DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is a final rejection
Claims 1-20 were cancelled
Claims 21-40 were added
Claims 21-40 are pending
Claims 21-40 are rejected under 35 USC § 101
Claims 21-40 are rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a domestic priority date of 1-9-2023
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 6-11-2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21-40 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 21-40 the claims recite an abstract idea of “crisis detection and healthcare response”.
Independent Claims 21, 29 & 37 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claims 21, 29, 37 recite a method, a non-transitory processor readable storage medium and a system respectively for crisis message classification.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
receiving healthcare communication data from a patient;
processing the healthcare communication data in real-;
generating a patient risk assessment score,
responsive to the risk assessment score exceeding a predetermined threshold, transmit real-time notifications to healthcare personnel; and
receiving response data from the healthcare personnel.
belonging to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “crisis detection and healthcare response”. (refer to MPP 2106.04(a)(2)). Alternatively the claim belongs to certain methods of organizing human activity under managing personal relationships or interactions between people as it recites “crisis detection and healthcare response”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claims 21, 29 recite:
a healthcare communication interface integrated with a healthcare provider system;
crisis detection system configured to generate patient risk assessments utilizing a crisis message classifier;
automatically triggering a technical alert system integrated with healthcare response infrastructure
Claim 29 recites:
one non-transitory processor readable storage medium storing a computer program of instructions configured to be readable by at least one processor for instructing the at least one processor to execute a computer process for performing a method.
Claims 37 recites:
a healthcare communication interface integrated with a healthcare provider system;
a crisis detection system;
a risk assessment component;
a technical alert system that integrates with healthcare response infrastructure;
the technical alert system is configured to automatically transmit real-time notifications;
a response interface configured to receive response data.
Such that it amounts to no more than mere instructions to apply the exception using a generic computer component (refer to MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two:
Claims 21, 29 recite:
a healthcare communication interface integrated with a healthcare provider system;
crisis detection system configured to generate patient risk assessments utilizing a crisis message classifier;
automatically triggering a technical alert system integrated with healthcare response infrastructure
Claim 29 recites:
one non-transitory processor readable storage medium storing a computer program of instructions configured to be readable by at least one processor for instructing the at least one processor to execute a computer process for performing a method.
Claims 37 recites:
a healthcare communication interface integrated with a healthcare provider system;
a crisis detection system;
a risk assessment component;
a technical alert system that integrates with healthcare response infrastructure;
the technical alert system is configured to automatically transmit real-time notifications;
a response interface configured to receive response data.
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, even when viewed as a whole the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The following dependent claims recites additional limitations that further define the abstract idea of “crisis detection and healthcare response”. The claim limitations include:
Claim 22: improve risk assessment accuracy;
Claims 24, 32, 40: performs a binary classification of the healthcare communication data;
Claims 25, 33: wherein the real-time notifications include a graphical element selectable by the healthcare personnel, and the response data includes feedback on the patient risk assessment score provided by healthcare personnel;
Claims 26, 34: wherein the response data includes a message provided by the healthcare personnel directly to the patient;
Claims 27, 35: utilizes optimization parameters based on cost ratios for minimizing total misclassification costs in patient safety risk assessment;
Claims 28, 36: determining, in real-time, that the patient risk assessment score exceeds the predetermined threshold;
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception. The claims include:
Claim 22: automatically updating the crisis message classifier based on the response data;
Claims 23, 30, 38: wherein the crisis message classifier includes a trained natural language processing (NLP) model;
Claims 24, 32, 40: crisis detection system;
Claims 27, 35: crisis detection system;
Claims 28, 36: the crisis detection system;
Claims 31, 39: automatically updating the crisis detection system utilizing the response data from the healthcare personnel and the received healthcare communication data.
Accordingly, these additional elements, when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception.
