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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-16 in the reply filed on 6/23/2026 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims are directed to the terms “non-imaging system” but does not provide any further clarification on type of “non-imaging probes”. Claims do not further define the abbreviation “DSLVM”. Claims do not specify the type of treatment or target anatomy that is being considered with respect to the “selected treatment”.
“Transcranial ultrasound systems (TUS) and methods use domain-specific large vision models (DSLVM) artificial intelligence systems to improve the efficacy of non-imaging probes. A positioning DSLVM assists an operator in improving the placement of the probe on a patient's head. The positioning DSLVM uses a pre-procedure MRI of the patient's head, target dose plan, target anatomy, and the probe's position information on the scalp, and it outputs the control parameters for the probe's beamformer. A segmenting DSLVM helps an operator with the optimal initial placement of the non-imaging probe by highlighting anatomical structures in color” [0010].
“In this application, Type 1 annular arrays and Type 2 low-element-count non-imaging arrays collectively are referred to as “simple probes” to distinguish them from probes capable of imaging inside the skull to provide stimulation guidance. Simple probes may be used for tFUS (transcranial focused ultrasound) or TUS treatment if some means other than ultrasound can guide the stimulation beam. Referring to FIG. 2, System 200 includes simple probes 210. Associated with probe 210 are the electronics needed to control the US probe, such as beamformer, etc. Probe 210 is placed on the user, subject, or patient's head. User, subject, and patient are used interchangeably and have the same meaning in this application. Neuro-Navigation 230 computes the position and orientation (pose) of probe 210 on the user's head” [0014].
As per the specification, the disclosure is specific to the use Transcranial Focused Ultrasound System (tFUS) using domain-specific large vision models where the positioning DSLVM assists the operator in improving placement of probe on a patient’s head where the target anatomy is scalp. Disclosure further states the use of “simple probes” used for transcranial focused ultrasound or TUS treatment where the probes are placed on the patient’s head.
The claim language terms (as set forth above) are considered indefinite as the claims do not clarify the terms with respect to the type of probe, the target anatomy, the full form of the learning model, the type of treatment and how these elements are connected to provide the adjusted location of the probe.
The dependent claims do not provide further clarity and therefore stand rejected under 112(b).
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) 1-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (2024/0164758) in view of Vega Romero et al. (2024/0260946).
With respect to claim 1, Wu et al. teach of a non-imaging system and method for ultrasonically treating a target anatomy comprising an ultrasonic probe 106, a control subsystem in communication with an input device to receive patient record repository that stores diagnostic and/or treatment histories and based on past scans, body geometry of the patient and other preferences, the processing device may determine the parameters and/or configuration of a device such as the position and/or orientation of the probe 106 [0018, 0019, 0033, 0041, 0042]. Wu et al. teach of determining the probe location based on infrared based navigation system [0018]. Wu et al. teach of generating an adjusted probe location using a large vision model DSLVM such as a convolution neural network based on the probe location, and the dose distribution specification associated with ultrasonic field emitted by probe or the operating parameters of the ultrasound device [0041, 0042]. Wu et al. teach of the neural network outputting parameters to program the probe where the training process includes processing an input using presently assigned parameters of the neural network and making a prediction for a desired result and adjusting the presently assigned network parameters and transmitting the adjusted location to the input device [0042].
With respect to claim 1, Wu et al. teach of receiving treatment history from an input device [0033] but do not teach of the specific treatments or generating a probe location based on the selected treatment. In a similar field of endeavor Vega Romero et al. teach of directing treatment for a variety of diseases and conditions by providing user guidance to achieve a higher quality ultrasound image relative to the initial images by adjusting the power of the probe or adjusting percentage and/or density of transducers that are activated in the probe and therefor selecting treatment for a plurality of beams emitted by the probe to provide guidance to the user of the ultrasound via a recurrent neural network or convolutional neural network that maps the ultrasound image to a percentage or distribution of active transducers [0128, 0131]. Under broadest reasonable interpretation, the combination of the references teach of a computing and simulation subsystem to generate an ultrasonic field emitted by the probe based on the treatment history, the position of the probe, and the patient data and learning network that generates an adjusted location associated with probe based on the dose distribution specification associated with ultrasonic field emitted by the probe, selected treatment, and position of the probe. It would have therefore been obvious to one of ordinary skill in the art to use the teaching by Vega Romero et al. to modify Wu et al. to determine one or more quality requirements for the first scan to improve overall diagnostic quality [Vega Romero et al., 0015, 0016].
