Description:Please refer to the attached complete specification document (Form 2) submitted as part of this application. , Claims:1. An intelligent system for calculating contributional energy, comprising:
an input acquisition module configured to receive data corresponding to one or more entities via a processor or computing unit comprising physical memory, the data comprising physical, cognitive, behavioral, digital, or perceptual information;

an entity efficiency computational module configured to calculate an entity efficiency value (U?) based on said input data, the U? representing a quantitative measure of the entity’s operational or behavioral performance;

an awareness determination module configured to compute an awareness level (A) for the entity, based on contextual, cognitive, environmental, or semantic recognition parameters;

an activation efficiency evaluation module configured to determine the activation efficiency (A?) of the entity, wherein A? represents the degree of timeliness, intentionality, and context-responsiveness of actions performed by the entity;

a PURE computation engine configured to calculate a contributional energy output (E?) using the following mathematical formulation:
E? = ?n ( U?× A × A?)
wherein n denotes each individual entity or sub-entity included in the system's evaluation;

an output module configured to generate, display, or transmit the computed contributional energy E? as a result signal; and

a feedback optimization module configured to analyze the output E? and automatically adjust computation parameters in the U?, A, and A? modules for iterative system refinement.

2. A method for calculating contributional energy using the PURE Equation, the method comprising the steps of:
a. receiving input data for one or more entities via a sensor, digital interface, or data processing pipeline;
b. calculating an entity efficiency metric (U?) using entity-related metrics such as performance rate, behavioral precision, cognitive effectiveness, or sensor outputs;
c. determining an awareness value (A) based on entity's understanding or interpretation of its task, environment, or system state;
d. computing an activation efficiency (A?) by evaluating the quality and responsiveness of the entity's actions with respect to time and context;
e. applying the PURE Equation:
E? = ?? (U?? × A? × A??)
to generate a contributional energy output;
f. transmitting or visualizing the calculated E? value; and
g. performing a feedback refinement step that modifies at least one of U?, A, or A? based on deviation analysis or predictive learning.

3. The system of claim 1, wherein the input data is received from at least one of:
real-time physiological sensors;
digital activity logs;
robotic actuators;
semantic or emotional analysis engines;
institutional, social, or machine networks.
4. The system of claim 1, wherein the awareness module employs semantic parsing, contextual comprehension models, or artificial neural networks to derive the awareness parameter A.
5. The system of claim 1, wherein the activation efficiency module processes action data comprising one or more of:
time-stamped behavioral logs,
precision-alignment metrics,
latency-response intervals,
or motivational alignment parameters.
6. The PURE Engine of claim 1, wherein the computational logic is embedded in at least one of:
software algorithms;
hardware accelerators;
AI-based learning modules;
or edge/cloud hybrid environments.
7. The method of claim 2, wherein the feedback optimization module updates model parameters using supervised learning, reinforcement learning, or deviation-based calibration strategies.
8. The system of claim 1, further comprising a visual analytics interface that displays the entity's transition from a non-optimized energy state to a contributional energy-activated state using dynamic indicators, graphs, or E? trend charts.
9. The system of claim 1, wherein the contributional energy computation is performed in real-time and supports adaptive application in dynamic environments including but not limited to:
autonomous robotics,
educational platforms,
wellness tracking systems,
enterprise optimization systems,
AI decision-making engines,
or cognitive augmentation devices.
10. The system as claimed in claim 1, wherein the contributional energy computation is calibrated through one or more simulation-based parameter adjustment models, the models utilizing historical, empirical, or hypothetical datasets for U? (entity efficiency), A (awareness), and A? (activation efficiency), to optimize the resulting contributional energy output (E?) under varying operational conditions.
11. The system as claimed in claim 1, further comprising a simulation module configured to model and compare the effect of isolated parameter optimization versus simultaneous parameter optimization on the computed contributional energy output (E?), wherein said simulation utilizes hypothetical, empirical, or real-world entity profiles to evaluate contributional performance dynamics under varying U?, A, and A? configurations.
12. Use of the system as claimed in claim 1 for evaluating real-time team contributional efficiency in organizational or corporate environments, wherein said evaluation is based on individual employee U? (efficiency), A (awareness), and A? (activation) parameters computed continuously, and the E? value is used to generate comparative dashboards for team performance optimization, training recommendations, or goal alignment.
13. Use of the system as claimed in claim 1 in autonomous robotic systems for dynamic energy optimization, wherein robotic agents use the PURE Equation to evaluate and adjust their cognitive focus, contextual awareness, and motor activation timing in real-time operational scenarios.
14. The system as claimed in claim 1, wherein the PURE computation engine is further configured to compute aggregated contributional energy across a plurality of entities, and to generate optimization recommendations based on inter-entity relational patterns derived from the collective U?, A, and A? values.
15. The system as claimed in claim 1, wherein the feedback optimization module is integrated with external knowledge bases or ontologies to dynamically refine U?, A, or A? using contextual updates, expert systems, or semantic enrichment engines.