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 in reply to the amendment filed on 02/09/24.
Claims 1, 6, 8, 12 have been amended and are hereby entered.
Claims 3, 5, 7 have been canceled.
Claim 11 was previously canceled.
Claims 1-2, 4, 6, 8-10, 12 are currently pending and have been examined.
This action is made final.
Foreign Priority
Acknowledgment is made of Applicant's claim for foreign priority based on applications filed in Korea on KR10-2022-0163845 (filed 11/30/2022) and KR10-2023-0124578 (filed 09/19/23). Certified foreign copies were received by USPTO on 08/05/2025. Accordingly, a priority date of 11/30/2022 has been given to the instant application.
IDS
The information disclosure statement (IDS) submitted on 12/7/25 and 2/27/26 has been considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
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 1-2, 4, 6, 8-10, 12 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1
Claims 1-2, 4, 6, 8-10 are drawn to a method, and Claim 12 is drawn to a computing device for predicting a bio event, both of which are within the four statutory categories. Claims 1-2, 4, 6, 8-10, 12 are further directed to an abstract idea on the grounds set out in detail below.
Step 2A Prong 1
Claim 1 and Claim 12 recite implementing the steps of:
obtaining bio information of a patient,
generating a first output indicating an expected time of occurrence of the bio event based on the bio information,
generating a second output including a probability value of occurrence of the bio event within a predetermined time period,
providing comprehensive prediction information related to the bio event based on the first output and the second output to a user, wherein the comprehensive prediction information includes information used to determine a priority for each patient among all patients
These steps amount to managing personal behavior or relationships or interactions
between people and therefore recite certain methods of organizing human activity. Obtaining bio information (e.g., biometric data such as heart rate, blood pressure, etc.) of a patient and using the bio information to predict (“generate”) an estimated time that a bio event (e.g., stroke, sepsis, cardiac arrest) may occur, along with a probability value of the bio event occurring within a particular time period, in order to provide comprehensive prediction information used to determine a priority of a patient is a personal behavior that may be performed by a healthcare provider.
The above claims are therefore directed to an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application because the additional
elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
The independent claims additionally recite:
a computing device as implementing the steps of the abstract idea (Claim 1)
a computing device comprising at least one processor and a memory, wherein the processor is configured to implement the steps of the abstract idea (Claim 12)
an artificial neural network model, wherein the artificial neural network model is a model trained by multi-task learning based on a first task of predicting an expected time of occurrence of the bio event and a second task of predicting a probability of occurrence of the bio event, as implementing the steps of generating a first output indicating an expected time of occurrence of the bio event based on the bio information and generating a second output including a probability value of occurrence of the bio event within a predetermined time period (Claims 1 and 12)
The broad recitation of general purpose computing elements (a computing device, processor, memory, artificial neural network) at a high level of generality only amount to mere instructions to implement the abstract idea using computing components as tools.
Regarding the computing device, Page 8 discloses “The computing device 100 may include a processor 110, a memory 130, and a network unit 150”; the following descriptions of processor (pages 8-9), memory (pages 11-12) and network unit (page 12) disclose the components of the computing device at a high level of generality and are understood to be general purpose computing components. Further, page 31 discloses “the method of the
present disclosure may be carried out by a personal computer, a hand-held computing
device, a microprocessor-based or programmable home appliance (each of which may be
connected with one or more relevant devices and be operated), and other computer system
configurations, as well as a single-processor or multiprocessor computer system, a mini
computer, and a main frame computer”. Therefore, the computing device is given its broadest reasonable interpretation as a general purpose computing device functioning in its ordinary capacity.
Regarding the processor and memory, page 8 of specification discloses “The processor 110 may be constituted by one or more cores and may include processors for data analysis and deep learning, which include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), and the like of the computing device”, while page 11 of specification discloses “According to an exemplary embodiment of the present disclosure, the memory 130 may include at least one type of storage medium of a flash memory type storage medium, a hard disk type storage medium, a multimedia card micro type storage medium, a card type memory (for example, an SD or XD memory, or the like), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. Therefore, these elements are given their broadest reasonable interpretation of general purpose computing components functioning in their ordinary capacities.
