DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 1, 2025 has been entered.
Response to Amendment
Claims 1-2 and 16 have been amended. Claims 3-13, 15, and 17-20 have not been modified. Claim 14 has been cancelled. Claims 1-13 and 15-20 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments filed December 1, 2025 have been fully considered but they are not persuasive. A response is provided below.
Applicant argues Claim Objections, pg. 9 of Remarks:
Examiner acknowledges Applicant amendment and withdraws the claim objections.
Applicant argues 35 U.S.C. §101 Rejections, pg. 9 of Remarks:
Applicant argues that the claims and fact pattern mirror Desjardins. Examiner respectfully disagrees.
The decision is specific to the facts before the Appeals Review Panel and follows the subject matter eligibility analysis set forth in MPEP 2106. As found by the Panel, the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" represents “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application; “improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel.
Applicant’s claims do not provide such an improvement. There is no indication in the cited portion of the Specification that the claimed invention provides an improvement as to how model is trained, rather that only accuracy of the diagnosis is improved. Improving the accuracy of a machine learning model by supplying it with specific data is not an improvement to how the model is trained within the meaning of Desjardins. This is how all machine learning models are optimized (i.e., select training data, train the model, compare the output to validation data, receive feedback, adjust the parameters of the training data according to the comparison/feedback, and repeat until an accuracy threshold is met). Put another way, the particular way the machine learning model of applicant’s invention uses the data to train itself is not improved, which is the holding of Desjardins. As indicated by Applicant citing to paragraph [0031] of the as-filed Specification, Applicant is merely improving the accuracy of the model by optimizing the data selected/used by the model. Improving the accuracy of a model is not an improvement by any measure in MPEP 2106.
Regarding Applicant’s further assertion that the training is not non-specific, Examiner respectfully disagrees. There is no indication in the claims that the training being performed is beyond how any machine learning model is trained. [0100] of Applicant specification also supports the lack of any specific improvements to how machine learning models are trained, “The AI model may utilize a neural network, such as a convolutional neural network, trained using any of supervised, semi-supervised, unsupervised, and reinforcement learning techniques.” The claims nor Applicant specification recite any specific, technical improvements to the field of machine learning as any generic training method may be applied to perform the training to improve upon the abstract idea of patient diagnosis, which is supported by [0031] of Applicant specification as well as Applicant’s own arguments on pg. 12 of the previously filed Remarks (“increase in diagnosis accuracy”).
Applicant argues 35 U.S.C. §103 Rejections, pg. 12 of Remarks:
Regarding I, Applicant argues that Caffarel in view of Bagchi does not teach the amended claim limitation “wherein training the patient care model on the care insight increases the predicted probability corresponding to each diagnosis”. Applicant argument is moot as new art is provided to teach the amended limitation.
Regarding II, Applicant argues that Bagchi does not teach “the recommendation weight indicating a change in a predicted probability of a diagnosis based on the information corresponding to the recommendation being considered in the corresponding diagnosis”, specifically citing [0077] of Bagchi. Examiner acknowledges that [0077] of Bagchi fails to disclose the claim limitation as [0077] refers to a different, unrelated functionality. Instead, Examiner now cites [0071], which corresponds to the cited text on pg. 28 of the previous Office Action, in addition to the previously provided [0069] and [0070]. Additional citations are provided in the response to Remarks only to illustrate Examiner’s explanation.
[0069], “this embodiment automatically identifies information relevant to the answers that is not contained within the problem case information as missing information, and further automatically identifies the amount the missing information affects the corresponding confidence values (both using the using the question-answering module) and outputs this information to the user.”
In Bagchi, the confidence value refers to a confidence that a diagnosis is correct amongst an outputted differential diagnosis list ([0049], “The computer processor 104/110 also automatically generates a plurality of diagnosis answers for each diagnosis query, and calculates confidence values for each of the answers based on numerical values for several dimensions of evidence that are relevant to the problem-solving domain.” [0075], “FIG. 7, the decision-support application 104 has generated a set of possible answers to the query with associated confidence scores associated with each answer and the same is displayed in area 514.”). Examiner interprets this confidence value to be functionally analogous to the predicted probability of the instant application.
[0070], “the application 104 may be used to identify missing information that has potential for affecting the confidence in answers. For a given answer, the decision-maker 108 may want to know what hypothetical information, if provided, can produce the greatest change in the confidence. For example, in the medical domain, if the answer is a disease, the missing information may be a lab test that confirms or rules out the disease.”
Examiner interprets identifying missing information, which may be a lab test to confirm or rule out a possible disease, to encompass the recommendation to increase the care provider’s probability of an accurate diagnosis. Specifically, the lab test would be analogous to the blood test recited in [0032] of Applicant’s specification, as the function of the lab test is the same, which is to change the predicted probability of a possible diagnosis being correct.
[0071], “When two answers have similar confidences, making it difficult to choose between them, it is helpful to identify the missing information that will cause the biggest difference between these confidences. For example, in the medical domain, the answers may be two related diseases and the missing information may be a lab test designed to differentiate between them. This evidence could increase as well as decrease the confidence of one answer thus helping to ascertain the correct diagnosis in the case of a medical diagnostic system.”).
[0071] provides further support that the identified missing information/suggested lab test is considered in the corresponding diagnosis, as it may differentiate one diagnosis from another.
As [0069-0071] of Bagchi teaches wherein a lab test may be identified to increase or decrease confidence in a possible diagnosis amongst several, which is analogous to the example of the blood test in [0032] of Applicant specification, Examiner submits that Bagchi teaches the claim limitation.
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 1-13 and 15-20 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a method and a machine (claims 1-13 and 15-20). Accordingly, claims 1-13 and 15-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified system claim 2 as the claim that represent the claimed invention for analysis, and are similar to system claim 1 and method claim 16.
Claim 2:
A system, comprising:
a power source;
a communication interface configured to communicate with one or more patient monitoring devices and at least one database over a network;
a human-machine interface configured to provide information to a user and obtain information from the user;
a memory configured to store instructions; and
at least one processor configured to execute the instructions to:
obtain, through the communication interface, a care plan for a patient;
generate, by a differential diagnosis model, a differential diagnosis list for the patient, the differential diagnosis list being obtained by inputting least one of diagnostic medical information corresponding to the patient and diagnostic monitoring data corresponding to the patient into the differential diagnosis model, the differential diagnosis list including one or more diagnoses, a predicted probability corresponding to each diagnosis, each predicted probability indicating an estimated accuracy of a corresponding diagnosis, one or more recommendations for a first diagnosis of the differential diagnosis list, and a recommendation weight for each of the one or more recommendations, the recommendation weight indicating a change in a predicted probability of a diagnosis based on the information corresponding to the recommendation being considered in the corresponding diagnosis;
obtain the diagnosis from the differential diagnosis model based on a user selection;
obtain a care recommendation by inputting the diagnosis and one or more of medical information corresponding to the patient, monitoring data corresponding to the patient, and medical facility inventory information into a patient care model;
compare the care recommendation to the care plan to obtain a care insight, the care insight including recommended care that is not included in the care plan or recommended removal of care that is included in the care plan;
output, through the human-machine interface, information corresponding to the care insight, and
responsive to a user input selecting the care insight through the human-machine interface, revise the care plan based on the care insight,
wherein the differential diagnosis model and the patient care model are artificial intelligence (AI) models, and wherein the patient care model is trained on the care insight,
wherein training the patient care model on the patient care insight increases the predicted probability corresponding to each diagnosis.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity as managing personal behaviors. The claim elements are directed towards “obtain[ing] a care recommendation”, “obtain[ing] a care insight”, “revis[ing] the care plan based on the care insight” after receiving a user input, and receiving a “recommendation weight indicating a change in predicted probability of a diagnosis based on the information corresponding to the recommendation being considered”, which falls under diagnosing and managing patient care. Diagnosis and managing patient care fall under the abstract concept of managing personal behaviors of people, as it is a human activity regularly performed by healthcare providers for their patients. It is important to note that the examples provided by the MPEP such as social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing and that 2106.04(a)(2)II states certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping.
