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 .
Information Disclosure Statement
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered. The listed reference is U.S. Pat. No. 9,322,937.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 10-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Banerjee et al. (US 2021/0133589).
With respect to claim 10, Banerjee et al. teaches a method for estimating a signal measured on a pixel of a detector, the detector comprising a plurality of electrodes, forming pixels, connected to a detector material, the detector material being a semiconductor, each electrode being configured to collect charge carriers moving, through the detector material, under the effect of an electric field, following an interaction of ionising radiation in the detector material, the method comprising:
irradiating the detector with X-ray or gamma-ray photons (Para. 24-27, 75), so as to generate interactions in the detector material, with each interaction forming a detection signal (Sα) measured by at least one electrode (Para. 24-27);
associating, for various detected interactions, a state vector, comprising at least a position, a charge, and the detection signal, with the state vector transitioning from an initial state, when the interaction occurs, to a final state, when the electrons generated by the interaction are collected by at least one electrode (Para. 24-27);
wherein the method comprises, for each detected interaction:
(i) initialising the initial state vector (Para. 24-27);
(ii) implementing, based on the initial state, or on an initial state resulting from a previous iteration, a charge carrier propagation model in the semiconductor material, in order to estimate the state vector in the final state, with the propagation model being based on a parameter vector, the parameter vector parameterising at least one transport property of the charge carriers through the detector material, towards the electrodes (Para. 29);
(iii) computing an error, representing a difference between the estimated detection signal state vector in the final state and the measured detection signal (Para. 24-27);
(iv) backpropagating a gradient of the error from the final state to the initial state, with the gradient of the error being computed relative to at least one term of the state vector, and relative to a plurality of parameters of the parameter vector (Para. 49);
(v) updating the state vector, at the initial statet, and the parameter vector (Para. 24-27), and repeating steps (ii) to (v) until a criterion for stopping the iterations is met; and wherein:
steps (i) to (v) are implemented by a processing unit connected to the detector (Para. 56).
With respect to claim 11, Banerjee et al. teaches the method according to Claim 10, wherein: steps (i) to (v) are implemented for various interactions, so as to update the parameter vector each time steps (i) to (v) are implemented; the parameter vector is then updated by combining the parameter vectors updated during each interaction (Para. 35-38).
With respect to claim 12, Banerjee et al. teaches the method according to Claim 10, wherein the detector material is discretised into voxels, and wherein the parameter vector comprises, for each voxel, a transport property for the charge carriers in the detector (Para. 34).
With respect to claim 13, Banerjee et al. teaches the method according to Claim 12, wherein the transport property for charge carriers comprises: a value of the electric field; and/or a gradient of the electric field in at least one direction; and/or a value of a divergence of the electric field; and/or a probability of trapping in the voxel (Para. 26-34).
With respect to claim 14, Banerjee et al. teaches the method according to Claim 11, wherein: for each interaction, the time between the initial state and the final state is discretised into time steps (Para. 26-34); step (ii) comprises a digital integration of an evolution function, with the evolution function representing a temporal evolution of the position and of the charge of the charge carriers, and; an evolution of each detection signal; wherein the digital integration is successively carried out between each time step, between the initial state and the final state (Para. 26-34).
With respect to claim 15, Banerjee et al. teaches the method according to Claim 14, wherein step (iv) comprises a digital integration of an adjoint propagation equation, translating a temporal evolution of the gradient of the error, with the digital integration being successively carried out between each time step, between the final state and the initial state (Para. 37-41).
With respect to claim 16, Banerjee et al. teaches a method for training a supervised artificial intelligence algorithm, configured to simulate a response of a detector (Para. 37-41);
wherein the detector comprises a plurality of electrodes, forming pixels, connected to a semiconductor material, each pixel being configured to collect charge carriers moving, through the semiconductor material, under the effect of an electric field, following an interaction of ionising radiation in the semiconductor material (Para. 26-34), wherein the method
comprises: a) defining a position of an interaction in the detector and energy released during said interaction; b) estimating a detection signal measured by at least one electrode of the detector, by implementing the steps of the method according to Claim 10; c) repeating steps a) and b) so as to form a database connecting, for each defined interaction, the measured signal to at least one pixel; d) using the database to carry out supervised learning of the artificial intelligence algorithm (Para. 37-41).
With respect to claim 17, Banerjee et al. teaches the method according to Claim 16, wherein the artificial intelligence algorithm is of the multilayer perceptron type (Para. 37-41).
With respect to claim 18, Banerjee et al. teaches a detector, comprising a plurality of electrodes, forming pixels, connected to a semiconductor material, with each pixel being configured to collect charge carriers moving, through the semiconductor material, under the effect of an electric field, following an interaction of ionising radiation in the semiconductor material (Para. 75-81); wherein the detector is connected to a processing unit configured to implement steps (i) to (v) of the method according to claim 10.
With respect to claim 19, Banerjee et al. teaches a detector, comprising a plurality of electrodes, forming pixels, connected to a semiconductor material, with each pixel being configured to collect charge carriers moving, through the semiconductor material, under the effect of an electric field, following an interaction of ionising radiation in the semiconductor material (Para. 75-81); wherein the detector is connected to a processing unit configured to estimate energy released by the interaction and/or a position of the interaction wherein the processing unit implements a supervised artificial intelligence algorithm that is trained according to the method of Claim 16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wang (US 2024/0346628), Banerjee et al. (US 12524666) and Siow et al. (US 11699231) teach using unsupervised training of models for X-ray detectors.
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DAVID J. MAKIYA
Supervisory Patent Examiner
Art Unit 2884
/DAVID J MAKIYA/Supervisory Patent Examiner, Art Unit 2884
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