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 .
Claim Objections
Claims 1-20 are objected to because of the following informalities: Claims 1, 8, and 15 are objected to for reciting “a rule-based model comprising rules for classifies text”. This better reads as “a rule-based model comprising rules for classifying text”. Appropriate correction is required.
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.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bayahya et al, hereinafter Bayahya (US 11250723 B1, published 2/22/2025, filed on 11/4/2020), in view of Brisimi et al, hereinafter Brisimi US PG Pub 20200160191 A1.
Regarding claim 1, Bayahya teaches inputting of collected data to be classified by a number of machine learning classifiers. Each classifier algorithm classifies the input data into 3 hierarchical categories-normal, mild cognitive impairment, demented, (col.7, lines 42-55, Fig. 5)-- A computer-implemented method for text classification comprising: receiving an input text for classification based on a hierarchy of categories; providing the input text to a knowledge model,
Additionally, Bayahya teaches inputting of collected data to be classified by a number of classifiers. The classifiers include a Decision tree, and Extra Tree classifiers-- rule-based model comprising rules for classifies text, which use decision trees to improve accuracy. Each classifier algorithm classifies the input data into 3 hierarchical categories-normal, mild cognitive impairment, demented, (col. 11, lines 39-57, col.7, lines 42-55, Fig. 5)-- wherein the knowledge model is a rule-based model comprising rules for classifies text; executing the knowledge model to generate a first output representing a first category for the input text;
In addition, Bayahya teaches inputting of collected data to be classified by a number of classifiers. Each classifier, which includes a machine learning algorithm, classifies the input data into 3 hierarchical categories-normal, mild cognitive impairment, demented, (col.7, lines 42-55, Fig. 5)-- providing the input text to a machine learning based model trained for classifying text; executing the machine learning based model to generate a second output representing a second category for the input text;
Moreover, Bayahya teaches an Ensemble system of voting for determining a final classification, which votes on the classification from each classifier to determine the most frequent resulting classification input from the machine learning algorithms (col.7, lines 50-55, Fig. 5)-- providing the first output and the second output to an ensemble model configured to combine results of the knowledge model and the machine learning based model; executing the ensemble model to determine a category for the input text based on the first category and the second category;
Bayahya, fails to explicitly teach and sending the category for the input text determined by the ensemble model to a client device. However, Brisimi teaches a server providing a rules correction service that performs machine learning processing, and for communicating with clients (45, 56, figs.1-2, 4). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yahya, and , because Brisimi discloses maintaining the accuracy and correctness of policy data source in an active learning environment (14-15).
Claim 8 is directed towards a non-transitory computer readable medium to implement the limitations of claim 1, and is likewise rejected.
Claim 15 is directed towards a computer system to implement the limitations of claim 1, and is likewise rejected.
Claims 2-6, 9-13, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bayahya et al, hereinafter Bayahya (US 11250723 B1, published 2/22/2025, filed on 11/4/2020), in view of Brisimi et al, hereinafter Brisimi US PG Pub 20200160191 A1, as applied to claim 1 above, and further in view of Freund US PG Pub 20200160191 A1 (9/24/2002).
Regarding claim 2, Bayahya teaches testing the models for accuracy by splitting the training data into 10 folds. The testing process is repeated for each of the 10 folds, and determines the models with above 99% accuracy classification—“ RANDOM FOREST® as well as for Extra Trees as shown in Table II.” (col.14, lines 14-65) -- receiving a new set of text inputs for classifying, wherein the machine learning based model has low accuracy of classification for text inputs from the new set of text inputs; wherein the ensemble model determines the category of an input text from the new set of text inputs as the second category responsive to determining that an accuracy of classification of the machine learning based model for the input text from the new set of text inputs is below a threshold value.
Bayahya fails to explicitly teach adding one or more rules to the knowledge model for classifying documents of the new set of text inputs. However, Freund teaches adding rules to a Tree based classifiers (abstract, col.7 , lines 1-14). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yahya, and , because Freund discloses above, increasing accuracy of the classifier
Regarding claim 3, Bayahya teaches testing the models for accuracy by splitting the training data into 10 folds. The testing process is repeated for each of the 10 folds, and determines the models with above 99% accuracy classification—“ RANDOM FOREST® as well as for Extra Trees as shown in Table II.” (col.14, lines 14-65) -- using the input text from the new set of text inputs and the category determined for the input text by the ensemble model as training data for training the machine learning based model.
Regarding claim 4, Bayahya teaches testing the models for accuracy by splitting the training data into 10 folds. The testing process is repeated for each of the 10 folds, and records accuracy and errors—(col.14, lines 27-65) -- generating synthetic data based on the input text from the new set of text inputs and the category determined for the input text by the ensemble model as additional training data for the machine learning based model.
Regarding claim 5, Bayahya teaches testing the models for accuracy and after testing determining the models with highest class prediction—“ RANDOM FOREST® as well as for Extra Trees as shown in Table II.” (col.14, lines 14-65) -- wherein determining the category of the input text by the ensemble model comprises: receiving a first measure of accuracy of the first output generated by the knowledge model; receiving a second measure of accuracy of the second output generated by the machine learning based model; and determining the category for the input text based on the first output and the second output based on at least one of the first measure of accuracy or the second measure of accuracy.
Regarding claim 6, Bayahya teaches testing the models for accuracy and after testing determining the models with highest class prediction or predicted the classes most accurately, namely correct or incorrect class(es)—“ RANDOM FOREST® as well as for Extra Trees as shown in Table II.” (col.14, lines 14-65) -- wherein the ensemble model determines that the category of the input text is the first category if a comparison of the first measure of accuracy and the second measure of accuracy indicates that the knowledge model has higher accuracy compared to the machine learning based model.
Claims 9-13 are directed towards a non-transitory computer readable medium to implement the limitations of claims 2-6 and are likewise rejected.
Claims 16-20 are directed towards a computer system to implement the limitations of claims 2-6, and are likewise rejected.
Claims 7, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bayahya et al, hereinafter Bayahya (US 11250723 B1, published 2/22/2025, filed on 11/4/2020), in view of Brisimi et al, hereinafter Brisimi US PG Pub 20200160191 A1, as applied to claim 1 above, and further in view of Williams US PG Pub US 20150161636 A1.
Regarding claim 7, Bayahya, fails to explicitly teach wherein the input text represents articles retrieved from a website. However, Williams teaches using models to determine best prices by comparing websites (137-138, 221). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yahya, and Williams, because William discloses minimizing time, energy, and storage capacity (14-15).
Claim 14 is directed towards a non-transitory computer readable medium to implement the limitations of claim 7 and is likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Van Dusen et al US 20170235848 A1, which comprises categorization of services and commonplace information.
Srinivasan et al US 20210279606 A1, which comprises a plurality of correlations may be identified between the new attribute and existing attributes of the entities in a knowledge base using a rule-based model, such that attribute rules may be associated with each identified correlation exceeding a predetermined confidence threshold.
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/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145