IP Library › Granted Patent US 11,315,201
Granted Patent B2
US 11,315,201 · App. 17/022,683 · Granted Apr 26, 2022

Intelligent bill of materials advisor

Inventors: Nitin Bajaj (Overland Park, KS); Teresa Sheausan Tung (Tustin, CA); Sonali Parthasarathy (San Francisco, CA); Reymonrod Geli Vasquez (Vallejo, CA); Steven Robert Roberts (Burlingame, CA); Colin Anil Puri (Walnut Creek, CA); Elizabeth Ann Shay (San Francisco, CA); Unnati Amin (Voorhees, NJ); Michael Nai-An Chen (San Francisco, CA); Analiz Cabrera Hernandez (Somerville, MA); Trinidy Ray Combs (Minneapolis, MN)
Assignee: Accenture Global Solutions Limited
G06Q50/08G06Q10/06313G06Q10/0875
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Quick Facts
Patent No.
US 11,315,201
App. No.
17/022,683
Granted
Apr 26, 2022
Kind
B2
Abstract

A method and system for intelligently generating Bill of Materials (BoM) and tracking components of a build site. The method and system include querying a knowledge graph for a collection of historical bill of materials (BoMs), automatically generating a digital twin of a built site based on a selected group of completed build site, and intelligently generating the BoM for the digital twin based on BoMs of the selected group of completed built site.

Claims (44)

1. A computer system for automatic generation of a Bill of Materials (BoM) for a new construction project, comprising:

a memory including instruction code; and

a processor in communication with the memory, and when executing the instruction code, configured to:

obtain a plurality of predefined properties associated with the new construction project;

automatically query a knowledge graph to obtain a population of construction projects with project characteristics that best match the plurality of the predefined properties of the new construction project in a multi-dimensional vector space comprising an embedding space by:

vectorizing the project characteristics of each of the population of construction projects and the plurality of predefined properties into the embedding space;

calculating distances between the population of construction projects and the new construction project in the embedding space; and

obtaining the population of construction projects that are similar to the new construction project in the embedding space by determining whether the population of construction projects are similar to the new construction project;

wherein the population of construction projects collectively forming a digital representation of the new construction project;

retrieve, from the knowledge graph, a set of BoMs associated with the population of construction projects; and

automatically aggregate the set of BoMs to generate a base BoM for the new construction project.

2. The computer system of claim 1 , wherein, to automatically aggregate the set of BoMs to generate the base BoM, the processor is configured to select components from the set of BoMs as recorded in the knowledge graph to generate the base BoM for the new construction project in at least one of a plurality of user-selectable optimization modes.

3. The computer system of claim 2 , wherein the processor is further configured to deduplicate the components selected from the set of BoMs to generate the base BoM for the new construction project.

4. The computer system of claim 2 , wherein the plurality of user-selectable optimization modes comprises a first mode for optimizing a cost of the new construction project, a second mode for optimizing a construction time for the new construction project, and a third hybrid mode of the first mode and the second mode.

5. The computer system of claim 2 , wherein the processor is configured to select components from the set of BOMs to generate the base BoM for the new construction project by optimizing a cost of the new construction project projected according to the knowledge graph.

6. The computer system of claim 2 , wherein the processor is configured to select components from the set of BoMs to generate the base BoM for the new construction project by optimizing a construction time of the new construction project projected according to at least predicted supplier lead times of the selected components according to the knowledge graph.

7. The computer system of claim 1 , wherein, to automatically aggregate the set of BoMs to generate the base BoM, the processor is configured to select components from the set of BoMs as recorded in the knowledge graph according to popularity of the components in the set of BOMs to generate the base BoM for the new construction project.

8. The computer system of claim 1 , wherein the processor is further configured to:

provide a first user interface for modifying the base BoM to generate a modified BoM by selecting additional components from a set of candidate components extracted from the knowledge graph.

9. The computer system of claim 8 , wherein the processor is further configured to provide a reason of inclusion as extracted from the knowledge graph for at least one of the set of candidate components in the first user interface.

10. The computer system of claim 8 , wherein the processor is further configured to provide a reliability indication as extracted from the knowledge graph for at least one of the set of candidate components in the first user interface.

11. The computer system of claim 8 , wherein the processor is further configured to generate a project timeline based on at least component lead times in the base BoM or the modified BoM and provide a second user interface to displaying the project timeline.

12. A method for automatic generation of a BoM for a new construction project, comprising:

obtaining a plurality of predefined properties associated with the new construction project;

automatically querying a knowledge graph to obtain a population of construction projects with project characteristics that best match the plurality of the predefined properties of the new construction project in a multi-dimensional vector space comprising an embedding space by:

vectorizing the project characteristics of each of the population of construction projects and the plurality of predefined properties into the embedding space;

calculating distances between the population of construction projects and the new construction project in the embedding space; and

obtaining the population of construction projects that are similar to the new construction project in the embedding space by determining whether the population of construction projects are similar to the new construction project;

wherein the population of construction projects collectively forming a digital representation of the new construction project;

retrieving, from the knowledge graph, a set of BoMs associated with the population of construction projects; and

automatically aggregating the set of BoMs to generate a base BoM for the new construction project.

13. The method of claim 12 , wherein automatically aggregating the set of BoMs to generate the base BoM comprises selecting components from the set of BoMs as recorded in the knowledge graph to generate the base BoM for the new construction project in at least one of a plurality of user-selectable optimization modes.

14. The method of claim 12 , further comprising providing a first user interface for modifying the base BoM to generate a modified BoM by selecting additional components from a set of candidate components extracted from the knowledge graph.

15. A non-transitory computer readable medium for storing instructions, wherein the instructions, when executed by a computer processor, causes the computer processor to:

obtain a plurality of predefined properties associated with a new construction project;

automatically query a knowledge graph to obtain a population of construction projects with project characteristics that best match the plurality of the predefined properties of the new construction project in a multi-dimensional vector space comprising an embedding space by:

vectorizing the project characteristics of each of the population of construction projects and the plurality of predefined properties into the embedding space;

calculating distances between the population of construction projects and the new construction project in the embedding space; and

obtaining the population of construction projects that are similar to the new construction project in the embedding space by determining whether the population of construction projects are similar to the new construction project;

wherein the population of construction projects collectively forming a digital representation of the new construction project;

retrieve, from the knowledge graph, a set of BoMs associated with the population of construction projects; and

automatically aggregate the set of BoMs to generate a base BoM for the new construction project.

16. The non-transitory computer readable medium of claim 15 , wherein, to automatically aggregate the set of BoMs to generate the base BoM, the instructions cause the computer processor to select components from the set of BoMs as recorded in the knowledge graph to generate the base BoM for the new construction project in at least one of a plurality of user-selectable optimization modes.

17. The non-transitory computer readable medium of claim 15 , wherein the instructions further cause the computer processor to provide a first user interface for modifying the base BoM to generate a modified BoM by selecting additional components from a set of candidate components extracted from the knowledge graph.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 16, 2020
From: BAJAJ, NITIN; TUNG, TERESA SHEAUSAN; PARTHASARATHY, SONALI; VASQUEZ, REYMONROD GELI; ROBERTS, STEVEN ROBERT; PURI, COLIN ANIL; SHAY, ELIZABETH ANN; AMIN, UNNATI; CHEN, MICHAEL NAI-AN; HERNANDEZ, ANALIZ CABRERA; COMBS, TRINIDY RAY
To: ACCENTURE GLOBAL SOLUTIONS LIMITED
Reel/Frame 053791/0453 →
Continuity (2)
Provisional Application 62964107 · Jan 21, 2020
Related Publication 20210224929A1 · Jul 22, 2021