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
Claims 1, 3-11, and 13-20 are currently pending and have been examined.
Claims 1, 3, 11, and 20 have been amended.
Claims 2 and 12 have been cancelled.
Claims 1, 3-11, and 13-20 have been rejected.
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, 3-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea without significantly more. Claims 1, 3-11, and 13-20 are directed to a system, method, or product which are one of the statutory categories of invention. (Step 1: YES).
Independent Claim 1 discloses a method comprising: digitally introducing data associated with at least one patient into a quantum classifier for classifying patients such that patients having a predetermined medical condition or being in risk of having the predetermined medical condition in a predetermined time span are classified into a predetermined risk group, the data comprising biological measurements of the at least one patient, wherein the biological measurements are obtained by way of multiple sensors which have a granularity of 5 milliseconds or less and the quantum classifier having been trained with a training dataset comprising both historical data of biometrics of historical patients and historical data related to the medical condition of the historical patients; digitally commanding a quantum device or system to run the quantum classifier to classify the at least one patient in a time span of 1 minute or less; and digitally determining a first action to be taken with respect to the at least one patient when the at least one patient has been classified into the predetermined risk group, or either a second action or no action to be taken with respect to the at least one patient when the at least one patient has been classified into a predetermined group different from the predetermined risk group; digitally commanding an electronic device to take the first action and/or the second action; after the at least one patient has been classified by the quantum classifier, digitally introducing manually -introduced data related to the medical condition of the at least one patient into the training dataset, thereby providing an updated training dataset; and digitally commanding the quantum device or system to run the quantum classifier to retrain itself with the updated training dataset.
Independent Claim 11 discloses a device comprising: at least one processor and at least one memory, wherein the at least one memory being is configured, with the at least one processor, to cause the device to: introduce data associated with at least one patient into a quantum classifier for classifying patients such that patients having a predetermined medical condition or being in risk of having the predetermined medical condition in a predetermined time span are classified into a predetermined risk group, the data comprising biological measurements of the at least one patient, wherein the biological measurements are obtained by way of multiple sensors which have a granularity of 5 milliseconds or less, and the quantum classifier having been trained with a training dataset comprising both historical data of biometrics of historical patients and historical data related to the medical condition of the historical patients; command a quantum device or system to run the quantum classifier to classify the at least one patient in a time span of 1 minute or less; and determine a first action to be taken with respect to the at least one patient when the at least one patient has been classified into the predetermined risk group, or either a second action or no action to be taken with respect to the at least one patient when the at least one patient has been classified into a predetermined group different from the predetermined risk group; digitally commanding an electronic device to take the first action and/or the second action; after the at least one patient has been classified by the quantum classifier, digitally introducing manually -introduced data related to the medical condition of the at least one patient into the training dataset, thereby providing an updated training dataset; and digitally commanding the quantum device or system to run the quantum classifier to retrain itself with the updated training dataset.
Independent Claim 20 discloses a non-transitory computer-readable medium encoded with instructions that, when executed by at least one processor or hardware, perform or make a device to at least perform the following steps: introducing data associated with at least one patient into a quantum classifier for classifying patients such that patients having a predetermined medical condition or being in risk of having the predetermined medical condition in a predetermined time span are classified into a predetermined risk group, the data comprising biological measurements of the at least one patient, wherein the biological measurements are obtained by way of multiple sensors which have a granularity of 5 milliseconds or less, and the quantum classifier having been trained with a training dataset comprising both historical data of biometrics of historical patients and historical data related to the medical condition of the historical patients; commanding a quantum device or system to run the quantum classifier to classify the at least one patient in a time span of 1 minute or less; and determining a first action to be taken with respect to the at least one patient when the at least one patient has been classified into the predetermined risk group, or either a second action or no action to be taken with respect to the at least one patient when the at least one patient has been classified into a predetermined group different from the predetermined risk group; digitally commanding an electronic device to take the first action and/or the second action; after the at least one patient has been classified by the quantum classifier, digitally introducing manually -introduced data related to the medical condition of the at least one patient into the training dataset, thereby providing an updated training dataset; and digitally commanding the quantum device or system to run the quantum classifier to retrain itself with the updated training dataset.
