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
Priority is acknowledged from Provisional application 63/613,835 with a filing date of 12/22/2023.
Information Disclosure Statement
The information disclosure statements (“IDS”) filed on 01/03/2025 and 04/28/2025 were reviewed and the listed references were noted.
Drawings
The 7-page drawings have been considered and placed on record in the file.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, and the claimed invention is directed to non-statutory subject matter as follows. The claims recite receiving a medical image of a subject with an abnormality, receiving a selection of a healthy model representing the subject from a plurality of models, receiving a selection of locations on the healthy model to place transducers to treat the abnormality without identifying a location, receiving an indication of a region of interest in the model, and calculating a dosage of tumor treating fields in the region without including abnormal tissue in the model.
Step 1:
With regard to Step 1, the instant claims are directed to a method, which is among the statutory categories of invention.
Step 2A – Prong 1:
With regard to Step 2A – Prong 1, for example in Claim 1, the limitations of " and calculating, by at least one processor, for each of the locations, a dosage of tumor treating fields treatment in the region of interest of the healthy model without modifying the healthy model to include abnormal tissue", as drafted only involves mental processes or mathematical calculations, such as the calculation of dosage for a region of interest. That is, nothing in the above-described claim elements preclude the steps from practically being performed in the mind or on a piece of paper. If a claim limitation, under its broadest reasonably interpretation covers performance of the limitation in the mind or through mathematical calculations, but for the recitation of a generic apparatus components, such as a processor, computer program, or machine-readable media, then it falls within the "mental processes", which include concepts performed in the human mind, including an observation, evaluation, judgement, opinion, or mathematical calculations groupings of the abstract idea. Accordingly, the claim recites an abstract idea.
Step 2A – Prong 2:
The 2019 PEG defines the phrase “integration into a practical application” to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception. In the instant case, the additional elements in the claims do not apply, rely on, or use the judicial exception.
This judicial exception is not integrated into a practical application because the claim only recites the following additional steps "receiving a medical image of a subject having an abnormality; receiving a selection of a healthy model from a plurality of healthy models, the healthy model being representative of the subject, the selection based on the medical image of the subject; receiving a selection of locations on the healthy model to place transducers to treat the abnormality of the subject without identifying a location of the abnormality in the healthy model; receiving an indication of a region of interest in the healthy model”, i.e., insignificant extra-solution activities. The other additional recited element in certain other claims is just a processor and a computer-readable storage medium, which are generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it is a field-of-use limitation that does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim as a whole, recites an abstract idea.
Step 2B:
Because the claim fails under Step 2A, the claims are further evaluated under Step 2B. The claim herein does not include additional steps that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into practical application, the additional elements/steps amount to no more than insignificant extra-solution activities. Mere instructions to apply an exception using generic apparatus component, such as a processor, cannot provide an inventive concept. The claim is not patent eligible. It should be noted that a similar analysis may be performed with respect to independent Claims 19 and 20.
Further, with regard to dependent Claims 2-18 viewed individually, these additional steps are under their broadest reasonable interpretation, cover performance of the limitation in the mind and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims limitations amount to significantly more than the abstract idea itself. For example, the selection of a model based on identifying a landmark in the image and corresponding the landmark in the model as recited in Claim 2 or segmenting the model based on tissue type as recited in Claim 8 are only examples of routine and conventional image processing steps or steps that could be completed within the human mind and do not amount to significantly more to consider as inventive steps. Accordingly, Claims 1-20 are rejected under 35 U.S.C. 101.
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 19 and 20 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 pre-AIA the applicant regards as the invention. Examiner respectfully requests that appropriate corrections be made to clarify the scope of the claims.
Claims 19 and 20 recite the limitation “the tumor”. There is insufficient antecedent basis for these limitations in the claim.
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.
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.
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.
Claims 1-9 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Urman et al. (US 20210162228 A1) in view of Landon et al. (US 20220160430 A1) and Avraham et al. (US 20210060334 A1).
