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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
Claims 2, 11 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 9, 17 of U.S. Patent No. 11783223
18378068 (present application)
11783223 (Parent Patent)
2. A method for using graphical user interface to generate a machine learning model, the method, performed by an electronic device, the method comprising:
receiving, via the graphical user interface, a selection of a template of a plurality of templates, each template corresponding to a category of a type of data, wherein the category of the type of data comprises one of motion, images, text, sound, or tabular data;
training the machine learning model using training data to generate a trained model, the training data comprising first structured data records and first associated metadata records, each first associated metadata record including a classification label of a first structured data record;
displaying an accuracy score of the trained model applied to validation data comprising second structured data records and second associated metadata records, each second associated metadata record including the classification label of a second structured data record; and
generating executable code for the trained model, the executable code including the trained model and executable on a mobile device to classify data obtained from a sensor of the mobile device, wherein the sensor corresponds to the type of data of the selected template.
Claim 11 (similar to claim 1)
Claim 20 (similar to claim 1)
1. A method for using graphical user interface to generate a machine learning model, the method, performed by an electronic device, comprising:
receiving, via a user interface, a selection of a template of a plurality of templates defining a machine learning model, each template corresponding to a category of a type of data, wherein the category of the type of data comprises one of images, text, sound, or tabular data;
identifying a location of a plurality of training data comprising a first plurality of structured data records and first associated metadata records, each metadata record of the first associated metadata records identifying at least a classification label of the first plurality of structured data records;
training the machine learning model by analyzing each of the first plurality of structured data records and the first associated metadata records to generate a trained model;
identifying a location of a plurality of validation data comprising a second plurality of structured data records and second associated metadata records, each metadata record of the second associated metadata records identifying at least the classification label of the second plurality of structured data records; validating the trained model by analyzing each of the second plurality of structured data records to generate an identification for each of the second plurality of structured data records;
displaying an accuracy score of the identification provided by the machine learning model that is generated by comparing the identification for each of the second plurality of structured data records against the second associated metadata records; and
generating executable code for the trained model, the executable code including the trained model and executable on a mobile device to classify data obtained from a sensor of the mobile device, wherein the sensor corresponds to the type of data of the selected template.
Claim 9 (similar to claim 1)
Claim 17 (similar to claim 1)
Claims 11 and 20 recite similar limitations as claim 1 and are likewise rejected.
Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 9, 17 of the Patent no. 11783223 application is narrower than and includes all of the features of claims 1, 11, 20 of the present application.
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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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 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 2-21 are rejected under 35 U.S.C. 103 as being unpatentable over Skiles et al. (Skiles) US 2018/0314939 A1 in view of Rugel et al. (Rugel) US 2019/0095822 A1 and Pedersen et al. (Pedersen) US 2020/0142999
In regard to claim 2, Skiles disclose A method for using graphical user interface to generate a machine learning model, the method, performed by an electronic device, the method comprising: (Fig. 1-Fig. 7, Fig. 1, 102, [0004][0033] using GUI to define and assign document to classes using ML)
receiving, via the graphical user interface, a selection of a template of a plurality of templates, each template corresponding to a category of a type of data, (Fig. 1-2, 130, [0021], [0025]-[0033] using GUI to define and assign documents to classes using ML, selecting a document corresponding to a classification from document classes (a class and a sub-class of documents)
training the machine learning model using training data to generate a trained model, the training data comprising first structured data records and first associated metadata records, each first associated metadata record including a classification label of a first structured data record; ([0004]-[0005] [0025]-[0028][0033]-[0042] generate a document classifier based on the training data and train ML to determine the module to use, the data is arranged in folder tree with tags indicating the documents assigned to the classes and sub-classes)
an accuracy score of the trained model applied to validation data comprising second structured data records and second associated metadata records, each second associated metadata record including the classification label of a second structured data record is generated; ([0030]-[0032] [0038]-[0046] generate the best classifier by applying the test data to the classifier and by comparing the classifiers to determine the performing classifier with accuracy greater than or equal to a threshold, which is criterion set by the user, the second portion of data has classification tagged)
But Skiles fail to explicitly disclose “displaying the accuracy score, generating executable code for the trained model, the executable code including the trained model and executable on a mobile device to classify data obtained from a sensor of the mobile device, wherein the sensor corresponds to the type of data of the selected template.”
Rugel disclose displaying the accuracy score, ([0034[0054][0115] displaying the generated output)
generating executable code for the trained model, the executable code including the trained model and executable on a mobile device to classify data obtained from a sensor of the mobile device, wherein the sensor corresponds to the type of data of the selected template. (abstract, [0006][0009] [0026][0044]-[0046][0052][0155] an instruction transmitted to a computer device and execute on the device to enable the functionality of sensor that may be used on the identified test with types of sensor data, and processed using ML datasets, and sensor categorizes data collected, and sensor collect data corresponding to type of data interested based on the identified test)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Rugel’s data processing with ML to provide output into Skiles’s invention as they are related to the same field endeavor of generating output display using ML on a user interface. The motivation to combine these arts, as proposed above, at least because Rugel’s testing output display using sensor data collected using ML would help to provide test validation method into Skiles’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that display testing output using sensor data collected would facilitate verification of the ML model developed.
