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公开(公告)号：US20240267239A1
公开(公告)日：2024-08-08
申请号：US18164483
申请日：2023-02-03
摘要： A method includes determining, by a trained machine learning model, a score based at least on one or more latent features. The method also includes monitoring the determining of the score by the trained machine learning model. The monitoring includes determining one or more production statistics associated with the one or more latent features, derived variables and input data elements, and accessing one or more reference assets persisted on a model governance blockchain. The one or more reference assets includes one or more reference statistics and a threshold indicating a deviation between the one or more production statistics and the one or more reference statistics. The method also includes generating an alert based on the one or more production statistics associated with the one or more latent features meeting the threshold. Related methods and articles of manufacture are also disclosed.
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公开(公告)号：US20240232683A9
公开(公告)日：2024-07-11
申请号：US17972510
申请日：2022-10-24
发明人： Matthew Kennel , Scott Zoldi
CPC分类号： G06N20/00 , G06K9/6262
摘要： Explanatory dropout systems and methods for improving a computer implemented machine learning model are provided using on-manifold/on-distribution evaluation of dropout of key features to explain model outputs. The machine learning model is trained using a plurality of input examples, including input records with explicit dropout operators applied effectuating the removal of influence of features associated with an explanation reason class. One or more dropout operators may be stochastically applied to one or more input examples. The procedure includes on-manifold/on-distribution evaluation of the machine learning model under conditions of absence or presence of the one or more dropout operators for reliable calculation of numerical statistics associated with reason classes to yield model explanations. The training and evaluation procedures present advantages over traditional off-manifold or off-distribution perturbative explanation procedures.
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公开(公告)号：US20240220907A1
公开(公告)日：2024-07-04
申请号：US18608128
申请日：2024-03-18
发明人： Mary Krone , Ryan Weber , Ana Paula Azevedo Travassos , Laura Waterbury , Paulo Mei , Mayumi Assato , Shubham Kedia , Nitin Basant , Chisoo Lyons
IPC分类号： G06Q10/0639 , G06F16/9035 , G06F16/9038 , G06N20/00 , G06Q10/067
CPC分类号： G06Q10/06395 , G06F16/9035 , G06F16/9038 , G06N20/00 , G06Q10/067
摘要： Computer-implemented methods, systems and products for analytics and discovery of patterns or signals. The method includes a set of operations or steps, including collecting data from a plurality of data sources, the data having a plurality of associated data types, and filtering the collected data based on identifying viable data sources from which the data is collected. The method further includes prioritizing discovery objectives based on analyzing the filtering results, and enriching the filtered collected data from viable data sources according to the prioritized discovery objectives. The method further includes extracting one or more signals from the enriched data using one or more machine learning mechanisms in combination with qualified subject matter expertise input, and graphically displaying the extracted signals in a meaningful way to a human operator such that the human operator is enabled to understand importance of extracted signals.
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公开(公告)号：US20240086944A1
公开(公告)日：2024-03-14
申请号：US18509249
申请日：2023-11-14
发明人： Jun Zhang , Scott Michael Zoldi
IPC分类号： G06Q30/0201 , G06N3/045 , G06N5/045
CPC分类号： G06Q30/0201 , G06N3/045 , G06N5/045 , G06N20/00
摘要： A diagnostic system for model governance is presented. The diagnostic system includes an auto-encoder to monitor model suitability for both supervised and unsupervised models. When applied to unsupervised models, the diagnostic system can provide a reliable indication on model degradation and recommendation on model rebuild. When applied to supervised models, the diagnostic system can determine the most appropriate model for the client based on a reconstruction error of a trained auto-encoder for each associated model. An auto-encoder can determine outliers among subpopulations of consumers, as well as support model go-live inspections.
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公开(公告)号：US11875239B2
公开(公告)日：2024-01-16
申请号：US18103328
申请日：2023-01-30
发明人： Chong Huang , Arash Nourian , Feier Lian , Longfei Fan , Kevin Griest , Jari Koister , Andrew Flint
CPC分类号： G06N20/00 , G06F17/18 , G06F18/10 , G06F18/217 , G06F18/251 , G06V10/70
摘要： Computer-implemented machines, systems and methods for managing missing values in a dataset for a machine learning model. The method may comprise importing a dataset with missing values; computing data statistics and identifying the missing values; verifying the missing values; updating the missing values; imputing missing values; encoding reasons for why values are missing; combining imputed missing values and the encoded reasons; and recommending models and hyperparameters to handle special or missing values.