These claims further recite: mental processes. The claims recite elements, underlined above, that can be performed in the mind of a person, with pen and paper, or using a generic computer. See also MPEP 2106.04(a)(2) III C that teaches generic computer performing an abstract idea can also fall under mental processes. These encompass obtaining care plans, generating differential diagnoses with recommendation weights, obtaining care recommendations, comparing care plans and recommendations to obtain a care insight, outputting the care insight, and revising the care plan based on the insight.
Accordingly, the claim recites at least one abstract idea.
Claims 1 and 16 are abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional elements beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional elements” while the underlined portions continue to represent the at least one “abstract idea”):
Additional elements cited in the Claims:
One or more patient monitoring devices (1-2); at least one database (1-2); an electronic device (1); a power source (1-2); a communication interface (1-2); a network (1-2); a human-machine interface (1-2,16); a memory (1-2); at least one processor (1-3,5-6,8-13); a differential diagnosis model (1-2,16); a patient care model (1-3,5-6,8-9,11-13,16-17,19); AI models (1-2,16); training the patient care model (1-2,5,8,11,16); monitoring sub-model (5); treatment sub-model (8); equipment sub-model (11)
Any computing devices and their associated components (an electronic device, a power source, a human-machine interface, at least one processor) that would be able to perform the method are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0056] of Applicant specification recites: “the DAM assistant may interact with a user via a computing device having a user or human-machine interface, such as a personal computer, a tablet computer, or a handheld device such as a cell phone. The user may receive data from the computing device and also input data into the computing device.” No specific, technical improvements are being made to computing devices as a variety of generic computing devices are simply applied to perform the abstract idea.
Communication interfaces (communication interface, network) are also taught at a high level of generality. [0073] of Applicant specification recites: “As shown in Fig. 7, communication interface 642 may be connected with a network 710 through wireless or wired communication architecture to communicate with an external electronic device. Communication interface 642 may be a wired or wireless transceiver or any other component for transmitting and receiving signals.” [0077] further recites: “The network 710 includes at least one communication network, such as a computer network (like a local area network (LAN) or wide area network (WAN)), Internet, or a telephone network.” [0078] further recites: “Fig. 7. For example, the network configuration 700 could include any number of each component in any suitable arrangement. In general, computing and communication systems come in a wide variety of configurations, and Fig. 7 does not limit the scope of this disclosure to any particular configuration.” No specific, technical improvements are being made to device communication as a variety of generic communication architectures are simply applied to perform the insignificant extra-solution activity of transmitting and receiving data.
The storage devices (database, memory) are also taught at a high level of generality. [0028] of Applicant specification recites: “EMR database 109 may be an external database accessible by EMR module via a secured hospital interface, or EMR database 109 may be a local database (e.g., housed on a device of the hospital). EMR database 109 may be a database stored in a mass storage device configured to communicate with secure channels (e.g., HTTPS and TLS), and store data in encrypted form.” [0080] further recites: “The medium may continuously store the computer-executable programs or instructions, or temporarily store the computer-executable programs or instructions for execution or downloading. Also, the medium may be any one of various recording media or storage media in which a single piece or plurality of pieces of hardware are combined, and the medium is not limited to a medium directly connected to electronic device 600, but may be distributed on a network.” No specific, technical improvements are being made to storage devices as they are simply used to perform the insignificant extra-solution activity of storing data.
Patient monitoring devices are also taught at a high level of generality. [0027] of Applicant specification recites: “The patient care units 120 may include one or more patient monitoring devices that monitor physiological parameters of a patient. Non-limiting embodiments of patient monitoring devices include a non-invasive blood pressure monitor, a blood oxygen monitor, an electrocardiogram, an electroencephalogram, a temperature monitor, a heart rate monitor, a respiration rate monitor, a carboxyhemoglobin monitor, an end-tidal carbon dioxide monitor, a heart rhythm monitor, a cardiac output monitor, invasive blood pressure monitor, and a heart rate variability monitor.” No specific, technical improvements are being made to storage devices as they are simply used to perform the insignificant extra-solution activity of receiving data from.
Differential diagnosis models (patient care model, monitoring sub-model, treatment sub-model, equipment sub-model) and corresponding training methods are also taught at a high level of generality. [0027] of Applicant specification recites: “The DAM assistant may generate the differential diagnosis list using the AID model. The AID model may utilize a neural network, such as a convolutional neural network, trained using any of supervised, semi-supervised, unsupervised, and reinforcement learning techniques.” No specific, technical improvements are being made to machine learning as a generically, non-specifically trained neural network model is applied perform the abstract idea of patient diagnosis.
The patient model (patient care model, monitoring sub-model, treatment sub-model, equipment sub-model) is also taught at a high level of generality. [0113] of Applicant specification recites: “The patient care model may included AI based models as well a non-AI based models (e.g., statistical models, rules based algorithms). According to an aspect, the patient care model may utilize multiple sub-models.” No specific, technical improvements are being made to machine learning as any statistical model or rules based algorithm (that may or may not be AI) is applied to model patient care.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception 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, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 3: This claim recites wherein the at least one processor is configured to execute the instructions to obtain the care recommendation by obtaining a patient monitoring plan from the patient care model; which teaches an abstract idea of obtaining care recommendations by obtaining a patient monitoring plan, which is routinely obtained from healthcare professionals in charge of their patients. This claim further teaches the processor at a high level of generality such that it is only applied to perform the insignificant extra-solution activity of obtaining data.
Claim 4: This claim recites wherein the patient monitoring plan comprises a parameter to monitor and timing information for monitoring the parameter; which only serves to limit information comprised in the patient monitoring plan.
Claim 5: This claim recites wherein the at least one processor is configured to execute the instructions to obtain the patient monitoring plan from a monitoring sub-model of the patient care model, the monitoring sub-model being trained based on previous user inputs; which only serves to limit the abstract idea of the patient monitoring plan. This claim teaches the monitoring sub-model and its training at a high level of generality, such that it is only applied to perform the abstract idea of generating a patient monitoring plan.
Claim 6: This claim recites wherein the at least one processor is configured to execute the instructions to obtain the care recommendation by obtaining a treatment plan from the patient care model; which teaches an abstract idea of obtaining care recommendations by obtaining a patient monitoring plan, which is routinely obtained from healthcare professionals in charge of their patients. This claim further teaches the processor at a high level of generality such that it is only applied to perform the insignificant extra-solution activity of providing data.
Claim 7: This claim recites wherein the treatment plan comprises a medical treatment type, an amount of the type of treatment, and a timing information for the treatment; which only serves to limit the abstract idea of the treatment plan.
Claim 8: This claim recites wherein the at least one processor is configured to execute the instructions to obtain the treatment plan from a treatment sub-model of the patient care model, the treatment sub-model being trained based on previous user inputs; which teaches an abstract idea of the treatment plan, which is routinely obtained from healthcare professionals in charge of their patients. This claim further teaches the treatment sub-model and its training at a high level of generality such that it is only applied to perform abstract idea of generating a treatment plan.
