DETAILED ACTION
This communication is in response to the Application filed on 02/25/2025. Claims 1-20 are pending and have been examined. Claims 1, 8 and 15 are independent. This Application was published as US Pub 2026/0204253.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/25/2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority based on application IN202511002889 filed on 01/13/2025 in Indian Patent Office and receipt of a certified copy thereof.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "the mobile device" in line 1. There is insufficient antecedent basis for this limitation in the claim as the system of Claim 1 does not recite the limitation “a mobile device.”
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-8, 11-15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dame et al. (US Pub 2021/0020168) in view of Zuluaga-Gomez et al. ("ATCO2 corpus: A large-scale dataset for research on automatic speech recognition and natural language understanding of air traffic control communications." arXiv preprint arXiv:2211.04054 (2022)) further in view of Daniels et al. (US Pat 12,277,862).
Regarding Claim 1,
Dame discloses a system, comprising: at least one processor; at least one memory communicatively coupled to the at least one processor; and wherein the at least one processor is configured to (Fig.6, paras [087-097], "…data processing system 600 includes communications framework 602, which provides communications between processor unit 604, memory 606..."):
record, onboard an aircraft, spoken audio communication between air traffic control and a pilot onboard the aircraft (Fig.25, paras [138-139], "…Voice activity detection system 2500 may be configured to identify speech 2504 in any appropriate audio signal 2502 from any appropriate source...audio signal 2502 may comprise air traffic control radio communications 2508. Audio signal 2502 may be live audio signal 2510 or recorded audio signal 2512..."; Fig.24, par [134], "…sending audio dialogue over cellular wireless networks 2406 for both the ATC received message (clearance) as well as the pilot mic output…").
tag a source of the spoken audio communication as being at least one of the air traffic control or the pilot onboard the aircraft (Fig.19, paras [127-128], "…System 1900 receives both a pilot audio stream 1902 and an ATC audio stream 1904. The audio streams are fed into a multiplexor 1908 within a diarized speech-to-text system...", "…
The tuple 1914 of speaker and text are then sent to the natural language processor 1916 that is tuned for IFR clearance dialogue...").
Dame further discloses split the spoken audio communication into corresponding separate audio files for at least the source of an instruction (Dame, Fig.25, paras [139-140], "…Segmenter 2518 is configured to use various criteria to filter and combine the portions of audio signal 2502 that are determined to comprise speech 2504 into speech segments 2520...", "…speech segments 2520 derived by voice activity detection system 2500 from recorded audio signal 2512 may be used to train artificial intelligence speech recognition model 2522 to transcribe speech from an audio signal...")
extract corresponding text (par [127], "…The ASR 1910 processes the IFR clearance dialogue by using aviation context-dependent communication speech to text recognition models 1912 specifically tuned to the IFR clearance sequence...")
tag the corresponding text with a corresponding separate audio file (par [128], "…The tuple 1914 of speaker and text (i.e., tagging speaker identity to extracted text) are then sent to the natural language processor 1916 that is tuned for IFR clearance dialogue..."; par [119], "…enhanced IFR Clearance Speech Processor and popup generation and an interactive UI for confirming each step of a 'C-R-A-F-T' (Clearance limit, Route, Altitude, Frequency, Transponder) clearance...")
But the segmenter 2518 (Fig.25) of Dame is based on acoustic/noise/pause boundary, and not based on semantic content such as action, object type or object ID as recited in the limitation, "for the at least one of the action, the object type, or the object identification." Moreover, Dame does not explicitly disclose extracting or tagging corresponding text "by the action, the object type, or the object identification."
However, Zuluaga-Gomez, in the analogous field of an automatic speech recognition in air traffic control communication, discloses ATC-domain specific data collection (Zuluaga-Gomez, 3.2 Lexicon, "…We keep only words relevant to ATC domain, i.e. words present in ATC transcripts or other resource. The lexicon contains 29k unique word-symbols...") and annotation process (Zuluaga-Gomez, 1 Introduction, "…the ATCO2 corpus can be used to produce a robust ASR system for the ATC domain, and with its NER annotations it is possible to train models for SLU applications for extracting meaning from speech (e.g., callsign or command detection)..."; Appendix A Automatic Transcription Engine, Fig.9: Transcription and Annotations Subsystem).
