McNair2 teach the system of claim 9. Saunders/McNair/McNair2 further teach:
wherein the method further comprises generating the first healthcare event using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure. [Saunders teaches at para. [0283] the data is structured so that it is put into a consistent computer record structure, with consistent fields, consistent units for values (e.g., grams), and so forth. Saunders teaches at para. [0285] after the medical information is structured, which will include normalizing the information into a consistent and standardized record format, the information is stored in a diary for the particular patient in a database. Saunders teaches at para. [0285] information from a patient’s diary will be accessed by an analysis process, which performs analysis by processing the complex relationships between data. Saunders teaches at para. [0286] this analysis of the patient background, treatment and/or medication information and other patient data will be rendered on the screen visually at a process including but not limited to the form of color, highlighter, arrows, or indicators. This teaches determining the at least hidden relationship within the healthcare data with the consistent computer data structure comprises an indication of a clinical concept and/or value concept. Saunders teaches at para. [0286] the analysis will also be used by the system and method to suggest that a healthcare professional make changes to a drug or treatment, or to a clinical trial or experiment. The changes to a drug or treatment or to a clinical trial or experiment are generating the first healthcare event using the output recited. Collectively, Saunders teaches generating the first healthcare event using the output of the trained statistical model comprising the at least one hidden relationship within the healthcare data with the consistent computer data structure.]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2016/0239778 A1 (hereafter Suneja) teaches on the subject of workflows with procedural milestones.
US 20120004925 A1 (hereafter Braveman) teaches inputting healthcare data into a trained statistical model.
G. Quellec et al., "Real-Time Segmentation and Recognition of Surgical Tasks in Cataract Surgery Videos," in IEEE Transactions on Medical Imaging, vol. 33, no. 12, pp. 2352-2360, Dec. 2014. (Year: 2014). Quellec teaches on the subject of the recognition of surgical events, which is tangentially related to medical workflow development.
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/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683