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 .
Response to Amendment
Applicant's response to the last office action, filed June 8, 2026 has been entered and made of record. Claims 1, 6-7, 10-12, 14-16, and 20 are amended. Claims 1-20 are pending in this application for examination.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shamir et al, (US-PGPUB 20210196207) in view of Shamir et al, (US-PGPUB 20210196943), (thereafter referenced as “Shamir 843”); and further in view of Deslauriers et al, (US-PGPUB 20210299440)
Regarding claim 1, Shamir discloses a computer-implemented method for
generating at least one transducer location for delivering tumor treating fields to a subject, (see at least: Par. 0003, “method”, and Figs. 8A, 9A, 9B, 9C, 9D, and Par. 0055-0062, “measuring tumor location”, and Par. 0068, “TTFields delivery to a patient”), the method comprising:
obtaining a medical image of the subject, the medical image having a plurality of voxels, the medical image representing a plurality of tissue types of the subject, wherein at least one voxel is associated with each tissue type, (see at least: Par. 0038, the image data may include 3D data obtained from or generated by a 3D scanner, “i.e., implicitly obtaining a medical image of the subject”; and from Par. 0004, determining, for a patient, a set of image data, wherein the set of image data comprises a plurality of voxels, “i.e., the medical image having a plurality of voxels”; and from Par. 0067, segmenting the MRI data identifies the tissue type in each voxel, and electric properties may be assigned to each tissue type based on empirical data, “i.e., at least one voxel is associated with each tissue type”);
determining, using a trained machine learning model and the medical image of the subject, conductivities for the tissue types of the subject in the medical image, the trained machine learning model trained with medical images of a plurality of other subjects learning to associate dielectric properties with a patient model and/or determine dielectric (e.g., standard electrical properties, etc.) of tissues … , using medical images from a plurality of patients …, [i.e., determining, using a trained machine learning model and the medical image of the subject, “using trained artificial intelligence and/or machine learning”, conductivities for the tissue types of the subject in the medical image, “standard electrical properties”, the trained machine learning model trained with medical images of a plurality of other subjects, “training artificial intelligence and/or machine learning with medical images from a plurality of patients”]); and
identifying a location of a tumor in the medical image of the subject, (see at least: Figs. 9C-9D, and Par. 0059-0060, MRI measurements of the portion of the patient that is to receive the transducer arrays may be determined automatically, for example by way of artificial intelligence techniques, or may be performed manually, for example, by way of a physician; and from Par. 0064, the MRI measurements may comprise one or more tumor measurements, such as tumor location measurements, [i.e., identifying a location of a tumor in the medical image of the subject, “implicit by performing MRI measurements comprising one or more tumor measurements, such as tumor location measurements”]); and
generating the at least one transducer location for delivering the tumor treating fields to the subject based on the conductivities for the tissue types of the subject in the medical image and the location of the tumor in the medical image, (see at least: Par. 0056, analyzing the imaging data 610, such as MRI imaging data, by the patient modeling application 608 to identify a region of interest that comprises a tumor, …; and from Par. 0058-0065, the patient modeling application 608 may be configured to determine a desired (e.g., optimal) transducer array layout for a patient based on the location and extent of the tumor, …. , to generate the configuration which delivers maximal electric field intensity to the tumor site, [i.e., generating the at least one transducer location, “identifying a region of interest that comprises a tumor”, for delivering the tumor treating fields to the subject, “delivering maximal electric field intensity to the tumor site”, based on the conductivities for the tissue types of the subject in the medical image and the location of the tumor in the medical image, ”implicitly based on the desired (e.g., optimal) transducer array layout for a patient and the location and extent of the tumor”]).
Shamir does not expressly disclose that the machine learning model is trained with resistances obtained from an application of tumor treating fields to the other subjects.
However, Shamir 843 discloses training the machine learning model with features obtained from an application of tumor treating fields to the other subjects, (see at least: Fig. 1, and Par. 0081-0082, features that are related to the electric field distribution may be extracted from the patient MRI, where the extracted features may be incorporated in a compact model to quickly estimate, predict, and/or determine an electric field and/or electric field distributions for transducer arrays by providing a precomputed mapping from the MRI features to electric field values, [i.e., training the machine learning model with features obtained from an application of tumor treating fields to the other subjects, “implicit by training the ML model with features related to the electric field distribution extracted from the patient MRI”]).
Shamir and Shamir 843 are combinable because they are both concerned with tumor characterization. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Shamir, to train the machine learning with features extracted from patients that underwent TTFields therapy, as though by Shamir 843, in order to classify features extracted from 3D MRI images to enable fast approximation of electric field distribution based on a predictive model, (Shamir 843, Par. 0083)
Although Shamir discloses training the machine learning with the features related to the electric field distribution; the combination of Shamir and Shamir 843 as whole does not expressly disclose that the features comprise electrical resistances.
