DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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 the Application
2. Pursuant to the amendment filed August 6, 2026, claims 1-13 and 20-26 are pending in the application. The applicant has cancelled claims 14-19. The applicant has added clams 20-26. The applicant has amended claim 1.
Claim Rejections - 35 USC § 103
3. 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.
4. Claims 1-7, 21, and 23-26 are rejected under 35 U.S.C. 103 as being unpatentable over Couper et al. (US 2019/00190400).
Regarding claim 1, Couper discloses:
a non-volatile digital memory storing a series of instructions executable on a processor of a mobile computing device ([0011], [0025]; FIG. 1: 105, 110; Couper discloses a mobile device comprising a processor and memory; the mobile devices 105 and 110 can include a signature detector 115 and one or more sound signatures 120. The signature detector 115 can be a computer program that is executed by each respective mobile device 105 and 110) to carry out the steps of:
entering a standby listening mode to acquire sound data responsive to sound received at an acoustic transducer associated with the mobile computing device ([0028]; the signature detector 115 can cause the mobile devices 105 and 110 to keep the microphone active so that sounds are continually monitored, recorded, and compared with the stored sound signatures);
analyzing, by the processor of the mobile computing device, the acquired sound data to detect a sound produced by a shot fired by a weapon ([0049] each mobile device can analyze the detected sound and compare the detected sound against one or more sound signatures stored within the mobile device; the mobile device(s) can determine that the sound matches one of the sound signatures stored within the mobile device);
responsive to detecting a shot-fired sound:
capturing and storing metadata of the shot-fired sound ([0027]; the signature detector 115 can extract information from the detected sound and compare that information to the sound signatures 120; examples of the sort of data that can be extracted or determined from the detected sound can include, but are not limited to, volume or sound-pressure-level information, the frequency range of the detected sound, the amount of energy detected in different frequency bands of interest, a spectrum analysis, transient characteristics, the actual waveform of the detected sound, a Fourier Transform or FFT information, formant information, or the like); and
automatically and without manual input to a user interface of the mobile computing device, transmitting a not audible, shot-fired message to a server, the message including at least some of the stored metadata ([0020]; upon detecting a selected sound, the mobile device can send a communication to an event processor; the communication can specify information relating to the detected sound; the communication can provide one or more items of information relating to the detected sound 150. For example, one or more parameters of the detected sound 150 that may be determined or extracted for comparison with the sound signatures 120 can be sent; other data generated by the signature detector 115 also can be sent such as, for instance, a measure of how closely the sound 150 matches the sound signature 120),
Couper does not explicitly disclose transmitting a digital message, but Couper does disclose that the mobile computing device is a mobile phone enabled for wireless transmission over wireless links ([0024]), and Couper discloses that that the mobile device communicates over a WAN, a LAN, the Public Switched Telephone Network (PSTN), the Web, the Internet, and one or more intranets, short range wireless networks, a local wireless network built using Bluetooth or one of the IEEE 802 wireless communication protocols, e.g., 802.11a/b/g/i, 802.15, 802.16, 802.20, Wi- Fi Protected Access (WPA), or WPA2, a mobile, cellular, and or satellite- based wireless network and support voice, video, text, and/or any combination thereof, e.g., GSM, TDMA, CDMA, and/or WCDMA network ([0023]), which are typically digital networks and communication channels, all of which suggests that the Cuper’s mobile device transmits a digital message for the benefit of being enabled for the foregoing digital networks and communication channels;
it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have configured the mobile device of Couper in the foregoing manner because that would have enabled the mobile device to be enabled for the discloses digital networks and communication channels;
wherein the shot-fired message does not include any audio recording of the acquired sound data ([0034]; the information sent within the communication, i.e., SMS message 155, can include any information determined by the signature detector 115 and, in one embodiment, initially exclude any audio that have been recorded or collected by the mobile device 105).