Regarding the artificial neural network, the use of the ANN to output an expected time of a bio event and a prediction related to a bio event represents the application of the abstract idea (“apply it”) on a generic computer (see preceding paragraph regarding “generic computer”). This is because the use of the ANN in the claims does not place any limits on how the ANN functions. Where “the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished” (see MPEP 2106. 05(f)), e.g., where the use of an ANN in a claim does not recite how its functions are actually performed and are merely recited at a high, non-inventive level, the ANN itself represents applying the abstract idea on a computer because no improvement to the ANN itself is claimed. In the instant claims, the broadest reasonable interpretation in light of the specification, represent generic computer functionality. The use of a computer to output an expected time of a bio event or a prediction related to a bio event amount to applying data to an algorithm and reporting the results (MPEP § 2106.05(f)(2), see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Regarding the “wherein the ANN model is a model trained by multi-task learning…” clause of Claim 1 and 12 that further describes the ANN, Examiner respectfully submits that the training steps are performed outside of the instant claim, and only provide descriptive language of the types of training data.
These elements are therefore not sufficient to integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to
more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claim 1, 12 only recite the aforementioned computing elements as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f).
Thus, taken alone, the additional elements do not amount to significantly more than the
above-identified judicial exception. Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. Their
collective functions merely provide conventional computer implementation.
Depending Claims
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims.
Claim 2 recites wherein the bio event includes an event related to a disease determined to affect a health status of the patient, which further narrows the scope of parent claim 1. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more.
Claim 4 recites wherein the first output includes a value corresponding to a length of a remaining time from a time when the bio information is obtained up to a time when the bio event is predicted to occur, which further narrows the scope of parent claim 1. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more.
Claim 6 recites limitations pertaining to wherein the comprehensive prediction information includes a filtered result obtained through a comparison between the second output and a predetermined threshold, and priority information of the patient determined based on the filtered result and a size of the first output, which further narrows the scope of parent claim 5. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 8 recites limitations pertaining to wherein the artificial neural network model is trained based on two or more different training data sets; Claim 9 recites limitations pertaining to wherein the two or more different training data sets include first training data which is bio information with information on an occurrence time of the bio event of the patient set as a label; and Claim 10 recites limitations pertaining to wherein the two or more different training data sets include second training data which is bio information with binary information indicating whether the bio event occurs within a predetermined time period set as a label. The use of a computer to use training data sets to train a model such as an ANN, utilizing the known training embodiments offered in the instant specification (e.g., Page 16 discloses “The neural network may be trained in at least one scheme of supervised learning, unsupervised learning, semi supervised learning, or reinforcement learning. The training of the neural network may be a process in which the neural network applies knowledge for performing a specific operation to the neural network) amount to applying data to an algorithm and reporting the results (MPEP § 2106.05(f)(2), see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. The techniques outlined, and Examiner notes the known methods of training to one of ordinary skill in the art, are mathematical algorithms or certain methods of organizing human activity of labeling and fitting data to a particular model representation. These limitations are not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101 as they include all of the limitations of claim 1. The additional recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea. Beyond the limitations which recite the abstract idea, the claims recite additional elements consistent with those identified above with respect to the independent claims which encompass adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 2, 4, 6, 8-10 recite additional subject matter which amounts to additional elements consistent with those identified in the analysis of the independent claims above. As discussed above with respect to independent claims and integration of the abstract idea into a practical application, recitation of these additional elements (e.g., electronic storage medium, computing devices, processor, etc.) only amounts to invoking computers as a tool to perform the abstract idea. 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.
Dependent claims 1-2, 4, 6, 8-10, 12, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
For the reasons stated, Claims 1-2, 4, 6, 8-10, 12 fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 4, 6, 8-9, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Quinn et. al. (US Publication 20210022660A1) further in view of Agarwal et. al. (US Publication 20200242566A1), and further in view of Jin et. al. (US Publication 20220270240A1).
