# US 2019/0318204 A1 · Methods and apparatus to manage tickets

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- Number: US 2019/0318204 A1
- Document ID: US20190318204A1
- Status: Published application
- Kind: A1
- Application number: 16/452,040
- Filed: 2019-06-25
- Published: 2019-10-17
- Earliest claimed: 2019-06-25
- Assignees: Intel Corporation
- Applicants: Intel Corporation
- Inventors: Yatish Mishra; Cesar Martinez-Spessot; Alexander Heinecke; Justin Gottschlich
- Web page: https://patentgrep.com/patent/US20190318204A1

## CPC

- G06Q 10/06316: {Sequencing of tasks or work}
- G06F 18/2148: {characterised by the process organisation or structure, e.g. boosting cascade}
- G06F 18/24: Classification techniques
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections
- G06N 3/09: Supervised learning
- G06N 20/00: Machine learning
- G06Q 10/0631: Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- 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
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Methods and apparatus to manage tickets are disclosed. A disclosed example apparatus includes a ticket analyzer to read data corresponding to open tickets, a machine learning model processor to apply a machine learning model to files associated with previous tickets based on the read data to determine probabilities of relationships between the files and the open tickets, a grouping analyzer to identify at least one of a grouping or a dependency between the open tickets based on the determined probabilities, and a ticket data writer to store data associated with the at least one of the grouping or the dependency.

## Claims

### Claim 1

An apparatus comprising:
a ticket analyzer to read data corresponding to open tickets;
a machine learning model processor to apply a machine learning model to files associated with previous tickets based on the read data to determine probabilities of relationships between the files and the open tickets;
a grouping analyzer to identify at least one of a grouping or a dependency between the open tickets based on the determined probabilities; and
a ticket data writer to store data associated with the at least one of the grouping or the dependency.

### Claim 2 (depends on 1)

The apparatus as defined in claim 1, further including a machine model trainer to train the machine learning model based on the previous tickets.

### Claim 3 (depends on 2)

The apparatus as defined in claim 2, wherein the machine model trainer implements a long short term memory (LSTM) network to train the machine learning model.

### Claim 4 (depends on 2)

The apparatus as defined in claim 2, wherein the machine model trainer trains the machine learning model by assigning a first value to a first group of the files and a second value to a second group of the files corresponding to previously resolved issues.

### Claim 5 (depends on 1)

The apparatus as defined in claim 1, wherein the previous tickets correspond to closed tickets of a previous project.

### Claim 6 (depends on 1)

The apparatus as defined in claim 1, wherein the grouping analyzer is to implement a cost function analysis to identify the at least one of the grouping or the dependency.

### Claim 7 (depends on 1)

The apparatus as defined in claim 1, wherein the ticket data writer is to append at least one of the open tickets with the data associated with the at least one of the grouping or the dependency.

### Claim 8

At least one non-transitory computer-readable medium comprising instructions, which when executed, cause at least one processor to at least:
apply a machine learning model to files associated with previous tickets based on read data corresponding to open tickets to determine probabilities of relationships between the files and the open tickets;
identify at least one of a grouping or a dependency between the open tickets based on the determined probabilities; and
store data associated with the at least one of the grouping or the dependency.

### Claim 9 (depends on 8)

The at least one non-transitory computer-readable medium as defined in claim 8, wherein the instructions, when executed, cause the at least one processor to train the machine learning model based on the previous tickets.

### Claim 10 (depends on 9)

The at least one non-transitory computer-readable medium as defined in claim 9, wherein a long short term memory (LSTM) network is used to train the machine learning model.

### Claim 11 (depends on 9)

The at least one non-transitory computer-readable medium as defined in claim 9, wherein the machine learning model is trained by assigning a first value to a first group of the files and a second value to a second group of the files corresponding to previously resolved issues.

### Claim 12 (depends on 8)

The at least one non-transitory computer-readable medium as defined in claim 8, wherein the previous tickets correspond to closed tickets of a previous project.

### Claim 13 (depends on 8)

The at least one non-transitory computer-readable medium as defined in claim 8, wherein the instructions, when executed, cause the at least one processor to perform a cost function analysis to identify the at least one of the grouping or the dependency.

### Claim 14 (depends on 8)

The at least one non-transitory computer-readable medium as defined in claim 8, wherein the instructions, when executed, cause the at least one processor to append at least one of the open tickets with the data associated with the at least one of the grouping or the dependency.

### Claim 15

A method comprising:
applying, by executing an instruction with at least one processor, a machine learning model to files associated with previous tickets based on read data corresponding to open tickets to determine probabilities of relationships between the files and the open tickets;
identifying, by executing an instruction with the at least one processor, at least one of a grouping or a dependency between the open tickets based on the determined probabilities; and
storing, by executing an instruction with the at least one processor, data associated with the at least one of the grouping or the dependency.

### Claim 16 (depends on 15)

The method as defined in claim 15, further including training, by executing an instruction with the at least one processor, the machine learning model based on the previous tickets.

### Claim 17 (depends on 16)

The method as defined in claim 16, wherein a long short term memory (LSTM) network is used to train the machine learning model.

### Claim 18 (depends on 16)

The method as defined in claim 16, wherein the machine learning model is trained by assigning a first value to a first group of the files and a second value to a second group of the files corresponding to previously resolved issues.

### Claim 19 (depends on 15)

The method as defined in claim 15, wherein the previous tickets correspond to closed tickets of a previous project.

### Claim 20 (depends on 15)

The method as defined in claim 15, further including performing, by executing an instruction with the at least one processor, a cost function analysis to identify the at least one of the grouping or the dependency.