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 application filed on 03/06/2024.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 03/06/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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 1-20 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: receiving medical data of a patient; determining whether the patient has a medical condition using the medical data and a diagnostic model including artificial intelligence (AI) models; and transmitting or displaying information identifying the determination of whether the patient has the medical condition, wherein the diagnostic model including the AI models is trained by: receiving training data including a majority class of samples corresponding to medical data of patients that do not have the medical condition and a minority class of samples corresponding to medical data of patients that do have the medical condition; determining sub-groups of the majority class of samples based on features of the majority class of samples; generating sub-group training datasets that each include respective samples of the sub-groups of the majority class of samples and samples of the minority class of samples; and training the AI models of the diagnostic model using the sub-group training datasets.
Independent Claim 8 discloses a device comprising: a memory configured to store instructions; and one or more processors configured to execute the instructions to perform operations comprising: receiving medical data of a patient; determining whether the patient has a medical condition using the medical data and a diagnostic model including artificial intelligence (AI) models; and transmitting or displaying information identifying the determination of whether the patient has the medical condition, wherein the diagnostic model including the AI models is trained by: receiving training data including a majority class of samples corresponding to medical data of patients that do not have the medical condition and a minority class of samples corresponding to medical data of patients that do have the medical condition; determining sub-groups of the majority class of samples based on features of the majority class of samples; generating sub-group training datasets that each include respective samples of the sub-groups of the majority class of samples and samples of the minority class of samples; and training the AI models of the diagnostic model using the sub-group training datasets.
Independent Claim 15 discloses a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving medical data of a patient; determining whether the patient has a medical condition using the medical data and a diagnostic model including artificial intelligence (AI) models; and transmitting or displaying information identifying the determination of whether the patient has the medical condition, wherein the diagnostic model including the AI models is trained by: receiving training data including a majority class of samples corresponding to medical data of patients that do not have the medical condition and a minority class of samples corresponding to medical data of patients that do have the medical condition; determining sub-groups of the majority class of samples based on features of the majority class of samples; generating sub-group training datasets that each include respective samples of the sub-groups of the majority class of samples and samples of the minority class of samples; and training the AI models of the diagnostic model using the sub-group training datasets.
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The remaining un-bolded limitations, given the broadest reasonable interpretation, cover the abstract idea of a mental process because they recite a process that could be practically performed in the human mind (i.e. observations, evaluations, judgements, and / or opinions). In this case, the steps of receiving medical data of a patient, determining whether the patient has a medical condition using the medical data and a diagnostic model and transmitting or displaying information identifying the determination of whether the patient has the medical condition, and receiving majority class of samples corresponding to medical data of patients that do not have the medical condition and a minority class of samples corresponding to medical data of patients that do have the medical condition; determining sub-groups of the majority class of samples based on features of the majority class of samples; generating sub-group training datasets that each include respective samples of the sub-groups of the majority class of samples and samples of the minority class of samples is reasonably interpreted as at least evaluations that could be performed by a human mentally or using a pen and paper.
Further, the remaining un-bolded limitations, are also merely directed to rules or instructions to determine whether a patient has a medical condition. 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.
The abstract ideas are being considered together as a single abstract idea for further analysis. (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:
artificial intelligence (AI) models
Independent Claim 8 discloses the following additional elements:
a device comprising: a memory configured to store instructions; and one or more processors configured to execute the instructions to perform operations
artificial intelligence (AI) models
Independent Claim 15 discloses the following additional elements:
a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations
artificial intelligence (AI) models
In particular, the artificial intelligence (AI) models (of claims 1, 8, and 15), device comprising: a memory configured to store instructions; and one or more processors configured to execute the instructions to perform operations (of claim 8), and non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations (of claim 15) 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 para 31 states - The user device 180 may be configured to display information received from the diagnostic platform 120. For example, the user device 180 may be a smartphone, a laptop computer, a desktop computer, a wearable device, a medical device, a radiology device, or the like.