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, even when viewed as a whole the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 22: automatically updating the crisis message classifier based on the response data;
Claims 23, 30, 38: wherein the crisis message classifier includes a trained natural language processing (NLP) model;
Claims 24, 32, 40: crisis detection system;
Claims 27, 35: crisis detection system;
Claims 28, 36: the crisis detection system;
Claims 31, 39: automatically updating the crisis detection system utilizing the response data from the healthcare personnel and the received healthcare communication data;
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 21-23, 25-31, 33-39 are rejected by 35 U.S.C. 102(a)(1) as being anticipated by Amarasingham et.al (US 20150213224 A1) hereinafter “Amarasingham”
Regarding claims 21, 29, 37 Amarasingham teaches:
receiving healthcare communication data from a patient (to receive a variety of clinical ... data relating to patients .. requiring care) through a healthcare communication interface integrated with a healthcare provider system (holistic hospital patient care and management system 10); (See at least [0029] via: “...FIG. 1 is a simplified block diagram of an exemplary embodiment of a holistic hospital patient care and management system and method 10 according to the present disclosure. The holistic hospital patient care and management system 10 includes a computer system 12 adapted to receive a variety of clinical and non-clinical data relating to patients or individuals requiring care... It should be noted that the computer system 12 may comprise one or more local or remote computer servers operable to transmit data and communicate via wired and wireless communication links and computer networks...”; in addition see at least [0041] via: “...The computer system 12 may comprise a number of computing devices, including servers, that may be located locally or in a cloud computing farm...”; in addition see at least [0073] via: “...FIG. 7 is a simplified flowchart/block diagram of an exemplary embodiment of a clinical predictive modeling method 100 according to the present disclosure. A variety of data are received from a number of disparate data sources 102 related to particular patients admitted at a hospital or a healthcare facility. The incoming data may be received in real-time or the data may be pulled in batches. The incoming data are stored in a data store 104...”)
automatically processing the healthcare communication data (input data including the clinical ... patient data) in real-time (patient data are continually received, collected, and/or polled by the system 10) through a crisis detection system (The system 10) configured to generate patient risk assessments (configured to provide ... risk identification) utilizing a crisis message classifier (functionality may include patient risk stratification,); (See at least [0048] via: “...All of the above-described input data including the clinical and non-clinical patient data are continually received, collected, and/or polled by the system 10 whenever they become available and are used in analysis for a number of output data and results... The system 10 is configured to provide ... risk identification 51..”; in addition see at least [0054] via: “...The data extraction process 62 extracts clinical and non-clinical data from data sources in real-time ..”; in addition see at least [0056] via: “...The data integration logic module 60 then passes the pre-processed data to a disease/risk logic module 66. The disease/risk logic module 66 is operable to calculate a risk score associated with a specific disease or condition for each patient ..”; in addition see at least [0086] via: “...The enhanced predictive model is capable of serving as a reliable warning tool for the timely detection and prevention of adverse events. Its functionality may include patient risk stratification, notification of clinical staff of an adverse event,..”; in addition see at least [0073] via: “...In block 106, the received data undergo a data processing and integration process following data extraction (e.g., data cleansing and data manipulation), as described above. The resultant data then undergo the disease risk logic process 108 during which disease identification, and predictive modeling are performed. ..” )
generating a patient risk assessment score (calculate a risk score associated with a specific disease or condition for each patient) using the crisis detection system (The system 10),(See at least [0056] via: “...The data integration logic module 60 then passes the pre-processed data to a disease/risk logic module 66. The disease/risk logic module 66 is operable to calculate a risk score associated with a specific disease or condition for each patient ..”; in addition see at least [0073] via: “...The risk score (with specific regard to high risk) computed for each patient for a disease of interest is compared to a disease high risk threshold in block 110. Each disease is associated with its own high risk threshold. If the risk score is less than the high risk threshold, then the process determines if the patient's risk score falls into the medium or low risk categories, otherwise the process returns to data integration and is repeated when new data associated with a patient become available. If the risk score is greater than or equal to the high risk threshold, then the identified patient having the high risk score is identified as `high risk` and included in a patient list in block 112...”)
