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 .
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 2 and 3 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "the image" in the preamble. There is insufficient antecedent basis for this limitation in the claim as there is no prior definition for image. However, claim 1 defines a first image and a second image and it is unclear which image the applicant is referring to as “the image”. For the purpose of examination, the Examiner is reading as “the first image”. Appropriate corrections are required
Claim 3 is objected to for being dependent on rejected claim 2.
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 – 4, 8, 10 and 14 – 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion, organizing human activity and mathematical concepts and calculations). The claim(s) recite(s) a method, and computer-readable storage medium configured to detect a focus of attention. This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved .The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such except for the generic computer elements at high level of generality (i.e., processor, memory).
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
• STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
• STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
o STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
o STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
o STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1 and 10 are directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories? YES. Claims 1, 14 and 15 are directed to a method, a system and a computer readable medium
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? YES, the claims are directed toward a mental process (i.e. abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
• Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
• Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
• Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The method in claim 1 (and computer-readable storage in claim(s) 10) comprise a mental process that can be practicably performed in the human mind (or generic computers or components configured to perform the method) and, therefore, an abstract idea.
Regarding Claims 1, 14 and 15: the method recites the steps (functions) of:
determining a location for simulating a structure within the first image (mental process including observation and evaluation, and can be done mentally in the human mind or using a generic computer program; determining location for simulating a structure within the image….);
simulating, according to the location, a shape for the structure (mental process including observation and evaluation, and can be done mentally in the human mind or using a generic computer program; simulating a shape of a structure can be done by using a pen and paper ….);
generating a mask according to the location and the shape for the structure (mental process including observation and evaluation, and can be done mentally in the human mind or using a generic computer program; penetrating a mask based on location and shape structure can be done by using a pen and paper ….);
applying the mask to the first image to generate a second image simulating the structure (mental process including observation and evaluation, and can be done mentally in the human mind or using a generic computer program; penetrating a mask based on location and shape structure can be done by using a pen and paper ….).
These limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). As such, a person could mentally analyze an image and determine a location for simulating structure within the image, simulate a shape for the structure according to the location, generate mask according to the location and shape and applying the mask to generate a second image, either mentally or using a pen and paper. The mere nominal recitation that the various steps are being executed by a device/in a device (e.g. processing unit) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
• an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
• an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
• an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
• an additional element effects a transformation or reduction of a particular article to a different state or thing; and
• an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
• an additional element merely recites the words “simulating”, “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
• an additional element adds insignificant extra-solution activity to the judicial exception; and
• an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1, 14 and 15 does/do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. Claims 1, 14 and 15 recites additional elements:
obtaining a first image of a subject (insignificant extra solution activity of data gathering);
Claims 15 recites the further limitations of:
non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to (instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea).
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
• adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
• simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claims 1, 14 and 15 are directed to an abstract idea of image simulation, shape generation masking, location determination and masking. These claims are recited at a high level of generality of simulating structures in images though generic image processing steps (obtaining, determining, simulating shape, generating mask, applying mask etc.) and they do not provide any specific technical domain or concrete limitations and lacks details regarding technological improvements. The claims does/do not recite any additional elements that are not well-understood, routine or conventional. The use of a computer to “obtaining, determining, and simulating, applying, etc., as claimed in Claims 1, 14 and 15 is a routine, well-understood and conventional process that is performed by computers.
Thus, since Claims 1, 14 and 15 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that Claim(s) 1, 14 and 15 are not eligible subject matter under 35 U.S.C 101.
Regarding Claim 2: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The limitation(s):
wherein determining the location within the image comprises: identifying at least one anatomical region associated with a body part of the subject (mental process including observation and evaluation, and can be done mentally in the human mind or using a generic computer program; identifying….);
extracting, using the identified at least one anatomical region, a plurality of territories associated with the first image (mental process including observation and evaluation, and can be done mentally in the human mind or using a generic computer program; extracting ….); and
selecting at least one first territory associated with the first image as the location for the mask (mental process including observation and evaluation, and can be done mentally in the human mind or using a generic computer program; selecting….).
