US-12619634-B2 - Method and an apparatus for meta-model optimization of an engagement portfolio

US12619634B2US 12619634 B2US12619634 B2US 12619634B2US-12619634-B2

Abstract

A method for meta-model optimization may include receiving an entity and engagement profile, classify each entity and engagement profile to one or more descriptors, compiling a digital model for each entity and engagement profile, identifying an optimal compatibility allocation of entities to engagements; and generating a user display summarizing the optimal meta-model to the user.

Inventors

Barbara Sue Smith
Daniel J. Sullivan

Assignees

THE STRATEGIC COACH INC.

Dates

Publication Date: 20260505
Application Date: 20240801

Claims (20)

1 . An apparatus for meta-model optimization, the apparatus comprising: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive entity profile data and engagement profile data from a user; apply, by a descriptor manager, one or more data tags to the entity profile data and the engagement profile data in order to convert the entity profile data and the engagement profile data into a machine-readable format; apply, by the descriptor manager, one or more labels to an entity profile of the entity data and an engagement profile of the engagement profile data; classify, using the one or more labels, the entity profile and the engagement profile to a plurality of descriptors, wherein classifying each descriptor comprises; receiving training data, wherein the training data comprises historical user inputs and assigned descriptors; and training a machine-learning model as a function of the training data; identify an optimal compatibility allocation of entities to engagements, wherein the optimal compatibility allocation is determined as a function of prioritizing the entity profile and the engagement profile by generating and training a descriptor classifier to categorize the assigned descriptors; generate a user display summarizing the optimal compatibility allocation; and reassess entity allocations to continually optimize user engagement portfolio.
2 . The apparatus of claim 1 , wherein receiving the entity profile data and the engagement profile data from the user comprises receiving the entity profile data and the engagement profile data from a graphical user interface.
3 . The apparatus of claim 1 , wherein receiving the entity profile data and the engagement profile data from the user comprises receiving the entity profile data and the engagement profile data from a data repository.
4 . The apparatus of claim 1 , wherein the entity profile data uses a set of proprietary or industry standard multiple-choice questions to identify an entity's most effective contribution qualities and store the entity profile data to a data repository.
5 . The apparatus of claim 1 , wherein prioritizing the entity profile comprises identifying a future goal of the entity profile.
6 . The apparatus of claim 1 , wherein the classify each entity profile of the entity profile data and engagement profile of the engagement profile data to a plurality of descriptors comprises: generating classifier training data, wherein the classifier training data comprises exemplary entity profiles and exemplary engagement profiles classified to exemplary descriptors, which are subsequently successfully paired as assessed by the user; training a descriptor classifier as a function of the classifier training data; and correlating the entity profile data and the engagement profile data using the descriptor classifier.
7 . The apparatus of claim 1 , wherein the classify each entity profile comprises: associating the entity profile and the engagement profile to affiliated descriptors; incorporating affiliated relative prioritization of any related descriptor into the entity profile and the engagement profile; integrating any historical data available for the entity profile and the engagement profile into the machine-learning model; and categorizing each profile based on any statistically significant characteristics as determined by the machine-learning model.
8 . The apparatus of claim 1 , wherein identifying the optimal compatibility allocation of the entities to the engagements comprises: accepting user-defined priorities for each engagement; training the machine-learning model as a function of the optimal compatibility allocation of the entities; constructing a compatibility profile for each potential entity-engagement pair which evaluates availability and priority of each profile with regard to a likelihood of engagement success; and calculating the optimal allocation of the entities across all known engagements to optimize execution based on the user-defined priorities.
9 . The apparatus of claim 8 , wherein calculating the optimal allocation of the entities across the all known engagements comprises: isolating mandatory attribute requirements; isolating unique features of each entity model and its assigned descriptors from all entity models and matching a best fit entity model with needs of each engagement; comparing all possible allocations and ranking them based on optimization of all user engagements; and filtering all of the possible allocations based on a set of user refinement criteria.
10 . The apparatus of claim 8 , wherein calculating the optimal allocation of the entities across the all known engagements comprises calculating the optimal allocation as a function of a machine-learning model.
11 . A method for meta-model optimization, the method comprising; receiving, by a computing device, entity profile data and engagement profile data; applying, by a descriptor manager, one or more data tags to the entity profile data and the engagement profile data in order to convert the entity profile data and the engagement profile data into a machine-readable format; applying, by the descriptor manager, one or more labels to an entity profile of the entity data and an engagement profile of the engagement profile data; correlating, by the computing device, using the one or more labels, the entity profile and the engagement profile to a plurality of descriptors; compiling, by the computing device, a digital model for each entity profile and the engagement profile; identifying, by the computing device, an optimal compatibility allocation of entities to engagements, wherein the optimal compatibility allocation is determined as a function of prioritizing the entity profile and the engagement profile by generating and training a descriptor classifier to categorize the assigned descriptors; generating, by the computing device, a user display summarizing the optimal compatibility allocation; and reassessing entity allocations to continually optimize user engagement portfolio.
12 . The method of claim 11 , wherein receiving, by the computing device, the entity profile data and the engagement profile data comprises receiving, by a graphical user interface communicatively connected to the computing device, the entity profile data and the engagement profile data.
