Abstract: Systems and methods receive, by a backend system and from a user device, input(s) indicating dataset(s) should be stored to storage location(s) of the backend system, and dynamically compare data of the dataset(s) to stored data of stored dataset(s), the comparing including applying the dataset(s) to a deployed predictive model that is trained to quantify a percentage of similarity between the dataset(s) and the stored dataset(s). Based on the applying, it is dynamically determined that the percentage of similarity of one dataset of the dataset(s) surpasses a predefined threshold percentage, and electronic notification(s) indicating the percentage of similarity between the one dataset and a stored dataset of the dataset(s) is transmitted to the user device based on the percentage of similarity surpassing the predefined threshold percentage.

Abstract: Systems and methods receive input(s) facilitating dataset management, the input(s) initiating a machine learning process configured to detect data redundancies of two or more datasets. Entity data stored to entity data storage location(s) are accessed and processed to conform with formatting requirements for the machine learning process. Validation is performed on the processed entity data, the validation ensuring the processed entity data satisfy the formatting requirements, where the validation produces training data that is inserted into an iterative training and testing loop.

Abstract: Systems and methods facilitate saving data storage through data storage management of storage location(s) by training, via an iterative training and testing loop, a predictive model using training data to detect data redundancies from two or more datasets stored to data storage location(s), the training includes testing the predictive model by predicting a target variable and iteratively adjusting weights and calculations during each subsequent iteration to improve predictability of the target variable, where the predictive model is trained to identify data similarities among the two or more datasets. Based on any error in predicting the target variable being less than a predetermined level, the predictive model is deployed and applied to at least two datasets to quantify a percentage of similarity among the at least two datasets. If it is determined the percentage of similarity surpasses a predefined threshold percentage, then an electronic notification is transmitted.

Abstract: Systems and methods perform data analysis on at least two separate datasets to identify any redundancies. The data analysis includes comparing first data values of a first dataset with second data values of a second dataset, the comparing including evaluating similarities of the first data values and second data values, identifying, from the comparing, that the first data values and the second data values include at least a portion of substantially similar data, and interpreting similarities of the portion of substantially similar data, the interpreting including determining that a dataset of the first dataset and second dataset is a subset of the other dataset. Further, control signal(s) are transmitted to a user device to initiate displaying, via a user interface of the user device, a prompt indicating that the at least one dataset is likely the subset of the other dataset.

Abstract: Systems and methods access, from data storage location(s), at least two separate datasets, and perform data analysis on the at least two separate datasets to identify any redundancies. The data analysis includes comparing at least one first column name of first column(s) of first data values of a first dataset with at least one second column name of second column(s) of second data values of a second dataset, the comparing including evaluating similarities of the at least one first column name and the at least one second column name. The data analysis also includes determining whether statistical information of the first data values and the second data values are replicas and deriving semantic logic from the first dataset and the second dataset to interpret importance of retaining both the first dataset and the second dataset.

Abstract: Systems and methods access, from data storage location(s), at least two separate datasets, and perform data analysis on the at least two separate datasets to identify any redundancies. The data analysis includes comparing at least one first column name of first column(s) of first data values of a first dataset with at least one second column name of second column(s) of second data values of a second dataset, the comparing including evaluating similarities of the at least one first column name and the at least one second column name. The data analysis also includes determining whether statistical information of the first data values and the second data values are replicas and deriving semantic logic from the first dataset and the second dataset to interpret importance of retaining both the first dataset and the second dataset.

Abstract: Systems and methods are disclosed for creating a governance graph representing data set interconnection. The data set interconnections may be based on common fields, sources, databases, applications, or patterns of usage. For example, the interconnections may be direct connections, where one data set is directly downstream from another data set. Alternatively, the interconnections may be indirect connections based patterns showing the data sets are commonly used together. For example, given data sets “A”, “B”, and “C”, if “B” is directly connected to “A” because it is downstream from “A”, and a particular group of users commonly use “B” and “C” together, “A” may be indirectly related to “C” based on the pattern of usage. In this example, the governance graph is configured to indicate the connection between “A” and “B” is stronger than the connection between “A” and “C”, whilst still showing said connection.

