IP Library Granted Patent US 9,600,908
Granted Patent B2
US 9,600,908 · App. 14/792,160 · Granted Mar 21, 2017

System and method for color paint selection and acquisition

Inventors: Michael S. Gordon (Yorktown Heights, NY); James R. Kozloski (New Fairfield, CT); Peter K. Malkin (Yorktown Heights, NY); Clifford A. Pickover (Yorktown Heights, NY)
Assignee: International Business Machines Corporation
G06T11/001G06N5/04G06Q30/0631G06N99/005
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Quick Facts
Patent No.
US 9,600,908
App. No.
14/792,160
Granted
Mar 21, 2017
Kind
B2
Abstract

A method for paint color recommendation. The method obtains measures of an environment to be painted and trains a learned model to input data received from customers including data representing each customer's initial color paint and pigment selection, and one or more of: a customer perceptual, a customer context, and environment measure (P/C/E data) to generate a sparse matrix. One or more paint vendors may then use the generated sparse matrix to determine a color pigment recommendation from a pigments color space for a customer. From a user selected color/pigment, and using the learned model, the method maps the selection, together with the user's P/C/E data back to the color/pigments space. User feedback representing a degree of satisfaction that the recommended color pigment applied to the user environment has matched the user's initial color paint and color pigment selection is elicited.

Claims (25)

1. A method of paint color recommendation for a color paint vendor comprising:

receiving, at a processor device, input data representing an initial paint color or pigment selection from a user;

receiving, at the processor device, further input data representing one or more of a user's perceptual/cognitive/environmental (P/C/E) context, a user's environmental context associated with an environment in which the selected initial paint color or pigment is to be applied to a surface thereof;

training, using a machine learning technique, a sparse regression model to received input data from multiple user's including each user's initial paint color or pigment selection data, a received user profile data, a received user context data, and a received environment measures data to generate a sparse matrix;

mapping, using the generated sparse matrix, said user's initial paint color or pigment selection and said received user context data including said received environment measures data to a pigments color space;

determining, based on said map, a color pigment or pigment mixture from said pigments color space for recommendation to the user; and

communicating data representing said color pigment or pigment mixture recommendation to a device.

2. The method of claim 1 , wherein said context may include the user's cognitive context obtained from social network information, said method further comprising:

conducting a search in a social networking site or network infrastructure to obtain user context information comprising one or more of: the user's interest in art, design goods, music and reading material, and the user's purchase history of art and design goods, music and reading material.

3. The method of claim 2 , wherein said color pigment or pigment mixture recommendation data specifies a unique combination of and amounts of paint colors and color pigments, said paint vendor using said specified color pigment recommendation data to mix a test supply of color paint of the recommended color pigment for said user, wherein said user applies said test supply of color paint of the recommended color pigment data to a surface within said environment, said method further comprising:

receiving from said user, at the processor device, a feedback data representing a degree of satisfaction that the recommended color pigment from said test supply and applied to the user environment has matched the user's initial paint color or pigment selection; and

updating said learned model with a positive or negative label based on said received user feedback data.

4. The method of claim 3 , wherein said updating said learned model comprises:

applying, in the model, a positive label to an output recommended color pigment or pigment mixture based on a received favorable user feedback data, associated with the received input initial paint color or pigment selection, and said one or more user P/C/E context data.

5. The method of claim 1 , wherein said received user profile data comprises one or more of:

a gender, a socioeconomic status; an age of said user.

6. The method of claim 1 , further comprising: receiving, at the processor device, user context data representing one or more of:

a picture or real-time image of a room or environment to be painted with said color paint and color pigment selection;

a result of an administered psychophysical assessment of the user's color perception to obtain components of the user's color perception; and

a measure associated with a feature of an environment in which the selected paint color is to be applied to a surface thereof.

7. The method of claim 1 , wherein said received environment measures data comprises one or more of:

an ambient light level as recorded over a period of time of a room or environment to be painted with a color paint and color pigment selection; and

an amount and color of items to be located in said room or environment to be painted with a color paint and color pigment selection.

8. The method of claim 1 , wherein said mapping of said user's initial paint color or pigment selection, said received user context data including said received environment measures data to a pigments color space further comprises:

mapping one or more color components of the painted environment, a measure of the user's color perception, and components of the user's context to one or more of: a mixture of wavelengths corresponding to a desired color, a mixture of pigments corresponding to a desired wavelength of light sufficient to produce the desired color, and a base paint into which pigments are mixed.

Assignments (7)
SECURITY AGREEMENT Recorded May 20, 2026
From: WAYFAIR LLC
To: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION, AS NOTES COLLATERAL AGENT
Reel/Frame 075591/0399 →
SECURITY INTEREST Recorded Nov 10, 2025
From: WAYFAIR LLC
To: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION, AS NOTES COLLATERAL AGENT
Reel/Frame 073514/0326 →
SECURITY AGREEMENT Recorded Mar 13, 2025
From: WAYFAIR LLC
To: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION, AS NOTES COLLATERAL AGENT
Reel/Frame 070513/0542 →
SECURITY AGREEMENT Recorded Oct 10, 2024
From: WAYFAIR LLC
To: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION, AS NOTES COLLATERAL AGENT
Reel/Frame 069143/0399 →
SECURITY AGREEMENT Recorded Mar 24, 2021
From: WAYFAIR LLC
To: CITIBANK, N.A.
Reel/Frame 055708/0832 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 29, 2019
From: INTERNATIONAL BUSINESS MACHINES CORPORATION
To: WAYFAIR LLC
Reel/Frame 050867/0899 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 6, 2015
From: GORDON, MICHAEL S.; KOZLOSKI, JAMES R.; MALKIN, PETER K.; PICKOVER, CLIFFORD A.
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 036001/0553 →
Continuity (2)
Continuation 14618503 · Feb 10, 2015
Related Publication 20160232688A1 · Aug 11, 2016