However, Deslauriers et al disclose schematic example, depicting a resistance that is encountered when one of the electrodes is positioned in contact with either blood, CSF, or lymph, and the remaining electrodes are conventional electrodes 40 on the person's skin for establishing an electrical path to the tissue, [which implicitly enables measuring the electrical resistances based on applying a voltage to the plurality of electrodes and the TTFields induced to portions of a person's body, (tumor)], (see Figs. 8, 10, and Par. 0083-0084).
Shamir, Shamir 843, and Deslauriers are combinable because they are all concerned with tumor characterization. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Shamir and Shamir 843, to apply a voltage to one of the electrodes positioned in contact with either blood, CSF, or lymph, and the remaining electrodes the person's skin, as though by Deslauriers to measure one or more resistances based on establishing an electrical path to the tissue, such as inducing an electric field to brain tumors, (Par. 0083)
Regarding claim 2, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir discloses determining conductivities for the tissue types of the subject produces a conductivity mapping of the medical image of the subject, (see at least: Par. 0074, the dielectric property and tissue type correlation/ estimation rule may be applied to the labeled baseline data to generate conductivity maps for various tissue types represented in an image, [i.e., determining conductivities for the tissue types of the subject produces a conductivity mapping of the medical image of the subject, “implicit by generating conductivity maps for various tissue types represented in an image based on dielectric property and tissue type correlation/ estimation rule”]).
Regarding claim 3, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir discloses wherein identifying the location of the tumor in the medical image of the subject is based on user input, (see at least: Par. 0099, interfaces 1212 can be used to receive user input; and from Par. 0058-0064, three-dimensional array layout map 800 may be used by the patient and/or caregiver in arranging arrays on the scalp during the normal course of TTFields therapy as shown in FIG. 8B, based on the location and extent of the tumor, [i.e., identifying the location of the tumor in the medical image of the subject is based on user input, “implicit by using three-dimensional array layout map 800 by caregiver and performing manual MRI measurement determination by implicitly the user]).
Regarding claim 4, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir discloses wherein identifying the location of the tumor in the medical image of the subject is based on segmenting tumor tissue from other tissue in the medical image, (see at least: Par. 0064, MRI measurements may comprise one or more tumor measurements, such as tumor location measurements, which the tumor location measurements may be made using T1 postcontrast MRI sequences, firstly on the axial image demonstrating maximal enhancing tumor diameter (FIG. 9C), [i.e., identifying the location of the tumor in the medical image of the subject, “implicit by the MRI measurements”, is based on segmenting tumor tissue from other tissue in the medical image, “implicit by enhancing the tumor”]).
Regarding claim 5, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir discloses wherein the plurality of tissue types includes one or more of skin, bone, skull, organ, or brain, (see at least: Par. 0050, a tumor 304 is located below the skin surface 302 and bone tissue 306 and is located within brain tissue 308, [i.e., the plurality of tissue types includes one or more of skin, bone, skull, organ, or brain, “skin surface 302 , bone tissue 306, and brain tissue 308”]).
Regarding claim 6, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir 843 discloses wherein the features obtained from the application of tumor treating fields are for a plurality of voltages at one or more frequencies of the tumor treating fields, (Shamir 843, see at least: Par. 0050, generating one or more electric signals, which may be configured to generate an alternating voltage waveform at frequencies in the range from about 50 kHz to about 500 kHz, “the features obtained from the application of tumor treating fields are for a plurality of voltages at one or more frequencies of the tumor treating fields, “implicit by generating alternating voltage waveform at frequencies in the range from about 50 kHz to about 500 kHz”])
In other hand, Deslauriers discloses obtaining resistances based on applying a voltage to the plurality of electrodes, that implicitly indices TTFields to portions of a person's body, (Par. 0083-0084).
Regarding claim 7, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir 843 discloses wherein the features obtained from the application of tumor treating fields are based on currents measured from applying tumor treating fields to the other subjects at associated voltages, (Par. 0050-0055, generating one or more electric signals, which may be configured to generate an alternating voltage waveform at frequencies in the range from about 50 kHz to about 500 kHz, where output parameters of the signal generator 108 may comprise, for example, an intensity of the field, a frequency of the waves (e.g., treatment frequency), and a maximum allowable temperature of the one or more transducer arrays 104, and the control software 110 can increase current until the current reaches maximal treatment current, [i.e., wherein the features obtained from the application of tumor treating fields are based on currents measured, “implicit by increasing current until the current reaches maximal treatment current”, from applying tumor treating fields to the other subjects at associated voltages, “implicit by generating alternating voltage waveform”]).
