Office Action 1: Non-Final Rejection (2024-05-23)
Rejection types: 103 - Obviousness

CLAIMS:
CLAIMS WHAT IS CLAIMED IS:
1\. An apparatus comprising at least one processor and at least one non-transitory memory comprising program code stored thereon, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to at least: transform an original training data set corresponding to a multi-class classification task into at least two adapted training data sets each corresponding to a binary classification task, the at least two adapted training data sets including a coarse-level adapted training data set and at least one fine-level adapted training data set, wherein a coarse-level instance of a binary classification model configured to perform the binary classification task is trained based at least in part on the coarse-level adapted training data set, and at least one fine-level instance of the binary classification model is trained based at least in part on, respectively, each at least one fine-level adapted training data set; transform binary output data generated with respect to an input data set using the trained coarse-level instance of the binary classification model and the trained at least one fine-level instance of the binary classification model into multi-class output data corresponding to the multiclass classification task; and cause performance of at least one enterprise management operation based at least in part on the multi-class output data.
2\. The apparatus of claim 1, wherein the original training data set comprises a multi-class classification of training objects each into an original classification set of at least three original classification sets.
3\. The apparatus of claim 2, wherein the coarse-level adapted training data set comprises a binary classification of the training objects from the original training data set each into one of: a majority classification set corresponding to a particular original classification set of the at least three original classification sets into which a largest proportion of the training objects are classified in the original training data set relative to other original classification sets of the at least three original classification sets; and a non-majority classification set representing a combination of the other original classification sets of the at least three original classification sets other than the particular original classification set to which the majority classification set corresponds.
4\. The apparatus of claim 3, wherein the at least one fine-level adapted training data set each comprises a binary classification of a selected subset of the training objects, including only training objects classified into the non-majority classification set in the coarse-level adapted training data set, each into one of a first classification set and a second classification set, each of which corresponds to a different original classification set from among the combination of original classification sets represented by the non-majority classification set of the coarse-level adapted training data set.
5\. The apparatus of claim 3, wherein a coarse-level portion of the binary output data, which coarse-level portion is generated using the trained coarse-level instance of the binary classification model, comprises a binary classification of all production objects from the input data set each into one of: the majority classification set and the non-majority classification set.
6\. The apparatus of claim 5, wherein a fine-level portion of the binary output data, which fine-level portion is generated using the trained at least one fine-level instance of the binary classification model, comprises a binary classification of a selected subset of the production objects including only production objects classified into the non-majority classification set in the coarse-level portion of the binary output data each into a particular original classification set from among the combination of original classification sets represented by the non-majority classification set.
7\. The apparatus of claim 1, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, further cause the apparatus to at least: train the coarse-level instance of the binary classification model based at least in part on the coarse-level adapted training data set; and train each at least one fine-level instance of the binary classification model based at least in part on, respectively, each at least one fine-level adapted training data set.
8\. The apparatus of claim 1, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, further cause the apparatus to at least: generate the binary output data with respect to the input data set using the trained coarse-level instance of the binary classification model and the trained at least one fine-level instance of the binary classification model.
9\. The apparatus of claim 8, wherein generating the binary output data and transforming the binary output data into the multi-class output data comprises: generating a coarse-level portion of the binary output data by performing, using the trained coarse-level instance of the binary classification model, a coarse-level classification of all production objects from the input data set into one of the majority classification set and the non-majority classification set; generating a fine-level portion of the binary output data by performing, using the trained at least one fine-level instance of the binary classification model, a fine-level classification of a selected subset of the production objects including only production objects classified into the non-majority classification set in the coarse-level portion of the binary output data; and combining the coarse-level portion of the binary output data and the fine-level portion of the binary output data to produce the multi-class output data.
10\. The apparatus of claim 1, wherein the multi-class classification task includes classification of text objects containing text characterizing quality management events associated with products produced by an enterprise into classification sets corresponding to predicted levels of severity of the quality management events.
11\. A computer-implemented method comprising: transforming an original training data set corresponding to a multi-class classification task into at least two adapted training data sets each corresponding to a binary classification task, the at least two adapted training data sets including a coarse-level adapted training data set and at least one fine-level adapted training data set, wherein a coarse-level instance of a binary classification model configured to perform the binary classification task is trained based at least in part on the coarse-level adapted training data set, and at least one fine-level instance of the binary classification model is trained based at least in part on, respectively, each at least one fine-level adapted training data set; transforming binary output data generated with respect to an input data set using the trained coarse-level instance of the binary classification model and the trained at least one fine-level instance of the binary classification model into multi-class output data corresponding to the multiclass classification task; and causing performance of at least one enterprise management operation based at least in part on the multi-class output data.
12\. The method of claim 11, wherein the original training data set comprises a multi-class classification of training objects each into an original classification set of at least three original classification sets.
