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 .
Status of Claims
Claims 1-20 are currently pending in application 19/092,824.
Double Patenting
The rejection of Claims 1-20 on the grounds of nonstatutory double patenting, as being unpatentable over claims 1-17 of U.S. Patent No. 12,271,971, is withdrawn due to Applicant’s arguments (Applicant Arguments/ Remarks, 10/6/2025).
Claim Rejections – 35 USC §101
The rejection of Claims 1-19 under 35 U.S.C. § 101, because the claimed invention is directed to non-statutory subject matter, is withdrawn due to Applicant’s arguments (Applicant Arguments/ Remarks, 10/6/2025) and amendments to independent claims 1, 10, 19, and 20 (Claims, 10/6/2025).
Claim Rejections - 35 USC § 112 (a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 1-20 are rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Independent Claims 1, 10, 19, and 20 have been amended by Applicant as follows:
Independent Claim 1 has been amended to recite, “a threat detection and response system, comprising:
at least one sound sensor configured to sense sound within a detection zone and transmit sound data corresponding to the sensed sound; and a system gateway comprising a processor configured to:
train at least one model to detect an existence of a threat following a period of time, retrain the at least one model using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone;
receive the sound data;
analyze the sound data using the at least one model to detect a vocal phrase within the sensed sound;
determine a confidence score for the threat based on an extent to which the detected vocal phrase is correlated to a pre-defined phrase associated with a threat;
further analyze the sound data using the at least one model to detect one of more additional vocal phrases;
determine that at least one of the one or more additional vocal phrases is correlated to the pre-defined phrase associated with the threat;
responsive to determining that at least one of the one or more additional vocal phrases is correlated to the pre-defined phrase associated with the threat, update the confidence score to reflect an increased confidence in an existence of the threat; and
communicate a possible existence of the threat to a recipient device after determining the confidence score is greater than a threshold value.”
Independent Claim 10 has been amended to recite, “a threat detection and response method, comprising, by one or more processors:
training at least one model to detect an existence of a threat;
following a period of time, retraining the at least one model using real-world background noise data comprising sounds from a detection zone, thereby improving threat detection in the detection zone;
receiving sound data from at least one sound sensor configured to sense sound within a detection zone;
analyzing the sound data using the at least one model to detect a vocal phrase within the sensed sound;
determining that the detected vocal phrase corresponds to a pre-defined vocal phrase associated with a threat; and
communicating an existence of the threat to a recipient device.
Independent Claim 19 has been amended to recite, “a threat detection and response system, comprising a processor configured to:
train at least one model to detect an existence of a threat;
following a period of time, retrain the at least one model using real-world background noise data comprising sounds from a detection zone, thereby improving threat detection in the detection zone;
receive sound data generated by at least one sound sensor configured to sense sound within a detection zone;
analyze the sound data using the at least one model to detect a vocal phrase within the sensed sound;
determine that the detected vocal phrase corresponds to a pre-defined vocal phrase associated with a threat; and
communicate an existence of the threat to a recipient device.”
Independent Claim 20 has been amended to recite, “a threat detection sensor, comprising: at least one sound sensor configured to sense sound within a detection zone; and a processor unit coupled to the at least one sound sensor and configured to:
receive sound data generated by the at least one sound sensor;
analyze the sound data to detect initial evidence of the vocal phrase within the sensed sound; and
after detecting the initial evidence of the vocal phrase, transmit the sound data corresponding to the sensed sound, wherein the transmitted sound data is used to confirm an existence of a threat by confirming that that the vocal phrase is within the sensed sound, wherein the existence of the threat is confirmed using at least one model, wherein after the at least one model is trained, the at least one model is retrained using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone.”
However, Applicant’s Specification describes the method/system for detecting a vocal phrase by correlating received voice data with predefined vocal phrase data as follows:
[0013] According to another aspect of the present disclosure, the determining further includes: analyzing vocal phrases from the first sensor data (raw or processed); correlating the vocal phrases to pre-defined phrases corroborated with the threat; assigning the confidence score based on the correlation of the vocal phrase to the pre-defined phrase; analyzing additional vocal phrases from the second sensor data (raw or processed); correlating the additional vocal phrases to pre-defined phrases corroborated with the threat; and updating the confidence score based on the correlation of the vocal phrases.