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The remaining un-bolded limitations are merely directed to classifying a patient in a group based on patient data. The series of steps recited above describe managing personal behavior or relationships or interactions between people and thus are grouped as certain methods of organizing human activity which is an abstract idea. (Step 2A- Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) 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 (MPEP 2106.05.f), (2) Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Independent Claim 1 discloses the following additional elements:
Digitally [by means of digital or computer technology] introducing data
A quantum classifier
Biological measurements are obtained by way of multiple sensors which have a granularity of 5 milliseconds or less
Digitally [by means of digital or computer technology] commanding a quantum device or system to run the quantum classifier in a time span of 1 minute or less
Digitally commanding an electronic device to take the first action and/or the second action
Digitally commanding the quantum device or system to run the quantum classifier to retrain itself with the updated training dataset
Independent Claim 11 discloses the following additional elements:
At least one processor
At least one memory
A quantum classifier
Biological measurements are obtained by way of multiple sensors which have a granularity of 5 milliseconds or less
Commanding a quantum device or system to run the quantum classifier in a time span of 1 minute or less
Digitally commanding an electronic device to take the first action and/or the second action
Digitally commanding the quantum device or system to run the quantum classifier to retrain itself with the updated training dataset
Independent Claim 20 discloses the following additional elements:
A non-transitory computer-readable medium encoded with instructions
At least one processor or hardware
A quantum classifier
Biological measurements are obtained by way of multiple sensors which have a granularity of 5 milliseconds or less
Commanding a quantum device or system to run the quantum classifier in a time span of 1 minute or less
Digitally commanding an electronic device to take the first action and/or the second action
Digitally commanding the quantum device or system to run the quantum classifier to retrain itself with the updated training dataset
In regards to commanding a quantum device or system to run the quantum classifier in a time span of 1 minute or less and digitally commanding the quantum device or system to run the quantum classifier to retrain itself with the updated training dataset, the Applicant’s specification on Page 3, lines 10-13 discloses the quantum device or system can be or comprise a universal quantum computer and/or a quantum annealer. Wherein the universal quantum computer or quantum annealer can be built with components (such as a superconducting circuit) that are known in the art. Further, the Applicant’s specification on Page 5, lines 29-32 discloses the quantum classifier comprises one of the following: a quantum support vector machine, a variational quantum classifier with data reuploading, a quantum boost algorithm based on variational quantum optimization, a quantum boost algorithm based on optimization, and a quantum neural network (as claimed in dependent claims 9 and 18). Thus, disclosing that the classifier is an algorithm or neural network. Therefore, the claim language discloses commanding a computer to run an algorithm or neural network in a time span of 1 minute or less wherein the computer processor is being used to apply the neural network or algorithm. The result of running that algorithm on the computer is that it classifies within a minute or less. The quantum computer is not being improved and instead comprises components well known in the art. Further, merely retraining the quantum classifier with updated training dataset is not improving the quantum classifier. The action of updating the training data to retrain the quantum classifier is not improving how the quantum classifier is trained on the specific data, it is merely training the quantum classifier on more data. If the quantum classifier is trained more and thus learns more, it is going to perform better than a quantum classifier that is trained less. This is not an improvement to the quantum classifier itself. The quantum classifier is not trained to analyze data that was it was previously unable to analyze, nor is it performing faster than expected as a quantum classifier, nor is it analyzing data in a new way. Further, the quantum classifier is composed of an algorithm or neural network that is not being improved and thus, the quantum computer implementing the quantum classifier is being used in a conventionally and as such amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception. See MPEP 2106.05(f), “As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965).”