responsive to the risk assessment score exceeding a predetermined threshold, automatically triggering a technical alert system integrated with healthcare response infrastructure to transmit real-time notifications (automatically generate, transmit, and present information such as high-risk patient lists with risk scores) to healthcare personnel (notify caregiver or healthcare provider ) ; (See at least [0069] via: “...This output may be transmitted wirelessly or via LAN, WAN, the Internet, and delivered to healthcare facilities' electronic medical record stores, user electronic devices (e.g., pager, text messaging program, mobile telephone, tablet computer, mobile computer, laptop computer, desktop computer, and server), health information exchanges, and other data stores, databases, devices, and users. The system and method 10 may automatically generate, transmit, and present information such as high-risk patient lists with risk scores,..”; in addition see at least [0086] via: “...The system 10 may notify caregiver or healthcare provider via, for example, pages, best practice alerts, conventional alerts, and visualization reports...”; in addition see at least [0073] via: “...In block 114, the patient list and other associated information may then be presented to the intervention coordination team in one or more possible ways, such as transmission to and display on a desktop or mobile device in the form of a text message, e-mail message, web page, etc. In this manner, an intervention coordination team is notified and activated to target the patients identified in the patient list for assessment, and inpatient and outpatient treatment and care, as shown in block 118. The process may thereafter provide feedback data to the data sources 102 and/or return to data integration 106 that continues to monitor the patient during his/her targeted inpatient and outpatient intervention and treatment. Data related to the patient generated during the inpatient and outpatient care, such as prescribed medicines and further laboratory results, radiological images, etc. may be continually monitored to track intervention completion...” ; in addition see at least [0104] via: “...Cardiology and surgical services are two areas of medicine that can be aided by innovative tools to risk stratify patients in real-time to notify the healthcare providers that individuals at high risk for developing a specific disease or condition, such as AAA rupture..”; in addition see at least [0108] via: “...In the Neuro-ICU, the patient may be monitored by the facial and biological recognition system that is able to detect a mild change in pupillary responsiveness signaling an early change in intra-cranial pressure. This information is immediately transmitted to the healthcare staff as an alert. The healthcare staff responds by taking immediate action to intubate the patient and administer treatment for increased intra-cranial pressure. Therefore, early and aggressive treatment aided by the system 10 helps this patient regain complete neurologic functioning...”) and
receiving response data (timely or real time information to ... the patient's family) through the technical alert system from the healthcare personnel (patient care and management system 10). (See at least [0044] via: “... During surgery, the system transmits the patient's conditions and status on a real time basis to the patient's family. Therefore, throughout the patient's stay in the hospital as well as after discharge, the holistic hospital patient care and management system 10 continually monitors the patient's condition, collects patient data in real-time, arranges for efficient delivery of care, manages the hospital's resources and supplies, and communicates timely or real time information to healthcare providers and the patient's family...”; in addition see at least [0096] via: “...FIG. 15 is a simplified flowchart of an exemplary embodiment of a patient/family engagement process 210 according to the present disclosure... patient's family is also provided with opportunities to be notified of patient status in an effort to increase awareness and shared decision-making during complicated situations, such as surgery.. .. a selective subset of the patient's data are retrieved from the data store and displayed, as shown in block 214. Also displayed are resources available to the patient, such as information related to a particular disease that the patient is being treated for, information related to a therapy or treatment that the patient is undergoing, information about available support groups, etc....”; in addition see at least [0069] via: “...This output may be transmitted wirelessly or via LAN, WAN, the Internet, and delivered to healthcare facilities' electronic medical record stores, user electronic devices (e.g., pager, text messaging program, mobile telephone, tablet computer, mobile computer, laptop computer, desktop computer, and server), health information exchanges, and other data stores, databases, devices, and users. The system and method 10 may automatically generate, transmit, and present information such as high-risk patient lists with risk scores,..”)
Regarding claim 22: Amarasingham teaches the invention as claimed and detailed above with respect to claim 21. Amarasingham also teaches:
further comprising automatically updating the crisis message classifier based on the response data to improve risk assessment accuracy. (See at least [0076] via: “...As new, updated, or additional patient data become available, as shown in block 128, the data is evaluated to identify or verify disease/condition. The patient may be reclassified if the data now indicate the patient should be classified differently, for example. A patient may also be identified as potentially being diagnosed with an additional disease and be classified as such. For example, in the first 24 hours of admissions, the system identifies a particular patient as having CHF. Upon receiving more information, such as lab results and new physician notes, the system identifies this patient as also having AMI. Thus, this patient is identified as an AMI candidate and a CHF candidate...”; in addition see at least [0087] via: “...If there are no new patient data available or accessible to the disease component/risk logic modules, then there is no change to the patient classification and the display reflects the current state of patient classification, as shown in block 129. Accordingly, as real-time or near real-time patient data become available, the patients' disease and adverse event classifications are re-evaluated and updated as necessary...”)
