# US 2019/0171928 A1 · Dynamically managing artificial neural networks

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2019/0171928 A1
- Document ID: US20190171928A1
- Status: Published application
- Kind: A1
- Application number: 16/313,697
- Filed: 2017-06-27
- Published: 2019-06-06
- Earliest claimed: 2016-06-27
- Inventors: Robin Young
- Web page: https://patentgrep.com/patent/US20190171928A1

## CPC

- G06N 3/08: Learning methods
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections
- G06N 3/09: Supervised learning
- G06N 20/20: Ensemble learning
- G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202)
- G06Q 10/063112: {Skill-based matching of a person or a group to a task}
- G06Q 10/10: Office automation; Time management
- G06Q 10/1053: Employment or hiring
- G06Q 30/0201: Market modelling; Market analysis; Collecting market data
- G06Q 30/0202: Market predictions or forecasting for commercial activities
- 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

In some embodiments, the disclosed subject matter involves using socket layers with a plurality of artificial neural networks in a machine learning system to create customizable inputs and outputs for a machine learning service. The machine learning service may include a plurality of convolutional neural networks and a plurality of pre-trained fully connected neural networks to find the best fits. In an embodiment, when the customized input or output data is not a good fit with the pre-trained artificial neural networks, a socket layer may automatically request additional convolutional layers or new training of a neural network to dynamically manage the machine learning system to accommodate the customized input or customized output. Other embodiments are described and claimed.

## Claims

### Claim at position 1

1-24. (canceled)

### Claim 25

A computer implemented method for managing a plurality of artificial neural networks in a machine learning system, comprising:
receiving input data for a customized input data layer for the plurality of artificial neural networks, the input data having identified physical,
organizational or chemical properties, and processing the input data by at least one convolutional layer to produce convolutionally processed input data;
confirming a range of the identified physical, organizational or chemical properties of the input data;
confirming that the convolutionally processed input data fits with the customized input layer;
responsive to an indication that the convolutionally processed input data fits with the customized input layer: converting properties of the customized input layer into an input socket layer of the plurality of artificial neural networks, placing the properties of the customized input layer into the input socket layer of the plurality of artificial neural networks, and proceeding to prepare an output prediction using the plurality of artificial neural networks; and
responsive to an indication that the convolutionally processed input data does not fit with the customized input layer, requesting at least one of an additional convolutional layer process or a training of a new neural network model, and proceeding to prepare an output prediction,
wherein the input socket layer is configured to automatically and dynamically initiate changes to the machine learning system to accommodate the input data.

### Claim 26 (depends on 25)

The computer implemented method as recited in claim 25, further comprising:
receiving at an output socket layer, an output prediction from the plurality of artificial neural networks;
confirming the range of the identified physical, organizational or chemical properties of the output prediction;
confirming the output prediction is a fit with a customized output layer; responsive to an indication that the output prediction fits with the customized output layer: converting properties of the output prediction by an output socket layer to the customized output layer, placing the properties of the output socket layer into the customized output layer; and
responsive to an indication that the output prediction at the output socket layer does not fit with the customized output layer, requesting at least one of an additional output convolutional layer process or a training of a new neural network model, and placing the properties of the output socket layer into the customized output layer,
wherein the output socket layer is configured to automatically and dynamically initiate changes to the machine learning system to accommodate the output prediction.

### Claim 27 (depends on 26)

The computer implemented method as recited in claim 26, further comprising:
calculating probabilities of phenomena occurring in a predictive model; processing the customized output layer through a convolutional model to produce a convoluted output prediction; and
providing the convoluted output prediction to a user as output data.

### Claim 28 (depends on 26)

The computer implemented method as recited in claim 26, wherein confirming the output prediction is a fit with a customized output layer further comprises:
receiving an output vector from the customized output layer;
measuring a distance between the output vector and a pre-trained model of the plurality of artificial neural networks, wherein the distance indicates whether there is a sufficient match between the customized output layer and the pre-trained model;
responsive to an indication of a sufficient match with the pre-trained model and the output vector, indicating that the output prediction fits with the customized output layer;
responsive to an indication that there is not a sufficient match with the pre-trained model and the output vector; identifying whether the output vector is a sufficient match with an additional output convolutional layer; responsive to an indication that the output vector is a sufficient match with an additional output convolutional layer, automatically requesting processing of an additional output convolutional layer; and
responsive to an indication that the output vector is not a sufficient match with an additional output convolutional layer, automatically requesting the training of the new neural network model for use with the plurality of artificial neural networks.

### Claim 29 (depends on 25)

The computer implemented method as recited in claim 25, wherein confirming that the convolutionally processed input data fits with the customized input layer further comprises:
receiving an input vector from the customized input layer;
measuring a distance between the input vector and a pre-trained model of the artificial neural network, wherein the distance indicates whether there is a sufficient match between the input data and the pre-trained model;
responsive to an indication of a sufficient match with the pre-trained model and the input vector, indicating that the convolutionally processed input data fits with the customized input layer;
responsive to an indication that there is not a sufficient match with the pre-trained model and the input vector; identifying whether the input vector is a sufficient match with an additional convolutional layer;
responsive to an indication that the input vector is a sufficient match with an additional convolutional layer, automatically requesting processing of an additional convolutional layer; and
responsive to an indication that the input vector is not a sufficient match with an additional convolutional layer, automatically requesting the training of the new neural network model.

### Claim 30 (depends on 25)

The computer implemented method as recited in claim 25, further comprising:
identifying properties to be trained in the machine learning system; selecting a range of outcomes for the output prediction;
tagging data based on the selected range of outcomes, to generate tagged data; and providing the tagged data to train a first neural network model.