Regarding Claim 3: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The limitation(s): selecting at least one second territory associated with the first image as a second location for another mask for generating a third image (mental process including observation and evaluation, and can be done mentally in the human mind or using a generic computer program; selecting….).
Regarding Claim 4: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The limitation(s): wherein determining the location and simulating the shape comprises:
receiving an indication of at least one territory associated with the first image as the location (insignificant pre/post-solution extra activity of generating data); and
simulating the shape of the structure based on the at least one territory associated with the first image (mental process including observation and evaluation, and can be done mentally in the human mind or using a paper and pencil).
Regarding Claim 8: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The limitation(s): wherein simulating the shape comprises: generating an elliptical shape according to one or more parameters, the one or more parameters comprising a long axis or a short axis of the first image defining a dimension of the structure (mental process including observation and evaluation, and can be done mentally in the human mind or using a paper and pencil or using a generic computer program);
applying an elastic distortion to the elliptical shape to simulate the shape of the structure (mental process including observation and evaluation, and can be done mentally in the human mind or using a paper and pencil or using a generic computer program).
Regarding Claim 10: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The limitation(s): wherein generating the mask comprises determining an appearance of the mask associated with at least an intensity of one or more voxels of the first image (mental process including observation and evaluation, and can be done mentally in the human mind or using a pen and paper or using a generic computer program).
Regarding Claims 5 – 7, 9, and 11 – 13: these claims recite additional limitations that integrate the mental process into practical application or add significantly more to the mental process. Therefore, claims 5 – 7, 9 and 11 – 13 are eligible subject matter under 35 U.S.C 101. However, claims 5 – 7, 9 and 11 – 13 are objected to for being dependent on rejected base claims.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
(g)(1) during the course of an interference conducted under section 135 or section 291, another inventor involved therein establishes, to the extent permitted in section 104, that before such person’s invention thereof the invention was made by such other inventor and not abandoned, suppressed, or concealed, or (2) before such person’s invention thereof, the invention was made in this country by another inventor who had not abandoned, suppressed, or concealed it. In determining priority of invention under this subsection, there shall be considered not only the respective dates of conception and reduction to practice of the invention, but also the reasonable diligence of one who was first to conceive and last to reduce to practice, from a time prior to conception by the other.
A rejection on this statutory basis (35 U.S.C. 102(g) as in force on March 15, 2013) is appropriate in an application or patent that is examined under the first to file provisions of the AIA if it also contains or contained at any time (1) a claim to an invention having an effective filing date as defined in 35 U.S.C. 100(i) that is before March 16, 2013 or (2) a specific reference under 35 U.S.C. 120, 121, or 365(c) to any patent or application that contains or contained at any time such a claim.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1 – 4, 14 – 15 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Ben-Haim (US 20160027342 A1; hereafter referred to as Ben-Haim).
Regarding Claim 1, Ben-Haim teaches:
A method for simulating structures in images (Ben-Haim, Fig. 5, [0461] “[0461] At 518, the model is optionally simulated”), comprising:
obtaining a first image of a subject (Ben-Haim, [0213] “imaging modality means an imaging modality that is designed to or otherwise configured to produce functional based data and/or images (e.g., of an intrabody organ or a part thereof) for example, a nuclear based modality such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), or other modalities. The images may be based on changes within tissues, for example, chemical composition (e.g., at nerve synapses), released chemicals (e.g., at synapses), metabolism, blood flow, absorption, and/or other changes. The images may provide physiological functional data, for example, activity of nervous system tissue”);
determining a location for simulating a structure within the first image (Ben-Haim, [0220] “a method of processing functional images to identify and/or locate nerves (e.g., GPs) within tissues (e.g., heart, stomach, intestines, kidney, aorta, or other organs or structures). Optionally, anatomical images used to reconstruct the functional image and/or process the functional data are combined with the functional images, the combined image may be used as a basis for locating GPs”);