Further, biological measurements obtained by way of multiple sensors which have a granularity of 5 milliseconds or less (of claims 1, 11, and 20), digitally [by means of digital or computer technology] introducing data of claim 1, the at least one processor of claim 11, the at least one memory of claim 11, the at least one processor or hardware of claim 20, and the non-transitory computer-readable medium encoded with instructions of claim 20 are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception.
Applicant’s specification states at page 8 - The system 1 at least comprises one or more computing devices 2, each comprising at least one processor 3 and at least one memory 4; for example, a personal computer, a laptop, a field-programmable gate array or FPGA, an application specific integrated circuit or ASIC, etc. Methods according to the present disclosure can be carried out by the system 1. To this end, the at least one memory 4 may include instructions, for example in the form of a computer program code, so that a method according to the present disclosure is carried out upon execution by the at least one processor 3.
Further, in regards to the multiple sensors which have a granularity of 5 milliseconds or less, the claims and specification do not recite anything to show how the sensors are modified in a way so that they now have the claimed granularity, or that the claimed particular granularity allows the sensors to obtain certain biological measurements that would otherwise not be obtainable or measurable. Instead, the claims recite that sensors of the claimed granularity already exist and are simply being used to obtain the biological measurements. Thus, the sensors are deemed to be used in an “apply it” manner; the involvement of these sensors is not a technical solution as the invention does not improve the sensors themselves.
In regards to, ”digitally commanding an electronic device to take the first action and/or the second action,” the Applicant’s specification on page 4 specifically states, “the first action to be taken comprises at least one of the following: moving the patient to an intensive care unit, administering one or more drugs to the patient, activating one or more devices for notification, and sending a notification to one or more electronic devices associated with medical staff,” and, “the second action to be taken comprises at least one of the following: logging in at least one memory unit that the at least one patient is not classified into the predetermined risk group together with the biometrics; and moving the patient out from an intensive care unit.” Thus, the Examiner notes that the action could be sending a notification (to one or more electronic devices associated with medical staff) and therefore could simply be applying an electronic device to transmit data as opposed to controlling a device to perform the action such as automatically administering drugs or controlling conveyor belts to move the wheels of a bed. Therefore, the amendment of digitally commanding an electronic device to take the first action and/or the second action… does not integrate the abstract idea into a practical application or provide significantly more.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, claim(s) 1, 11, and 20 are directed to an abstract idea(s) without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of biological measurements obtained by way of multiple sensors which have a granularity of 5 milliseconds or less (of claims 1, 11, and 20), digitally [by means of digital or computer technology] commanding a quantum device or system to run the quantum classifier in a time span of 1 minute or less (a quantum device or system and a quantum classifier), digitally [by means of digital or computer technology] introducing data of claim 1, the at least one processor of claim 11, the at least one memory of claim 11, the at least one processor or hardware of claim 20, and the non-transitory computer-readable medium encoded with instructions of claim 20 amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more’). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more").
Accordingly, even in combination, this additional element does not provide significantly more. As such the independent claims 1, 11, and 20 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent claim(s) 3-10 and 13-19 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Dependent claim 3, 5, 9, 13, 18 discloses the additional element of machine learning (claim 3 and 13), one or more devices for notification or one or more electronic devices associated with medical staff (claim 5), and one of the following: a quantum support vector machine, a variational quantum classifier with data reuploading, a quantum boost algorithm based on variational quantum optimization, and a quantum boost algorithm based on optimization (claim 9 and 18). Dependent claims 4, 6-8, 10, 14-17 and 19 do not further disclose any additional elements.
