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 .
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.
Response to Amendment
Claims 1-4, 6, and 8-20 are currently pending.
Independent claim(s) 1, 10 and 19 and dependent claims 6, 8-9 and 16 have been amended by applicant’s amendments received 9 January 2026. No new matter has been introduced.
Claims 5 and 7 have been canceled, and therefore the prior rejections is/are moot.
Prior objections of the drawings have been overcome by amendment and are therefore withdrawn.
Prior objections of the specification have been overcome by amendment and are therefore withdrawn.
Response to Arguments
Applicant’s arguments, see pg. 12 of remarks, lines 24-29, filed 9 January 2026, with respect to the rejection(s) of claim(s) 1-4 and 10-13 under 35 U.S.C. 102(a)(1) and (a)(2) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of amendments to the independent claims (1, 10 and 19) incorporating limitations from previously dependent claims (5 and/or 7) thereby overcoming the current rejection under 35 U.S.C. 102(a)(1) and (a)(2) by Liu et al. (hereinafter Liu, US 20210286051 A1). However, as discussed below, the priorly cited secondary reference Wang et al. (hereinafter Wang, US 10490288 B1) is still applicable to these limitations, and the rejection of claims 1-4, 10-13 and 19 have been updated accordingly under 35 U.S.C. 103.
Applicant's arguments filed 09 January 2026 have been fully considered but they are not persuasive.
Firstly, applicant’s arguments (pg. 12 Remarks, lines 10-17) discuss Wang being limited to two alternative fitting processes, citing that Figs. 6A and 6B (which discuss linear and non-linear fitting processes, respectively) must be performed separately, and never simultaneously with one another. Wang teaches (Col. 16 line 61 – Col. 17 line 18) that “For example, at least portions of the functionality of the linear function coefficients calibration processes 600 and 650 of FIGS. 6A and 6B and/or the gradient descent process 700 of FIG. 7 are illustratively implemented in the form of software running on one or more processing devices.”, which when combined with Fig. 9’s disclosure of multiple simultaneously running processing devices (902-1 to 902-D) , as is noted in “Multiple elements of the system may be collectively implemented on a common processing platform of the type shown in FIG. 9, or each such element may be implemented on a separate processing platform.” (Col. 16, lines 26-29). To one of ordinary skill in the art, this exemplifies a system which can run multiple, simultaneous processes, where one processor may run a linear based fitting calibration process while a separate processor runs a non-linear based fitting calibration process.
Applicant also notes that (Remarks, pg. 12 lines 18-24) if Wang only teaches a linear or a non-linear fitting process that this teaches away from a simultaneous operation of the two. While Wang does not teach only one process or the other (see above), it is also not explained how teaching both processes as alternatives to one another inherently is teaching away from using both, as both are well known mathematical fitting processes in the art of data analysis.
Claim Objections
Claim 6 objected to because of the following informalities:
Claim 6 currently is dependent upon a canceled claim (claim 5). As claim 5 was previously dependent upon claim 1, for examination purposes, claim 6 will be interpreted to be dependent on independent claim 1.
Appropriate correction is required.
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) 1-4, 6, 8, 10-17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (hereinafter Liu, US 20210286051 A1), and in view of Wang et al. (hereinafter Wang, US 10490288 B1).
Regarding claim 1, Liu teaches a system for use in time of flight (ToF) distance measurement, the system comprising:
a sampling circuit configured to detect a ToF measurement signal and provide digital ToF signal sample data ([0265] - [0274]; Fig. 13 receiving circuit sends electrical signal to sampling circuit within detector (105) with sampled points) for a plurality of threshold-based samples of a detected ToF measurement signal ([0117] - [0118]; Fig. 12 comparison circuit (130) within the control circuit (140) may include plurality of TDCs for signal point vs. threshold comparison);
and a sample processing circuit in data communication with the sampling circuit ([0120]; Fig. 8 control circuit (140), in connection with comparison circuit (130)) and configured to apply one or more curve fitting techniques to the digital ToF signal sample data and generate a signal waveform representing the detected ToF measurement signal from which the ToF distance measurement is determinable ([0059] - [0062], [0093], [0120]; Fig. 5, where signal is compared to thresholds to create waveforms (520) and (530) and the system can further determine distance between the object and system).