Regarding claims 23, 30 & 38: Amarasingham teaches the invention as claimed and detailed above with respect to claims 21, 29 & 37 respectively. Amarasingham also teaches:
wherein the crisis message classifier includes a trained natural language processing (NLP) model. (See at least [0053] via: “...FIG. 5 is a simplified logical block diagram of an exemplary embodiment of the holistic hospital patient care and management system and method 10..”; in addition see at least [0057] via: “...in addition see at least [0086] via: “...during disease identification, natural language processing is conducted on unstructured clinical and non-clinical data to determine the potential disease(s) that the physician believes are likely to be diagnosed for the patient...”; in addition see at least [0058] via: “...The disease/risk logic 66 includes a hybrid model of natural language processing and generation 70, which combines a rule-based model and a statistically-based learning model. During natural language processing 70, raw unstructured data, for example, physicians' notes and reports, first go through a process called tokenization. ..”; in addition see at least [0063] via: “...The natural language generation module 70 is adapted to receive the unstructured clinical information for a patient, and "translate" that data to present the textual evidence that the patient is at high-risk for a specific disease...”)
Regarding claims 25, & 33: Amarasingham teaches the invention as claimed and detailed above with respect to claims 21 and 29 respectively. Amarasingham also teaches:
wherein the real-time notifications include a graphical element selectable by the healthcare personnel, and the response data includes feedback on the patient risk assessment score provided by healthcare personnel. (See at least [0037] via: “...The system 10 is further adapted to receive and display user preferences and system configuration data from clinicians' computing devices (mobile devices, tablet computers, laptop computers, desktop computers, servers, etc.) 19 in a wired or wireless manner. These computing devices 19 are equipped to display a system dashboard and/or another graphical user interface to present data, reports, and alerts. The system is further in communication with a number of display monitors 20 mounted and located in a number of locations, including patient rooms, hallways, etc. A clinician (physicians, nurses, physician assistants, and other healthcare personnel) may use the system to access a number of patient data, including immediately generating a list of patients that have the highest congestive heart failure readmission risk scores using real-time data, e.g., top n numbers or top x %. A display in a patient's room may be used to provide care plan and/or discharge information to the patient and family. The graphical user interfaces are further adapted to receive the user's (healthcare personnel) input of preferences and configurations, etc. The data may be transmitted, presented, and displayed to the clinician/user in the form of web pages, web-based message, text files, video messages, multimedia messages, text messages, e-mail messages, and in a variety of suitable ways and formats..”; in addition see at least [0048] via: “...All of the above-described input data including the clinical and non-clinical patient data are continually received, collected, and/or polled by the system 10 whenever they become available and are used in analysis for a number of output data and results. The data may be presented in ... graphical format...”; in addition see at least [0067] via: “...The dashboard user interface 75 allows interactive requests for a variety of views, reports and presentations of extracted data and risk score calculations from an operational database within the system, including for example, ... graphical representations of the data for a patient or population over time..”; in addition see at least [0070] via: “...The data presentation and system configuration logic module 74 further includes a system configuration interface 77. Local clinical preferences, knowledge, and approaches may be directly provided as input to the predictive models through the system configuration interface 77. This system configuration interface 77 allows the institution or health system to set or reset variable thresholds, predictive weights, and other parameters in the predictive model directly. The system configuration interface 77 preferably includes a graphical user interface designed to minimize user navigation time..”)