Abstract: Systems and methods are disclosed for creating a governance graph representing data set interconnection. The data set interconnections may be based on common fields, sources, databases, applications, or patterns of usage. For example, the interconnections may be direct connections, where one data set is directly downstream from another data set. Alternatively, the interconnections may be indirect connections based patterns showing the data sets are commonly used together. For example, given data sets “A”, “B”, and “C”, if “B” is directly connected to “A” because it is downstream from “A”, and a particular group of users commonly use “B” and “C” together, “A” may be indirectly related to “C” based on the pattern of usage. In this example, the governance graph is configured to indicate the connection between “A” and “B” is stronger than the connection between “A” and “C”, whilst still showing said connection.

Abstract: Systems and methods are disclosed for creating a governance graph representing data set interconnection. The data set interconnections may be based on common fields, sources, databases, applications, or patterns of usage. For example, the interconnections may be direct connections, where one data set is directly downstream from another data set. Alternatively, the interconnections may be indirect connections based patterns showing the data sets are commonly used together. For example, given data sets “A”, “B”, and “C”, if “B” is directly connected to “A” because it is downstream from “A”, and a particular group of users commonly use “B” and “C” together, “A” may be indirectly related to “C” based on the pattern of usage. In this example, the governance graph is configured to indicate the connection between “A” and “B” is stronger than the connection between “A” and “C”, whilst still showing said connection.

Abstract: A casino gaming system includes gaming machines which are configured to accept and read tickets. The ticket may comprise cash-value tickets or non-cash value/promotional tickets. The tickets may also include secondary feature triggering indicia. The player may present the cash-value ticket having a feature triggering indicia or a non-cash value/promotional ticket to a gaming machine, such as by inserting it into a media reader such as a bill acceptor. Associated monetary value or non-monetary credits may be credited to the machine. In addition, if the ticket includes a feature triggering indicia, when such an indicia is detected, the gaming machine preferably triggers or initiates the feature.

Abstract: This invention relates to a process for making phenolic fatty acid compounds having a reduced phenolic ester content. The invention also relates to method for chemically bonding a phenolic resin with a non-phenolic polymer (e.g., a synthetic fabric). The method comprises contacting a phenolic fatty acid compound with a non-phenolic polymer to introduce a hydroxy phenyl functional group into the non-phenolic polymer; and reacting the hydroxy phenyl functional group contained in the non-phenolic polymer with a phenolic resin or a phenolic crosslinker composition capable of forming a phenolic resin, to chemically bond the phenolic resin with the non-phenolic polymer. The invention is particularly useful for making a synthetic fabric-reinforced article, such as synthetic fabric-reinforced rubber article, circuit board substrate, or fiberglass.

Abstract: This invention relates to a process for making phenolic fatty acid compounds having a reduced phenolic ester content. The invention also relates to method for chemically bonding a phenolic resin with a non-phenolic polymer (e.g., a synthetic fabric). The method comprises contacting a phenolic fatty acid compound with a non-phenolic polymer to introduce a hydroxy phenyl functional group into the non-phenolic polymer; and reacting the hydroxy phenyl functional group contained in the non-phenolic polymer with a phenolic resin or a phenolic crosslinker composition capable of forming a phenolic resin, to chemically bond the phenolic resin with the non-phenolic polymer. The invention is particularly useful for making a synthetic fabric-reinforced article, such as synthetic fabric-reinforced rubber article, circuit board substrate, or fiberglass.