Regarding claim 2, Couper discloses that the stored instructions further cause the processor to present an indication to the user interface of the mobile device that the shot-fired message was transmitted to the server. ([0024], [0034]; Couper discloses that a mobile phone sends information by Short Messenger Service (SMS), which is typically displayed on the display of a mobile phone.)
Regarding claim 3, Couper discloses that the acoustic transducer comprises an audio microphone, an acoustic pressure sensor, or other acoustic transducer. ([0028]; the signature detector 115 can cause the mobile devices 105 and 110 to keep the microphone active so that sounds are continually monitored, recorded, and compared with the stored sound signatures 120.)
Regarding claim 4, Couper discloses that the metadata includes an identifier of a person associated with the mobile device. ([0024], [0034]; Couper discloses that a mobile phone sends information by Short Messenger Service (SMS), and SMS comprises metadata that includes an identifier associated with the mobile device.)
Regarding claim 5, Couper discloses the metadata includes at least one of distance, direction, and location data of the shot-fired sound. ([0035]; Spatial information, such as the location of the mobile device 105 when the sound 150 is detected can be included in the communication.)
Regarding claim 6, Couper discloses that the metadata includes a timestamp of the shot-fired sound. ([0035]; Temporal information, e.g., a timestamp specifying the time of day and the date, when the sound 150 is detected also can be specified within the communication.)
Regarding claim 7, Couper discloses that the stored instructions comprise an application executable on a smartphone and the message is transmitted using a data communications feature of the smartphone. ([0024], [0025]; The mobile devices 105 and 110 can be implemented as mobile phones, personal digital assistants, or any other device capable of sending and receiving data over wireless communication links 140 and 145 via the communication network 135. For example, each of the mobile devices 105 and 110 can include a microphone, a processor, and memory. The mobile devices 105 and 110 further can include a wireless transceiver capable of establishing the wireless communication links 140 and 145 with the communication network 135. The wireless transceiver can support one or more of the various communication protocols noted herein, though the present invention is not intended to be limited by the type of communication scheme or channel used. The signature detector 115 and/or the sound signatures 120 can be installed on the mobile devices 105 and 110 at some other point in time, for example, after purchasing the device by downloading the computer programs and data via a wireless connection.)
Regarding claim 21, Couper discloses that the stored instructions cause the processor to extract, from the acquired sound data, an approximation of an acoustic pressure wave produced by the shot fired, the approximation extracted using linear digital signal processing filtering, nonlinear digital signal processing filtering, and/or a deep-learning network. ([0027] The signature detector 115 can extract information from the detected sound and compare that information to the sound signatures 120. Examples of the sort of data that can be extracted or determined from the detected sound can include, but are not limited to, volume or sound-pressure-level information, the frequency range of the detected sound, the amount of energy detected in different frequency bands of interest, a spectrum analysis, transient characteristics, the actual waveform of the detected sound, a Fourier Transform or FFT information, formant information, or the like.)
Regarding claim 23, Couper discloses that the stored instructions cause the processor to break the acquired sound data into chunks and process each of the chunks to detect the shot-fired sound. ([0027]; Examples of the sort of data that can be extracted or determined from the detected sound can include, but are not limited to, volume or sound-pressure-level information, the frequency range of the detected sound, the amount of energy detected in different frequency bands of interest, a spectrum analysis, transient characteristics, the actual waveform of the detected sound, a Fourier Transform or FFT information, formant information, or the like. The FFT involves breaking data into chunks.)
Regarding claim 24, Couper discloses that the mobile computing device is structured to be worn or carried by a person such that the mobile computing device travels with the person. ([0024] The mobile devices 105 and 110 can be implemented as mobile phones, personal digital assistants, or any other device capable of sending and receiving data over wireless communication links 140 and 145 via the communication network 135. A mobile phone is carried by a person and travels with that person.)
Regarding claim 25, Couper discloses that no audio recording of the acquired sound data is retained in the mobile computing device. ([0044]; the mobile device can record a continuous loop and, if a detected sound matches a sound signature 120, the signature detector 115 can prevent that audio from being recorded over until the mobile device either provides the audio to the event processor 125 or determines that the audio is not needed by the event processor 125. Couper discloses that audio recordings in the mobile device are recorded over.)
