DETAILED ACTION
1. This office action is in response to the Application No. 17249177 filed on 07/08/2021. Claim 1 is presented for examination and are currently pending.
It appears the inventor(s) filed the current application pro se (i.e., without the benefit of representation by a registered patent practitioner). While inventors named as applicants in a patent application may prosecute the application pro se, lack of familiarity with patent examination practice and procedure may result in missed opportunities in obtaining optimal protection for the invention disclosed. The inventor(s) may wish to secure the services of a registered patent practitioner to prosecute the application, because the value of a patent is largely dependent upon skilled preparation and prosecution. The Office cannot aid in selecting a patent practitioner.
A listing of registered patent practitioners is available at https://oedci.uspto.gov/OEDCI/. Applicants may also obtain a list of registered patent practitioners located in their area by writing to Mail Stop OED, Director of the U.S. Patent and Trademark Office, P.O. Box 1450, Alexandria, VA 22313-1450.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
3. Claim 1 is objected to because of the following informalities:
In claim 1, line 7, the limitation recites “2nd training data set”. It should be “second training data set”.
In claim 1, line 9, the limitation recites “3rd training data set”. It should be “third training data set”.
In claim 1, line 10, the limitation recites “1st training data set”. It should be “first training data set”.
In claim 1, line 14, the limitation recites “2nd model”. It should be “second model”.
In claim 1, line 17, the limitation recites “3rd model”. It should be “third model”.
Appropriate correction is needed to make the limitation of the claims consistent.
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.
4. Claim 1 is rejected under 35 U.S.C 101 because the claimed invention is directed towards an abstract idea without significantly more.
Step 1
Independent claim 1 is directed to a method, and falls into one of the four statutory categories.
Step 2A, Prong 1
Claim 1 recites the following abstract ideas:
assigning, a risk value to each or to some data points in the data set using the following framework (Mental process of assigning a value to some data points which can be done with a pen and paper);
determining, a subset of data points (predictable subset) in a data set with unknown target values (an unseen data set) that are "predictable" (e.g., have a better probability of the value to be predicted in the 3rd model) (Mental process directed to determining a predictable subset which can be done with a pen and paper);
assigning, a predicted probability value to each data point in the predictable subset (Mental process of assigning a value to data points which can be done with a pen and paper).
Step 2A, Prong 2
Claim 1 recites the following additional elements:
receiving a data set having multiple data points (This limitation is directed to mere data gathering. This does not integrate the abstract idea into a practical application);
using a meta-model (e.g., a meta-model consisting of three sub-models that are built on any known modeling algorithms - the same or different algorithms for the sub-models) (This limitation is directed to using multiple modeling algorithms. This is directed to a high-level generic use of modeling algorithms. This does not integrate the abstract idea into a practical application),
building the first model (e.g., sub-model 1), by training it with a set of data points having target value (the 1st training data set) (This limitation is directed to training a model using training datasets. This is directed to a high-level generic process of training a model. This does not integrate the abstract idea into a practical application); and
generating the 2nd training data set for the second model (e.g., sub-model 2), by assigning to each data point a new target value (either "predictable" or "unpredictable") (This limitation is directed to generating training data sets. This is directed to mere data gathering. This does not integrate the abstract idea into a practical application); and
generating the 3rd training data set for the third model (e.g., sub-model 3), by selecting a subset of data points from the 1st training data set that are successfully predicted by the first model (e.g., sub-model 1) (This limitation is directed to generating training data sets. This is directed to mere data gathering. This does not integrate the abstract idea into a practical application); and
building a second model (e.g., sub-model 2), by training it on the 2nd training data set (This limitation is directed to training a model using training datasets. This is directed to a high-level generic process of training a model. This does not integrate the abstract idea into a practical application); and
building a third model (e.g., sub-model 3), by training it on the 3rd training data set (This limitation is directed to training a model using training datasets. This is directed to a high-level generic process of training a model. This does not integrate the abstract idea into a practical application); and
using the 3rd model (e.g., sub-model 3) (This limitation is directed to using a model. This is directed to a high-level generic use of a model. This does not integrate the abstract idea into a practical application),
using the 2nd model (e.g., sub-model 2) (This limitation is directed to using a model. This is directed to a high-level generic use of a model. This does not integrate the abstract idea into a practical application),
Step 2B
Claim 1 recites the following additional elements:
