# US 2022/0284368 A1 · Automatically Learning Process Characteristics for Model Optimization

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- Number: US 2022/0284368 A1
- Document ID: US20220284368A1
- Status: Published application
- Kind: A1
- Application number: 17/194,920
- Filed: 2021-03-08
- Published: 2022-09-08
- Earliest claimed: 2021-03-08
- Applicants: Aible Inc.
- Inventors: Arijit Sengupta; Jonathan Wray; Rodney Butters
- Web page: https://patentgrep.com/patent/US20220284368A1

## CPC

- G06Q 10/06315: {Needs-based resource requirements planning or analysis}
- G06N 5/04: Inference or reasoning models
- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G06Q 30/0202: Market predictions or forecasting for commercial activities
- 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
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Data characterizing inputs to a prediction process that classifies events, an output of the prediction process, and feedback data characterizing a performance of the outcome is monitored. A resource capacity affecting the outcome of the prediction process, and/or a cost-benefit affecting the outcome of the prediction process is determined from the monitoring. The determined resource capacity and/or the determined cost-benefit is provided. Related apparatus, systems, techniques and articles are also described.

## Claims

### Claim 1

A method comprising:
monitoring data characterizing inputs to a prediction process that classifies events, an output of the prediction process, and feedback data characterizing a performance of the outcome;
determining, from the monitoring, a resource capacity affecting the outcome of the prediction process, and/or a cost-benefit affecting the outcome of the prediction process; and
providing the determined resource capacity and/or the determined cost-benefit.

### Claim 2 (depends on 1)

The method of claim 1, wherein determining the resource capacity includes determining a number of outputs assigned to a first class of at least two classes over a period of time.

### Claim 3 (depends on 2)

The method of claim 2, wherein the resource capacity characterizes a number of events that an entity has resources to process over the period of time.

### Claim 4 (depends on 1)

The method of claim 1, wherein determining the cost-benefit affecting the outcome of the prediction process includes estimating a cost of a false positive, a cost of a false negative, a benefit of a true positive, and a benefit of a true negative.

### Claim 5 (depends on 1)

The method of claim 1, wherein the inputs to the prediction process include information associated with sales leads, the events including sales opportunities, and the feedback data characterizes whether pursuing the sales leads resulted in conversion of the sales leads.

### Claim 6 (depends on 1)

The method of claim 1, wherein the feedback data characterizes whether the output of the prediction process was accurate.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
determining, based on the monitoring, that a sufficient amount of data characterizing the inputs, the output, and the feedback data has been received, and training a model to perform the prediction process using the monitored data; and
deploying the trained model within an enterprise resource management system for operating on new input data to the prediction process.

### Claim 8 (depends on 7)

The method of claim 7, further comprising:
receiving new data characterizing input data for a new event;
determining a first class of at least two classes for the new event and using the model, the capacity, and/or the cost-benefit;
determining an impact value of the first class of the new event; and
providing the first class and the determined impact value.

### Claim 9 (depends on 7)

The method of claim 7, wherein training the model includes training a set of models, each model in the set of models trained for at least one resource capacity value; and
wherein determining the first class using the model includes selecting the model from the set of models according to the determined resource capacity.

### Claim 10

A system comprising:
at least one data processor; and
memory storing instructions which, when executed by the at least one data processor, causes the data processor to perform operations comprising:
monitoring data characterizing inputs to a prediction process that classifies events, an output of the prediction process, and feedback data characterizing a performance of the outcome;
determining, from the monitoring, a resource capacity affecting the outcome of the prediction process, and/or a cost-benefit affecting the outcome of the prediction process; and
providing the determined resource capacity and/or the determined cost-benefit.

### Claim 11 (depends on 10)

The system of claim 10, wherein determining the resource capacity includes determining a number of outputs assigned to a first class of at least two classes over a period of time.

### Claim 12 (depends on 11)

The system of claim 11, wherein the resource capacity characterizes a number of events that an entity has resources to process over the period of time.

### Claim 13 (depends on 10)

The system of claim 10, wherein determining the cost-benefit affecting the outcome of the prediction process includes estimating a cost of a false positive, a cost of a false negative, a benefit of a true positive, and a benefit of a true negative.

### Claim 14 (depends on 10)

The system of claim 10, wherein the inputs to the prediction process include information associated with sales leads, the events including sales opportunities, and the feedback data characterizes whether pursuing the sales leads resulted in conversion of the sales leads.

### Claim 15 (depends on 10)

The system of claim 10, wherein the feedback data characterizes whether the output of the prediction process was accurate.

### Claim 16 (depends on 10)

The system of claim 10, the operations further comprising:
determining, based on the monitoring, that a sufficient amount of data characterizing the inputs, the output, and the feedback data has been received, and training a model to perform the prediction process using the monitored data; and
deploying the trained model within an enterprise resource management system for operating on new input data to the prediction process.

### Claim 17 (depends on 16)

The system of claim 16, the operations further comprising:
receiving new data characterizing input data for a new event;
determining a first class of at least two classes for the new event and using the model, the capacity, and/or the cost-benefit;
determining an impact value of the first class of the new event; and
providing the first class and the determined impact value.

### Claim 18 (depends on 16)

The system of claim 16, wherein training the model includes training a set of models, each model in the set of models trained for at least one resource capacity value; and
wherein determining the first class using the model includes selecting the model from the set of models according to the determined resource capacity.

### Claim 19

A non-transitory computer readable medium storing computer readable instructions which, when executed by at least one processor forming part of at least one computing system, causes the at least one processor to perform operations comprising:
monitoring data characterizing inputs to a prediction process that classifies events, an output of the prediction process, and feedback data characterizing a performance of the outcome;
determining, from the monitoring, a resource capacity affecting the outcome of the prediction process, and/or a cost-benefit affecting the outcome of the prediction process; and
providing the determined resource capacity and/or the determined cost-benefit.

### Claim 20 (depends on 19)

The computer readable medium of claim 19, wherein determining the resource capacity includes determining a number of outputs assigned to a first class of at least two classes over a period of time.