Regarding Claim 1, Quinn discloses a method performed by a computing device to predict a bio event ([0004], teaching on system and method for collecting and analyzing vital sign information to predict a likelihood having an adverse health condition (“bio event”); [0051] defines “adverse health condition” as “occurrence of a complication”); [0140]-[0147] teaches on computer systems for implementing the methods of the disclosure), the method comprising:
obtaining bio information of a patient ([0005]-[0008] teach on collecting and analyzing vital sign information or other clinical health data obtained by blood testing or imaging to predict an adverse health condition in the patient; data may be collected from an ECG sensor; health data of a patient is received from sensors through a wireless transceiver; vital sign measurements may include one or more measurements selected from the group of “heart rate, heart rate variability, blood pressure (e.g., systolic and diastolic), respiratory rate, blood oxygen concentration (SpO2), carbon dioxide concentration in respiratory gases, a hormone level, sweat analysis, blood glucose, body temperature, impedance (e.g., bioimpedance), conductivity, capacitance, resistivity, electromyography, galvanic skin response, neurological signals (e.g., electroencephalography), immunology markers, and other physiological measurements” – all of the aforementioned measurements are interpreted as “bio information of a patient”); and
generating a first output indicating an expected time of occurrence of the bio event based on the bio information by using an artificial neural network model ([0006] teaches on after the health data is received, processing the health data using a trained algorithm to generate an output indicative of a progression in a health condition of the subject; [0009] teaches on the trained algorithm comprising a machine learning based classifier which may be a neural network (interpreted as synonymous with “artificial neural network”); [0010] teaches on processing the health data using the trained algorithm to indicate the progression of the health condition over a period of time which includes various windows, such as 2, 4, 6, 8 or 10 hours prior to onset of the health condition and ending at the onset of the health condition; “onset of the health condition” is interpreted as “occurrence of the bio event”; [0063] teaches on the machine learning classifier generating predictions which may include “a prediction of the time at which the patient is expected to have developed the disease or disorder”; [0070] teaches on outputting an alert when the machine learning classifier predicts a disease, disorder or complication, which may comprise information such as prediction of the disease, disorder or complication, likelihood of disease, disorder or complication, and time until onset of the disease, disorder or complication – “time until onset…” is interpreted as “expected time of occurrence”);
generating a second output indicating a probability value of occurrence of the bio event within a predetermined time period by using the artificial neural network model ([0070] teaches on when the ML classifier generates a prediction of a disease, disorder of complication, an alert may be transmitted to the patient’s team; the alert may comprise information such as a “likelihood of the predicted disease, disorder or complication”; per [0063] the likelihood is understood to be a relative likelihood or probability (likelihood and probability are interpreted as indicating a “possibility”) and the output may include a prediction including likelihood of developing the disease and prediction of the time at which the patient is expected to have developed the disease; per [0068], the ML classifier may be one or more neural networks (interpreted as synonymous with artificial neural network);
providing comprehensive prediction information related to the bio event based on the first output and the second output to a user ([0070] teaches on the ML classifier generating a prediction of a disease, disorder or complication and generating and transmitting an alert to a healthcare provider; the alert may comprise output information such as “prediction of a disease, disorder, or complication, a likelihood of the predicted disease, disorder, or complication, a time until an expected onset of the disease, disorder, or condition” – interpreted as “comprehensive prediction information” as it consists of expected onset time of occurrence (first output) and likelihood (second output).
wherein the artificial neural network model is a model trained ([0057] teaches on an ML classifier which may be a machine learning algorithm which may be a neural network, interpreted as synonymous with “artificial neural network”; the ML classifier (e.g., the NN) may be trained using “one or more training datasets” corresponding to patient data) based on a first task of predicting an expected time of occurrence of the bio event ([0058] teaches on training the NN using one or more patient datasets; [0058] teaches on generating training data sets from patient features and labels (clinical characteristics/outcomes); features may include vital sign measurements or biometric data; [0060] teaches on labels comprising clinical outcomes such as occurrence of a disease or complication (bio event) in the patient; clinical outcomes (labels) may include a “temporal characteristic” associated with the diagnosis/presence of the adverse health condition (bio event), e.g., temporal characteristic may indicate the patient having an occurrence of the adverse condition (sepsis) within a certain time period which may be a number of hours, days, weeks, months) and a second task of predicting a probability of occurrence of the bio event ([0056] teaches on applying a “trained algorithm” to patient health to generate a likelihood (synonymous with “probability”) of the subject having an adverse health condition (bio event); the trained algorithm may be a ML classifier; the ML classifier may be trained using clinical datasets from one or more patients as inputs and known clinical health outcomes (occurrence of disease/disorder, “bio event”); if the algorithm/ML classifier has been trained to generate the likelihood of the subject having the adverse condition/disease/disorder, it is interpreted as training the model based on a task of predicting a probability of occurrence of the bio event).
