DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/28/2025 has been entered.
Remarks
This action is in response to the Remarks filed 02/28/2025.
Claims 1-3, 5, 7-13, 15, and 17-20 are pending.
Response to Arguments
Applicant’s arguments, see pages 9-15, filed 02/28/2025, with respect to the rejection of claims 1-3, 5, 7-13, 15, and 17-20 under 35 U.S.C. 103 have been fully considered. Independent claims 1 and 11 have been amended to include “receiving condition score training data from a data store, wherein the condition score training data comprises the plurality of patient profiles as input correlated to a plurality of condition scores as output”, “training a condition score machine-learning model using the condition score training data”, generating a condition score “as a function of the trained condition score machine-learning model”, and “wherein the associated plurality of condition identifiers comprise clinical measurements associated with the condition score generated using the trained condition score machine-learning model”. Applicant argues that Attia or Attia in view of Groot, Attia’313, Karlsson, and Lou fails to disclose the amended limitations. Examiner agrees. Therefore, the rejection has been withdrawn.
Applicant's arguments (see Remarks, pages 1-8) filed 02/28/2025 have been fully considered but they are not persuasive.
Rejection of claims 1-3, 5, 7-13, 15, and 17-20 under 35 U.S.C. 101
Independent claims 1 and 11 have been amended to include “receiving condition score training data from a data store, wherein the condition score training data comprises the plurality of patient profiles as input correlated to a plurality of condition scores as output”, “training a condition score machine-learning model using the condition score training data”, generating a condition score “as a function of the trained condition score machine-learning model”, and “wherein the associated plurality of condition identifiers comprise clinical measurements associated with the condition score generated using the trained condition score machine-learning model”. Applicant argues that the claims do not recite a mental process, that any alleged abstract idea is incorporated into a practical application, and that the claims recite additional elements that provides a technical improvement in the field. Examiner respectfully disagrees.
First, the argument that the claims do not recite a mental process (see Remarks, pages 3-5) is not found to be persuasive. Applicant argues that the amended claims require the use of machine learning models, and therefore do not recite a mental process. However, this is not found to be persuasive. As claimed, the claims recite a method and system for determining a patient survival profile comprising receiving patient profile data, defining cohort labels, receiving condition score training data, training a condition score model, generating a condition score each patient profile, defining the cohort labels as a function of a generated condition score, assigning the cohort labels to the patient profiles, generating condition training data by correlating the patient profiles with condition identifiers, generating a condition evaluation model using iteratively updated training data and a machine learning algorithm, determining a patient survival profile using the model, and displaying the patient survival profile at a visual interface of a display. The limitation for determining a patient survival profile, under its broadest reasonable interpretation, is directed towards an abstract idea.
The abstract idea includes the following steps:
Receiving patient profile data;
Defining cohort labels;
Receiving condition score training data;
Training a condition score model;
Generating a condition score each patient profile;
Defining the cohort labels as a function of a generated condition score;
Assigning the cohort labels to the patient profiles;
Generating condition training data by correlating the patient profiles with condition identifiers by preconditioning the data;
Generating a condition evaluation model using the condition training data and a machine learning algorithm;
Training the condition evaluation model by iteratively retraining the condition evaluation model with feedback from previous iterations;
Determining a patient survival profile using the model; and
Displaying the patient survival profile at a visual interface of a display.
These are all steps that can be performed in the mind or using pen and paper or generic computer components. For example, in the context of this claim, a user could receive patient profile data, define cohort labels, receive condition score training data, train a condition score model, generate a condition score each patient profile, define the cohort labels as a function of a generated condition score, assign the cohort labels to the patient profiles, generate condition training data by correlating the patient profiles with condition identifiers by preconditioning the data, generate a condition evaluation model using condition training data and a machine learning algorithm, train the condition evaluation model by iteratively retraining the condition evaluation model with feedback from previous iterations, determine a patient survival profile using the model, and output the patient survival profile. Further, the machine learning as claimed is claimed broadly and does not require any specific type of machine learning or algorithm. Therefore, the claims recite a mental process.
Applicant then argues (see Remarks, pages 5-7) that the claims are integrated into a practical application and points to Examples 47 and 48 of the USPTO 101 examples. However, this is not found to be persuasive. As best understood, the crux of the invention is the data analysis performed to determine a patient survival profile. The claimed technical improvement appears to reside within the abstract idea. Regarding the comparison to claim 3 of Example 47 of the USPTO 101 guidance, claim 3 was found eligible because it recites actions that are executed to remediate or prevent network intrusions. In the instant claims, the patient survival profile is simply displayed. Regarding the comparison to claim 3 of Example 48 of the USPTO 101 examples, the facts of this case do not match the fact pattern of example 48.
The argument (see Remarks, pages 7-8) that the non-conventional and specific arrangement of steps provides a technical improvement in the field is not found to be persuasive. As explained above, the claimed technological improvement in the field is within the abstract idea itself.
Therefore, there is no further description, in the claims or the specification, of any particular technology for performing the steps recited in the claim other than generic computer components used in their ordinary capacity as tools to apply the abstract idea. Nor does the claimed invention use a particular, or special, machine. In other words, the claims “are not tied to any particular novel machine or apparatus” capable of rescuing them from the realm of an abstract idea. Further, these components are being used to perform the extra-solution activity of data gathering and analysis (i.e. an insignificant extra-solution activity, see MPEP 2106.05(g)).
Therefore, the claims do not recite any additional elements that: (1) improve the functioning of a computer or other technology, (2) are applied with any particular machine, (3) effect a transformation of a particular article to a different state, and (4) are applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment or field of use. Please See MPEP § 2106.05(a)(c), (e)-(h). Therefore, the rejection of the claims under 35 U.S.C. 101 is maintained.