Zuluaga-Gomez further discloses split the spoken audio communication into the corresponding separate audio files for at least the source of the instruction (Zuluaga-Gomez, 4.3 Data Processing Pipeline, "…Acoustic-based speaker diarization: a single recording can have multiple speakers in it, so the per-speaker segments are identified by diarization. We do it before the automatic transcripts are generated, so the NLU modules always process segments of a single speaker...") and the at least one of the action, the object type, or the object identification (Zuluaga-Gomez, 4.3 Data Processing Pipeline, "…Post-processing by NLP: in ATCO2 project, we focused on extracting knowledge from the text produced by the ASR system...Callsign recognition and ATCO/pilot classification: decide who is speaking (i.e., instruction source)...ATC-Entity recognition: highlight the callsign, command and value entities in text (i.e., object ID, action, and object type, respectively)...");
extract corresponding text for the at least one of the action, the object type, or the object identification from text (Zuluaga-Gomez, 6 Automatic Speech Recognition, Figure 6: Hybrid-based ASR system, "…The seed ASR system is part of the ‘data processing pipeline’ (see Section 4.3). This ASR produced the automatic transcripts for the ATCO2-PL-set corpus and also the initial transcripts for the ATCO2-test-set corpus...Inference in a hybrid speech recognizer has three stages: feature extraction, acoustic matching and decoding...The contextual adaptation improves the accuracy of the ASR system by feeding-in a rapidly changing contextual information…") regarding the clearances that are executed;
tag the corresponding text with a corresponding separate audio file for the at least one of the action, the object type, or the object identification (Zuluaga-Gomez, 7 Natural Language Processing and Understanding, Figure 7: (a) Named entity recognition and (b) speaker role detection based on sequence classification (SC) for ATC utterances. Both systems are based on fine-tuning to ATC tasks from a pre-trained BERT [54] model.; 7.1 Named Entity Recognition, "…Air traffic control communications typically carry structured information, including callsigns, commands
and values. These can be seen as ‘named entities’. ATCO2-test-set corpus provides annotation on the word level that assigns pieces of text to these specific classes. We developed a baseline system to extract such information from ASR utterances"; 7.3 Speaker Role Detection, "…In NLP, text classification or sequence classification (SC) is a task that assigns a label or a class to a sequence of words...we propose some baselines on the SC task aimed at detecting the speaker role from transcribed ATC communications (sentences)...").
Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have applied diarization, hybrid-based ASR, and entity-tagging of Zuluaga-Gomez to Dame's onboard avionics and data processing system with a reasonable expectation of success to have yielded the predictable results of an onboard aircraft system with an automatic ATC transcription and Annotation engine capable of acoustic-based speaker diarization, automatic speech recognition, named entity recognition, tagging ATC-related information based on content (Zuluaga-Gomez, Abstract).
Dame in view of Zuluaga-Gomez does not explicitly disclose limitations regarding clearance tracking and monitoring. Daniels, in the analogous field of auditing pilot log entry records against Automatic Dependent Surveillance-Broadcast (ADSB) data, discloses remove any corresponding separate audio files for any non-standard words from the spoken audio communication which are not included in a list of standard vocabulary for communication between the air traffic control and pilots (Daniels, col.13:47-67, "…These communications are processed through a speech-to-text engine specifically trained on aviation terminology to convert the audio into structured text. This allows the system to capture key ATC instructions and pilot acknowledgments, transforming them into machine-readable data while filtering out irrelevant content…").