Quinn does not disclose, but Agarwal, which is directed to systems and methods for prioritizing patients for clinician management, teaches wherein the comprehensive prediction information includes information capable of determining a priority for each patient among all patients (Fig. 2 / Paras. [0041]-[0050] teach on an overview of prioritizing patients for clinician management: [0042] specifically teaches on monitoring pulmonary artery pressure (PAP); [0043], the detected PAP is used to estimate a probability that the patient will experience a predetermined event during a predetermined time period; [0046], a risk score is determined – per [0035] the risk score may be related to the probability/likelihood a patient will have a heart failure within a given period, e.g., one week – interpreted as “prediction information”; [0048], a priority score is determined based on the risk score and the patient’s medical historical data; [0049], the priority score is compared to priority scores of other patients to rank the patient compared to other patients (interpreted as determining a priority for each patient if they are ranked)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Quinn with these teachings of Agarwal, to include information capable of determining a priority for each patient among a plurality of patients within the comprehensive prediction information of Quinn, with the motivation of accommodating a high volume of patients so that lower risk patients can be scheduled to be seen by a doctor further out so that higher risk patients can be given first available time slots for a medical appointment with a doctor (Agarwal [0004]/[0051]).
Quinn teaches on training the model which may be a neural network with more than one dataset as discussed above, but does not teach on training the model using multi-task learning. Jin, which is directed to processing medical images using machine learning, teaches
training a model with multi-task learning ([0035] teaches on a machine learning based model trained with multi-task learning).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Quinn/Agarwal with these teachings of Jin, to implement the training of the Quinn’s neural network using multi-task learning as taught by Jin, with the motivation of simultaneously training the model to perform a plurality of tasks and enabling the model to learn a more complete representation of the data context by using all available training data together ([0035]).
Regarding Claim 2, Quinn/Agarwal/Jin teach the limitations of Claim 1. Quinn further discloses wherein the bio event includes an event related to a disease determined to affect a health status of the patient ([0042] teaches on monitoring and analysis of vital sign of a patient having an occurrence or recurrence of a disease or disorder; the patient treated for a disease or disorder in a hospital/clinical setting may need to be monitored for occurrence/recurrence of the disease or a complication related to the treatment administered for the disease/disorder; a patient who had an operation may need to be monitored for sepsis or post-surgery complications; patient monitoring may detect conditions causing sepsis; patient monitoring may detect complications such as stroke, heart failure, chronic obstructive pulmonary disease, etc.)
Regarding Claim 4, Quinn/Agarwal/Jin teach the limitations of Claim 1. Quinn further discloses wherein the first output includes a value corresponding to a length of a remaining time from a time when the bio information is obtained up to a time when the bio event is predicted to occur ([0070] teaches on the ML classifier generating/outputting a prediction of a disease, disorder or complication, including “time until an expected onset of the disease, disorder or condition”, and transmitting an alert to a healthcare provider – a time until onset is interpreted as a value corresponding to the length of remaining time up until when the bio event is predicted to occur; per [0045] the vital sign data may undergo real-time processing and analysis; when an adverse health condition (occurrence of disease or complication) is predicted, a real-time alert may be sent to a healthcare provider – if the data is monitored in real-time and an alert is sent in real-time to healthcare provider, and the output includes a “time until expected onset”, it is interpreted as the value corresponding to length of time remaining up to the time when bio event is predicted to occur).
