DETAILED ACTION
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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5, 11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Schools et al. (US 2024/0397288 A1, “Schools”).
As to claim 1, Schools discloses a system to perform artificial intelligence (AI) augmented voice recognition within telecommunications networks, the system comprising:
a processing system including one or more electronic processors configured to:
receive, via a telecommunications network, audio data captured with a user equipment (UE), wherein the UE is a push-to-talk over cellular (PTToC) enabled device (monitoring PTT over broadband services activity, the broadband network including 5G cellular network, and the activity including voice call data, the para. 0022, 0024-0025);
receive, via the telecommunications network, metadata that corresponds to the audio data, wherein the metadata is generated based on a preprocessing operation executed by the UE with respect to the audio data (monitoring data includes metadata, which may be based on analysis of audio during the event as well as audio collected over time, para. 0031-0032, 0034, 0060, 0072);
execute an AI model to evaluate the audio data and the metadata to determine a circumstantial context of the audio data (AI model analyzes collected monitoring data to identify a community operation situation, para. 0021, 0024, 0034, 0050, 0060);
determine, based on execution of the AI model, the circumstantial context of the audio data (based on the analysis performed by the AI model, determines community operational situations gatherings such as riots, police actions, disasters, fires, religious services, concerts, political rallies, sporting event, etc., para. 0007, 0033, 0039, 0053, 0074); and
control, based on the circumstantial context, a response system to execute an automated response protocol (based on the inferred community operational situation, an alert can be sent to a dispatcher, manager, office, or other point of contact in order to form a response to the situation, para. 0007, 0055, 0062, including actions taken by law enforcement, fire and ambulance services, military responses, opening emergency shelters, etc., 0074).
As to claim 2, Schools discloses: wherein the processing system is configured to: determine that the circumstantial context is associated with an emergency event (based on the analysis performed by the AI model, determines community operational situations gatherings such as riots, police actions, disasters, fires, religious services, concerts, political rallies, sporting event, etc., para. 0007, 0033, 0039, 0053, 0074).
As to claim 3, Schools discloses: wherein the processing system is configured to control the response system to:
generate an alert notification that indicates at least one of: occurrence of an emergency event (operational alert can assist by presenting information associated with the community operational situation, para. 0035);
a first portion of the audio data; a second portion of the metadata; or
a transcription of the first portion of the audio data.
As to claim 5, Schools discloses: wherein the processing system is configured to control the response system to: interface with an emergency response system of an emergency response entity (notification to an emergency services dispatcher, para. 0076).
As to claim 11, Schools discloses: wherein the AI model is configured to evaluate the audio data and the metadata to determine the circumstantial context by performing at least one of:
a tonal pitch analysis that analyzes a tonal pitch of the audio data in order to detect a fluctuation in tonal pitch;
a volume analysis that monitors a volume level of the audio data in order to detect a change in volume level that exceeds a volume level threshold; and
a word choice analysis that monitors the audio data in order to detect an occurrence of a predetermined word (para. 0034).
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.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schools in view of Kolandasamy (US 2026/0106936 A1).
Schools differs from claim 4 in that it does not disclose: wherein the processing system is configured to control the response system to: identify a second UE, wherein the second UE is located within a first distance range from the UE; and transmit the alert notification to the second UE such that the second UE outputs the alert notification.
Kolandasamy teaches an artificial intelligence driven emergency alert system which identifies wireless devices of emergency responders within a geo-fenced area of emergence call or sensor location (para. 0080-0082). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Schools with the above teaching of Kolandasamy in order to provide faster, more accurate, and more efficient emergency services (para. 0087).
Claim(s) 6, 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schools in view of Murthy et al. (US 2026/0059283 A1, “Murthy”).
Schools differs from claim 6 in that it does not disclose: wherein the metadata includes at least one of: a tonal pitch of the audio data; a volume of the audio data; a word choice of the audio data; a word cadence of the audio data; or a reverberation of the audio data.
Murthy teaches providing context-aware emergency information to an emergency response service such as urgency keywords detected during pre-call dialog, stress tone analysis (para. 0027-0044). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Schools with the above teaching of Murthy in order to facilitate effective and safe communications (Murthy: para. 0046).