Regarding claims 26 & 34: Amarasingham teaches the invention as claimed and detailed above with respect to claims 21 and 29 respectively. Amarasingham also teaches:
wherein the response data includes a message provided by the healthcare personnel directly to the patient. (See at least [0044] via: “... During surgery, the system transmits the patient's conditions and status on a real time basis to the patient's family. Therefore, throughout the patient's stay in the hospital as well as after discharge, the holistic hospital patient care and management system 10 continually monitors the patient's condition, collects patient data in real-time, arranges for efficient delivery of care, manages the hospital's resources and supplies, and communicates timely or real time information to healthcare providers and the patient's family...”; in addition see at least [0096] via: “...FIG. 15 is a simplified flowchart of an exemplary embodiment of a patient/family engagement process 210 according to the present disclosure... patient's family is also provided with opportunities to be notified of patient status in an effort to increase awareness and shared decision-making during complicated situations, such as surgery.. .. a selective subset of the patient's data are retrieved from the data store and displayed, as shown in block 214. Also displayed are resources available to the patient, such as information related to a particular disease that the patient is being treated for, information related to a therapy or treatment that the patient is undergoing, information about available support groups, etc....”; in addition see at least [0069] via: “...This output may be transmitted wirelessly or via LAN, WAN, the Internet, and delivered to healthcare facilities' electronic medical record stores, user electronic devices (e.g., pager, text messaging program, mobile telephone, tablet computer, mobile computer, laptop computer, desktop computer, and server), health information exchanges, and other data stores, databases, devices, and users. The system and method 10 may automatically generate, transmit, and present information such as high-risk patient lists with risk scores,..”)
Regarding claims 27 & 35: Amarasingham teaches the invention as claimed and detailed above with respect to claims 21 and 29 respectively. Amarasingham also teaches:
wherein the crisis detection system utilizes optimization parameters based on cost ratios for minimizing total misclassification costs in patient safety risk assessment. (See at least [0097] via: “...FIG. 16 is a simplified flowchart of an exemplary embodiment of a situation analysis simulation process 230 according to the present disclosure. This function gives the hospital administrator the ability to simulate `what-if` scenarios by adjusting different parameters and observing the expected impact on operations will facilitate appropriate planning to optimize existing resources, thereby enhancing operational efficiency. The use of real-time data used to run the simulations will provide reasonable confidence in the application of simulated results to current and future resource planning In block 232, the method displays input parameters that can be varied to simulate certain scenarios. The parameters may include the number of available beds, the number of patients, then number of physicians, the number of nurses, the number of certain medical equipment, the amount of certain medical supplies, etc. In block 234, the user is provided the ability to alter or change these parameter values to see what would happen to the operations of the hospital. For example, the user may increase the number of patients needing care in the emergency department by two fold due to a multi-car accident. The user may reduce the number of available beds and decrease the number of physicians available to tend to the patients due to a high patient volume day. The user may lower the number of physicians, and increase the number of nurses available due to more severe cases (e.g., surgeries) requiring physician (rather than nurse) supervision. The user may indicate the time period of the simulation in terms of days, weeks, months, for example. The system receives the user input, as shown in block 234, and uses the predictive model to simulate the scenario described by the user input in block 236 in order to evaluate options based on potential financial, operational, or clinical outcomes (as selected by the user) as demonstrated by the simulation. The system has access to current real-time data about patient status, healthcare staff availability, resource and supply availability, and other information that are modified or influenced by the user simulation input. The system may identify and display if, when, where, and how patient care would be compromised with the simulation input, as shown in block 238. The system may further identify recommended actions or advanced precautions that can be taken to address shortcomings identified in the simulation, as shown in block 240..”)
Regarding claims 28 & 36: Amarasingham teaches the invention as claimed and detailed above with respect to claims 21 and 29 respectively. Amarasingham also teaches:
the crisis detection system determining, in real-time, that the patient risk assessment score exceeds the predetermined threshold. (See at least [0073] via: “... FIG. 7 is a simplified flowchart/block diagram of an exemplary embodiment of a clinical predictive modeling method 100 according to the present disclosure. A variety of data are received from a number of disparate data sources 102 related to particular patients admitted at a hospital or a healthcare facility. The incoming data may be received in real-time or the data may be pulled in batches. The incoming data are stored in a data store 104. In block 106, the received data undergo a data processing and integration process following data extraction (e.g., data cleansing and data manipulation), as described above. The resultant data then undergo the disease risk logic process 108 during which disease identification, and predictive modeling are performed. The risk score (with specific regard to high risk) computed for each patient for a disease of interest is compared to a disease high risk threshold in block 110. Each disease is associated with its own high risk threshold. If the risk score is less than the high risk threshold, then the process determines if the patient's risk score falls into the medium or low risk categories, otherwise the process returns to data integration and is repeated when new data associated with a patient become available. If the risk score is greater than or equal to the high risk threshold, then the identified patient having the high risk score is identified as `high risk` and included in a patient list in block 112. In block 114, the patient list and other associated information may then be presented to the intervention coordination team in one or more possible ways, such as transmission to and display on a desktop or mobile device in the form of a text message, e-mail message, web page, etc. In this manner, an intervention coordination team is notified and activated to target the patients identified in the patient list for assessment, and inpatient and outpatient treatment and care, as shown in block 118. The process may thereafter provide feedback data to the data sources 102 and/or return to data integration 106 that continues to monitor the patient during his/her targeted inpatient and outpatient intervention and treatment. Data related to the patient generated during the inpatient and outpatient care, such as prescribed medicines and further laboratory results, radiological images, etc. may be continually monitored to track intervention completion...”)
