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 .
Claims 1 – 20 are presented for examination.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 7-8, 10-12, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yanoff et al. (US 2020/0193654 A1; pub. Jun. 18, 2020) in view of Byoungil et al. “Deep Learning-Based Pulse Height Estimation for Separation of Pile-Up Pulses from NaI(TI) Detector”, IEEE Transactions on Nuclear Science, Vol. 69, No. 6, Jun. 2022, pg.1344 - 1351.
Regarding claim 1, Yanoff et al. disclose: A computer-implemented method comprising: receiving at least one signal characterizing a detection event (para. [0051]); generating sampled signal data based on sampling the at least one signal (para. [0051]); applying a trained machine learning process to the sampled signal data (para. [0068], [0119], [0124])
Li et al. are silent about: based on the application of the trained machine learning process, generating pulse data characterizing a plurality of pulses; and transmitting the pulse data characterizing the plurality of pulses.
In a similar field of endeavor Byoungil et al. disclose: based on the application of the trained machine learning process (pg.1344 Abstract), generating pulse data characterizing a plurality of pulses; and transmitting the pulse data characterizing the plurality of pulses (pg.1344 Abstract, pg.1346 col.2 last para., pg.1350 col.2) motivated by the benefits for improved pulse restoration and separation (Byoungil et al. pg.1350 col.2 2nd para.).
In light of the benefits for improved pulse restoration and separation as taught by Byoungil et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Yanoff et al. with the teachings of Byoungil et al.
Regarding claim 2, Yanoff et al. and Byoungil et al. disclose: the pulse data comprises an energy value for each of the plurality of pulses (the claim is rejected on the same basis as claim 1 because pulses are representative of energy).
Regarding claim 3, Byoungil et al. disclose: the pulse data comprises a time offset value characterizing a time offset between the plurality of pulses (pg.1349 Table IV & V) motivated by the benefits for improved pulse restoration and separation (Byoungil et al. pg.1350 col.2 2nd para.).
Regarding claim 7, Yanoff et al. and Byoungil et al. disclose: applying the trained machine learning process to the sampled signal data comprises: reading model parameters from a memory device; executing a machine learning model based on the model parameters; and inputting the sampled signal data to the executed machine learning model (the claim is rejected on the basis as claim 1).
Regarding claim 8, Yanoff et al. and Byoungil et al. disclose: generating synthetic signals; training the machine learning model based on the synthetic signals; reading the model parameters from the trained machine learning model; and storing the model parameters in the memory device (the claim is rejected on the basis as claim 1, especially abstract of Byoungil et al. teaches simulated pulses = synthetic signals).
Regarding claim 10, Yanoff et al. and Byoungil et al. disclose: the plurality of pulses consists of two pulses (the claim is rejected on the basis as claim 1, especially abstract of Byoungil et al.).
Regarding claim 11, Yanoff et al. and Byoungil et al. disclose: receiving the at least one signal from a scanner of an image scanning system (the claim is rejected on the basis as claim 1, especially abstract of Byoungil et al.).
Regarding claim 12, Yanoff et al. and Byoungil et al. disclose: the at least one signal comprises a first signal that characterizes energy levels of the detection event (the claim is rejected on the basis as claim 1, especially abstract of Byoungil et al.).
Regarding claim 15, Yanoff et al. and Byoungil et al. disclose: A non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: receiving at least one signal characterizing a detection event; generating sampled signal data based on sampling the at least one signal; applying a trained machine learning process to the sampled signal data and, based on the application of the trained machine learning process, generating pulse data characterizing a plurality of pulses; and transmitting the pulse data characterizing the plurality of pulses (the claim contains the save substantive limitations as claim 1, therefore the claim is rejected on the same basis).
Claims 4-6, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Yanoff et al. (US 2020/0193654 A1; pub. Jun. 18, 2020) in view of Byoungil et al. “Deep Learning-Based Pulse Height Estimation for Separation of Pile-Up Pulses from NaI(TI) Detector”, IEEE Transactions on Nuclear Science, Vol. 69, No. 6, Jun. 2022, pg.1344 – 1351 and further in view of Konno (US 2017/0231584 A1; pub. Aug. 17, 2017).
Regarding claim 4, the combined references are silent about: generating a first time for a first pulse of the plurality of pulses based on sampling a system time; and generating a second time for a second pulse of the plurality of pulses based on the first time and the time offset.
In a similar field of endeavor Konno discloses: changing the sampling time (para. [0250]-0253]) motivated by the benefits for improving the quantitativeness of obtained image quality (Konno para. [0270]).
In light of the benefits for improving the quantitativeness of obtained image quality as taught by Konno, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Yanoff et al. and Byoungil et al. with the teachings of Konno to generate a first time for a first pulse of the plurality of pulses based on sampling a system time; and generate a second time for a second pulse of the plurality of pulses based on the first time and the time offset.
Regarding claim 5, the combination of Yanoff et al., Byoungil et al. and Konno disclose: generating image measurement data based on the energy value for each of the plurality of pulses and the time offset value characterizing the time offset between the plurality of pulses (the claim is rejected on the same basis as claim 4).
Regarding claim 6, Yanoff et al. disclose: the image measurement data is positron emission tomography (PET) measurement data (para. [0034]).
Regarding claim 16, the combination of Yanoff et al., Byoungil et al. and Konno disclose: the pulse data comprises an energy value for each of the plurality of pulses and a time offset value characterizing a time offset between the plurality of pulses, and wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform operations comprising: generating a first time for a first pulse of the plurality of pulses based on sampling a system time; and generating a second time for a second pulse of the plurality of pulses based on the first time and the time offset (the claim is rejected on the same basis as claim 4).
