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 .
Priority
This Application claims priority to U.S. Provisional Application No. 63/613,858, filed December 22, 2023, which is incorporated by reference herein in its entirety.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/3/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 4/28/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1-11, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wong et al. (US 11446487 B2) referred to as Wong hereinafter and further in view of Urman et al. (US 20180160933 A1) referred to as Urman hereinafter and Mylonas et al. (US 20250360339 A1) referred to as Mylonas hereinafter.
Regarding claim 1, Wong teaches A method for determining transducer locations for delivery of tumor treating fields based on approximate abnormality location, the method comprising: (“System and methods for determining placement of a transducer array relative to a subject's head, which may be used in treating cancer in the subject,” Wong, abstract)
receiving an identification of a location of an abnormality of the subject (“In embodiments where the one or more transducer arrays are used to treat brain cancer, intracranial structures (e.g., necrotic core, resection cavity, white matter, grey matter, brain matter) and/or extracranial structures (e.g., an implantable electrode, modulator) may be considered. Intracranial and/or extracranial structures can vary in position and/or geometry for different individuals. Characteristics of a tumor including, location, size, shape, geometry, and one or more material properties may vary depending on the type and/or stage of the cancer.” Wong, col. 9, lines 22-31)
associating, by at least one processor, a model of the abnormality with the healthy model based on the size of the abnormality to obtain a customized model of the subject; (“The apparatus includes circuitry configured to construct, based on at least one image, a representation of a subject's head that includes information for a plurality of structures including at least one tumor positioned within the subject's brain. The circuitry is further configured to determine, by using the representation of the subject's head to calculate electric field propagation for a plurality of arrangements of at least one transducer on a surface of the subject's head, at least one electric field distribution within the subject's brain for the plurality of arrangements. The at least one electric field distribution indicates an amount of electric field at individual regions of the subject's brain that include the at least one tumor and span across the entirety of the subject's brain.” Wong, col. 5, lines 31-43) and (“To determine the electric field coverage of GTV as influenced by the conductivity of the necrotic core, both EVH and SARVH were constructed as the GTV was modeled with or without the necrotic core.” Wong, col. 21, lines 34-37), the model without the necrotic core is the claimed healthy model, however the reference does not teach “receiving a selection of a healthy model from a plurality of healthy models” which is explained below by the secondary reference.
receiving a selection of locations on the customized model of the subject to place transducers to treat the abnormality; (“Different transducer arrangements may be evaluated based on one or more characteristics (e.g., amount of electric field, rate of energy absorption, amount of energy absorbed) at a tumor location determined from the distributions of electric field and/or rate of energy absorption. By comparing different distributions for different transducer arrangements, a particular transducer arrangement may be selected from among the different transducer arrangements as providing a particular amount of energy absorption and/or electric field to the tumor.” Wong, col. 10, lines 30-39)
receiving an indication of a region of interest in the customized model of the subject based at least in part on the model of the abnormality; (“the at least one electric field distribution indicates an amount of electric field at individual regions of the subject's brain that include the at least one tumor and span across the entirety of the subject's brain.” Wong, col. 4, lines 20-24)
calculating, by the at least one processor, for each of the locations, a dosage of tumor treating fields treatment for the region of interest in the customized model of the subject; (“The different electric field and/or rate of energy absorption distributions may be assessed to identify an arrangement that provides a desired amount of electric field and/or energy absorption rate to a tumor or a region of a tumor.” Wong, col. 13, lines 43-47)
selecting one or more of the locations as recommended locations based on the dosage; and providing the one or more recommended locations. (“At step 280, an indication identifying the selected transducer array arrangement may be generated, such as by array operation analyzer 122. The indication may be transmitted to user interface 124 and/or array controller 126. The indication may include positional information identifying locations of the subject's head where one or more transducers should be placed when providing treatment.” Wong, col. 16, lines 32-38)
