Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a variety of machine learning models and a base limit value model to generate user interface elements that transparently and efficiently present current and future base limit values for user accounts. For example, the disclosed systems can utilize a machine learning model to determine a base limit value, subsequent base limit value, and user activity conditions to achieve the subsequent base limit value for a user account. Then, the disclosed systems can display a base limit progress element that indicates progress towards fulfilling the user activity conditions to achieve the subsequent base limit value. For example, the disclosed systems can display, within a graphical user interface, multiple base limit progress elements that indicate progress towards fulfilling the user activity conditions in separate time-based segments (e.g.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a variety of machine learning models and a base limit value model to generate user interface elements that transparently and efficiently present current and future base limit values for user accounts. For example, the disclosed systems can utilize a machine learning model to determine a base limit value, subsequent base limit value, and user activity conditions to achieve the subsequent base limit value for a user account. Then, the disclosed systems can display a base limit progress element that indicates progress towards fulfilling the user activity conditions to achieve the subsequent base limit value. For example, the disclosed systems can display, within a graphical user interface, multiple base limit progress elements that indicate progress towards fulfilling the user activity conditions in separate time-based segments (e.g.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a variety of machine learning models and a base limit value model to generate user interface elements that transparently and efficiently present current and future base limit values for user accounts. For example, the disclosed systems can utilize a machine learning model to determine a base limit value, subsequent base limit value, and user activity conditions to achieve the subsequent base limit value for a user account. Then, the disclosed systems can display a base limit progress element that indicates progress towards fulfilling the user activity conditions to achieve the subsequent base limit value. For example, the disclosed systems can display, within a graphical user interface, multiple base limit progress elements that indicate progress towards fulfilling the user activity conditions in separate time-based segments (e.g.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that increase a base limit value for a contact based on a user interaction with a graphical user interface. Generally, the disclosed system provides a graphical user interface for display to a user that includes base limit increase elements to increase base limit values for contacts of the user. Based on user selection of a base limit increase element, the disclosed system can provide an increased base limit value to an account associated with the contact.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that increase a base limit value for a contact based on a user interaction with a graphical user interface. Generally, the disclosed system provides a graphical user interface for display to a user that includes base limit increase elements to increase base limit values for contacts of the user. Based on user selection of a base limit increase element, the disclosed system can provide an increased base limit value to an account associated with the contact.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a variety of machine learning models and a base limit value model to generate user interface elements that transparently and efficiently present current and future base limit values for user accounts. For example, the disclosed systems can select from between multiple activity machine learning models and utilize the selected activity machine learning model with user activity data to determine an activity score. Then, the disclosed systems can determine a base limit value using a base limit value model that includes relations between activity scores and various user activity conditions. Additionally, the disclosed systems can generate user interface elements within a graphical user interface to display the determined base limit value, a subsequent base limit value, and user activity conditions to achieve the subsequent base limit value within a graphical user interface.

Abstract: A tool bit assembly includes a tool bit having a shaft with a first working region disposed at a first end of the shaft and a first reduced diameter portion proximate the working region and disposed between a first shoulder and a second shoulder. A floating sleeve has a rear portion with a pair of opposed window openings, a front portion supporting a magnet, and a pair of balls, with each ball disposed in one of the window openings. The floating sleeve is receivable over the tool bit with the magnet proximate the first working regions and the balls received in the first reduced diameter portion to allow the floating sleeve to move between a first position in which the balls abut the first shoulder and a second position in which the balls abut the second shoulder.

Abstract: A fastener driving tool includes a shaft with a working region disposed at a first end of the shaft and configured to drive a fastener, a shank portion disposed proximate a second end of the shaft and configured to couple the shaft to a power tool, and a reduced diameter torsion zone disposed between the shank portion and the working region, the torsion zone extending from a first shoulder closer to the working region to a second shoulder closer to the shank portion. A length (L) and a radius (c) of the torsion zone have been optimized so that approximately half of breakages occur in the working region and approximately half of breakages occur in the torsion zone.