Claim 9: This claim recites wherein the at least one processor is configured to execute the instructions to obtain the care recommendation by obtaining an equipment list from the patient care model; which an abstract idea of managing equipment for care recommendations. This claim further teaches the processor at a high level of generality such that it is only applied to perform the insignificant extra-solution activity of obtaining data.
Claim 10: This claim recites wherein the equipment list comprises at least one of a device for monitoring the patient, a device for treating the patient, and patient care equipment; which only serves to limit the abstract idea of the equipment list.
Claim 11: This claim recites wherein the at least one processor is configured to execute the instructions to obtain the equipment list from an equipment sub-model of the patient care model, the equipment sub-model being trained based on previous user inputs; which teaches an abstract idea of the equipment list, which is routinely obtained from healthcare professionals in charge of their patients. This claim further teaches the equipment sub-model and its training at a high level of generality such that it is only applied to perform abstract idea of generating an equipment list.
Claims 12 and 19: These claims recite wherein the at least one processor is configured to execute the instructions to, based on receiving additional medical information and/or additional patient monitoring data: obtain an updated care recommendation by inputting the additional medical information and/or additional patient monitoring data into the patient care model; compare the updated care recommendation to the care plan to obtain an updated care insight, the updated care insight including recommended care that is not included in the care plan; output information corresponding to the updated care insight, and responsive to a user input selecting the updated care insight of the updated care recommendation, revise the care plan based on the updated care insight; which is found to be abstract for the same reasons as claim 2, as described above, as it performs the same abstract steps again (“receiving additional … information and/or additional… data”).
Claim 13: This claim recites wherein the at least one processor is configured to execute the instructions to, at a preset time interval: obtain an updated care recommendation by inputting additional medical information and/or additional patient monitoring data into the patient care model, the additional medical information and/or additional patient monitoring data being created after the care recommendation was obtained; compare the updated care recommendation to the care plan to obtain an updated care insight, the updated care insight including recommended care that is not included in the care plan; output information corresponding to the updated care insight, and responsive to a user input selecting the updated care insight of the updated care recommendation, revise the care plan based on the updated care insight; which is found to be abstract for the same reasons as claim 2, as described above, as this claim only serves to limit the time interval that the abstract steps are performed.
Claim 15: This claim recites wherein the user input selecting the care insight comprises a user input revising the care insight; which teaches an abstract idea of certain methods of organizing human activity, as obtaining a user input requires a user to perform an action, as well as modification to the abstract idea of the care insight.
Claim 17: This claim recites wherein the obtaining the care recommendation comprises obtaining a patient monitoring plan, a treatment plan, and an equipment plan from the patient care model; which teaches the patient care model at a high level of generality such that it is only applied to perform the abstract idea of providing a patient monitoring plan, a treatment plan, and an equipment plan.
Claim 18: This claim recites the method further comprising obtaining a user input for each of the patient monitoring plan, the treatment plan, and the equipment plan; which teaches an abstract idea of certain methods of organizing human activity, as obtaining a user input requires a user to perform an action, as well as modification to the abstract ideas of the patient monitoring plan, the treatment plan, and the equipment plan.
Claim 20: This claim recites wherein the monitoring data comprises data from multiple real time or near real time data feeds produced by multiple sources; which teaches obtaining data for the purpose of performing the abstract idea of generating diagnoses.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 3-13, 15, and 17-20, additional limitations which amount to elements that have been recognized as activities in particular fields, claims 3-13, 15, and 17-20, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 3-13, 15, and 17-20, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-13 and 15-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 6-7, 15-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Caffarel (US 20160171177) in view of Bagchi (US 20120078062) further in view of Macoviak (US 20140058755).
Regarding claim 1, Caffarel teaches a medical system comprising:
one or more patient monitoring devices, each patient monitoring device being configured to generate patient monitoring data by monitoring a physiological parameter of a patient ([0031], “The patient data include physiological data collected from one or more sensors, laboratory data, imaging data acquired by one or more imaging devices, clinical decision outputs (e.g., early warning scores, state of the patient, etc.), and the like… the patient data includes a unique identifier, medical indication, age, gender, body mass index, systolic/diastolic blood pressure, relevant blood markers, the results of medical questionnaires about the patient's medical and quality of life, and the like.” [0068], “home medical devices (e.g., weight scales, blood pressure devices, CPAP devices, nebulizers, and the like)”);
at least one database storing medical information corresponding to the patient ([0031], “the patient data is stored in the patient information database 26. Examples of patient information systems include, but are not limited to, electronic medical record systems, departmental systems, and the like.”); and
an electronic device comprising: a power source; a communication interface configured to communicate with the one or more patient monitoring devices and the at least one database over a network ([0057], “The patient care plan system 10 is connected to a wide range of sensors and other input data sources. For example, the patient care plan system 10 can be connected to a hospital's information system (e.g., that includes the patient's current and past medical status), other diagnostic data sources (e.g., lab information systems, pharmacy records, monitoring of vital signs, and the like), home medical devices (e.g., weight scales, blood pressure devices, CPAP devices, nebulizers, and the like), home devices (e.g., tablets, television, activity monitors, and the like), ...” [0074], “Examples of clinical interface systems 18 include, but are not limited to, a software application that could be accessed and/or displayed on a personal computer, web-based applications, tablets, mobile devices, cellular phones, and the like.” [0030], “The patient care plan system 10 suitably includes a patient information system 12, a medical information system 14, a decision support system (DSS) 16, and a clinical interface system 18 and the like, interconnected via a communications network 20.”). It would be obvious to one of ordinary skill in the art that devices such as computers, tablets, and phones would include a power source.