Liu does not teach use of hardware accelerators specifically for curve fitting purposes, or the specifics of the curve fitting techniques.
Wang teaches an apparatus for curve fitting a distribution of data, which utilizes dedicated processing for supporting curve fitting, where the system has
One or more curve fitting hardware accelerators of the one or more curve fitting hardware accelerators (Col. 15 line 52 - Col. 16 line 10, Col. 16 line 61 - Col. 17 line 2; where processes (600) and (650) are running on one or more processing devices, which may include dedicated circuits for curve fitting),
wherein the curve fitting techniques include a linear curve fitting technique and a non-linear curve fitting technique (Col. 10 lines 1-22, Col. 12 lines 4-26, Col. 16 line 61-Col. 17 line 18, ; Fig. 6A shows process (600) shows a linear function process and where Fib. 6B shows process (650) which entails a non-linear process and both may be completed by the system simultaneously),
a first curve fitting hardware accelerator (Col. 8 line 63 - Col. 9 line 9, Col. 15 line 52 - Col. 16 line 10, Col. 16 line 61 - Col. 17 line 2; where processes (600) and (650) are running on one or more processing devices, which may include dedicated circuits for accelerating curve fitting) configured to implement the linear curve fitting technique (Col. 10 lines 1-22; Fig. 6A where process (600) shows a linear function process and may be implemented on priorly noted first processing platform);
and a second curve fitting hardware accelerator (Col. 8 line 63 - Col. 9 line 9, Col. 15 line 52 - Col. 16 line 10, Col. 16 line 61 - Col. 17 line 2; where processes (600) and (650) are running on one or more processing devices, which may include dedicated circuits for accelerating curve fitting) configured to implement the non-linear curve fitting technique (Col. 12 lines 4-26; Fig. 6B where process (650) shows a non-linear function process and may be implemented on priorly noted second processing platform).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Liu to incorporate the teachings of Wang to utilize hardware specifically for increasing the processing speed of curve fitting, and combined linear and non-linear curve fitting techniques for use in a ToF distance measuring device with a reasonable expectation of success. Use of integrated accelerator circuits, such as FPGAs or ASICs, for improving processing times is known in the art, and therefore using one or more for curve fitting within a system as taught by Wang in the ToF system of Liu would have a predictable result of reducing processing and distance determination times, especially allowing for potential simultaneous curve fitting within the receiving, sampling, and arithmetic circuits of Liu ([0270]). Additionally, Liu notes that other processing circuits can be used in the system near the comparison circuit ([0084]). Use of many different curve fitting techniques, including linear and non-linear, is well known in the art, and therefore use of both simultaneously as taught by Wang in the ToF system of Liu would have a predictable result of reducing processing and distance determination times, specifically for curve fitting of digitized ToF sample signals which may not entirely be linear or non-linear in nature.
Regarding claim 2, Liu as modified above teaches the system of claim 1, wherein
the digital ToF signal sample data includes a first plurality of data points for a rising edge of the detected ToF measurement signal as detected by the sampling circuit and a corresponding second plurality of data points for a falling edge of the detected ToF measurement signal as detected by the sampling circuit, and wherein the first plurality of data points and the corresponding second plurality of data points each comprise a data point of the digital ToF signal sample data for each threshold of the plurality of threshold- based samples of the detected ToF measurement signal ([0059] - [0062], [0286]; Fig. 5 where both rising and falling edges of digitized received signal have points for specific voltage thresholds).
Regarding claim 3, Liu as modified above teaches the system of claim 1, wherein the sampling circuit comprises:
one or more time-to-digital converters (TDCs) configured to apply a plurality of thresholds for the plurality of threshold-based samples of the detected ToF measurement signal ([0077], [0081] - [0082]; Fig. 12 where multiple TDC's each apply a threshold to the measurement signal).
Regarding claim 4, Liu as modified above teaches the system of claim 3, wherein
the plurality of thresholds comprise voltage thresholds, and wherein the digital ToF signal sample data comprise time data with respect to the detected ToF measurement signal crossing each voltage threshold of the plurality of thresholds ([0059] - [0062]; Fig. 5 where the thresholds are voltage thresholds and include time data of when measurement signal crosses said thresholds).
Regarding claim 6, Liu as modified above teaches the system of claim 5 (1).
