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 .
Election/Restrictions
In response to Election/Restrictions of 07/02/2026, applicant elected group II claims 13-26 without traverse. Claims 1-6 are non-elected, and 7-12 are cancelled.
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 13-26 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Following is the analysis under 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) published on January 7,
Step 1: Claims 13-26 are directed to a device (apparatus) or method (process), and thus meets the requirements for step 1 since the claims are directed toward one of the four statutory categories of invention.
Step 2A, prong one: Independent claim 13 recites the following limitations that when given their broadest reasonable interpretation, fall within the mental processes grouping of abstract ideas for the reasons set forth below:
“determine prognosis of a cardiac arrest patient” (a human can practically perform this limitation in the mind or with the aid of pen and paper by having a data set or information regarding cardiology, medicine or patient conditions regarding cardiac arrest)
Step 2A, prong two: Claim 13 recites the following additional elements that when considered individually and as a whole do not integrate the judicial exception into a practical application for the reasons set forth below:
brain magnetic resonance imaging (MRI) analysis device using a plurality of neural network models, comprising: a memory for storing a plurality of neural network models; and a processor, which is connected to the memory so as to control the analysis device, wherein the processor is configured to receives input data comprising main data and/or auxiliary data, and performs one or more tasks for the input data (the brain magnetic resonance imaging (MRI) analysis device, processor and memory are recited at a high level of generality and is only involved in the insignificant extra-solution activity of data gathering)
using a pre-trained model comprising a first neural network model, a second neural network model, and a third neural network model, wherein the first neural network model is trained to generate a feature vector for the main data, wherein the second neural network model is trained to perform one or more tasks for the main data, and wherein the third neural network model is trained to perform one or more tasks for the main data and/or auxiliary data. (The recited device is recited at a high level of generality, i.e., as a generic computer performing generic computer functions. merely adding insignificant pre-solution activity to the abstract idea- see MPEP 2106.05(g). In limitations, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).);
Step 2B: Claim 13 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as a whole, they do not add an inventive concept for the same reasons set forth above in step 2A, prong two. Additionally, when reconsidering the limitations that were considered insignificant extra-solution activity, the following evidence shows the limitations are well-understood, routine and conventional functions:
using a pre-trained model… trained to generate a feature vector … trained to perform one or more tasks (These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. These amounts to no more than mere instructions to apply the exception using a generic computer component).
Claims 14-26 have limitations similar to claim 1 and are rejected for same reasons as above. Claims 14-17, 19,21-19, 21-24, 26 only recite the type of data, or training being applied to the different neural networks. These are rejected because they include limitations that further limit the extra solution activity.
Claims 18,25 recite training with respect to particular tasks. However, there is no additional limitations. These claims 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; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process. These limitations only recite the outcome of using the neural networks and do not include any details about how the “tasks” are performed. See MPEP 2106.05(f).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 13-26 rejected under 35 U.S.C. 103 as being unpatentable over Pugin [Resting-State Brain Activity for Early Prediction Outcome in Postanoxic Patients in a Coma with Indeterminate Clinical Prognosis, AJNR Am J Neuroradiol 2020, 41 (6) 1022-1030] in view of Chung [Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks, Journal of the Neurological Sciences 425 (2021)].
As per claim 13, Pugin teaches a brain magnetic resonance imaging (MRI) analysis device using a plurality of neural network models (Pugin Fig 2), comprising:
a memory for storing a plurality of neural network models; and a processor, which is connected to the memory so as to control the analysis device (Pugin page 1023 protocol, MR acquisition and prediction as discussed requires memory and processor),
wherein the processor is configured to receives input data comprising main data and/or auxiliary data (Pugin Fig 2 A.1, A.2 MRI data received), and
performs one or more tasks for the input data to determine prognosis of a cardiac arrest patient (Pugin page 1022 conclusions “Resting-state fMRI might bridge the gap left in early prognostication” 1028 “The essential point of the current study was to perform fMRI early after cardiac arrest and to focus on those cases”)
by using a pre-trained model (Pugin page 1025-1026,” We then used rs-fMRI to train a machine learning…” In machine learning training is performed prior to prediction) comprising a first neural network model, a second neural network model, Pugin Fig 2 items B, C2 first and second model as claimed below),
wherein the first neural network model is trained to generate a feature vector for the main data (Pugin Fig 2 items B, page 1026 “We used all the features derived from connectivity matrices of all patients …to predict” hence feature vectors are generated as connectivity matrices CM), wherein the second neural network model is trained to perform one or more tasks for the main data (Pugin Fig 2 items C2, outcome prediction based on CM), and
Pugin does not expressly teach a third neural network model is trained to perform one or more tasks for the main data and/or auxiliary data.