Wherein Fig. 1 of the instant application discloses the diagnostic platform implements the diagnostic model which comprises of AI models. Thus, the user device is applying the AI model to display information received from the diagnostic platform to implement the abstract idea. The user device is behaving as expected and is not improved in any way.
Further, the Applicant’s specification para 26 discloses, “the medical device 110 may be an electrocardiogram (ECG) device, an electroencephalogram (EEG) device, an ultrasound device, a magnetic resonance imaging (MRI) device, an X-ray device, a computed tomography (CT) device, or the like.” Therefore, the medical device is collected data as is expected and is not being improved. The data is not collected in a more efficient way nor is the data collected that which could not be collected before.
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, 8, and 15 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 the artificial intelligence (AI) models (of claims 1, 8, and 15), device comprising: a memory configured to store instructions; and one or more processors configured to execute the instructions to perform operations (of claim 8), and non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations (of claim 15) 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, 8, and 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent claim(s) 2-7, 9-14, and 16-20 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 claims 4, 11, and 18 do further disclose the additional element(s) of a deep learning ensemble model which is further narrowing the additional element of the AI models as described above.
As such, the deep learning ensemble model (of claims 4, 11, and 18) further narrows the AI models of claims 1, 8, and 15 and further is 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. 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, even in combination, these additional elements do not integrate the abstract idea 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 element of the deep learning ensemble model 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, this additional element does not provide significantly more.
Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claim(s) 1-3, 6-10, 13-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kashiwagi (US PG Pub 2023/0284983 A1) in view of Mufti (Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study).
Regarding Claim 1, Kashiwagi discloses:
A method comprising:
receiving medical data of a patient; determining whether the patient has a medical condition using the medical data and a diagnostic model including artificial intelligence (AI) models; and (Para 55 discloses a therapy selection support program that generate a discriminator (identifier) as a diagnostic marker or a classifier as a stratification marker through machine learning on the basis of measurement data on brain activities [received medical data] and that provide information related to selection of a therapy for a subject with depression symptoms on the basis of the results of measurement of brain activities of the subject using the discriminator (identifier) or the classifier as a biomarker.)
transmitting or displaying information identifying the determination of whether the patient has the medical condition, wherein (Para 59 discloses the support information providing device includes a clustering processor and an interface device; the clustering processor calculates a probability at which the first subject is classified as belonging to each of the clusters by the clustering classifier, and reads at least two pieces of the therapy information selected in accordance with the probability from the database device; and the interface device outputs data for displaying the selected clusters and the corresponding pieces of the therapy information in association with each other.)
determining sub-groups of the majority class of samples based on features of the majority class of samples; generating sub-group training datasets that each include respective samples of the sub-groups of the majority class of samples and samples of the minority class of samples; and training the AI models of the diagnostic model using the sub-group training datasets (Para 58 discloses the processor is configured to, in the machine learning to generate the identifier model, generate a plurality of training sub-samples by executing under-sampling and sub-sampling from the first cohort and the second cohort, select features for clustering from a sum-set of features that are used to generate the identifier through the machine learning in accordance with a degree of importance of features that belong to the sum-set for each of the training sub-samples, and generate the clustering classifier by the multiple co-clustering method on the basis of the selected features for the clustering. Para 76 discloses the plurality of second subjects include a first cohort having a diagnosis label of a depression and a second cohort not having the diagnosis label of the depression. The clusters obtained as a result of the stratification are obtained by a clustering classifier obtained through a clustering process for results of measurement of brain functional connectivity correlation values. The clustering classifier is generated through a processing step of executing the clustering process for the plurality of second subjects.)