As to claim 9, Schools in view of Murthy teach: wherein the processing system is configured to: access, via an open application programming interface (API), a network function of the telecommunications network (Murthy: para. 0100).
As to claim 10, Schools in view of Murthy teaches: wherein the processing system is configured to: determine, via the network function, location data associated with the UE; and control the response system to execute the automated response protocol based on the location data associated with the UE (Murthy: response service based on device location, para. 0060, 0063).
Claim(s) 7, 13-15, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schools in view of Allu Balan et al. (US 2026/0025415 A1, “Allu Balan”).
School discloses: wherein the telecommunications network is a fifth generation (5G) telecommunications network (5G network, para. 0025, 0027, 0044, 0067-0068), but differs from claim 7 in that it does not disclose: wherein the processing system is configured to receive the audio data via a voice over new radio (VoNR) wireless communication standard.
Allu Balan teaches the well known use of VoNR communication standard for handling emergency communication between two user devices (para. 0026, 0041, 0046, 0059, claim 9). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Schools with the above teaching of Allu Balan using VoNR as a well known form of wireless broadband communication (para. 0028).
As to claim 13, Schools in view of Allu Balan teaches a method to perform artificial intelligence (AI) augmented voice recognition within telecommunications networks, the method comprising:
receiving, with a processing system including one or more electronic processors, using a voice over new radio (VoNR), an audio data package, the audio data package including audio data captured with a push-to-talk (PTT) device, wherein the PTT device is a push-to-talk over cellular (PTToC) enabled device (Schools: monitoring PTT over broadband services activity, the broadband network including 5G cellular network, and the activity including voice call data, the para. 0022, 0024-0025; Allu Balan: VoNR, para. 0026, 0028 0041, 0046, 0059, claim 9);
executing, with the processing system, an AI model to determine a circumstantial context of the audio data from the audio data package (Schools: AI model analyzes collected audio data to identify a community operation situation, para. 0021, 0024, 0034, 0050, 0060);
determining, with the processing system, that the circumstantial context of the audio data triggers an automated response protocol (Schools: based on the inferred community operational situation, it is determined that an alert should be sent to a dispatcher, manager, office, or other point of contact in order to form a response to the situation, para. 0007, 0055, 0062); and
executing, with the processing system, based on the circumstantial context, an automated response pursuant to the automated response protocol (Schools: para. 0007, 0055, 0062, 0074).
As to claim 14, Schools in view of Allu Balan teaches: wherein receiving, with the processing system, using the VoNR, the audio data package includes receiving metadata that corresponds to the audio data, wherein the metadata is generated based on a preprocessing operation executed by the PTT device with respect to the audio data (Schools: monitoring data includes metadata, which may be based on analysis of audio during the event as well as audio collected over time, para. 0031-0032, 0034, 0060, 0072).
As to claim 15, Schools in view of Allu Balan teaches: wherein receiving, with the processing system, using the VoNR, the audio data package includes receiving, from the PTT device using VoNR, a data stream that includes the audio data and metadata corresponding to the audio data, wherein the audio data is raw data and the metadata corresponding to the audio data is pre-processed data (Schools: monitoring data comprises voice call data, text data and metadata, para. 0024; Allu Balan: VoNR, para. 0026, 0028 0041, 0046, 0059, claim 9).
As to claim 17, Schools in view of Allu Balan teaches: determining, with the processing system, a classification for the audio data based on the circumstantial context; and selecting, with the processing system, the automated response from a plurality of automated responses based on the classification (Schools: type of emergency response is dependent on the particular community operational situation, para. 0033, 0074).