The system/ method for detecting a threat based on vocal phrases is not described in the specification as a model that is trained and then retrained, “wherein the existence of the threat is confirmed using at least one model, wherein after the at least one model is trained, the at least one model is retrained using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone.” To detect a threat based on the vocal phrase data, Applicant’s Specification describes a rule-based system or traditional signal processing model, where a human expert has explicitly programmed the exact rules and the specific "pre-defined phrase" for detection. The described system/ method follows these explicit, static instructions every time. The Applicant’s Specification describes using the trained/retrained machine learning model for a different threat detection embodiment using threat-positive audio data, threat-negative audio data, and background noise audio data. See Applicant’s Specification Para 0064-0065:
[0064] According to an aspect of the present disclosure, a threat detection system includes a memory storing training datasets including threat-positive audio data, threat-negative audio data, and background noise audio data; and instructions to implement a first machine learning model and a second machine learning model. The system includes a processor coupled to the memory and configured to train the first machine learning model to detect an existence of a threat in arbitrary audio data using the threat-positive audio data and the threat-negative audio data; overlay the background noise audio data with the threat-positive audio data to generate overlayed threat-positive audio data; overlay the background noise audio data with the threat-negative audio data to generate overlayed threat-negative audio data; train the second machine learning model to detect the existence of the threat in arbitrary audio data using the overlayed threat-positive audio data and the overlayed threat-negative audio data; following training the first machine learning model and the second machine learning model: receive audio data from one or more sensors positioned in or around a space monitored by the threat detection system; determine, by providing the received audio data to the first machine learning model, a first confidence score that the threat is detected in the space; determine, by providing the received audio data to the second machine learning model, a second confidence score that the threat is detected in the space; and communicate the existence of the threat to a recipient device based on at least one of the first and second confidence scores.
[0065] In some embodiments, the threat detection system further includes an audio sensor configured to record real-world background noise data in the space monitored by the threat detection system, wherein the processor is further configured to overlay the real-world background noise data with the threat-positive audio data to generate space-specific threat-positive audio data; overlay the real-world background noise data with the threat-negative audio data to generate space-specific threat-negative audio data; and retrain the second machine learning model to detect the existence of the threat in arbitrary audio data using the space-specific threat-positive audio data and the space-specific threat-negative audio data.
Therefore, the newly amended Independent Claims 1, 10, 19, and 20 contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Claims 2-9 and 11-18 are also rejected as being dependent from claims 1 and 10, under the same rationale and reasoning as identified above.
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 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Bernotas et al (US 2021/0158685 A1) in view of Graciarena et al. (US 2025/0046333 A1).
As per independent Claim 1, Bernotas discloses a threat detection and response system, comprising:
at least one sound sensor configured to sense sound within a detection zone and transmit sound data corresponding to the sensed sound; and a system gateway comprising a processor (See at least Figs.1-3, Fig.6, and Para 0017-Para 0039) configured to:
training at least one model to detect an existence of a threat following a period of time, retrain the at least one model, thereby improving threat detection in a location (See at least Para 0088-0090, Machine Learning model);
receive the sound data (See at least Figs. 4-5A; Para 0039, “The safety system may receive and/or monitor signals received from devices. At 402, one or more first signals received from one or more first devices may be monitored. The one or more first signals may comprise one or more image signals, one or more video signals, one or more audio signals, one or more infrared signals and/or one or more biometric signals.”; See also Para 0054);
analyze the sound data using the at least one model to detect a vocal phrase within the sensed sound (See at least Para 0074, “In an example where the first signal comprises audio, the audio may be classified as being associated with one or more sounds associated with a threat to safety. Alternatively and/or additionally, the audio may be classified as being associated with the one or more sounds based upon a comparison of the audio with the one or more first sets of audio features. … Alternatively and/or additionally, one or more sets of audio features of the one or more first sets of audio features may comprise one or more phrases that a person may say when a threat event occurs.”; See also Para 0054-0055);
determine a confidence score for the threat based on an extent to which the detected vocal phrase is correlated to a pre-defined phrase associated with a threat (See at least Para 0094 “In some examples, the first probability may be determined based upon audio information of the one or more second signals. For example, the one or more second signals may comprise one or more first audio signals (e.g., signals comprising audio information and/or other information). It may be determined that one or more second audio signals, of the one or more first audio signals, are associated with one or more threat indicators (e.g., each signal of the one or more second audio signals may be classified as being associated with one or more threat indicators). For example, a threat indicator may be detected within a signal of the one or more second audio signals. The threat indicator may correspond to one or more of a gunshot, an explosion, a phrase that a person may say when a threat events occurs, one or more voice properties of a person, etc.”);
further analyze the sound data using the at least one model to detect one of more additional vocal phrases (See at least Figs. 4; Para 0039, Para 0074, and Para 0078-0078);
determine that at least one of the one or more additional vocal phrases is correlated to the pre-defined phrase associated with the threat (See at least Figs. 4; Para 0039, “… one or more audio signals”; Para 0085, Para 0094);
responsive to determining that at least one of the one or more additional vocal phrases is correlated to the pre-defined phrase associated with the threat, update the confidence score to reflect an increased confidence in an existence of the threat (See at least Figs. 4; Para 0094); and
communicate a possible existence of the threat to a recipient device after determining the confidence score is greater than a threshold value (See at least Figs. 4-5F; Para 0054-0056, Para 0094, Para 0102).
