# US 12,182,498 B1 · Redacting portions of text transcriptions generated from inverse text normalization > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,182,498 B1 - Document ID: US12182498B1 - Status: Granted - Kind: B1 - Application number: 17/810,302 - Filed: 2022-06-30 - Granted: 2024-12-31 - Earliest claimed: 2022-06-30 - Assignees: Amazon Technologies, Inc. - Applicants: Amazon Technologies, Inc. - Inventors: Monica Lakshmi Sunkara; Deepthi Devaiah Devanira; Chaitanya Shivade; Sravan Babu Bodapati; Katrin Kirchhoff; Srikanth Ronanki - Web page: https://patentgrep.com/patent/US12182498B1 ## CPC - G06F 40/166: Editing, e.g. inserting or deleting - G06F 40/279: Recognition of textual entities - G10L 15/16: using artificial neural networks - G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue - G10L 15/26: Speech to text systems (G10L15/08 takes precedence) - G06F 21/6245: {Protecting personal data, e.g. for financial or medical purposes} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract Portions of text data generated from inverse text normalization may be redacted. Text data for redaction may be obtained. One or more inverse text normalization models may be applied to the text data to generate normalized text data. A machine learning model, trained to recognize text for redaction, may be applied to identify portions of the normalized text data for redaction. The identified portions may be redacted and the redacted normalized text provided to a destination. ## Claims ### Claim 1 A system, comprising: at least one processor; and a memory, storing program instructions that when executed by the at least one processor, cause the at least one processor to implement a machine transcription system, configured to: obtain text data generated from one or more automated speech recognition techniques for redaction; cause application of a number of inverse text normalization models to the text data to generate normalized text data based, at least in part, on a predicted normalization by a first inverse text normalization model for the text data; cause application of a machine learning model, trained to recognize text for redaction, to identify one or more portions of the normalized text data for redaction; redact the identified one or more portions of the normalized text data; and provide the redacted text data to a destination. ### Claim 2 (depends on 1) The system of claim 1, wherein the text data is obtained to perform a batch transcription job and wherein the destination is a data storage system specified by the batch transcription job. ### Claim 3 (depends on 1) The system of claim 1, wherein the text data is a stream of text data for a streaming transcription job, and wherein the machine transcription system is further configured to apply a scout network to determine a number of words in the text data upon which to perform the application of the machine learning model to identify the one or more portions of the normalized text data for redaction. ### Claim 4 (depends on 1) The system of claim 1, wherein the machine transcription system is a machine transcription service offered by a provider network that performs a received transcription job request that includes the text data on behalf of a client of the provider network. ### Claim 5 A method, comprising: obtaining, by a machine transcription system, text data for redaction; applying, by the machine transcription system, a number of inverse text normalization models to the text data to generate normalized text data based, at least in part, on a predicted normalization by a first inverse text normalization model for the text data; applying, by the machine transcription system, a machine learning model, trained to recognize text for redaction, to the normalized text data to identify one or more portions of the normalized text data for redaction; redacting, by the machine transcription system, the identified one or more portions of the normalized text data; and providing, by the machine transcription system, the redacted text data to a destination. ### Claim 6 (depends on 5) The method of claim 5, wherein the text data is obtained to perform a batch transaction job and wherein the destination is a data storage system specified by the batch transaction job. ### Claim 7 (depends on 5) The method of claim 5, wherein the text data is a stream of text data and wherein the method further comprises applying a scout network to determine a number of words in the text data upon which to perform the applying of the machine learning model to identify the one or more portions of the normalized text data for redaction. ### Claim 8 (depends on 5) The method of claim 5, wherein the application of the machine learning model is performed to evaluate the normalized text data for one or more redaction classifications specified in a request to the machine transcription system. ### Claim 9 (depends on 5) The method of claim 5, wherein the first inverse text normalization model is a neural network-based inverse text normalization model. ### Claim 10 (depends on 9) The method of claim 9, wherein applying the number of the one or more inverse text normalization models to the text data to generate normalized text data comprises: determining that a normalization confidence score for the predicted normalization of the text data satisfies confidence criteria; and responsive to the determination, applying a finite state transducer to the predicted normalization to produce the normalized text data. ### Claim 11 (depends on 5) The method of claim 5, wherein applying the number of the one or more inverse text normalization models to the text data to generate normalized text data comprises applying a finite state transducer to the predicted normalization to produce the normalized text data. ### Claim 12 (depends on 5) The method of claim 5, wherein the text data is obtained as the output of an automatic speech recognition system. ### Claim 13 (depends on 5) The method of claim 5, wherein the applying of the machine learning model to the normalized text data to initially identifies a further one or more portions of the normalized text data for redaction and where the method further comprises not applying redaction to the further one or more portions of the normalized text data according to a determination that a confidence score for a classification for the further one or more portions is below a confidence threshold. ### Claim 14 One or more non-transitory, computer-readable storage media, storing program instructions that when executed on or across one or more computing devices cause the one or more computing devices to implement: receiving text data for redaction; causing application of a number of one or more inverse text normalization models to generate normalized text data based, at least in part, on a predicted normalization by a first inverse text normalization model for the text data; causing application of a machine learning model, trained to recognize text for redaction, to the normalized text data to identify one or more portions of the normalized text data for redaction; redacting the identified one or more portions of the normalized text data; and providing the redacted text data to a destination. ### Claim 15 (depends on 14) The one or more non-transitory, computer-readable storage media of claim 14, wherein the text data is received as part of performing a batch transaction job and wherein the destination is a data storage system specified by the batch transaction job. ### Claim 16 (depends on 14) The one or more non-transitory, computer-readable storage media of claim 14, wherein the text data is a stream of text data and wherein the one or more non-transitory, computer-readable storage media store further program instructions that when executed on or across the one or more computing devices, cause the one or more computing devices to further implement applying a scout network to determine a number of words in the text data upon which to perform the applying of the machine learning model to identify the one or more portions of the normalized text data for redaction. ### Claim 17 (depends on 14) The one or more non-transitory, computer-readable storage media of claim 14, wherein the application of the machine learning model is performed to evaluate the normalized text data for one or more redaction classification specified in a received request. ### Claim 18 (depends on 14) The one or more non-transitory, computer-readable storage media of claim 14, wherein the first inverse text normalization model is a neural network-based inverse text normalization model. ### Claim 19 (depends on 18) The one or more non-transitory, computer-readable storage media of claim 18, wherein, in applying the number of the the one or more inverse text normalization models to the text data to generate normalized text data, the program instructions cause the one or more computing devices to implement: determining that a normalization confidence score for the predicted normalization of the text data satisfies confidence criteria; and responsive to the determination, applying a finite state transducer to the predicted normalization to produce the normalized text data. ### Claim 20 (depends on 14) The one or more non-transitory, computer-readable storage media of claim 14, wherein the one or more computing devices are implemented as part of a machine transcription service offered by a provider network.