As presented above, the Applicant’s specification on Page 3, lines 10-13 discloses the quantum device or system can be or comprise a universal quantum computer and/or a quantum annealer. Wherein the universal quantum computer or quantum annealer can be built with components (such as a superconducting circuit) that are known in the art. Further, the Applicant’s specification on Page 5, lines 29-32 discloses the quantum classifier comprises one of the following: a quantum support vector machine, a variational quantum classifier with data reuploading, a quantum boost algorithm based on variational quantum optimization, a quantum boost algorithm based on optimization, and a quantum neural network (as claimed in dependent claims 9 and 18). Thus, disclosing that the classifier is an algorithm or neural network. Therefore, the claim language discloses digitally commanding a computer to run an algorithm or neural network with a minute or less wherein the computer processor is being used to apply the neural network or algorithm. The quantum computer is not being improved and instead comprises components well known in the art. Further, the quantum classifier is composed of an algorithm or neural network that is not being improved and thus, the quantum computer implementing the quantum classifier is being used in a conventionally and as such amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception.
Further, the machine learning of claim 3 and 13 and the one or more devices for notification or one or more electronic devices associated with medical staff of claims 5 and 20 are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception. See MPEP 2106.05(f), “As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965).”
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the machine learning of claim 3 and 13, and one or more devices for notification or one or more electronic devices associated with medical staff of claim 5, one of the following: a quantum support vector machine, a variational quantum classifier with data reuploading, a quantum boost algorithm based on variational quantum optimization, and a quantum boost algorithm based on optimization of claim 9 and 18 amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more’). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more").
Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1, 3-11, and 13-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free of Prior Art
Relevant prior art is as follows:
Maheshwari (Machine learning applied to diabetes dataset using Quantum versus Classical computation) Abstract discloses the proposed system tackles a binary classification problem of patients with diabetes into two different classes: diabetes patients with acute diseases and diabetes patients without acute diseases [patients having a predetermined medical condition (diabetes) … classified into a predetermined risk group (having acute disease or not)]. Section IV (B) Results discloses in this study, CPU-based algorithms (Decision Trees, Random Forest, XGB and Adaboost, Voting Model 1, and Voting Model 2) and QPU-based classifiers (Qboost, QBoostPlus, New Model 1 and New Model 2) are used to solve a binary classification problem… The Quantum algorithms perform better than the classical algorithms in terms of computation time of training and testing time of classical models, like Voting Model 1, Voting Model 2 are 19s, and 3.632s respectively, whereas the QPU running time of Qboost, Qboost plus, New Model 1 and New Model 2 classifiers 0.866s, 0.346s, 0.347s, and 0.347s respectively [all less than a minute], but the quantum system is 55 times faster than the classical system on diabetes data, as shown in table 11 and fig 2 which compares the results of all the classifiers.)
Majumder (Wearable Sensors for Remote Health Monitoring) Table 7 discloses a plurality of different sensors for measuring a plurality of parameters (Temperature, pulse, SpO2, heart rate (HR), ECG, systolic blood pressure (SBP), and respiration rate (RR)). Wherein the plurality of different sensors sample at a rate of 8 kHz, 240 Hz, and 200 Hz [wherein the sampling rate = 1 / time, thus a granularity of 5 ms is equivalent to a sampling rate of 200 Hz. 240 Hz is equivalent to 4 ms, 8 kHz is equivalent to 0.125 ms, thus Majumder discloses multiple sensors that have a granularity of 5 milliseconds or less].)
Ansari (US PG Pub 2021/0304855 A1) Para 57-59 discloses other adverse events may be indicated in the training data (e.g., transfer to the ICU [historical data related to the medical condition of the historical patients]…). Para 91 discloses the coding architecture 100 may integrate heterogeneous data to predict the onset of sepsis in advance of a patient developing clinical sepsis. The training data may be merged with EHR data, including laboratory results and vital signs [biometrics of historical patients], to create a predictive model of sepsis. In some embodiments, training data may be discovered by querying a cohort of patients to identify a number of patients whose historical EHR include ECG data up to 14 days after the start of the hospital encounter, wherein the patients developed sepsis, with concomitant ECG data (e.g., up to 24 hours before the first time sepsis was clinically identified). The data set may be partitioned into training, validation, and test sub datasets. See further: Paras 97-98.)