Bernotas fails to expressly disclose training at least one model to detect an existence of a threat following a period of time, retrain the at least one model using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone.
However, the analogous art of Graciarena discloses training at least one model to detect an existence of a threat following a period of time, retraining the at least one model using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone (See at least Para 0021, “Embeddings extractor 106 may receive input audio waveform 116, e.g., converted to audio spectrogram 104 in some examples, and output an embedding 114 of input audio waveform 116. Embeddings extractor 106 may be trained using an audio space comprising a plurality of sounds, which in some examples includes non-speech sounds. Non-speech sounds may include a sounds generated in nature, e.g., an avalanche, bird songs, waves on a shore, along with mechanically generated sounds such as motors, clocks, bells, explosions, and similar sounds.”; Para 0022, Para 0033, and Para 0060).
Therefore, at the time the invention was effectively filed, it would have been obvious for one of ordinary skill in the art to have included training at least one model to detect an existence of a threat following a period of time, retraining the at least one model using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone., as disclosed by Graciarena in the system disclosed by Bernotas, for the advantage of providing a threat detection and response system, with the ability to increase system/ method effectiveness and efficiency by incorporating a variety of data modeling techniques and comparing a variety of data types (See KSR [127 S Ct. at 1739] “The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
As per Claim 2, Bernotas and Graciarena disclose wherein the processor is further configured to: determine that multiple individuals spoke the vocal phrase; and responsive to determining that multiple individuals spoke the vocal phrase, update the confidence score to reflect the increased confidence in the existence of the threat (See at least Figs. 4; Para 0054-0056).
As per Claim 3 (2), Bernotas and Graciarena disclose wherein the process or is further configured to: determine that at least one of the one or more additional vocal phrases is correlated to a second pre-defined phrase associated with a non-existence the threat; and responsive to determining that at least one of the one or more additional vocal phrases is correlated to the second pre-defined phrase associated with the non-existence of the threat, update the confidence score to reflect a decreased confidence in the existence of the threat (See at least Figs. 4; Para 0054-0056, Para 0085).
As per Claim 4 (2), Bernotas and Graciarena disclose a second sensor configured to collect second sensor data from within at least a portion of the detection zone and transmit the second sensor data, wherein the second sensor is a motion sensor, a temperature sensor, an image sensor, a light sensor, a sound sensor, a pressure sensor, a gas sensor, a chemical sensor, or a biologic sensor; wherein the processor is further configured to: receive the second sensor data; and update the confidence score based on an extent to which the second sensor data corroborates the existence of the threat (See at least Para 0040, Para 0054-0058, Para 0070).
As per Claim 5, Bernotas and Graciarena disclose wherein the at least one sound sensor is mounted on or inside a structure (See at least Para 0040).
As per Claim 6, Bernotas and Graciarena disclose wherein the possible existence of the threat is communicated to the recipient device via a secure communication protocol along with evidence of the threat (See at least Figs. 1-4; Para 0022-0023, Para 0054-0056).