Regarding Claim 6, Quinn/Agarwal/Jin teach the limitations of Claim 1. Quinn further discloses wherein the comprehensive prediction information includes a filtered result obtained through a comparison between the second output and a predetermined threshold ([0051] teaches on monitoring a patient for adverse health conditions and alerting a healthcare provider when a certain predetermined criterion is met, e.g., a minimum threshold for a likelihood of deterioration of patient’s state, occurrence of a disease or disorder or occurrence of a complication such as sepsis; [0052] provides various threshold percentages as examples of the minimum threshold; per Claim 3 the “second output” is likelihood of occurrence of disease/complication; Examiner interprets comparing the patient’s state to a “minimum threshold” and subsequently causing an alert to be generated to read on “filtering” information through the comparison to generate the resulting alert)
Quinn does not disclose, but Agarwal further teaches: priority information of the patient determined based on [a] result ([0049] teaches on comparing the priority score of one patient to other patients’ priority scores to rank each patient; the ranking is interpreted priority information based on the result of the comparison); and a size of a first output ([0046] teaches on a risk score being determined for a patient which represents the probability that the patient will experience the predetermined event (e.g., heart failure) during the predetermined period; the probability may be represented as a risk score or in terms of “low, medium, and high”; a risk score and “low, medium, high” are all interpreted as indicating the “size” of the output of the patient experiencing the event within the predetermined time period; Examiner submits that Applicant has not defined how “size” of the first output (“a first output indicating an expected time of occurrence of the bio event”) is measured or determined in instant claim 6, and as such, the applied reference of a risk score or a low/medium/high probability is interpreted as teaching on the broadest reasonable interpretation of information based on the size of the output).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of Quinn/Agarwal/Jin with these teachings of Agarwal, to include, with the comprehensive priority information of Quinn, priority information based on a result (e.g., the filtered result of Quinn) along with a size of the first output as taught by Agarwal, with the motivation of using priority information of the patient compared to other patients, in order to accommodate a high volume of patients so that lower risk patients can be scheduled to be seen by a doctor further out so that higher risk patients can be given first available time slots for a medical appointment with a doctor (Agarwal [0004]/[0051]).
Regarding Claim 8, Quinn/Agarwal/Jin teach the limitations of Claim 1. Quinn further discloses wherein the artificial neural network model is trained based on two or more different training data sets ([0057] teaches on an ML classifier which may be a machine learning algorithm which may be a neural network, interpreted as synonymous with “artificial neural network”; the ML classifier (e.g., the NN) may be trained using “one or more training datasets” corresponding to patient data; [0058] teaches on training the NN using one or more patient datasets; [0058] teaches on generating training data sets from patient features and labels (clinical characteristics and outcomes, respectively); features may include vital sign measurements or biometric data (“bio information”); features may comprise patient information such as patient age, patient medical history, other medical conditions, current or past medications, and time since the last observation – if training datasets comprise a “set of features” it is interpreted as more than one feature used in training which is interpreted as “two of more different training datasets”, e.g. vital sign measurements and patient age or medical history are interpreted as being different types of training datasets; further, [0064] teaches on training the ML classifier model to generate real-time predictions, the model can be trained using “datasets” (plural); datasets may comprise: “intensive care unit (ICU) databases of de-identified data including vital sign observations (e.g., labeled with an appearance of ICD9 or ICD10 diagnosis codes), databases of ambulatory vital sign observations collected via tele-health programs, databases of vital sign observations collected from rural communities, vital sign observations collected from fitness trackers, vital sign observations from a hospital or other clinical setting, vital sign measurements collected using an FDA-approved wearable monitoring device, and vital sign measurements collected using wearable monitoring devices of the present disclosure” – any two datasets of the aforementioned are interpreted as “two or more different training data sets”; per [0057] the ML algorithm can comprise a neural network which is interpreted as synonymous with artificial neural network).
Regarding Claim 9, Quinn/Agarwal/Jin teach the limitations of Claim 8. Quinn further discloses wherein the two or more different training data sets include first training data which is bio information with information on an occurrence time of the bio event of the patient set as a label ([0057] teaches on an ML classifier which may be a machine learning algorithm which may be a neural network, interpreted as synonymous with “artificial neural network”; the ML classifier (e.g., the NN) may be trained using “one or more training datasets” corresponding to patient data; [0058] teaches on training the NN using one or more patient datasets; [0058] teaches on generating training data sets from patient features and labels (clinical characteristics and outcomes, respectively); features may include vital sign measurements or biometric data (“bio information”); [0060] teaches on labels comprising clinical outcomes such as occurrence of a disease or complication (bio event) in the patient; clinical outcomes (labels) may include a “temporal characteristic” associated with the diagnosis/presence of the adverse health condition (bio event), e.g., temporal characteristic may indicate the patient having an occurrence of the adverse condition (e.g., sepsis) within a certain time period which may be a number of hours, days, weeks, months).
Regarding Claim 12, Quinn/Agarwal/Jin teach the limitations of Claim 1. Claim 12 recites limitations that are the same or substantially similar to Claim 1, and the discussion above with respect to Claim 1 is equally applicable to Claim 12. Claim 1 additionally recites the following additional limitations which are also taught by Quinn: at least one processor; and a memory, wherein the at least one processor is configured to (paras. [0140]-[0147] teach on computer system architecture, including memory and processor).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Quinn et. al. (US Publication US20210022660A1), further in view of Agarwal et. al. (US Publication 20200242566A1), and further in view of Jin et. al. (US Publication 20220270240A1) as applied to claim 8 above, and further in view of Fornwalt et. al. (US Publication 20210076960A1).