In other hand, Deslauriers discloses obtaining resistances based on applying a voltage to the plurality of electrodes, that implicitly indices TTFields to portions of a person's body, (Par. 0083-0084).
Regarding claim 8, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir 843 discloses wherein the trained machine learning model was trained over a range of voltages and over a range of frequencies for the application of tumor treating fields to the other subjects, (see at least: Par. 0047-0048, incorporates random forest regression (and/or other machine learning) for fast estimation of TTFields, by generating tumor treatment fields (TTFields) (e.g., at 150 kHz) via the electric field generator 102 and deliver the TTFields to an area of the body through the one or more transducer arrays 104, and from Par. 0050, frequencies in the range from about 50 kHz to about 500 kHz, … and voltages in the range of about 0.1 V/cm to about 10 V/cm).
Regarding claim 9, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir discloses wherein the trained machine learning model is for determining conductivities for a designated tumor treating fields frequency and a designated cancer, (see at least: Par. 0068, the patient modeling application 608 may use artificial intelligence and/or machine learning to associate dielectric properties with a patient model and/or determine dielectric (e.g., standard electrical properties, etc.) of tissues to map one or more dielectric properties (e.g., conductivity, relative permittivity, etc.) to a tissue type (e.g., combined skin and muscle tissue, skull/bone, cerebrospinal fluid, gray matter, white, tumor/cancerous tissue, etc.), [i.e., training ML model for determining conductivities for a designated tumor treating fields frequency and a designated cancer, “implicit by training ML model to map one or more dielectric properties (e.g., conductivity, relative permittivity, etc.) to a tissue type, (tumor/cancerous tissue)”]).
Regarding claim 10, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir discloses training a machine learning model to obtain the trained machine learning model, wherein the machine learning model is trained with the medical images of the other subjects medical images from a plurality of patients may be used to train the patient modeling application 608 (or any other predictive model) to map one or more dielectric properties (e.g., conductivity, relative permittivity, etc.) to a tissue type (e.g., combined skin and muscle tissue, skull/bone, cerebrospinal fluid, gray matter, white, tumor/cancerous tissue, etc.), [i.e., training a machine learning model to obtain the trained machine learning model, wherein the machine learning model is trained with the medical images of the other subjects, “implicit by using medical images from a plurality of patients to train the patient modeling application 60”], …. where the tissue types represented in a medical image, [i.e., wherein at least one medical image of the other subjects includes a tumor, “tumor/cancerous tissue”]).
In other hand, Deslauriers discloses obtaining resistances based on applying a voltage to the plurality of electrodes, that implicitly indices TTFields to portions of a person's body, (Par. 0083-0084).
Regarding claim 11, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir discloses wherein training the machine learning model to obtain the trained machine learning model comprises: calculating the frequencies in the range from about 50 kHz to about 500 kHz, where output parameters of the signal generator 108 may comprise, for example, an intensity of the field, a frequency of the waves (e.g., treatment frequency), and a maximum allowable temperature of the one or more transducer arrays 104, and the control software 110 can increase current until the current reaches maximal treatment current, [i.e., calculating the features based on currents measured from applying the tumor treating fields to the other subjects, “implicit by increasing current until the current reaches maximal treatment current”, at associated voltages, “implicit by generating alternating voltage waveform”]).
In other hand, Deslauriers discloses obtaining resistances based on applying a voltage to the plurality of electrodes, that implicitly indices TTFields to portions of a person's body, (Par. 0083-0084).