simulating, according to the location, a shape for the structure (Ben-Haim, [0220] - [0222] “The size and/or shape of the image masks may be defined, for example, by the ability of software to segment the anatomical image, by the resolution of the anatomical and/or functional images, by the resolution of the ablation treatment, by the size of the structure being identified”; Ben-Haim, [0364] The image mask may be a 2D and/or 3D volume with a shape and/or size selected based on tissues and/or organ parts within the anatomical image. The image mask may correspond to anatomical parts believed to contain the neural tissue for imaging (e.g., GPs), for example, corresponding to the walls of the four heart chambers, corresponding to the intestinal wall, bladder wall, renal artery, aortic branch region of the renal artery, kidney, or other structures”; Ben-Haim, [0367] “The templates may define: the location of the innervated organ (or tissue) and/or the location of the GPs within and/or in proximity to the innervated organ, outside of the organ. The templates may be generated, for example based on a predefined anatomical atlas that maps nerve structures to tissues and/or organs of the body”);
generating a mask according to the location and the shape for the structure (Ben-Haim, [0220] “The method may comprise generating image masks corresponding to regions of the anatomical image contain the GPs and/or the innervations of the organ… the selected image masks are applied to corresponding locations on the functional image, for example, by a registration process. GP characteristics within the functional image are reconstructed, instructed by the applied mask”); and
applying the mask to the first image to generate a second image simulating the structure (Ben-Haim, [0364] “The image mask may correspond to anatomical parts believed to contain the neural tissue for imaging (e.g., GPs), for example, corresponding to the walls of the four heart chambers, corresponding to the intestinal wall, bladder wall, renal artery, aortic branch region of the renal artery, kidney, or other structures”; Ben-Haim, [0370] “The image masks are applied to the functional image… the image masks are applied to the functional data…the image masks are applied to combined functional and anatomical images and/or data, for example, overlaid images”).
Regarding Claim 2, Ben-Haim teaches the method of claim 1, wherein determining the location within the image comprises:
identifying at least one anatomical region associated with a body part of the subject (Ben-Haim, [0220] “a method of processing functional images to identify and/or locate nerves (e.g., GPs) within tissues (e.g., heart, stomach, intestines, kidney, aorta, or other organs or structures)”);
extracting, using the identified at least one anatomical region, a plurality of territories associated with the first image (Ben-Haim, [0215] “hidden functional portions of an organ may be localized by visualizing their functionality. In some embodiments, this may be combined with enhancing the resolution of functional imaging, for example, by focusing the functional imaging on regions expected to include the anatomical imaging hidden functional portions. These regions may be identified, in some embodiments, based on structural imaging); and
selecting at least one first territory associated with the first image as the location for the mask (Ben-Haim, [0220] “anatomical images used to reconstruct the functional image and/or process the functional data are combined with the functional images, the combined image may be used as a basis for locating GPs. The method may comprise generating image masks corresponding to regions of the anatomical image contain the GPs and/or the innervations of the organ… The anatomical information, in the form of the mask, may be used for guiding the processing to certain regions of the functional image, to help in locating the GPs of interest”).
Regarding Claim 3, Ben-Haim teaches the method of claim 2, further comprising, subsequent to selecting the at least one first territory,
selecting at least one second territory associated with the first image as a second location for another mask for generating a third image (Ben-Haim, [0363] “Different image masks may be generated for different tissue types, and/or for GPs at different locations within the organ. For example, for GPs within the epicardium one set of image masks is generated. For GPs within the myocardium another set of image masks may be generated. Image masks may be generated for fat pads”).
Regarding Claim 4, Ben-Haim teaches the method of claim 2, wherein determining the location and simulating the shape comprises:
receiving an indication of at least one territory associated with the first image as the location (Ben-Haim, [0186] “the representation includes location indications, for example, an anatomical location, body coordinates and/or a functional location. Optionally or alternatively to static data, dynamic data per ganglion may be stored, for example, a time-based activation profile, correlation with organ data and/or other dynamic data”; Ben-Haim, [0187] “data is provided as location indications, size/shape indications”); and
simulating the shape of the structure based on the at least one territory associated with the first image (Ben-Haim, Fig.5, [0458] “the visualization is with reference to an organ (515). In one example, a 2D or 3D visualization of an organ or part thereof is used with reference (e.g., mapping) to one or more ANS model portions”; Ben-Haim, [0461] “At 518, the model is optionally simulated… the model is provided in a visualized and/or simulated form… for example physiological state and/or a simulation of other body systems are used as an input into the model simulation”).