While Kashiwagi discloses the above limitations, and “under-sampling is done for building the classifier since the numbers of the MDD patients and the healthy persons HC are imbalanced in the training dataset. In addition, a prescribed number, for example, 130, of the MDD patients and the same number of healthy persons are sampled at random from the training dataset as the sub-sampling process,” (Para 368), it does not fully disclose the following limitations that Mufti discloses:
the diagnostic model including the AI models is trained by: receiving training data including a majority class of samples corresponding to medical data of patients that do not have the medical condition and a minority class of samples corresponding to medical data of patients that do have the medical condition; (The issue of outcome class imbalance section discloses in our dataset, the outcome class distribution is notably imbalanced (only 11.4% of patients developed delirium [minority class = patients that do have the medical condition, thus reads on majority class = patients that do not have the medical condition]). Typically, classification algorithms tend to predict the majority class very well but perform poorly on the minority class due to 3 main reasons [47-49]: (1) the goal of minimizing the overall error (maximize accuracy), to which the minority class contributes very little; (2) algorithm’s assumption that classes are balanced; and (3) the assumption that impact of making an error is equal. See further: Prediction model’s performance evaluation section)
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 therapy selection support device (brain functional connectivity correlation value clustering device, method, system) as taught by Kashiwagi with the majority and minority classes as taught by Mufti in order to generate a new balanced dataset by decreasing the number of the majority class instances, in order to reduce the difference between the minority and the majority classes and make the training more efficient (Mufti: The Issue of Outcome Class Imbalance section, para 3).
Regarding Claim 2, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Kashiwagi and Mufti discloses the following limitation that Kashiwagi further discloses:
The method of claim 1, wherein the features are determined using clinical metadata associated with the majority class of samples. (Para 74 discloses The processing step includes i) a step of acquiring features based on a plurality of brain functional connectivity correlation values [broadest reasonable interpretation of clinical metadata] that represent time correlation of brain activities among a plurality of predetermined brain area pairs for each of the plurality of second subjects, ii) a step of executing machine learning, through supervised learning, to generate an identifier model for discriminating presence or absence of the diagnosis label on the basis of the acquired features, iii) a step of selecting features for clustering.)
Regarding Claim 3, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Kashiwagi and Mufti discloses the following limitation that Kashiwagi further discloses:
The method of claim 1, wherein the features are determined by extracting the features from the training data using a feature extraction technique. (Para 74 discloses the processing step includes i) a step of acquiring features based on a plurality of brain functional connectivity correlation values [extracting the features] that represent time correlation of brain activities among a plurality of predetermined brain area pairs for each of the plurality of second subjects, ii) a step of executing machine learning, through supervised learning, to generate an identifier model for discriminating presence or absence of the diagnosis label on the basis of the acquired features, iii) a step of selecting features for clustering.)
Regarding Claim 6, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Kashiwagi and Mufti discloses the following limitation that Kashiwagi further discloses:
The method of claim 1, wherein each sub-group training dataset is based on a different type of feature. (Paras 472-487 disclose as a result of learning of an identifier for identifying label M and label H through learning with feature selection on each sub-sample, different features would be selected by different identifiers as represented by black dots, from each group having high correlation, in the area surrounded by the chain-dotted line representing the sum-set mentioned above, for each sub-sample [thus reading on each sub-group training dataset being based on a different type of feature].)
Regarding Claim 7, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Kashiwagi and Mufti discloses the following limitation that Mufti further discloses:
The method of claim 1, wherein a ratio between a number of samples of the majority class of samples and a number of samples of the minority class of samples for each of the sub-group training datasets is less than a ratio between a number of samples of the majority class of samples and a number of samples of the minority class of samples for the training data. (The issue of outcome class imbalance section discloses we used the SpreadSubSample filter in WEKA [46] to produce a random subsample by undersampling the majority class (which can be done by either specifying a ratio or the number of observations). In our case, we specified a ratio of 1:1. [a ratio of the training dataset is less than that of the overall training data] By doing so, the filter generates a new balanced dataset by decreasing the number of the majority class instances, which reduces the difference between the minority and the majority classes. Undersampling is considered an effective method for dealing with class imbalance [50]. In this approach, a subset of the majority class is used to learn the model. Many of the majority class examples are ignored; the training set becomes more balanced, which makes the training more efficient.)
