DETAILED ACTION
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, 16, 20 are objected to because of the following informalities:
In Claim 1, lines 5, 12 and 14, “a processor” was probably meant to be: the processor. The same objection is made for Claim 16 at lines 6, 13 and 15; and Claim 20 at lines 5, 12 and 14.
Appropriate correction is required.
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).
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 § 2146 et seq. 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).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 16, 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1, 9, 16 respectively of U.S. Patent No. 11,783,233. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application claims as pointed out above are anticipated by the corresponding patent claims. The “first plurality of novelty classes” and the “second plurality of novelty classes” of the current application claims being equivalent to the “plurality of novelty classes” of the patent claims. And, “the second plurality of novelty classes subdividing the first plurality of novelty classes based on the prediction model” of the current application claims being equivalent to the “the feature segmentation model dividing the plurality of training data observations into the plurality of feature data segments” of the patent claims.
Claims 1, 16, 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1, 13, 10 respectively of U.S. Patent No. 12,547,942. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the current application as stated above are anticipated by the respective corresponding claims of the patented application. The “distance metrics” of the current application being equivalent to the “confusion matrix” used in “determining whether the predicted target value matches the actual target value and whether the feature data segment matches the case attribute data segment”; and the “plurality of novelty classes” of the current application being equivalent to the “plurality of feature data segments” which will indicate classes, in the patent.
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 1-10, 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
All claims are directed towards either a method, a system or a non-transitory computer-readable media and thus satisfies Step 1 as falling into one of the statutory categories.
Step 2A, Prong One:
Independent Claim 1 recites (the same analysis applies to similar independent Claims 16 and 20):
determining a predicted target value;
determining one or more distance metrics, each of the one or more distance metrics representing a respective distance between the test data observation and the feature data segment along one or more dimensions;
determining, via a processor, a first novelty class of a first plurality of novelty classes for the test data observation based on the one or more distance metrics;
determining, via a processor, a second novelty class of a second plurality of novelty classes for the test data observation based on the one or more distance metrics and the first novelty class, the second plurality of novelty classes subdividing the first plurality of novelty classes based on the prediction model;
selecting a model updating mechanism from a plurality of model updating mechanisms based on the first novelty class and the second novelty class;
these limitations, under their broadest reasonable interpretation, covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of determining target values and distance metrics between data points for determining classes for the data points and selecting an updating mechanism based on the classes, using observation, evaluation and judgement.
Step 2A, Prong Two:
Claim 1 recites the additional elements of (the same analysis applies to similar independent Claims 16 and 20):
via a processor by applying to a test data observation a prediction model pre-trained via a plurality of training data observations
determining a designated feature data segment of a plurality of feature data segments by applying a feature segmentation model to the test data observation via a processor, the feature segmentation model being pre-trained to classify a respective training data observation of the plurality of training data observations as belonging to a respective feature data segment of the plurality of feature data segments;
and determining based on the model updating mechanism an updated prediction model and an updated feature segmentation model that both incorporate the test data observation and the training data observations.
these limitations are considered as applying instructions to use a learning model as a tool to perform the abstract idea that includes updating/retraining the model - see MPEP 2106.05(f).
The additional element of a “processor” as recited in the claims is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea.
Step 2B:
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are considered as applying instructions to use a learning model as a tool to perform the abstract idea - see MPEP 2106.05(f). The additional element of a “processor” as recited in the claims 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. The claims are therefore not patent eligible.
Dependent Claims 2 and 17 are considered as using the learning model as a tool to perform the abstract idea - see MPEP 2106.05(f).
Dependent Claims 3-4 and 18-19 are also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of determining distance metrics/values between data points for determining classes for the data points and selecting an updating mechanism based on the classes, using observation, evaluation and judgement.
Dependent Claim 5 is considered as using the learning model as a tool to perform the abstract idea that includes its training - see MPEP 2106.05(f).
Dependent Claims 6 and 10 are also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of comparing distance metrics to value ranges to determine classes of data, using observation, evaluation and judgement.
Dependent Claims 7-9 are considered as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g); and therefore subsequently considered as adding well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality (defining data features, or metadata values and distance metric), to the judicial exception - see MPEP 2106.05(d).
Dependent Claims 12-13 are also considered as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g); and therefore subsequently considered as adding well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality (defining representation of observation data) to the judicial exception - see MPEP 2106.05(d).