a human-machine interface configured to provide information to a user and obtain information from the user ([0031], “the patient information system 12 includes one or more display devices 24 that provides users a user interface within which to manually enter the patient data and/or for displaying generated patient data.”);
a memory configured to store instructions; and at least one processor configured to execute the instructions ([0035], “The components of the patient care plan system 10 suitably include processors 46 executing computer executable instructions embodying the foregoing functionality, where the computer executable instructions are stored on memories 48 associated with the processors 46.”) to:
obtain, through the communication interface, a care plan for the patient ([0005], “the personalized service plan 90 is generated and transferred to the outcome processor 64.” [0052], “The personalized service processor 84 creates a personalized service plan 90 that includes the selected medical services 52 and the selected social services 54 that correspond to the personalized needs of the target patient based on the status vector 80 thereof.”);
obtain a care recommendation by inputting a diagnosis and one or more of medical information corresponding to the patient, monitoring data corresponding to the patient, and medical facility inventory information into a patient care model ([0033], “The clinical models and algorithms typically include one or more suggested or entered diagnosis and/or treatment options/orders as a function of the patient data and the clinical problem of the patient being treated. Further, the clinical models and algorithms typically generate medical, lifestyle, and/or psycho-social data that include one or more interventions for the various diagnosis and/or treatment options and the clinical context based on the state of the patient and the patient data.” [0031], “The patient data include physiological data collected from one or more sensors, laboratory data, imaging data acquired by one or more imaging devices, clinical decision outputs (e.g., early warning scores, state of the patient, etc.), and the like. The patient data may also include the patient's medical records, the patient's administrative data (e.g., patient's name, location, and the like), the patient's clinical problem(s), the patient's demographics such as weight, age, family history, co-morbidities, and the like.” [0040], “The patient care plan processor 40 generates patient care plan model in the form of a support vector machine, Bayesian classifier, or any other statistical model that can be used to associates cases with outcomes and recommendations for interventions or services.”);
compare the care recommendation to the care plan to obtain a care insight, the care insight including recommended care that is not included in the care plan or recommended removal of care that is included in the care plan ([0066], “The selected outcomes 92 constitute the patient care plan 106.” [0067], “The medical outcome vector 116 and the social outcome vector 118 are compared to similar vectors contained within the historical patient database 28 using the machine-learning processor 120… For example, the status vector 80 is updated to state that the patient's heart failure is stable, the COPD is controlled, and the patient no longer has pneumonia, but that the patient's coping skills did not improve. As a result, the patient medical care plan 106 is updated to discharge the patient from all other services 52 and 54 except for the psychological care service (e.g., anger management classes, waiting list independent for senior living).”);
output, through the human-machine interface, information corresponding to the care insight ([0034], “The clinical interface system 18 enables the user to input the patient values, lifestyle regimes, ability-to-pay, and preferences related to diagnosis and treatment from a patient's perspective which are used to select the most cost-effective service for a specific patient from multiple service programs applicable to that patient's clinical condition… the clinical interface system 18 displays the quantitative evaluation and comparison of the choices of services including a comparison of alternative choices on the same measure, such as allowing the patients to adjust for lifestyle regime and preferences, outcome parameters, patient pathways, a desired probability of an overall outcome or of a specific outcome parameter, and the like including the cost effects of those choices.”), and
responsive to a user input selecting the care insight through the human-machine interface, revise the care plan based on the care insight ([0034], “The clinical interface system 18 includes a display 42 (such as a CRT display, a liquid crystal display, a light emitting diode display, and the like) to display the evaluation and/or comparison of choices and a user input device 44 such as a keyboard and a mouse, for the user to input the patient values and preferences and/or modify the evaluation and/or comparison.” [0067], “the patient medical care plan 106 is updated to discharge the patient from all other services 52 and 54 except for the psychological care service (e.g., anger management classes, waiting list independent for senior living).”),
wherein the patient care model is an artificial intelligence (AI) model ([0040], “The patient care plan processor 40 generates patient care plan model in the form of a support vector machine, Bayesian classifier, or any other statistical model that can be used to associates cases with outcomes and recommendations for interventions or services.” [0058], “a machine-learning processor 120 (e.g., K-nearest neighbors, support vector machines, decision tree learning, support vector machines, neural networks, inductive logic programming, clustering, association rule learning, Bayesian networks, reinforcement learning, representation learning, similarity learning, sparse dictionary learning, and the like).”), and
wherein the patient care model is trained on the care insight ([0058], “Consequently, the patient care plan 106 can be updated and improved based on patient care plans for other patients similar to the target patient. The evaluation updates 108, once computed, are recirculated into the status processor 98 to further refine the patient care plan 106.”).
Caffarel does not teach executing instructions to:
generate, by a differential diagnosis model, a differential diagnosis list for the patient, the differential diagnosis list being obtained by inputting least one of diagnostic medical information corresponding to the patient and diagnostic monitoring data corresponding to the patient into the differential diagnosis model, the differential diagnosis list including one or more diagnoses, a predicted probability corresponding to each diagnosis, each predicted probability indicating an estimated accuracy of a corresponding diagnosis, one or more recommendations for a first diagnosis of the differential diagnosis list, and a recommendation weight for each of the one or more recommendations, the recommendation weight indicating a change in a predicted probability of a diagnosis based on the information corresponding to the recommendation being considered in the corresponding diagnosis; and obtain the diagnosis from the differential diagnosis model based on a user selection; wherein the differential diagnosis model is an AI model, and wherein training the patient care model on the care insight increases the predicted probability corresponding to each diagnosis.
However, Bagchi does teach executing instructions to:
generate, by a differential diagnosis model, a differential diagnosis list for the patient, the differential diagnosis list being obtained by inputting least one of diagnostic medical information corresponding to the patient and diagnostic monitoring data corresponding to the patient into the differential diagnosis model, the differential diagnosis list including one or more diagnoses, a predicted probability corresponding to each diagnosis, each predicted probability indicating an estimated accuracy of a corresponding diagnosis, one or more recommendations for a first diagnosis of the differential diagnosis list, and a recommendation weight for each of the one or more recommendations ([0041], “the embodiments herein can be used as a clinical decision support tool by physicians who are providing care to a patient. Examples of queries include (but are not limited to): what clinical conditions are characterized by a set of symptoms?; what is the "differential diagnosis" (a ranked list of diseases) that could potentially cause a set of symptoms, conditions, findings? (this can be conditioned by providing other pertinent patient information such as active diseases, current medications, allergies, past disease history, family disease history and patient demographics); what tests would increase or decrease confidence in a given disease hypothesis present in the differential diagnosis?” [0037], “the system can continuously monitor relevant case input” [0070], “the application 104 may be used to identify missing information that has potential for affecting the confidence in answers. For a given answer, the decision-maker 108 may want to know what hypothetical information, if provided, can produce the greatest change in the confidence. For example, in the medical domain, if the answer is a disease, the missing information may be a lab test that confirms or rules out the disease.”). Please see the output differential diagnosis list with corresponding probabilities in Fig. 11, below.
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the recommendation weight indicating a change in a predicted probability of a diagnosis based on the information corresponding to the recommendation being considered in the corresponding diagnosis ([0069], “this embodiment automatically identifies information relevant to the answers that is not contained within the problem case information as missing information, and further automatically identifies the amount the missing information affects the corresponding confidence values (both using the using the question-answering module) and outputs this information to the user.” [0070], “the application 104 may be used to identify missing information that has potential for affecting the confidence in answers. For a given answer, the decision-maker 108 may want to know what hypothetical information, if provided, can produce the greatest change in the confidence. For example, in the medical domain, if the answer is a disease, the missing information may be a lab test that confirms or rules out the disease.” [0071], “When two answers have similar confidences, making it difficult to choose between them, it is helpful to identify the missing information that will cause the biggest difference between these confidences. For example, in the medical domain, the answers may be two related diseases and the missing information may be a lab test designed to differentiate between them. This evidence could increase as well as decrease the confidence of one answer thus helping to ascertain the correct diagnosis in the case of a medical diagnostic system.”). Examiner interprets identifying missing information, which may be a lab test to confirm or rule out a possible disease, to encompass the recommendation to increase the care provider’s probability of an accurate diagnosis. Specifically, the lab test would be analogous to the blood test recited in [0032] of Applicant’s specification, as the function of the lab test is the same, which is to change the predicted probability of a possible diagnosis being correct. As the identified missing information/suggested lab test may differentiate one diagnosis from another, Examiner notes that the test would be considered in the corresponding diagnosis.
and obtain the diagnosis from the differential diagnosis model based on a user selection ([0077], “FIG. 12, the decision-support application 104 allows the user to select the answer Lyme disease in order to view the evidence profile 516 for the answer.”); and
wherein the differential diagnosis model is an AI model ([0053], “ the method automatically analyzes the problem case information 102, using the problem case analysis module, in order to identify semantic concepts, relations and other relevant knowledge (e.g., medical patient data)… They can be procedural code, rule based, using programmed logic or in many other forms for determining concepts, relations and other relevant information. They could, for example, be based on machine learning, using a set of training data containing known concepts and relations.”).
Caffarel in view of Bagchi are considered analogous to the claimed invention because they are in the field of patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel with Bagchi for the advantage of “targeting differential diagnosis and treatment” (Bagchi; [0036]).
