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 § 103
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) 1, 10, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 12,182,674 B2).
Claim 1, Huang et al. disclose of a system comprising microphones distributed around a construction site, the system further comprising at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the system at least to perform: receiving audio information obtained by at least a first microphone of the microphones (fig.1 (102-107); fig.2; col.16 line 35-40); and determining a type of equipment in-use based on the received audio information, via a machine learning engine (col.2 line 40-45 & col.3 line 60-col.4 line 20/various equipment devices may be determined based on machine learning engine).
However, Huang et al. never limit the device to being a construction equipment. But, one of the ordinary skills in the art could have modified the prior art with variety devices which may be determined by also adding if desired such construction equipment for achieving the same expected result as to allow the user to recognize the noise generated by such construction equipment device according to it particular sound.
The system of claim 1, wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the system at least to perform: generating samples of the audio information, and wherein determining the type of construction equipment in-use comprises processing at least one of the samples via the machine learning engine (col.4 line 40-45; abstract).
However, although the art explicitly mentioned of sample of audio information to be generated according to timing, but it never limit such aspect as having the samples having a duration selected from the range 0.5 seconds to five seconds, but one of the ordinary skills in the art could have varied the generated sample according to timing as mentioned by specifying any ranges including having the samples having a duration selected from the range 0.5 seconds to five seconds for achieving the same result as to train the device for effectively determined specific sounds.
18. The system of claim 1, wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the system at least to perform: causing, at least in part, outputting of an alert in dependence on the determined type of construction equipment in-use (col.17 line 20-35).
The claim(s) 19-20 which in substance disclose of the same limitation as in claim (s) 1 has been analyzed and rejected accordingly.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 12,182,674 B2) and Angkititrakul et al. (US 2025/0189970 A1).
The system of claim 1, but the prior art never disclose of wherein the microphones comprise omnidirectional microphones.
But it shall be noted the prior art herein Angkititrakul et al disclose of the concept regarding having microphones comprise omnidirectional microphones (par [17]). Thus, one of the ordinary skills in the art could have modified the prior art by adding such noted omnidirectional microphones so to enable the device to pick up sound in all directions.
Claim(s) 3-4, 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 12,182,674 B2) and Briggs et al. (US 11,956,588 B2).
The system of claim 1, wherein the system comprises devices distributed around the construction site (fig.1 (102-107); col.4 line 5-35). But the prior art never specify of having each device comprising a different one of the microphones.
But, Briggs et al. disclose of a system wherein a device comprise different microphones (col.6 line 17-22). Thus, one of the ordinary skills in the art could have modified the prior art by adding such noted device with each different one of the microphones so as to allow capturing of sound at particular location around the site.
The system of claim 3, wherein each device is an edge device comprising one of the at least one memory and the computer program code, configured to, with the at least one processor, cause the edge device to perform determining the type of construction equipment in-use, the computer program code of the edge device including a trained copy of the machine learning engine (fig.2 (202); col.5 line 10-20 & col.7 line 5-30 & line 45-55/the device may be trained and process perform at the device ).
The system of claim 3, but, the prior art never specify as wherein each device comprises a securing point enabling attachment of the device to a support.
But the examiner takes official notice having a device with securing point enabling attachment of the device to a support is very well known in the art. Thus, one of the ordinary skills in the art could have modified the prior art by adding such noted securing point enabling attachment of the device to a support so as to allow the user free movements without necessity to manually hold on to the device.
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 12,182,674 B2) and Briggs et al. (US 11,956,588 B2) and Sundaram et al. (US 12,387,727 B1).
The system of claim 3, but the prior art never specify as wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the system at least to perform: obtaining information indicating a location of a first device of the devices, the first device comprising the first microphone; and associating the determined type of construction equipment in-use with the information indicating the location of the first device.
However, Sundaram et al. disclose of a similar system wherein system at least to perform: obtaining information indicating a location of a first device of the devices, the first device comprising the first microphone; and associating the determined type of construction equipment in-use with the information indicating the location of the first device (fig.7; col.5 line 50-67; col.31 line 20-37). Thus, one of the ordinary skills in the art could have modified the prior art by adding such noted system at least to perform: obtaining information indicating a location of a first device of the devices, the first device comprising the first microphone; and associating the determined type of construction equipment in-use with the information indicating the location of the first device so as to provide the contextual data associated with the device for improving accuracy of the model with corresponding data receive of the device.
The system of claim 7, wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the system at least to perform causing, at least in part, outputting of an alert in dependence on the determined type of construction equipment in-use and on the information indicating the location of the first device (Huang-col.17 line 20-30).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 12,182,674 B2) and Constantino (US 2024/0249567 A1).
The system of claim 1, but the art never specify as wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the system at least to perform: filtering the audio information based on an intensity of the audio information, and determining the type of construction equipment in-use based on the filtered audio information.
However, Constantino disclose of the similar aspect related to filtering the audio information based on an intensity of the audio information, and determining the type of audio based on the filtered audio information (par [50]). Thus, one of the ordinary skills in the art could have modified the prior art by adding such method regarding filtering the audio information based on an intensity of the audio information, and determining the type of audio based on the filtered audio information so as to accurately determined the desired signal according to audio analysis.
Claim(s) 11-14, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 12,182,674 B2) and Stuart (US 12,529,789 B2).
The system of claim 1, but the prior art as in Huang failed to mentioned as wherein determining the type of construction equipment in-use via the machine learning engine is dependent on a first above-threshold frequency and on whether the audio information contains one or more further above-threshold frequencies which are simultaneous with the first frequency over a period of time.