Dependent Claims 14-15 are also considered as falling under the “Mental Processes” groupings of abstract ideas. That is the human mind is capable of determining whether a value falls below a threshold and selecting an updating mechanism accordingly, using observation, evaluation and judgement.
Claim Rejections - 35 USC § 102
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 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)(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.
Claims 1, 5-6, 16, 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mishra, US 2023/0076083 A1.
Regarding Claim 1, Mishra teaches:
A method comprising: determining a predicted target value via a processor by applying to a test data observation a prediction model pre-trained via a plurality of training data observations (paragraph 84: “In some examples, ML training computing device 102 may apply one or more trained machine learning models to detect fraudulent activity, such as a fraudulent return. The trained machine learning models may be trained with training data 370, which may include labelled data 372 and, in some examples, unlabeled data 374”. The detection of fraudulent activity corresponding to the predicted target value. Examiner's note: see also Schwiep, US 2022/0292308 A1, for example paragraph 3);
determining a designated feature data segment of a plurality of feature data segments by applying a feature segmentation model to the test data observation via a processor, the feature segmentation model being pre-trained to classify a respective training data observation of the plurality of training data observations as belonging to a respective feature data segment of the plurality of feature data segments (Abstract; paragraph 31: “The positively labelled training data, along with the negatively labelled training data and the unlabeled training data are then clustered into similar segments. For example, a k-Means clustering algorithm may be applied to the positively labelled training data, negatively labelled training data, and the unlabeled training data to generate the segments. In some examples, application of the k-Means clustering algorithm includes applying a neural network to the positively labelled training data, negatively labelled training data, and the unlabeled training data to determine a subset of features (e.g., key features). The features are then clustered into the segments”);
determining one or more distance metrics, each of the one or more distance metrics representing a respective distance between the test data observation and the feature data segment along one or more dimensions (Abstract; paragraphs 31, 33: “The features are then clustered into the segments (e.g., cluster groups of a particular cluster size) based on determining, for example, a pairwise Euclidean distance between the features. Application of the Euclidean distance may identify unlabeled training data that is similar to, for example, positively labelled training data”. The Euclidean distance measure including measurements in n-dimensional space);
determining, via a processor, a first novelty class of a first plurality of novelty classes for the test data observation based on the one or more distance metrics; determining, via a processor, a second novelty class of a second plurality of novelty classes for the test data observation based on the one or more distance metrics and the first novelty class, the second plurality of novelty classes subdividing the first plurality of novelty classes based on the prediction model (Abstract; paragraph 8: “The computing device generates clusters of the training data based on one or more corresponding attributes of the training data. Further, the computing device determines a distance metric between positively labelled samples and unlabeled samples within each cluster, and generates, for each of the clusters, a plurality of sub-clusters based on the determined distance metrics. The computing device also determines, from each of the plurality of sub-clusters, one or more of the unlabeled samples based on a corresponding reward value and a corresponding sampling rate value. The computing device may train a machine learning model with the determined unlabeled samples from each of the plurality of sub-clusters”. The clusters representing the novelty classes and the sub-clusters representative of the subdividing of the first plurality of novelty classes);
selecting a model updating mechanism from a plurality of model updating mechanisms based on the first novelty class and the second novelty class (paragraph 36: “In some examples, one or more models, such as one or more rule-based models or machine learning based models, are applied to the selected samples for labelling. Once labelled, machine learning models may be trained not only with the originally positively and negatively labelled data, but with the newly labelled training data as well”. The subsequent usage of the newly labelled training data representative of the model updating);
and determining based on the model updating mechanism an updated prediction model and an updated feature segmentation model that both incorporate the test data observation and the training data observations (paragraph 90: “ML training computing device 102 may train the one or more machine learning models based on the updated labelled data”. Examiner’s note: see also Schwiep, US 2022/0292308 A1, for example paragraph 131).
Regarding Claim 5, Mishra further teaches:
The method recited in claim 1, wherein the plurality of model updating mechanisms includes training a new model using a subset of the training data observations that occurred after a cutoff threshold point in time (paragraph 100: “after each iteration (e.g., the processing of a threshold amount of training data 370), reward/sampling rates determination engine 410 may update the reward rates based on a proportion (e.g., percentage) of positively labelled data samples from all data samples in a sub-cluster or category that were provided for labelling”. The processing of the threshold amount of training data necessarily based on a cutoff threshold point in time).