monitor, onboard the aircraft, clearances to track when the clearances are executed (Daniels, col.7:32-47, "…An aspect of the invention focuses on detecting verbal ATC instructions and monitoring aircraft actions by correlating ADS-B feedback, providing a comprehensive system for ensuring compliance with ATC instructions..."; col.5:63- col.6:62, "…The model performs semantic analysis of ATC communications to anticipate the required aircraft actions and compare them to the actual operations observed through ADS-B data...the invention incorporates a model that is trained on extensive datasets containing thousands of hours of labeled ADS-B data and ATC communications...");
Therefore, it would have been obvious to a person of ordinary skill in the art to apply Daniels' known ADS-B based execution monitoring and verification technology to the onboard aircraft ASR/VAD and annotation system for the ATC/pilot communication taught by Dame in view of Zuluaga-Gomez with a reasonable expectation that the combination would have yielded the predictable results of automated generation of execution-confirmed audio-text pairs as a training corpus to enhance post-flight analysis and investigations and provide real-time validation of aircraft operations against ATC instructions, thereby improving safety and compliance in civil aviation (Daniels, col.3:24 - col.4:15).
Claim 4 is a system claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale.
Regarding Claim 4,
Dame in view of Zuluaga-Gomez further in view of Daniels discloses the system of claim 1, wherein the at least one processor is configured to implement:
a voice transcription system (Dame, Fig.2, Automated Speech Transcriber 208) configured to: record the spoken audio communication between the air traffic control and the pilot onboard the aircraft (Dame, Fig.25, paras [138-139], "…Voice activity detection system 2500 may be configured to identify speech 2504 in any appropriate audio signal 2502 from any appropriate source...audio signal 2502 may comprise air traffic control radio communications 2508. Audio signal 2502 may be live audio signal 2510 or recorded audio signal 2512..."; Fig.24, par [134], "…sending audio dialogue over cellular wireless networks 2406 for both the ATC received message (clearance) as well as the pilot mic output…"); and
tag the source of the spoken audio communication as being the at least one of the air traffic control or the pilot onboard the aircraft (Dame, Fig.19, paras [127-128], "…System 1900 receives both a pilot audio stream 1902 and an ATC audio stream 1904. The audio streams are fed into a multiplexor 1908 within a diarized speech-to-text system..."; Zuluaga-Gomez, 4.3 Data Processing Pipeline, "…Acoustic-based speaker diarization: a single recording can have multiple speakers in it, so the per-speaker segments are identified by diarization. We do it before the automatic transcripts are generated, so the NLU modules always process segments of a single speaker...");
an audio separation system (Zuluaga-Gomez, 4.3 Data Processing Pipeline, Fig.9, Transcription and Annotation Subsystem) configured to: split the spoken audio communication into the corresponding separate audio files for at least the source of the instruction (Zuluaga-Gomez, 4.3 Data Processing Pipeline, "…Acoustic-based speaker diarization: a single recording can have multiple speakers in it, so the per-speaker segments are identified by diarization. We do it before the automatic transcripts are generated, so the NLU modules always process segments of a single speaker...") and the at least one of the action, the object type, or the object identification (Zuluaga-Gomez, 4.3 Data Processing Pipeline, "…Post-processing by NLP: in ATCO2 project, we focused on extracting knowledge from the text produced by the ASR system...Callsign recognition and ATCO/pilot classification: decide who is speaking (i.e., instruction source)...ATC-Entity recognition: highlight the callsign, command and value entities in text (i.e., object ID, action, and object type, respectively)..."); and
remove any corresponding separate audio files for any non-standard words from the spoken audio communication which are not included in the list of the standard vocabulary for the communication between the air traffic control and the pilots (Zuluaga-Gomez, 3.2 Lexicon, "…We keep only words relevant to ATC domain, i.e. words present in ATC transcripts or other resource. The lexicon contains 29k unique word-symbols..."; Daniels, col.13:47-67, "…These communications are processed through a speech-to-text engine specifically trained on aviation terminology to convert the audio into structured text. This allows the system to capture key ATC instructions and pilot acknowledgments, transforming them into machine-readable data while filtering out irrelevant content…");
