US-20260127547-A1 - SYSTEMS AND METHODS FOR CONTROLLING RESOURCE ALLOCATION

US20260127547A1US 20260127547 A1US20260127547 A1US 20260127547A1US-20260127547-A1

Abstract

A system for controlling inventory-specific resource allocation comprises, for each particular inventory item of a set of inventory items: (i) determine a plurality of evaluation score deltas, each of the plurality of evaluation score deltas being associated with a respective inventory dimension of a plurality of inventory dimensions associated with the particular inventory item, the plurality of inventory dimensions comprising at least views and age; (ii) determine an evaluation score for the particular inventory item using the plurality of evaluation score deltas; (iii) determine an inventory resource allocation level for the particular inventory item based on the evaluation score; and (iv) automatically update resource allocation settings for the particular inventory item on one or more promotional communications platforms based on the inventory resource allocation level for the particular inventory item.

Inventors

Herbert Ramon Anderson
Alison Thompson
Taft Allan Thompson
Jorge Jesus Gomez-Ramirez
Diego Alberto Morfin

Assignees

CARUMAI, LLC

Dates

Publication Date: 20260507
Application Date: 20251230

Claims (13)

1 . A computer system for controlling inventory-specific resource allocation, the system comprising: one or more processors; and one or more computer-readable recording media that store instructions that are executable by the one or more processors to configure the system to: for each particular inventory item of a set of inventory items: determine a plurality of evaluation score deltas corresponding to a plurality of inventory dimensions of an asset retrieved from a plurality of geographic locations, the plurality of inventory dimensions comprising at least a number of user views and age of the asset, wherein determining the plurality of evaluation score deltas of the asset comprises: updating a machine learning algorithm executed by the system using the plurality of evaluation deltas for the asset, wherein updating the machine learning algorithm includes weighting the plurality of evaluation score deltas for the asset in real-time, and determining based on the weighting that an asset qualifies for a first asset allocation threshold; receiving a plurality of new evaluation score deltas corresponding to the plurality of inventory dimensions of the asset and updating the machine learning algorithm to determine, based on a weighting by the machine learning algorithm of the new evaluation score deltas that the asset qualifies for a second asset allocation threshold that is different from the first asset allocation threshold; display on a display screen the first asset allocation assigned to the asset after receipt by the system of the plurality of inventory dimensions of the asset; and without input from an end user of the system, the one or more machine learning algorithms of the system automatically changing the display screen to show that the asset is assigned to the second asset allocation threshold.
2 . The computer system of claim 1 , wherein a views weight for determining the views evaluation score delta is based on a default views weight and/or a user-defined views weight.
3 . The computer system of claim 1 , wherein determining the plurality of evaluation score deltas further comprises: defining an age evaluation score delta for the plurality of evaluation score deltas based on the age for the particular inventory item and an age weight, wherein the age weight is selected based on the age for the particular inventory item.
4 . The computer system of claim 1 , wherein the plurality of inventory dimensions further comprises one or more of leads, appointments, and condition of the inventory item.
5 . The computer system of claim 4 , wherein determining the plurality of evaluation score deltas further comprises: if the age of the particular inventory item fails to satisfy the turnover age threshold, determining a leads threshold for the particular inventory item based on a function of the age for the particular inventory item and an expected leads metric; if the age of the particular inventory item satisfies a turnover age threshold, determining the leads threshold for the particular inventory item based on the expected leads metric and not based on the function of the age for the particular inventory item; accessing a leads metric for the particular inventory item; if the leads metric fails to satisfy the leads threshold, defining a leads evaluation score delta for the plurality of evaluation score deltas as a weighted ratio of the leads threshold to the leads metric; and if the leads metric satisfies the leads threshold, defining the leads evaluation score delta for the plurality of evaluation score deltas as a negative weighted ratio of the leads metric to the leads threshold, wherein a leads weight for determining the leads evaluation score delta is based on a default leads weight and/or a user-defined leads weight.