However, Stuart et al. disclose of a similar aspect related to a system including determining the type of construction equipment in-use via the machine learning engine is dependent on a first above-threshold frequency and on whether the audio information contains one or more further above-threshold frequencies which are simultaneous with the first frequency over a period of time (col.14 line 20-45). Thus, one of the ordinary skills in the art could have modified the prior art with determining a certain particular type of device being equipment by adding such specific aspect related signal as being is dependent on a first above-threshold frequency and on whether the audio information contains one or more further above-threshold frequencies which are simultaneous with the first frequency over a period of time so as to detect a signal of a particular characteristic.
Claim 12, the system of claim 1, but the prior art never specify as wherein the determination of a type of construction equipment in-use is dependent on frequency content from the range 1.5kHz to 8kHz.
However, Stuart et al. disclose of a similar aspect related to a system including implementing the determination of a type of a signal that is dependent on frequency content from the range 1.5kHz to 8kHz (col.14 line 20-30). Thus, one of the ordinary skills in the art could have modified the prior art with determining a certain particular type of device being equipment by adding such specific aspect related signal as being dependent on frequency content from the range 1.5kHz to 8kHz so as to detect a signal of a particular characteristic.
13. The system of claim 1, wherein the determination of a type of construction equipment in-use is based on two or more of the following variables: whether the audio information contains two simultaneous frequency bands; whether the audio information contains three simultaneous frequency bands; a centre frequency of at least one frequency band; a bandwidth of at least one frequency band; or an intensity of at least part of the audio information.
Stuart et al. disclose of a system including determination of a type of audio data in use is based on two or more of the following variables: whether the audio information contains two simultaneous frequency bands; or an intensity of at least part of the audio information (fig.10a; col.14 line 25-45). Thus, one of the ordinary skills in the art could have modified the art by adding the aspect concerning determination of a type of audio data in use is based on two or more of the following variables: whether the audio information contains two simultaneous frequency bands; or an intensity of at least part of the audio information so as to detect a signal of a particular characteristic so as to provide the immersive acoustic image to user.
14. The system of claim 13, wherein the machine learning engine is trained to recognize , based on the two or more of the variables, at least two of the following types of construction equipment, however, the art as mentioned never specify of the equipment being: angle grinder; saw; router; drill; vacuum cleaner; scaffold wrench; screw gun; pad sander; grinder; electric plane; or grinding wheel.
But, one of the ordinary skills in the art could have modified the prior art with variety devices which may be determined by also adding if desired such construction equipment f equipment being: angle grinder; saw; router; drill; vacuum cleaner; scaffold wrench; screw gun; pad sander; grinder; electric plane; or grinding wheel for achieving the same expected result as to allow the user to recognize the noise generated by such construction equipment device according to it particular sound.
17. The system of claim 1, but the art never specify as wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the system at least to perform: causing, at least in part, outputting of an alert in dependence on the determined type of construction equipment in-use and on a noise threshold.
However, Stuart et al. disclose of the general concept related to a system to perform: causing, at least in part, outputting of an alert in dependence on the determined type of construction equipment in-use and on a noise threshold (fig.10; col.28 line 30-40). Thus, one of the ordinary skills in the art could have modified the prior art by adding such aspect related to a system to perform: causing, at least in part, outputting of an alert in dependence on the determined type of construction equipment in-use and on a noise threshold as to allow the user to recognize the loudest sound in the scene being generated according to it particular sound characteristic.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 12,182,674 B2) and Port et al. (US 11,817,114 B2).
16. The system of claim 1, but the prior art never specify as wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the system at least to perform: causing, at least in part, outputting of an alert in dependence on the determined type of construction equipment in-use and on time of day.
However Port et al. disclose of a system to perform: causing, at least in part, outputting of an alert in dependence on the determined type of data and on time of day (col.22 line 45-55). Thus, one of the ordinary skills in the art could have modified the art detecting construction equipment by adding such noted system at least to perform: causing, at least in part, outputting of an alert in dependence on the determined type of construction equipment in-use and on time of day so a to adjust the output based one noise and additional parameter such as timing for providing corresponding signal indicator to user.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 12,182,674 B2) and Goran et al. (US 10,880,641 B2).
15. The system of claim 1, but the prior art never specify as (further comprising human presence detectors distributed around the construction site, wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the system at least to perform: causing, at least in part, outputting of an alert in dependence on the determined type of construction equipment in-use, and on information from the human presence detectors indicating an above-threshold number of humans proximal to the first microphone.
However, Goran et al. disclose of a system to perform: causing, at least in part, outputting of an alert in dependence on the determined a condition, and on information from the human presence detectors indicating an above-threshold number of humans proximal to the device (col.30 line 5-20 & col.31 line 35-50). thus, one of the ordinary skills in the art could have modified the device with microphone by adding thereto such aspect related to causing, at least in part, outputting of an alert in dependence on the determined type of construction equipment in-use, and on information from the human presence detectors indicating an above-threshold number of humans proximal there to so as to provide the adjusted output according to users’ condition.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (US 12,182,674 B2) and Briggs et al. (US 11,956,588 B2) Ravi et al. (US 2022/0374719 A1).
The system of claim 3 wherein each device comprise a battery (col.3 line 40-45/the device may be a trained smartphone which inherently has a battery to implement designated function), but the prior art never specify as wherein each device comprises each trained copy of the machine learning engine represents weights and activations with a precision less than 32 bits, or less than 16 bits.
However, it shall be noted Ravie et al. disclose of such aspect concerning device comprises each trained copy of the machine learning engine represents weights and activations with a precision less than 32 bits, or less than 16 bits (par [34, 70, 101]). Thus, one of the ordinary skills in the art could have modified the art by adding such noted aspect related to device comprises each trained copy of the machine learning engine represents weights and activations with a precision less than 32 bits, or less than 16 bits so as to reduce processing power associated with the model performance.
Conclusion
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/DISLER PAUL/Primary Examiner, Art Unit 2695