Controller systems and methods of limiting the operation of neural networks to be within one or more conditions

• US 10,242,665 B1
• Filed: 06/04/2018
• Issued: 03/26/2019
• Est. Priority Date: 12/29/2017
• Status: Active Grant

• Current Assignee: Apex AI Industries LLC
• Original Assignee: Apex Artificial Intelligence Industries, Inc.
• Inventors: Abeloe, Kenneth A.
• Primary Examiner(s): Tzeng, Feng-Tzer
• Application Number:
  US15/997,031
• Time in Patent Office: 295 Days
• CPC Class Codes: B60Q 5/006 indicating risk of collision between vehicles or with pedestrians
• CPC Class Codes: B60R 11/0217 for loud-speakers
• CPC Class Codes: B60R 11/04 Mounting of cameras operative during drive; Arrangement of controls thereof relative to the vehicle
• CPC Class Codes: B60R 2300/102 using 360 degree surveillance camera system
• CPC Class Codes: B60R 2300/103 using camera systems provided with artificial illumination device, e.g. IR light source
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0221 involving a learning process
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G10L 13/00 Speech synthesis; Text to speech systems
• CPC Class Codes: G10L 13/047 Architecture of speech synthesisers
• CPC Class Codes: G10L 15/16 : using artificial neural networks
• CPC Class Codes: G10L 15/22 : Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 2015/223 : Execution procedure of a spoken command

Abstract

Systems and methods for automatically self-correcting or correcting in real-time one or more neural networks after detecting a triggering event, or breaching boundary conditions are provided. Such a triggering event may indicate incorrect output signal or data being generated by the one or more neural networks. In particular, machine controllers of the invention limit the operations of neural networks to be within boundary conditions. Autonomous machines of the invention can be self-corrected after a breach of a boundary condition is detected. Autonomous land vehicles of the invention are capable of determining the timing of automatic transition to the manual control from automated driving mode. The controller of the invention filters and saves input-output data sets that fall within boundary conditions for later training of neural networks. The controllers of the invention include security architectures to prevent damages from virus attacks or system malfunctions.

Claims

1. A method of operating an apparatus using a control system that includes at least one neural network, comprising:

receiving an input vector captured by the apparatus;

processing the input vector using the at least one neural network of the control system;

obtaining an output from the at least one neural network resulting from processing the input vector;

processing the output with a second neural network to determine whether the output breaches a predetermined condition that is unchangeable after an initial installation onto the control system, wherein the second neural network is prevented from being retrained; and

using the output from the at least one neural network to control the apparatus unless the output breaches the predetermined condition.

2. The method of claim 1, further comprising re-training the at least one neural network when the output is determined to breach the predetermined condition.

3. The method of claim 1, further comprising defining the predetermined condition to prevent a damage to the apparatus.

4. The method of claim 1, further comprising defining the predetermined condition with a machine recognizable human speech part.

5. The method of claim 1, wherein the apparatus is a human speech generator with a loudspeaker and the step of using the obtained output further includes the step of generating human speech parts to be played on the loudspeaker.

6. The method of claim 1, wherein the apparatus is an autonomous land vehicle and the step of using the obtained output further includes the step of generating a signal to control the autonomous land vehicle.

7. The method of claim 1, further comprising:

in response to determining the obtained output from the at least one neural network breaches a predetermined condition, replacing nodal values of the at least one neural network with previously stored nodal values.

17. The method of claim 1, wherein the at least one neural network is trained with a first data set and the second neural network is trained with a second data set, the second data set different than the first data set.

8. An apparatus being operated in part by a controller, the apparatus comprising:

an input device coupled to the apparatus, the input device constructed to generate an input vector;

a controller;

at least one neural network coupled to the controller, the at least one neural network constructed to receive the input vector and to generate an output; and

wherein the controller comprises a second neural network constructed to receive the output from the at least one neural network and determine whether the output breaches a predetermined condition unchangeable after an initial installation onto the controller,

wherein the second neural network is prevented from being retrained, and

wherein the controller is constructed to operate the apparatus using the output from the at least one neural network unless the output from the at least one neural network is determined to breach the predetermined condition.

9. The apparatus of claim 8, wherein the controller is further constructed to re-train the at least one neural network when the output is determined to breach the predetermined condition.

10. The apparatus of claim 8, wherein the controller is further constructed to generate a human recognizable notification when the output is determined to breach the predetermined condition.

11. The apparatus of claim 8, wherein the predetermined condition is defined to prevent damage to the apparatus.

12. The apparatus of claim 8, wherein the predetermined condition is defined with a machine recognizable human speech part.

13. The apparatus of claim 8, wherein the apparatus is an autonomous land vehicle coupled to the at least one neural network and constructed to generate a signal to control the autonomous land vehicle.

14. The apparatus of claim 8, wherein nodal values of the at least one neural network are replaced by to a previously stored nodal values when the obtained output from the at least one neural network is determined to breach a predetermined condition.

18. The apparatus of claim 8, wherein the at least one neural network is trained with a first data set and the second neural network is trained with a second data set, the second data set different than the first data set.

15. An apparatus being operated in part by a controller, comprising:

an input means coupled to the apparatus for generating an input vector;

at least one neural network coupled to the controller and constructed to receive the input vector and to generate an output; and

a second neural network coupled to the at least one neural network and constructed for comparing the output from the at least one neural network with a predetermined condition that is unchangeable after an initial installation onto the control system, wherein the second neural network is prevented from being retrained, and

wherein the controller is constructed to operate the apparatus using the output from the at least one neural network unless the output is determined, by the second neural network, to breach the predetermined condition.

16. The apparatus of claim 15, wherein the apparatus is an autonomous land vehicle coupled to the at least one neural network, and the at least one neural network is constructed to generate a signal to control the autonomous land vehicle.

19. The apparatus of claim 15, wherein the at least one neural network is trained with a first data set and the second neural network is trained with a second data set, the second data set different than the first data set.