Liu does not teach use of hardware accelerators, which may specifically be a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC).
Wang teaches a method and apparatus for curve fitting a distribution of data, where at least one of the first curve fitting hardware accelerator or the second curve fitting hardware accelerator are implemented on a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC) configured to perform a curve fitting technique of the one or more curve fitting techniques (Col. 8 lines 43-62, Col. 15 line 52 - Col. 16 line 10; where curve fitting techniques may be operated on individual processing platforms (902-1), (902-2)...(902-N) which may include an ASIC or FPGA).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Liu to incorporate the teachings of Wang to specifically utilize FPGAs or ASICs, which can be integrated into a distance measuring device’s processor, for increasing the processing speed of curve fitting of TOF data with a reasonable expectation of success. Use of integrated accelerator circuits, such as FPGAs or ASICs, for improving processing times is known in the art, and therefore using one or more for curve fitting within a system as taught by Wang in the ToF system of Liu would have a predictable result of reducing processing and distance determination times. Additionally, use of integrated chips as part of a system as a whole is known to those of ordinary skill in the art to reduce the physical size requirement of a system.
Regarding claim 8, Liu as modified above teaches the system of claim 1, wherein
parallel processing circuitry configured to perform parallel processing of iterations of instances of the digital ToF signal sample data being processed by the sample processing circuit ([0270], where the number of receiving, sampling, and arithmetic circuits may be two or more, and therefore a second or more arithmetic circuit can be dedicated to parallel processing of non-linear curve fitting as discussed above.).
Regarding claim 10, Liu teaches a method for use in time of flight (ToF) distance measurement, the method comprising:
sampling a plurality of threshold-based samples of a detected ToF measurement signal ([0122] – [0126]; Fig. 10);
providing digital ToF signal sample data for the plurality of threshold-based samples of the detected ToF measurement signal ([0117] - [0118], [0265] - [0274]; Fig. 13, receiving circuit sends electrical signal to sampling circuit within detector (105) where input signals are sampled and compared to multiple thresholds);
and generating a signal waveform representing the detected ToF measurement signal by applying one or more curve fitting techniques to the digital ToF signal sample data ([0059] - [0062], [0093], [0120]; Fig. 5, where signal is compared to thresholds to create waveforms (520) and (530) and the system can further determine distance between the object and system).
Liu does not teach the specifics of the curve fitting techniques.
Wang teaches an apparatus for curve fitting a distribution of data, which utilizes dedicated processing for supporting curve fitting, wherein
the curve fitting techniques include a linear curve fitting technique and a non-linear curve fitting technique (Col. 10 lines 1-22, Col. 12 lines 4-26, Col. 16 line 61-Col. 17 line 18, ; Fig. 6A shows process (600) shows a linear function process and where Fib. 6B shows process (650) which entails a non-linear process and both may be completed by the system simultaneously).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Liu to incorporate the teachings of Wang to utilize a combined linear and non-linear curve fitting techniques for use in a ToF distance measuring device with a reasonable expectation of success. Use of many different curve fitting techniques, including linear and non-linear, is well known in the art, and therefore use of both simultaneously as taught by Wang in the ToF system of Liu would have a predictable result of reducing processing and distance determination times, specifically for curve fitting of digitized ToF sample signals which may not entirely be linear or non-linear in nature.
As Liu teaches both a system (as per independent claim 1) and method (as per independent claim 10), claim 11 is similarly rejected to claim 2.
As Liu teaches both a system (as per independent claim 1) and method (as per independent claim 10), claim 12 is similarly rejected to claim 3.
As Liu teaches both a system (as per independent claim 1) and method (as per independent claim 10), claim 13 is similarly rejected to claim 4.
Regarding claim 14, Liu as modified above teaches the method of claim 10, but does not teach use of hardware accelerators specifically for curve fitting purposes.
