Predicting aggregate value of objects representing potential transactions based on potential transactions expected to be created

Patent metadata

Field	Value
Publication number	US-2018096372-A1
Application number	US-201715721346-A
Country	US
Kind code	A1
Filing date	Sep 29, 2017
Priority date	Sep 30, 2016
Publication date	Apr 5, 2018
Grant date	—

Abstract

An online system stores objects representing potential transactions of an enterprise. The online system uses predictor models to determine an aggregate score based on values of the objects associated with a time interval, for example, a month. Each object is configured to take one of a plurality of states. The online system stores historical data describing activities associated with potential transaction objects and uses the stored data for generating the predictor models. The online system categorizes the objects into bins based on states of the objects. The online system may generate different predictions for each category. The online system may use machine learning based models as predictor models. The online system extracts features describing potential transaction objects and provides these as input to the predictor model.

First claim

We claim:

1 . A computer-implemented method comprising:
storing, by a computer system, a plurality of objects, each object associated with a value and having one of a plurality of states, each object configured to perform state transitions responsive to changes in data associated with the object; identifying a time interval comprising a sequence of sub-intervals and a current sub-interval from the sequence; identifying a set of objects from the plurality of objects, the identified set of objects associated with the time interval; accessing one or more predictor models configured to receive as input the identified set of objects and predict an estimated value associated with objects representing potential transactions expected to be created before the end of the time interval; determining, by the computer system, an estimated value of objects for potential transactions that are expected to be created within the time interval based on the one or more predictor models and the identified set of objects; determining an estimated total score for the end of the time period based on the estimated value of objects for potential transactions that are expected to be created within the time interval; and sending the estimated total value for display by a user interface.

2 . The method of claim 1 , wherein the one or more predictor models are machine learning based models trained using historical data describing the plurality of objects.

3 . The method of claim 1 , wherein the one or more predictor models comprise a plurality of linear regression models, each of the plurality of linear regression models associated with a category and a position of a sub-interval in the sequence of sub-intervals, each linear regression model determined based on historical data associated with the stored objects.

4 . The method of claim 1 , wherein the one or more predictor models comprise a model that predicts the aggregate value associated with objects that are expected to be created before the end of the time interval based on a total expected value for potential transaction scheduled to close by the end of the time interval.

5 . The method of claim 1 , wherein the one or more predictor models comprise a model that predicts the aggregate value associated with objects that are expected to be created before the end of the time interval based on a total expected value for potential transactions scheduled to close by the end of the time interval and a total expected value for potential transactions scheduled to close after the end of the time interval.

6 . The method of claim 1 , wherein historical data associated with the stored objects is stored in a database table, the method comprising:
detecting a change in a value associated with an object representing a potential transaction; and adding a row to the database table responsive to detecting the change in the value.

7 . The method of claim 1 , wherein each object represents a potential transaction and the object score of the object represents a value of the potential transaction.

8 . The method of claim 1 , wherein each object is represented as a database record.

9 . The method of claim 1 , wherein each category corresponds to one of the plurality of states of an object representing a potential transaction.

10 . The method of claim 1 , wherein the time interval represents a month and each sub-interval represents a day of the month.

11 . A non-transitory computer readable storage medium storing instructions for:
storing, by a computer system, a plurality of objects, each object associated with a value and having one of a plurality of states, each object configured to perform state transitions responsive to changes in data associated with the object; identifying a time interval comprising a sequence of sub-intervals and a current sub-interval from the sequence; identifying a set of objects from the plurality of objects, the identified set of objects associated with the time interval; accessing one or more predictor models configured to receive as input the identified set of objects and predict an estimated value associated with objects representing potential transactions expected to be created before the end of the time interval; determining, by the computer system, an estimated value of objects for potential transactions that are expected to be created within the time interval based on the one or more predictor models and the identified set of objects; determining an estimated total score for the end of the time period based on the estimated value of objects for potential transactions that are expected to be created within the time interval; and sending the estimated total value for display by a user interface.

12 . The non-transitory computer readable storage medium of claim 11 , wherein the one or more predictor models are machine learning based models trained using historical data describing the plurality of objects.

13 . The non-transitory computer readable storage medium of claim 11 , wherein the one or more predictor models comprise a plurality of linear regression models, each of the plurality of linear regression models associated with a category and a position of a sub-interval in the sequence of sub-intervals, each linear regression model determined based on historical data associated with the stored objects.

14 . The non-transitory computer readable storage medium of claim 11 , wherein the one or more predictor models comprise a model that predicts the aggregate value associated with objects that are expected to be created before the end of the time interval based on a total expected value for potential transaction scheduled to close by the end of the time interval.

15 . The non-transitory computer readable storage medium of claim 11 , wherein the one or more predictor models comprise a model that predicts the aggregate value associated with objects that are expected to be created before the end of the time interval based on a total expected value for potential transactions scheduled to close by the end of the time interval and a total expected value for potential transactions scheduled to close after the end of the time interval.

16 . The non-transitory computer readable storage medium of claim 11 , wherein historical data associated with the stored objects is stored in a database table, the stored instructions further comprising instructions for:
detecting a change in a value associated with an object representing a potential transaction; and adding a row to the database table responsive to detecting the change in the value.

17 . A computer-implemented system comprising:
a computer processor; and a computer readable non-transitory storage medium storing instructions thereon, the instructions when executed by a processor cause the processor to perform the steps of:
storing, by a computer system, a plurality of objects, each object associated with a value and having one of a plurality of states, each object configured to perform state transitions responsive to changes in data associated with the object;
identifying a time interval comprising a sequence of sub-intervals and a current sub-interval from the sequence;
identifying a set of objects from the plurality of objects, the identified set of objects associated with the time interval;
accessing one or more predictor models configured to receive as input the identified set of objects and predict an estimated value associated with objects representing potential transactions expected to be created before the end of the time interval;
determini

Assignees

• Salesforce Com Inc

Inventors

• Rickard Jr Scott Thurston
• Balsam Elizabeth Rachel
• Backes Tracy Morgan
• Alexander Zachary

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06Q30/0204 Primary

  Market segmentation · CPC title

• G06Q30/0203

  Market surveys; Market polls · CPC title

• G06Q30/0201

  Market modelling; Market analysis; Collecting market data · CPC title