Regarding Claim 9, Ben-Haim teaches the method of claim 1, wherein generating the mask comprises:
obtaining a plurality of historical masks associated with a plurality of images from one or more subjects (Ben-Haim, [0376] “the image masks are generated and/or applied based on templates. The templates may define: the location of the innervated organ (or tissue) and/or the location of the GPs within and/or in proximity to the innervated organ, outside of the organ. The templates may be generated, for example based on a predefined anatomical atlas that maps nerve structures to tissues and/or organs of the body”);
training a model using at least one machine learning technique based on the plurality of historical masks (Ben-Haim, [0449] ““learning data set” is used to train the machine to sort through multiple inputs per patient and find the combination of those that predict the wanted outcome…. the learned data set or tagged data set is provided by stimulating a patient and measuring reaction”; Ben-Haim, [0452] “The machine is trained the machine to identify this response (e.g., in the collected data)”);
generating, using the trained model, the mask for the location according to the plurality of historical masks (Ben-Haim, [0481] “diagnosis sub-system 86 uses a diagnosis database 808 (e.g., rules, example diagnoses, machine learning data) to assist in providing a diagnosis...the diagnosis database is updatable and/or parts thereof are available at different and/or additional cost. The result may be a personalized diagnosis 810. In an exemplary embodiment of the invention, the diagnosis database includes a plurality of templates, each one optionally associated with one or more possible diagnoses and/or including instruction for missing data to assist in diagnosis”); and
refining the mask based on a comparison between the generated mask and the plurality of historical masks associated with the location for simulating the structure (Ben-Haim, [0422]” adapting/refining the model may also uses data about the organ (318) (also referred as organ data)”; Ben-Haim, Fig. 8, [0481] “diagnosis sub-system 86 uses a diagnosis database 808 (e.g., rules, example diagnoses, machine learning data) to assist in providing a diagnosis...the diagnosis database is updatable and/or parts thereof are available at different and/or additional cost. The result may be a personalized diagnosis 810. In an exemplary embodiment of the invention, the diagnosis database includes a plurality of templates, each one optionally associated with one or more possible diagnoses and/or including instruction for missing data to assist in diagnosis”).
Regarding Claim 10, Ben-Haim teaches the method of claim 1, wherein generating the mask comprises determining an appearance of the mask associated with at least an intensity of one or more voxels of the first image (Ben-Haim, [0393] “the identified GP is automatically related to a tissue type… related to the tissue type based on the applied image mask. Alternatively, or additionally, the identified GP is related to the tissue type based on the characteristics of the intensity readings, for example, large sizes (denoting large GPs) may only be found in certain tissues”; Ben-Haim, [0394] Optionally, at 4830, one or more parameters are calculated for the identified GPs (also referred to herein as GP parameters). Examples of parameters include: average size, specific activity (e.g., counts per voxel of GP/average counts in the corresponding image mask volume)”).
Regarding Claim 11, Ben-Haim teaches the method of claim 10, wherein determining the appearance comprises:
selecting an aggregated pixel intensity of the mask (Ben-Haim, [0391] “the GP may be identified by comparing calculated activity (e.g., image intensity) of a certain region to surrounding activity in the same image mask. Alternatively, or additionally, the GP may be identified by comparing calculated activity (e.g., image intensity) within the image mask to activity in another image mask”; Ben-Haim, [0393] “the identified GP is related to the tissue type based on the characteristics of the intensity readings, for example, large sizes (denoting large GPs) may only be found in certain tissues”);
applying at least one pattern for the mask (Ben-Haim, [0367] “the image masks are generated and/or applied based on templates. The templates may define: the location of the innervated organ (or tissue) and/or the location of the GPs within and/or in proximity to the innervated organ, outside of the organ. The templates may be generated, for example based on a predefined anatomical atlas that maps nerve structures to tissues and/or organs of the body”); and
simulating at least one noise for the mask (Ben-Haim, [0344] “Noise may be received from areas corresponding to the inside of the heart chamber, even though no activity is expected. Optionally, the noise is removed from the functional data based on the corresponding anatomical image (e.g., after image registration). Optionally, intensity denoting noise within blood (or other fluid) filled chambers and/or vessels is removed. For example, intensity readings of the functional data corresponding to heart chambers and/or surrounding blood vessels are removed, e.g., by applying one or more image mask on functional image”).