With respect to claims 2 and 3, Wu et al. in view of Vega Romero et al. teach of generating an adjusted probe location using a large vision model such as a convolution neural network based on the probe location, 3D MRI model, and the dose distribution specification or the operating parameters of the ultrasound device [0041, 0042]. Wu et al. teach of the neural network outputting parameters to program the probe where the training process includes processing an input using presently assigned parameters of the neural network and making a prediction for a desired result and adjusting the presently assigned network parameters [0042]. Vega Romero et al. also teach of the convolution neural network providing guidance on the set of parameters to program a transducer associated with the probe [0128, 0131]. It would have therefore been obvious to one of ordinary skill in the art to use the teaching by Vega Romero et al. to modify Wu et al. to determine one or more quality requirements for the first scan to improve overall diagnostic quality [Vega Romero et al., 0015, 0016].
With respect to claims 4 and 8, Wu et al. do not teach of the specific treatments. Vega Romero et al. teach of the providing user guidance to achieve a higher quality ultrasound image relative to the initial images by adjusting the power of the probe or adjusting percentage and/or density of transducers that are activated in the probe and therefor selecting treatment for a plurality of beams emitted by the probe to provide guidance to the user of the ultrasound via a recurrent neural network or convolutional neural network that maps the ultrasound image to a percentage or distribution of active transducers [0128, 0131]. Vega Romero et al. therefore teach of activating one subset of transducers during the first scan and then activating a second subset of transducers during the second scan [0134, 0137]. It would have therefore been obvious to one of ordinary skill in the art to use the teaching by Vega Romero et al. to modify Wu et al. to determine one or more quality requirements for the first scan to improve overall diagnostic quality [Vega Romero et al., 0015, 0016].
With respect to claims 5 and 6, Wu et al. teach of commands to adjust the FOV of the sensor or transmit a command to change the resolution at which the sensor takes images [0051] and other commands with respect to the sensing device [0057, 0058] and generating a visual representation showing the alignment of the image and the 3D model [0046] but do not explicitly teach of generating a recommendation to move the probe based on visual or auditory prompts. Vega Romero et al. teach of a system and method for controlling an ultrasound probe including providing guidance through audio or visual mechanisms for one or more recommended movements to be executed by the ultrasound probe [0162]. Vega Romero et al. teach of the computing device determining the first anatomic plane of the first target anatomical structure by using the first imaging data as input data to a trained neural network that is configured to generate as output data one or more angles of the anatomic plane of the target anatomical structure or a distance between the anatomic plane and an origin of the model of the target anatomical structure [0159]. It would have therefore been obvious to one of ordinary skill in the art to use the teaching by Vega Romero et al. to modify Wu et al. to determine one or more quality requirements for the first scan to improve overall diagnostic quality [Vega Romero et al., 0015, 0016].
With respect to claim 7, Wu et al. in view of Vega Romero et al. teach of determining parameters and/or configurations of a ultrasound probe to such as the position and/or orientation of the probe based on past medical scans of the 3D model MRI image, probe location [0033] and treatment history of the transducers (as set forth by Vega Romero).
With respect to claim 9, Wu et al. teach of a system and method of obtaining a structural magnetic resonance image of a patient or a 3D model for a patient [0017], providing the MRI to an input device and receiving from the input device, a selected treatment associated with an ultrasonic probe 106 or the database may comprise a patient record repository that stores diagnostic and/or treatment histories and based on past scans, body geometry of the patient and other preferences, the processing device may determine the parameters and/or configuration of a device such as the position and/or orientation of the probe 106 [0033].
