Abstract: Disclosed embodiments pertain to automated payment splitting. A bill to a user for goods or services can be detected. Contacts that are collocated with the user can be determined via social media image analysis. A notification can be sent to the contacts to join a payment group. The payment group can be created upon receiving a response to the notification. A split payment rubric can be determined for the payment group. The split payment rubric can enumerate payment responsibilities of each person in the payment group using social media image analysis. Payment of the bill can be completed via a split payment amongst the payment group according to the split payment rubric.

Abstract: Disclosed embodiments relate to a smart video conference environment. A live video of a conference participant can be segmented into a foreground, including the participant, and a background that excludes the participant. The background can be replaced by a virtual background determined based on one or more factors. The factors can include context data acquired from a calendar, content based on the context data, and transaction history. Further, interaction amongst conference participants can be monitored and utilized to update the background in one instance based on topics of conversation. Interactive content presented in the virtual background permits interaction by participants with the background. Furthermore, content can also be overlaid on the participant and the background in one instance.

Abstract: Aspects described herein may provide modification of recommendations from a recommendation engine for a product or a device. The recommendation engine may provide initial search results for a particular type of device. The initial search results may be modified to ensure inclusion of devices that include features that the user considers important. Features that the user considers important may be determined based on observing the user's interaction with another device of the same type. By observing the user's interaction with the other device over a period of time, features that the user commonly uses and features that the user sparingly uses may be determined. The initial search results may then be modified to remove devices that do not include the features frequently used by the user and therefore considered important to the user.

Abstract: A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

Abstract: A server device may be configured to obtain image data that depicts a set of objects associated with a user. The server device may be configured to process, using at least one image analysis technique, the image data to determine identification information for each object of the set of objects. The server device may be configured to obtain exchange data related to at least one exchange log of the user and may be configured to determine, based on the exchange data and the identification information, estimated exchange information for each object of a subset of objects of the set of objects. The server device may be configured to determine, based on the estimated exchange information, estimated assessment information for each object of the subset of objects and may be configured to generate, based on the estimated assessment information, presentation information for display via a graphical user interface (GUI).

Abstract: In some implementations, a system may generate a first exchange profile for a first user and a second exchange profile for a second user. The first exchange profile may identify one or more characteristics of an exchange behavior of the first user based on exchange data of the first user, and the second exchange profile may identify one or more characteristics of an exchange behavior of the second user based on exchange data of the second user. The system may receive, from a first device of the first user, a request for a selection for the first user. The system may determine, based on the first exchange profile and the second exchange profile, an entity that is relevant to the first user. The system may transmit, to the first device of the first user, information indicating the selection for the first user, where the selection identifies the entity.

Abstract: Methods and systems disclosed herein describe customizing searching. Search queries may be customized according to a user's preferences. A user may emphasize or indicate that additional weight should be given to one or more terms in a search query. Terms that are weighted higher may have a larger impact on the results that are returned in response to the search query. In addition to changing the terms in a search query, a user may provide a weight for each term. Each term in a search query may be weighted to varying degrees, giving a user more control over the results that are returned. The weights may be used with machine learning techniques to generate a vector representation of a search query. The vector representation of the search query may be compared with vector representations of search objects to determine results that match the search query.

Abstract: Aspects described herein may provide modification of recommendations from a recommendation engine for a product or a device. The recommendation engine may provide initial search results for a particular type of device. The initial search results may be modified to ensure inclusion of devices that include features that the user considers important. Features that the user considers important may be determined based on observing the user's interaction with another device of the same type. By observing the user's interaction with the other device over a period of time, features that the user commonly uses and features that the user sparingly uses may be determined. The initial search results may then be modified to remove devices that do not include the features frequently used by the user and therefore considered important to the user.

Abstract: A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

Abstract: In some implementations, a trend prediction system may identify, using a machine learning model, one or more consumers having a historical tendency to adopt one or more trends near a beginning of one or more trend adoption curves. The trend prediction system may predict, using the machine learning model, a consumer trend near a beginning of a trend adoption curve based on a subset of consumer data associated with the one or more consumers having the historical tendency to adopt the trends near the beginning of the trend adoption curves. The trend prediction system may determine, based on the consumer trend that is near the beginning of the trend adoption curve, local trend information in an area associated with a client. The trend prediction system may provide, to a device associated with the client, the local trend information.