a clearance monitoring system (Daniels, examples at Figs.12-25) configured to: monitor the clearances to track when the clearances are executed (Daniels, col.7:32-47, "…An aspect of the invention focuses on detecting verbal ATC instructions and monitoring aircraft actions by correlating ADS-B feedback, providing a comprehensive system for ensuring compliance with ATC instructions..."; col.5:63- col.6:62, "…The model performs semantic analysis of ATC communications to anticipate the required aircraft actions and compare them to the actual operations observed through ADS-B data...the invention incorporates a model that is trained on extensive datasets containing thousands of hours of labeled ADS-B data and ATC communications...");
a text extraction system (Zuluaga-Gomez, Figure 6: Hybrid-based ASR system) configured to: extract the corresponding text for the at least one of the action, the object type, or the object identification from the text (Zuluaga-Gomez, 6 Automatic Speech Recognition, "…The seed ASR system is part of the ‘data processing pipeline’ (see Section 4.3). This ASR produced the automatic transcripts for the ATCO2-PL-set corpus and also the initial transcripts for the ATCO2-test-set corpus...Inference in a hybrid speech recognizer has three stages: feature extraction, acoustic matching and decoding...The contextual adaptation improves the accuracy of the ASR system by feeding-in a rapidly changing contextual information…") regarding the clearances that are executed (Daniels, col.5:63- col.6:62, "…The model performs semantic analysis of ATC communications to anticipate the required aircraft actions and compare them to the actual operations observed through ADS-B data...the invention incorporates a model that is trained on extensive datasets containing thousands of hours of labeled ADS-B data and ATC communications..."); and
a voice and text tagging system (Zuluaga-Gomez, Figure 7: BERT-based Named entity recognition and speaker role detection) configured to: tag the corresponding text with the corresponding separate audio file for the at least one of the action, the object type, or the object identification (Zuluaga-Gomez, 7 Natural Language Processing and Understanding, Figure 7: (a) Named entity recognition and (b) speaker role detection based on sequence classification (SC) for ATC utterances. Both systems are based on fine-tuning to ATC tasks from a pre-trained BERT [54] model.; 7.1 Named Entity Recognition, "…Air traffic control communications typically carry structured information, including callsigns, commands and values. These can be seen as ‘named entities’. ATCO2-test-set corpus provides annotation on the word level that assigns pieces of text to these specific classes. We developed a baseline system to extract such information from ASR utterances"; 7.3 Speaker Role Detection, "…In NLP, text classification or sequence classification (SC) is a task that assigns a label or a class to a sequence of words...we propose some baselines on the SC task aimed at detecting the speaker role from transcribed ATC communications (sentences)...").
Regarding Claim 5,
Dame in view of Zuluaga-Gomez further in view of Daniels discloses the system of claim1, wherein the at least one processor is further configured to: output the corresponding text with the corresponding separate audio file for the at least one of the action, the object type, or the object identification (Zuluaga-Gomez, 6 Automatic Speech Recognition, Figure 6: Hybrid-based ASR system, "…The seed ASR system is part of the ‘data processing pipeline’ (see Section 4.3). This ASR produced the automatic transcripts for the ATCO2-PL-set corpus and also the initial transcripts for the ATCO2-test-set corpus…"; 4.3 Data Processing Pipeline, "…Post-processing by NLP: in ATCO2 project, we focused on extracting knowledge from the text produced by the ASR system...Callsign recognition and ATCO/pilot classification: decide who is speaking (i.e., instruction source)...ATC-Entity recognition: highlight the callsign, command and value entities in text (i.e., object ID, action, and object type, respectively)...").
Regarding Claim 6,
Dame in view of Zuluaga-Gomez further in view of Daniels discloses the system of claim 5, wherein the at least one processor is further configured to: retraining a machine learning model based on the corresponding text and the corresponding separate audio file (Dame, Fig.25, par [140], "...speech segments 2520 derived by voice activity detection system 2500 from recorded audio signal 2512 may be used to train artificial intelligence speech recognition model 2522 to transcribe speech from an audio signal..."; par [170], "…receive a number of user-created transcriptions of the selected audio file through the number of user interfaces; and use the user-created transcriptions as labeled training data for speech recognition machine learning…").
