Granted Patent B2

US 12,657,542 · App. 18/753,278 · Granted Jun 16, 2026

Inventors: Jeremiah Woods (Cincinnati, OH); Asad Lalani (Houston, TX); Catherine Knuff (Brooklyn, NY)

Assignee: Procore Technologies, Inc.

G06Q10/0639G06Q50/08

Abstract

Claims (69)

1 . A computing platform comprising:

at least one processor;

at least one non-transitory computer-readable medium; and

program instructions stored on the at least one non-transitory computer-readable medium that, when executed by the at least one processor, cause the computing platform to:

create a data science model that is configured to (i) receive a value for a metric that provides insight regarding a party's usage of a software tool of a construction management software application on a construction project and (ii) based on an evaluation of the received value for the metric, output a prediction of the party's performance on the construction project, wherein the data science model is created by:

obtaining project data for a universe of past construction projects;

for each respective construction project in the universe of past construction projects, utilizing the respective project data for the respective construction project to determine (i) a respective metric value of the metric for the respective construction project and (ii) a respective performance value that quantifies performance on the respective construction project;

partitioning the respective metric values that are determined for the universe of past construction projects into a plurality of discrete ranges of metric values;

for each respective range of metric values in the plurality of discrete ranges of metric values, determining a corresponding performance value that quantifies performance on a construction project having a metric value within the respective range of metric values; and

encoding the plurality of discrete ranges of metric values and corresponding performance values into the data science model;

after creating the data science model, utilize the data science model to produce a prediction of a given party's performance on a given construction project that is based on the given party's usage of the software tool by:

obtaining project data for a given construction project;

based on the obtained project data, determining a given value for the metric; and

inputting the given value for the metric into the data science model and thereby causing the data science model to (i) evaluate the given value of the metric, and (ii) based on the evaluation of the given value, output the prediction of the given party's performance on the given construction project;

based on the prediction of the given party's performance on the given construction project, determine that the given party's usage of the software tool is negatively impacting the given party's performance on the given construction project; and

in response to the determination that the given party's usage of the software tool is negatively impacting the given party's performance on the given construction project, generate a recommendation for changing how the software tool is being utilized by the given party.

2 . The computing platform of claim 1 , wherein the metric comprises a first metric, the data science model comprises a first data science model, and the prediction of the given party's performance on the given construction project comprises a first prediction of the given party's performance on the given construction project, and wherein the computing platform further comprises program instructions stored on the at least one non-transitory computer-readable medium that, when executed by the at least one processor, cause the computing platform to:

create a second data science model that is configured to (i) receive a value for a second metric that provides insight regarding a party's usage of the software tool on a construction project and (ii) based on an evaluation of the received value for the second metric, predict the party's performance on the construction project; and

after creating the second data science model, utilize the second data science model to produce a second prediction of the given party's performance on the given construction project that is based on the given party's usage of the software tool.

3 . The computing platform of claim 2 , wherein the determination that the given party's usage of the software tool is negatively impacting the given party's performance on the given construction project is further based on the second prediction.

4 . The computing platform of claim 1 , wherein the prediction of the given party's performance on the given construction project comprises a predicted performance value that quantifies the given party's performance on the given construction project.

5 . The computing platform of claim 4 , wherein the predicted performance value quantifies the given party's performance on the given construction project in terms of the given party's ability to meet one of a timing goal, a budget goal, a quality goal, or a safety goal.

6 . The computing platform of claim 1 , wherein the data science model's evaluation of the given value of the metric involves:

identifying, from the plurality of discrete ranges of metric values, a given range of metric values that encompasses the given value; and

identifying a corresponding performance value for the given range of metric values.

7 . The computing platform of claim 1 , wherein the plurality of discrete ranges of metric values comprise quantiles.

8 . The computing platform of claim 1 , wherein the universe of past construction projects comprise past construction projects that were managed using the construction management software application.

9 . A non-transitory computer-readable medium having stored thereon program instructions that, when executed by at least one processor, cause a computing platform to:

create a data science model that is configured to (i) receive a value for a metric that provides insight regarding a party's usage of a software tool of a construction management software application on a construction project and (ii) based on an evaluation of the received value for the metric, output a prediction of the party's performance on the construction project, wherein the data science model is created by:

obtaining project data for a universe of past construction projects;

for each respective construction project in the universe of past construction projects, utilizing the respective project data for the respective construction project to determine (i) a respective metric value of the metric for the respective construction project and (ii) a respective performance value that quantifies performance on the respective construction project;

partitioning the respective metric values that are determined for the universe of past construction projects into a plurality of discrete ranges of metric values;

for each respective range of metric values in the plurality of discrete ranges of metric values, determining a corresponding performance value that quantifies performance on a construction project having a metric value within the respective range of metric values; and

encoding the plurality of discrete ranges of metric values and corresponding performance values into the data science model;

after creating the data science model, utilize the data science model to produce a prediction of a given party's performance on a given construction project that is based on the given party's usage of the software tool by:

obtaining project data for a given construction project;

based on the obtained project data, determining a given value for the metric; and

inputting the given value for the metric into the data science model and thereby causing the data science model to (i) evaluate the given value of the metric, and (ii) based on the evaluation of the given value, output the prediction of the given party's performance on the given construction project;

based on the prediction of the given party's performance on the given construction project, determine that the given party's usage of the software tool is negatively impacting the given party's performance on the given construction project; and

in response to the determination that the given party's usage of the software tool is negatively impacting the given party's performance on the given construction project, generate a recommendation for changing how the software tool is being utilized by the given party.