receiving a data set having multiple data points (This limitation is directed to mere data gathering. This does not amount to significantly more than judicial exception. See MPEP 2106.05(g));
using a meta-model (e.g., a meta-model consisting of three sub-models that are built on any known modeling algorithms - the same or different algorithms for the sub-models) (This limitation is directed to using multiple modeling algorithms. This is directed to a high-level generic use of modeling algorithms. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)),
building the first model (e.g., sub-model 1), by training it with a set of data points having target value (the 1st training data set) (This limitation is directed to training a model using training datasets. This is directed to a high-level generic process of training a model. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)); and
generating the 2nd training data set for the second model (e.g., sub-model 2), by assigning to each data point a new target value (either "predictable" or "unpredictable") (This limitation is directed to generating training data sets. This is directed to mere data gathering. This does not amount to significantly more than judicial exception. See MPEP 2106.05(g)); and
generating the 3rd training data set for the third model (e.g., sub-model 3), by selecting a subset of data points from the 1st training data set that are successfully predicted by the first model (e.g., sub-model 1) (This limitation is directed to generating training data sets. This is directed to mere data gathering. This does not amount to significantly more than judicial exception. See MPEP 2106.05(g)); and
building a second model (e.g., sub-model 2), by training it on the 2nd training data set (This limitation is directed to training a model using training datasets. This is directed to a high-level generic process of training a model. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)); and
building a third model (e.g., sub-model 3), by training it on the 3rd training data set (This limitation is directed to training a model using training datasets. This is directed to a high-level generic process of training a model. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)); and
using the 3rd model (e.g., sub-model 3) (This limitation is directed to using a model. This is directed to a high-level generic use of a model. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)),
using the 2nd model (e.g., sub-model 2) (This limitation is directed to using a model. This is directed to a high-level generic use of a model. This does not amount to significantly more than judicial exception. See MPEP 2106.05(f)),
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
5. Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the phrases that follows "e.g." and that is in parenthesis - “()”
renders the claim indefinite because it is unclear whether the limitation(s) following the phrase and limitations in parenthesis are part of the claimed invention. See MPEP § 2173.05(d).
For the purpose of examination, the limitations that follows "e.g.," and the limitations in “()” is not given patentable weight.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
6. Claim 1 is rejected under 35 U.S.C 102(a)(1) as being anticipated by Baron et al. (US20210350930 filed 05/11/2020)
Regarding claim 1, Baron teaches a method, comprising: receiving a data set having multiple data points (receiving data corresponding to a plurality of data categories of a patient, abstract);
assigning, using a meta-model (e.g., a meta-model consisting of three sub-models that are built on any known modeling algorithms - the same or different algorithms for the sub-models) (Disclosed herein are techniques for performing a clinical prediction using a combined learning model (herein after, a “meta-model”) comprising a plurality of machine learning models, to address some of the issues above [0059]),
a risk value to each or to some data points in the data set using the following framework (Predictor module 306 can then use the machine learning models included in the meta-model to process data 308 to generate prediction outputs for the patient, such as survival rates 310 a and 310 b [0062]; while the patient data of the second subset of the group of patients can be used to generate the performance metric (e.g., AUC of ROC) [0088]);
building the first model (e.g., sub-model 1) (The identified portions of clinical data having the requisite data categories for each individual prediction model, as shown in FIG. 4B, are then used to build and train the individual prediction models [0101]; … perform training of the individual prediction model, … as described in FIG. 3D [0102]; learning model 304a is the first built learning model, Fig. 3D),
by training it with a set of data points having target value (the 1st training data set) (For example, to perform the training for machine learning model 304 a, patient data 364 a of a first group of patients having data categories S0, S1, S2, S3 required by machine learning model 304 a can be identified [0087]; For example, based on the survival statistics, a percentage of the surviving patients in the group (with the group size adjusted with respect to time to account for deaths) at different times can be determined to represent the target survival rate [0026]); and
generating the 2nd training data set for the second model (e.g., sub-model 2) (Moreover, machine learning model 304 b is trained using a set of data categories S2, S4, and S5; Five different splits can be performed to create five different first and second subset of the clinical data [0102]. The Examiner notes the first subset is generated as second training data),