As per Claim 7, Bernotas and Graciarena disclose wherein the recipient device is an alarm system, a mobile communication device, or an access control system (See at least Para 0040, Para 0054-0056).
As per Claim 8, Bernotas and Graciarena disclose wherein the at least one sound sensor is positioned indoors or outdoors (See at least Para 0040, Para 0054-0056).
As per Claim 9, Bernotas and Graciarena disclose a processing unit communicatively coupled to the at least one sound sensor, wherein the processing unit is configured to, prior to transmission of the sound data: analyze the sound data to detect initial evidence of the vocal phrase within the sensed sound; and after detecting the initial evidence of the vocal phrase, allow or cause the sound data to be transmitted (See at least Para 0038-0040, Para 0054-0056).
As per independent Claims 10 and 19, Bernotas discloses a threat detection and response method/ system (See at least Figs.1-3, Fig.6, and Para 0017-Para 0039), comprising, by one or more processors:
training at least one model to detect an existence of a threat; following a period of time, retraining the at least one model, thereby improving threat detection in the detection zone in a location (See at least Para 0088-0090, Machine Learning model);
receiving sound data from at least one sound sensor configured to sense sound within a detection zone (See at least Figs. 4-5A; Para 0039, “The safety system may receive and/or monitor signals received from devices. At 402, one or more first signals received from one or more first devices may be monitored. The one or more first signals may comprise one or more image signals, one or more video signals, one or more audio signals, one or more infrared signals and/or one or more biometric signals.”; See also Para 0054);
analyzing the sound data using the at least one model to detect a vocal phrase within the sensed sound (See at least Para 0074, “In an example where the first signal comprises audio, the audio may be classified as being associated with one or more sounds associated with a threat to safety. Alternatively and/or additionally, the audio may be classified as being associated with the one or more sounds based upon a comparison of the audio with the one or more first sets of audio features. … Alternatively and/or additionally, one or more sets of audio features of the one or more first sets of audio features may comprise one or more phrases that a person may say when a threat event occurs.”; See also Para 0054-0055);
determining that the detected vocal phrase corresponds to a pre-defined vocal phrase associated with a threat (See at least Para 0094 “In some examples, the first probability may be determined based upon audio information of the one or more second signals. For example, the one or more second signals may comprise one or more first audio signals (e.g., signals comprising audio information and/or other information). It may be determined that one or more second audio signals, of the one or more first audio signals, are associated with one or more threat indicators (e.g., each signal of the one or more second audio signals may be classified as being associated with one or more threat indicators). For example, a threat indicator may be detected within a signal of the one or more second audio signals. The threat indicator may correspond to one or more of a gunshot, an explosion, a phrase that a person may say when a threat events occurs, one or more voice properties of a person, etc.”); and
communicating an existence of the threat to a recipient device (See at least Figs. 4-5F; Para 0094, Para 0054-0056).
Bernotas fails to expressly disclose training at least one model to detect an existence of a threat following a period of time, retrain the at least one model using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone.
However, the analogous art of Graciarena discloses training at least one model to detect an existence of a threat following a period of time, retraining the at least one model using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone (See at least Para 0021, “Embeddings extractor 106 may receive input audio waveform 116, e.g., converted to audio spectrogram 104 in some examples, and output an embedding 114 of input audio waveform 116. Embeddings extractor 106 may be trained using an audio space comprising a plurality of sounds, which in some examples includes non-speech sounds. Non-speech sounds may include a sounds generated in nature, e.g., an avalanche, bird songs, waves on a shore, along with mechanically generated sounds such as motors, clocks, bells, explosions, and similar sounds.”; Para 0022, Para 0033, and Para 0060).
Therefore, at the time the invention was effectively filed, it would have been obvious for one of ordinary skill in the art to have included training at least one model to detect an existence of a threat following a period of time, retraining the at least one model using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone., as disclosed by Graciarena in the system disclosed by Bernotas, for the advantage of providing a threat detection and response system, with the ability to increase system/ method effectiveness and efficiency by incorporating a variety of data modeling techniques and comparing a variety of data types (See KSR [127 S Ct. at 1739] “The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
As per Claim 11, Bernotas and Graciarena disclose determining a confidence score for the threat based on an extent to which the detected vocal phrase is correlated to the pre-defined phrase; and communicating the existence of the threat to the recipient device if the confidence score is greater than a threshold value (See at least Para 0102, “At 408, one or more first alert devices of a plurality of alert devices may be selected, based upon the first probability, for transmission of one or more messages associated with the threat event. In some examples, a threat level of the threat event may be determined based upon the first probability. For example, a first range of probabilities may correspond to a first threat level of a plurality of threat levels, a second range of probabilities may correspond to a second threat level of the plurality of threat levels, etc. In an example, the threat level of the threat event may correspond to the first threat level based upon a determination that the first probability is within the first range of probabilities.”; See also Figs. 4; Para 0054-0056, Para 0078-0079).