Regarding claim 26, Couper discloses that the shot-fired message is transmitted to the server using Bluetooth, WiFi, and/or a cellular data network. ([0023]; the communication network 135 can include a local wireless network built using Bluetooth or one of the IEEE 802 wireless communication protocols, e.g., 802.11a/b/g/i, 802.15, 802.16, 802.20, Wi-Fi Protected Access (WPA), or WPA2. In terms of long range wireless networks, the communication network 135 can include a mobile, cellular, and or satellite-based wireless network and support voice, video, text, and/or any combination thereof, e.g., GSM, TDMA, CDMA, and/or WCDMA network.)
5. Claims 8-13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Couper in view of Anderson et al. (US 2019/0347920 A1).
Regarding claim 8, Couper does not disclose that the stored instructions further cause the processor to apply machine learning to detect shots and differentiate shot-fired sounds from non-shot sounds.
Andreson, in the same field of analyzing sound data to identify a sound produced by a shot fired by a weapon, teaches a firearm discharge detector and system ([0004]), wherein the stored instructions further cause the processor to apply machine learning to detect shots and differentiate shot-fired sounds from non- shot sounds. ([0041], [0042]; FIG. 8; the server 190 is trained on a large number of audio recordings of gunshots) for the benefit of training the machine learning model to determine the difference between, for example, a car backfire and a gunshot ([0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have combined the teachings of Anderson with the invention of Couper because that would have enabled the invention to train a machine learning model to determine the difference between, for example, a car backfire and a gunshot.
Regarding claim 9, the above combination does not disclose that the machine learning incorporates anomaly detection to differentiate shot-fired sounds from non-shot sounds.
Andreson, in the same field of analyzing sound data to identify a sound produced by a shot fired by a weapon, teaches a firearm discharge detector and system ([0004]), wherein the machine learning incorporates anomaly detection to differentiate shot-fired sounds from non-shot sounds ([0041], [0043]; with each validation of “gunshot” or “not gunshot” the model “learns” to distinguish between the sound of a gunshot and other noises, such as a car backfire) for the benefit of training the model to identify a likelihood that a noise is a gunshot, with any likelihood over 75% considered as positive for a gunshot ([0043]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have combined the teachings of Anderson with the invention of the above combination because that would have enabled the invention to train a machine learning model to identify a likelihood that a noise is a gunshot, with any likelihood over 75% considered as positive for a gunshot.
Regarding claim 10, Couper discloses a server remote from the mobile computing devices (FIG. 1: 105, 110, 125). The above combination does not disclose that the machine learning is cloud-based and is executed on computing resources remote from the mobile computing device.
Andreson, in the same field of analyzing sound data to identify a sound produced by a shot fired by a weapon, teaches a firearm discharge detector and system ([0004]), wherein cloud-based machine learning is performed by training the server 190 on a large number of audio recordings of gunshots; a sufficiently designed and trained machine learning model is configured to determine the difference between, for example, a car backfire and a gunshot ([0002], [0041]) for the benefit of training the machine learning model to determine the difference between, for example, a car backfire and a gunshot ([0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have combined the teachings of Anderson with the invention of the above combination because that would have enabled the invention to train a machine learning model to determine the difference between, for example, a car backfire and a gunshot.
Regarding claim 11, the above combination does not disclose that the machine learning employs a classifier model to differentiate shot-fired sounds that is based at least in part on a dataset of acoustic data acquired by firing weapons and storing acoustic data emitted by the weapons when fired.