Regarding Claim 1, Urman teaches "A computer-implemented method for determining transducer locations for delivery of tumor treating fields based on models of healthy subjects, the method comprising: receiving a medical image of a subject having an abnormality"; (Urman, Paras. 65-67, teaches MRI imaging data which is analyzed by the patient modeling application wherein MRI measurements of the portion of the patient that is to receive the transducer arrays is determined and the MRI measurements may be received via a standard Digital Imaging and Communications in Medicine viewer and wherein the patient modeling application is configured to determine a desired transducer layout for a patient based on the location and extent of the tumor, i.e., receive medical image of a subject having an abnormality);
"
"
"receiving an indication of a region of interest in the healthy model"; (Urman, Pars. 3-4 and 75, teaches determining a region of interest within the 3D model of a portion of a subject's body wherein a healthy head model may be generated to serve as a deformable template from which patient models can be created).
However, Urman does not explicitly teach "receiving a selection of a healthy model from a plurality of healthy models, the healthy model being representative of the subject, the selection based on the medical image of the subject; receiving a selection of locations on the healthy model to place transducers to treat the abnormality of the subject without identifying a location of the abnormality in the healthy model; and calculating, by at least one processor, for each of the locations, a dosage of tumor treating fields treatment in the region of interest of the healthy model without modifying the healthy model to include abnormal tissue”.
In an analogous field of endeavor, Landon teaches "receiving a selection of a healthy model from a plurality of healthy models, the healthy model being representative of the subject, the selection based on the medical image of the subject"; (Landon, Paras. 8, 11, 183, and 230, teaches identifying, based on the at least on 2D image, a first representative bone comprising identifying a plurality of potential representative bones from the library of representative bones and selecting a first representative bone from the plurality of potential representative bones wherein the representative bone is a 3D model and wherein the library is healthy anatomy, i.e., receive selection of a healthy model being the representative bone 3D model which is representative of the subject due to being based on the 2D image of the subject).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Urman by including the selection of a healthy model from a plurality of models based on the image of the subject taught by Landon. One of ordinary skill in the art would be motivated to combine the references since it helps generate custom models (Landon, Abstract, teaches the motivation of combination to be to generate a custom 3D model of the subject for modifications).
However, the combination of references of Urman in view of Landon does not explicitly teach “receiving a selection of locations on the healthy model to place transducers to treat the abnormality of the subject without identifying a location of the abnormality in the healthy model; and calculating, by at least one processor, for each of the locations, a dosage of tumor treating fields treatment in the region of interest of the healthy model without modifying the healthy model to include abnormal tissue”.
In an analogous field of endeavor, Avraham teaches "receiving a selection of locations on the healthy model to place transducers to treat the abnormality of the subject without identifying a location of the abnormality in the healthy model"; (Avraham, Paras. 30-36, 42, and 51, teaches example layouts for pairs of transducer arrays that provides adequately high field intensities in the region of interest wherein the field intensities depicted were generated by running simulations using a DUKE model wherein the optimum position of each of the transducer arrays may be determined using simulations for each individual person to calculate the resulting electric field for each combination of positions for the transducer arrays, and selecting the combination that provides the best results and wherein it is sufficient for the user to define the region of interest in which they want to optimize the electric field without the need for accurately identifying the tumor, i.e., receive a selection of locations on the healthy model being the DUKE model to treat the abnormality without having to explicitly identify a location of the abnormality that would be within the healthy model);
"and calculating, by at least one processor, for each of the locations, a dosage of tumor treating fields treatment in the region of interest of the healthy model without modifying the healthy model to include abnormal tissue"; (Avraham, Paras. 30-36, 41-42 and 51, teaches the mean intensity, the median field intensity, and the percentage of the ROI with an intensity above 1 V/cm were all obtained by simulating the electric fields when the electrode elements within each transducer array were positioned as depicted in FIGS. 2-7 wherein the optimum position of each of the transducer arrays may be determined using simulations for each individual person to calculate the resulting electric field for each combination of positions for the transducer arrays, and selecting the combination that provides the best results and wherein it is sufficient for the user to define the region of interest in which they want to optimize the electric field without the need for accurately identifying the tumor, i.e., calculate dosage of tumor treating fields treatment in the region of interest of the healthy model being the mean intensity, field intensity, and percentage of the ROI above a specific intensity for each of the transducer locations without having to explicitly modify the DUKE model to include the tumor or abnormal tissue).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Urman and Landon by including the selection of transducer locations and calculation a dosage of tumor treating fields in a region without identifying or including the location of the abnormality taught by Avraham. One of ordinary skill in the art would be motivated to combine the references since it provides desired levels of intensities (Avraham, Para. 29, teaches the motivation of combination to be to provide layouts for positioning of the transducer arrays to provide desired level of field intensities for treating cancer in the ROI).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 2, the combination of references of Urman in view of Landon and Avraham teaches "The method of claim 1, wherein the selection of the healthy model is based on identifying a landmark in the medical image of the subject and a corresponding landmark in the healthy model"; (Landon, Paras. 12 and 224, teaches identifying one or more key points on the at least one 2D image wherein identifying a first representative bone is further based on the one or more key points, i.e., selection of the model is based on identifying a landmark in the image and corresponding the landmark in the model being the identification of the key points and corresponding them to the model).