As per Claim 12, Bernotas and Graciarena disclose further analyzing the sound data to detect one of more additional vocal phrases; determining that at least one of the one or more additional vocal phrases is correlated to the pre-defined phrase associated with the threat; and responsive to determining that at least one of the one or more additional vocal phrases is correlated to the pre-defined phrase associated with the threat, updating the confidence score to reflect an increased confidence in the existence of the threat (See at least Figs. 4; Para 0054-0056).
As per Claim 13, Bernotas and Graciarena disclose receiving second sensor data from a second sensor configured to collect second sensor data from within at least a portion of the detection zone, wherein the second sensor is a motion sensor, a temperature sensor, an image sensor, a light sensor, a sound sensor, a pressure sensor, a gas sensor, a chemical sensor, or a biologic sensor; and updating the confidence score based on an extent to which the second sensor data corroborates the existence of the threat (See at least Para 0040, Para 0054-0058, Para 0070).
As per Claim 14, Bernotas and Graciarena disclose wherein the at least one of the at least one sound sensors is mounted on or inside a structure (See at least Para 0040).
As per Claim 15, Bernotas and Graciarena disclose wherein the existence of the threat is communicated to the recipient device via a secure communication protocol (See at least Figs. 1-4; Para 0022-0023, Para 0054-0056).
As per Claim 16, Bernotas and Graciarena disclose wherein the recipient device is an alarm system, a mobile communication device, or an access control system (See at least Para 0040, Para 0054-0056).
As per Claim 17, Bernotas and Graciarena disclose wherein the at least one sound sensor is positioned indoors or outdoors (See at least Para 0040, Para 0054-0056).
As per Claim 18, Bernotas and Graciarena disclose prior to transmission of the sound data: analyzing the sound data to detect initial evidence of the vocal phrase within the sensed sound; and after detecting the initial evidence of the vocal phrase, allowing or causing the sound data to be transmitted (See at least Para 0038-0040, Para 0054-0056).
As per independent Claim 20, Bernotas discloses a threat detection sensor, comprising:
at least one sound sensor configured to sense sound within a detection zone; and a processor unit coupled to the at least one sound sensor (See at least Figs.1-3, Fig.6, and Para 0017-Para 0039) and configured to:
receive sound data generated by the at least one sound sensor (See at least Figs. 4-5A; Para 0039, “The safety system may receive and/or monitor signals received from devices. At 402, one or more first signals received from one or more first devices may be monitored. The one or more first signals may comprise one or more image signals, one or more video signals, one or more audio signals, one or more infrared signals and/or one or more biometric signals.”; See also Para 0040 and Para 0054);
analyze the sound data to detect initial evidence of the vocal phrase within the sensed sound(See at least Para 0074, “In an example where the first signal comprises audio, the audio may be classified as being associated with one or more sounds associated with a threat to safety. Alternatively and/or additionally, the audio may be classified as being associated with the one or more sounds based upon a comparison of the audio with the one or more first sets of audio features. … Alternatively and/or additionally, one or more sets of audio features of the one or more first sets of audio features may comprise one or more phrases that a person may say when a threat event occurs.”; See at least Para 0094 “In some examples, the first probability may be determined based upon audio information of the one or more second signals. For example, the one or more second signals may comprise one or more first audio signals (e.g., signals comprising audio information and/or other information). It may be determined that one or more second audio signals, of the one or more first audio signals, are associated with one or more threat indicators (e.g., each signal of the one or more second audio signals may be classified as being associated with one or more threat indicators). For example, a threat indicator may be detected within a signal of the one or more second audio signals. The threat indicator may correspond to one or more of a gunshot, an explosion, a phrase that a person may say when a threat events occurs, one or more voice properties of a person, etc.”; See also Para 0054-0055); and
after detecting the initial evidence of the vocal phrase, transmit the sound data corresponding to the sensed sound, wherein the transmitted sound data is used to confirm an existence of a threat by confirming that that the vocal phrase is within the sensed sound (See at least Figs. 4-5F; Para 0094, Para 0054-0056), wherein the existence of the threat is confirmed using at least one model, wherein after the at least one model is trained, the at least one model is retrained, thereby improving threat detection in a location (See at least Para 0088-0090, Machine Learning model).