13\. The method of claim 12, wherein the coarse-level adapted training data set comprises a binary classification of the training objects from the original training data set each into one of: a majority classification set corresponding to a particular original classification set of the at least three original classification sets into which a largest proportion of the training objects are classified in the original training data set relative to other original classification sets of the at least three original classification sets; and a non-majority classification set representing a combination of the other original classification sets of the at least three original classification sets other than the particular original classification set to which the majority classification set corresponds.
14\. The method of claim 13, wherein the at least one fine-level adapted training data set each comprises a binary classification of a selected subset of the training objects, including only training objects classified into the non-majority classification set in the coarse-level adapted training data set, each into one of a first classification set and a second classification set, each of which corresponds to a different original classification set from among the combination of original classification sets represented by the non-majority classification set of the coarse-level adapted training data set.
15\. The method of claim 13, wherein a coarse-level portion of the binary output data, which coarse-level portion is generated using the trained coarse-level instance of the binary classification model, comprises a binary classification of all production objects from the input data set each into one of: the majority classification set and the non-majority classification set.
16\. The method of claim 15, wherein a fine-level portion of the binary output data, which fine-level portion is generated using the trained at least one fine-level instance of the binary classification model, comprises a binary classification of a selected subset of the production objects including only production objects classified into the non-majority classification set in the coarse-level portion of the binary output data each into a particular original classification set from among the combination of original classification sets represented by the non-majority classification set.
17\. The method of claim 11, further comprising: training the coarse-level instance of the binary classification model based at least in part on the coarse-level adapted training data set; and training each at least one fine-level instance of the binary classification model based at least in part on, respectively, each at least one fine-level adapted training data set.
18\. The method of claim 11, further comprising generating the binary output data with respect to the input data set using the trained coarse-level instance of the binary classification model and the trained at least one fine-level instance of the binary classification model.
19\. The method of claim 18, wherein generating the binary output data and transforming the binary output data into the multi-class output data comprises: generating a coarse-level portion of the binary output data by performing, using the trained coarse-level instance of the binary classification model, a coarse-level classification of all production objects from the input data set into one of the majority classification set and the non-majority classification set; generating a fine-level portion of the binary output data by performing, using the trained at least one fine-level instance of the binary classification model, a fine-level classification of a selected subset of the production objects including only production objects classified into the non-majority classification set in the coarse-level portion of the binary output data; and combining the coarse-level portion of the binary output data and the fine-level portion of the binary output data to produce the multi-class output data.
20\. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising an executable portion configured to: transform an original training data set corresponding to a multi-class classification task into at least two adapted training data sets each corresponding to a binary classification task, the at least two adapted training data sets including a coarse-level adapted training data set and at least one fine-level adapted training data set, wherein a coarse-level instance of a binary classification model configured to perform the binary classification task is trained based at least in part on the coarse-level adapted training data set, and at least one fine-level instance of the binary classification model is trained based at least in part on, respectively, each at least one fine-level adapted training data set; transform binary output data generated with respect to an input data set using the trained coarse-level instance of the binary classification model and the trained at least one fine-level instance of the binary classification model into multi-class output data corresponding to the multiclass classification task; and cause performance of at least one enterprise management operation based at least in part on the multi-class output data.

OFFICE ACTION:
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
This instant application No. 18/332,905 has claims 1-20 pending.
Priority / Filing Date
There is no priority being claimed. The effective filing date of this application is June 12, 2023.
Abstract
The abstract of the disclosure is acceptable for examination purposes.
Drawings
The drawings filed on June 12, 2023 are acceptable for examination purposes.
Notes
Claim 1 recites system for multi-class classification comprising at least one processor and at least one non-transitory memory. The claim appears as not implementable in a human mind and/or with the aid of pen/paper (i.e., transforming a multi-class training data set into at least two binary-class training data sets, and transforming outputs associated with the trained binary classification models into multi-class output data). Thus, claim 1 and its dependent claims qualify as eligible subject matters under 35 U.S.C. 101.
Claim 11 recites a method for multi-class classification. The claim appears as not implementable in a human mind and/or with the aid of pen/paper (i.e., transforming a multi-class training data set into at least two binary-class training data sets, and transforming outputs associated with the trained binary classification models into multi-class output data). Thus, claim 11 and its dependent claims qualify as eligible subject matters under 35 U.S.C. 101.
Claim 20 recites a non-transitory computer-readable medium for multi-class classification. The claim appears as not implementable in a human mind and/or with the aid of pen/paper (i.e., transforming a multi-class training data set into at least two binary-class training data sets, and transforming outputs associated with the trained binary classification models into multi-class output data). Thus, claim 20 qualifies as eligible subject matters under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
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 1-2, 7-9, 11-12, and 17-20 are rejected under AIA  35 U.S.C. 103 as being unpatentable over Ezhov et al. (Pub. No. US 2022/0122261, published April 21, 2022; hereinafter Ezhov) in view of Nicacio Braga (Pub. No. US 2023/0139437, filed November 2, 2021; hereinafter Braga).