by assigning to each data point a new target value (either "predictable" or "unpredictable") (At step 342, data of a group of patients whose survival statistics at the pre-determined time are known, can be input to the machine learning model to predict a survival rate of each patient in the group [0073]. The Examiner notes predictable implies ability to survive are known); and
generating the 3rd training data set for the third model (e.g., sub-model 3) (a third machine learning model trained using third data of a third data category, see claim 4; Further, machine learning model 304 c is trained using a set of data categories S0, S8, and S9 [0063]; Five different splits can be performed to create five different first and second subset of the clinical data [0102]; The Examiner notes the second subset is generated as third training data),
by selecting a subset of data points from the 1st training data set (Select module 304 can accept data 308 of a patient as input. Based on the data categories included in data 308 [0062])
that are successfully predicted by the first model (e.g., sub-model 1) (Moreover, as the prediction outputs of the selected machine learning models are combined to generate the combined prediction output, the combined prediction output can reflect all data categories present in the patient data, while the combination also reflects the confidence level of each machine learning model [0030]); and
building a second model (e.g., sub-model 2), by training it on the 2nd training data set (The identified portions of clinical data having the requisite data categories for each individual prediction model, as shown in FIG. 4B, are then used to build and train the individual prediction models [0101]; … perform training of the individual prediction model, … as described in FIG. 3D [0102]; learning 304b is the second learning model built, Fig. 3D); and
building a third model (e.g., sub-model 3) (The identified portions of clinical data having the requisite data categories for each individual prediction model, as shown in FIG. 4B, are then used to build and train the individual prediction models [0101]; … perform training of the individual prediction model, … as described in FIG. 3D [0102]; learning 304C is the third learning model built, Fig. 3D),
by training it on the 3rd training data set (a third machine learning model trained using third data of a third data category, see claim 4; Further, machine learning model 304 c is trained using a set of data categories S0, S8, and S9 [0063]); and
determining, using the 2nd model (e.g., sub-model 2) (Moreover, machine learning model 304 b is trained using a set of data categories S2, S4, and S5 [0063]),
a subset of data points (predictable subset) in a data set with unknown target values (an unseen data set) that are "predictable" (e.g. have a better probability of the value to be predicted in the 3rd model) (Predictor module 306 can use machine learning models … 304 b … to perform a prediction … machine learning model 304 b to generate survival rate 310 b. [0066]; The second prediction result can include, for example, a second survival rate predicted for the patient by second machine learning module 304 b [0118]; The regression model parameters can then be determined based on the values of the patients for the different data categories at different times, such that the regression model outputs the target survival rates. [0026]; Specifically, machine learning models database 302 may store a plurality of machine learning models including model 304 a, 304 b, 304 c, etc. Each machine learning model can include, for example, …, a mathematical model (e.g., regression model 250 of FIG. 2D) [0063]; The weight of each individual prediction model is then determined based on an average of the five AUC values obtained from the training and validation operations [0102]);
assigning (Each decision tree in a random forest model can be generated in a training process over patient data of a set of data categories [0048]; Each decision tree can be assigned to process different subsets of the data categories of patient data to generate the CHF value [0046]),
using the 3rd model (e.g., sub-model 3) ((a third machine learning model trained using third data of a third data category, see claim 4; Specifically, machine learning models database 302 may store a plurality of machine learning models including model 304 a, 304 b, 304 c … Each machine learning model can include, for example, a random forest model (e.g., random forest model 230 of FIG. 2C), … The random forest model and the mathematical model can include a set of sub-models each associated with a respective time [0063]),
a predicted probability value to each data point in the predictable subset (The value of CHF can represent the risk of death (or a survival rate) at a particular time … The random forest model 230 shown in FIG. 2C can be trained to predict a CHF value for a particular time [0044]; A cumulative hazard function can be interpreted as the probability of survival (or death) of a patient at a particular time from a diagnosis [0022]; At step 342, data of a group of patients whose survival statistics at the pre-determined time are known, can be input to the machine learning model to predict a survival rate of each patient in the group [0073]. The Examiner notes predictable implies ability to survive are known).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MORIAM MOSUNMOLA GODO whose telephone number is (571)272-8670. The examiner can normally be reached Monday-Friday 7:30am-5:30pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B. Zhen can be reached on (571)272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.G./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121