# US 2018/0308010 A1 · Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems

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- Number: US 2018/0308010 A1
- Document ID: US20180308010A1
- Status: Published application
- Kind: A1
- Application number: 15/816,690
- Filed: 2017-11-17
- Published: 2018-10-25
- Earliest claimed: 2017-04-25
- Assignees: Xaxis, Inc.
- Applicants: Xaxis, Inc.
- Inventors: Karl Edward Bunch; Adam Branyan Cushner; Jacob Grabczewski; Sara Sue Robertson; Inga Silkworth
- Web page: https://patentgrep.com/patent/US20180308010A1

## CPC

- G06N 20/00: Machine learning
- G06F 8/315: {Object-oriented languages}
- G06F 9/4881: {Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues}
- G06F 15/76: Architectures of general purpose stored program computers (with program plugboard G06F15/08; multicomputers G06F15/16)
- G06F 16/24578: {using ranking}
- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/20: Ensemble learning
- G06Q 30/0241: Advertisements
- G06Q 30/0246: {Traffic}
- G06Q 30/0275: {Auctions}
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- 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

## Abstract

The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (“DBMLII”) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A decoupled machine learning workflow generation request is obtained. A set of decoupled tasks specified via the decoupled machine learning workflow generation request is determined, wherein each decoupled task in the set of decoupled tasks is associated with a corresponding class. Dependencies among decoupled tasks in the set of decoupled tasks are determined. A decoupled machine learning workflow structure comprising the set of decoupled tasks and the determined dependencies is generated, wherein the decoupled machine learning workflow structure is executable via a decoupled machine learning workflow controller to produce machine learning results.

## Claims

### Claim 1

A machine learning workflow decoupling apparatus, comprising:
a memory;
a component collection in the memory, including:
a machine learning workflow decoupler component;
a processor disposed in communication with the memory, and configured to issue a plurality of processing instructions from the component collection stored in the memory,
wherein the processor issues instructions from the machine learning workflow decoupler component, stored in the memory, to:
obtain, via at least one processor, a decoupled machine learning workflow generation request;
determine, via at least one processor, a set of decoupled tasks specified via the decoupled machine learning workflow generation request, wherein each decoupled task in the set of decoupled tasks is associated with a corresponding class;
determine, via at least one processor, dependencies among decoupled tasks in the set of decoupled tasks; and
generate, via at least one processor, a decoupled machine learning workflow structure comprising the set of decoupled tasks and the determined dependencies, wherein the decoupled machine learning workflow structure is executable via a decoupled machine learning workflow controller to produce machine learning results.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein each decoupled task in the set of decoupled tasks is independent of other decoupled tasks.

### Claim 3 (depends on 1)

The apparatus of claim 1, wherein each decoupled task in the set of decoupled tasks is configured to include a specification of a set of inputs and of a set of outputs.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein a first user interface is provided for creation of a decoupled task by a first entity, and a second user interface is provided for creation of the corresponding class of the decoupled task by a second entity.

### Claim 5 (depends on 1)

The apparatus of claim 1, wherein dependencies among decoupled tasks in the set of decoupled tasks specify the order of execution of the decoupled tasks.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein dependencies among decoupled tasks in the set of decoupled tasks specify for each decoupled task which other decoupled tasks provide inputs utilized by the respective decoupled task.

### Claim 7 (depends on 1)

The apparatus of claim 1, wherein the decoupled machine learning workflow structure employs a directed acyclic graph to organize the set of decoupled tasks and the determined dependencies.

### Claim 8 (depends on 7)

The apparatus of claim 7, wherein the decoupled machine learning workflow controller is configured to process the directed acyclic graph and to store the produced machine learning results.

### Claim 9 (depends on 1)

The apparatus of claim 1, wherein the produced machine learning results are stored in a Bonsai tree format.

### Claim 10 (depends on 1)

The apparatus of claim 1, wherein the produced machine learning results are stored in JSON format.