Regarding Claim 7,
Dame in view of Zuluaga-Gomez further in view of Daniels discloses the system of claim 5, wherein the at least one processor is further configured to: combine the corresponding text and the corresponding separate audio file with other corresponding text and corresponding audio files to generate a corpus of data (Dame, par [170], "…receive a number of user-created transcriptions of the selected audio file through the number of user interfaces; and use the user-created transcriptions as labeled training data for speech recognition machine learning…"; Figs.9-11, par [098], "…ATC speech labeling provided by the illustrative embodiments randomly selects from thousands of segmented ATC recordings (short audio clips)...");and
retrain a machine learning model based on the corpus of data (Dame, Fig.25, par [140], "...speech segments 2520 derived by voice activity detection system 2500 from recorded audio signal 2512 may be used to train artificial intelligence speech recognition model 2522 to transcribe speech from an audio signal...").
Claim 8 is a method claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale. Rationale for combination is similar to that provided for Claim 1.
Claim 11 is a method claim with limitations similar to the limitations of Claim 4 and is rejected under similar rationale.
Regarding Claim 11,
Dame in view of Zuluaga-Gomez further in view of Daniels discloses the method of claim 8, further comprising: recording, using a voice transcription system (Dame, Fig.2, Automated Speech Transcriber 208), the spoken audio communication between the air traffic control and the pilot onboard the aircraft (Dame, Fig.25, paras [138-139], "…Voice activity detection system 2500 may be configured to identify speech 2504 in any appropriate audio signal 2502 from any appropriate source...audio signal 2502 may comprise air traffic control radio communications 2508. Audio signal 2502 may be live audio signal 2510 or recorded audio signal 2512..."; Fig.24, par [134], "…sending audio dialogue over cellular wireless networks 2406 for both the ATC received message (clearance) as well as the pilot mic output…");
tagging, using a voice and text tagging system (Zuluaga-Gomez, Figure 7: BERT-based Named entity recognition and speaker role detection), the source of the spoken audio communication as being the at least one of the air traffic control or the pilot onboard the aircraft (Dame, Fig.19, paras [127-128], "…System 1900 receives both a pilot audio stream 1902 and an ATC audio stream 1904. The audio streams are fed into a multiplexor 1908 within a diarized speech-to-text system..."; Zuluaga-Gomez, 4.3 Data Processing Pipeline, "…Acoustic-based speaker diarization: a single recording can have multiple speakers in it, so the per-speaker segments are identified by diarization. We do it before the automatic transcripts are generated, so the NLU modules always process segments of a single speaker...");
splitting, using an audio separation system (Zuluaga-Gomez, 4.3 Data Processing Pipeline, Fig.9, Transcription and Annotation Subsystem), the spoken audio communication into the corresponding separate audio files for the at least the source of the instruction (Zuluaga-Gomez, 4.3 Data Processing Pipeline, "…Acoustic-based speaker diarization: a single recording can have multiple speakers in it, so the per-speaker segments are identified by diarization. We do it before the automatic transcripts are generated, so the NLU modules always process segments of a single speaker...") and the at least one of the action, the object type, or the object identification (Zuluaga-Gomez, 4.3 Data Processing Pipeline, "…Post-processing by NLP: in ATCO2 project, we focused on extracting knowledge from the text produced by the ASR system...Callsign recognition and ATCO/pilot classification: decide who is speaking (i.e., instruction source)...ATC-Entity recognition: highlight the callsign, command and value entities in text (i.e., object ID, action, and object type, respectively)...");
removing, using the audio separation system, any corresponding separate audio files for any non-standard words from the spoken audio communication which are not included in the list of the standard vocabulary for the communication between the air traffic control and the pilots (Zuluaga-Gomez, 3.2 Lexicon, "…We keep only words relevant to ATC domain, i.e. words present in ATC transcripts or other resource. The lexicon contains 29k unique word-symbols..."; Daniels, col.13:47-67, "…These communications are processed through a speech-to-text engine specifically trained on aviation terminology to convert the audio into structured text. This allows the system to capture key ATC instructions and pilot acknowledgments, transforming them into machine-readable data while filtering out irrelevant content…");