Abstract: A tool bit assembly includes a tool bit having a shaft with a first working region disposed at a first end of the shaft and a first reduced diameter portion proximate the working region and disposed between a first shoulder and a second shoulder. A floating sleeve has a rear portion with a pair of opposed window openings, a front portion supporting a magnet, and a pair of balls, with each ball disposed in one of the window openings. The floating sleeve is receivable over the tool bit with the magnet proximate the first working regions and the balls received in the first reduced diameter portion to allow the floating sleeve to move between a first position in which the balls abut the first shoulder and a second position in which the balls abut the second shoulder.

Abstract: A tool bit assembly includes a tool bit having a shaft with a first working region disposed at a first end of the shaft and a first reduced diameter portion proximate the working region and disposed between a first shoulder and a second shoulder. A floating sleeve has a rear portion with a pair of opposed window openings, a front portion supporting a magnet, and a pair of balls, with each ball disposed in one of the window openings. The floating sleeve is receivable over the tool bit with the magnet proximate the first working regions and the balls received in the first reduced diameter portion to allow the floating sleeve to move between a first position in which the balls abut the first shoulder and a second position in which the balls abut the second shoulder.

Abstract: A fastener driving tool includes a shaft with a working region disposed at a first end of the shaft and configured to drive a fastener, a shank portion disposed proximate a second end of the shaft and configured to couple the shaft to a power tool, and a reduced diameter torsion zone disposed between the shank portion and the working region, the torsion zone extending from a first shoulder closer to the working region to a second shoulder closer to the shank portion. A length (L) and a radius (c) of the torsion zone have been optimized so that approximately half of breakages occur in the working region and approximately half of breakages occur in the torsion zone.

Abstract: A tool bit assembly includes a tool bit having a shaft with a first working region disposed at a first end of the shaft and a first reduced diameter portion proximate the working region and disposed between a first shoulder and a second shoulder. A floating sleeve has a rear portion with a pair of opposed window openings, a front portion supporting a magnet, and a pair of balls, with each ball disposed in one of the window openings. The floating sleeve is receivable over the tool bit with the magnet proximate the first working regions and the balls received in the first reduced diameter portion to allow the floating sleeve to move between a first position in which the balls abut the first shoulder and a second position in which the balls abut the second shoulder.

Abstract: The invention relates to an apparatus and method to obtain a bitumen or heavy oil sample from an oil reservoir sample, such as a core sample, to enable measurement of physical properties such as viscosity, API gravity, or chemical properties such as sulphur content of the obtained bitumen or heavy oil sample. The analyses performed on the samples obtained in accordance with the invention are effective in assisting oil field operators in making timely drilling and production decisions at the oil reservoir or for routine laboratory extraction of oils and bitumens. The invention also permits the collection of samples from simulated thermal recovery operations and also allows the collection of bitumens and oils for online analysis of live oil physical properties.

Abstract: Techniques for determining viscosity of oil in a sample include extracting the oil from the sample using a volatile polar first solvent; using a portion of the extracted oil to determine a concentration factor of oil in the sample; determining a dry weight of the extracted oil using the concentration factor; adding a second solvent to a remaining portion of the extract, where the second solvent is less volatile than the first solvent; removing the first solvent from the remaining portion of the extract; taking a series of sub-samples from the remaining portion of the extract and sequentially removing different amounts of the second solvent from each sub-sample; measuring solvent concentrations and viscosity values of the solvent-oil mixtures of each sub-sample; and based on the measured solvent concentrations and viscosity values of each sub-sample, determining the second-solvent-free viscosity of the oil in the sample.

Abstract: A process is described for measuring a property of oil extracted from a sample, for example, the viscosity or density. The oil is extracted from the sample using a volatile polar first solvent. A portion of the extracted oil is used to determine a concentration factor. A dry weight of the oil in the sample is calculated using the concentration factor. A second solvent less volatile than the first solvent is added to the remaining extract. The first solvent is substantially removed from the remaining extract. Different amounts of the second solvent are removed from each of a series of sub-samples taken from the remaining extract. Solvent concentrations and viscosity values and/or density values of the solvent-oil mixtures are measured for each sub-sample. Based on the measured solvent concentrations and viscosity values and/or density values, the second-solvent free viscosity and/or density of the oil in the sample is estimated.