A computer system includes a transceiver that receives over a data communications network different types of input data from multiple source nodes and a processing system that defines for each of multiple data categories, a set of groups of data objects for the data category based on the different types of input data. Predictive machine learning model(s) predict a selection score for each group of data objects in the set of groups of data objects for the data category for a predetermined time period. Control machine learning model(s) determine how many data objects are permitted for each group of data objects based on the selection score. Decision-making machine learning model(s) prioritize the permitted data objects based on one or more predetermined priority criteria. Subsequent activities of the computer system are monitored to calculate performance metrics for each group of data objects and for data objects actually selected during the predetermined time period. Predictive machine learning model(s) and decision-making machine learning model(s) are adjusted based on the performance metrics to improve respective performance(s).

Systems and methods for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative AI model about structured data in a SaaS environment, enabling users to interact with and manipulate data through AI-assisted interfaces. One of the systems maintains a generative AI agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative AI agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AI. The systems and methods represent advancement in AI-driven SaaS customization and data analysis.

The invention relates to methods and systems for integrating generative artificial intelligence (AI) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining AI agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows AI agents to be added as platform users. The generative AI agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of AI outputs, and management of AI resources as limited assets. This approach enhances SaaS functionality by enabling AI-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

It is determined that a device associated with a subscriber is accessing an online system of a service provider. Data usage information associated with the subscriber as accessed. Based on the data usage information, an offering from a plurality of offerings is selected, the offering including a service offering or a product offering. Information that identifies the offering is caused to be sent to the device for display.

It is determined that a device associated with a subscriber is accessing an online system of a service provider. Data usage information associated with the subscriber as accessed. Based on the data usage information, an offering from a plurality of offerings is selected, the offering including a service offering or a product offering. Information that identifies the offering is caused to be sent to the device for display.

A central computing platform provides users with control over multi-party information involving third-party services during different stages of a travel journey, or while users are provided with data connectivity via different networks or systems. The central computing platform provides a digital ledger on which the multi-party information is recorded. Recording of the multi-party information, as well as modification thereof, requires authentication of each party as well as input (e.g., approval) by the computing platform. In some examples, the central computing platform provides users with control over digital records of real-world interactions with other users. The platform generates and publishes digital records of real-world interactions between users that include media data capturing the real-world interactions. In generating the digital records, the platform authenticates the users involved in the real-world interactions and can execute exchanges of the digital records with third-party users using authentication data for each of the interacting users.

Described herein are methods and systems for enabling retail subscribers to dynamically reallocate their individual subscriptions to different retail data plans in a billing cycle. In one embodiment, a plan grid and a corresponding cost grid for each subscriber are generated prior to the start of a billing cycle based on predicted daily data usage over the billing cycle. Then, on each day of the remaining days in the billing cycle, the plan grid and the cost grid for each subscriber are reconstructed based on actual data usage of each individual subscriber as well as for all subscribers included or eligible to be included in a family pooled plan with the subscriber. On any day of the billing cycle, there may be some reconstructed plan grids that include a cost-reduction time window that can reduce the total predicted cost of some subscribers. These subscribers can then be reallocated to a retail data plan associated with that cost-reduction time window.

Described herein are methods and systems for enabling retail subscribers to dynamically reallocate their individual subscriptions to different retail data plans in a billing cycle. In one embodiment, a plan grid and a corresponding cost grid for each subscriber are generated prior to the start of a billing cycle based on predicted daily data usage over the billing cycle. Then, on each day of the remaining days in the billing cycle, the plan grid and the cost grid for each subscriber are reconstructed based on actual data usage of each individual subscriber as well as for all subscribers included or eligible to be included in a family pooled plan with the subscriber. On any day of the billing cycle, there may be some reconstructed plan grids that include a cost-reduction time window that can reduce the total predicted cost of some subscribers. These subscribers can then be reallocated to a retail data plan associated with that cost-reduction time window.

Provided is a content recommendation method. The content recommendation method includes: receiving, by a user terminal, text information from a user through a text input window of a chatting room, outputting, by the user terminal, a first content set including at least a part of contents related to keyword information to be mapped to the text information, outputting, by the user terminal, a first preview content for a first content included in the first content set, receiving, by the user terminal, an interaction from the user, and in response to the interaction, outputting, by the user terminal, a second preview content for a second content to replace the first preview content, wherein the second content is a content related to the keyword information.

A central computing platform provides users with control over multi-party information involving third-party services during different stages of a travel journey, or while users are provided with data connectivity via different networks or systems. The central computing platform provides a digital ledger on which the multi-party information is recorded. Recording of the multi-party information, as well as modification thereof, requires authentication of each party as well as input (e.g., approval) by the computing platform. In some examples, the central computing platform provides users with control over digital records of real-world interactions with other users. The platform generates and publishes digital records of real-world interactions between users that include media data capturing the real-world interactions. In generating the digital records, the platform authenticates the users involved in the real-world interactions and can execute exchanges of the digital records with third-party users using authentication data for each of the interacting users.