monitoring, using a clearance monitoring system, the clearances to track when the clearances are executed
(Daniels, col.7:32-47, "…An aspect of the invention focuses on detecting verbal ATC instructions and monitoring aircraft actions by correlating ADS-B feedback, providing a comprehensive system for ensuring compliance with ATC instructions..."; col.5:63- col.6:62, "…The model performs semantic analysis of ATC communications to anticipate the required aircraft actions and compare them to the actual operations observed through ADS-B data...the invention incorporates a model that is trained on extensive datasets containing thousands of hours of labeled ADS-B data and ATC communications...");
extracting, using a text extraction system (Zuluaga-Gomez, Figure 6: Hybrid-based ASR system), the corresponding text for the at least one of the action, the object type, or the object identification from the text (Zuluaga-Gomez, 6 Automatic Speech Recognition, "…The seed ASR system is part of the ‘data processing pipeline’ (see Section 4.3). This ASR produced the automatic transcripts for the ATCO2-PL-set corpus and also the initial transcripts for the ATCO2-test-set corpus...Inference in a hybrid speech recognizer has three stages: feature extraction, acoustic matching and decoding...The contextual adaptation improves the accuracy of the ASR system by feeding-in a rapidly changing contextual information…") regarding the clearances that are executed (Daniels, col.5:63- col.6:62, "…The model performs semantic analysis of ATC communications to anticipate the required aircraft actions and compare them to the actual operations observed through ADS-B data...the invention incorporates a model that is trained on extensive datasets containing thousands of hours of labeled ADS-B data and ATC communications..."); and
tagging, using the voice and text tagging system (Zuluaga-Gomez, Figure 7: BERT-based Named entity recognition and speaker role detection), the corresponding text with the corresponding separate audio file for the at least one of the action, the object type, or the object identification (Zuluaga-Gomez, 7 Natural Language Processing and Understanding, Figure 7: (a) Named entity recognition and (b) speaker role detection based on sequence classification (SC) for ATC utterances. Both systems are based on fine-tuning to ATC tasks from a pre-trained BERT [54] model.; 7.1 Named Entity Recognition, "…Air traffic control communications typically carry structured information, including callsigns, commands and values. These can be seen as ‘named entities’. ATCO2-test-set corpus provides annotation on the word level that assigns pieces of text to these specific classes. We developed a baseline system to extract such information from ASR utterances"; 7.3 Speaker Role Detection, "…In NLP, text classification or sequence classification (SC) is a task that assigns a label or a class to a sequence of words...we propose some baselines on the SC task aimed at detecting the speaker role from transcribed ATC communications (sentences)...").
Claim 12 is a method claim with limitations similar to the limitations of Claim 5 and is rejected under similar rationale. Rationale for combination is similar to that provided for Claim 1.
Claim 13 is a method claim with limitations similar to the limitations of Claim 6 and is rejected under similar rationale.
Claim 14 is a method claim with limitations similar to the limitations of Claim 7 and is rejected under similar rationale.
Claim 15 is a non-transitory computer-readable medium claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale. Additionally,
Dame further discloses a non-transitory computer-readable medium comprises a set of instructions that, when executed by at least one processor (par [088], "…Processor unit 604 serves to execute instructions for software that may be loaded into memory 606..."; par [093], "…The program code in the different embodiments may be embodied on different physical or computer-readable storage media, such as memory 606 or persistent storage 608..."), cause the at least one processor to:
…
Rationale for combination is similar to that provided for Claim 1.
Claim 18 is a non-transitory computer-readable medium claim with limitations similar to the limitations of Claim 5 and is rejected under similar rationale. Rationale for combination is similar to that provided for Claim 1.
Claim 19 is a non-transitory computer-readable medium claim with limitations similar to the limitations of Claim 5 and is rejected under similar rationale.