However, Wong does not teach receiving a selection of a healthy model from a plurality of healthy models, the healthy model being representative of a subject;
Urman teaches receiving a selection of a healthy model from a plurality of healthy models, the healthy model being representative of a subject; (“when the body area is the head of a patient and the abnormal tissue corresponds to a tumor in the head of the patient, the model template corresponds to the head of a healthy individual and lacks any tumors. In some embodiments, the model template may be selected from multiple existing model templates based on similarities between the image and each of the multiple model templates.” Urman, para. [0095])
associating, by at least one processor, a model of the abnormality with the healthy model based on the location of the abnormality to obtain a customized model of the subject; (“The first method further includes retrieving a model template from a memory device of the computer system, the model template comprising tissue probability maps that specify positions of a plurality of tissue types in a healthy version of the body area of the patient, and deforming the model template in space so that features in the deformed model template line up with corresponding features in the data set. The first method also includes modifying portions of the deformed model template that correspond to the masked-out portion of the data set so that the modified portions represent the abnormal tissue, and generating a model of electrical properties of tissues in the body area based on (a) the positions of the plurality of tissue types in the deformed and modified model template and (b) the position of the abnormal tissue in the deformed and modified model template.” Urman, para. [0010])
Wong and Urman are combinable because they are from the same field of endeavor, image processing in treating tumors.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong in light of Urman’s selecting healthy models. One would have been motivated to do so because it can be useful for creating an accurate head model. (Urman, para. [0040])
However, the combination of Wong and Urman does not teach receiving an identification of a size of the abnormality of the subject;
Mylonas teaches receiving an identification of a size of the abnormality of the subject; (“The CT scan provides high-resolution three-dimensional images of the patient's body, offering valuable details about the shape, size, and location of the tumour or the organ. It serves as comprehensive and detailed training data for the cGAN model, enabling it to accurately understand the patient's unique anatomy and the specific characteristics of the tumour or organ… These contours, drawn on the 3D pre-treatment CT images, provide the exact shape that the physician has identified as the treatment target. This personalised contouring provides an accurate representation of the target area, facilitating precise treatment planning and execution.” Mylonas, para. [0048])
Wong, Urman, and Mylonas are combinable because they are from the same field of endeavor, image processing in treating tumors.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong and Urman in light of Mylonas’s receiving size of the abnormality. One would have been motivated to do so because it can improve the accuracy of real-time tracking during treatment. (Mylonas, para. [0048])
Regarding claim 2, Wong teaches wherein the model of the abnormality is a simplified model of the abnormality. (“Standard geometric solids, including cube, cylinder, sphere, icosahedron, and cone, were used to represent the shape of the tumor for studying changes in electric fields distribution and energy deposition in the GTV (FIGS. 10A and 10B). To simplify the comparisons, the necrotic core properties across all models, including the original brain model, were set equal to the GTV” Wong, col. 22, lines 16-22)
Regarding claim 3, Wong teaches wherein the model of the abnormality is representative of an organ. (“a representation of the subject's head that includes information for a plurality of structures including one or more tumors positioned within the subject's brain.” Wong, abstract)
Regarding claim 4, Wong teaches wherein the model of the abnormality is a geometric shape. (“FIGS. 10A-10B show geometric analysis of GTV on EVH and SARVH. The glioblastoma was represented by standard relatively symmetric geometric solids, including cube, cylinder, sphere, and icosahedron, for studying changes in electric fields distribution and specific absorption rate in the GTV (FIG. 10A).” Wong, col. 8, lines 21-26)
Regarding claim 5, Wong teaches wherein the model of the abnormality is based on a shape of the abnormality (“The conical shape was also chosen because it is relatively more asymmetrical. Its position in the brain, as represented by the anterior, posterior, left lateral, right lateral, inferior, and superior orientations, were also used for studying changes in electric fields distribution and energy deposition in the GTV (FIG. 10B).” Wong, col. 8, 26-31)
Wong does not teach wherein the model of the abnormality is based on the size of the abnormality.