Rosinko (US PG Pub 2023/0166035 A1) Para 200 discloses alert threshold condition may be associated with the health condition of the subject. For example, alert threshold condition may include subject's glucose level (e.g., blood glucose level) is above or below a set value (hyperglycemia or hypoglycemia). Para 204 discloses the display systems that may receive alerts 1611 from the AMD may include: a medical practitioner 1614 (e.g., such as a doctor, nurse, . . . ), a guardian of the subject 1616 (e.g., subject's parents), an emergency service provider 1618, an authorized user 1620 (e.g., a user authorized by the subject such as spouse, relative, friend, and the like), a healthcare provider 1622, or a device of the subject 1612 (e.g., cell phone, personal computer, tablet and the like). Para 256 discloses once the therapy is suspended the user may be at the risk of entering a hyperglycemic state (high glucose that may result in complications such as diabetic ketoacidosis or neurovascular complications), if the user forgets to reactivate the drug delivery after exercise. Further, the subject's glucose level may rise above or below a dangerous level during the period of exercise. In these situations, the automatic medicament delivery resumption may improve the health of the subject [administering one or more drugs to the patient]. See Further: Para 278.)
Patel (US PG Pub 2023/0329630 A1) Para 167 discloses an initial respiratory-condition score (or a first set of respiratory-condition scores) may be determined from user voice samples collected as described herein. After some time interval, such as a week, a second respiratory-condition score may indicate a change in the user's respiratory condition. A change indicating the user's condition is improving (which may be determined as described below) may imply that the antibiotic is working. A change indicating that the user's condition is not improving or is staying the same may [different classification – second time the patient is sick. Predetermined number of classifications is one] imply that the antibiotic is not working, in which case the user's clinician may want to prescribe a different treatment.
While Gambetta (US PG Pub 2020/0320437 A1) Para 91-92 and Fig. 5 discloses classical processor 502 runs a classical optimization scheme to generate update parameters for a kernel alignment algorithm and sends the update parameters to quantum processor 504. If classical processor 502 determines that the parameters for the quantum feature kernel alignment optimization problem are to be updated, classical processor 502 runs the classical optimization scheme using the updated parameters to generated further updated parameters. Classical processor 502 then sends the further updated parameters to quantum processor 504 it does not disclose “after the at least one patient has been classified by the quantum classifier, digitally introducing manually-introduced data related to the medical condition of the at least one patient into the training dataset, thereby providing an updated training dataset; and digitally commanding the quantum device or system to run the quantum classifier to retrain itself with the updated training dataset’ wherein the quantum classifier classifies “patients such that patients having a predetermined medical condition or being in risk of having the predetermined medical condition in a predetermined time span are classified into a predetermined risk group, the data comprising biological measurement of the at least one patient, wherein the biological measurements are obtained by way of multiple sensors which have a granularity of 5 milliseconds or less.”
Response to Arguments
Applicant’s arguments filed 3/25/2026 with respect to 35 U.S.C. § 101 have been fully considered, but are not persuasive.
The Applicant argues that the claims are not directed to certain methods of organizing human activity. The Examiner respectfully disagrees. 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 a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to [summarize the invention]. The Examiner notes that Applicant’s Background describes thoroughly monitoring the condition of the patient and inferring the possible health problems the patient has or might have in a short time period (see Spec. Background Para 1) as a human task, and just states that the doctors and nurses do not have the time to do so. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to classify patients into a predetermined risk group based on their biological measurements, the claimed invention is directed to an abstract idea.