As to claim 18, Schools in view of Allu Balan teaches a non-transitory computer-readable medium storing instructions that, when executed by one or more electronic processors of a processing system in a telecommunications network, cause the processing system to perform operations comprising:
receiving, over a fifth generation (5G) telecommunications network, using voice over new radio (VoNR), audio data captured with a push-to-talk over cellular (PTToC) device (Schools: monitoring PTT over broadband services activity, the broadband network including 5G cellular network, and the activity including voice call data, the para. 0022, 0024-0025; Allu Balan: VoNR, para. 0026, 0028 0041, 0046, 0059, claim 9);
receiving, over the 5G telecommunications network, using VoNR, metadata corresponding to the audio data (Schools: monitoring data includes metadata, which may be based on analysis of audio during the event as well as audio collected over time, para. 0031-0032, 0034, 0060, 0072);
providing the audio data and the metadata to an artificial intelligence (AI) model, the AI model configured to extract a plurality of voice features from the audio data and the metadata (Schools: AI model analyzes collected monitoring data to identify a community operation situation, para. 0021, 0024, 0034, 0050, 0060; keywords and phrases, para. 0034);
determining that one or more voice features of the plurality of voice features indicate an event (Schools: based on the analysis performed by the AI model, determines community operational situations gatherings such as riots, police actions, disasters, fires, religious services, concerts, political rallies, sporting event, etc., para. 0007, 0033-0034, 0039, 0053, 0074); and
responsive to determining that the one or more voice features of the plurality of voice features indicate the event, controlling execution of an automated response associated with the event (Schools: based on the inferred community operational situation, an alert can be sent to a dispatcher, manager, office, or other point of contact in order to form a response to the situation, para. 0007, 0055, 0062, including actions taken by law enforcement, fire and ambulance services, military responses, opening emergency shelters, etc., 0074).
As to claim 19, Schools in view of Allu Balan teaches: wherein determining that the one or more voice features of the plurality of voice features indicate the event includes at least one of:
detecting, in the audio data, a change in tonal pitch that satisfies a pitch change threshold;
detecting, in the audio data, a change in volume that satisfies a volume change threshold;
detecting, in the audio data, a use of a predetermined word (Schools: keywords and phrases, para. 0034); or
detecting, in the audio data, a deviation from a normalized voice pattern.
As to claim 20, Schools in view of Allu Balan teaches: accessing, via an open application programming interface (API), a virtual network function of the 5G telecommunications network (Allu Balan: 5GNR, para. 0028, 0031); and executing, using the virtual network function, functionality of the virtual network function with respect to the audio data (Allu Balan: Virtual Network Functions, para. 0075).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schools in view of Gersten (US 2018/0053394 A1).
Schools differs from claim 8 in that it does not disclose: wherein the audio data is time-series data that is continuously captured by a microphone of the UE.
Gersten teaches a mobile device’s microphones continually listening for the acoustic fingerprint of a gunshot (para. 0039). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Schools with the above teaching of Gersten in order to allow background use of mobile devices to be part of a network that is constantly looking for danger (Gersten: para. 0065).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schools in view of Cheng et al. (US 2003/0009341 A1, “Cheng”).
Schools differs from claim 12 in that it does not disclose: identify an entity associated with the audio data; determine a normalized voice pattern of the entity; determine, based on the audio data and the metadata, a present voice pattern of the entity; detect a deviation between the normalized voice pattern of the entity and the present voice pattern of the entity; and determine the circumstantial context based on the deviation.
Cheng teaches determining an emergency situation when a user’s voice pattern deviates from normal voice patterns (para. 0046). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Schools with the above teaching of Cheng in order to detect abnormal conditions based on voice intonation, speech and/or emotions of a user (Cheng: para. 0002).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schools in view of Allu Balan, as applied to claim 13 above, and further in view of Cheng.
Schools in view of Allu Balan differs from claim 16 in that it does not teach: wherein determining, with the processing system, the circumstantial context of the audio data triggers the automated response protocol includes: identifying, with the processing system, a previous instance of a feature of the audio data; comparing, with the processing system, a present instance of the feature of the audio data with the previous instance of the feature of the audio data; and determining, with the processing system, that the present instance of the feature of the audio data deviates from the previous instance of the feature of the audio data.
Cheng teaches determining an emergency situation when a user’s voice pattern deviates from normal voice patterns (para. 0046). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Schools in view of Allu Balan with the above teaching of Cheng in order to detect abnormal conditions based on voice intonation, speech and/or emotions of a user (Cheng: para. 0002).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Song et al. (US 2025/0380120 A1) teach emergency incident detection and alerting to nearby devices.
Bernotas et al. (US 2021/0158685 A1) teach continuously monitoring audio recorded by a microphone to detect a threat event (para. 0054).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stella L Woo whose telephone number is (571)272-7512. The examiner can normally be reached Monday - Friday, 8 a.m. to 5 p.m.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ahmad Matar can be reached at 571-272-7488. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Stella L. Woo/ Primary Examiner, Art Unit 2693
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