# US 2025/0094879 A1 · Generation and Utilization of Channel Allocation Models for Resource Allocation Recommendations

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- Number: US 2025/0094879 A1
- Document ID: US20250094879A1
- Status: Published application
- Kind: A1
- Application number: 18/013,322
- Filed: 2022-12-20
- Published: 2025-03-20
- Earliest claimed: 2022-12-20
- Assignees: Google Inc.
- Applicants: Google LLC
- Inventors: Xinghua Zhao
- Web page: https://patentgrep.com/patent/US20250094879A1

## CPC

- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 20/20: Ensemble learning
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/047: Probabilistic or stochastic networks
- G06N 3/0475: Generative networks
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Example embodiments of the present disclosure provide for an example method including obtaining data associated with media channels. The example method includes inputting the data into a machine learning model. The example method includes estimating, by the machine learning model, a structure of a causal graph. The example method includes applying a second machine learning model to the causal graph to estimate one or more parameters associated with the causal graph. The example method includes determining an allocation of resources to the media channels based on the causal graph.

## Claims

### Claim 1

A computer-implemented method comprising:
obtaining data associated with a plurality of media channels;
inputting the data into a machine learning model;
estimating, by the machine learning model, a structure of a causal graph;
applying a second machine learning model to the causal graph to estimate one or more parameters associated with the causal graph; and
determining an allocation of resources to the plurality of media channels based on the causal graph.

### Claim 2 (depends on 1)

The method of claim 1, wherein the causal graph comprises a Bayesian belief network and the second machine learning model comprises a kernel-based machine learning model.

### Claim 3 (depends on 1)

The method of claim 1, wherein estimating, by the machine learning model, the structure of the causal graph comprises:
performing structure learning to generate the causal graph comprising a plurality of nodes and a plurality of edges.

### Claim 4 (depends on 3)

The method of claim 3, wherein the structure learning comprises:
applying a machine learning model for feature selection; and
performing bootstrap aggregation for updating the causal graph.

### Claim 5 (depends on 3)

The method of claim 3, wherein estimating the structure of the causal graph comprises: performing parameter learning to determine a (i) magnitude and (ii) direction for each respective edge of the plurality of edges.

### Claim 6 (depends on 5)

The method of claim 5, wherein performing the parameter learning comprises:
performing parameter regularization.

### Claim 7 (depends on 3)

The method of claim 3, wherein the causal graph comprises a directed acyclic graph representing conditional probabilities between the nodes.

### Claim 8 (depends on 1)

The method of claim 1, comprising performing a validation method by:
transmitting data comprising instructions that cause a graphical representation of the causal graph to display via a graphical user interface of a device associated with a user; and
obtaining data indicative of user input of acceptance of the graphical representation of the causal graph.

### Claim 9 (depends on 1)

The method of claim 1, wherein determining the allocation of resources to the plurality of media channels based on the causal graph comprises:
determining a relationship between a first node, a second node, and a third node; and
based on the relationship between the first node, the second node, and the third node, determining an optimal budget allocation for a first media channel associated with the first node, a second medial channel associated with the second node, and a third media channel associated with the third node.

### Claim 10 (depends on 9)

The method of claim 9, wherein determining the optimal budget allocation for the first media channel, the second media channel, and the third media channel comprises:
simulating data indicative of a plurality of budget allocation scenarios, wherein each budget allocation scenario is indicative of an amount of budget allocated to each of the first media channel, the second media channel, and the third media channel;
obtaining data indicative of user input of a target parameter;
for each respective budget allocation scenario of the plurality of budget allocation scenarios, determining a respective parameter for a respective budget allocation scenario of the plurality of budget allocation scenarios;
comparing each respective parameter and the target parameter;
determining that a first respective parameter associated with a first budget allocation scenario is within a threshold of the target parameter; and
in response to determining that the first respective parameter is within the threshold of the target parameter, selecting the first budget allocation scenario as an optimal budget allocation scenario.

### Claim 11 (depends on 10)

The method of claim 10, wherein the target parameter and the respective parameters associated with each respective budget allocation scenario are indicative of a percent reduction in cost per sale.

### Claim 12 (depends on 11)

The method of claim 11, wherein the cost per sale is determined by calculating a media spend by the respective media channel divided by unit sales driven by the respective media channel.

### Claim 13 (depends on 10)

The method of claim 10, wherein the target parameter and the respective parameters associated with each respective budget allocation scenario are indicative of an attribution associated with an effectiveness of media channel and promotion usage on target actions.

### Claim 14 (depends on 13)

The method of claim 13, wherein the attribution for the respective media channel is determined by calculating a unit sale driven by the respective media channel divided by overall sales.

### Claim 15 (depends on 10)

The method of claim 10, wherein the target parameter and the respective parameters associated with each respective budget allocation scenario are an overall total media spend budget.

### Claim 16 (depends on 1)

The method of claim 1, comprising:
generating data indicative of a graphical representation of the causal graph; and
transmitting data comprising instructions that, when executed, cause the graphical representation of the causal graph to render via a graphical user interface of a user device.

### Claim 17 (depends on 1)

The method of claim 1, comprising:
generating data indicative of a graphical representation of the determined allocation of resources to the plurality of media channels; and
transmitting data comprising instructions that, when executed, cause the graphical representation of the allocation of resources to the plurality of media channels to render via a graphical user interface of a user device.

### Claim 18 (depends on 1)

The method of claim 1, where the data associated with the plurality of media channels comprises outcome data, predictive variables, and control variables.

### Claim 19

A computing system, comprising:
one or more processors; and
one or more one or more computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising;
obtaining data associated with a plurality of media channels;
inputting the data into a machine learning model;
estimating, by the machine learning model, a structure of a causal graph;
applying a second machine learning model to the causal graph to estimate one or more parameters associated with the causal graph; and
determining an allocation of resources to the plurality of media channels based on the causal graph.

### Claim 20

One or more non-transitory computer readable media storing instructions that are executable by one or more processors to perform operations comprising:
obtaining data associated with a plurality of media channels;
inputting the data into a machine learning model;
estimating, by the machine learning model, a structure of a causal graph;
applying a second machine learning model to the causal graph to estimate one or more parameters associated with the causal graph; and
determining an allocation of resources to the plurality of media channels based on the causal graph.