Although Caffarel teaches a patient care model ([0040], “The patient care plan processor 40 generates patient care plan model in the form of a support vector machine, Bayesian classifier, or any other statistical model that can be used to associates cases with outcomes and recommendations for interventions or services.”) and Bagchi teaches training a model ([0053], “They could, for example, be based on machine learning, using a set of training data containing known concepts and relations.”), Caffarel in view of Bagchi does not explicitly teach wherein training the patient care model on the care insight increases the predicted probability corresponding to each diagnosis.
However, Macoviak does teach wherein training the patient care model on the care insight increases the predicted probability corresponding to each diagnosis ([0266], “a module configured to enhance predictive accuracy by comparing an expected result or outcome to an actual result or outcome to train, re-train, or validate at least one statistical model” [0232], “Referring to FIG. 25, in a particular embodiment, a module for applying a diagnostic or therapeutic analysis accepts subjective 169 and objective 170 subject inputs. Trained statistical models extract a differential diagnosis 171... In other embodiments, conditional random fields (CRF), hidden Markov models (HMM), or deep learning methods, such as multilayer neural networks, convolutional neural networks, and the like, are used to extract differential diagnoses. Further, in this embodiment, extracted diagnoses are ranked 172 by likelihood.” [0231], “a plan includes list of recommended treatment plans given the assessment of a subject's condition. In further embodiments, recommended treatment plans include, by way of non-limiting examples, ordering further labs, ordered radiological work, referrals, performed procedures, and medications. In still further embodiments, a recommended treatment plan addresses each item of the differential diagnosis.”). Examiner notes that Macoviak teaches care insights (treatment plan) that are an expected or real result or outcome that can be used to train the model to enhance predictive accuracy, including accuracy of the differential diagnosis.
Caffarel in view of Bagchi further in view of Macoviak are considered analogous to the claimed invention because they are in the field of patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi with Macoviak for the advantage of providing a system wherein “"enhancement" algorithms are used to optimize predictive performance” (Macoviak; [0185]).
Claims 2 and 16 are rejected for the same reasons as claim 1, as described above.
Regarding claim 6, Caffarel in view of Bagchi further in view of Macoviak teaches the system of claim 2, as described above. Caffarel further teaches wherein the at least one processor is configured to execute the instructions to obtain the care recommendation by obtaining a treatment plan from the patient care model ([0066], “The selected outcomes 92 constitute the patient care plan 106.” [0067], “The status processor 98 produces self-effectiveness evaluation updates 108 of the patient care plan 106. To do so, the scores 104 are collected the medical outcome classifier 112 and the social outcome classifier 114. The medical outcome vector 116 and the social outcome vector 118 are compared to similar vectors contained within the historical patient database 28 using the machine-learning processor 120… As a result, the patient medical care plan 106 is updated to discharge the patient from all other services 52 and 54 except for the psychological care service (e.g., anger management classes, waiting list independent for senior living).”). It would be obvious to one of ordinary skill in the art that a medical care plan would include a treatment plan.
Regarding claim 7, Caffarel in view of Bagchi further in view of Macoviak teaches the system of claims 2 and 6, as described above. Caffarel further teaches wherein the treatment plan comprises a medical treatment type ([0067], “The status processor 98 produces self-effectiveness evaluation updates 108 of the patient care plan 106. To do so, the scores 104 are collected the medical outcome classifier 112 and the social outcome classifier 114. The medical outcome vector 116 and the social outcome vector 118 are compared to similar vectors contained within the historical patient database 28 using the machine-learning processor 120… As a result, the patient medical care plan 106 is updated to discharge the patient from all other services 52 and 54 except for the psychological care service (e.g., anger management classes, waiting list independent for senior living).”). It would be obvious to one of ordinary skill in the art being able to discriminate between types of service would encompass the medical treatment type.
an amount of the type of treatment ([0041], “the cost analysis processor 42 receives predictions of medical outcomes and healthcare resource consumption to estimate costs and effects of the services of interest specific to the specific patient.” [0042], ““ability-to-pay” value (e.g., the amount of money the patient is able and/or willing to pay for the services)”). Amount of money falls under the broadest reasonable interpretation of ‘amount’.
and a timing information for the treatment ([0065], “The personalized service processor 84 creates the personalized service plan 90 that includes the selected medical services 52 and the selected social services 54 that correspond to the personalized needs of the patient based on the status vector 80 thereof… In another example, the personalized social services 54 include: (1) a temporary home care service for 2 weeks;”).
Regarding claim 15, Caffarel in view of Bagchi further in view of Macoviak teaches the system of claim 2, as described above. Caffarel further teaches wherein the user input selecting the care insight comprises a user input revising the care insight ([0034], “The clinical interface system 18 includes a display 42 (such as a CRT display, a liquid crystal display, a light emitting diode display, and the like) to display the evaluation and/or comparison of choices and a user input device 44 such as a keyboard and a mouse, for the user to input the patient values and preferences and/or modify the evaluation and/or comparison.” [0067], “the patient medical care plan 106 is updated to discharge the patient from all other services 52 and 54 except for the psychological care service (e.g., anger management classes, waiting list independent for senior living).”).
Regarding claim 20, Caffarel in view of Bagchi further in view of Macoviak teaches the method of claim 16, as described above. Although Caffarel teaches receiving data in a continuous-fashion, Caffarel in view of Bagchi does not explicitly teach wherein the monitoring data comprises data from multiple real time or near real time data feeds produced by multiple source.
However, Macoviak does teach wherein the monitoring data comprises data from multiple real time or near real time data feeds produced by multiple source ([0266], “module configured to transform individualized emerging health or economic data that has been acquired in real-time into at least one model set” [0153], “emerging patient-specific parameters include, by way of non-limiting examples, severity of illness, real-time vital signs, and current symptoms.” [0123], “The networked medical devices described herein optionally utilize biosensors to perform a wide range of suitable diagnostic tests. In various embodiments, suitable diagnostic tests include, by way of non-limiting examples, blood sugar test (e.g., diabetes), complete blood count or CBC blood test (e.g., anemia, infection, etc.), troponin blood test (e.g., myocardial infarction), serum creatinine blood test (e.g., kidney function), Chem 7 blood test (e.g., nutritional status, electrolytes imbalances, etc.), ultrasound and fiber optic camera examination, spirometer test (e.g., asthma, COPD, etc.), INR blood test (e.g., Coumadin patient), urine test detecting blood (e.g., gross and microscopic hematuria), blood cholesterol test (e.g., hyperlipidemia), blood pressure test (intermittent vs. continuous) (e.g., hypertension or hypotension), pulse oximetry test (e.g., hypoxia, etc.), and temperature measurement (e.g., fever, etc.), and 12 lead EKG (e.g., myocardial infarction, arrhythmias, etc.).” [0132], “the systems, devices, software, and methods described herein include hardware and software elements for establishing, conducting, and maintaining telecommunications. In further embodiments, telecommunications are used by the devices and systems described herein, for example, …; to monitor, regulate, control, and exchange data with biosensors;” [0133], “a module for telecommunications creates a communications link… one or more communications links are interactive and provide real-time (e.g., synchronous) or near real-time (e.g., asynchronous) two-way communication or transfer of data and/or information.”).
Caffarel in view of Bagchi further in view of Macoviak are considered analogous to the claimed invention because they are in the field of patient diagnosis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi with Macoviak for the advantage of “provid[ing] real-time (e.g., synchronous) … two-way communication or transfer of data and/or information” (Macoviak; [0133]).
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Caffarel (US 20160171177) in view of Bagchi (US 20120078062) further in view of Macoviak (US 20140058755) and Goldner (US 20220361823).