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公开(公告)号：US11818147B2
公开(公告)日：2023-11-14
申请号：US17102295
申请日：2020-11-23
CPC分类号： H04L63/1416 , G06N3/04 , G06N3/08
摘要： Systems, methods and computer program products for improving security of artificial intelligence systems. The system comprising processors for monitoring one or more transactions received by a machine learning decision model to determine a first score associated with a first transaction. The first transaction may be identified as likely adversarial, in response to the first score being lower than a certain score threshold and the first transaction having a low occurrence likelihood. A second score may be generated in association with the first transaction based on one or more adversarial latent features associated with the first transaction. At least one adversarial latent feature may be detected as being exploited by the first transaction, in response to determining that the second score falls above the certain score threshold. Accordingly, an abnormal volume of activations of adversarial latent features spanning across a plurality of transactions scored may be detected and blocked.
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公开(公告)号：US11804302B2
公开(公告)日：2023-10-31
申请号：US17469794
申请日：2021-09-08
发明人： Gerald Fahner , Brad Vancho
IPC分类号： G06Q10/06 , G06Q30/02 , G16H50/20 , G06N20/20 , G16H50/30 , G06Q10/0637 , G06Q10/0635 , G06Q40/03
CPC分类号： G16H50/20 , G06N20/20 , G06Q10/0635 , G06Q10/06375 , G06Q40/03 , G16H50/30
摘要： A sensitivity index model for predicting the sensitivity of an entity to a potential future disruption can be trained using a process that includes dividing a population of entities for which data attributes are available into matched pairs in a first sub-population and a second sup-population based on matching propensity scores for the entities using supervised machine learning, modeling outcomes for the two sub-populations, using the resultant models to calculate expected performances of the entities under differing conditions, and generating the sensitivity index model using supervised learning techniques based on quantification of differences between the calculated expected performances for the entities.
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8.发明授权公开(公告)号：US11694292B2
公开(公告)日：2023-07-04
申请号：US16395112
申请日：2019-04-25
发明人： Scott Michael Zoldi , Qing Lin
摘要： A system and method includes soft-segment based rules optimization that can mitigate the overall false positives while maintaining 100% true positive detection. The soft clustering allows real-time re-assignment of an account to a dominate archetype behavior, as well as rule optimization based on a logical order with more relaxation on thresholds for the most inefficient rules is performed within each archetype. The rule optimization provides false positive reduction compared to a baseline rule system. The method can be used to reduce false positives for any rule-based detection system in which the same true positive detection is required.
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9.发明公开公开(公告)号：US20230162277A1
公开(公告)日：2023-05-25
申请号：US17531709
申请日：2021-11-19
IPC分类号： G06Q40/02 , G06F40/194
CPC分类号： G06Q40/025 , G06F40/194
摘要： Systems, methods, and products for detection of selective omissions in an open data sharing computing platform comprises monitoring a plurality of events associated with a first digital record stored in a database of digital records, the first digital record uniquely identifying a first entity; associating a first detected event with a first set of words at least partially descriptive of the first detected event; associating a second detected event with a second set of words at least partially descriptive of the second detected event, the first event and the second event being detected, in response to digital records associated with the first event and the second event being shared over an open data sharing computing platform with express authorization provided by the first entity.
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10.发明授权公开(公告)号：US11636485B2
公开(公告)日：2023-04-25
申请号：US15947717
申请日：2018-04-06
发明人： Scott Michael Zoldi , Alexei Betin
摘要： Parallelized computation by a real-time transaction scoring system that incorporates global behavior profiling of transacting entities includes dividing a global profile computing component of a transaction scoring model of a real-time behavioral analytics transaction scoring system into a plurality of global profile component instances. The transaction scoring model uses a plurality of global profile variables, each of the plurality of global profile component instances using its own global profile partition that contains the estimate of global profile variables and being configured for update by a dedicated thread of execution of the real-time transaction scoring system, each dedicated thread being configured for receiving and scoring a portion of input transactions. The method further includes partitioning, based on one or more transaction routing shuffling algorithms, the input transactions for receipt across the plurality of global profile component instances, and updating each of the plurality of global profile partitions by the corresponding global profile component running in the dedicated thread according to the scoring algorithm.
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