The proposed combination as well as the motivation for combining the Urman in view of Landon and Avraham references presented in the rejection of Claim 1, applies to claim 2. Thus, the method recited in claim 2 is met by Urman in view of Landon and Avraham.
Regarding Claim 3, the combination of references of Urman in view of Landon and Avraham teaches "The method of claim 1, wherein the selection of the healthy model comprises: determining a plurality of measurements of the subject from the medical image of the subject"; (Landon, Para. 222, teaches the system may additionally acquire data including one or more properties related to the bones of the 2D image, such as dimensions, measurements, calculated properties, deformities, features, or other differentiating information wherein data relating to properties of the bones may be received with the 2D images, i.e., determine a plurality of measurements from the image);
"comparing the plurality of measurements of the subject to a plurality of measurements for each health model"; (Landon, Para. 236, teaches the system may identify one or more potential representative bones for comparison with the patient bone wherein potential representative bones may be identified from a library of representative bones in which the system may utilize one or more key points and/or any calculated properties of the patient bone, including, but not limited to, bone dimensions, bone deformities, bone thickness, mechanical axis, and anatomical axis to identify substantial matches among the historical bone image data, i.e., compare the measurements of the subject to the plurality of measurements of the model representative bones);
"and selecting the healthy model being representative of the subject as the healthy model having measurements most similar to measurements of the subject"; (Landon, Para. 213 and 236, teaches the system may identify one or more potential representative bones for comparison with the patient bone wherein potential representative bones may be identified from a library of representative bones in which the system may utilize one or more key points and/or any calculated properties of the patient bone, including, but not limited to, bone dimensions, bone deformities, bone thickness, mechanical axis, and anatomical axis to identify substantial matches among the historical bone image data and wherein landmarks are used as a means of selecting the bone that most resembles the imaged bone, i.e., select the model having measurements most similar to measurements of the subject being the identification of a substantial match among the representative bone data).
The proposed combination as well as the motivation for combining the Urman in view of Landon and Avraham references presented in the rejection of Claim 1, applies to claim 3. Thus, the method recited in claim 3 is met by Urman in view of Landon and Avraham.
Regarding Claim 4, the combination of references of Urman in view of Landon and Avraham teaches "The method of claim 3, wherein the selection of the healthy model is further based on a location of an organ of the subject"; (Landon, Paras. 12 and 224, teaches identifying one or more key points on the at least one 2D image wherein identifying a first representative bone is further based on the one or more key points in which each of the one or more key points corresponds to a portion of bony anatomy, a location of ligament attachment, a bony landmark, an anatomic landmark, a knee center, etc., i.e., selection of the model is based on a location of an organ being the bone location).
The proposed combination as well as the motivation for combining the Urman in view of Landon and Avraham references presented in the rejection of Claim 1, applies to claim 4. Thus, the method recited in claim 4 is met by Urman in view of Landon and Avraham.