Regarding claims 31 & 39: Amarasingham teaches the invention as claimed and detailed above with respect to claims 29 and 37 respectively. Amarasingham also teaches:
wherein the method further comprises automatically updating the crisis detection system utilizing the response data from the healthcare personnel and the received healthcare communication data. (See at least [0076] via: “...As new, updated, or additional patient data become available, as shown in block 128, the data is evaluated to identify or verify disease/condition. The patient may be reclassified if the data now indicate the patient should be classified differently, for example. A patient may also be identified as potentially being diagnosed with an additional disease and be classified as such. For example, in the first 24 hours of admissions, the system identifies a particular patient as having CHF. Upon receiving more information, such as lab results and new physician notes, the system identifies this patient as also having AMI. Thus, this patient is identified as an AMI candidate and a CHF candidate...”; in addition see at least [0087] via: “...If there are no new patient data available or accessible to the disease component/risk logic modules, then there is no change to the patient classification and the display reflects the current state of patient classification, as shown in block 129. Accordingly, as real-time or near real-time patient data become available, the patients' disease and adverse event classifications are re-evaluated and updated as necessary...”)
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
non-obviousness.
Claims 24, 32, 40 are rejected under 35 U.S.C. 103 as being un-patentable by Amarasingham, in view of Ehrclassifier et.al (JP 2023529647 A) hereinafter “Ehrclassifier”.
Regarding claims 24, 32, 40 Amarasingham teaches the invention as claimed and detailed above with respect to claims 22, 29 and 37 respectively. However, Amarasingham is silent the following claim that is taught by Ehrclassifier:
wherein the crisis detection system performs a binary classification of the healthcare communication data. (See at least [Page 45, lines 21-30] via: “..An exemplary logistic regression ARDS classifier distinguishes patient populations.... Patient mortality was determined by assigning each patient to a high or low mortality risk group using the same dataset and model input variables outlined above in Example 1, rather than using the K-means clustering model. A binary classifier was trained to predict the rate. Although in some embodiments the binary classifier can be trained using a variety of machine learning methods ... , in this particular embodiment, a standard scalar ... and then run a logistic regression .. was applied..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Amarasingham to incorporate the teachings of Ehrclassifier. Those in the art would have recognized that Amarasingham’s teaching regarding A holistic hospital patient care and management system comprising a data store to receive and store patient data including clinical data; at least one predictive model including a plurality of weighted risk variables and risk thresholds in consideration of the clinical data and configured to identify at least one medical condition associated with the patients; a risk logic module configured to apply at least one predictive model to the clinical data to determine at least one risk score associated with each patient; a patient monitoring logic module configured to determine patient status; and a user interface module configured to display patient medical condition, risk score, and status information could be modified to include Ehrclassifier’s teaching regarding the use of a binary classifier. The combination of Amarasingham and Ehrclassifier is useful to healthcare providers in differentiating among high risk patients that need immediate medical assistance from lower risk patients that may safely require medical assistance at a later time.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
MacGabann (US 20190313230 A1)- EMERGENCY RESPONSE SYSTEM teaches: distributed computing systems for coordinating emergency rescue operations. The disclosed distributed emergency response system includes a mobile rescue application that increases the efficiency and reliability of users submitting rescue requests, a server-based application that coordinates dispatches in response to incoming rescue requests, and a mobile responder application that provides more efficient rescue through real-time location updates received from the mobile rescue application. The rescue application can include a dynamic, menu-based user interface to quickly solicit the emergency information needed for automated triage and dispatch.