Regarding claim 12, Shamir discloses a computer-implemented method for obtaining a trained machine learning model to identify conductivities in a medical image, (see at least: Par. 0003, “method”, and Par. 0070, implicit by training ML model for generating conductivity maps for various tissue types represented in an image”), the method comprising:
obtaining a plurality of medical images for a plurality of subjects, each medical image having a plurality of voxels, each medical image comprising a plurality of tissue types of the subject, wherein at least one voxel in each medical image is associated with each tissue type of the subject, (see at least: Par. 0038, the image data may include 3D data obtained from or generated by a 3D scanner, “i.e., implicitly obtaining a plurality of medical images of the subject”; and from Par. 0004, determining, for a patient, a set of image data, wherein the set of image data comprises a plurality of voxels, “i.e., the medical image having a plurality of voxels”; and from Par. 0067, segmenting the MRI data identifies the tissue type in each voxel, and electric properties may be assigned to each tissue type based on empirical data, “i.e., at least one voxel is associated with each tissue type”);
training a machine learning model to determine the conductivities in a medical image, the machine learning model being trained using the plurality of medical images for the plurality of subjects learning to associate dielectric properties with a patient model and/or determine dielectric (e.g., standard electrical properties, etc.) of tissues … , using medical images from a plurality of patients …, [i.e., determining, using a trained machine learning model and the medical image of the subject, “using trained artificial intelligence and/or machine learning”, conductivities for the tissue types of the subject in the medical image, “standard electrical properties”, the trained machine learning model trained with medical images of a plurality of other subjects, “training artificial intelligence and/or machine learning with medical images from a plurality of patients”]); and
Shamir does not expressly disclose obtaining measured resistances of each subject from application of tumor treating fields to each subject; and training a machine learning model to determine the conductivities in a medical image, the machine learning model being trained using the measured resistances of each subject from the application of tumor treating fields to each subject
Shamir 843 discloses obtaining measured features of each subject from application of tumor treating fields to each subject, (see at least: Par. 0081, features that are related to the electric field distribution may be extracted from the patient MRI); and training a machine learning model to determine the conductivities in a medical image, the machine learning model being trained using the measured resistances of each subject from the application of tumor treating fields to each subject, (see at least: Par. 0081-0082, features that are related to the electric field distribution may be extracted from the patient MRI, where the extracted features may be incorporated in a compact model to quickly estimate, predict, and/or determine an electric field and/or electric field distributions for transducer arrays by providing a precomputed mapping from the MRI features to electric field values, [i.e., training the machine learning model with features obtained from an application of tumor treating fields to the other subjects, “implicit by training the ML model with features related to the electric field distribution extracted from the patient MRI”]).
Although Shamir discloses training the machine learning with the features related to the electric field distribution; the combination of Shamir and Shamir 843 as whole does not expressly disclose that the features comprise electrical resistances.
However, Deslauriers et al disclose schematic example, depicting a resistance that is encountered when one of the electrodes is positioned in contact with either blood, CSF, or lymph, and the remaining electrodes are conventional electrodes 40 on the person's skin for establishing an electrical path to the tissue, [which implicitly enables measuring the electrical resistances based on applying a voltage to the plurality of electrodes and the TTFields induced to portions of a person's body, (tumor)], (see Figs. 8, 10, and Par. 0083-0084).
Shamir, Shamir 843, and Deslauriers are combinable because they are all concerned with tumor characterization. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Shamir and Shamir 843, to apply a voltage to one of the electrodes positioned in contact with either blood, CSF, or lymph, and the remaining electrodes the person's skin, as though by Deslauriers to measure one or more resistances based on establishing an electrical path to the tissue, such as inducing an electric field to brain tumors, (Deslauriers, Par. 0083)
Regarding claim 13, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 12.
Furthermore, Shamir discloses wherein two or more of the medical images are associated with a similar region of a subject of the plurality of subjects, (Par. 0056, the imaging data 610, such as MRI imaging data, may be analyzed by the patient modeling application 608 to identify a region of interest that comprises a tumor; and from Par. 0068, the medical images may include images derived from magnetic resonance imaging (MRI), radiography, ultrasound, elastography, …., and/or any other imaging technique, [i.e., the two or more of the medical images, “imaging data such as MRI images”, are associated with a similar region of a subject of the plurality of subjects, “identify a region of interest that comprises a tumor by implicitly using the imaging data of similar region of a subject of the plurality of subjects”]).
Regarding claim 15, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 12.
Furthermore, Shamir discloses calculating the measured features frequencies in the range from about 50 kHz to about 500 kHz, where output parameters of the signal generator 108 may comprise, for example, an intensity of the field, a frequency of the waves (e.g., treatment frequency), and a maximum allowable temperature of the one or more transducer arrays 104, and the control software 110 can increase current until the current reaches maximal treatment current, [i.e., calculating the features based on currents measured from applying the tumor treating fields to the other subjects, “implicit by increasing current until the current reaches maximal treatment current”, at associated voltages, “implicit by generating alternating voltage waveform”]).
In other hand, Deslauriers discloses obtaining resistances based on applying a voltage to the plurality of electrodes, that implicitly indices TTFields to portions of a person's body, (Par. 0083-0084).
Regarding claim 16, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 12.