Regarding Claim 5, the combination of references of Urman in view of Landon and Avraham teaches "The method of claim 1, wherein the selection of the locations on the healthy model to place transducers to treat the abnormality is based at least in part on conductivities for at least one tissue type included in the healthy model"; (Urman, Para. 65, teaches each tissue type may be assigned dielectric properties for relative conductivity and permittivity, and simulations may be run whereby different transducer array configurations are applied to the surface of the model to understand how an externally applied electric field, of preset frequency, will distribute throughout any portion of a patient's body to properly optimize array placement on a portion of a patient's body, i.e., selection of locations in the model to place transducers for treatment based at least in part on conductivities of tissue types in the model being the optimization of array placement based on assigned properties for conductivity for each tissue type during simulations).
Regarding Claim 6, the combination of references of Urman in view of Landon and Avraham teaches "The method of claim 5, 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"; (Urman, Paras. 65 and 84, teaches simulations wherein each tissue type may be assigned dielectric properties for relative conductivity and permittivity, and simulations may be run whereby different transducer array configurations are applied to the surface of the model to understand how an externally applied electric field, of preset frequency, will distribute throughout any portion of a patient's body to properly optimize array placement on a portion of a patient's body and wherein dose metrics for each of a plurality of pairs of transducer arrays are determined after the simulated electric field distributions are determined, i.e., calculation of the dosage of the tumor treating fields is based on the conductivities for tissue types of the models being the simulated electric field distributions).
Regarding Claim 7, the combination of references of Urman in view of Landon and Avraham teaches "The method of claim 1, wherein the healthy model defines healthy tissue having an electrical property, wherein calculating the dosage of treatment is based at least in part on the electrical property"; (Urman, Paras. 65, 75, and 84, teaches a healthy head model is generated as a deformable template for generating patient models wherein MRI data is segmented and the tissue type is assigned electric properties to each tissue type based on empirical data in which the standard electrical properties of tissues are used in the simulations wherein dose metrics for each of a plurality of pairs of transducer arrays are determined after the simulated electric field distributions are determined, i.e., healthy model defines tissue having electrical properties wherein calculating the dosage treatment is based on electrical properties).
Regarding Claim 8, the combination of references of Urman in view of Landon and Avraham teaches "The method of claim 1, wherein each of the plurality of healthy models is segmented based on tissue type"; (Urman, Para. 75, teaches segmenting tissue from MRI data when creating a model wherein segmenting the MRI data identifies the tissue type in each voxel, i.e., models are segmented based on tissue type).
Regarding Claim 9, the combination of references of Urman in view of Landon and Avraham teaches "The method of claim 1, wherein each of the plurality of healthy models is representative of a group of the healthy subjects without abnormalities"; (Landon, Paras. 8, 11, 183, and 230, teaches identifying, based on the at least on 2D image, a first representative bone comprising identifying a plurality of potential representative bones from the library of representative bones and selecting a first representative bone from the plurality of potential representative bones wherein the representative bone is a 3D model and wherein the library is healthy anatomy, i.e., healthy models in the library are representative of a group of the healthy subjects without the abnormalities).
The proposed combination as well as the motivation for combining the Urman in view of Landon and Avraham references presented in the rejection of Claim 1, applies to claim 9. Thus, the method recited in claim 9 is met by Urman in view of Landon and Avraham.
Regarding Claim 18, the combination of references of Urman in view of Landon and Avraham teaches "The method of claim 1, wherein the medical image is at least one of a computed tomography (CT) image, a magnetic resonance imaging (MRI) medical image, or a positron emission tomography (PET) medical image"; (Urman, Para. 64, teaches imaging data comprising x-ray CT data, PET data, and MRI data).
Claim 19 recites a system with elements corresponding to the steps recited in Claim 1. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Urman in view of Landon and Avraham references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Urman in view of Landon and Avraham references discloses a processor and a memory to execute instructions (for example, see Urman, Paragraphs 98-99).
Claim 20 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Urman in view of Landon and Avraham references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Urman in view of Landon and Avraham references discloses a computer readable storage medium (for example, see Urman, Paragraph 41).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Urman in view of Landon, Avraham, and Schmidt et al. (US 20200265579 A1).