The Applicant disagrees that the quantum classifier, the quantum device or system for running the quantum classifier, the biological measurements obtained by way of multiple sensors which have granularity of 5 ms or less are deemed to be used in an “apply it” manner. The Examiner respectfully disagrees. Applicant does not provide any explanation for why they do not believe it is used in an “apply it” manner. As previously presented, In regards to commanding a quantum device or system to run the quantum classifier in a time span of 1 minute or less, the Applicant’s specification on Page 3, lines 10-13 discloses the quantum device or system can be or comprise a universal quantum computer and/or a quantum annealer. Wherein the universal quantum computer or quantum annealer can be built with components (such as a superconducting circuit) that are known in the art. Further, the Applicant’s specification on Page 5, lines 29-32 discloses the quantum classifier comprises one of the following: a quantum support vector machine, a variational quantum classifier with data reuploading, a quantum boost algorithm based on variational quantum optimization, a quantum boost algorithm based on optimization, and a quantum neural network (as claimed in dependent claims 9 and 18). Thus, disclosing that the classifier is an algorithm or neural network. Therefore, the claim language discloses commanding a computer to run an algorithm or neural network in a time span of 1 minute or less wherein the computer processor is being used to apply the neural network or algorithm. The result of running that algorithm on the computer is that it classifies within a minute or less. The quantum computer is not being improved and instead comprises components well known in the art. Further, the quantum classifier is composed of an algorithm or neural network that is not being improved and thus, the quantum computer implementing the quantum classifier is being used in a conventionally and as such amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception. See MPEP 2106.05(f), “As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965).”
Further, in regards to the multiple sensors which have a granularity of 5 milliseconds or less, the claims and specification do not recite anything to show how the sensors are modified in a way so that they now have the claimed granularity, or that the claimed particular granularity allows the sensors to obtain certain biological measurements that would otherwise not be obtainable or measurable. Instead, the claims recite that sensors of the claimed granularity already exist and are simply being used to obtain the biological measurements. Thus, the sensors are deemed to be used in an “apply it” manner; the involvement of these sensors is not a technical solution as the invention does not improve the sensors themselves.
The Applicant than argues that the claimed subject matter clearly relates to a solution that is both technical and specific. The Examiner does not disagree that the solution involves technical components. However, MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. As discussed further below, there is no technical problem and thus, this argument is not persuasive.
The Applicant then argues that the quantum classifier makes the classification “more reliable when the input for the classification relies on big data” and points to page 3, line 2 of the Applicant’s substitute specification. The Examiner notes that the specification is describing the reason for selecting a quantum classifier. It does not disclose how the quantum classifier is improved in any way, for example: it does not analyze the data in a new way, it does not analyze data that a quantum classifier was not previously able to analyze. The Applicant merely recites using the quantum classifier, they did not invent the quantum classifier and have not otherwise pointed to any way that the quantum classifier is improved over the prior art.
The Applicant then argues that digitally determining the first or second actions and digitally commanding an electronic device to take the first action and/or the second action have the specific technical effect of freeing medical staff from having to take said actions. Here, the Applicant’s argued problem is not a technological problem caused by the technological environment to which the claims are confined. The problem of medical staff having to take actions is not a problem caused by the device that is involved in the process. Automating activity (manual or otherwise) using computers, devices and the like is regarded as the computer and devices being utilized in an “apply it” manner. As such, this argument is not persuasive.
The Applicant then points to page 4, lines 28-32 discloses “freeing the medical staff from doing such tasks. For example, a drug dispenser may administer drugs automatically, a bed of the patient may have its wheels moved or be moved by way of e.g. conveyor belts, etc.” However, the claim does not specifically disclose automatically causing a drug dispenser to administer drugs or controlling conveyor belts to move the wheels of a bed. The specification on page 4 specifically states, “the first action to be taken comprises at least one of the following: moving the patient to an intensive care unit, administering one or more drugs to the patient, activating one or more devices for notification, and sending a notification to one or more electronic devices associated with medical staff,” and, “the second action to be taken comprises at least one of the following: logging in at least one memory unit that the at least one patient is not classified into the predetermined risk group together with the biometrics; and moving the patient out from an intensive care unit.” Thus, the Examiner notes that the action could be sending a notification (to one or more electronic devices associated with medical staff) and therefore could simply be applying an electronic device to transmit data as opposed to controlling a device to perform the action such as automatically administering drugs or controlling conveyor belts to move the wheels of a bed. Furthermore, the specification is silent as to how the patient would be moved to an ICU, as it only discusses the bed being moved by way of conveyer belts. Therefore, the amendment of digitally commanding an electronic device to take the first action and/or the second action… does not integrate the abstract idea into a practical application or provide significantly more.