But Skiles and Rugel fail to explicitly disclose “wherein the category of the type of data comprises one of motion, images, text, sound, or tabular data;”
Pedersen disclose wherein the category of the type of data comprises one of motion, images, text, sound, or tabular data; ([0025]-[0030] the various classes of data have different categories, such as image, text, video, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Pedersen’s method of classifying data with ML into Rugel and Skiles’s invention as they are related to the same field endeavor of generating output display using ML on a user interface. The motivation to combine these arts, as proposed above, at least because Pedersen’s classifying data with various data categories using ML would provide classifying method into Rugel and Skiles’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that provide classifying method with various data categories would facilitate ML model training and improve user experience using the device.
In regard to claim 3, Skiles, Rugel and Pedersen disclose The method of claim 2,
Skiles disclose wherein the accuracy score is generated by:
analyzing, using the trained model, each of the second structured data records to generate an identification for each of the second structured data records; ([0030]-[0032] [0038]-[0046] evaluate, using the ML model, the second portion of the data to generate a document classifier) and
comparing the identification for each of the second structured data records against the classification label for each of the second associated metadata records. ([0042]-[0044] generate the best classifier by comparing the classifiers of the portions of data to determine the performing classifier with accuracy greater than or equal to a threshold)
In regard to claim 4, Skiles, Rugel and Pedersen disclose The method of claim 2,
Skiles disclose further comprising:
analyzing the training data to determine a number of classes, each class corresponding to a different classification label; and comparing the classification label for the training data to determine consistency with the category of the type of data. ([0035]-[0042][0046]-[0049] [0089]-[0090] multiple classifiers are identified with different classifiers and tags are compared with matching data type)
In regard to claim 5, Skiles, Rugel and Pedersen disclose The method of claim 4, Skiles disclose further comprising:
displaying a list of the classes of the training data on the graphical user interface; ([0035]-[0040] classifiers are displayed on the GUI) and
receiving a selection of one of more classes of a plurality of the classes for training the machine learning model. ([0052]-[0057] [0071]-[0073]selecting a classifier to train the model)
In regard to claim 6, Skiles, Rugel and Pedersen disclose The method of claim 2, Skiles disclose further comprising:
analyzing the training data to determine a number of classes, each class corresponding to a different classification label; ([0035]-[0042][0046]-[0049] [0089]-[0090] multiple classifiers are identified with different classifiers and tags are compared with matching data type)
displaying a list of the classes of the training data on the graphical user interface; ([0035]-[0040] classifiers are displayed on the GUI) and
receiving a selection of one of more classes of a plurality of the classes for training the machine learning model. ([0052]-[0057] [0071]-[0073]selecting a classifier to train the model)
In regard to claim 7, Skiles, Rugel and Pedersen disclose The method of claim 2,
Skiles disclose further comprising receiving an identification of a location of the training data by selecting an icon associated with the training data and dragging the icon onto a designated area on the graphical user interface. (Fig. 1, [0029]-[0032] drag the doc to a folder representing a class on the GUI)
In regard to claim 8, Skiles, Rugel and Pedersen disclose The method of claim 2,
Skiles disclose wherein identifying a location of the validation data comprises automatically selecting a random portion of the training data, wherein the random portion of the training data is withheld from training the machine learning model. ([0040]-[0044] [0052]-[0057] [0088]-[0090]using a larger subset of the supervised training data to generate the best classifier, the portion of the supervised training data is not used.)
In regard to claim 9, Skiles, Rugel and Pedersen disclose The method of claim 2, further comprising:
Skiles disclose receiving a selection that the validation data is to be automatically selected from the training data; ([0040]-[0044] [0052]-[0057] [0088]-[0090] selecting the test data from the training data)
withholding a preselected percentage of the training data from the training, the withheld training data comprising the second structured data records; ([0040]-[0044] [0088]-[0090] partition the training data in training portion and test portion, the test portion are the test data)
validating the trained model by analyzing each of the second structured data records to generate an identification for each of the second structured data records and the second associated metadata records; ([0038]-[0046] validating the model by comparing the classifications indicated in the training data and generate classifiers for the test data) and
generating the accuracy score by comparing the identification for each of the second structured data records against the second associated metadata records. ([0042]-[0044] generate the best classifier by comparing the classifiers to determine the performing classifier with accuracy greater than or equal to a threshold, which is criterion set by the user)
In regard to claim 10, Skiles, Rugel and Pedersen disclose The method of claim 2,
Skiles disclose further comprising:
detecting reaching a threshold for the training data; automatically training the machine learning model; and generating the executable code for the trained model. ([0042]-[0049] generate the best classifier by comparing the classifiers to determine the performing classifier with accuracy greater than or equal to a threshold, and train the model and generate instructions for the model)
In regard to claims 11-19, claims 11-19 are computer device claims corresponding to the method claims 2-10 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 2-10.
In regard to claim 20-21, claim 20-21 are medium claims corresponding to the method claim 1-2 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1-2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US 20190392257 A1 2019-12-26 Foley et al.
APPARATUS AND METHOD FOR IMPROVED INTERFACE-BASED DECISION ANALYSIS
Foley et al. disclose An apparatus, method, and computer program product for the improved development of training data sets for use in connection with machine learning models capable of operating on natural language data records and other unstructured data in a network environment. Some example implementations provide for the generation and presentation of record images in a user interface that allows captures user actions reflecting higher-order data analysis and discernment for incorporation into training protocols used for machine learning models… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
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