Regarding Claim 10, Quinn/Agarwal/Jin teach the limitations of Claim 8. Quinn further discloses two or more different training data sets (paras. [0057], [0058], [0064] as cited above with respect to Claim 8 teach on two or more training datasets).
Quinn does not disclose the following, but Fornwalt, which is directed to using ECG to predict future atrial fibrillation (AF) in a patient, teaches: wherein the training data sets include training data which is bio information with binary information indicating whether the bio event occurs within a predetermined time period set as a label ([0097] teaches on a training database including a number of ECGs (“bio information”) and clinical data; the clinical data can include outcome data such as “whether or not a patient developed AF (atrial fibrillation, interpreted as “bio event”) within a time period following the day that the ECG was taken, where the time period may include 1, 2, 3, 4, etc. months or years; Examiner interprets outcome data indicating “whether or not a patient developed AF” to read on “binary information indicating whether the bio event occurs” as the patient either has AF or they do not have AF).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Quinn/Agarwal/Jin with these teachings of Fornwalt, to include a training dataset with bio information (ECG) and binary information indicating whether the bio event (AF) occurs within a predetermined time period as a label as the second training data of Quinn, with the motivation of using ECG data with known time-to-event data for training the model to predict occurrence of an AF in a patient (Fornwalt [0122]).
Response to Applicant’s Remarks/Arguments
Please note: When referencing page numbers of Applicant’s response, references are to page numbers as printed.
Claim Objections
The objection to Claim 6 is withdrawn in view of Applicant’s amendment to Claim 6.
Rejections under 35 USC 101
Applicant’s remarks have been fully considered but are not persuasive. Regarding remarks at page 5 that the processes “do not constitute a mere mental act or mathematical concept”, Examiner respectfully submits that the abstract idea has been categorized in the non-final action, as well as in this action, as certain methods of organizing human activity. Therefore, arguments directed towards “mental act” or “mathematical concept” are not persuasive. Regarding recitation of the artificial neural network, in this case to generate the first and second outputs of an expected time of occurrence and probability of bio event occurring in a predetermined time period, this only amounts to using the ANN model as a tool to apply data to a model and generate a result (see MPEP 2106.05(f)(2)). Regarding the “computing device including a processor”, Examiner respectfully submits that using a general purpose computer and processor only amount to mere instructions to apply the abstract idea (MPEP 2106.05(f)(2)). Examiner maintains the position that Claim 1 and 12 indeed recite an abstract idea, as set out in detail in the 101 analysis section in non-final action and above in this action.
MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent personal behaviors that a person or persons, with or without the aid of a computer, would perform to obtain bio information of a patient, use the bio information to predict an estimated time that a bio event may occur, along with a probability value of the bio event occurring within a particular time period, in order to determine a priority of a patient. Because the claim elements fall under such a series of personal behaviors that may be performed by a healthcare provider to ultimately determine a priority of a patient, the claimed invention is directed to an abstract idea. This argument is not persuasive.
Regarding remarks directed to a practical application and Step 2A Prong 2 at top of page 6, the Examiner respectfully disagrees. MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves the functioning of a computer. See also MPEP 2106.05(a)(I). The technological environment of Applicant’s claim is a general-purpose computer (see Spec. pages 8-12, 31 as cited in 101 additional elements section above). Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the computer that results from the implementation of Applicant’s claim. There is no indication that the computer is made to run faster, more efficiently, or utilize less power. Because there is no improvement to the function of the computer, a practical application is not present.
MPEP 2106.04(d)(1) also states that a practical application may be present where the claimed invention improves another technology. See also MPEP 2106.05(a)(II). Applicant’s claim is confined to a general-purpose computer (see Spec. pages 8-12, 31 as cited with respect to specific elements in 101 analysis section above) and does not recite “another technology.” Because no other technology is recited in the claim, the claim cannot improve another technology (see, e.g., MPEP 2106.05(I)(A)(i) describing an example of an improvement to another technology where the abstract idea implemented on a computer improved the claimed additional element of a rubber molding machine).