The Applicant further argues that the amendment of providing an updated training data set and digitally commanding the quantum device or system to run the quantum classifier to retrain itself with the updated training dataset improves the quantum classifier. The Examiner respectfully disagrees. The action of updating the training data to retrain the quantum classifier is not improving how the quantum classifier is trained on the specific data, it is merely training the quantum classifier on more data. If the quantum classifier is trained more and thus learns more, it is going to perform better than a quantum classifier that is trained less. This is not an improvement to the quantum classifier itself. The quantum classifier is not trained to analyze data that was it was previously unable to analyze, nor is it performing faster than expected as a quantum classifier, nor is it analyzing data in a new way.
Finally, the Applicant argues that the technical features included in the claim address the specific technical problem of how to, on the basis of data about patient biometrics and medical conditions, make the classification on the patient possible in a short time span, and with a richer and more complex classifying algorithm, especially when the classification relies on big data, and simultaneously to free the medical staff from a first and/or a second action determine to be taken depending on the result of the classification and to improve and adapt the classification as new data comes in. The majority of these limitations have been discussed above (the need to free medical staff from implementing actions is not a technical problem), however, to clarify these do not provide a technical problem. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by the technological environment to which the claims are confined. The problem of there being too many patients that a medical staff must monitor and doctors and nurses not being able to devote the time needed for thorough monitoring (Applicant’s specification page 1, line 18) and not classifying patients fast enough (per the Applicant’s remarks) and the need to update training data as new data comes in was not a problem caused by the device that is involved in the process. At best, Applicant’s identified problem is a business problem. There is no technical problem present, thus utilizing a quantum classifier to achieve a classification is not a technical solution to a technical problem. Because no technological problem is present, the claims do not provide a practical application. As such, providing a technical solution to a business problem does not integrate the abstract idea into a practical application and this argument is not persuasive.
Applicant’s arguments filed 3/25/2026 with respect to 35 U.S.C. § 103 have been fully considered, but are not persuasive
The Applicant argues that it would not have been obvious to combine Maheshwari and Ansari. Specifically because Ansari does not disclose diabetes as Maheshwari does and because Ansari’s waveforms are a different type of data compared to the data mentioned in Maheshwari. The Examiner respectfully disagrees. Ansari discloses analyzing patient data to “facilitate prediction, diagnosis and/or monitoring of acute and chronic conditions and overall health.” Whereas, Maheshwari utilizes QPU-based classifiers to “solve a binary classification problem” wherein the binary classification problem determines patients with diabetes that either have acute disease or do not have acute disease to determine the performance of the quantum algorithms compared to classical algorithms. Thus, both references facilitate prediction, diagnosis, and/or monitoring of acute conditions and overall health and thus are analogous art. Therefore, this argument is not persuasive. As previously presented, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the combination of the quantum computation as taught by Maheshwari and the wearable sensors as taught by Majumder with the coding architectures for automatic analysis of waveforms as taught by Ansari in order to incorporate EHR to provide better model accuracy and allow experts and non-experts to make better diagnoses (Ansari Para 129) where EHR data includes patient vitals, patient labs, etc. (Ansari para 134).
The Applicant further argues that Gambetta does not disclose limitation (ii). This argument is persuasive, and after further search and consideration, the previous 35 U.S.C 103 rejection has been withdraw in light of the amendments to the independent claims of the claimed invention.
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.
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/SARA JESSICA MORICE DE VARGAS/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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