Abstract: An example operation may include one or more of storing a portfolio of assets of a user in memory, receiving text from a conversation between the user on a first device and a second user on a second device, identifying an upcoming life event of the user based on execution of a generative artificial intelligence (GenAI) model on the received text from the conversation, determining a change to the portfolio of assets of the user based on the upcoming life event and existing assets within the portfolio of assets, and displaying the change to the portfolio of assets of the user via a user interface.

Abstract: An example operation may include one or more of displaying a report on a user interface of a software application on a user device, listening to a call between a user on the user device and a different user on a second user device that is connected to the user device via a network, executing a generative artificial intelligence (GenAI) model based on content that is heard during the call and content within the report displayed on the user interface to identify content within the displayed report that is discussed during the call, and modifying the displayed report to emphasize the identified content within the displayed report on the user interface.

Abstract: An example operation may include one or more of storing a portfolio of assets of a user in memory, receiving contextual data of the user from a user device of the user, identifying an asset of interest that is not included in the portfolio of assets of the user based on execution of a generative artificial intelligence (GenAI) model on the portfolio of assets of the user and the received contextual data of the user, generating a different portfolio of assets based on the asset of interest that is not included in the portfolio of assets of the user, and displaying the different portfolio of assets via a user interface.

Abstract: An example operation may include one or more of storing a current portfolio of a user in memory, receiving contextual data of the user from a user device of the user, identifying an asset of interest of the user based on execution of a generative artificial intelligence (GenAI) model based on the received contextual data of the user and the current portfolio of the user stored in memory, predicting a performance of the current portfolio with the identified asset of interest included therein at a future point in time, and displaying the predicted performance of the current portfolio with the identified asset of interest included therein on a user interface.

Abstract: An example operation may include one or more of receiving content from a conversation between a user on a user device and a second user on a second user device that is connected to the user device via a network, identifying a topic of the conversation based on execution of a generative artificial intelligence (GenAI) model on the received content from the conversation, identifying a call list that is previously stored in memory that is associated with the topic of the conversation based on keywords included in the identified topic of the conversation, and adding an identifier of the user to the call list stored in memory.

Abstract: An example operation may include one or more of receiving a current status of a portfolio of a user and previous actions taken on the portfolio of the user over a predetermined period of time, determining a goal for the user based on execution of a generative artificial intelligence (GenAI) model on the current status of the portfolio of the user and the previous actions taken, receiving a request for the portfolio of the user via a user device, and in response to the request, generating a text-based description of the goal and displaying the text-based description of the goal with portfolio content from the portfolio of the user on a user interface of the user device.

Abstract: An example operation may include one or more of receiving a conversation of a user, identifying a goal of the user from the conversation, identifying a different user that is associated with the identified goal of the conversation, generating a call script comprising a description of content therein to be discussed with a different user based on execution of a generative artificial intelligence (GenAI) model on the identified goal, and integrating the call script into a digital calendar of the different user.

Abstract: This application provides a speaker and an electronic device. The speaker includes a ring-shaped housing, a magnetic circuit system, a first diaphragm disposed at a first end of the ring-shaped housing, a first component, a first voice coil, a second diaphragm, a second component, and a second voice coil, a first channel, and the first channel is located in at least one of the magnetic circuit system and the ring-shaped housing. The dual-diaphragm speaker in embodiments of this application has a small volume, and can improve low- and medium-frequency sensitivity.

Abstract: The disclosed embodiments include computer-implemented systems, apparatuses, and processes that automatically generate and provision for presenting actionable icons on a graphical user interface (GUI) of a computer device.

Abstract: The disclosed embodiments include computer-implemented systems, apparatuses, and processes that automatically generate and provision a system of machine learning models specifically configured and trained for providing a signal output indicative of a prediction and associated confidence metrics derived via retraining the prediction model for providing the initial prediction and comparing outputs of the set of machine learning models to expected thresholds for the prediction and generating, based on the comparison, a set of actions to be performed on a networked computing environment relating to one or more transactions associated with the prediction.

Abstract: This application relate to the field of electronic device technologies, and in particular, to a loudspeaker and an electronic device. Embodiments of this application are provided to resolve a technical problem of insufficient sensitivity of a loudspeaker caused by a small electromagnetic driving force that drives a voice coil to vibrate. According to a loudspeaker and an electronic device in embodiments of this application, a yoke includes a bottom part and a sleeve located on the side of the bottom part. An annular magnet is sleeved on the outer side of the sleeve. A voice coil is sleeved on the sleeve and located between the magnet and the sleeve. A damper is located in a region enclosed by the sleeve. An outer edge of the damper is connected to the voice coil, and a middle part of the damper is connected to the yoke.