Ferentz (US 20200258606 A1)- APPARATUS AND METHOD FOR EMERGENCY RESPONSE DATA ACQUISITION AND RETRIEVAL -teaches: method includes receiving an emergency data request with a patient identifier; retrieving protected data from a database corresponding to the patient identifier; and displaying the protected data on an emergency responder device display. Another disclosed method includes pushing a plurality of anonymized candidate profiles associated with a device identifier to an emergency network entity, in response to initiation of an emergency session by a device having the device identifier where the anonymized candidate profiles provide anonymized patient information without exposing protected data related to a patient and correspond to a non-anonymized medical profile for the patient; receiving a request from the emergency network entity for a specific non-anonymized medical profile corresponding to one of the plurality of anonymized candidate profiles; and providing a non-anonymized medical data profile corresponding to the specific anonymized candidate profile to the emergency network entity in response to the request
Response to Arguments
The Applicant's arguments filed 11-18-2025, have been fully considered but not found
persuasive.
Applicant cancelled claims 1-20 and added claims 21-40 as posted in the above analysis.
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 101.
Several steps are taken in the analysis as to whether an invention is rejected under 101. The first step is to determine if the claim falls within a statutory category. In this case it does for claims 21, 29 and 37 since the claims recite a method, a non-transitory processor readable storage medium and a system respectively for crisis detection and healthcare response.
The second step under 2A prong one is to determine if the claims recite an abstract idea, which would be the case if the invention can be grouped as either: a) mathematical concepts; (b) mental processes; or (c) certain methods of organizing human activity (encompassing (i) fundamental economic principles, (ii) commercial or legal interactions or (iii) managing personal behavior or relationships or interactions between people). The current invention is classified as an abstract idea since it may be grouped as a mental process as it recites “crisis detection and healthcare response”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “crisis detection and healthcare response”.
The third step under 2A Prong Two is to determine if additional elements in the claim imposes a meaningful limit on the abstract idea in order to integrate it into a practical idea. The current invention does not represent a practical idea since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
the fourth step under 2B is to determine if additional elements of the claim provide an inventive concept. An invention may be classified as an inventive concept if a computer-implemented processes is determined to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic, and non-conventional even if generic computer operations on a generic computing device is used to implement the abstract idea.
Step 2A Prong ONE
The Applicant argues that independent claims 21, 29, 37 do not recite an abstract idea as posted in the Office Action since they are not directed towards a method of organizing human activity or mental processes as alleged, but rather is directed towards a computer-implemented method for real-time patient safety monitoring in healthcare systems using a specific technical architecture. This result is achieved through a particular system including healthcare communication interfaces integrated with healthcare provider systems, crisis detection systems configured for real-time patient risk assessment, and technical alert systems that integrate with healthcare response infrastructure for real-time coordination of crisis intervention.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The method to select the abstract idea is to strip the additional elements from the claims. As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of amended limitation of claims 21, 29 & 37:
receiving healthcare communication data from a patient through a healthcare communication interface integrated with a healthcare provider system;
automatically processing the healthcare communication data in real-time through a crisis detection system configured to generate patient risk assessments utilizing a crisis message classifier;
generating a patient risk assessment score using the crisis detection system,
responsive to the risk assessment score exceeding a predetermined threshold, automatically triggering a technical alert system integrated with healthcare response infrastructure to transmit real-time notifications to healthcare personnel; and
receiving response data through the technical alert system from the healthcare personnel. .
The selected abstract idea (boldened limitations) of claims 21, 29, 37 can be implemented by pencil and paper and thus belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “crisis detection and healthcare response”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “crisis detection and healthcare response”. (refer to MPP 2106.04(a)(2)). Accordingly independent claims 21, 29 and 37 recite an abstract idea.
Step 2A Prong TWO
The Applicant argues that even if the invention recites an abstract idea it is directed to patentable subject matter since it integrates the abstract idea into a practical application. The Applicant further argues that the claims improves the technique of healthcare crisis detection and response as it requires specific technical architecture including healthcare communication interfaces integrated with healthcare provider systems, crisis detection systems configured for real-time patient risk assessment, and technical alert systems that integrate with healthcare response infrastructure to coordinate automated crisis intervention. Applicant's claimed invention provides a method for effectively creating real-time patient safety monitoring because the method recites technical elements that coordinate processing across multiple healthcare technical systems to analyze patient communication data and generate automated crisis response coordination through real-time technical integration. Furthermore the Applicant argues that independent Claims 21, 29, 37 recites the coordination of healthcare communication interfaces, crisis detection systems, and technical alert systems integrated with healthcare response infrastructure to generate real-time crisis intervention coordination (i.e., "responsive to the risk assessment score exceeding a predetermined threshold, automatically triggering a technical alert system that integrates with healthcare response infrastructure to transmit real-time notifications to healthcare personnel"), which directly parallels the technical integration that made Examples 47 and 42 successful. This coordination emphasizes the integration into a practical application because these technical systems
The Examiner disagrees with the Applicant since the Applicant’s arguments are not persuasive.