Abstract: A computer-implemented method including receiving an image for evaluation, the image includes video and/or still image data; analyzing the image to extract profile features using a pattern recognition or a facial recognition algorithm, an object recognition, and/or an object character recognition, the profile features include first profile features related to a vehicle and second profile features related to an image verification indicator, the second profile features include one or more features related to an identity of a user; generating an image verification score including a first comparison and a second comparison of at least a portion of one or more of the first profile features and at least a portion of one or more of the second profile features, respectively, and the image verification indicator; and updating a vehicle profile when the image verification score satisfies a threshold of similarity based on the comparisons.

Abstract: In some embodiments, the disclosure describes a system that allows for advanced payment instructions to be included in electronic transmissions without the need to hard-code the payment instructions into an online platform. In some embodiments, the system is configured to intercept electronic transaction transmissions from an acquirer processors. In some embodiments, the system is configured to receive payment instructions from an acquirer processor and replace and/or augment the payment instructions. In some embodiments, the system is configured to consolidate information from multiple sources and display analysis of the information. In some embodiments, information displayed by the system includes artificial intelligence driven analytics. In some embodiments, the AI is configured to determine the most relevant information to display to a user. In some embodiments, analytics provided by the system include sales, authorizations, settlements, deposits, and disputes.

Abstract: Systems and methods receive input(s) facilitating dataset management, the input(s) initiating a machine learning process configured to detect data redundancies of two or more datasets. Entity data stored to entity data storage location(s) are accessed and processed to conform with formatting requirements for the machine learning process. Validation is performed on the processed entity data, the validation ensuring the processed entity data satisfy the formatting requirements, where the validation produces training data that is inserted into an iterative training and testing loop.

Abstract: A high voltage power supply can be compact with shielded electronic components. The power supply can include multiple stages separated by circuit boards. Electronic components for each stage can be directly soldered to adjacent circuit boards. Traces can pass through and electrically couple electronic components on each side of the circuit board between them.

Abstract: A video analytics based image verification system for obtaining initial vehicle profiles is presented. The system may include an external processing server that receives a location of a vehicle and proximate traffic information to determine whether it is safe for a user to obtain an initial vehicle profile. The external processing server may further determine first and second profile features from video data indicative of the vehicle. The external processing server may compare the second profile feature to an image verification indicator to generate an image verification score. A provider server may receive the first profile feature and the image verification score from the external processing server, and update a risk evaluation to include the initial vehicle profile if the image verification score is above an image verification threshold.

Abstract: Systems and methods for analyzing documentation for assessing potential fraudulent user submissions are provided. According to certain aspects, a server computer may receive an initial set of documentation descriptive of damage to a property asset, and may analyze the initial set of documentation to determine whether additional documentation is needed. The server computer may initiate a communication channel with a user device via which the additional documentation may be submitted, and the server computer may similarly analyze the additional documentation to determine a likelihood of fraud. The server computer may process the user submission accordingly.

Abstract: Disclosed herein is an example track pad having a ground-contacting portion for use in a track-based machine to propel and support the machine across a supporting surface. The track pad may be a track pad that forms a link of a chain forming the track for the track-based machine. The track pad includes a cast body formed of a first alloy steel having a first strength and toughness. The track pad includes a roll path that contacts a roller of the track-based machine. The roll path is hardened through an induction hardening process to achieve a hardness level at a depth of 32 millimeters or more from the surface of the roll path. The roll path is shaped and positioned to contact rollers of the track drive assembly and thereby reduce wear and fatigue on the track pad as a result of contact and force transferred through the roller.

Abstract: A method for automatically classifying datasets is implemented on a computing system. A dataset is received by the computing system from a source wherein the dataset includes a plurality of data entries. The method includes the steps of: providing a plurality of predetermined semantic types; processing the data entries to identify each of the data entries as one of the semantic types, the processing including examining the data entries using two different models; generating a confidence score for each of the models based upon the examination of the data entries; generating a confidence label based upon a predetermined combination of the confidence scores; and generating a classification recommendation for the dataset based upon the identified semantic types and associating the confidence label with the dataset.