Regarding claims 1, 11, and 20, Ezhov clearly shows and discloses an apparatus comprising at least one processor and at least one non-transitory memory comprising program code stored thereon, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to at least implement a method; a computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising an executable portion configured to implement the method (Abstract and Figures 2A-2B), wherein the method comprising:
transforming an original training data set corresponding to a multi-class classification task (Formulating the problem of tooth localization as a 33-class semantic segmentation. Therefore, each of the 32 teeth and the background are interpreted as separate classes, [0052]) into at least two adapted training data sets each corresponding to a binary classification task, the at least two adapted training data sets including a coarse-level adapted training data set and at least one fine-level adapted training data set (Figure 10 shows training input images are transformed into soft targets and coarse images used to train coarse model in steps 2.1 [Symbol font/0xAE] 2.6 [Symbol font/0xAE] 2.7 [Symbol font/0xAE] 2.8, and fine images are used to train the fine model in steps 2.1 [Symbol font/0xAE] 2.3 [Symbol font/0xAE] 2.4 [Symbol font/0xAE] 2.13, [0084]-[0098]. The coarse and fine models are trained to predict 2-class segmentation (tooth or other anatomical structure of interest, [0035]-[0037]),
wherein a coarse-level instance of a binary classification model configured to perform the binary classification task is trained based at least in part on the coarse-level adapted training data set, and at least one fine-level instance of the binary classification model is trained based at least in part on, respectively, each at least one fine-level adapted training data (Figure 10 shows training input images are transformed into soft targets and coarse images used to train coarse model in steps 2.1 [Symbol font/0xAE] 2.6 [Symbol font/0xAE] 2.7 [Symbol font/0xAE] 2.8, and fine images are used to train the fine model in steps 2.1 [Symbol font/0xAE] 2.3 [Symbol font/0xAE] 2.4 [Symbol font/0xAE] 2.13, [0084]-[0098]. The coarse and fine models are trained to predict 2-class segmentation (tooth or other anatomical structure of interest, [0035]-[0037]);
transforming binary output data generated with respect to an input data set using the trained coarse-level instance of the binary classification model and the trained at least one fine-level instance of the binary classification model into multi-class output data corresponding to the multiclass classification task (At step 1102, combining a coarse model output with a coarse input image at fine resolution for a coarse output; passing the output through a fine model to generate the probability 1104, [0099]. The system outputs 33-channel image (say, 33×100×100×100-dimensional tensor) that is interpreted as a probability distribution for non-tooth vs. each of 32 possible (for adult human) teeth, for every pixel, [0036]); and
causing performance of at least one enterprise management operation based at least in part on the multi-class output data (receiving a volumetric image comprising a jaw/tooth structure in terms of voxels; and defining each voxel as a distinct anatomical identifier based on a probabilistic distribution for each of anatomical structure. Apply a computer segmentation model to output probability distribution or discrete assignment of each voxel in the image to one or more classes (probabilistic of discrete segmentation), [0066]).
Braga then additionally or alternatively discloses:
transforming an original training data set corresponding to a multi-class classification task (a training dataset sample has data points for three classes that will be referred to as Class 1, Class 2, and Class 3, [0102]) into at least two adapted training data sets each corresponding to a binary classification task (The first binary classifier, being associated with class 1, labels all data points it predicts as Class 1 with a “1” and labels the rest with a “0.” The data points having the label “1” are set aside, and the data points having the label “0” are inputted into the second binary classifier, [0102]-[0103]. It is clear that the data points labeled “1” belong to a coarse-level adapted training dataset since they are set aside and not further inputted into any next binary classifier. The data points labeled “0” belong to a fine-level training dataset since they are further inputted into a next binary classifier);
It would have been obvious to an ordinary person skilled in the art at the time of the invention was effectively filed to incorporate the teachings of Braga with the teachings of Ezhov for the purpose of transforming a multi-class training problem into a series of binary class training problems in which data points are gradually refined from the training dataset as the training process progresses through each of the binary classifiers to provide at least one prediction for a multi-class input dataset.
Regarding claims 2, and 12, Braga further discloses the original training data set comprises a multi-class classification of training objects each into an original classification set of at least three original classification sets (a training dataset sample has data points for three classes that will be referred to as Class 1, Class 2, and Class 3. The classification system builds a multi-class classifier having three binary classifiers connected in series. Each binary classifier is associated with a respective one of the three classes, [0034]).
Regarding claims 7, and 17, Ezhov further discloses:
training the coarse-level instance of the binary classification model based at least in part on the coarse-level adapted training data set (Figure 10 shows training input images are transformed into soft targets and coarse images used to train coarse model in steps 2.1 [Symbol font/0xAE] 2.6 [Symbol font/0xAE] 2.7 [Symbol font/0xAE] 2.8, [0084]-[0098]. The coarse and fine models are trained to predict 2-class segmentation (tooth or other anatomical structure of interest, [0035]-[0037]); and
training each at least one fine-level instance of the binary classification model based at least in part on, respectively, each at least one fine-level adapted training data set (Figure 10 shows fine images are used to train the fine model in steps 2.1 [Symbol font/0xAE] 2.3 [Symbol font/0xAE] 2.4 [Symbol font/0xAE] 2.13, [0084]-[0098]. The coarse and fine models are trained to predict 2-class segmentation (tooth or other anatomical structure of interest, [0035]-[0037]).