Mylonas teaches wherein the model of the abnormality is based on the size of the abnormality. (“The CT scan provides high-resolution three-dimensional images of the patient's body, offering valuable details about the shape, size, and location of the tumour or the organ. It serves as comprehensive and detailed training data for the cGAN model, enabling it to accurately understand the patient's unique anatomy and the specific characteristics of the tumour or organ… These contours, drawn on the 3D pre-treatment CT images, provide the exact shape that the physician has identified as the treatment target. This personalised contouring provides an accurate representation of the target area, facilitating precise treatment planning and execution.” Mylonas, para. [0048])
Wong, Urman, and Mylonas are combinable because they are from the same field of endeavor, image processing in treating tumors.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong and Urman in light of Mylonas’s size of the abnormality. One would have been motivated to do so because it can improve the accuracy of real-time tracking during treatment. (Mylonas, para. [0048])
Regarding claim 6, Wong teaches wherein associating the model of the abnormality with the healthy model comprises defining electrical values of the abnormality for voxels of the healthy model that correspond to the location of the abnormality (“Array operation analyzer 122 may determine an electric field and/or rate of energy absorption (e.g., specific absorption rate) distribution using any suitable computational techniques, including one or more suitable finite element solving methods. Some computational techniques may include an iterative process to reduce error for each voxel in the representation of the subject's head and/or other anatomical sites. In some embodiments, array operation analyzer 122 may implement a Newton-Raphson method and/or other numerical methods to determine an electric field distribution.” Wong, col. 14, lines 11-22)
Wong does not teach model that corresponds to the size of the abnormality. (“The volumes are forward projected to digitally produce 2D projections every 0.1 degree over 360 degrees, generating 3,600 projections using the Reconstruction Toolkit (RTK) and Insight Toolkit (ITK). The projections produced from the planning CT are each paired 522 with the respective projection produced from the prostate contour. The projections of size 550×550 pixels are cropped 525 to 512×512 pixels. The crop position is randomly shifted, resulting in a maximum motion of 10 mm to replicate or simulate possible treatment setup error and anatomical motion.” Mylonas, para. [0095])
Wong, Urman, and Mylonas are combinable because they are from the same field of endeavor, image processing in treating tumors.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong and Urman in light of Mylonas’s size of the abnormality. One would have been motivated to do so because it can improve the accuracy of real-time tracking during treatment. (Mylonas, para. [0048])
Regarding claim 7, Wong teaches wherein the region of interest defined by a gross tumor volume (GTV) for the abnormality, wherein the dosage of treatment is calculated based at least in part on the GTV. (“To investigate the strength of electric field and the rate of energy deposited into the GTV and various intracranial structures, EVH and SARVH were generated for the comparison between models that use the primary position for transducer array placement as outlined in FIG. 5E and incorporate the isotropic conductivity and relative permittivity values listed in Table 1. As expected, the highest E.sub.AUC was found at the scalp and skull while the lowest was located at the orbits, bilateral ventricles, and brainstem (FIGS. 4A & 4C). In the GTV, 95% of the volume had an electric field intensity of >50 V/m while 50% had >80 V/m and 20% has >150 V/m (FIG. 4A).” Wong, col. 19, lines 41-52)
Regarding claim 8, Wong teaches wherein the selection of locations on the customized model of the subject to place the transducers to treat the abnormality is based at least in part on conductivities for at least one tissue type included in the healthy model and at least one tissue type included in the model of the abnormality. (“FIGS. 8A-8F show sensitivity analysis for electric field strength and specific absorption rate with or without the necrotic core.” Wong, col. 7, lines 55-57)
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Wong, col. 21, lines 50-67, col. 22, lines 1-6
Regarding claim 9, Wong teaches wherein calculating the dosage of tumor treating fields treatment is based at least in part on the conductivities for the at least one tissue type included in the healthy model and the at least one tissue type included in the model of the abnormality. (“FIGS. 8A-8F show sensitivity analysis for electric field strength and specific absorption rate with or without the necrotic core. EVH (FIG. 8A) and SARVH (FIG. 8C) were modeled with the necrotic core, which consisted of highly conductive fluid. When the necrotic core was replaced with poorly conductive tissue, such as white matter, the electric field coverage and specific absorption rate were increased as shown in the EVH (FIG. 8B) and SARVH (FIG. 8D), respectively. The electric field diagrams showed differences in the electric field coverage at the GTV with (FIG. 8E) and without (FIG. 8F) the necrotic core. EVH, electric field-volume histogram; SARVH, specific absorption rate-volume histogram.” Wong, col. 7, lines 55-67) no tumor is the claimed healthy model
Regarding claim 10, Wong teaches wherein the healthy model defines healthy tissue and the model of the abnormality defines unhealthy tissue, wherein the healthy tissue has a first electrical property and the unhealthy tissue has a second electrical property, wherein calculating the dosage of treatment is based at least in part on the first electrical property and the second electrical property. (“FIGS. 9A-9C show influence of cerebrospinal fluid on the electric field strength and specific absorption rate at the GTV and necrotic core. The layer of cerebrospinal fluid was altered by +1, −1, and −2 pixels at the convexity of the brain and the respective EVH (FIG. 9A) and SARVH (FIG. 9B) at the GTV were generated. The electric field quantities E.sub.AUC, V.sub.E150, and E.sub.50%, as well as quantities for the specific absorption rate SAR.sub.AUC, V.sub.SAR7.5, and SAR.sub.50%, all increased progressively when the cerebrospinal fluid space was narrowed progressively from +1 pixel to −1 pixel, and then to −2 pixels on the convexity of the brain. GTV, gross tumor volume; EVH, electric field-volume histogram; SARVH, specific absorption rate-volume histogram; E.sub.AUC, electric field area under the curve; V.sub.E150, volume covered with electric field intensity of 150 volts per meter; and E.sub.50%, the electric field intensity encompassing 50% of volume; SAR, specific absorption rate; SAR.sub.AUC, SAR area under the curve; V.sub.SAR7.5, volume covered with specific absorption rate of 7.5 watts per kilogram; SAR.sub.50%, the magnitude of specific absorption rate encompassing 50% of volume.” Wong, col. 8, lines 1-20)
Regarding claim 11, Wong teaches wherein each of the plurality of healthy models is segmented based on tissue type. (“a set of images of a subject's head may be segmented into regions corresponding to different structures, such as white matter, grey matter, scalp, necrotic core of a tumor, and cerebrospinal fluid, and volumes associated with those regions may be determined as estimated volumes for those structures.” Wong, col. 15, lines 22-27)
Regarding claim 16, Wong does not teach wherein the healthy model is selected from the plurality of healthy models based at least in part on a medical image of the subject.