6 . The computer system of claim 4 , wherein determining the plurality of evaluation score deltas further comprises: accessing an appointments metric for the particular inventory item; if the appointments metric fails to satisfy an appointments threshold, defining an appointments evaluation score delta for the plurality of evaluation score deltas as an appointments weight, wherein the appointments weight is based on a default appointments weight and/or a user-defined appointments weight; if the appointments metric satisfies the appointments threshold, defining the appointments evaluation score delta for the plurality of evaluation score deltas as a negative appointments weight; and if the age of the particular inventory item fails to satisfy the turnover age threshold, reducing the appointments evaluation score delta.
7 . The system of claim 5 , wherein determining the plurality of evaluation score deltas further comprises: accessing a condition label for the particular inventory item; and defining a condition evaluation score delta for the plurality of evaluation score deltas based on the condition label and a condition weight, wherein the condition weight is based on a default condition weight and/or a user-defined condition weight.
8 . The system of claim 1 , wherein determining the plurality of evaluation score deltas further comprises: determining a local scarcity and a national scarcity for the particular inventory item based on one or more labels associated with the particular inventory item; if the local scarcity is less than an average local scarcity, defining a scarcity evaluation score delta for the plurality of evaluation score deltas as a weighted ratio of the local scarcity to the average local scarcity; if the local scarcity is greater than or equal to the average local scarcity, defining the scarcity evaluation score delta for the plurality of evaluation score deltas as a negative weighted ratio of the local scarcity to the average local scarcity; if the national scarcity is less than an average national scarcity, modifying the scarcity evaluation score delta by adding a weighted ratio of the national scarcity to the average national scarcity; and if the national scarcity is greater than or equal to the average national scarcity, modifying the scarcity evaluation score delta by subtracting a weighted ratio of the national scarcity to the average national scarcity, wherein a scarcity weight for determining the scarcity evaluation score delta is based on a default scarcity weight and/or a user-defined scarcity weight.
9 . The system of claim 1 , wherein determining the evaluation score for the particular inventory item comprises modifying an initial evaluation score with the plurality of evaluation score deltas.
10 . The system of claim 1 , wherein a user interface frontend displays an inventory resource allocation level determined for each particular inventory item of the set of inventory items.
11 . A computer-implemented method for controlling inventory-specific resource allocation based on scarcity data of an inventory item at a remote location, the method comprising: for each particular inventory item of a set of inventory items: determining a plurality of evaluation score deltas corresponding to a plurality of inventory dimensions of an asset retrieved from a plurality of geographic locations, the plurality of inventory dimensions comprising at least a number of user views and age of the asset, wherein determining the plurality of evaluation score deltas of the asset comprises: updating a machine learning algorithm executed by a computer system using the plurality of evaluation deltas for the asset, wherein updating the machine learning algorithm includes weighting the plurality of evaluation score deltas for the asset in real-time, and determining based on the weighting that an asset qualifies for a first asset allocation threshold; receiving a plurality of new evaluation score deltas corresponding to the plurality of inventory dimensions of the asset and updating the machine learning algorithm to determine, based on a weighting by the machine learning algorithm of the new evaluation score deltas that the asset qualifies for a second asset allocation threshold that is different from the first asset allocation threshold; displaying on a display screen the first asset allocation assigned to the asset after receipt by the system of the plurality of inventory dimensions of the asset; and without input from an end user of the computer system, the machine learning algorithm of the computer system automatically changing the display screen to show that the asset is assigned to the second asset allocation threshold.
12 . The method as recited in claim 11 , further comprising: determining the plurality of evaluation score deltas, determine the evaluation score for the particular inventory item using the plurality of evaluation score deltas, and determining an inventory resource allocation level for the particular inventory item based on the evaluation score using one or more artificial intelligence modules.