Regarding claim 3, Caffarel in view of Bagchi further in view of Macoviak teaches the system of claim 2, as described above. Although Caffarel teaches wherein the at least one processor is configured to execute the instructions to obtain the care recommendation by obtaining a patient monitoring plan ([0037], “the plurality of medical services 52 are each associated with a plurality of medical service outcomes 56, and the plurality of social services 54 are each associated with a plurality of social service outcomes 58. In one example, the medical services 52 are selected from a group that includes inpatient services (e.g., surgery and the like), specialized outpatient services (e.g., disease management programs and the like), primary care (e.g., a community nurse and the like), rehabilitation services (e.g., physical therapy and the like), mental medical services (e.g., psychiatry and the like), and palliative care (e.g., preventative care and the like). The medical service outcomes 56 include readmission risk, mortality, and medical assessment of the patient, among others.” [0065], “recommendation that the patient be closely monitored to follow the dementia progression”), Caffarel in view of Bagchi further in view of Macoviak does not explicitly teach wherein the at least one processor is configured to execute the instructions to obtain the care recommendation by obtaining a patient monitoring plan from the patient care model.
However, Goldner does teach wherein the at least one processor is configured to execute the instructions to obtain the care recommendation by obtaining a patient monitoring plan from the patient care model ([0053], “The system can provide recommendations based on the rankings. For example, the system can develop an exercise schedule, exercise routines, sleeping schedule, or the like based on one or more goals.” [0058], “the system 102 can analyze the obtained input data, including historical data, current real-time data, continuously supplied data, and/or any other data (e.g., using a statistical analysis, machine learning analysis, etc.), and generate output data… the output data can also include predictions of a patient's health state, interpretations, recommendations, notifications, instructions, support, and/or other information related to the obtained input data.”).
Caffarel in view of Bagchi further in view of Macoviak and Goldner are considered analogous to the claimed invention because they are in the field of patient care planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi further in view of Macoviak with Goldner for the advantage of continuously obtaining data “from the patient over a particular time period” and setting the “time interval for data collection” (Goldner; [0028]).
Regarding claim 4, Caffarel in view of Bagchi further in view of Macoviak and Goldner teaches the system of claims 2 and 3, as described above. Caffarel in view of Bagchi does not explicitly teach wherein the patient monitoring plan comprises a parameter to monitor and timing information for monitoring the parameter.
However, Goldner does teach wherein the patient monitoring plan comprises a parameter to monitor and timing information for monitoring the parameter ([0027], “The biosensor 104a can include various types of sensors, such as chemical sensors, electrochemical sensors, optical sensors (e.g., optical enzymatic sensors, opto-chemical sensors, fluorescence-based sensors, etc.), spectrophotometric sensors, spectroscopic sensors, polarimetric sensors, calorimetric sensors, iontophoretic sensors, radiometric sensors, and the like, and combinations thereof. In some embodiments, the biosensor 104a is or includes a blood pressure sensor.” [0028], “some or all of the user devices 104 are configured to continuously obtain any of the above data (e.g., health-related information and/or contextual information) from the patient over a particular time period (e.g., hours, days, weeks, months, years). For example, data can be obtained at a predetermined time interval (e.g., once every minute, 2 minutes, 5 minutes, 10 minutes, 15 minutes, 20 minutes, 30 minutes, 60 minutes, 2 hours, etc.), at random time intervals,”).
Caffarel in view of Bagchi further in view of Macoviak and Goldner are considered analogous to the claimed invention because they are in the field of patient care planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi further in view of Macoviak with Goldner for the advantage of setting the “time interval for data collection” (Goldner; [0028]).
Regarding claim 5, Caffarel in view of Bagchi further in view of Macoviak and Goldner teaches the system of claims 2 and 3, as described above. Caffarel in view of Bagchi further in view of Macoviak does not explicitly teach wherein the at least one processor is configured to execute the instructions to obtain the patient monitoring plan from a monitoring sub-model of the patient care model, the monitoring sub-model being trained based on previous user inputs.
However, Goldner does teach wherein the at least one processor is configured to execute the instructions to obtain the patient monitoring plan from a monitoring sub-model of the patient care model, the monitoring sub-model being trained based on previous user inputs ([0108], “the electronics assembly 212 and/or blood pressure prediction system (e.g., blood pressure prediction system 102 of FIG. 1) can determine blood pressure monitoring settings based on output from the at least one machine-learning model to achieve a threshold confidence score for additional predictions.” [0011], “The models can be trained on data related to personalized body, health, and/or physical characteristics of the user, e.g., the current or previous blood pressure, amount of sleep, heart rate, blood glucose (BG), activity, weight, etc.” [0037], “This allows a blood pressure prediction system 102 to analyze available user devices 104, identify available data types that match data types used for training, and retrieval of all or a portion of the available user data to input into a machine learning model that was trained using similar data.”). It would be obvious to one of ordinary skill in the art that retrieval of data indicates that the data was previously input into the system.
Caffarel in view of Bagchi further in view of Macoviak and Goldner are considered analogous to the claimed invention because they are in the field of patient care planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi further in view of Macoviak with Goldner for the advantage of training based on “previous blood pressure” (Goldner; [0011]).
Claims 8, 12-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Caffarel (US 20160171177) in view of Bagchi (US 20120078062) further in view of Macoviak (US 20140058755) and Moturu (US 20170004260).
Regarding claim 8, Caffarel in view of Bagchi further in view of Macoviak teaches the system of claims 2 and 6, as described above. Although Caffarel teaches usage of clinical models that provide treatment plans ([0067], “The clinical models and algorithms typically include one or more suggested or entered diagnosis and/or treatment options/orders as a function of the patient data and the clinical problem of the patient being treated.”), Caffarel in view of Bagchi further in view of Macoviak does not explicitly teaches wherein the at least one processor is configured to execute the instructions to obtain the treatment plan from a treatment sub-model of the patient care model, the treatment sub-model being trained based on previous user inputs.
However, Moturu does teach wherein the at least one processor is configured to execute the instructions to obtain the treatment plan from a treatment sub-model of the patient care model, the treatment sub-model being trained based on previous user inputs ([0033], “utilizing computer models for selecting therapeutic interventions tailored to a patient health state inferred from digital communication behavior and/or sensor data; dynamically modifying a dynamic care plan based on user behavior data,” [004], “generation of the therapeutic intervention predictive model includes utilization of one or more machine learning techniques and training data (e.g., from the user, from a population of users), data mining, and/or statistical approaches to generate more accurate models pertaining to the user's disorder state (e.g., over time, with aggregation of more data).”).
Caffarel in view of Bagchi further in view of Macoviak and Moturu are considered analogous to the claimed invention because they are in the field of patient care planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi further in view of Macoviak with Moturu for the advantage of “utilizing computer models for selecting therapeutic interventions tailored to a patient health state” (Moturu; [0033]) for “an improved assessment of patient states of need”(Moturu; [0025]).