Regarding claims 8, and 18, Ezhov further discloses generating the binary output data with respect to the input data set using the trained coarse-level instance of the binary classification model and the trained at least one fine-level instance of the binary classification model (At step 1102, combining a coarse model output with a coarse input image at fine resolution for a coarse output; passing the output through a fine model to generate the probability 1104, [0099]. The system outputs 33-channel image (say, 33×100×100×100-dimensional tensor) that is interpreted as a probability distribution for non-tooth vs. each of 32 possible (for adult human) teeth, for every pixel, [0036]).
Regarding claims 9, and 19, Ezhov further discloses generating the binary output data and transforming the binary output data into the multi-class output data (At step 1102, combining a coarse model output with a coarse input image at fine resolution for a coarse output; passing the output through a fine model to generate the probability 1104, [0099]. The system outputs 33-channel image (say, 33×100×100×100-dimensional tensor) that is interpreted as a probability distribution for non-tooth vs. each of 32 possible (for adult human) teeth, for every pixel, [0036]) comprises:
generating a coarse-level portion of the binary output data by performing, using the trained coarse-level instance of the binary classification model (Figure 10 shows training input images are transformed into soft targets and coarse images used to train coarse model in steps 2.1 [Symbol font/0xAE] 2.6 [Symbol font/0xAE] 2.7 [Symbol font/0xAE] 2.8, [0084]-[0098]. The coarse and fine models are trained to predict 2-class segmentation (tooth or other anatomical structure of interest, [0035]-[0037]);
generating a fine-level portion of the binary output data by performing, using the trained at least one fine-level instance of the binary classification model (Figure 10 shows fine images are used to train the fine model in steps 2.1 [Symbol font/0xAE] 2.3 [Symbol font/0xAE] 2.4 [Symbol font/0xAE] 2.13, [0084]-[0098]. The coarse and fine models are trained to predict 2-class segmentation (tooth or other anatomical structure of interest, [0035]-[0037]).
Braga then discloses:
performing a coarse-level classification of all production objects from the input data set into one of the majority classification set and the non-majority classification set (the first binary classifier, being associated with class 1, labels all data points it predicts as Class 1 with a “1” and labels the rest with a “0.” The data points having the label “1” are set aside, and the data points having the label “0” are inputted into the second binary classifier, [0036]. The process is repeated through a series of binary classifier, and it is clear data with labels “1” can be non-majority data sets since they can be set aside and not further inputted to a next classifier. Data with labels “0” are majority data sets since they are further input into next classifiers to predict the remaining classes);
performing a fine-level classification of a selected subset of the production objects including only production objects classified into the non-majority classification set in the coarse-level portion of the binary output data (The process is repeated through a series of binary classifier, and it is clear data with labels “1” can be non-majority data sets since they can be set aside and not further inputted to a next classifier. Data with labels “0” are majority data sets since they are further input into next classifiers to predict the remaining classes, [0036]); and
combining the coarse-level portion of the binary output data and the fine-level portion of the binary output data to produce the multi-class output data (At step 1102, combining a coarse model output with a coarse input image at fine resolution for a coarse output; passing the output through a fine model to generate the probability 1104, [0099]. The system outputs 33-channel image (say, 33×100×100×100-dimensional tensor) that is interpreted as a probability distribution for non-tooth vs. each of 32 possible (for adult human) teeth, for every pixel, [0036]).
Claim 10 is rejected under AIA  35 U.S.C. 103 as being unpatentable over Ezhov in view of Braga and further in view of Rapowitz et al. (Pub. No. US 2023/0342691, filed on April 22, 2022; hereinafter Rapowitz).
Regarding claim 10, Rapowitz then discloses the multi-class classification task includes classification of text objects containing text characterizing quality management events associated with products produced by an enterprise into classification sets corresponding to predicted levels of severity of the quality management events (labeled data generator 118 tag a particular input or output or group of inputs or outputs as being accurate or inaccurate. Labeled data generator 118 may tag a particular input or output group with a severity indicator. In another embodiment, labeled data generator 118 may employ natural language processing tools 116 to perform text-analyses and determine conclusions about the input or output group. This technique allows labeled data generator 118 to programmatically generate labeled data from the unlabeled data to further prepare the corpus of data for ingestion by machine learning model 114, [0042]-[0044]. Product name 214 may be included in results 211 and may display a relevant product or product(s) related to the initiative request. In some embodiments, the product name may be submitted by intent requestor 102 in the intake form. In another embodiment, risk initiative mitigation system 110 may identify a subset of relevant consumer facing applications from consumer facing applications 130 potentially impacted by the initiative request, [0061]).