The Examiner restates that claims 21, 29 & 37 do not integrate the abstract idea into a practical application.
Regarding Example 47, claim 1 is related to the use of an artificial neural network to identify or detect anomalies does not recite any abstract ideas under step 2A Prong One, such as a mathematical concept, mental process, or a method of organizing human activity, such as a fundamental economic concept or managing interactions between people, since the claim recites a plurality of neurons, which are hardware components comprising a register and a microprocessor, and a plurality of synaptic circuits which together form an artificial neural network and hence is eligible. Nevertheless there is no step 2A Prong Two analysis as argued by the Applicant since the claim was determined to be eligible under step 2A Prong One. The instant application nevertheless does recite an abstract idea as explained in this office action under the heading Step 2A Prong ONE.
Regarding Example 42, claim 1 is related to a method for transmission of notifications when medical records are updated, whereby under 2A Prong Two analysis the claim is eligible since it recites a combination of additional elements including storing information, providing remote access over a network, converting updated information that was input by a user in a non-standardized form to a standardized format, automatically generating a message whenever updated information is stored, and transmitting the message to all of the users. The claim as a whole integrates the method of organizing human activity into a practical application. Specifically, the additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user. Thus, the claim is eligible because it is not directed to the recited judicial exception (abstract idea). However as explained below by the Examiner the instant invention does not impose a meaningful limit on the abstract idea as explained below since the additional elements as recited above amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea
Neither claim 21, 29 or 37 recite additional elements that impose a meaningful limit on the abstract idea:
Claims 21 & 29 recite:
a healthcare communication interface integrated with a healthcare provider system;
crisis detection system configured to generate patient risk assessments utilizing a crisis message classifier;
automatically triggering a technical alert system integrated with healthcare response infrastructure.
Claim 29 recites:
one non-transitory processor readable storage medium storing a computer program of instructions configured to be readable by at least one processor for instructing the at least one processor to execute a computer process for performing a method;
Claim 37 recites:
a healthcare communication interface integrated with a healthcare provider system;
a crisis detection system;
a risk assessment component;
a technical alert system that integrates with healthcare response infrastructure;
the technical alert system is configured to automatically transmit real-time notifications;
a response interface configured to receive response data.
The additional elements as recited above amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Providing a method for effectively creating real-time patient safety monitoring because the method recites technical elements that coordinate processing across multiple healthcare technical systems to analyze patient communication data and generate automated crisis response coordination through real-time technical integration is not enough to classify the claims as integrated into a practical application.
In order to integrate the abstract idea into a practical application the additional elements should be shown to impose a meaningful limit on the abstract idea. A colloquial interpretation of a practical application is not enough.
In order to integrate the abstract idea into a practical idea the Applicant could demonstrate at least one of the conditions enumerated below applies:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using 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 is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Applicant has not demonstrated any of the above listed conditions.
Regarding Step 2B
Similar to the analysis under Step 2A Prong Two, the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
In order evaluate whether the claim recites additional elements that amount to an inventive concept what could be shown is:
Adding a specific limitation (unconventional other than what is well-understood, routine, conventional (WURC) activity in the field - see MPEP 2106.05(d)
The Applicant has not demonstrated the above listed condition.
As a result, the Examiner restates the rejection of the invention under 35 USC §101.
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 103.
The applicant's arguments with respect to claim Claims 21, 29, 37 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument:
For reasons of record and as set forth above, the examiner maintains the rejection of claims 21-40 as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101, in addition to maintaining the rejection under 35 USC §103. In reaching this decision, the Examiner considered all evidence presented and all arguments actually made by Applicant.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571)270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684