Regarding claim 17, the combination of Yanoff et al., Byoungil et al. and Konno disclose: the instructions, when executed by the at least one processor, further cause the at least one processor to perform operations comprising generating image measurement data based on the energy value for each of the plurality of pulses and the time offset value characterizing the time offset between the plurality of pulses (the claim is rejected on the same basis as claim 5).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Yanoff et al. (US 2020/0193654 A1; pub. Jun. 18, 2020) in view of Byoungil et al. “Deep Learning-Based Pulse Height Estimation for Separation of Pile-Up Pulses from NaI(TI) Detector”, IEEE Transactions on Nuclear Science, Vol. 69, No. 6, Jun. 2022, pg.1344 – 1351 and further in view of Connell et al. (US 12,494,287 B1; pub. Dec. 9, 2025).
Regarding claim 9, the combined references are silent about: the machine learning model is a Random Forrest model.
In a similar field of endeavor Connell et al. disclose: the machine learning model is a Random Forrest model (col.20 L32-35) motivated by the benefits for high accuracy and robustness to overfitting.
In light of the benefits for high accuracy and robustness to overfitting, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Yanoff et al. and Byoungil et al. with the teachings of Connell et al.
Claims 13 - 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yanoff et al. (US 2020/0193654 A1; pub. Jun. 18, 2020) in view of Byoungil et al. “Deep Learning-Based Pulse Height Estimation for Separation of Pile-Up Pulses from NaI(TI) Detector”, IEEE Transactions on Nuclear Science, Vol. 69, No. 6, Jun. 2022, pg.1344 – 1351 and further in view of Teshigawara (US 2016/0054455 A1; pub. Feb. 25, 2016).
Regarding claim 13, the combined references are silent about: the at least one signal comprises a second signal that characterizes a first dimension location of a crystal that detected the detection event.
In a similar field of endeavor Teshigawara discloses: the at least one signal comprises a second signal that characterizes a first dimension location of a crystal that detected the detection event (para. [0096], [0104]) motivated by the benefits for images with an improved spatial resolution.
In light of the benefits for images with an improved spatial resolution, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Yanoff et al. and Byoungil et al. with the teachings of Teshigawara.
Regarding claim 14, the combination of Yanoff et al., Byoungil et al. and Teshigawara disclose: the at least one signal comprises a third signal that characterizes a second dimension location of the crystal that detected the detection event (the claim is rejected on the same basis as claim 13).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Yanoff et al. (US 2020/0193654 A1; pub. Jun. 18, 2020) in view of Byoungil et al. “Deep Learning-Based Pulse Height Estimation for Separation of Pile-Up Pulses from NaI(TI) Detector”, IEEE Transactions on Nuclear Science, Vol. 69, No. 6, Jun. 2022, pg.1344 – 1351 and further in view of Bradbury et al. (US 2015/0182118 A1; pub. Jul. 2, 2015).
Regarding claim 18, Yanoff et al. and Byoungil et al. disclose all the limitations of claim 18 (see rejection of claim 1) except for: a transceiver; and at least one processor communicatively coupled to the transceiver and to the memory device, the at least one processor configured to execute the instructions to: receive, via the transceiver, at least one signal characterizing a detection event.
In a similar field of endeavor Bradbury et al. disclose: a transceiver (para. [0133], [0289]); and at least one processor communicatively coupled to the transceiver and to the memory device, the at least one processor configured to execute the instructions (para. [0289]) to: receive, via the transceiver, at least one signal characterizing a detection event (para. [0215]) motivated by the benefits for imaging with improved signal to noise ratio (Bradbury et al. para. [0006]).
In light of the benefits for imaging with improved signal to noise ratio as taught by Bradbury et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Yanoff et al. and Byoungil et al. with the teachings of Bradbury et al.
Claims 19 -20 are rejected under 35 U.S.C. 103 as being unpatentable over Yanoff et al. (US 2020/0193654 A1; pub. Jun. 18, 2020) in view of Byoungil et al. “Deep Learning-Based Pulse Height Estimation for Separation of Pile-Up Pulses from NaI(TI) Detector”, IEEE Transactions on Nuclear Science, Vol. 69, No. 6, Jun. 2022, pg.1344 – 1351 in view of Bradbury et al. (US 2015/0182118 A1; pub. Jul. 2, 2015) and further in view of Konno (US 2017/0231584 A1; pub. Aug. 17, 2017).
Regarding claim 19, Byoungil et al. disclose: the pulse data comprises an energy value for each of the plurality of pulses and a time offset value characterizing a time offset between the plurality of pulses (pg.1349 Table IV & V). The combined references are silent about: the at least one processor is configured to execute the instructions to: generate a first time for a first pulse of the plurality of pulses based on sampling a system time; and generate a second time for a second pulse of the plurality of pulses based on the first time and the time offset.
In a similar field of endeavor Konno discloses: changing the sampling time (para. [0250]-0253]) motivated by the benefits for improving the quantitativeness of obtained image quality (Konno para. [0270]).
In light of the benefits for improving the quantitativeness of obtained image quality as taught by Konno, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Yanoff et al., Byoungil et al. and Bradbury et al. with the teachings of Konno to: generate a first time for a first pulse of the plurality of pulses based on sampling a system time; and generate a second time for a second pulse of the plurality of pulses based on the first time and the time offset.
Regarding claim 20, the combination of Yanoff et al., Byoungil et al., Bradbury et al. and Konno disclose: the at least one processor is configured to execute the instructions to generate image measurement data based on the energy value for each of the plurality of pulses and the time offset value characterizing the time offset between the plurality of pulses (the claim is rejected on the same basis as claim 19).
Conclusion
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/MAMADOU FAYE/Examiner, Art Unit 2884
/UZMA ALAM/Supervisory Patent Examiner, Art Unit 2884