Intelligent auditory humanoid robot and computerized verbalization system programmed to perform auditory and verbal artificial intelligence processes

• US 9,302,393 B1
• Filed: 02/03/2015
• Issued: 04/05/2016
• Est. Priority Date: 04/15/2014
• Status: Active Grant

• Current Assignee: Alan Rosen, David B. Rosen
• Original Assignee: Alan Rosen, David B. Rosen
• Inventors: Rosen, David B., Rosen, Alan
• Primary Examiner(s): Tran, Khoi
• Assistant Examiner(s): Oh, Harry
• Application Number:
  US14/613,054
• Time in Patent Office: 427 Days
• Field of Search: 701245-264
• US Class Current: 1/1
• CPC Class Codes: B25J 9/1694 characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
• CPC Class Codes: B62D 57/032 with alternately or sequentially lifted supporting base and legs; with alternately or sequentially lifted feet or skid B62D57/024 takes precedence
• CPC Class Codes: G06N 3/008 based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour
• CPC Class Codes: Y10S 901/01 Mobile robot
• CPC Class Codes: Y10S 901/46 Sensing device
• CPC Class Codes: Y10S 901/47 Optical
• CPC Class Codes: Y10S 901/50 Miscellaneous

Abstract

The disclosed Auditory RRC-Humanoid Robot equipped with a verbal-phoneme sound generator is a computer-based system programmed to reach high levels of human-like verbal-AI. Behavioral programming techniques are used to reach human-like levels of identification-AI, recognition-AI, and comprehension-AI of all the words and sentences presented to the robot as verbal input signals. An innovative behavioral speech processing methodology is used to recognize and repeat the acoustic sequential set of phoneme signals that comprise the verbally generated speech of human speakers. The recognized and repeated sequential set of phoneme signals are then mapped onto a unique phonetic structure such as all the words and clauses listed in a 50,000 word lexicon that may then make up the vocabulary of the RRC-Robot. The system is programmed to hear and understand verbal speech with its auditory sensors, and intelligently responds by verbally talking with its verbal-phoneme sound generator.

Claims

1. An auditory relational robotic controller (RRC)-humanoid robot comprising:

a human-like mechanical robotic system comprising a human-like tactile recording monitor, a human-like visual recording monitor, and a human-like robotic body comprising a set of bipedal limbs, a set of arms, a set of hands, a set of fingers, an energy power source, and sets of motors and gears used to move the body, limbs, arms, hands, and fingers;

an auditory human-like recording monitor sensitive to an auditory frequency range of 1-20,000 cycles per second (cps), said auditory human-like recording monitor comprising a set of linear pickup microphones and a set of spectrum analyzers that convert incoming sound into electronic phonetic (a-f-t)-signal output characterized by an amplitude, frequency, time (a-f-t) chart showing the amplitude and frequency of the incoming sound as a function of time;

a relational robotic controller (RRC) that satisfies a set of specification requirements for relational robotic controllers;

a verbal-phoneme sound generator that generates sequences of phoneme sounds that are controlled by the RRC;

an interface circuit positioned between the auditory human-like recording monitor and the RRC, said interface circuit configured to decompose an electronic phonetic (a-f-t)-signal output of each spectrum analyzer into collective modalities tuned to the characteristics of verbal speech, generate a q-magnitude and p-direction p-phoneme vector that is a suitable input to a multi-dimensional function space Nodal Map Module (NMM), standardize the p-phoneme vector to operate in a Task Selector Module (TSM), the NMM, a Sequence Stepper Module (SSM) of the RRC, and successfully activate the verbal-phoneme sound generator, and develop a speech processing methodology for obtaining a one-to-one mapping of the acoustic signals onto a phonetic structure free of segmentation errors; and

a programming methodology defined by a Declarative Hierarchical Task Diagram (DHTD) specification that provides the robot a human-like, high IQ form of verbal artificial intelligence (AI).

2. The auditory RRC-humanoid robot of claim 1, wherein the TSM is included in a Hierarchical Task Diagram (HTD) structure for the assignment of priorities to Task Initiating Trigger TIT-patterns that activate tasks selected by the TSM.

3. The auditory RRC-humanoid robot of claim 1, wherein the verbal-phoneme sound generator generates 120 different phoneme sounds.