Claim 20 is a non-transitory computer-readable medium claim with limitations similar to the limitations of Claim 5 and is rejected under similar rationale.
Claims 2-3, 9-10 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Dame in view of Zuluaga-Gomez further in view of Daniels further in view of Sundararajan et al. (US Pub 2017/0069312).
Claim 2 is a system claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale. But Dame in view of Zuluaga-Gomez further in view of Daniels does not explicitly disclose a mobile device onboard the aircraft performing the speech recognition and annotation between the ATC and pilots.
Sundararajan, in the analogous field of speech recognition system on an aircraft cockpit, discloses at least a second processor integrated into a mobile device onboard the aircraft (Sundararajan, par [004], "…The method includes capturing voice data at a carry-on-device from a user during a preflight check-in performed by the user for an upcoming flight; extracting features associated with the user from the captured voice data at the carry-on-device..."; par [013], "…The COD incorporates and enables a speech recognition system adaptation for a speech-based cockpit system..."; Fig.1, par [015], "…The carry-on-device 100 includes an audio sensor 110, at least one processor 120, a memory 125, a transmitter 130, a microphone 140, and an interface 101..."; Fig.3, paras [020-025], "…At block 302, voice data is captured at a carry-on device 100 from the user during a pre-flight check-in per formed by the user for an upcoming flight...At block 304, features 126 associated with the user are extracted from the captured voice data at the carry-on device 100…").
Therefore, it would have been obvious to a person of ordinary skill in the art to implementing the audio-clip/text tagging method taught by Dame in view of Zuluaga-Gomez further in view of Daniels on the carry-on-device architecture of Sundararajan with a reasonable expectation that this implementation would have yielded the predictable results of offloading the tagging/labeling tasks to a separate mobile device onboard the aircraft since Sundararajan's carry-on-device already performs the similar general function of capturing audio and extracting features for use by the cockpit system.
Regarding Claim 3,
Dame in view of Zuluaga-Gomez further in view of Daniels discloses the system of claim 1. However, Claim 1 does not recite a mobile device. Instead, Claim 2 discloses a mobile device.
Assuming, arguendo, that Claim 3 is dependent on Claim 2,
Sundararajan discloses wherein the mobile device is selected from at least one of a tablet, a laptop, and a smartphone (Sundararajan, par [013], "…The COD incorporates and enables a speech recognition system adaptation for a speech-based cockpit system..."; Fig.1, par [015], "…The carry-on-device 100 includes an audio sensor 110, at least one processor 120, a memory 125, a transmitter 130, a microphone 140, and an interface 101...").
While the specific device type for the carry-on-device is not explicitly disclosed, Sundararajan discloses generic components (audio sensor, processor, transmitter, memory, microphone, and interface). A POSITA at the priority date (September 2015) would recognize this as standard smartphone/tablet/laptop architecture, making substitution of a named commercial device an obvious design choice - a known, interchangeable substitute performing the identical function.
Claim 9 is a method claim with limitations similar to the limitations of Claim 2 and is rejected under similar rationale. Rationale for combination is similar to that provided for Claim 2.
Claim 10 is a method claim with limitations similar to the limitations of Claim 3 and is rejected under similar rationale.
Claim 16 is a non-transitory computer-readable medium claim with limitations similar to the limitations of Claim 2 and is rejected under similar rationale.
Rationale for combination is similar to that provided for Claim 2.
Claim 17 is a non-transitory computer-readable medium claim with limitations similar to the limitations of Claim 3 and is rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wei (US Pat 12,062,375) discloses systems and methods for processing audio files to perform audio segmentation and Speaker Role Identification (SRID) by training low level classifier and high-level clustering components to separate and identify audio from different sources in an audio file by unifying audio separation and automatic speech recognition (ASR) techniques in a single system (Wei, Abstract).
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/JANGWOEN LEE/ Examiner, Art Unit 2656
/EDGAR X GUERRA-ERAZO/ Primary Examiner, Art Unit 2656
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