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, 5, 7-13, 15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claims recite an apparatus and method for determining a patient survival profile. To determine whether a claim satisfies the criteria for subject matter eligibility, the claim is evaluated according to a stepwise process as described in MPEP 2106(III) and 2106.03-2106.04. The instant claims are evaluated according to such analysis.
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 is directed towards an apparatus and claim 11 is directed towards a method, and thus meet the requirements for step 1.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claims 1 and 11 recite an apparatus and method for determining a patient survival profile comprising receiving patient profile data, defining cohort labels, receiving condition score training data, training a condition score model, generating a condition score each patient profile, defining the cohort labels as a function of a generated condition score, assigning the cohort labels to the patient profiles, generating condition training data by correlating the patient profiles with condition identifiers by preconditioning the data, generating a condition evaluation model using condition training data and a machine learning algorithm, training the condition evaluation model by iteratively retraining the condition evaluation model with feedback from previous iterations, determining a patient survival profile using the model, and displaying the patient survival profile at a visual interface of a display. The limitation of an apparatus and method for determining a patient survival profile, as drafted in claims 1 and 11, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with pen and paper using generic computer components, but for the recitation of a generic memory, processor, and display. For example, in the context of this claim, a user could receive patient profile data, define cohort labels, receive condition score training data, train a condition score model, generate a condition score each patient profile, define the cohort labels as a function of a generated condition score, assign the cohort labels to the patient profiles, generate condition training data by correlating the patient profiles with condition identifiers by preconditioning the data, generate a condition evaluation model using condition training data and a machine learning algorithm, train the condition evaluation model by iteratively retraining the condition evaluation model with feedback from previous iterations, determine a patient survival profile using the model, and output the patient survival profile. Other than reciting a generic memory, processor, and display, nothing in the elements of the claims precludes the step from practically being performed in the mind.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Therefore, claims 1 and 11 recite an abstract idea of a mental process.
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
The additional elements of a memory, processor, and display device are recited at a high level of generality (i.e. as a generic memory to store data and a generic processor configured to process and analyze the data and a generic interactive display to output the data) such that they amount to no more than mere instructions to apply the exception using generic computer components. The limitation of a graphical user interface does not add integrate the judicial exception into a practical application, as graphical user interfaces are well known, routine, and conventional, and further is being used for the extra-solution activity of displaying the data.
The steps of receiving patient profile data and receiving condition score training data are considered to be a data gathering step (i.e. an insignificant extra-solution activity, see MPEP 2106.05(g)). The steps of defining the cohort labels as a function of a generated condition score, training a condition score model, assigning the cohort labels to the patient profiles, generating condition training data by correlating the patient profiles with condition identifiers by preconditioning the data, generating a condition evaluation model using condition training data and a machine learning algorithm, training the condition evaluation model by iteratively retraining the condition evaluation model with feedback from previous iterations, and determining a patient survival profile using the model are considered to be data analysis steps, and the step of displaying the patient survival profile at a visual interface of a display is considered to be a data output step (i.e. an insignificant extra-solution activity, see MPEP 2106.05(g)).
Further, the processor as claimed and described in the Specification appear to function in a generic manner (e.g. par. [0008] of the instant specification describes that the processor can include without limitation, “a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC)”, “Processor 104 may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Processor 104 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like.”; par. [0093]: “ Processor 704 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 704 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 704 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC).”).
Accordingly, the additional elements do not integrate the abstract idea into a practical application. See MPEP 2106.06(b) and (f) and MPEP 2106.04(a)(2)(III)(C).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The additional elements when considered individually and in combination are not enough to qualify as significantly more than the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a memory, processor, and display device amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the additional elements do not amount to more than generically linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Therefore, the claims are not patent eligible.
Claims 2, 3, 5, 7-10, 12, 13, 15, and 17-20 depend on claims 1 and 11 and recite the same abstract idea as claims 1 and 11 from which they depend. Further, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the mental process). For example, the additional limitations recited in claims 2, 5, 12, and 15 (i.e. further defining ECG data and patient profiles used) are simply expanding upon the types of data gathered. The additional limitations of claims 3 and 13 (i.e. extracting the condition score using regular expressions, wherein the patient profile data further includes electronic health record data) are simply further data analysis steps. The additional limitations of claims 7 and 17 (i.e. identifying sub-cohort labels based on certain criteria) are simply further data analysis steps. The additional limitations of claims 8 and 18 (i.e. explaining that the model is a time series convolutional neural network) are simply expanding upon the data analysis used. The additional limitations of claims 9, 10, 19, and 20 (i.e. generating a condition risk factor identifier and classifier) are simply further data analysis steps. The additional elements individually do not amount to significantly more than the judicial exception explained above (the abstract idea). Looking at the limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves any technology or includes a particular solution to a computer-based problem or a particular way to achieve a computer-based outcome. Rather, the collective functions of the claimed invention merely provides a conventional computer implementation, i.e. the computer (processor) is simply a tool to perform the claimed invention.
While there are no prior art rejections for claims 1-3, 5, 7-13, 15, and 17-20, they are not indicated as allowable due to the rejection under 35 U.S.C. 101, as explained above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHREYA P ANJARIA whose telephone number is (571)272-9083. The examiner can normally be reached M-F: 8:00-5:00 EST.
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/SHREYA ANJARIA/Examiner, Art Unit 3796
/Jennifer Pitrak McDonald/Supervisory Patent Examiner, Art Unit 3796