Regarding claim 12, Caffarel in view of Bagchi further in view of Macoviak teaches the system of claim 2, as described above. Caffarel further teaches wherein the at least one processor is configured to execute the instructions to, based on receiving additional medical information and/or additional patient monitoring data ([0059], “In addition, the outcomes statuses 100, the references statuses 102, and the scores 104 are similarly inputted back into the patient assessment processor 60, the historical patient database 28, and the general service processor 82, respectively, to continuously update the patient care plan 106.” Claim 8, “continuously monitoring a profile and a status of the target during implementation of the patient care plan; and updating the patient care plan to achieve a highest net care benefit.”):
obtain an updated care recommendation by inputting the additional medical information and/or additional patient monitoring data into the patient care model ([0033], “The clinical models and algorithms typically include one or more suggested or entered diagnosis and/or treatment options/orders as a function of the patient data and the clinical problem of the patient being treated. Further, the clinical models and algorithms typically generate medical, lifestyle, and/or psycho-social data that include one or more interventions for the various diagnosis and/or treatment options and the clinical context based on the state of the patient and the patient data.” [0031], “The patient data include physiological data collected from one or more sensors, laboratory data, imaging data acquired by one or more imaging devices, clinical decision outputs (e.g., early warning scores, state of the patient, etc.), and the like. The patient data may also include the patient's medical records, the patient's administrative data (e.g., patient's name, location, and the like), the patient's clinical problem(s), the patient's demographics such as weight, age, family history, co-morbidities, and the like.” [0040], “The patient care plan processor 40 generates patient care plan model in the form of a support vector machine, Bayesian classifier, or any other statistical model that can be used to associates cases with outcomes and recommendations for interventions or services.”);
compare the updated care recommendation to the care plan to obtain an updated care insight ([0066], “The selected outcomes 92 constitute the patient care plan 106.” [0067], “The medical outcome vector 116 and the social outcome vector 118 are compared to similar vectors contained within the historical patient database 28 using the machine-learning processor 120.” [0064], “the selected general medical services 52 include pulmonary rehabilitation service upon discharge and physical activity. The selected general social services 54 include temporary home care (e.g., a home medical care worker to provide services hygiene, food, logistics, and the like), nutritional services, psychological care, neurological consultations, and the like.”);
output information corresponding to the updated care insight ([0034], “The clinical interface system 18 enables the user to input the patient values, lifestyle regimes, ability-to-pay, and preferences related to diagnosis and treatment from a patient's perspective which are used to select the most cost-effective service for a specific patient from multiple service programs applicable to that patient's clinical condition… the clinical interface system 18 displays the quantitative evaluation and comparison of the choices of services including a comparison of alternative choices on the same measure, such as allowing the patients to adjust for lifestyle regime and preferences, outcome parameters, patient pathways, a desired probability of an overall outcome or of a specific outcome parameter, and the like including the cost effects of those choices.”), and
responsive to a user input selecting the updated care insight of the updated care recommendation, revise the care plan based on the updated care insight ([0034], “The clinical interface system 18 includes a display 42 (such as a CRT display, a liquid crystal display, a light emitting diode display, and the like) to display the evaluation and/or comparison of choices and a user input device 44 such as a keyboard and a mouse, for the user to input the patient values and preferences and/or modify the evaluation and/or comparison.” [0067], “the patient medical care plan 106 is updated to discharge the patient from all other services 52 and 54 except for the psychological care service (e.g., anger management classes, waiting list independent for senior living).”).
Caffarel in view of Bagchi further in view of Macoviak does not teach wherein the updated care insight includes recommended care that is not included in the care plan.
However, Moturu does teach wherein the updated care insight includes recommended care that is not included in the care plan ([0053], “Block S150 can include receiving manual input by the care provider that can be used in updating the dynamic care plan (e.g., to delete a therapeutic intervention, to add a therapeutic intervention, to modify a therapeutic intervention provision parameter, etc.)”).
Caffarel in view of Bagchi further in view of Macoviak and Moturu are considered analogous to the claimed invention because they are in the field of patient care planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi further in view of Macoviak with Moturu for the advantage of being able to “add a therapeutic intervention” (Moturu; [0125]).
Regarding claim 13, Caffarel in view of Bagchi further in view of Macoviak teaches the system of claim 2, as described above. Caffarel further teaches wherein the at least one processor is configured to execute the instructions to:
obtain an updated care recommendation by inputting additional medical information and/or additional patient monitoring data into the patient care model, the additional medical information and/or additional patient monitoring data being created after the care recommendation was obtained ([0005], “the personalized service plan 90 is generated and transferred to the outcome processor 64.” [0052], “The personalized service processor 84 creates a personalized service plan 90 that includes the selected medical services 52 and the selected social services 54 that correspond to the personalized needs of the target patient based on the status vector 80 thereof.” [0059], “In addition, the outcomes statuses 100, the references statuses 102, and the scores 104 are similarly inputted back into the patient assessment processor 60, the historical patient database 28, and the general service processor 82, respectively, to continuously update the patient care plan 106.” Claim 8, “continuously monitoring a profile and a status of the target during implementation of the patient care plan; and updating the patient care plan to achieve a highest net care benefit.”);
compare the updated care recommendation to the care plan to obtain an updated care insight ([0066], “The selected outcomes 92 constitute the patient care plan 106.” [0067], “The medical outcome vector 116 and the social outcome vector 118 are compared to similar vectors contained within the historical patient database 28 using the machine-learning processor 120.” [0064], “the selected general medical services 52 include pulmonary rehabilitation service upon discharge and physical activity. The selected general social services 54 include temporary home care (e.g., a home medical care worker to provide services hygiene, food, logistics, and the like), nutritional services, psychological care, neurological consultations, and the like.”);
output information corresponding to the updated care insight ([0034], “The clinical interface system 18 enables the user to input the patient values, lifestyle regimes, ability-to-pay, and preferences related to diagnosis and treatment from a patient's perspective which are used to select the most cost-effective service for a specific patient from multiple service programs applicable to that patient's clinical condition… the clinical interface system 18 displays the quantitative evaluation and comparison of the choices of services including a comparison of alternative choices on the same measure, such as allowing the patients to adjust for lifestyle regime and preferences, outcome parameters, patient pathways, a desired probability of an overall outcome or of a specific outcome parameter, and the like including the cost effects of those choices.”), and
responsive to a user input selecting the updated care insight of the updated care recommendation, revise the care plan based on the updated care insight ([0034], “The clinical interface system 18 includes a display 42 (such as a CRT display, a liquid crystal display, a light emitting diode display, and the like) to display the evaluation and/or comparison of choices and a user input device 44 such as a keyboard and a mouse, for the user to input the patient values and preferences and/or modify the evaluation and/or comparison.” [0067], “the patient medical care plan 106 is updated to discharge the patient from all other services 52 and 54 except for the psychological care service (e.g., anger management classes, waiting list independent for senior living).”).
Caffarel in view of Bagchi further in view of Macoviak does not explicitly teach executing the instructions at a preset time interval; and wherein the updated care insight includes recommended care that is not included in the care plan.
However, Moturu does teach executing the instructions at a preset time interval ([0145], “Block S180, generating one or more evaluations can be performed continuously, in response to a condition (e.g., promotion of a therapeutic intervention, updating of a dynamic care plan, etc.), at predetermined time intervals (e.g., as part of a daily patient assessment), and/or at any suitable time”); and
wherein the updated care insight includes recommended care that is not included in the care plan ([0053], “Block S150 can include receiving manual input by the care provider that can be used in updating the dynamic care plan (e.g., to delete a therapeutic intervention, to add a therapeutic intervention, to modify a therapeutic intervention provision parameter, etc.)”).
Caffarel in view of Bagchi further in view of Macoviak and Moturu are considered analogous to the claimed invention because they are in the field of patient care planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi further in view of Macoviak with Moturu for the advantage of “generating one or more evaluations… at predetermined time intervals” (Moturu; [0145]) and being able to “add a therapeutic intervention” (Moturu; [0125]).
Regarding claim 19, this claim is rejected for the same reasons as claim 12, as described above.
Claims 9-11 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Caffarel (US 20160171177) in view of Bagchi (US 20120078062) further in view of Macoviak (US 20140058755) and Allen (US 20180082030).
