# US 2022/0277263 A1 · System and method for predictive inventory

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- Number: US 2022/0277263 A1
- Document ID: US20220277263A1
- Status: Published application
- Kind: A1
- Application number: 17/187,421
- Filed: 2021-02-26
- Published: 2022-09-01
- Earliest claimed: 2021-02-26
- Applicants: Fiix Inc.
- Inventors: Mohammad ESMALIFALAK; Akshay IYENGAR; Seyedmorteza Mirhoseininejad; Francis EMERY; Taylor MATHEWSON; Peter DOULAS
- Web page: https://patentgrep.com/patent/US20220277263A1

## CPC

- G06Q 10/00: Administration; Management
- G06Q 10/0875: Itemisation or classification of parts, supplies or services, e.g. bill of materials
- G06N 20/20: Ensemble learning
- G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q 10/0631: Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q 10/06315: {Needs-based resource requirements planning or analysis}
- G06Q 10/08: Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q 10/0838: {Historical data}
- 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

There is provided systems and methods for forecasting usage of one or more parts. Such systems may receive historical usage data, generate machine learning models, generate predictions using the machine learning models, and perform one or more actions based on the generated predictions.

## Claims

### Claim 1

A method of forecasting part usage, comprising:
receiving, by a computing device, historical usage data for a part;
training, by the computing device, a machine learning model based on the historical usage data, wherein the training comprises:
discovering correlations between usage of the part and work orders based on analysis of the historical usage data, and
training the machine learning model based on the correlations;
determining, by the computing device, a predicted amount of future demand for the part based on the machine learning model; and
performing, by the computing device, an action based on the predicted amount of future demand for the part, wherein the action comprises at least initiating an order for the part.

### Claim 2 (depends on 1)

The method of claim 1, further comprising generating clean data based on the historical usage data, wherein the generating comprises at least one of:
detecting and removing outlier data within the historical usage data, or
inferring and adding missing values to the historical usage data.

### Claim 3 (depends on 2)

The method of claim 2, wherein the clean data comprises time stamp data and part usage data.

### Claim 4 (depends on 2)

The method of claim 2, wherein the determining of the predicted amount of future demand comprises applying the clean data to the machine learning model.

### Claim 5 (depends on 1)

The method of claim 1, wherein the determining comprises determining the predicted amount of future demand based on an ensemble model generated based on the machine learning model.

### Claim 6 (depends on 1)

The method of claim 1, wherein the performing of the action further comprises generating a notification.

### Claim 7 (depends on 6)

The method of claim 6, wherein the notification comprises one or more of an email notification, a notification directed to a client device, or a text message.

### Claim 8

(canceled)

### Claim 9

A system for forecasting usage of parts, comprising:
a memory that stores executable components; and
a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
a data accumulator module configured to receive historical usage data for a part;
a model generation module configured to discover, based on analysis of the historical usage data, a correlation between usage of the part and work orders and to train a machine learning model based on the correlation;
a part forecasting module configured to determine a predicted amount of future demand for the part based on the machine learning model; and
a notification module configured to perform an action based on the predicted amount of future demand for the part, wherein the action comprises at least initiating an order for the part.

### Claim 10 (depends on 9)

The system of claim 9, wherein the data accumulator module is further configured to at least one of remove outlier data from the historical usage data or add missing data to the historical usage data.

### Claim 11 (depends on 10)

The system of claim 10, wherein the data accumulator module is further configured to add time stamp data to the historical usage data.

### Claim 12 (depends on 9)

The system of claim 9, wherein the part forecasting module is further configured to determine the predicted amount of future demand based on an ensemble model generated based on the machine learning model.

### Claim 13 (depends on 9)

The system of claim 9, wherein the notification module is configured to generate a notification in response to determining that the predicted amount of future demand satisfies a criterion.

### Claim 14 (depends on 13)

The system of claim 13, wherein the notification is at least one of an email notification, a notification directed to a client device, or a text message.

### Claim 15 (depends on 9)

The system of claim 9, wherein the model generation module is configured to train the machine learning model based on data obtained from an Internet of Things device installed on an industrial asset.

### Claim 16

A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:
receiving historical usage data for a part;
training a machine learning model based on the historical usage data, wherein the training comprises
discovering correlations between usage of the part and work orders based on analysis of the historical usage data, and
training the machine learning model based on the correlations;
determining a predicted amount of future demand for the part based on the machine learning model; and
performing an action based on the predicted amount of future demand for the part, wherein the action comprises at least initiating an order for the part.

### Claim 17 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein the operations further comprise at least one of removing outlier data from the historical usage data or adding missing data to the historical usage data.

### Claim 18 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein the operations further comprise adding time stamp data to the historical usage data.

### Claim 19 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein the determining comprises determining the predicted amount of future demand based on an ensemble model generated based on the machine learning model.

### Claim 20 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein the training comprises training the machine learning model based on data obtained from an Internet of Things device installed on an industrial asset.

### Claim 21 (depends on 1)

The method of claim 1, wherein the training comprises training the machine learning model based on data obtained from an Internet of Things device installed on an industrial asset.