Urman teaches wherein the healthy model is selected from the plurality of healthy models based at least in part on a medical image of the subject. (“when the body area is the head of a patient and the abnormal tissue corresponds to a tumor in the head of the patient, the model template corresponds to the head of a healthy individual and lacks any tumors. In some embodiments, the model template may be selected from multiple existing model templates based on similarities between the image and each of the multiple model templates.” Urman, para. [0095])
Wong and Urman are combinable because they are from the same field of endeavor, image processing in treating tumors.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong in light of Urman’s selecting healthy models. One would have been motivated to do so because it can be useful for creating an accurate head model. (Urman, para. [0040])
Regarding claim 17, Wong does not teach wherein the healthy model is selected from the plurality of healthy models based at least in part on a physical measurement of the subject.
Urman teaches wherein the healthy model is selected from the plurality of healthy models based at least in part on a physical measurement of the subject. (“the model template may be selected from multiple existing model templates based on similarities between the image and each of the multiple model templates. For example, a measure of similarity such as mutual information or a distance may be determined between the patient data set (derived by masking out abnormalities in the patient image) and each one of several model templates, and the model template that is most similar to the patient data set (e.g., has the least distance or the most mutual information) may be selected accordingly.” Urman, para. [0095])
Wong and Urman are combinable because they are from the same field of endeavor, image processing in treating tumors.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong in light of Urman’s selecting healthy models. One would have been motivated to do so because it can be useful for creating an accurate head model. (Urman, para. [0040])
Regarding claim 18, Wong does not teach wherein the healthy model is selected from the plurality of healthy models based at least in part on a classification of the subject.
Urman teaches wherein the healthy model is selected from the plurality of healthy models based at least in part on a classification of the subject. (“a measure of similarity such as mutual information or a distance may be determined between the patient data set (derived by masking out abnormalities in the patient image) and each one of several model templates, and the model template that is most similar to the patient data set (e.g., has the least distance or the most mutual information) may be selected accordingly.” Urman, para. [0095])
Wong and Urman are combinable because they are from the same field of endeavor, image processing in treating tumors.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong in light of Urman’s selecting healthy models. One would have been motivated to do so because it can be useful for creating an accurate head model. (Urman, para. [0040])
Regarding claim 19, refer to the explanation of claim 1.
Regarding claim 20, Wong teaches A non-transitory processor readable medium containing a set of instructions thereon for determining transducer locations for delivery of tumor treating fields using simulations based on approximate abnormality location, wherein when executed by a processor, the instructions cause the processor to (“computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by computer 1210. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.” Wong, col. 26, lines 12-29)
Regarding rest of claim 20, refer to the explanation of claim 1.
Claim(s) 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Wong, Urman, Mylonas as mentioned above, and further in view of Zhang et al. (US 20220108445 A1) referred to as Zhang hereinafter.
Regarding claim 12, Urman teaches wherein the plurality of healthy models are generated by: receiving training data for a plurality of healthy subjects; analyzing the training data to identify commonalities among the plurality of healthy subjects; (“the tissue probability maps are derived from images of a healthy individual from whom the model template has been derived. Optionally, in these embodiments, the tissue probability maps are derived by simultaneously registering and segmenting the images of the healthy individual using existing tissue probability maps, and wherein the existing tissue probability maps are derived from images of multiple individuals.” Urman, para. [0013]
However, the combination of Wong, Urman, and Mylonas does not teach clustering the plurality of healthy subjects into clusters based at least in part on the commonalities among the plurality of healthy subjects; and generating the plurality of healthy models, wherein the generating comprises, for each cluster, generating one of the plurality of healthy models based at least in part on the training data for the plurality of healthy subjects that are within the cluster.