Regarding the specific features cited to by Applicant at page 6, as shown above in 101 analysis section, the steps of predicting the expected time and probability value of a bio event are within the scope of the abstract idea; they are not additional elements. Predicting an expected time and probability are both steps that could be performed by a healthcare provider. Utilizing a neural network model trained with multi-task learning only amounts to mere instructions to apply the abstract idea on a computer, e.g., applying particular data to a model to output results. Regarding remarks directed to “synthesizing two distinct outputs to determine a priority for each patient… the present disclosure improves resource allocation efficiency in clinical settings”, Examiner respectfully submits that this may provide an improvement to the abstract idea (e.g., a better way of determining patient priority), but is not a technical improvement. Regarding integration of a judicial exception into a practical application, please see 2106.04(d)(II) which states, “The analysis under Step 2A Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon (including products of nature). Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h)”, and, see also MPEP 2106.05(a) which states, “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” Applicant has not provided, nor can Examiner find evidence of, how any of the additional elements identified above in main 101 analysis section are providing an improvement over prior art systems. The additional elements identified above are understood to be computing components functioning in their normal operating capacity, which is not sufficient to integrate the judicial exception into a practical application. Therefore, this argument is not persuasive.
Regarding concluding remarks at page 6, Examiner reiterates that the claims were not categorized as “mental process” or “mathematical process”; this argument is not persuasive. As discussed in this response section, the abstract idea was categorized as certain methods of organizing human activity. Recitation of the ANN model only amounts to using the ANN implemented on a general purpose to apply the abstract idea. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
For all of the above reasons, Applicant’s remarks are not persuasive and the rejections of Claims 1-2, 4, 6, 8-10, 12 under 35 USC 101 are maintained.
35 USC 102 Rejections
Applicant’s remarks have been fully considered but are not persuasive with regard to the Quinn reference and the specific limitation Applicant presents at page 7. Applicant remarks that Quinn fails to disclose every element of amended Claim 1; specifically, Applicant argues that Quinn fails to disclose or suggest the specific configuration of predicting a specific “expected time of occurrence” and generating it as a first output. Examiner respectfully disagrees with Applicant’s position that Quinn teaches these elements. Examiner has recopied the same claim mapping that was included with non-final action with emphasis on relevant paragraphs:
generating a first output indicating an expected time of occurrence of the bio event based on the bio information by using an artificial neural network model ([0006] teaches on after the health data is received, processing the health data using a trained algorithm to generate an output indicative of a progression in a health condition of the subject; [0009] teaches on the trained algorithm comprising a machine learning based classifier which may be a neural network (interpreted as synonymous with “artificial neural network”); [0010] teaches on processing the health data using the trained algorithm to indicate the progression of the health condition over a period of time which includes various windows, such as 2, 4, 6, 8 or 10 hours prior to onset of the health condition and ending at the onset of the health condition; “onset of the health condition” is interpreted as “occurrence of the bio event”; [0063] teaches on the machine learning classifier generating predictions which may include “a prediction of the time at which the patient is expected to have developed the disease or disorder”; [0070] teaches on outputting an alert when the machine learning classifier predicts a disease, disorder or complication, which may comprise information such as prediction of the disease, disorder or complication, likelihood of disease, disorder or complication, and time until onset of the disease, disorder or complication – “time until onset…” is interpreted as “expected time of occurrence” ).
Examiner submits that [0063] teaches on providing a “prediction of the time” of a health condition, which is synonymous with “expected time of occurrence” of the bioevent in the instant claims. Therefore, this argument is not persuasive. Examiner maintains that Quinn teaches on the portions of Claim 1 to which it has been mapped.
Regarding the amended limitations in Claim 1, Examiner submits that Applicant’s amendments have necessitated new grounds of rejection and will address the individual references below in 35 USC 103 section.