Abstract: A server device may be configured to obtain image data that depicts a set of objects associated with a user. The server device may be configured to process, using at least one image analysis technique, the image data to determine identification information for each object of the set of objects. The server device may be configured to obtain exchange data related to at least one exchange log of the user and may be configured to determine, based on the exchange data and the identification information, estimated exchange information for each object of a subset of objects of the set of objects. The server device may be configured to determine, based on the estimated exchange information, estimated assessment information for each object of the subset of objects and may be configured to generate, based on the estimated assessment information, presentation information for display via a graphical user interface (GUI).

Abstract: A system may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold. When the busyness score for the ATM does not exceed the busyness score threshold, the system may cause the ATM to present, via a first graphical user interface, a default ATM experience. When the busyness score for the ATM exceeds the busyness score threshold, the system may cause the ATM to present via, a second graphical user interface, a busy ATM experience.

Abstract: A server device may be configured to obtain image data that depicts a set of objects associated with a user. The server device may be configured to process, using at least one image analysis technique, the image data to determine identification information for each object of the set of objects. The server device may be configured to obtain exchange data related to at least one exchange log of the user and may be configured to determine, based on the exchange data and the identification information, estimated exchange information for each object of a subset of objects of the set of objects. The server device may be configured to determine, based on the estimated exchange information, estimated assessment information for each object of the subset of objects and may be configured to generate, based on the estimated assessment information, presentation information for display via a graphical user interface (GUI).

Abstract: A modular transaction card assembly includes a card frame having the traditional dimensions of a credit card, and a transaction card that is smaller than a traditional card and that fits into a receptacle of the card frame. The card frame may include a window in which contact pads of the transaction card are positioned and flush with the surface of the card frame when the transaction card is secured in the receptacle. The card frame and transaction card assembled together may form a uniform assembly that appears and functions as a traditional transaction card (e.g., credit card).

Abstract: A modular transaction card assembly includes a card frame having the traditional dimensions of a credit card, and a transaction card that is smaller than a traditional card and that fits into a receptacle of the card frame. Each of the card frame and the transaction card may be capable of performing contactless data transactions individually. In some instances, the combined assembly of the card frame with the transaction card may detect a proximity of a mobile device to the assembly and generate an authentication credential that is unique to the combination of the card frame, the transaction card, and the mobile device. The authentication credential may be used to authentic the transaction card when conducting a data transaction.

Abstract: A modular transaction card assembly includes a card frame having the traditional dimensions of a credit card, and a transaction card that is smaller than a traditional card and that fits into a receptacle of the card frame. Each of the card frame and the transaction card may be capable of performing contactless data transactions individually. The combined assembly of the card frame with the transaction card secured in the receptacle is also capable of performing contact data transactions, and in some instances, with an identifier that is distinct from the card frame or the transaction card alone. The card frame may include a processor enabling it to perform data encryption and authentication of the smaller transaction card.

Abstract: A modular transaction card assembly includes a card frame having the traditional dimensions of a credit card, and a transaction card that is smaller than a traditional card and that fits into a receptacle of the card frame. Each of the card frame and the transaction card may be capable of performing contactless data transactions individually. The combined assembly of the card frame with the transaction card secured in the receptacle is also capable of performing contact data transactions. The transaction card and/or the card frame may include one or more visual or tactile regions that allow the transaction card and card frame to be distinguished from other cards and/or to be properly oriented and aligned with one another when combined to form the card assembly.

Abstract: A modular transaction card assembly includes a card frame having the traditional dimensions of a credit card, and a transaction card that is smaller than a traditional card and that fits into a receptacle of the card frame. Each of the card frame and the transaction card may be capable of performing contactless data transactions individually. The combined assembly of the card frame with the transaction card secured in the receptacle is also capable of performing contact data transactions, and in some instances, with an identifier that is distinct from the card frame or the transaction card alone. The card frame may include a processor enabling it to perform data encryption and authentication of the smaller transaction card.

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).