Chung, in a related field of technology of predictions for in-hospital cardiac arrest, teaches a third neural network model is trained to perform one or more tasks for the main data and/or auxiliary data (Chung Fig 1 ANN model 1 for main data or ANN model 2 for auxiliary data or ANN model 3 for all data. Training discussed throughout the article, required for ANNs).
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the system in Pugin by utilizing additional learning networks as in Chung. The motivation would be to provide significant clinical value in assisting with decision-making regarding in-hospital cardiac arrest patients.
As per claims 14-15, Pugin in view of Chung further teaches wherein when the input data comprises main data, the second neural network model is trained to perform one or more tasks for the input data, and when the input data comprises auxiliary data, the third neural network model is trained to perform one or more tasks for the input data, wherein the processor is configured to: when the input data comprises only the main data, performs one or more tasks for the input data by using the first neural network model and the second neural network model, and when the input data comprises the auxiliary data, performs one or more tasks for the input data by using the first neural network model and the third neural network model (Pugin Fig 2, Chung Fig 1 as discussed above teaches the structure (neural networks) for performing the functions recited, should the conditions occur. See MPEP 2111.04).
As per claim 16, Pugin in view of Chung further teaches wherein the main data is data generated from one or more brain MRI images of a cardiac arrest patient or a non-cardiac arrest patient (Pugin page 1028 “perform fMRI early after cardiac arrest and to focus on those cases with uncertain outcome”).
As per claim 17, Pugin in view of Chung further teaches wherein the auxiliary data is data generated from data comprising at least one of age information, gender information, blood test result information, cardiac arrest duration information, cardiac arrest cause information, body temperature information, consciousness information, and MRI equipment characteristic information of each of the cardiac arrest patient and the non-cardiac arrest patient (Pugin Table 1, page 1024 “We screened 351 patients comatose following CA, 17 of whom underwent the whole protocol and were included in the final analysis (…Of these 17, nine regained consciousness (mean, 4 67.8 days after the MR imaging session; good outcome group)”).
As per claim 18, Pugin in view of Chung further teaches wherein the second neural network model and the third neural network model are trained to analyze, as the one or more tasks, the input data with respect to at least one of a probability of death within a predetermined period, a probability of recovery of neurological function at a specific level or higher within the predetermined period, a lesion presence/absence probability, presence/absence 23 of a specific lesion at a specific location, a probability of presence/absence of a specific disease, and an auxiliary task (Pugin abstract, “prognostication is accurate for predicting poor outcome (i.e., death), … We specifically assessed whether resting-state fMRI provides prognostic information”, page 1023 LHS “predict coma outcome (ie, consciousness recovery versus remaining comatose; namely good-versus-poor outcome) using rs-fMRI and machine learning methods”).
As per claim 19, Pugin in view of Chung further teaches wherein the second neural network model and the third neural network model are trained to perform classification or regression analysis on the input data based on a type of a task performed on the input data (Pugin page 1023 RHS “we trained a machine learning classifier to identify patients with good and poor outcomes based on brain network”, Chung page 6 RHS “AUC values indicate that the models can distinguish the classes of interest, namely the favorable versus unfavorable neurological outcomes of patients with IHCA”).
As per claims 20-26, they are directed to method of claims 13-19 and are rejected for same reasons as above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OOMMEN JACOB whose telephone number is (571)270-5166. The examiner can normally be reached 8:00-4:00.
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/Oommen Jacob/Primary Examiner, Art Unit 3797
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