Furthermore, Shamir 843 discloses applying tumor treating fields to the plurality of subjects at associated voltages to obtain measured currents; and calculating the measured features using the measured currents and the associated voltages, (Par. 0050-0055, 0065, generating one or more electric signals, which may be configured to generate an alternating voltage waveform at frequencies in the range from about 50 kHz to about 500 kHz, where output parameters of the signal generator 108 may comprise, for example, an intensity of the field, a frequency of the waves (e.g., treatment frequency), and a maximum allowable temperature of the one or more transducer arrays 104, and the control software 110 can increase current until the current reaches maximal treatment current, [i.e., implicitly obtaining the features by applying tumor treating fields to the plurality of subjects at associated voltages to obtain measured currents, “implicit by increasing current until the current reaches maximal treatment current implicit by generating alternating voltage waveform),
In other hand, Deslauriers discloses obtaining resistances based on applying a voltage to the plurality of electrodes, that implicitly indices TTFields to portions of a person's body, (Par. 0083-0084).
Regarding claim 17, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 16.
Furthermore, Shamir 843 discloses wherein the measured currents are obtained from locations on the subject receiving the tumor treating fields, (see at least: Par. 0125-0127, the current density (vector field) was computed from the electric field using Ohm's law, based on defining a reference coordinate system positioning of the transducer arrays relative to the tumor location, [i.e., wherein the measured currents are obtained, “implicit by obtaining current density (vector field)”, from locations on the subject receiving the tumor treating fields, “implicitly based on the defined reference coordinate”]).
Regarding claim 18, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 16.
Furthermore, Shamir discloses wherein the associated voltages are generated by a voltage generator used to generate the tumor treating fields, (see at least: Par. 0041, The signal generator 108 may be configured to generate an alternating voltage waveform at frequencies in the range from about 50 kHz to about 500 kHz, (e.g., the TTFields), [i.e., wherein the associated voltages are generated by a voltage generator, “signal generator 108 corresponds to the voltage generator”, used to generate the tumor treating fields, “TTFields”]).
Regarding claim 19, the combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
Furthermore, Shamir discloses wherein the trained machine learning model is able to determine conductivities for voxels in a medical image associated with tissue of a subject, (see at least: Par. 0068, medical images from a plurality of patients may be used to train the patient modeling application 608 (or any other predictive model) to map one or more dielectric properties (e.g., conductivity, relative permittivity, etc.) to a tissue type (e.g., combined skin and muscle tissue, skull/bone, cerebrospinal fluid, gray matter, white, tumor/cancerous tissue, etc.), to determine conductivity values, at a set of points (e.g., different tissue locations, etc.), [i.e., wherein the trained machine learning model is able to determine conductivities for voxels in a medical image associated with tissue of a subject, “implicit by determining conductivity values, at a set of points based on mapping one or more dielectric properties to a tissue type”]).
Regarding claim 20, claim 20 recites substantially similar limitations as set forth in claim 1. As such, claim 20 is rejected for at least similar rational.
The Examiner further acknowledged the following additional limitation(s): “an apparatus for selecting transducer locations for delivering tumor treating fields to a subject, the apparatus comprising: one or more processors; and memory accessible by the one or more processors, the memory storing instructions that when executed by the one or more processors”. However, Shamir discloses an apparatus, (Fig. 1, Par. 0039, “apparatus 100”, for selecting transducer locations for delivering tumor treating fields to a subject, (see at least: Fig. 8A, Par. 0037, “TTFields are typically delivered through two pairs of transducer arrays that generate perpendicular fields within the treated tumor, based on the location of one pair of transducer arrays relative to the tumor”).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Shamir, Shamir 843, and Deslauriers, as applied to claim 12 above; and further in view of HEBB (US-PGPUB 20230045652)
The combination of Shamir, Shamir 843, and Deslauriers as whole discloses the limitations of claim 1.
The combination of Shamir, Shamir 843, and Deslauriers as whole does not expressly disclose wherein the measured electrical resistances are associated with a range of voltages and a range of frequencies from the application of tumor treating fields to the plurality of subjects.
However, HEBB discloses wherein the measured electrical resistances are associated with a range of voltages and a range of frequencies from the application of tumor treating fields to the plurality of subjects, (see at least: Par. 0090-0091, especially the eq. (1), where the resistances can be implicitly measured from the range of voltages and a range of frequencies).
Shamir, Shamir 843, Deslauriers, and HEBB are combinable because they are all concerned with tumor characterization. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Shamir, Shamir 843, and Deslauriers, to measure resistances from the range of voltages and a range of frequencies, as though by HEBB, in order to plan of treatment of tumors, cancer and other medical conditions by delivering in situ electrical stimulation, (HEBB, Par. 0001).
Conclusion
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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Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMARA ABDI whose telephone number is (571)272-0273. The examiner can normally be reached 9:00am-5:30pm.
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/AMARA ABDI/Primary Examiner, Art Unit 2668 10/01/2026