10 . The non-transitory computer-readable medium of claim 9 , wherein the metric comprises a first metric, the data science model comprises a first data science model, and the prediction of the given party's performance on the given construction project comprises a first prediction of the given party's performance on the given construction project, and wherein the non-transitory computer-readable medium also has stored thereon program instructions that, when executed by at least one processor, cause the computing platform to:

create a second data science model that is configured to (i) receive a value for a second metric that provides insight regarding a party's usage of the software tool on a construction project and (ii) based on an evaluation of the received value for the second metric, predict the party's performance on the construction project; and

after creating the second data science model, utilize the second data science model to produce a second prediction of the given party's performance on the given construction project that is based on the given party's usage of the software tool.

11 . The non-transitory computer-readable medium of claim 10 , wherein the determination that the given party's usage of the software tool is negatively impacting the given party's performance on the given construction project is further based on the second prediction.

12 . The non-transitory computer-readable medium of claim 9 , wherein the prediction of the given party's performance on the given construction project comprises a predicted performance value that quantifies the given party's performance on the given construction project.

13 . The non-transitory computer-readable medium of claim 12 , wherein the predicted performance value quantifies the given party's performance on the given construction project in terms of the given party's ability to meet one of a timing goal, a budget goal, a quality goal, or a safety goal.

14 . The non-transitory computer-readable medium of claim 9 , wherein the data science model's evaluation of the given value of the metric involves:

identifying, from the plurality of discrete ranges of metric values, a given range of metric values that encompasses the given value; and

identifying a corresponding performance value for the given range of metric values.

15 . The non-transitory computer-readable medium of claim 9 , wherein the plurality of discrete ranges of metric values comprise quantiles.

16 . The non-transitory computer-readable medium of claim 9 , wherein the universe of past construction projects comprise past construction projects that were managed using the construction management software application.

17 . A method implemented by a computing platform, the method comprising:

creating a data science model that is configured to (i) receive a value for a metric that provides insight regarding a party's usage of a software tool of a construction management software application on a construction project and (ii) based on an evaluation of the received value for the metric, output a prediction of the party's performance on the construction project, wherein the data science model is created by:

obtaining project data for a universe of past construction projects;

for each respective construction project in the universe of past construction projects, utilizing the respective project data for the respective construction project to determine (i) a respective metric value of the metric for the respective construction project and (ii) a respective performance value that quantifies performance on the respective construction project;

partitioning the respective metric values that are determined for the universe of past construction projects into a plurality of discrete ranges of metric values;

for each respective range of metric values in the plurality of discrete ranges of metric values, determining a corresponding performance value that quantifies performance on a construction project having a metric value within the respective range of metric values; and

encoding the plurality of discrete ranges of metric values and corresponding performance values into the data science model;

after creating the data science model, utilizing the data science model to produce a prediction of a given party's performance on a given construction project that is based on the given party's usage of the software tool by:

obtaining project data for a given construction project;

based on the obtained project data, determining a given value for the metric; and

inputting the given value for the metric into the data science model and thereby causing the data science model to (i) evaluate the given value of the metric, and (ii) based on the evaluation of the given value, output the prediction of the given party's performance on the given construction project;

based on the prediction of the given party's performance on the given construction project, determining that the given party's usage of the software tool is negatively impacting the given party's performance on the given construction project; and

in response to the determination that the given party's usage of the software tool is negatively impacting the given party's performance on the given construction project, generating a recommendation for changing how the software tool is being utilized by the given party.

18 . The method of claim 17 , wherein the metric comprises a first metric, the data science model comprises a first data science model, and the prediction of the given party's performance on the given construction project comprises a first prediction of the given party's performance on the given construction project, and wherein the method further comprises:

creating a second data science model that is configured to (i) receive a value for a second metric that provides insight regarding a party's usage of the software tool on a construction project and (ii) based on an evaluation of the received value for the second metric, predict the party's performance on the construction project; and

after creating the second data science model, utilizing the second data science model to produce a second prediction of the given party's performance on the given construction project that is based on the given party's usage of the software tool.

19 . The method of claim 18 , further comprising: wherein the determining that the given party's usage of the software tool is negatively impacting the given party's performance on the given construction project is further based on the second prediction.

20 . The method of claim 17 , wherein the prediction of the given party's performance on the given construction project comprises a predicted performance value that quantifies the given party's performance on the given construction project.

Assignments (1)

Continuity (1)

References Cited (9)