Bernotas fails to expressly disclose wherein after the at least one model is trained, the at least one model is retrained using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone.
However, the analogous art of Graciarena discloses training at least one model to detect an existence of a threat following a period of time, retraining the at least one model using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone (See at least Para 0021, “Embeddings extractor 106 may receive input audio waveform 116, e.g., converted to audio spectrogram 104 in some examples, and output an embedding 114 of input audio waveform 116. Embeddings extractor 106 may be trained using an audio space comprising a plurality of sounds, which in some examples includes non-speech sounds. Non-speech sounds may include a sounds generated in nature, e.g., an avalanche, bird songs, waves on a shore, along with mechanically generated sounds such as motors, clocks, bells, explosions, and similar sounds.”; Para 0022, Para 0033, and Para 0060).
Therefore, at the time the invention was effectively filed, it would have been obvious for one of ordinary skill in the art to have included training at least one model to detect an existence of a threat following a period of time, retraining the at least one model using real-world background noise data comprising sounds from the detection zone, thereby improving threat detection in the detection zone., as disclosed by Graciarena in the system disclosed by Bernotas, for the advantage of providing a threat detection and response system, with the ability to increase system/ method effectiveness and efficiency by incorporating a variety of data modeling techniques and comparing a variety of data types (See KSR [127 S Ct. at 1739] “The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Response to Arguments
Applicant’s arguments filed on 10/6/2025, with respect to Claims 1-20, have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of References Cited. The Examiner suggests the applicant review all of these documents before submitting any amendments; especially, the following:
Wang et al. (US 2022/0165297 A1) - See at least Para 0005, “… obtaining a speech segment and a non-speech segment of an audio signal to be detected, extracting a first audio feature of the speech segment and a second audio feature of the non-speech segment, detecting the first audio feature using a predetermined speech segment detection model to obtain a first detection score, detecting the second audio feature using a predetermined non-speech segment detection model to obtain a second detection score, and determining whether the audio signal belongs to a target audio based on the first detection score and the second detection score.”; Para 0026, “After an apparatus for detecting an audio signal collects the audio to be detected, the speech segment and the non-speech segment in the audio may be extracted through a voice activity detection. The speech segment is an audio segment containing human voice information in the audio to be detected, and the non-speech segment is an audio segment with various background sounds and noises, an audio segment with low volume, and so on.”; and Para 0028, “In the embodiment of the present disclosure, for the speech segment and the non-speech segment, different detection models are used for feature detection. For the detection model, a training may be performed based on a predetermined speech audio and a predetermined non-speech audio, respectively, such that targeted models can be obtained.”; See also Para 0057-0065).
Thiruvenkatanathan et al. (US 2023/0060936 A1) - See at least Para 0053-0056; Para 0061, “The receiver may include a notification application program to notify information in relation to the identified audio signal to one or more users. The notification application program may notify a user depending on preconfigured notification settings selected by the user. The notification application may or may not alert the user depending on whether the sound identified is of interest to the user.”; Para 0075, “ receiving audio data at a receiver module from at least one audio sensor.”; Para 0076, “processing the audio data using a signal recognition module.”; Para 0077, “wherein processing the audio data using the signal recognition module”; Para 0078, “extracting one or more feature vectors from the received audio data”; Para 0208, “Optionally, the known or ‘labelled’ sounds are obtained under different background noise conditions to enable robust sound classification under real world conditions.”)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN P OUELLETTE whose telephone number is (571)272-6807. The examiner can normally be reached on M-F 8am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda C Jasmin, can be reached at telephone number (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
November 17, 2025
/JONATHAN P OUELLETTE/Primary Examiner, Art Unit 3629