Zhang teaches clustering the plurality of healthy subjects into clusters based at least in part on the commonalities among the plurality of healthy subjects; (“In a non-mask related embodiment, similar operations are performed using patches to process the entire facial image, for example, including non-skin portions such as background, hair, eyes and lips. For example, at inference time, in the non-mask related embodiment, patches of resolution are created within the entire image by scanning from top-left to bottom-right, with a stride of one third (⅓) patch width as described.” Zhang, para. [0081], areas of the face without acne are the claimed healthy subjects)
and generating the plurality of healthy models, wherein the generating comprises, for each cluster, generating one of the plurality of healthy models based at least in part on the training data for the plurality of healthy subjects that are within the cluster. (“At inference time, in the mask related embodiment, patches of resolution are created within the generated skin mask by scanning from top-left to bottom-right, with a stride of one third (⅓) patch width. Patch width is normalized by defining it as a width of the face divided by 15. Each patch is passed through a trained convolutional neural network (i.e. a second model embodiment) described below. In an embodiment, the model outputs a list of probabilities of the following classes: inflammatory, retentional, pigmented (three acne types or classes) and healthy skin. In an embodiment, non-maximum Suppression (NMS) is applied to select the best boxes among the returned detected acne candidates.” Zhang, para. [0080])
Wong, Urman, Mylonas, and Zhang are combinable because they are from the same field of endeavor, image processing in treating abnormalities.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong, Urman, and Mylonas in light of Zhang’s clustering healthy models. One would have been motivated to do so because it can improve showing results. (Zhang, para. [0103])
Regarding claim 13, Zhang teaches wherein generating the plurality of healthy models comprises selecting, for each cluster, a model of one of the plurality of healthy subjects within the cluster to be the healthy model. (“In an embodiment, non-maximum Suppression (NMS) is applied to select the best boxes among the returned detected acne candidates. The healthy skin class gives the classifier an option other than one of three acne classes.” Zhang, para. [0081])
Wong, Urman, Mylonas, and Zhang are combinable because they are from the same field of endeavor, image processing in treating abnormalities.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong, Urman, and Mylonas in light of Zhang’s clustering healthy models. One would have been motivated to do so because it can improve showing results. (Zhang, para. [0103])
Regarding claim 14, Zhang teaches wherein generating the plurality of healthy models comprises generating, for each cluster, the healthy model using information concerning at least two of the plurality of healthy subjects within the cluster. (“To create a patch-based model dataset, in accordance with an embodiment, 2450 healthy patches and 3577 acne patches (patches including instances of any of the three acne classes previously described herein) were respectively sampled from the full face images of the dataset described above with reference to the first model embodiments.” Zhang, para. [0085])
Wong, Urman, Mylonas, and Zhang are combinable because they are from the same field of endeavor, image processing in treating abnormalities.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong, Urman, and Mylonas in light of Zhang’s clustering healthy models. One would have been motivated to do so because it can improve showing results. (Zhang, para. [0103])
Regarding claim 15, Zhang teaches wherein analyzing the training data comprises performing a principal component analysis to identify the commonalities among the plurality of healthy subjects. (“At inference time, in the mask related embodiment, patches of resolution are created within the generated skin mask by scanning from top-left to bottom-right, with a stride of one third (⅓) patch width. Patch width is normalized by defining it as a width of the face divided by 15. Each patch is passed through a trained convolutional neural network (i.e. a second model embodiment) described below. In an embodiment, the model outputs a list of probabilities of the following classes: inflammatory, retentional, pigmented (three acne types or classes) and healthy skin.” Zhang, para. [0080], skin region with no acne is the claimed commonality)
Wong, Urman, Mylonas, and Zhang are combinable because they are from the same field of endeavor, image processing in treating abnormalities.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wong, Urman, and Mylonas in light of Zhang’s identifying commonalities between healthy subjects. One would have been motivated to do so because it can improve showing results. (Zhang, para. [0103])
Conclusion
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/PARDIS SOHRABY/ Examiner, Art Unit 2664
/JENNIFER MEHMOOD/ Supervisory Patent Examiner, Art Unit 2664
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