4. The auditory RRC-humanoid robot of claim 3, wherein each phoneme sound output of the verbal-phoneme sound generator is activated by one of the 120 different auditory p-vectors.

5. The auditory RRC-humanoid robot of claim 1, wherein the set of linear pickup microphones comprises a right-side linear pickup microphone positioned on the right of an electronic enclosure and a left-side linear pickup microphone positioned on the left of the electronic enclosure.

6. The auditory RRC-humanoid robot of claim 5, wherein the set of spectrum analyzers comprises a first spectrum analyzer that converts incoming sound from the right-side linear pickup microphone and a second spectrum analyzer that converts incoming sound from the left-side linear pickup microphone.

7. The auditory RRC-humanoid robot of claim 1, wherein the DHTD specification comprises a programming methodology that is used to program each auditory TSM.

8. The auditory RRC-humanoid robot of claim 7 further comprising a declarative memory system comprising all programmed auditory TSMs within the RRC.

9. The auditory RRC-humanoid robot of claim 8, wherein the TSMs identify a sequential set of phonemes as a TIT-grouping.

10. The auditory RRC-humanoid robot of claim 9, wherein a TIT-processing period associated with each TIT-grouping determines the number of sequential phonemes that must be processed before the RRC recognizes the total sequence as a TIT.

11. The auditory RRC-humanoid robot of claim 10, wherein a TSM is programmed to repeat sounds spoken by a trainer-supervisor.

12. The auditory RRC-humanoid robot of claim 11, wherein the TSM is programmed to accurately repeat all the words and sentences taken from a lexicon comprising at least 50,000 words that represents a total vocabulary of the robot.

13. The auditory RRC-humanoid robot of claim 12, wherein a TSM is optimized to properly enunciate words and sentences listed in the lexicon by refining a design of the verbal-phoneme sound generator to assure that lexical segmentation and timing intervals between successive phonemes correctly reflect phonetic structure, expanding the number of phoneme sounds to reduce segmentation problems, and refining tuning of collective modalities generated by the set of spectrum analyzers to the actual collective modalities present in an English language verbal input signal.

14. The auditory RRC-humanoid robot of claim 13, wherein a one-to-one mapping between acoustic properties of speech and phonetic structure of an utterance is performed by training the TSM to repeat, recognize, and remember the heard words and sentences.

15. The auditory RRC-humanoid robot of claim 1, wherein the behavioral programming methodology is experiential in a manner such that the system relates, correlates, prioritizes and remembers input patterns by performing verbalization tasks with the verbal phoneme sound generator and relating the performed verbalization tasks to manual dexterity manipulation task TITs, locomotion tasks, and visualization task TITs associated with the self-location and identification coordinate frame.

16. The auditory RRC-humanoid robot of claim 15, wherein behavioral programming techniques are guided by a Hierarchical Task Diagram (HTD) in order to achieve high levels of AI for robotic self-knowledge, identification, recognition, visualization or comprehension of input sensory patterns.

17. The auditory RRC-humanoid robot of claim 16, wherein the robotic self-knowledge level of artificial intelligence, analogous to human proprioceptive self-knowledge, comprises training/programming the robot to reach and touch all itch points located on the surface of the robotic body, and all points in the near space surrounding the robotic body, identify and locate all itch points, and identify and locate all end joint body parts used to reach and touch all the itch points.

18. The auditory RRC-humanoid robot of claim 1, wherein a stand-alone RRC-auditory-verbalizing answering machine system is formed by combining three core parts of a fully trained auditory RRC-humanoid robot, wherein the three core parts comprise an RRC system, an auditory RRC recording monitor comprising a set of pick-up microphones and an interface spectrum analyzer, and a verbal phoneme sound generator.

19. The auditory RRC-humanoid robot of claim 18, wherein the stand-alone RRC-auditory-verbalizing answering machine system is experientially trained by an entire RRC-humanoid robotic system comprising sensors and a complete robotic body and limbs.

20. The auditory RRC-humanoid robot of claim 19, wherein the stand-alone RRC-auditory-verbalizing answering machine system obtains specialized trained expertise and verbal IQ AI level that is a subset of the total AI level experientially trained by an entire RRC-humanoid robotic system.