Abstract: Electroactive anode materials for energy storage devices and applications and electrodes comprising them are presented herein. Additionally, methods of manufacturing such materials are disclosed, including forming a precursor electroactive anode material via solid-state synthesis or co-precipitation and converting the precursor electroactive anode material to an electroactive material through ion-exchange. Compositions comprising electroactive anode material (e.g., H2W2O7) and electrodes incorporating these materials for use in electrochemical cells are also presented.

Abstract: Arrangements for using generative AI models for ATM process generation are provided. A computing platform may receive user input from an ATM and may identify, based on the user input, a first generative AI model associated with a first functionality of the ATM. The platform may execute the first model to output a preferred language and a first plurality of options in the preferred language. The platform may generate and transmit, to the ATM, a first output providing the first plurality of options. In response, the ATM may receive second user input selecting an option of the first plurality of options. The platform may identify a second generative AI model associated with second functionality of the ATM. The platform may execute the second model to output a second plurality of options. The platform may generate and transmit, to the ATM, a second user output including the second plurality of options.

Abstract: Predictive analysis for potential future security events may be performed using machine learning and an artificial intelligence model. Machine learning may be based on historical security event data, and analyzed on micro and macro levels to determine possible future events and a likelihood that those future events may occur. Further, machine learning and artificial intelligence models may be used to determine mitigation strategies to address possible future events.

Abstract: Systems, computer program products, and methods are described herein for data integration and predictive analytics using tokenized artificial intelligence agents. The present disclosure is configured to extract invoicing data from an invoicing database, service ticket data from a service ticket system, and contract data from a contract warehouse using a tokenized API interface; normalize and integrate the extracted data within a data rationalization warehouse; process the integrated data using a proprietary tokenized AI agent to identify patterns and trends; apply a machine learning model to the processed data to generate predictive maintenance recommendations; present the predictive maintenance recommendations and underlying data to end users through a presentation layer; and allow end users to interact with and adjust the predictive maintenance recommendations, with changes tracked and updated in the system.

Abstract: Predictive analysis for potential future security events may be performed using machine learning and an artificial intelligence model. Machine learning may be based on historical security event data, and analyzed on micro and macro levels to determine possible future events and a likelihood that those future events may occur. Further, machine learning and artificial intelligence models may be used to determine mitigation strategies to address possible future events.

Abstract: Predictive analysis for potential future security events may be performed using machine learning and an artificial intelligence model. Machine learning may be based on historical security event data, and analyzed on micro and macro levels to determine possible future events and a likelihood that those future events may occur. Further, machine learning and artificial intelligence models may be used to determine mitigation strategies to address possible future events.

Abstract: Systems, computer program products, and methods are described herein for data integration and predictive analytics using tokenized artificial intelligence agents. The present disclosure is configured to extract invoicing data from an invoicing database, service ticket data from a service ticket system, and contract data from a contract warehouse using a tokenized API interface; normalize and integrate the extracted data within a data rationalization warehouse; process the integrated data using a proprietary tokenized AI agent to identify patterns and trends; apply a machine learning model to the processed data to generate predictive maintenance recommendations; present the predictive maintenance recommendations and underlying data to end users through a presentation layer; and allow end users to interact with and adjust the predictive maintenance recommendations, with changes tracked and updated in the system.

Abstract: Arrangements for using generative artificial intelligence models for ATM process generation are provided. In some examples, a computing platform may receive, from at least one image or measurement capture device, dimension data associated with an ATM. The ATM may have a plurality of components arranged on a face of the ATM. The dimension data may be input to a generative artificial intelligence model and the model may be executed to output, based on the dimension data, a position or location of each ATM component relative to a reference point on the ATM. In some examples, the model may further output at least one audio script describing the location of each component. The model may output one or more translations of the at least one audio script. The computing platform may transmit or send the at least one audio script to the ATM for presentation during user interaction with the ATM.

Abstract: Arrangements for using generative AI models for ATM process generation are provided. A computing platform may receive user input from an ATM and may identify, based on the user input, a first generative AI model associated with a first functionality of the ATM. The platform may execute the first model to output a preferred language and a first plurality of options in the preferred language. The platform may generate and transmit, to the ATM, a first output providing the first plurality of options. In response, the ATM may receive second user input selecting an option of the first plurality of options. The platform may identify a second generative AI model associated with second functionality of the ATM. The platform may execute the second model to output a second plurality of options. The platform may generate and transmit, to the ATM, a second user output including the second plurality of options.

Abstract: A module for a modular furniture assembly includes a front frame rail, a rear frame rail, a first side rail, a support system, and a support bridge. The first side rail extends between the front frame rail and the rear frame rail. The first side rail defines a saddle that recesses below the front end portion and the rear end portion. The support system is disposed between the front frame rail, the rear frame rail, and the first side rail of the module. The support system is configured to support a user on the module. The support bridge is attached to the support system and is configured to attached to a support system of an adjacent module to functionally connect the support system of the module to the support system of the adjacent module such that forces are transferred therebetween.

Abstract: Arrangements for using generative artificial intelligence models for ATM operations control are provided. In some examples, a computing platform may receive, from a plurality of ATMs, operation data. The operation data may be analyzed using a first generative artificial intelligence (AI) model to identify one or more potential issues. If an issue is identified, additional data related to the issue may be retrieved from an impacted ATM. A second generative AI model associated with the particular identified issue may be identified. The model may be executed using the additional data as inputs to identify or output a corrective action. The corrective action may be transmitted to the ATM for execution. The one or more generative AI models may be updated to continuously improve accuracy.