13 . The method as recited in claim 11 , further comprising: configuring the machine learning algorithm to, for each particular inventory item of the set of inventory items: determine the plurality of evaluation score deltas, determine the evaluation score for the particular inventory item using the plurality of evaluation score deltas, and determine an inventory resource allocation level for the particular inventory item based on the user input received at a user interface frontend.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part of U.S. patent application Ser. No. 18/911,108, filed Oct. 9, 2024, and entitled, “SYSTEMS AND METHODS FOR CONTROLLING RESOURCE APPLICATION,” and a continuation-in-part of U.S. patent application Ser. No. 18/911,129, filed Oct. 9, 2024, and entitled, “SYSTEMS AND METHODS FOR GENERATING AND PRESENTING USER INTERFACE FRONTENDS FOR FACILITATING SCORE-BASED INVENTORY-SPECIFIC RESOURCE ALLOCATION,” which claim priority to U.S. Provisional Patent Application No. 63/589,919, filed on Oct. 12, 2023, and entitled “SYSTEMS AND METHODS FOR FACILITATING MANAGEMENT OF VENDOR OPERATIONS”, and to U.S. Provisional Patent Application No. 63/589,921, filed on Oct. 12, 2023, and entitled, “SYSTEMS AND METHODS FOR FACILITATING INVENTORY ANALYSIS”, and to U.S. Provisional Patent Application No. 63/589,923, filed on Oct. 12, 2023, and entitled, “SYSTEMS AND METHODS FOR CONTROLLING INVENTORY-SPECIFIC EXPENDITURE”, the entirety of which are incorporated herein by reference for all purposes. BACKGROUND Car dealerships face a range of challenges when it comes to marketing and advertising. For instance, many car dealerships collaborate with various marketing and/or advertising vendors (hereinafter “vendors”) to fulfill marketing/advertising needs vis-à-vis available inventory. However, analyzing and managing vendor relationships and/or operations can be a daunting task. Existing communication and/or analysis tools used between dealerships and vendors often fail to give dealerships a clear understanding of vendors' strategies, methodologies, specific services, and/or results. Dealerships thus often experience difficulty in determining return on investment (ROI) metrics associated with the vendor's marketing and advertising efforts. Such a lack of information can make it difficult for dealerships to keep vendors accountable for their performance and/or decisions, or to modify strategies/solutions to adapt to industry or market changes. Conventional tools can thus make it difficult for dealerships to ensure that campaigns are both effective and financially sustainable/sensible. Dealerships also find difficulty in allocating advertising spending vis-à-vis available inventory. For instance, many existing advertising management tools make it difficult for dealerships to effectively account for fluctuating advertising costs, fees/commissions, market changes, and/or other factors. Furthermore, many existing advertising management tools fail to convey meaningful ROI information related to advertising spending, particularly on an inventory-specific basis. Existing advertising management tools can thus fail to enable dealerships to make well-reasoned decisions with respect to advertising spending. For example, a dealership may fail to throttle advertising spending for a piece of underperforming inventory (or a piece of inventory associated with low demand under current market conditions), which can result in wasted resources. The subject matter claimed herein is not limited to embodiments that solve any challenges or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one exemplary technology area where some embodiments described herein may be practiced. BRIEF DESCRIPTION OF THE DRAWINGS In order to describe the manner in which the above-recited and other advantages and features can be obtained, a more particular description of the subject matter briefly described above will be rendered by reference to specific embodiments which are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments and are not therefore to be considered limiting in scope, embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings in which: FIG. 1 illustrates example components of a system that may comprise or implement the disclosed embodiments. FIG. 2 illustrates an example vendor overview section associated with a vendor report tool. FIG. 3 illustrates an example real-time analytics section associated with a vendor report tool. FIG. 4 illustrates a vendor display section associated with a vendor report tool. FIGS. 5, 6, and 7 displays a vendor detail section associated with a vendor report tool. FIG. 8 provides an alternative vendor overview section associated with a vendor report tool. FIG. 9 provides an alternative vendor detail section associated with a vendor report tool. FIG. 10 illustrates an example vendor data input section associated with a vendor report tool. FIGS. 11-17 illustrate example aspects of a vendor database layout 1100 that can support components of a vendor report tool. FIG. 18 illustrates a user interface frontend associated with an inventory analysis tool. FIG. 19 illustrates an example views breakdown associated with an inventory analysis tool. FIG. 20 illustrates an example leads b