### Claim 31 (depends on 30)

The computer implemented method as recited in claim 30, wherein the first neural network model includes language and contextual classifiers for natural language responses.

### Claim 32 (depends on 31)

The computer implemented method as recited in claim 31, wherein the output prediction provides matching for a job matching service.

### Claim 33 (depends on 32)

The computer implemented method as recited in claim 32, wherein the natural language responses include open ended textual response from a job candidate subscribed to the job matching service, responsive to a request from an employer for information.

### Claim 34 (depends on 33)

The computer implemented method as recited in claim 33, wherein the first neural network model uses the tagged data to identify semantic and sentiment contextual data in the natural language response.

### Claim 35 (depends on 25)

A computer readable storage medium having instructions stored thereon, the instructions when executed on a machine cause the machine to perform the method of claim 25.

### Claim 36

A machine learning system having a plurality of artificial neural networks and using customized layers, comprising;
a processor coupled to memory, including a plurality of trained neural network models;
a customized input layer coupled to an input socket layer, wherein the input socket layer is configured to provide input data to a plurality of fully connected layers of the plurality of trained neural network models, wherein the customized input layer is configured to receive the input data processed by at least one input convolutional layer;
a customized output layer coupled to an output socket layer, wherein the output socket layer is configured to receive output data from the plurality of fully connected layers of the plurality of trained neural network models, wherein the customized output layer is configured to send the output data to at least one output convolutional layer configured to generate output data; and
input fit logic operable by the processor configured to initiate automatic and dynamic changes to the machine learning system when the customized input layer is identified as not being a sufficient fit with the plurality of trained neural network models.

### Claim 37 (depends on 36)

The machine learning system as recited in claim 36, wherein the input fit logic is further configured to request at least one of an additional convolutional layer process or a training of a new neural network model to make the dynamic change of the machine learning system, responsive to an indication that the customized input layer is identified as not being a sufficient fit with the plurality of trained neural network models.

### Claim 38 (depends on 37)

The machine learning system as recited in claim 37, wherein the input fit logic is further configured to:
receive an input vector from the customized input layer;
measure a distance between the input vector and a trained model of the plurality of trained neural network models, wherein the distance indicates whether there is a sufficient match between the input data and the trained model; responsive to an indication of a sufficient match with the trained model and the input vector, indicate that the input data fits with the customized input layer;
responsive to an indication that there is not a sufficient match with the trained model and the input vector; identify whether the input vector is a sufficient match with an additional convolutional layer;
responsive to an indication that the input vector is a sufficient match with an additional convolutional layer, automatically request processing of an additional convolutional layer; and
responsive to an indication that the input vector is not a sufficient match with an additional convolutional layer, automatically request the training of the new neural network model.

### Claim 39 (depends on 37)

The machine learning system as recited in claim 37, further comprising: tagging logic operable by the processor to:
identify properties to be trained in the machine learning system; select a range of outcomes for the output prediction; tag data based on the selected range of outcomes, to generate tagged data; and
provide the tagged data to train a first neural network model.

### Claim 40 (depends on 39)

The machine learning system as recited in claim 39, wherein the first neural network model includes language and contextual classifiers for natural language responses.

### Claim 41 (depends on 40)

The machine learning system as recited in claim 40, wherein the output prediction provides matching for a job matching service.

### Claim 42 (depends on 41)

The computer implemented method as recited in claim 41, wherein the natural language responses include open ended textual response from a job candidate subscribed to the job matching service, responsive to a request from an employer for information.

### Claim 43 (depends on 42)

The machine learning system as recited in claim 42, wherein the first neural network model uses the tagged data to identify semantic and sentiment contextual data in the natural language response.

### Claim 44 (depends on 36)

The machine learning system as recited in claim 36, further comprising:
output fit logic operable by the processor configured to initiate dynamic changes to the machine learning system when the customized output layer is identified as not being a sufficient fit with the plurality of trained neural network models.

### Claim 45 (depends on 44)

The machine learning system as recited in claim 44, wherein the output fit logic is further configured to: receive at an output socket layer, an output prediction from the artificial neural network;
confirm the range of the identified physical, organizational or chemical properties of the output prediction;
confirm the output prediction is a fit with a customized output layer;
responsive to an indication that the output prediction fits with the customized output layer: convert properties of the output prediction by an output socket layer to the customized output layer, placing the properties of the output socket layer into the customized output layer; and
responsive to an indication that the output prediction at the output socket layer does not fit with the customized output layer, requesting at least one of an additional output convolutional layer process or a training of a new neural network model, and placing the properties of the output socket layer into the customized output layer.

### Claim 46 (depends on 45)

The machine learning system as recited in claim 45, wherein the logic is further configured to:
receive an output vector from the customized output layer;
measure a distance between the output vector and a trained model of the artificial neural network, wherein the distance indicates whether there is a sufficient match between the customized output layer and the trained model;
responsive to an indication of a sufficient match with the trained model and the output vector, indicate that the output prediction fits with the customized output layer;
responsive to an indication that there is not a sufficient match with the trained model and the output vector;
identify whether the output vector is a sufficient match with an additional output convolutional layer;
responsive to an indication that the output vector is a sufficient match with an additional output convolutional layer, automatically request processing of an additional output convolutional layer; and
responsive to an indication that the output vector is not a sufficient match with an additional output convolutional layer, automatically request the training of the new neural network model.

### Claim 47 (depends on 36)

The machine learning system as recited in claim 36, wherein the input socket layer and output socket layer are configured to enable the dynamic changes to the machine learning system to provide logic for fitting the plurality of artificial neural networks to the customized input layer and the customized output layer.

### Claim 48 (depends on 25)

A machine learning system comprising
means to performing the operations of claim 25.