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 therapy selection support device (brain functional connectivity correlation value clustering device, method, system) as taught by Kashiwagi with the majority and minority classes as taught by Mufti in order to generate a new balanced dataset by decreasing the number of the majority class instances, in order to reduce the difference between the minority and the majority classes and make the training more efficient (Mufti: The Issue of Outcome Class Imbalance section, para 3).
As to claims 8-10 and 13-14, the claims are directed to the device implementing the method of claims 1-3 and 6-7 and further recite a memory configured to store instructions and one or more processors configured to execute the instructions to perform operations (e.g., see Kashiwagi Para 283 teaching a computer main body including: a processor, a RAM for temporarily storing instructions of an application program…a non-volatile storage device for storing application programs, system programs, and data and para 284 teaching various functions… are realized by operation processes performed by CPU in accordance with a program) and are similarly rejected.
As to claims 15-17 and 19-20, the claims are directed to the non-transitory computer-readable medium implementing the method of claims 1-3 and 6-7 and further recite instructions that, when executed by one or more processors, cause the one or more processors to perform operations (e.g., see Kashiwagi Para 283 teaching a computer main body including: a processor, a RAM for temporarily storing instructions of an application program…a non-volatile storage device for storing application programs, system programs, and data and para 284 teaching various functions… are realized by operation processes performed by CPU in accordance with a program) and are similarly rejected. Examiner notes that claim 19 specifically discloses the limitations of claims 5 or claim 6, as such, the claim is rejected with the citations from Kashiwagi as disclosed above under claim 6.
Claim(s) 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kashiwagi (US PG Pub 20230284983 A1) in view of Mufti (Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study), further in view of Nguyen (Deep Ensemble Learning Approaches in Healthcare to Enhance the Prediction and Diagnosing Performance: The Workflows, Deployments, and Surveys on the Statistical, Image-Based, and Sequential Datasets).
Regarding Claim 4, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. While Kashiwagi para 62 discloses an ensemble model, “a process of generating the identifier through the machine learning involves ensemble learning in which a plurality of identifier sub-models are generated for the plurality of training sub-samples and the plurality of identifier sub-models are integrated with each other to generate the identifier model,” the combination of Kashiwagi and Mufti does not fully disclose the following limitation that Nguyen discloses:
The method of claim 1, wherein the AI models are deep learning ensemble models. (Section 1: Introduction, para 3 discloses in the healthcare support system, reaching the optimal performance is always the top priority for prediction and classification, where the decision of the healthcare workers needs to be accurate and tailored to each patient. Under these circumstances, ensemble techniques are one of the best choices [ 3]. Essentially, the ensemble learning technique combines many similar or different weak prediction models into a robust model. In other words, the technique is able to reduce variance and prevent overfitting phenomena in the training process [ 4,5]. As a result, it improves the accuracy and stability of the prediction model in classification and regression tasks. By incorporating DL models with ensemble learning techniques in this study, we propose three approaches collectively known as deep ensemble learning: deep-stacked generalization ensemble learning, gradient deep learning boosting, and Deep aggregation learning. In other words, by replacing the core learning units of the corresponding ensemble technique with suitable DL models, our proposed method can perform well with higher efficiency on all three data types… Section 2: Literature Review section, para 6 discloses the ensemble learning technique gains much reliability owing to its performance in combining multiple predictive and classification models into one strong model. Ensemble learning with the core learning unit as DL is an innovative and prospective method. Several studies implemented this combination and showed its high performance and reliability as a result. Suk et al. presented a deep ensemble sparse regression network model to diagnose brain diseases [ 20]… A study by An et al. proposed the concept of deep ensemble learning, named deep belief network, to classify Alzheimer’s disease [ 21]. See further Section 3.3 proposed deep ensemble learning approaches section.)