Regarding Claim 10, the combination of references of Urman in view of Landon and Avraham does not explicitly teach "The method of claim 1, wherein the plurality of healthy models is based on the healthy subjects clustered into groups".
In an analogous field of endeavor, Schmidt teaches "The method of claim 1, wherein the plurality of healthy models is based on the healthy subjects clustered into groups"; (Schmidt, Para. 44, teaches a subset of medical images is extracted from the database based on a similarity score of the medical images wherein the subset is identified by clustering the medical images in latent space and identifying a subset as a specific cluster using a suitable granularity, i.e., models based on subjects clustered into groups).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Urman, Landon, and Avraham wherein the images are 3D healthy models of the healthy subjects by including the clustering of models into groups taught by Schmidt. One of ordinary skill in the art would be motivated to combine the references since it provides a refined learning model (Schmidt, Abstract, teaches the motivation of combination to be to provide a refined machine learning model).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claims 11-17 are rejected under 35 U.S.C. 103 as being unpatentable over Urman in view of Landon, Avraham, Schmidt, and Mori (US 20150302171 A1).
Regarding Claim 11, the combination of references of Urman in view of Landon, Avraham, and Schmidt teaches "The method of claim 1, wherein the plurality of healthy models are generated by: receiving training data for the healthy subjects"; (Schmidt, Para. 43, teaches the initial machine learning model may have been trained using the medical images in the database for segmentation or classification of a physiological feature, i.e., receive training data for the subjects being the medical images comprising CT scans or MRI images);
"analyzing the training data to identify commonalities among the healthy subjects"; (Schmidt, Para. 44, teaches extracting a subset of medical images from the database based on a similarity score of the medical images determined based on content information of the medical images and/or based on latent space representation in the database, i.e., analyze the training data to identify commonalities being similarities);
"clustering the healthy subjects into clusters based at least in part on the commonalities among the healthy subjects"; (Schmidt, Para. 44, teaches a subset of medical images is extracted from the database based on a similarity score of the medical images wherein the subset is identified by clustering the medical images in latent space and identifying a subset as a specific cluster using a suitable granularity and wherein clustering can be done using the k-means algorithm or incorporating additional content information, i.e., clustering the models/subjects into clusters based on the commonalities).
However, the combination of references of Urman in view of Landon, Avraham, and Schmidt does not explicitly teach "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 healthy subjects that are within the cluster".
In an analogous field of endeavor, Mori teaches "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 healthy subjects that are within the cluster"; (Mori, Paras. 61 and 68-69, teaches a cluster may be created within the image data stored in the clinical database in which a representing case for each cluster is selected by calculating using population-averaging tools based on linear or non-linear image transformation, i.e., generating a plurality of models includes generating one of the models for each cluster based on the training data for the models in that cluster being the representing case for each cluster selected by averaging tools for the image data).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Urman in view of Landon, Avraham, and Schmidt wherein the models are healthy by including the generation of one of the models for each cluster based on the data of the subjects within the cluster taught by Mori. One of ordinary skill in the art would be motivated to combine the references since it improves mapping accuracy (Mori, Para. 12, teaches the motivation of combination to be to improve mapping and parcellation accuracy).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 12, the combination of references of Urman in view of Landon, Avraham, Schmidt, and Mori teaches "The method of claim 11, wherein generating the plurality of healthy models comprises selecting, for each cluster, a model of one of the healthy subjects within the cluster to be the healthy model"; (Mori, Paras. 61 and 68-69, teaches a cluster may be created within the image data stored in the clinical database in which a representing case for each cluster is selected by calculating using population-averaging tools based on linear or non-linear image transformation, i.e., model within the cluster of each cluster is selected to be the representing model).
The proposed combination as well as the motivation for combining the Urman in view of Landon, Avraham, Schmidt, and Mori references presented in the rejection of Claim 11, applies to claim 12. Thus, the method recited in claim 12 is met by Urman in view of Landon, Avraham, Schmidt, and Mori.