Wang teaches a method and apparatus for curve fitting a distribution of data, which utilizes dedicated processing for supporting curve fitting, wherein the generating the signal waveform representing the detected ToF measurement signal comprises:
generating the signal waveform representing the detected ToF measurement signal, at least in part, using one or more curve fitting hardware accelerators (Col. 15 line 52 - Col. 16 line 10, Col. 16 line 61 - Col. 17 line 2; where processes (600) and (650) are running on one or more processing devices, which may include dedicated circuits for curve fitting.).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Liu to incorporate the teachings of Wang to utilize hardware specifically for increasing the processing speed of curve fitting for use in a ToF distance measuring device with a reasonable expectation of success. Additionally, Liu notes that other processing circuits can be used in the system near the comparison circuit ([0084]). As use of integrated accelerator circuits for improving processing times is known in the art, using one or more for curve fitting within a system as taught by Wang in the ToF system of Liu would have a predictable result of reducing processing and distance determination times, specifically for curve fitting of digitized ToF sample signals.
As Liu teaches both a system (as per independent claim 1) and method (as per independent claim 10), claim 15 is similarly rejected to claim 6.
Regarding claim 16, Liu as modified above teaches the method of claim 14, but does not teach using both a linear and non-linear techniques for curve fitting.
Wang teaches use of two curve fitting techniques, which may be run on at least two curve fitting hardware accelerators (as taught in claim 14), wherein the one or more curve fitting hardware accelerators comprise
a first curve fitting hardware accelerator (Col. 8 line 63 - Col. 9 line 9, Col. 16 line 61 - Col. 17 line 2) configured to implement the linear curve fitting technique (Col. 10 lines 1-22; Fig. 6A where process (600) shows a linear function process and may be implemented on priorly noted first processing platform);
and a second curve fitting hardware accelerator (Col. 8 line 63 - Col. 9 line 9, Col. 16 line 61 - Col. 17 line 2) configured to implement the non-linear curve fitting technique (Col. 12 lines 4-26; Fig. 6B where process (650) shows a non-linear function process and may be implemented on priorly noted second processing platform);
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Liu to incorporate the teachings of Wang to utilize two dedicated processors, such as hardware accelerators or FPGAs, to separately implement linear and non-linear curve fitting techniques with a reasonable expectation of success. Use of integrated accelerator circuits, such as FPGAs or ASICs, for improving processing times is known in the art, and therefore using one or more for curve fitting within a system as taught by Wang in the ToF system of Liu would have a predictable result of reducing processing and distance determination times, especially allowing for potential simultaneous curve fitting within the receiving, sampling, and arithmetic circuits of Liu ([0270]).
As Liu teaches both a system (as per independent claim 1) and method (as per independent claim 10), claim 17 is similarly rejected to claim 8.
Regarding claim 19, Liu teaches a system for time of flight (ToF) distance measurement, the system comprising:
a sampling circuit having a signal detector in communication with one or more time-to- digital converters (TDCs) ([0265] - [0274]; Figs. 8, 12 control circuit (140) sends electrical signal from sensor (110) to sampling circuit and comparison circuit (130) which includes a plurality of TDCs), wherein the signal detector is configured to detect a ToF measurement signal and provide a detected ToF measurement signal to the one or more TDCs ([0059] - [0063]), wherein the one or more TDCs are configured to apply a plurality of thresholds and output digital ToF signal sample data for a plurality of threshold-based samples of the detected ToF measurement signal ([0117] - [0118]; Fig. 12 comparison circuit (130) within the control circuit (140) may include plurality of TDCs for signal point vs. threshold comparisons and output of digital waveform);
and a sample processing circuit in data communication with the sampling circuit ([0120]; Fig. 8 control circuit (140), in connection with comparison circuit (130)) are configured to apply one or more curve fitting techniques to the digital ToF signal sample data and generate a signal waveform representing the detected ToF measurement signal, and wherein the ToF-based distance computation logic is configured to determine a ToF distance measurement based on the signal waveform representing the detected ToF measurement signal ([0059] - [0062], [0093], [0120]; Fig. 5, where signal is compared to thresholds to create waveforms (520) and (530) and the system can further determine distance between the object and system).
Liu does not teach use of hardware accelerators specifically for curve fitting purposes, or the specifics of the curve fitting techniques.