Abstract: In an aspect, the present application describes a method including: obtaining account data associated with an account; passing at least some of the account data through a non-sufficient funds classifier, the non-sufficient funds classifier being a machine learning classifier configured to classify the account as either likely to have a non-sufficient funds event or unlikely to have a non-sufficient funds event, the machine learning classifier configured to evaluate a plurality of parameters associated with the account data; in response to determining that the account is likely to have a non-sufficient funds event, forecasting a balance for the account, wherein forecasting the balance for the account includes; and providing, to a client device associated with the account, a notification based on a forecasted balance.

Abstract: In an aspect, the present application describes a method including: obtaining account data associated with an account; passing at least some of the account data through a non-sufficient funds classifier, the non-sufficient funds classifier being a machine learning classifier configured to classify the account as either likely to have a non-sufficient funds event or unlikely to have a non-sufficient funds event, the machine learning classifier configured to evaluate a plurality of parameters associated with the account data; in response to determining that the account is likely to have a non-sufficient funds event, forecasting a balance for the account, wherein forecasting the balance for the account includes; and providing, to a client device associated with the account, a notification based on a forecasted balance.

Abstract: A modified solid electrolyte membrane is disclosed, and a preparation method therefor, and a lithium battery. The modified solid electrolyte membrane includes a base membrane and a solid electrolyte layer. The solid electrolyte layer is formed by a coating slurry, the basic membrane being coated by the coating slurry, or the basic membrane being coated by the coating slurry and the coating slurry even permeating into the basic membrane. The solid electrolyte layer is formed by a NASICON type solid electrolyte material and has a particle size smaller than 1 ?m.

Abstract: In an aspect, the present application describes a method including: obtaining account data associated with an account; passing at least some of the account data through a non-sufficient funds classifier, the non-sufficient funds classifier being a machine learning classifier configured to classify the account as either likely to have a non-sufficient funds event or unlikely to have a non-sufficient funds event, the machine learning classifier configured to evaluate a plurality of parameters associated with the account data; in response to determining that the account is likely to have a non-sufficient funds event, forecasting a balance for the account, wherein forecasting the balance for the account includes; and providing, to a client device associated with the account, a notification based on a forecasted balance.

Abstract: In an aspect, the present application describes a method including: identifying, from a set of accounts each having a transfer listing that includes a plurality of transfers with a first counterparty, a first rule; identifying, for a first account not included in the set of accounts, an initial transfer with the first counterparty; identifying one or more projected future transfers for first account based on a date associated with the initial transfer and the first rule; detecting one or more further transfers with the first counterparty from the transfer listing for the first account; determining, based on the one or more further transfers and the initial transfer, that a second rule matches a pattern defined by the further transfers and the initial transfers better than the first rule; and providing a notification to a client device associated with the first account based on the second rule.

Abstract: In an aspect, the present application describes a computer-implemented method including: storing a set of candidate rules, the set of candidate rules defining payment cycles; identifying, from a transaction history for an account, an actual set of transfers made to a first recipient; identifying a first reference transfer from the actual set of transfers; for each of at least a plurality of candidate rules in the set of candidate rules, identifying, based on the first reference transfer, an expected set of transfers for that candidate rule; identifying one of the candidate rules as a closest rule for the actual set of transfers; identifying a future expected transfer based on the identified one of the candidate rules; and providing a notification to a client device associated with the account of the future expected transfer.

Abstract: An apparatus for use in a digital messaging system includes a storage device and a processor coupled to the storage device. The storage device storing software instructions for controlling the processor that when executed by the processor configured the processor to: generate a first message comprising a payload portion; encrypt the payload portion of the message; derive a first session key from a domain-specific key; and sign the message using the first session key.

Abstract: An apparatus for use in a digital messaging system includes a storage device and a processor coupled to the storage device. The storage device storing software instructions for controlling the processor that when executed by the processor configured the processor to: generate a master private and public key pair; associate the master private and public key pair with a first certificate; and derive at least one domain-specific key from the one of the master private and public key pair. The first certificate is registered to a group comprising a plurality of domains. The domain-specific key is associated with one of the plurality of domains.