Regarding claim 9, Caffarel in view of Bagchi further in view of Macoviak teaches the system of claim 2, as described above. Caffarel in view of Bagchi further in view of Macoviak does not teach wherein the at least one processor is configured to execute the instructions to obtain the care recommendation by obtaining an equipment list from the patient care model.
However, Allen does teach wherein the at least one processor is configured to execute the instructions to obtain the care recommendation by obtaining an equipment list from the patient care model ([0022], “The illustrative embodiments will be described in the context of the treatment recommendation being performed with regard to the prescribing of medications. However, it should be appreciated that the mechanisms of the illustrative embodiments may be implemented with regard to any treatment recommendation… the illustrative embodiments may be used with various medical procedures …, medical equipment being prescribed for the patient (e.g., CPAP machines, implants, assisted living equipment, etc.), or the like.” [0038], “generate a ranked listing of treatment recommendations for the patient's medical condition” [0007], “the method comprises outputting, by the cognitive medical treatment system, the selected treatment option for use in treating the medical condition of the patient.” [0064], “A cognitive system comprises artificial intelligence logic, such as natural language processing (NLP) based logic, for example, and machine learning logic,”).
Caffarel in view of Bagchi further in view of Macoviak and Allen are considered analogous to the claimed invention because they are in the field of patient care planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi with Allen for the advantage of considering “medical equipment being prescribed for the patient” (Allen; [0022]).
Regarding claim 10, Caffarel in view of Bagchi further in view of Macoviak and Allen teaches the system of claims 2 and 9, as described above. Caffarel in view of Bagchi further in view of Macoviak does not teach wherein the equipment list comprises at least one of a device for monitoring the patient, a device for treating the patient, and patient care equipment.
However, Allen does teach wherein the equipment list comprises at least one of a device for monitoring the patient, a device for treating the patient, and patient care equipment ([0022], “medical equipment being prescribed for the patient (e.g., CPAP machines, implants, assisted living equipment, etc.)” [0038], “generate a ranked listing of treatment recommendations for the patient's medical condition”).
Regarding claim 11, Caffarel in view of Bagchi further in view of Macoviak and Allen teaches the system of claims 2 and 9, as described above. Caffarel in view of Bagchi further in view of Macoviak does not teach wherein the at least one processor is configured to execute the instructions to obtain the equipment list from an equipment sub-model of the patient care model, the equipment sub-model being trained based on previous user inputs.
However, Allen does teach wherein the at least one processor is configured to execute the instructions to obtain the equipment list from an equipment sub-model of the patient care model, the equipment sub-model being trained based on previous user inputs ([0022], “medical equipment being prescribed for the patient (e.g., CPAP machines, implants, assisted living equipment, etc.)” [0038], “generate a ranked listing of treatment recommendations for the patient's medical condition” [0007], “the method comprises outputting, by the cognitive medical treatment system, the selected treatment option for use in treating the medical condition of the patient.” [0064], “A cognitive system comprises artificial intelligence logic, such as natural language processing (NLP) based logic, for example, and machine learning logic,” [0094], “through an iterative process using known patient economic status information, the patient economic status evaluation logic 122 may be trained or manually configured such that the results generated match known patient economic status for a plurality of patients”). It would be obvious to one of ordinary skill in the art that training based on already known data would be based on previous inputs.
Caffarel in view of Bagchi further in view of Macoviak and Allen are considered analogous to the claimed invention because they are in the field of patient care planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi further in view of Macoviak with Allen for the advantage of considering “medical equipment being prescribed for the patient” (Allen; [0022]).
Regarding claim 17, Caffarel in view of Bagchi further in view of Macoviak teaches the method of claim 16, as described above. Caffarel in view of Bagchi further in view of Macoviak does not teach wherein the obtaining the care recommendation comprises obtaining a patient monitoring plan, a treatment plan, and an equipment plan from the patient care model.
However, Allen does teach wherein the obtaining the care recommendation comprises obtaining a patient monitoring plan, a treatment plan, and an equipment plan from the patient care model ([0022], “The illustrative embodiments will be described in the context of the treatment recommendation being performed with regard to the prescribing of medications. However, it should be appreciated that the mechanisms of the illustrative embodiments may be implemented with regard to any treatment recommendation… the illustrative embodiments may be used with various medical procedures …, medical equipment being prescribed for the patient (e.g., CPAP machines, implants, assisted living equipment, etc.), or the like.” [0075], “depending upon the particular implementation, the healthcare based operations may comprise patient diagnostics, medical treatment recommendation systems, medical practice management systems, personal patient care plan generation and monitoring, patient electronic medical record (EMR) evaluation for various purposes, such as for identifying patients that are suitable for a medical trial or a particular type of medical treatment, or the like.” [0038], “generate a ranked listing of treatment recommendations for the patient's medical condition” [0007], “the method comprises outputting, by the cognitive medical treatment system, the selected treatment option for use in treating the medical condition of the patient.” [0064], “A cognitive system comprises artificial intelligence logic, such as natural language processing (NLP) based logic, for example, and machine learning logic,”).
Caffarel in view of Bagchi further in view of Macoviak and Allen are considered analogous to the claimed invention because they are in the field of patient care planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi further in view of Macoviak with Allen for the advantage of considering “medical equipment being prescribed for the patient” (Allen; [0022]).
Regarding claim 18, Caffarel in view of Bagchi further in view of Macoviak and Allen, teaches the method of claims 16 and 17, as described above. Caffarel further teaches wherein the method further comprises obtaining a user input for each of the patient monitoring plan and the treatment plan ([0013], “a method for creating a patient care plan for a target patient is provided. Inputs related to one or more social services and one or more medical services that are each associated with target patient data are received. One or more social and medical services are selected based on a target assessment.” [0037], “the medical services 52 are selected from a group that includes inpatient services (e.g., surgery and the like), specialized outpatient services (e.g., disease management program, primary care (e.g., a community nurse and the like), rehabilitation services (e.g., physical therapy and the like), mental medical services (e.g., psychiatry and the like), and palliative care (e.g., preventative care and the like).” [0038], “The social services 54 are selected from a group that includes financial assistance services, housing services, personal care services, psychological support services, patient group services, social activities services, dietary support services, employment services, income services, legal services, transportation and mobility services, care management services, home care services, information and assistance services, long-term care services, nutrition and meals services, respite services, senior services, volunteer and intergenerational services, and wellness and well-being services.”).
Caffarel in view of Bagchi further in view of Macoviak does not teach wherein the method further comprises obtaining a user input for the equipment plan.
However, Allen does teach wherein the method further comprises obtaining a user input for the equipment plan ([0062], “Content users input questions to cognitive system which implements the QA pipeline. The QA pipeline then answers the input questions using the content in the corpus of data by evaluating documents, sections of documents, portions of data in the corpus, or the like.” [0022], “The illustrative embodiments will be described in the context of the treatment recommendation being performed with regard to the prescribing of medications. However, it should be appreciated that the mechanisms of the illustrative embodiments may be implemented with regard to any treatment recommendation… the illustrative embodiments may be used with various medical procedures …, medical equipment being prescribed for the patient (e.g., CPAP machines, implants, assisted living equipment, etc.), or the like.” [0038], “generate a ranked listing of treatment recommendations for the patient's medical condition”).
Caffarel in view of Bagchi further in view of Macoviak and Allen are considered analogous to the claimed invention because they are in the field of patient care planning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Caffarel in view of Bagchi further in view of Macoviak with Allen for the advantage of considering “medical equipment being prescribed for the patient” (Allen; [0022]).
Conclusion
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684