System and method for predicting the desire to keep the door open when making decisions

Patent metadata

Field	Value
Publication number	US-2024386473-A1
Application number	US-202318319416-A
Country	US
Kind code	A1
Filing date	May 17, 2023
Priority date	May 17, 2023
Publication date	Nov 21, 2024
Grant date	—

Abstract

A method for monitoring user purchase making activity is described. The method includes logging a potential user purchase and purchase communications corresponding to a potential option available for purchase by a user. The method also includes predicting whether loss aversion is a factor in a purchase making process of the potential option available for purchase by the user. The method further includes determining a purchase recommendation in response to identifying that the loss aversion is the factor in the potential purchase making process of the potential option available for purchase by the user. The method also includes displaying the purchase recommendation based on a use frequency of the option purchased by the user.

First claim

What is claimed is:

1 . A method for monitoring user purchase making activity, comprising:
logging a potential user purchase and purchase communications corresponding to a potential option available for purchase by a user; predicting whether loss aversion is a factor in a purchase making process of the potential option available for purchase by the user; determining a purchase recommendation in response to identifying that the loss aversion is the factor in the potential purchase making process of the potential option available for purchase by the user; and displaying the purchase recommendation based on a use frequency of the option purchased by the user.

2 . The method of claim 1 , further comprising:
logging of purchases and communications by others of the potential option available for purchase by the user; and training a machine-learning model to predict whether the loss aversion is the factor in the potential purchase making process of the potential option available for purchase by the user and to advise the user on how likely the option purchased by the user is useful.

3 . The method of claim 2 , in which training the machine-learning model comprises examining social media comments regarding the potential option available for purchase by the user.

4 . The method of claim 2 , in which training the machine-learning model comprises examining a purchase history of the user.

5 . The method of claim 2 , in which training the machine-learning model comprises:
examining use of the potential option available for purchase by the user by others that purchased the option; and examining feedback by others that purchased the option on removal of the potential option available for purchase by the user.

6 . The method of claim 2 , in which training the machine-learning model comprises:
identifying a set of dimensions involved in the potential purchase making process of the potential option available for purchase by the user; inputting, into the machine-learning model, the set of dimensions and values for the set of dimensions; and predicting, by the machine-learning model, a strength of the loss aversion by the user.

7 . The method of claim 1 , in which predicting whether the loss aversion was the factor comprises:
analyzing, using a natural language processor, terms of the purchase communications of the user to predict whether the loss aversion was the factor; and analyzing, using a machine-learning model, how often purchasers incurred the loss aversion for options associated with different products.

8 . The method of claim 1 , in which logging the potential user purchase comprises compiling contexts surrounding the potential user purchase to generate a data log, in which the contexts comprise scenarios, environments, concerns of the user, and/or information relating to the potential user purchase.

9 . A non-transitory computer-readable medium having program code recorded thereon for monitoring user purchase making activity, the program code being executed by a processor and comprising:
program code to log a potential user purchase and purchase communications corresponding to a potential option available for purchase by the user; program code to predict whether loss aversion is a factor in a purchase making process of the potential option available for purchase by the user; program code to determine a purchase recommendation in response to identifying that the loss aversion is the factor in the potential purchase making process of the potential option available for purchase by the user; and program code to display the purchase recommendation based on a use frequency of the option purchased by the user.

10 . The non-transitory computer-readable medium of claim 9 , further comprising:
program code to log of purchases and communications by others of the potential option available for purchase by the user; and program code to train a machine-learning model to predict whether the loss aversion is the factor in the potential purchase making process of the potential option available for purchase by the user and to advise the user on how likely the option purchased by the user is useful.

11 . The non-transitory computer-readable medium of claim 10 , in which the program code to train the machine-learning model comprises program code to examine social media comments regarding the potential option available for purchase by the user.

12 . The non-transitory computer-readable medium of claim 10 , in which the program code to train the machine-learning model comprises program code to examine a purchase history of the user.

13 . The non-transitory computer-readable medium of claim 10 , in which the program code to train the machine-learning model comprises:
program code to examine use of the potential option available for purchase by the user by others that purchased the option; and program code to examine feedback by others that purchased the option on removal of the potential option available for purchase by the user.

14 . The non-transitory computer-readable medium of claim 10 , in which the program code to train the machine-learning model comprises:
program code to identify a set of dimensions involved in the potential purchase making process of the potential option available for purchase by the user; program code to input, into the machine-learning model, the set of dimensions and values for the set of dimensions; and program code to predict, by the machine-learning model, a strength of the loss aversion by the user.

15 . The non-transitory computer-readable medium of claim 9 , in which the program code to predict whether the loss aversion was the factor comprises:
program code to analyze, using a natural language processor, terms of the purchase communications of the user to predict whether the loss aversion was the factor; and program code to analyze, using a machine-learning model, how often purchasers incurred the loss aversion for options associated with different products.

16 . The non-transitory computer-readable medium of claim 9 , in which the program code to log the potential user purchase comprises program code to compile contexts surrounding the potential user purchase to generate a data log, in which the contexts comprise scenarios, environments, concerns of the user, and/or information relating to the potential user purchase.

17 . A system for monitoring user purchase making activity, the system comprising:
a purchase logging module to log a potential user purchase and purchase communications corresponding to a potential option available for purchase by the user; a loss averse purchase identification model to predict whether loss aversion is a factor in a purchase making process of the potential option available for purchase by the user; a purchase advice determination model to determine a purchase recommendation in response to identifying that the loss aversion is the factor in the potential purchase making process of the potential option available for purchase by the user; and a purchase advice display module to display the purchase recommendation based on a use frequency of the option purchased by the user.

18 . The system of claim 17 , further comprising a machine-learning model trained to predict whether the loss aversion is the factor in the potential purchase making process of the potential option available for purchase by the user and to advise the user on how likely the option purchased by the user is useful according to logged purchases and communications by others of the po

Assignees

• Toyota Res Inst Inc
• Toyota Motor Co Ltd

Inventors

• Chen Francine R
• Lyons Kenton Michael
• Harinen Totte Harri
• Filipowicz Alexandre Leo Stephen

Classifications

• G06Q30/0631 Primary

  Recommending goods or services · CPC title

• G06Q30/0633 Primary

  Managing shopping lists, e.g. compiling or processing purchase lists (shipping orders G06Q10/083; order filling G06Q10/087) · CPC title