# US 2020/0401879 A1 · Systems and methods for predicting whether experimental legislation will become enacted into law

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

- Number: US 2020/0401879 A1
- Document ID: US20200401879A1
- Status: Published application
- Kind: A1
- Application number: 16/446,003
- Filed: 2019-06-19
- Published: 2020-12-24
- Earliest claimed: 2019-06-19
- Assignees: LegInsight, LLC
- Applicants: LegInsight, LLC
- Inventors: Adrian Menard
- Web page: https://patentgrep.com/patent/US20200401879A1

## CPC

- G06N 3/08: Learning methods
- G06F 16/27: Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
- G06F 16/953: Querying, e.g. by the use of web search engines
- G06N 3/0499: Feedforward networks
- G06N 3/09: Supervised learning
- G06N 3/0985: Hyperparameter optimisation; Meta-learning; Learning-to-learn
- G06N 5/02: Knowledge representation; Symbolic representation
- G06Q 50/26: Government or public services (business processes related to the transportation industry G06Q50/40)
- 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

A system and method for predicting whether experimental legislation will become enacted into law may include memory and at least one processor configured to receive a first request which proposes test content as an experimental law and receive a second request pertaining to a selected number of sponsors. The processor may automatically import, over a network, data from databases, prepare the data as predictive modeling data, split the predictive modeling data into two sets of data, train a two-class neural network on training data to predict whether the test law will become law, generate a set of results from the training data, cross-validate the set of results with the test data, and deploy, over the network, a predictive performance.

## Claims

### Claim 1

A system, comprising:
memory, having processor readable code stored therein;
a plurality of databases each storing a repository of data; and
one or more processors communicatively coupled to the memory and configured to execute instructions in the processor readable code that cause the one or more processors to:
transmit from a user a first request which generates test content as an experimental law, the experimental law proposing language directed to a not-yet-drafted bill;
transmit from the user a second request pertaining to a selection by the user of an amount of potential sponsors of the not-yet-drafted bill;
automatically import, over a network, data from the plurality of databases;
prepare the data as a plurality of predictive modeling data;
split the plurality of predictive modeling data into two sets of data, comprising a set of training data and a set of test data;
train a two-class neural network on the training data to evaluate a predictive performance of the not-yet-drafted bill;
generate a set of results from the training data;
cross-validate the set of results with the test data; and
deploy, over the network, a predictive performance, wherein the predictive performance is viewable on a network browser.

### Claim 2 (depends on 1)

The system of claim 1, wherein the predictive performance comprises a probability to pass.

### Claim 3 (depends on 2)

The system of claim 2, wherein the predictive performance comprises a suggested primary sponsor.

### Claim 4 (depends on 3)

The system of claim 3, wherein the predictive performance comprises a total amount of campaign contribution dollars.

### Claim 5 (depends on 1)

The system of claim 1, wherein the plurality of databases comprise at least one database of a structured query language, a no structured query language, a key value store, and a MapReduce.

### Claim 6 (depends on 5)

The system of claim 5, wherein the plurality of databases comprise at least one of in memory cache, distributed cache, and disk cache.

### Claim 7 (depends on 1)

The system of claim 1, wherein the system further comprises a proxy server and a load balancer as a content delivery network communicatively coupled to the network browser.

### Claim 8

At least one non-transitory computer readable medium containing processor readable code for programming one or more processors to perform a method comprising:
transmitting from a user a first request which generates test content as an experimental law, the experimental law proposing language directed to a not-yet-drafted bill;
transmitting from the user a second request pertaining to a selection by the user of an amount of potential sponsors of the not-yet-drafted bill;
importing automatically over a network, data from the plurality of databases;
preparing the data as a plurality of predictive modeling data;
splitting the plurality of predictive modeling data into two sets of data, comprising a set of training data and a set of test data;
training a two-class neural network on the training data to evaluate a predictive performance of the not-yet-drafted bill;
generating a set of results from the training data;
cross-validating the set of results with the test data; and
deploying, over the network, a predictive performance, wherein the predictive performance is viewable on a network browser.

### Claim 9 (depends on 8)

The method of claim 8, wherein after the importing step, the method further comprises editing metadata.

### Claim 10 (depends on 9)

The method of claim 9, wherein after the editing metadata step, the method further comprises converting categorical identifiers to Boolean indicator values.

### Claim 11 (depends on 10)

The method of claim 10, wherein after the splitting step, the method further comprises optimizing hyperparameters to tune the two-class neural network.

### Claim 12 (depends on 8)

The method of claim 8, wherein the predictive performance comprises a probability to pass.

### Claim 13 (depends on 12)

The method of claim 12, wherein the predictive performance comprises a suggested primary sponsor.

### Claim 14 (depends on 13)

The method of claim 13, wherein the predictive performance comprises a total amount of campaign contribution dollars.

### Claim 15

A non-transitory computer readable medium storing instructions executable by at least one processing device, the instructions including instructions to:
transmit from a user a first request which generates test content as an experimental law, the experimental law proposing language directed to a not-yet-drafted bill;
transmit from the user a second request pertaining to a selection by the user of an amount of potential sponsors of the not-yet-drafted bill;
automatically import, over a network, data from the plurality of databases;
prepare the data as a plurality of predictive modeling data;
split the plurality of predictive modeling data into two sets of data, comprising a set of training data and a set of test data;
train a two-class neural network on the training data to evaluate a predictive performance of the not-yet-drafted bill;
generate a set of results from the training data;
cross-validate the set of results with the test data; and
deploy, over the network, a predictive performance, wherein the predictive performance is viewable on a network browser.

### Claim 16 (depends on 15)

The system of claim 15, wherein the predictive performance comprises a probability to pass.

### Claim 17 (depends on 16)

The system of claim 16, wherein the predictive performance comprises a suggested primary sponsor.

### Claim 18 (depends on 17)

The system of claim 17, wherein the predictive performance comprises a total amount of campaign contribution dollars.

### Claim 19 (depends on 15)

The system of claim 15, wherein the plurality of databases comprise at least one database of a structured query language, a no structured query language, a key value store, and a MapReduce.

### Claim 20 (depends on 19)

The system of claim 19, wherein the plurality of databases comprises at least one of in memory cache, distributed cache, and disk cache.

### Claim 21 (depends on 15)

The system of claim 15, wherein the system further comprises a proxy server and a load balancer as a content delivery network communicatively coupled to the network browser.