| CPC G06Q 10/1097 (2013.01) [G06Q 40/06 (2013.01)] | 14 Claims |
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1. A system for task scheduling and financial planning, comprising:
a computer system comprising at least one memory and at least one processor;
a user profile datastore;
a client profile datastore;
a user device, such as a smartphone or personal computer;
a smart scheduling application comprising at least a first plurality of programming instructions stored in the at least one memory, and operating on at least one processor of, the computer system, wherein the first plurality of programming instructions, when operating on the at least one processor, cause the computer system to:
create an initial profile for a user and client, if a profile for a user or client does not already exist in the user profile datastore or client profile datastore, respectively;
save any newly created user or client profile in the user profile datastore or client profile datastore, respectively;
load any existing profile for a user or client involved in usage of the application, from a user profile datastore or client profile datastore, respectively;
receive data of events, obligations, tasks, or notifications, from a data fusion suite or task management engine;
organize the events, obligations, tasks, or notifications, in a calendar-based schedule using an adaptive scheduling algorithm that dynamically adjusts to changing priorities and temporal constraints;
render the calendar-based schedule to the user, via a graphical user interface of the user device, wherein the rendering includes a visual representation of scheduling conflicts and dependency relationships between tasks;
a task management engine comprising at least a second plurality of programming instructions stored in the at least one memory of, and operating on at least one processor of, the computer system, wherein the second plurality of programming instructions, when operating on the at least one processor, cause the computer system to:
request updated data from heterogeneous, external, or third-party data sources, via a data fusion suite, as directed or requested by a user or client or their respective profiles or settings;
receive data from any heterogeneous, external, or third-party data sources, including but not limited to financial institutions, market providers, and other scheduling applications, from a data fusion suite;
determine, from the received data, what events, obligations, tasks, or notifications, may be worth scheduling for the specific user and client, based on their profiles, using a computational load balancing protocol that distributes processing across multiple processing nodes to maintain system responsiveness;
send the events, obligations, tasks, or notifications to a machine learning engine for determining possible scheduling optimization according to user and client profile data;
allow a user or client to add, remove, or modify any events, obligations, tasks, or notifications through a transaction processing protocol that ensures data integrity during concurrent modifications;
send any user or client additions, removals, or modifications to a machine learning engine;
a machine learning engine comprising at least a third plurality of programming instructions stored in the at least one memory of, and operating on at least one processor of, the computer system, wherein the third plurality of programming instructions, when operating on the at least one processor, cause the computer system to:
implement a recurrent neural network comprising multiple hidden layers, wherein outputs from later layers are fed back to influence previous layers exhibiting temporal dynamic behavior, wherein the recurrent neural network maintains stateful information across multiple scheduling iterations to identify patterns that stateless algorithms cannot detect;
use genetic or evolutionary programming to optimize neural network parameters through successive generations of models, wherein the genetic programming implements crossover and mutation operations that adapt to the specific scheduling domain;
read profile data from a user profile datastore and client profile datastore;
receive data regarding possible or planned events from the task management engine;
use the recurrent neural network to optimize the scheduling of the possible or planned events by operating on a recurrent neural network architecture that processes temporal dependencies between events;
record any user modifications to optimize the neural network parameters using genetic or evolutionary programming;
use optimized neural network to improve future scheduling by implementing incremental learning techniques that preserve previously learned patterns while adapting to new user preferences;
a data fusion suite comprising at least a fourth plurality of programming instructions stored in the at least one memory of, and operating on at least one processor of, the computer system, wherein the fourth plurality of programming instructions, when operating on the at least one processor, cause the computer system to:
process outgoing requests for data updates from heterogeneous, external, or third-party data sources using an adaptive protocol selection mechanism that automatically determines optimal communication methods for each external system;
format outgoing requests for data updates from heterogeneous, external, or third-party data sources, according to each data source's requirements, formatting, or protocol using an extensible transformation engine that supports multiple data formats;
receive any incoming data and request responses from heterogeneous, external, or third-party data sources using secure transmission protocols that maintain data integrity; and
send any requested data or regularly scheduled data notifications from heterogeneous, external, or third-party data sources, to a task management engine or smart scheduling application, or to any other authorized requestor using an encrypted data pipeline that prevents unauthorized access.
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