Wang teaches an apparatus for curve fitting a distribution of data, which utilizes dedicated processing for supporting curve fitting, where the system has
One or more curve fitting hardware accelerators of the one or more curve fitting hardware accelerators (Col. 15 line 52 - Col. 16 line 10, Col. 16 line 61 - Col. 17 line 2; where processes (600) and (650) are running on one or more processing devices, which may include dedicated circuits for curve fitting.),
wherein the curve fitting techniques include a linear curve fitting technique and a non-linear curve fitting technique (Col. 10 lines 1-22, Col. 12 lines 4-26, Col. 16 line 61-Col. 17 line 18, ; Fig. 6A shows process (600) shows a linear function process and where Fib. 6B shows process (650) which entails a non-linear process and both may be completed by the system simultaneously),
a first curve fitting hardware accelerator (Col. 8 line 63 - Col. 9 line 9, Col. 15 line 52 - Col. 16 line 10, Col. 16 line 61 - Col. 17 line 2; where processes (600) and (650) are running on one or more processing devices, which may include dedicated circuits for accelerating curve fitting) configured to implement the linear curve fitting technique (Col. 10 lines 1-22; Fig. 6A where process (600) shows a linear function process and may be implemented on priorly noted first processing platform);
and a second curve fitting hardware accelerator (Col. 8 line 63 - Col. 9 line 9, Col. 15 line 52 - Col. 16 line 10, Col. 16 line 61 - Col. 17 line 2; where processes (600) and (650) are running on one or more processing devices, which may include dedicated circuits for accelerating curve fitting) configured to implement the non-linear curve fitting technique (Col. 12 lines 4-26; Fig. 6B where process (650) shows a non-linear function process and may be implemented on priorly noted second processing platform).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Liu to incorporate the teachings of Wang to utilize hardware specifically for increasing the processing speed of curve fitting, and combined linear and non-linear curve fitting techniques for use in a ToF distance measuring device with a reasonable expectation of success. Use of integrated accelerator circuits, such as FPGAs or ASICs, for improving processing times is known in the art, and therefore using one or more for curve fitting within a system as taught by Wang in the ToF system of Liu would have a predictable result of reducing processing and distance determination times, especially allowing for potential simultaneous curve fitting within the receiving, sampling, and arithmetic circuits of Liu ([0270]). Additionally, Liu notes that other processing circuits can be used in the system near the comparison circuit ([0084]). Use of many different curve fitting techniques, including linear and non-linear, is well known in the art, and therefore use of both simultaneously as taught by Wang in the ToF system of Liu would have a predictable result of reducing processing and distance determination times, specifically for curve fitting of digitized ToF sample signals which may not entirely be linear or non-linear in nature.
Claim(s) 9, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (hereinafter Liu, US 20210286051 A1), in view of Wang et al. (hereinafter Wang, US 10490288 B1) and further in view of Chhabra et al. (hereinafter Chhabra, US 20210018611 A1).
Regarding claim 9, Liu as modified above teaches the system of claim 1.
Liu does not teach use of a multi-point filter to increase signal-to-noise ratios, or use of a hardware accelerator implemented on a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC).
Wang teaches implementing a hardware accelerator, wherein the hardware accelerator is implemented on a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC) (Col. 8 lines 12-20, Col. 15 line 52 - Col. 16 line 10; where processing of peak data may be operated on individual processing platforms (902-1), (902-2)...(902-N) which may include an ASIC or FPGA, and may include processes such as noise or outlier determination).
Chhabra teaches an object and detection system and method which uses a multi-point filter configured to increase a signal-to-noise ratio (SNR) with respect to a ToF signal ([0103] - [0104]; Figs. 1, 3, where AI module (40) may complete method (300), where a waveform from a sensing device is received and pre-processed by a Gaussian filter to attempt to improve signal-to-noise ratios).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Liu to incorporate the teachings of Wang to utilize hardware specifically for increasing the processing speed of data filtering of peaks and Chhabra for use of a multi-point filter to increase signal-to-noise ratios in a ToF distance measuring device with a reasonable expectation of success. Liu notes that other processing circuits can be used in the system near the comparison circuit ([0084]). As use of integrated accelerator circuits for improving processing times is known in the art, using one or more for curve fitting within a system as taught by Wang in the ToF system of Liu would have a predictable result of reducing processing and distance determination times, specifically for curve fitting of digitized ToF sample signals. Liu additionally notes that data may be filtered to reduce noise via a preset threshold ([0066] – [0069]), and therefore would integrate use of the Gaussian filter of Chhabra with a predictable result of further increasing the signal-to-noise ratios of the data being processed.