Regarding Claim 6, Mishra further teaches:
The method recited in claim 1, wherein the plurality of first novelty classes correspond to one or more value ranges for the one or more distance metrics, and wherein determining the first novelty class comprises comparing the one or more distance metrics to the one or more value ranges (Abstract: “The computing device generates clusters of the training data based on one or more corresponding attributes of the training data. Further, the computing device determines a distance metric between positively labelled samples and unlabeled samples within each cluster, and generates, for each of the clusters, a plurality of sub-clusters based on the determined distance metrics. The computing device also determines, from each of the plurality of sub-clusters, one or more of the unlabeled samples based on a corresponding reward value and a corresponding sampling rate value”. And, paragraph 33: “training data within a threshold distance of the positively labelled training data is associated with the exploit group, and training data not within the threshold distance is associated with the explore group. As a result, data items closer to known positively labelled data items are placed into an exploit sub-group and while the remaining data items are sub-grouped into an explore sub-group”).
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 2-3, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Mishra, US 2023/0076083 A1, in view of Dasgupta, US 11,556,746 B1.
Regarding Claim 2, Mishra teaches:
The method recited in claim 1, wherein the feature segmentation model classifies a subset of the training data observations as belonging to the designated feature data segment (paragraph 31: “the embodiments described herein may aggregate positively labelled training data, negatively labelled training data, and unlabeled training data. Positively labelled training data may include, for example, training data with known preferences, while negatively labelled training data may include training data with known non-preferences and unlabeled training data may include training data with unknown preferences. The positively labelled training data, along with the negatively labelled training data and the unlabeled training data are then clustered into similar segments”. The respective clusters representative of the designated feature data segment),
Mishra may not have taught the following, however, Dasgupta shows:
and wherein a confusion matrix for the prediction model further subdivides the subset of the training data observations into the second plurality of novelty classes (Clause 41: C70, L35 to C71, L14: “obtain prediction results of a ML image model that classifies a given image to one or more of a plurality of classes, wherein the ML image model was trained using training images from a training set and the prediction results were generated using test images from another set; generate a model performance interface configured with a zoomable confusion matrix that groups the test images into cells according to their respective truth classes and predicted classes”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Dasgupta with that of Mishra for using a confusion matrix for the prediction model that further subdivides the subset of the training data observations into the second plurality of novelty classes.
The ordinary artisan would have been motivated to modify Mishra in the manner set forth above for the purposes of allowing users to perform iterative model experiments to develop machine learning models and that allow the users to manage training data for models [Dasgupta: Abstract].
Regarding Claim 3, Mishra further teaches:
The method recited in claim 2, wherein determining the second novelty class involves determining a plurality of second novelty class distance values that each measure a respective distance between the test data observation and a respective one of the plurality of second novelty classes and selecting the second novelty class based on the plurality of second novelty class distance values (Abstract; paragraph 8: “The computing device generates clusters of the training data based on one or more corresponding attributes of the training data. Further, the computing device determines a distance metric between positively labelled samples and unlabeled samples within each cluster, and generates, for each of the clusters, a plurality of sub-clusters based on the determined distance metrics. The computing device also determines, from each of the plurality of sub-clusters, one or more of the unlabeled samples based on a corresponding reward value and a corresponding sampling rate value. The computing device may train a machine learning model with the determined unlabeled samples from each of the plurality of sub-clusters”. The determination of the distance metric used in determining the class/cluster for the test data observation).
Claims 4, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mishra, US 2023/0076083 A1, in view of Xu, US 2013/0034293 A1.
Regarding Claim 4, with Mishra teaching those limitations of the claim as previously pointed out, Mishra may not have taught all of the following, however, Xu shows:
The method recited in claim 1, wherein the plurality of model updating mechanisms are selected from the group consisting of: incremental model self-healing, batch-based model self-healing, and training a new model (paragraphs 80-82: “The system should also facilitate both batch and incremental training of the reference data’; and “If desired, previous models may be saved with a timestamp, so if the newly trained model does not work well, the system may be rolled back”. This training also uses ground truth data, see paragraph 78, that improves accuracy or self-healing).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Xu with that of Mishra for selecting from a set of model updating mechanisms including incremental model self-healing, batch-based model self-healing, and training a new model.