Abstract: Systems and methods for selective on-device latency reduction include receiving data requesting that an application associated with a user device be utilized and identifying on-device functionality enabled for the application. Data may be generated that includes a subset of content associated with the on-device functionality and is configured to be utilized by a device in the environment to perform the on-device functionality. This data may be sent to the device in question and may configure the device to process input data associated with the on-device functionality and utilize commands associated with the subset of content based at least in part on processing the input data with the on-device functionality.

Abstract: Arrangements for using generative artificial intelligence models for ATM process generation are provided. In some examples, a computing platform may receive, from at least one image or measurement capture device, dimension data associated with an ATM. The ATM may have a plurality of components arranged on a face of the ATM. The dimension data may be input to a generative artificial intelligence model and the model may be executed to output, based on the dimension data, a position or location of each ATM component relative to a reference point on the ATM. In some examples, the model may further output at least one audio script describing the location of each component. The model may output one or more translations of the at least one audio script. The computing platform may transmit or send the at least one audio script to the ATM for presentation during user interaction with the ATM.

Abstract: The present invention is generally directed to artificial intelligence (AI) systems and materials. In particular, the invention relates to human-like AI that utilizes a symbolic thinking engine to create explanatory theories. Such theories are based on an iterative process that adjusts for available information, context, and/or perspective, and can be created based on sparse information. The invention then iterates to return a useful result that can also be predictive. In one or more embodiments, the present invention is modular and scalable, enabling the addition of multiple domains that collaborate in parallel, similar to how the human mind/brain is believed to work.

Abstract: A transformer apparatus for an electrical power transformation system is provided. The transformer apparatus comprises three outer transformer limbs, an inner transformer limb a transfer star, and first and second connection portions. The transfer star comprises an electromagnetic transfer core and three transfer coils. The electromagnetic transfer core extends from the inner transformer limb to each of the three outer transformer limbs at a point on each outer transformer limb between the first coil assembly and the second coil assembly. The transfer coils are wound around the electromagnetic transfer core such that each transfer coil is arranged between the inner transformer limb and a respective outer transformer limb. The transfer star is configured to allow transfer of magnetomotive force between the outer transformer limbs and the inner transformer limb of the transformer apparatus. First and second connecting portions are to allow magnetic flux to flow between the inner and outer transformer limbs.

Abstract: A transformer apparatus for an electrical power transformation system is provided. The transformer apparatus comprises three outer transformer limbs, an inner transformer limb a transfer star, and first and second connection portions. The transfer star comprises an electromagnetic transfer core and three transfer coils. The electromagnetic transfer core extends from the inner transformer limb to each of the three outer transformer limbs at a point on each outer transformer limb between the first coil assembly and the second coil assembly. The transfer coils are wound around the electromagnetic transfer core such that each transfer coil is arranged between the inner transformer limb and a respective outer transformer limb. The transfer star is configured to allow transfer of magnetomotive force between the outer transformer limbs and the inner transformer limb of the transformer apparatus. First and second connecting portions are to allow magnetic flux to flow between the inner and outer transformer limbs.

Abstract: A carrier configured to support multiple panels includes a housing having a pair of opposing sidewalls extending in a first direction and a plurality of trays stacked between the pair of opposing sidewalls in the first direction. A lid is slidably coupled to the pair of opposing sidewalls and configured to be slid on the housing between an open configuration and a closed configuration. The plurality of trays are supported in the housing such that, in the closed configuration of the lid, two trays of the plurality of trays are blocked from moving relative to one another in the first direction, and in the open configuration of the lid, the two trays are configured to be moved relative to one another in the first direction.

Abstract: A three phase transformer apparatus for a three phase electrical power transformation system is provided. For each phase of the three phase transformer apparatus the three phase transformer apparatus comprises: an upper core limb having a first end and a second end, a lower core limb having a third end and a fourth end, a first coil assembly comprising a first primary coil and a first secondary coil, and a second coil assembly comprising a second secondary coil and a control coil. The first primary coil and the first secondary coil of each first coil assembly are wound concentrically around the upper core limb or the lower core limb of the respective phase. The second secondary coil and the control coil of each second coil assembly are wound concentrically around the other of the upper core limb and the lower core limb of the respective phase. The second secondary coil of each second coil assembly is connected in series to the first secondary coil of the first coil assembly of the respective phase.

Abstract: A vehicle storage system includes a base that is configured to be disposed on seating assemblies within a rear seating space. A first partition is coupled to the base, and the first partition extends along a first edge of the base. A second partition is spaced from the first partition. The second partition is coupled to the base and extends along a second opposing edge of the base. A cross member is coupled to a top edge of each of the first partition and the second partition. The cross member extends adjacent to a headliner.

Abstract: A module for a modular furniture assembly includes a front frame rail, a rear frame rail, a first side rail, a support system, and a support bridge. The first side rail extends between the front frame rail and the rear frame rail. The first side rail defines a saddle that recesses below the front end portion and the rear end portion. The support system is disposed between the front frame rail, the rear frame rail, and the first side rail of the module. The support system is configured to support a user on the module. The support bridge is attached to the support system and is configured to attached to a support system of an adjacent module to functionally connect the support system of the module to the support system of the adjacent module such that forces are transferred therebetween.