13 . The method of claim 11 , wherein receiving, by the computing device, the entity profile data and the engagement profile data comprises receiving, by a data repository communicatively connected to the computing device, the entity profile data and the engagement profile data.
14 . The method of claim 11 , wherein receiving, by the computing device, the entity profile data, the computing device uses a set of proprietary or industry standard multiple-choice questions to identify most effective contribution qualities of the entity and store the entity profile data to a data repository.
15 . The method of claim 11 , wherein prioritizing the entity profile comprises identifying a future goal of the entity profile.
16 . The method of claim 11 , wherein the classify each entity profile of the entity profile data and engagement profile of the engagement profile data to a plurality of descriptors comprises: generating, by the computing device, classifier training data, wherein the classifier training data comprises exemplary entity profiles and exemplary engagement profiles classified to exemplary descriptors; training, by the computing device, a descriptor classifier as a function of the classifier training data; and correlating, by the computing device, the entity profile data and the engagement profile data using the descriptor classifier.
17 . The method of claim 11 , wherein correlating, by the computing device, each entity profile comprises: associating the entity profile and the engagement profile to affiliated descriptors; incorporating affiliated relative prioritization of any related descriptor into the entity profile and the engagement profile; integrating any historical data available for the entity profile or the engagement profile into a machine-learning model; and categorizing each profile based on any statistically significant characteristics as determined by the machine-learning model.
18 . The method of claim 11 , wherein identifying, by the computing device, the optimal compatibility allocation of the entities to the engagements comprises: incorporating relative prioritization of each descriptor of the plurality of descriptors; training a machine-learning model as a function of the optimal allocation of resources; constructing a digital model for each correlated entity profile and each correlated engagement profile; and calculating the optimal allocation of the entities across all known engagements.
19 . The method of claim 18 , wherein calculating, by the computing device, the optimal allocation of the entities across the all known engagements comprises: isolating mandatory attribute requirements; isolating unique features of each entity model and its descriptors from all entity models and matching a best fit entity model with needs of each engagement; comparing all possible allocations and ranking them based on optimization of all engagement qualifiers; and filtering all of the possible allocations based on a set of user refinement criteria.
20 . The method of claim 18 , wherein calculating the optimal allocation of the entities across the all known engagements involves calculating the optimal allocation as a function of a machine-learning model.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of Non-provisional application Ser. No. 18/415,131, filed on Jan. 17, 2024, and entitled “METHOD AND AN APPARATUS FOR META-MODEL OPTIMIZATION OF AN ENGAGEMENT PORTFOLIO,” the entirety of which is incorporated herein by reference. FIELD OF THE INVENTION The present invention generally relates to the field of artificial intelligence. In particular, the present invention is directed to a method and an apparatus for quantifying and optimizing collaborative entity engagements. BACKGROUND Current systems and methods to maximize collaborative engagements are limited to superficial assessments of key attributes, or prioritization of an essential entity then assembling the remaining composition to fill the subordinate needs. While these current systems and methods are marginally capable of achieving their assigned objectives, they lack the honed analysis necessary to efficiently engage the available resources and skillsets to optimally reach each engagement's intended purpose. SUMMARY OF THE DISCLOSURE In an aspect an apparatus for meta-model optimization is provided. The apparatus includes a computing device configured to receive a plurality of entity profile data and engagement profile data, classify each entity and engagement profile to a plurality of descriptors, compile a digital model for each entity and engagement profile, identify an optimal compatibility allocation of entities to engagements, and generate a user display summarizing the optimal meta-model to the user. In another aspect, a method for meta-model optimization is provided. The method includes receiving, by a computing device, an entity and engagement profile, correlating, by the computing device, each entity and engagement profile to a plurality of descriptors, compiling, by the computing device, a digital model for each entity and engagement profile, identifying, by the computing device, an optimal compatibility allocation of entities to engagements, and generating, by the computing device, a user display summarizing the optimal meta-model to the user. These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: FIG. 1 is a block diagram of an exemplary embodiment of an apparatus for meta-model optimization; FIG. 2 is a block diagram of an exemplary machine-learning process; FIG. 3 is an illustrative embodiment of a user interface; FIG. 4 is an exemplary embodiment of a fuzzy set comparison; FIG. 5 is a flow diagram of an exemplary method for meta-model optimization; FIG. 6 is a block diagram of a computer device that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof. The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations, and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted. DETAILED DESCRIPTION At a high level, aspects of the present disclosure are directed to meta-model optimization of multiple isolated models. In an embodiment, apparatus and methods may include utilizing machine-learning to generate optimized alternative options for achieving each engagement's intended purpose. Further, the meta-model optimization may then assess the collective entirety of the user engagement portfolio to optimize an entire fleet of entities as balanced against all current engagements. Aspects of the present disclosure can be used to digitally capture an entity's collaboration synopsis. As used in the current disclosure, an “entity” is comprised of one or more persons, a single piece of equipment or discernible system of aligned pieces of equipment, or any other device capable of executing a role or function. An entity may include a corporation, organization, business, group one or more persons, and the like. Aspects of the present disclosure can also be used to digitally condense an engagement's task characteristics. Referring now to FIG. 1, an exemplary embodiment of an apparatus 100 for meta-model optimization is illustrated. Apparatus may include a computing device 104. Computing device 104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosu