# US 10,311,529 B1 · Systems, media, and methods of applying machine learning to create a digital request for proposal

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- Number: US 10,311,529 B1
- Document ID: US10311529B1
- Status: Granted
- Kind: B1
- Application number: 16/000,750
- Filed: 2018-06-05
- Granted: 2019-06-04
- Earliest claimed: 2018-06-05
- Assignees: Emprove, Inc.
- Applicants: Emprove, Inc.
- Inventors: James Noel; Shih-Chien Lee; Ervi Susanti Ali Bongso
- Web page: https://patentgrep.com/patent/US10311529B1

## CPC

- G06Q 50/08: Construction
- G06F 30/13: Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
- G06N 20/00: Machine learning
- G06Q 30/0283: Price estimation or determination
- G06Q 30/0611: {Request for offers or quotes}
- G06N 5/02: Knowledge representation; Symbolic representation
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Disclosed herein are systems, media, and methods of applying machine learning to create a digital request for proposal for use in property construction and improvement. Consumers are enabled by the systems, media, and methods disclosed herein to compose property improvement proposals and generate electronic documentation to assist contractors in evaluating said proposals and providing bids for the completion of the proposed property improvement.

## Claims

### Claim 1

A computer-implemented system comprising: a computer-readable storage device coupled to the at least one processor and having instructions stored thereon which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
a) receiving, from a user interface, project profile data for a user;
b) determining a plurality of peer users based on the project profile data;
c) training a first machine learning algorithm using a plurality of user profiles for the peer users;
d) assigning unrefined weights to predictor variables of the first machine learning algorithm;
e) processing the project profile data through the first machine learning algorithm to determine a plurality of recommended options, the recommended options comprising a plurality of independently selectable sub-regions;
f) providing the recommended options to the user interface;
g) receiving, from the user interface, a data set comprising selections of the independently selectable sub-regions;
h) adjusting the predictor variables of the first machine learning algorithm based on the selections of the independently selectable sub-regions;
i) processing the data set, and the project profile data through the first machine learning algorithm to generate a proposed project plan, the proposed project plan comprising a plurality of project line items;
j) training a second machine learning algorithm using verified data for a plurality of implemented property construction projects, the verified data comprising projects material costs, labor costs, overhead costs, profits, or contingency costs for the implemented property construction projects;
k) processing the proposed project plan through the second machine learning algorithm to generate a dynamic cost estimate for the proposed project plan, the dynamic cost estimate comprising itemized cost estimates for each of the project line items;
l) providing, to the user interface, the dynamic cost estimate for the proposed project plan;
m) receiving, from the user interface, a finalized project scope, the finalized project scope comprising sections from the dynamic cost estimate;
n) generating a digital request for proposal based on the finalized project scope; and
o) providing the digital request for proposal to the user interface.

### Claim 2 (depends on 1)

The system of claim 1, wherein the project profile data comprises an Internet Protocol address, zip code data, geographical data, a property address, a clickstream, a budget for a project, a scope for the project, a room specification, a user's quality preference, or a project specification.

### Claim 3 (depends on 1)

The system of claim 1, wherein the proposed project plan comprises a visual summary, a cost breakdown of each aspect of the proposed project plan, a Styleboard, or a pie chart.

### Claim 4 (depends on 1)

The system of claim 1, wherein the operations comprise:
receiving a non-user data set from a non-user.

### Claim 5 (depends on 4)

The system of claim 4, wherein the operations comprise:
training the first machine learning algorithm with the non-user data set to improve the generation of proposed project plan.

### Claim 6 (depends on 1)

The system of claim 1, wherein the proposed project plan comprises a plurality of adjustable parameters.

### Claim 7 (depends on 1)

The system of claim 1, wherein the second machine learning algorithm comprises a categorical cost of index adjustment by zip code and hierarchical cost data.

### Claim 8 (depends on 1)

The system of claim 1, wherein the second machine learning algorithm comprises a quantity manager comprising tethered quantity rules, overage rules, project quality adjustments rules, home area adjustment rules, home type, age, or room specification adjustment rules.

### Claim 9 (depends on 1)

The system of claim 1, wherein the second machine learning algorithm comprises extensible rule set comprising scenario exclusions, attributes inheritance, scope tethering, reverse tethering, default scope selection by project quality, default scope selection by budget, default scope plan by project scale, or default scope plan by budget.

### Claim 10 (depends on 1)

The system of claim 1, wherein the verified data is acquired from actual bids from contractors.

### Claim 11 (depends on 1)

The system of claim 1, wherein the operations comprise:
a) training a third machine learning algorithm trained with property construction project data;
b) processing the digital request for proposal through the third machine learning algorithm to determine at least one service provider from a plurality of service providers; and
c) providing the digital request for proposal to the at least one service provider as a bid.

### Claim 12 (depends on 11)

The system of claim 11, wherein the third machine learning algorithm comprises a scoring analysis, a thresholding analysis, and a scheduler analysis.

### Claim 13 (depends on 12)

The system of claim 12, wherein the scoring analysis comprises an analysis based on project expertise requirements, quality craftsmanship, project scale, service area, service provider ratings, activity score, bid response score, service provider booking, and service provider schedule.

### Claim 14 (depends on 13)

The system of claim 13, wherein the scheduler analysis comprises an analysis based on a speed of bid submissions over time or changes in the third machine learning algorithm.

### Claim 15 (depends on 12)

The system of claim 12, wherein the operations comprise:
providing a notification regarding a status of the bid to the user and the at least one service provider.

### Claim 16 (depends on 1)

The system of claim 1, wherein the project profile data comprises an age of a user, a gender of the user, a geographic location of the user, similar or liked images for the user, budget of a project, and overall quality benchmark for the project.

### Claim 17 (depends on 1)

The system of claim 1, wherein the operations comprise:
training the first machine learning algorithm using a plurality of past project plans generated for the user.

### Claim 18 (depends on 1)

The system of claim 1, wherein the operations comprise:
processing the data set, and the project profile data through the first machine learning algorithm to generate a user profile for the user, the user profile comprising an intent of the user, a plurality of preferences of the user, and the proposed project plan.

### Claim 19 (depends on 18)

The system of claim 18, wherein the user profile comprises the user's quality sensibilities, the user's design sensibilities, the user's material preferences, the user's looks preferences, the user's budget, a property type, a property age, a room specification by type, or level of attention to detail.

### Claim 20 (depends on 1)

The system of claim 1, wherein the data set comprises clickstream data or selected design elements.

### Claim 21

Non-transitory computer-readable storage media encoded with a computer program including instructions executable by a processor to create a digital request for proposal, the instructions comprising:
a) receiving, from a user interface, project profile data for a user;
b) determining a plurality of peer users based on the project profile data;
c) training a first machine learning algorithm using a plurality of user profiles for the peer users;
d) assigning unrefined weights to predictor variables of the first machine learning algorithm; and
e) processing the project profile data through the first machine learning algorithm to determine a plurality of recommended options, the recommended options comprising a plurality of independently selectable sub-regions;
f) providing the recommended options to the user interface;
g) receiving, from the user interface, a data set comprising selections of the independently selectable sub-regions;
h) adjusting the predictor variables of the first machine learning algorithm based on the selections of the independently selectable sub-regions;
i) processing the data set, and the project profile data through the first machine learning algorithm to generate a proposed project plan, the proposed project plan comprising a plurality of project line items;
j) training a second machine learning algorithm using verified data for a plurality of implemented property construction projects, the verified data comprising projects material costs, labor costs, overhead costs, profits, or contingency costs for the implemented property construction projects;
k) processing the proposed project plan through the second machine learning algorithm to generate a dynamic cost estimate for the proposed project plan, the dynamic cost estimate comprising itemized cost estimates for each of the project line items;
l) providing, to the user interface, the dynamic cost estimate for the proposed project plan;
m) receiving, from the user interface, a finalized project scope, the finalized project scope comprising sections from the dynamic cost estimate;
n) generating a digital request for proposal based on the finalized project scope; and
o) providing the digital request for proposal to the user interface.

### Claim 22 (depends on 21)

The media of claim 21 further comprising:
a) training a third machine learning algorithm trained with property construction project data
b) processing the digital request for proposal through the third machine learning algorithm to determine at least one service provider from a plurality of service providers; and
c) providing the digital request for proposal to the at least one service provider as a bid.

### Claim 23

A computer-implemented method for creating a digital request for proposal comprising:
a) receiving, from a user interface, project profile data for a user;
b) determining a plurality of peer users based on the project profile data;
c) training a first machine learning algorithm using a plurality of user profiles for the peer users;
d) assigning unrefined weights to predictor variables of the first machine learning algorithm;
e) processing the project profile data through the first machine learning algorithm to determine a plurality of recommended options, the recommended options comprising a plurality of independently selectable sub-regions;
f) providing the recommended options to the user interface;
g) receiving, from the user interface, a data set comprising selections of the independently selectable sub-regions;
h) adjusting the predictor variables of the first machine learning algorithm based on the selections of the independently selectable sub-regions;
i) processing the data set, and the project profile data through the first machine learning algorithm to generate a proposed project plan, the proposed project plan comprising a plurality of project line items;
j) training a second machine learning algorithm using verified data for a plurality of implemented property construction projects, the verified data comprising projects material costs, labor costs, overhead costs, profits, or contingency costs for the implemented property construction projects;
k) processing the proposed project plan through the second machine learning algorithm to generate a dynamic cost estimate for the proposed project plan, the dynamic cost estimate comprising itemized cost estimates for each of the project line items;
l) providing, to the user interface, the dynamic cost estimate for the proposed project plan;
m) receiving, from the user interface, a finalized project scope, the finalized project scope comprising sections from the dynamic cost estimate;
n) generating a digital request for proposal based on the finalized project scope; and
o) providing the digital request for proposal to the user interface.