The ordinary artisan would have been motivated to modify Mishra in the manner set forth above for the purposes of supporting different types of training [Xu: paragraph 80].
Claims 7-10, 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Mishra, US 2023/0076083 A1, in view of Schwiep, US 2022/0292308 A1.
Regarding Claim 7, with Mishra teaching those limitations of the claim as previously pointed out, Mishra may not have taught all of the following, however, Schwiep shows:
The method recited in claim 1, wherein the test data observation includes a feature vector including a plurality of feature values corresponding with a respective plurality of features included in the prediction model, and wherein the test data observation includes a case attribute vector including one or more metadata values characterizing the test data observation (paragraphs 30-31: “A feature of a data sample may be a measurable property of an entity (e.g., person, thing, event, activity, etc.) represented by or associated with the data sample. For example, a feature can be the price of a house. As a further example, a feature can be a shape extracted from an image of the house. In some cases, a feature of a data sample is a description of (or other information regarding) an entity represented by or associated with the data sample. A value of a feature may be a measurement of the corresponding property of an entity or an instance of information regarding an entity”; and “Features can also have data types. For instance, a feature can have an image data type, a numerical data type, a text data type (e.g., a structured text data type or an unstructured ("free") text data type), a categorical data type, or any other suitable data type”. Data types can be considered metadata).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Schwiep with that of Mishra for having a feature vector including a plurality of feature values corresponding with a respective plurality of features included in the prediction model, and wherein the test data observation includes a case attribute vector including one or more metadata values characterizing the test data observation.
The ordinary artisan would have been motivated to modify Mishra in the manner set forth above for the purposes of determining features for a model [Schwiep: paragraph 95].
Regarding Claim 8, Schwiep further teaches:
The method recited in claim 7, wherein the metadata values are excluded from the prediction model (paragraph 96: “Each model picked by the model development module 108 can be trained on features from a feature list that is considered to be suitable or optimal for the model. Other features that may be available but are not present in the feature list can be ignored during training of the model”).
Regarding Claim 9, Mishra further teaches:
The method recited in claim 7, wherein the one or more distance metrics include a first distance metric corresponding with the feature vector and a second distance metric corresponding with the case attribute vector (Abstract; paragraph 8: “the computing device determines a distance metric between positively labelled samples and unlabeled samples within each cluster, and generates, for each of the clusters, a plurality of sub-clusters based on the determined distance metrics”. The distance metrics between the samples representative of the distance metrics between the feature and case attribute vectors).
Regarding Claim 10, Mishra further teaches:
The method recited in claim 9, wherein the first plurality of novelty classes correspond to a first value ranges for the first distance metric and a second distance range corresponding with the second distance metric, and wherein determining the first novelty class comprises comparing the first and second distance metrics to the first and second value ranges (Abstract; paragraph 8: “The computing device generates clusters of the training data based on one or more corresponding attributes of the training data. Further, the computing device determines a distance metric between positively labelled samples and unlabeled samples within each cluster, and generates, for each of the clusters, a plurality of sub-clusters based on the determined distance metrics. The computing device also determines, from each of the plurality of sub-clusters, one or more of the unlabeled samples based on a corresponding reward value and a corresponding sampling rate value. The computing device may train a machine learning model with the determined unlabeled samples from each of the plurality of sub-clusters”. The determination of the distance metric used in a comparison in determining the class/cluster for the data).
Regarding Claim 12, Schwiep further teaches:
The method recited in claim 1, wherein the first plurality of novelty classes includes a first class indicating that an observation is well-represented among the training data observations and a second class indicating that an observation is unrepresented among the training data observations (paragraph 31: “In some cases, a feature of a data sample is a description of (or other information regarding) an entity represented by or associated with the data sample” and “In some cases, a value of a feature can indicate a missing value”. See also Mishra, paragraph 31).
Regarding Claim 13, Schwiep further teaches:
The method recited in claim 12, wherein the first plurality of novelty classes includes a third class indicating that an observation is under-represented among the training data observations (paragraph 72: “the feature engineering module 106 can: perform feature engineering of time series specific features; reduce or eliminate features that are not important or less important than other features; and/or partition data into walk-backward backtests”).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Mishra, US 2023/0076083 A1, in view of Bhattacharyya, US 2020/0285997 A1.