It would have been obvious to an ordinary person skilled in the art at the time of the invention was effectively filed to incorporate the teachings of Rapowitz with the teachings of Ezhov, as modified by Braga, for the purpose of training and deploying a machine learning model to generate an assessment of risks and mitigations in response to a novel initiative request regarding a product to provide various feedback reflecting the accuracy of the risks and mitigations.
Allowable Subject Matters
Claims 3-6, and 13-16 are objected for being dependent on a respective base rejected claim but would be allowable if rewritten in independent form to incorporate the limitations of the base claims and all intervening claim(s).
Related Prior Art
The following references are considered as relevant to the claims:
Kaufman et al. (Pub. No. US 2022/0237801) teaches generating a multiclass image segmentation model(s) can include receiving multiple single-class image datasets, receiving a target mask for each of the single-class image datasets, receiving a condition of an object associated with each of the single-class image datasets, and generating the multiclass image segmentation model(s) based on the single-class image datasets, the target masks, and the identification of the target objects.
Lee et al. (Pub. No. US 2020/0193153) teaches balancing the selection of high-quality matches for each input text segment, so that a stored text segment with a high similarity score is chosen for each input text segment, with the desire for the stored text segments to come from a single or relatively smaller number of reference documents. The combination generation algorithm used is an optimization algorithm that optimizes across two or more variables, where for example one of the variables is the text similarity scores for the individual input text segments and another is the number of reference documents used.
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NEXT OFFICE ACTION: 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
Claims 1, 4-11, and 14-20 are allowed in this Office action.
Examiner’s Comment / Summary of Related Prior Art
The prior art on record are summarized as follows:
Ezhov et al. (Pub. No. US 2022/0122261) teaches automated segmentation of volumetric images comprising the steps of: receiving a coarse volumetric image comprising a jaw/tooth structure in terms of voxels; defining each voxel a distinct anatomical identifier based on a probabilistic distribution for each of a tooth/non-tooth class; applying the defined coarse image through a coarse model for a coarse output; combining coarse output with the coarse volumetric image; and generating a probabilistic segmentation from applying the combined coarse output/image through a fine model; and converting the probabilistic to a polygonal mesh for each defined class applying a volume-to-mesh algorithm.
Nicacio Braga (Pub. No. US 2023/0139437) teaches a training batch of data points from training data for a plurality of classes and builds a multi-class classifier having a series of binary classifiers arranged in a first order. Each of the binary classifiers is associated with a respective class. The embodiment trains the multi-class classifier with the binary classifiers arranged in a first order and, at each binary classifier, the embodiment identifies data points as belonging to the class associated with the respective classifier and updates the training batch to exclude the classified data points. The multi-class classifier is modified by changing the order of classifiers and repeats the training of the multi-class classifier with the series of binary classifiers arranged in a second order. A final configuration of the multi-class classifier is selected based at least in part on a comparison of first training results to the second training results.
Rapowitz et al. (Pub. No. US 2023/0342691) teaches training and deploying a machine learning model to generate an assessment of risks and mitigations in response to a novel initiative request. After generating labeled data from a corpus of prior risk assessments, a machine learning model may be trained to programmatically generate a risk assessment in response to a novel initiative request. A risk auditor may subsequently review the risk assessment generated using the machine learning model to provide various feedback reflecting the accuracy of the risks and mitigations. The machine learning model may then be retrained based on the feedback provided by the risk auditor to provide more accurate risks and mitigations in response to future initiatives.
Kaufman et al. (Pub. No. US 2022/0237801) teaches generating a multiclass image segmentation model(s) can include receiving multiple single-class image datasets, receiving a target mask for each of the single-class image datasets, receiving a condition of an object associated with each of the single-class image datasets, and generating the multiclass image segmentation model(s) based on the single-class image datasets, the target masks, and the identification of the target objects.
Lee et al. (Pub. No. US 2020/0193153) teaches balancing the selection of high- quality matches for each input text segment, so that a stored text segment with a high similarity score is chosen for each input text segment, with the desire for the stored text segments to come from a single or relatively smaller number of reference documents. The combination generation algorithm used is an optimization algorithm that optimizes across two or more variables, where for example one of the variables is the text similarity scores for the individual input text segments and another is the number of reference documents used.
Sharma et al. (Pub. No. US 2020/0356813) teaches a new data layer, being used to avoid bias in training process by favoring the gradients' updates towards the class with the majority of the samples, with the concept of weighted buckets as class containers. Each bucket has three properties: the source file of its contents, the unique label that represents its contents, and the weight that determines the likelihood with which a sample from the bucket might get picked as a member of each training batch. In this design, samples of a single class may be represented using several buckets with the same label, and if desired different weights, but each bucket is not capable of holding more than a single class. Each bucket's weight enables us to uniformly represent classes for which we have different numbers of training images, by assigning equal weights to their corresponding buckets.