Regarding Claim 13, the combination of references of Urman in view of Landon, Avraham, Schmidt, and Mori teaches "The method of claim 11, wherein generating the plurality of healthy models comprises generating, for each cluster, the healthy model using information about at least two of the healthy subjects within the cluster"; (Mori, Paras. 61 and 68-69, teaches a cluster may be created within the image data stored in the clinical database in which a representing case for each cluster is selected by calculating using population-averaging tools based on linear or non-linear image transformation, i.e., generating the model for each clusters uses information about at least two of the subjects/models/images within the cluster due to the use of population-averaging tools).
The proposed combination as well as the motivation for combining the Urman in view of Landon, Avraham, Schmidt, and Mori references presented in the rejection of Claim 11, applies to claim 13. Thus, the method recited in claim 13 is met by Urman in view of Landon, Avraham, Schmidt, and Mori.
Regarding Claim 14, the combination of references of Urman in view of Landon, Avraham, Schmidt, and Mori teaches "The method of claim 11, wherein each of the healthy subjects has a medical image associated therewith, wherein measurements for each healthy subject are extracted from the medical image associated with each healthy subject"; (Mori, Para. 34-35, teaches a large amount of clinical data such as MRI, PET, and CT may be used as part of the clinical database wherein the images are converted to anatomical feature matrices and non-image clinical data such as gender, age, functional states, and diagnosis are incorporated to the clinical database wherein patient images are mapped to images in the database using the measurements of anatomical similarities from the patients to all data in the database, based on the structured anatomical information, i.e., each of the subjects has a medical image in which measurements for the subject/image are extracted from the image associated with the subject being the extracted anatomical feature matrices).
The proposed combination as well as the motivation for combining the Urman in view of Landon, Avraham, Schmidt, and Mori references presented in the rejection of Claim 11, applies to claim 14. Thus, the method recited in claim 14 is met by Urman in view of Landon, Avraham, Schmidt, and Mori.
Regarding Claim 15, the combination of references of Urman in view of Landon, Avraham, Schmidt, and Mori teaches "The method of claim 11, wherein analyzing the training data comprises performing a principal component analysis to identify the commonalities among the plurality of healthy subjects"; (Mori, Para. 31, teaches performing principal component analysis to extract independent components within the N-dimension anatomical space wherein mapping of anatomical similarity of the patient data are associated with the population data based on the distances in the PCA space, i.e., analyzing the training data comprises performing principal component analysis to identify commonalities being the mapping of anatomical similarity of the data based on the distances in the PCA space).
The proposed combination as well as the motivation for combining the Urman in view of Landon, Avraham, Schmidt, and Mori references presented in the rejection of Claim 11, applies to claim 15. Thus, the method recited in claim 15 is met by Urman in view of Landon, Avraham, Schmidt, and Mori.
Regarding Claim 16, the combination of references of Urman in view of Landon, Avraham, Schmidt, and Mori teaches "The method of claim 11, wherein the commonalities are based on principal components defined for each of the healthy subjects"; (Mori, Para. 31, teaches performing principal component analysis to extract independent components within the N-dimension anatomical space wherein mapping of anatomical similarity of the patient data are associated with the population data based on the distances in the PCA space, i.e., commonalities or similarity is based on principal components defined for each of the subjects' models/images).
The proposed combination as well as the motivation for combining the Urman in view of Landon, Avraham, Schmidt, and Mori references presented in the rejection of Claim 11, applies to claim 16. Thus, the method recited in claim 16 is met by Urman in view of Landon, Avraham, Schmidt, and Mori.
Regarding Claim 17, the combination of references of Urman in view of Landon, Avraham, Schmidt, and Mori teaches "The method of claim 16, wherein the clustering is performed using k-means clustering"; (Schmidt, Para. 44, teaches a subset of medical images is extracted from the database based on a similarity score of the medical images wherein the subset is identified by clustering the medical images in latent space and identifying a subset as a specific cluster using a suitable granularity wherein clustering can be done using the k-means algorithm).
The proposed combination as well as the motivation for combining the Urman in view of Landon, Avraham, Schmidt, and Mori references presented in the rejection of Claim 11, applies to claim 17. Thus, the method recited in claim 17 is met by Urman in view of Landon, Avraham, Schmidt, and Mori.
Conclusion
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/ANDREW S BUDISALICH/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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