Regarding Claim 11, with Mishra teaching those limitations of the claim as previously pointed out, Mishra may not have taught all of the following, however, Bhattacharyya shows:
The method recited in claim 1, the method further comprising: receiving information from a plurality of sensors monitoring a mechanical device (paragraph 82: “sensors from a fleet of turbofan engines were evaluated to determine engine degradation and future failure”);
determining the test data observation based on the received information, wherein the predicted target value corresponds with a physical state associated with the mechanical device or process (paragraph 487: “predicting anomalous operation of a system, comprising: characterizing statistical properties of a plurality of streams of data representing sensor readings over a range of states of the system”);
and transmitting to a remote computing device an instruction to update a parameter value controlling operation of the mechanical device (paragraph 503: “the stock model may initiate with population statistics for the class of system, and as individual-specific data is acquired, update the model to reflect the specific device”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Bhattacharyya with that of Mishra for sensor monitoring and controlling of a mechanical device.
The ordinary artisan would have been motivated to modify Mishra in the manner set forth above for the purposes of determining anomalous operation of a system [Bhattacharyya: Abstract].
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Mishra, US 2023/0076083 A1, in view of Haga, US 20230249582 A1.
Regarding Claim 14, with Mishra teaching those limitations of the claim as previously pointed out, Mishra may not have taught all of the following, however, Haga shows:
The method recited in claim 1, the method further comprising: determining whether the predicted target value falls above a designated minimum positive probability threshold or below a designated maximum negative probability threshold (paragraph 8: “the degradation state acquired by the acquisition unit may be predicted as a probability value of degradation in the period, the state in which the second criterion is satisfied may be one of a first state in which the probability value of the degradation state is at least a first threshold value indicating that a degradation pattern is included and is less than a second threshold value determined as a threshold value for which a degree of degradation is higher than for the first threshold value, or a second state in which the probability value of the degradation state is less than the first threshold value, the acquisition unit may acquire respective degradation states for respective information regarding the state quantity obtained from the plurality of targets, and the update unit may determine whether the first state or the second state is present for each of the respective degradation states, and may differentiate a method of updating the learned model between a first case in which a number of times representing the first state is at least a predetermined number of times and a second case in which a number of times representing the second state is at least a predetermined number of times”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the teachings of Haga with that of Mishra for using probability thresholds in prediction target values.
The ordinary artisan would have been motivated to modify Mishra in the manner set forth above for the purposes of updating a learned model [Haga: Abstract].
Regarding Claim 15, with Mishra teaching those limitations of the claim as previously pointed out, Mishra may not have taught all of the following, however, Haga further teaches:
The method recited in claim 14, wherein the model updating mechanism is selected upon determining that the predicted target value falls above the designated minimum positive probability threshold or below the designated maximum negative probability threshold (paragraph 8: “the degradation state acquired by the acquisition unit may be predicted as a probability value of degradation in the period, the state in which the second criterion is satisfied may be one of a first state in which the probability value of the degradation state is at least a first threshold value indicating that a degradation pattern is included and is less than a second threshold value determined as a threshold value for which a degree of degradation is higher than for the first threshold value, or a second state in which the probability value of the degradation state is less than the first threshold value, the acquisition unit may acquire respective degradation states for respective information regarding the state quantity obtained from the plurality of targets, and the update unit may determine whether the first state or the second state is present for each of the respective degradation states, and may differentiate a method of updating the learned model between a first case in which a number of times representing the first state is at least a predetermined number of times and a second case in which a number of times representing the second state is at least a predetermined number of times”).
Claims 16-19 are similar to Claims 1-4 and are rejected under the same rationale as stated above for those claims.
Claim 20 is similar to Claim 1 and is rejected under the same rationale as stated above for that claim.
Examiner’s Note:
The Examiner cites particular pages, sections, columns, line numbers, and/or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner and the additional related prior arts made of record that are considered pertinent to applicant's disclosure to further show the general state of the art. The Examiner's interpretations in parenthesis are provided with the cited references to assist the applicants to better understand how the examiner interprets the prior art to read on the claims. Such comments are entirely consistent with the intent and spirit of compact prosecution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See PTO-892 for the relevant prior art relating to this application where for example the NPL of Ding teaches novelty detection in data used for training machine learning models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVE MISIR whose telephone number is (571)272-5243. The examiner can normally be reached M-R 8-5 pm, F some hours.
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/DAVE MISIR/Primary Examiner, Art Unit 2127
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