PATENT CLAIM ANALYSIS

Application Number: 15952802
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-10
Patent Classification: ["704", "235000"]

Abstract:
In some embodiments, the present invention provides for an exemplary computer system which includes at least the following components: an adaptive self-trained computer engine programmed, during a training stage, to electronically receive an initial speech audio data generated by a microphone of a computing device; dynamically segment the initial speech audio data and the corresponding initial text into a plurality of user phonemes; dynamically associate a plurality of first timestamps with the plurality of user-specific subject-specific phonemes; and, during a transcription stage, electronically receive to-be-transcribed speech audio data of at least one user; dynamically split the to-be transcribed speech audio data into a plurality of to-be-transcribed speech audio segments; dynamically assigning each timestamped to-be-transcribed speech audio segment to a particular core of the multi-core processor; and dynamically transcribing, in parallel, the plurality of timestamped to-be-transcribed speech audio segments based on the user-specific subject-specific speech training model.

Claim (Index 1):
A computer system, comprising:\n at least one adaptive self-trained computer engine; at least one multi-core processor comprising a plurality of cores, wherein the at least one multi-core processor is associated with the at least one adaptive self-trained computer engine so that the at least one multi-core processor is configured to receive executing instructions from the at least one adaptive self-trained computer engine; wherein the computer system is configured to perform at least the following operations: electronically receiving, by the adaptive self-trained computer engine, an initial speech audio data generated by a microphone of a computing device, wherein the initial speech audio data corresponds to at least one initial utterance received by the microphone from a particular user wherein the at least one initial utterance corresponds to an initial text being read by the particular user; dynamically segmenting, by the adaptive self-trained computer engine, the initial speech audio data and the corresponding initial text into a plurality of user phonemes; dynamically querying, by the adaptive self-trained computer engine, at least one database object containing at least one computer dictionary of stored subject-specific phonetic pronunciations of subject-specific words which are related to a particular activity area to match the plurality of user phonemes to a plurality of subject-specific phonetic pronunciations to form a plurality of user-specific subject-specific phonemes; dynamically associating, by the adaptive self-trained computer engine, a plurality of first timestamps with the plurality of user-specific subject-specific phonemes, wherein each first timestamp corresponds to a time segment location of a respective matched user phoneme in the initial speech audio data so as to form at least one user-specific subject-specific data dictionary of timestamped user-specific subject-specific phonemes; dynamically generating, by the adaptive self-trained computer engine, a plurality of user-specific subject-specific training instructions by utilizing the at least one user-specific subject-specific data dictionary of timestamped user-specific subject-specific phonemes as a user-specific subject-specific speech training model, wherein each user-specific subject-specific training instruction comprises a training text and a synthetized user-specific speech audio of the training text; dynamically outputting, by the adaptive self-trained computer engine, the training text of each user-specific subject-specific training instruction to the computing device.

Metadata:
- Claim Count in Document: 51.0
- Percentile: 91.0
- Lexical Diversity: 2.0641
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15477909', '13492540', '15661550', '14600794', '13091509']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.265751493038222
- 35 USC 102 Novelty (BERT): 0.5592708901219758
- Combined Prediction Score: 0.2951034327465974
- Mean Citation Score: 253.023042
- Max Citation Score: 430.95062
- Similarity Product: 413.3357883574938

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test