35 USC 103 Rejections
Applicant’s remarks have been fully considered but are not persuasive. Regarding remarks to Quinn and “expected time of occurrence”, see Examiner’s response above as this has already been addressed and is not persuasive. Applicant makes the additional argument that “a configuration for predicting the remaining time until an event occurs as a specific numerical value is absent from Quinn”; Examiner submits that this is also addressed by previously cited portion of Quinn, specifically para. [0010], teaching on processing the health data using the trained algorithm to indicate the progression of the health condition over a period of time which includes various windows, such as 2, 4, 6, 8 or 10 hours prior to onset of the health condition and ending at the onset of the health condition. While the instant claim does not actually require “predicting the remaining time until an event occurs as a specific numerical value”, e.g., the instant claim recites “generating a first output indicating an expected time of occurrence”. Nonetheless, Examiner submits that Quinn teaches on predicting remaining time as a specific numerical value, e.g., 2, 4, 6, 8 or 10 hours “prior to onset of the health condition and ending at the onset of the health condition” as well as predicting a time of occurrence (para. [0063]). This argument is not persuasive with respect to Quinn.
Regarding remarks directed to Agarwal, beginning at page 8, Applicant appears to be arguing Agarwal alone rather than the combination of Quinn with Agarwal. As shown above with respect to Claim 1 and Quinn, Quinn already teaches “comprehensive prediction information” (see Claim 1). Agarwal’s teachings are applied to the comprehensive prediction information of Quinn to determine a priority for each patient of a plurality of patients. Applicant cites to paragraph [0086] of Agarwal, however, Examiner did not rely upon paragraph [0086] to teach on the instant limitations in Claim 1. Rather, cited portions of Agarwal teach on using information related to a probability/likelihood that a bio event (heart failure) will occur within a given time period. Examiner submits that while both values of the instant claim are taught by Agarwal (time of occurrence – e.g., within a week in the cited paragraph, and probability value of occurrence), the combination of Quinn/Agarwal reads on the claim limitation, e.g., to include information capable of determining a priority for each patient within Quinn’s comprehensive prediction information that teaches on the claimed limitation, as explained in non-final action and above in 103 section in this action. This argument is not persuasive.
Regarding remarks directed to Jin at page 9, Examiner respectfully submits that Jin was introduced to teach on a specific type of machine learning, e.g., multi-task learning. Examiner submits that multi-task learning has many applications, and while Jin may specifically teach multi-task learning with respect to PC-MRI images as data, the type of data used is irrelevant in this situation. Jin does not need to teach on “synthesizing diverse data types output by the model” as this is already taught by Quinn; Jin is merely introduced to teach on the particulars of how the ANN was trained (multi-task learning). This argument is not persuasive.
Regarding remarks at page 10 pertaining to motivation to combine, Examiner submits that a motivation has already been provided in non-final action (with respect to Claim 7) and above with respect to Claim 1: it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Quinn/Agarwal with these teachings of Jin, to implement the training of the Quinn’s neural network using multi-task learning as taught by Jin, with the motivation of simultaneously training the model to perform a plurality of tasks and enabling the model to learn a more complete representation of the data context by using all available training data together ([0035]).
Examiner submits that the image processing aspect of Jin is irrelevant as it is not being used to teach any data types or intended uses in the instant claim. Examiner has not provided citations to image processing or included any discussion of image processing with respect to Jin. Examiner respectfully submits that multi-task learning can be utilized in many data domains, and as such, finds the citation to Jin [0035] to be relevant to teaching on training an ANN using multi-task learning, as it serves the purpose of enabling the model to learn a more complete representation of data context by using all available training data together. This argument is not persuasive.
The discussion above with respect to Claim 1 is equally applicable to Claim 12.
Regarding the rejection of dependent Claims 2, 4, 6, 8-10, the Applicant has not offered any arguments with respect to these claims other than to reiterate the argument(s) present for the claims from which they depend. As such, the rejection of these claims is also maintained.
For all of the above reasons, Applicant’s remarks are not persuasive. The rejections of Claims 1-2, 4, 6, 8-10, 12 under 35 USC 103 are maintained.
Conclusion
Examiner respectfully requests that Applicant provides citations to relevant paragraphs of specification for support for amendments in future correspondence.
The following relevant prior art not cited is made of record:
US Publication 20230282356 A1, teaching on using a neural network to determine a risk probability from patient data
US Publication US 20200108260 A1, teaching on using machine learning model to generate a probability of a cardiac arrythmia occurring in a patient within a predetermined time period
US Publication 20220378379 A1, teaching on AI-based cardiac event predictor systems and methods which uses ECG data and a trained model to predict cardiac events in a patient
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 extension fee 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 date of this final action.
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/ANNE-MARIE K ALDERSON/Primary Examiner, Art Unit 3682