With respect to claims 9 and 10, Wu et al. teach of receiving treatment history from an input device but do not teach of the specific treatments or generating a probe location based on the selected treatment. In a similar field of endeavor Vega Romero et al. teach of the providing user guidance to achieve a higher quality ultrasound image relative to the initial images by adjusting the power of the probe or adjusting percentage and/or density of transducers that are activated in the probe and therefor selecting treatment for a plurality of beams emitted by the probe to provide guidance to the user of the ultrasound via a recurrent neural network or convolutional neural network that maps the ultrasound image to a percentage or distribution of active transducers [0128, 0131]. With respect to claim 10, Wu et al. in view of Vega Romero et al. teach of determining parameters and/or configurations of a ultrasound probe to such as the position and/or orientation of the probe based on past medical scans of the 3D model MRI image, probe location [0033] and treatment history of the transducers (as set forth by Vega Romero). It would have therefore been obvious to one of ordinary skill in the art to use the teaching by Vega Romero et al. to modify Wu et al. to determine one or more quality requirements for the first scan to improve overall diagnostic quality [Vega Romero et al., 0015, 0016].
With respect to claim 11, Wu et al. in view of Vega Romero et al. teach of determining the probe location based on infrared based navigation system [0018].
With respect to claims 12 and 13, Wu et al. in view of Vega Romero et al. teach of generating an adjusted probe location using a large vision model such as a convolution neural network based on the probe location, 3D MRI model, and the dose distribution specification or the operating parameters of the ultrasound device [0041, 0042]. Wu et al. teach of the neural network outputting parameters to program the probe where the training process includes processing an input using presently assigned parameters of the neural network and making a prediction for a desired result and adjusting the presently assigned network parameters [0042].
With respect to claim 14, Wu et al. do not teach of the specific treatments. Vega Romero et al. teach of the providing user guidance to achieve a higher quality ultrasound image relative to the initial images by adjusting the power of the probe or adjusting percentage and/or density of transducers that are activated in the probe and therefor selecting treatment for a plurality of beams emitted by the probe to provide guidance to the user of the ultrasound via a recurrent neural network or convolutional neural network that maps the ultrasound image to a percentage or distribution of active transducers [0128, 0131]. Vega Romero et al. therefore teach of activating one subset of transducers during the first scan and then activating a second subset of transducers during the second scan [0134, 0137]. It would have therefore been obvious to one of ordinary skill in the art to use the teaching by Vega Romero et al. to modify Wu et al. to determine one or more quality requirements for the first scan to improve overall diagnostic quality [Vega Romero et al., 0015, 0016].
With respect to claims 15 and 16, Wu et al. teach of commands to adjust the FOV of the sensor or transmit a command to change the resolution at which the sensor takes images [0051] and other commands with respect to the sensing device [0057, 0058] and generating a visual representation showing the alignment of the image and the 3D model [0046] but do not explicitly teach of generating a recommendation to move the probe based on visual or auditory prompts. Vega Romero et al. teach of a system and method for controlling an ultrasound probe including providing guidance through audio or visual mechanisms for one or more recommended movements to be executed by the ultrasound probe [0162]. Vega Romero et al. teach of the computing device determining the first anatomic plane of the first target anatomical structure by using the first imaging data as input data to a trained neural network that is configured to generate as output data one or more angles of the anatomic plane of the target anatomical structure or a distance between the anatomic plane and an origin of the model of the target anatomical structure [0159]. It would have therefore been obvious to one of ordinary skill in the art to use the teaching by Vega Romero et al. to modify Wu et al. to determine one or more quality requirements for the first scan to improve overall diagnostic quality [Vega Romero et al., 0015, 0016].
Conclusion
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BR
/BAISAKHI ROY/Primary Examiner, Art Unit 3797
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