Reasons for Allowance
The following is an examiner's statement of reasons for allowance of Claims 1, 4-11, and 14-20:
In interpreting the current set of claims, in view of Applicant’s latest remarks, and in light of the search/examination and the available prior art, the Examiner finds the claimed invention to be patentably distinct from the prior art of records. Specifically, the prior art of records, individually or in combination, fail to explicitly teach, suggest or render obvious the claimed invention as recited in independent claims 1, 11, and 20.
Other dependent claims are also allowed based on their dependencies on claims 1, 11, and 20.
Any comments considered necessary by the Applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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CITED REFERENCE SUMMARIES: Ref US20220122261A1: [content unavailable]
Ref US20230139437A1: [content unavailable]
Ref US20230342691A1: Claims:
A computer implemented method, comprising: generating, by one or more processors, using a machine learning model trained with existing input materials and existing output materials, a risk assessment for an initiative request received from an intent requestor, wherein the risk assessment comprises one or more risks and one or more mitigations; displaying a risk assessment interface that presents summary information for the risk assessment, the one or more risks in association with the one or more mitigations, and a feedback interface that allows the intent requestor to provide feedback for each risk in the one or more risks; receiving the feedback from the intent requestor, wherein the feedback comprises a relevancy indicator and an accuracy indicator; and retraining the machine learning model based on the relevancy indicator and the accuracy indicator to cause the machine learning model to provide more accurate results in response to a future initiative request.
generating, by one or more processors, using a machine learning model trained with existing input materials and existing output materials, a risk assessment for an initiative request received from an intent requestor, wherein the risk assessment comprises one or more risks and one or more mitigations;
displaying a risk assessment interface that presents summary information for the risk assessment, the one or more risks in association with the one or more mitigations, and a feedback interface that allows the intent requestor to provide feedback for each risk in the one or more risks;
receiving the feedback from the intent requestor, wherein the feedback comprises a relevancy indicator and an accuracy indicator; and
retraining the machine learning model based on the relevancy indicator and the accuracy indicator to cause the machine learning model to provide more accurate results in response to a future initiative request.
The method of claim 1 , further comprising: identifying a risk auditor; displaying the risk assessment interface to the risk auditor; receiving additional feedback from the risk auditor; and updating the machine learning model based on the additional feedback.
identifying a risk auditor;
displaying the risk assessment interface to the risk auditor;
receiving additional feedback from the risk auditor; and
updating the machine learning model based on the additional feedback.
The method of claim 1 , wherein the risk assessment further comprises a severity indicator associated with a risk in the one or more risks.
The method of claim 1 , further comprising: applying sentiment analysis to the feedback to extract a polarity of the feedback; and generating the relevancy indicator based on the polarity.
applying sentiment analysis to the feedback to extract a polarity of the feedback; and
generating the relevancy indicator based on the polarity.
The method of claim 1 , further comprising: determining, a risk advisor associated with a mitigation in the one or more mitigations; generating a summary of the intent request; and displaying a summary page in the risk assessment interface to the risk advisor comprising the summary, the one or more risks, and the one or more mitigations.
determining, a risk advisor associated with a mitigation in the one or more mitigations;
generating a summary of the intent request; and
displaying a summary page in the risk assessment interface to the risk advisor comprising the summary, the one or more risks, and the one or more mitigations.
The method of claim 1 , wherein the relevancy indicator comprises a selection of relevant or irrelevant.
The method of claim 1 , wherein the accuracy indicator quantifies the accuracy of the risk and comprises a numerical score or a letter grade.
A system, comprising: a memory; and at least one processor coupled to the memory and configured to: generate using a machine learning model trained with existing input materials and existing output materials, a risk assessment for an initiative request received from an intent requestor, wherein the risk assessment comprises one or more risks and one or more mitigations; display a risk assessment interface that presents summary information for the risk assessment, the one or more risks in association with the one or more mitigations, and a feedback interface that allows the intent requestor to provide feedback for each risk in the one or more risks; receive the feedback from the intent requestor, wherein the feedback comprises a relevance indicator and an accuracy indicator; and retrain the machine learning model based on the relevancy indicator and the accuracy indicator to cause the machine learning model to provide more accurate results in response to future initiative request.
at least one processor coupled to the memory and configured to: generate using a machine learning model trained with existing input materials and existing output materials, a risk assessment for an initiative request received from an intent requestor, wherein the risk assessment comprises one or more risks and one or more mitigations; display a risk assessment interface that presents summary information for the risk assessment, the one or more risks in association with the one or more mitigations, and a feedback interface that allows the intent requestor to provide feedback for each risk in the one or more risks; receive the feedback from the intent requestor, wherein the feedback comprises a relevance indicator and an accuracy indicator; and retrain the machine learning model based on the relevancy indicator and the accuracy indicator to cause the machine learning model to provide more accurate results in response to future initiative request.
generate using a machine learning model trained with existing input materials and existing output materials, a risk assessment for an initiative request received from an intent requestor, wherein the risk assessment comprises one or more risks and one or more mitigations;
display a risk assessment interface that presents summary information for the risk assessment, the one or more risks in association with the one or more mitigations, and a feedback interface that allows the intent requestor to provide feedback for each risk in the one or more risks;
receive the feedback from the intent requestor, wherein the feedback comprises a relevance indicator and an accuracy indicator; and
retrain the machine learning model based on the relevancy indicator and the accuracy indicator to cause the machine learning model to provide more accurate results in response to future initiative request.