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 combination of the ensemble model of the therapy selection support device (brain functional connectivity correlation value clustering device, method, system) as taught by Kashiwagi and the majority and minority classes as taught by Mufti with the deep ensemble learning approaches in healthcare to enhance the prediction and diagnosing performance as taught by Nguyen in order to reduce variance and prevent overfitting phenomena in the training process (Section 1: Introduction, para 3) and implement a reliable prediction model with high performance (Section 2: Literature Review, para 6).
As to claim 11, this claim is directed to the device implementing the method of claim 4 and further recites a memory configured to store instructions and one or more processors configured to execute the instructions to perform operations (e.g., see Kashiwagi Para 283 teaching a computer main body including: a processor, a RAM for temporarily storing instructions of an application program…a non-volatile storage device for storing application programs, system programs, and data and para 284 teaching various functions… are realized by operation processes performed by CPU in accordance with a program) and are similarly rejected.
As to claim 18, this claim is directed to the non-transitory computer-readable medium implementing the method of claim 4 and further recites instructions that, when executed by one or more processors, cause the one or more processors to perform operations (e.g., see Kashiwagi Para 283 teaching a computer main body including: a processor, a RAM for temporarily storing instructions of an application program…a non-volatile storage device for storing application programs, system programs, and data and para 284 teaching various functions… are realized by operation processes performed by CPU in accordance with a program) and are similarly rejected. Examiner notes that claim 19 specifically discloses the limitations of claims 5 or claim 6, as such, the claim is rejected with the citations from Kashiwagi as disclosed above under claim 6.
Claim(s) 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kashiwagi (US PG Pub 20230284983 A1) in view of Mufti (Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study), further in view of Narayanan (US PG Pub 2021/0327585 A1).
Regarding Claim 5, this claim recites the limitations of Claim 1 and as to those limitations is rejected for the same basis and reasons as disclosed above. The combination of Kashiwagi and Mufti does not fully disclose the following limitation that Narayanan discloses:
The method of claim 1, wherein each sub-group training dataset is based on a same type of feature. (Paras 49-51 disclose any individual mini-corpora may utilize any subset of the identified minority class data sample images which may result in differing mini-corpora sizes as well as an unequal amount of majority to minority class data sample images… any individual mini-corpora may utilize any subset of the identified majority class data sample images which may result in differing mini-corpora sizes having overlapping majority class samples among the mini-corpora… It shall be recognized that in some embodiments, the method 200 or the like may function to implement any suitable combination of the above-described configuration parameters to create a class-balanced mini-corpora [thus reading on a sub-group training data set based on same (or different) features], and/or the like.)
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 combination of the therapy selection support device (brain functional connectivity correlation value clustering device, method, system) as taught by Kashiwagi and the majority and minority classes as taught by Mufti with the systems and methods for transfer-to-transfer learning -based training of a machine learning model for detecting medical conditions as taught by Narayanan in order to create a class-balanced mini-corpora with any suitable combination of the configuration parameters (Narayanan Para 51) and to detect new and/or recently encountered diseases (to get over the class imbalance problem caused by a lack in availability of sample data) (Narayanan Para 5).
As to claim 12, this claim is directed to the device implementing the method of claim 5 and further recites a memory configured to store instructions and one or more processors configured to execute the instructions to perform operations (e.g., see Kashiwagi Para 283 teaching a computer main body including: a processor, a RAM for temporarily storing instructions of an application program…a non-volatile storage device for storing application programs, system programs, and data and para 284 teaching various functions… are realized by operation processes performed by CPU in accordance with a program) and are similarly rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARA J MORICE DE VARGAS whose telephone number is (703)756-4608. The examiner can normally be reached M-F 8:30-5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H. Choi can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SARA JESSICA MORICE DE VARGAS/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681