Regarding claim 18, Liu as modified above teaches the method of claim 10, but does not teach use of a multi-point filter to increase signal-to-noise ratios.
Chhabra teaches an object and detection system and method which uses a multi-point filter configured to increase a signal-to-noise ratio (SNR) with respect to a ToF signal ([0103] - [0104]; Figs. 1, 3, where AI module (40) may complete method (300), where a waveform from a sensing device is received and pre-processed by a Gaussian filter to attempt to improve signal-to-noise ratios).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Liu to incorporate the teachings of Chhabra for use of a multi-point filter to increase signal-to-noise ratios in a ToF distance measuring device with a reasonable expectation of success. Liu notes that data may be filtered to reduce noise via a preset threshold ([0066] – [0069]), and therefore would integrate use of the Gaussian filter of Chhabra with a predictable result of further increasing the signal-to-noise ratios of the data before being further processed.
Regarding claim 20, Liu as modified above teaches the system of claim 19, which further comprises:
a light source configured to generate laser pulses, wherein the ToF measurement signal comprises a laser pulse generated by the light source ([0191]; Figs. 13, 14, pulsed laser diode may be emitter (103)), wherein the signal detector comprises a receiver configured to detect a laser pulse generated by the light source and reflected by a target of the ToF distance measurement ([0006], [0083], [0273]; Fig. 13, returned light collected by detector (105));
a beam steerer configured to operate under control of a beam steering controller to direct laser pulses generated by the light source as ToF distance measurement signals for illuminating a target of the ToF distance measurement ([0282]; Fig. 13, scanning module (102) may include further optical component (115), which rotates to change direction of projected light which may be connected to driver (117));
Liu does not teach use of a multi-point filter to increase signal-to-noise ratios.
Chhabra teaches an object and detection system and method which uses a multi-point filter configured to increase a signal-to-noise ratio (SNR) with respect to a ToF signal ([0103] - [0104]; Figs. 1, 3, where AI module (40) may complete method (300), where a waveform from a sensing device is received and pre-processed by a Gaussian filter to attempt to improve signal-to-noise ratios).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Liu to incorporate the teachings of Chhabra for use of a multi-point filter to increase signal-to-noise ratios in a ToF distance measuring device with a reasonable expectation of success. Liu notes that data may be filtered to reduce noise via a preset threshold ([0066] – [0069]), and therefore would integrate use of the Gaussian filter of Chhabra with a predictable result of further increasing the signal-to-noise ratios of the data before being further processed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liu et al. (US 20190025413 A1) teaches systems and methods for optical distance measurement where the system comprises a field-programmable gate array (FPGA) coupled to the light sensor, where the FPGA is configured to convert the analog pulse signal to a plurality of digital signal values, and generate a plurality of time measurements corresponding to the plurality of digital signal values.
Velazquez et al. (US 20150032788 A1) teaches a linearizer for signal processing which operates on an analog-to-digital converter to provide linearization, or real-time parallel processing, which eclipses the processing power of digital signal processors (DSP), field programmable gate arrays (FPGA), or application specific integrated circuits (ASIC) alone.
Zimmer et al. (US 20210278540 A1) teaches a noise filtering system and method for LiDAR applications, which integrates a field programmable gate array (FPGA) that performs processing of the serialized return pulse data, and signal-to-noise ratio filtering.
Lin et al. (US 20230073970 A1) teaches an image processing method for images captured by devices such as cameras, which includes scaling procedures which may include linear and/or non-linear curve fitting.
Beri et al. (US 20180033168 A1) teaches an image processing system which may use both linear and non-linear curves in the processing of images and reducing dynamic spread.
Potyrailo et al. (US 20030130823 A1) teaches a method and apparatus for data processing from processes such as imaging from a camera where a mathematical transform analysis is applied to reduce integration time, where the multivariate analysis comprises neural networks analysis, principal components analysis, partial least squares analysis, linear multivariate analysis, or nonlinear multivariate analysis.
Liu et al. (US 20210270965 A1) teaches a LIDAR system which determines time of flight (ToF) signals, and utilizes a mapping relationship for a distance-gain curve where both a linear and non-linear compensation curves are used for determining gain values.
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.
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/K.M.R./Examiner, Art Unit 3645
/HELAL A ALGAHAIM/SPE , Art Unit 3645
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