Andreson, in the same field of analyzing sound data to identify a sound produced by a shot fired by a weapon, teaches a firearm discharge detector and system ([0004]), wherein the machine learning employs a classifier model to differentiate shot-fired sounds that is based at least in part on a dataset of acoustic data acquired by firing weapons and storing acoustic data emitted by the weapons when fired ([0041], [0042], [0043]; The model is trained through numerous iterations over training data including audio recordings of different types of gunshots with variations in ambient noise. In order to mimic real-life situations and to train the model to distinguish gunfire when ambient noise is present, the training data recordings will include different types and levels of ambient noise. The model stores the parameters associated with each recording, such as, by way of example and not limitation, frequency, decibel level and elapsed time) for the benefit of training the machine learning model to distinguish gunfire when ambient noise is present ([0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have combined the teachings of Anderson with the invention of the above combination because that would have enabled the invention to train a machine learning model to distinguish gunfire when ambient noise is present.
Regarding claim 12, the above combination does not disclose that the machine learning includes determining a probable type of weapon that was fired to cause the shot-fired sound.
Andreson, in the same field of analyzing sound data to identify a sound produced by a shot fired by a weapon, teaches a firearm discharge detector and system ([0004]), wherein the machine learning includes determining a probable type of weapon that was fired to cause the shot-fired sound ([0044]; the model can be fine-tuned to identify characteristics such as caliber, rate of fire, etc. in step 850. By listening to the frequency, reverberation, and duration of a gunshot (often combined into a term called the “report”), characteristics such as caliber can be determined. In this same way one could determine a suppressed firearm as well by listening for the specific noises of the gas expanding at an altered rate combined with the mechanical movement in the firearm) for the benefit of enabling the model to enable the police to identify the type of firearm used in the shooting ([0030]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have combined the teachings of Anderson with the invention of the above combination because that would have enabled the model to have enabled the police to identify the type of firearm used in a shooting.
Regarding claim 13, the above combination does not disclose that the machine learning includes determining a probable type of ammunition that was fired to cause the shot-fired sound.
Andreson, in the same field of analyzing sound data to identify a sound produced by a shot fired by a weapon, teaches a firearm discharge detector and system ([0004]), wherein the machine learning includes determining a probable type of ammunition that was fired to cause the shot-fired sound ([0022], [0044]; a data store 180 such as, for example, a look-up table contains a continually expanding catalog, or list, of gunshot “signatures” containing the acoustic signatures of known firearms and ammunition; the model can be fine-tuned to identify characteristics such as caliber, rate of fire, etc. in step 850; by listening to the frequency, reverberation, and duration of a gunshot (often combined into a term called the “report”), characteristics such as caliber can be determined).
for the benefit of enabling the model to enable the police to identify the type of firearm used in the shooting ([0030]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have combined the teachings of Anderson with the invention of the above combination because that would have enabled the model to have enabled the police to identify the type of firearm used in a shooting.
Regarding claim 20, the above combination does not disclose that detecting the shot-fired sound comprises determining that a likelihood that the acquired sound data corresponds to a shot fired by a weapon exceeds a selected confidence level.
Andreson, in the same field of analyzing sound data to identify a sound produced by a shot fired by a weapon, teaches a firearm discharge detector and system ([0004]), wherein detecting the shot-fired sound comprises determining that a likelihood that the acquired sound data corresponds to a shot fired by a weapon exceeds a selected confidence level ([0043]; the model is trained to identify a likelihood that a noise is a gunshot, with any likelihood over 75% considered as positive for a gunshot. Given one or more audio files with meta data such as the reading of the PIR sensor, and the timestamp of the recording, the model can return a percentage likelihood that the audio recording is in fact a gunshot, and the percent likelihood of a particular caliber) for the benefit of training the machine learning model to determine the difference between, for example, a car backfire and a gunshot ([0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have combined the teachings of Anderson with the invention of Couper because that would have enabled the invention to train a machine learning model to determine the difference between, for example, a car backfire and a gunshot.
Allowable Subject Matter
6. Claim 22 is objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Kane et al. (US 2016/0157032 A1) discloses a method for identifying a gunshot occurrence with a mobile hand-held device.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN F MORTELL whose telephone number is (571)270-1873. The examiner can normally be reached Monday - Friday 10-7 ET.
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/JOHN F MORTELL/Primary Examiner, Art Unit 2689