The system of claim 8 , the at least one processor further configured to: identify a risk auditor; display the risk assessment interface to the risk auditor; receive additional feedback from the risk auditor; and update the machine learning model based on the additional feedback.
identify a risk auditor;
display the risk assessment interface to the risk auditor;
receive additional feedback from the risk auditor; and
update the machine learning model based on the additional feedback.
The system of claim 8 , wherein the risk assessment further comprises a severity indicator associated with a risk in the one or more risks.
The system of claim 8 , the at least one processor further configured to: apply sentiment analysis to the feedback to extract a polarity of the feedback; and generate the relevancy indicator based on the polarity.
apply sentiment analysis to the feedback to extract a polarity of the feedback; and
generate the relevancy indicator based on the polarity.
The system of claim 8 , the at least one processor further configured to: determine a risk advisor associated with a mitigation in the one or more mitigations; generate a summary of the intent request; and display a summary page in the risk assessment interface to the risk advisor comprising the summary, the one or more risks, and the one or more mitigations.
determine a risk advisor associated with a mitigation in the one or more mitigations;
generate a summary of the intent request; and
display a summary page in the risk assessment interface to the risk advisor comprising the summary, the one or more risks, and the one or more mitigations.
The system of claim 8 , wherein the relevancy indicator comprises a selection of relevant or irrelevant.
The system of claim 8 , wherein the accuracy indicator quantifies the accuracy of the risk and comprises a numerical score or a letter grade.
A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: generating using a machine learning model trained with existing input materials and existing output materials, a risk assessment for an initiative request received from an intent requestor, wherein the risk assessment comprises one or more risks and one or more mitigations; displaying a risk assessment interface that presents summary information for the risk assessment, the one or more risks in association with the one or more mitigations, and a feedback interface that allows the intent requestor to provide feedback for each risk in the one or more risks; receiving the feedback from the intent requestor, wherein the feedback comprises a relevancy indicator and an accuracy indicator; and retraining the machine learning model based on the relevancy indicator and the accuracy indicator to cause the machine learning model to provide more accurate results in response to a future initiative request.
generating using a machine learning model trained with existing input materials and existing output materials, a risk assessment for an initiative request received from an intent requestor, wherein the risk assessment comprises one or more risks and one or more mitigations;
The non-transitory computer-readable device of claim 15 , the operations further comprising: identifying a risk auditor; displaying the risk assessment interface to the risk auditor; receiving additional feedback from the risk auditor; and updating the machine learning model based on the additional feedback.
The non-transitory computer-readable device of claim 15 , wherein the risk assessment further comprises a severity indicator associated with a risk in the one or more risks.
The non-transitory computer-readable device of claim 15 , the operations further comprising: applying sentiment analysis to the feedback to extract a polarity of the feedback; and generating the relevancy indicator based on the polarity.
The non-transitory computer-readable device of claim 15 , the operations further comprising: determining, a risk advisor associated with a mitigation in the one or more mitigations; generating a summary of the intent request; and displaying a summary page in the risk assessment interface comprising the summary, the one or more risks, and the one or more mitigations.
displaying a summary page in the risk assessment interface comprising the summary, the one or more risks, and the one or more mitigations.
The non-transitory computer-readable device of claim 15 , wherein the relevancy indicator comprises a selection of relevant or irrelevant.
Ref US20220237801A1: [content unavailable]
Ref US20200193153A1: [content unavailable]
Ref US20220122261: Claims:
1. A method for automated segmentation of volumetric images, said method comprising the steps of:
receiving a volumetric image comprising a jaw/tooth structure in terms of voxels; and
defining each voxel a distinct anatomical structure by identifier based on a probabilistic distribution for each of the anatomical structure.
2. The method of claim 1, wherein the at least one received volumetric image comprises a 3-D pixel array.
3. The method of claim 2, further comprising the step of pre-processing by converting the 3-D pixel array into an array of Hounsfield Unit (HU) radio intensity measurements.
4. The method of claim 1, further comprising the step of pre-processing the received image by rescaling using linear interpolation.
5. The method of claim 4, wherein the pre-processing comprises using any one of a normalization schemes to account for variations in image value intensity depending on at least one of an input or output of volumetric image.
6. The method of claim 1, further comprising the step of localizing using a V-Net-based fully convolutional neural network.
7. The method of claim 1, further comprising the step of extracting anatomical structure by finding a minimum bounding rectangle around the defined structure.
8. The method of claim 7, wherein the bounding rectangle extends by at least 15 mm vertically and 8 mm horizontally (equally in all directions) to capture the tooth and surrounding context.
9. The method of claim 1, further comprising the step of condition classification using a DenseNet 3-D convolutional neural network.
10. The method of claim 1, further comprising converting the probabilistic to a polygonal mesh for each defined class applying a volume-to-mesh algorithm.
11. A method for automated segmentation of volumetric images, said method comprising the steps of:
receiving a volumetric image comprising a jaw/tooth structure in terms of voxels; defining each voxel a distinct anatomical identifier based on a probabilistic distribution for each of an anatomical structure; and converting the probabilistic to a polygonal mesh for each defined class applying a volume-to-mesh algorithm.
12. The method of claim 11, wherein the at least one received volumetric image comprises a 3-D pixel array.
13. The method of claim 11, further comprising defining voxels using a V-Net-based fully convolutional neural network.
14. The method of claim 11, further comprising the step of extracting an anatomical structure by finding a minimum bounding rectangle around the localized and identified tooth/non-tooth.
15. The method of claim 14, wherein the bounding rectangle extends by at least 15 mm vertically and 8 mm horizontally (equally in all directions) to capture the tooth and surrounding context.
16. The method of claim 11, further comprising the step of condition classification using a DenseNet 3-D convolutional neural network.
17. A method for automated segmentation of volumetric images, said method comprising the steps of:
receiving a coarse volumetric image comprising a jaw/tooth structure in terms of voxels; defining each voxel a distinct anatomical identifier based on a probabilistic distribution for each of an anatomical structure; applying the defined coarse image through a coarse model for a coarse output; combining coarse output with the coarse volumetric image; and generating a probabilistic segmentation from applying the combined coarse output/image through a fine model; and converting the probabilistic to a polygonal mesh for each defined class applying a volume-to-mesh algorithm.
18. The method of claim 17, further comprising segmenting the polygonal mesh for each defined class.
19. The method of claim 17, further comprising a patch-based approach for the fine model segmentation.
20. The method of claim 17, further comprising a Region-of-Interest (RoI) approach for the fine model segmentation.
Ref US20230139437: Claims:
1. A computer implemented method comprising:
generating a training batch of data points from training data associated with a plurality of classes; building a multi-class classifier having a series of binary classifiers arranged in a first order, wherein each of the series of binary classifiers is associated with a respective class of the plurality of classes; training the multi-class classifier with the series of binary classifiers being arranged in the first order using the training batch of data points, the training comprising, at each binary classifier in succession:
identifying data points as belonging to the associated class resulting in classified data points; and
updating the training batch to exclude the classified data points prior to the training batch being inputted into a subsequent binary classifier;
 modifying the multi-class classifier by changing the order of the series of binary classifiers resulting in the series of binary classifiers being arranged in a second order; repeating the training of the multi-class classifier with the series of binary classifiers being arranged in the second order using the training batch of data points; and selecting a final configuration of the multi-class classifier based at least in part on a comparison of first training results from training with the series of binary classifiers arranged in the first order to second training results from training with the series of binary classifiers arranged in the second order.
2. The method of claim 1, wherein the generating of the training batch of data points comprises randomly selecting the data points from the training data.
3. The method of claim 1, wherein each of the series of binary classifiers outputs a first value for data points predicted to be part of the class associated with the binary classifier and outputs a second value for data points predicted to be part of any other classes of the plurality of classes.
4. The method of claim 1, wherein training of the multi-class classifier further comprises applying a multi-class voting classifier to any data points remaining unclassified after progressing through the series of binary classifiers.
5. The method of claim 1, further comprising:
generating a first random order as the first order of arrangement of the series of binary classifiers; and generating a second random order as the second order of arrangement of the series of binary classifiers.
6. The method of claim 1, wherein the modifying of the multi-class classifier comprises:
designating a plurality of groups of binary classifiers from the series of binary classifiers such that each of the binary classifiers is in exactly one of the plurality of groups; and arranging the binary classifiers in each of the groups according to respective random orders of arrangement.
7. The method of claim 6, further comprising:
modifying the multi-class classifier by changing the order of the series of binary classifiers resulting in the series of binary classifiers being arranged in a third order; and repeating the training of the multi-class classifier with the series of binary classifiers being arranged in the third order using the training batch of data points.
8. The method of claim 7, wherein the modifying of the multi-class classifier resulting in the series of binary classifiers being arranged in the third order comprises:
designating a plurality of sub-groups of binary classifiers from each of the groups of binary classifiers such that each of the binary classifiers is in exactly one of the plurality of sub-groups; and arranging the binary classifiers in each of the sub-groups according to respective random orders of arrangement.
9. The method of claim 8, wherein each of the groups of binary classifiers includes an equal number of the sub-groups.
10. The method of claim 1, further comprising:
generating a first overall confidence score based on cumulative confidence scores from classifying each of the data points with the binary classifiers arranged in the first order; and generating second overall confidence score based on cumulative confidence scores from classifying each of the data points with the binary classifiers arranged in the second order.
11. The method of claim 10, wherein the comparison of the first training results to the second training results comprises comparing the first overall confidence score to the second overall confidence score.
12. A computer program product, the computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable sto
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