Regarding Claim 12, Ben-Haim teaches the method of claim 10, wherein the at least one pattern comprises at least one of: edema pattern, hemorrhagic pattern, necrotic pattern, cystic pattern, inflammatory pattern (Ben-Haim, [0159] “the model includes information about particular GPs, for example, a degree of nearby physical stimulation (e.g., inflammations)”), tumoral pattern (Ben-Haim, [0641] physical or chemical properties of tumor), or ischemic pattern (Ben-Haim, [0193] “ischemia is diagnosed”).
Regarding Claim 14, Ben-Haim teaches:
A system for simulating structures in images (Fig.5, [0461] “the model is provided in a visualized and/or simulated form and visualization and/or simulation comprise display and/or usage of such provided for … for example physiological state and/or a simulation of other body systems are used as an input into the model simulation”), comprising one or more processors ([0474] “processor 704 modifies and/or times the imaging and/or data collection to such stimulation”) configured to:
obtain a first image of a subject (Ben-Haim, [0213] “imaging modality means an imaging modality that is designed to or otherwise configured to produce functional based data and/or images (e.g., of an intrabody organ or a part thereof) for example, a nuclear based modality such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), or other modalities. The images may be based on changes within tissues, for example, chemical composition (e.g., at nerve synapses), released chemicals (e.g., at synapses), metabolism, blood flow, absorption, and/or other changes. The images may provide physiological functional data, for example, activity of nervous system tissue”);
determine a location for simulating a structure within the first image (Ben-Haim, [0220] “a method of processing functional images to identify and/or locate nerves (e.g., GPs) within tissues (e.g., heart, stomach, intestines, kidney, aorta, or other organs or structures). Optionally, anatomical images used to reconstruct the functional image and/or process the functional data are combined with the functional images, the combined image may be used as a basis for locating GPs”);
simulate, according to the location, a shape for the structure (Ben-Haim, [0220] - [0222] “The size and/or shape of the image masks may be defined, for example, by the ability of software to segment the anatomical image, by the resolution of the anatomical and/or functional images, by the resolution of the ablation treatment, by the size of the structure being identified”; Ben-Haim, [0364] The image mask may be a 2D and/or 3D volume with a shape and/or size selected based on tissues and/or organ parts within the anatomical image. The image mask may correspond to anatomical parts believed to contain the neural tissue for imaging (e.g., GPs), for example, corresponding to the walls of the four heart chambers, corresponding to the intestinal wall, bladder wall, renal artery, aortic branch region of the renal artery, kidney, or other structures”; Ben-Haim, [0367] “The templates may define: the location of the innervated organ (or tissue) and/or the location of the GPs within and/or in proximity to the innervated organ, outside of the organ. The templates may be generated, for example based on a predefined anatomical atlas that maps nerve structures to tissues and/or organs of the body”);
generate a mask according to the location and the shape for the structure (Ben-Haim, [0220] “The method may comprise generating image masks corresponding to regions of the anatomical image contain the GPs and/or the innervations of the organ… the selected image masks are applied to corresponding locations on the functional image, for example, by a registration process. GP characteristics within the functional image are reconstructed, instructed by the applied mask”); and
apply the mask to the first image to generate a second image simulating the structure (Ben-Haim, [0364] “The image mask may correspond to anatomical parts believed to contain the neural tissue for imaging (e.g., GPs), for example, corresponding to the walls of the four heart chambers, corresponding to the intestinal wall, bladder wall, renal artery, aortic branch region of the renal artery, kidney, or other structures”; Ben-Haim, [0370] “The image masks are applied to the functional image… the image masks are applied to the functional data…the image masks are applied to combined functional and anatomical images and/or data, for example, overlaid images”).
Regarding Claim 15, Ben-Haim teaches:
A non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one processor (Ben-Haim, [0658] “The instructions executed by the processor/module may, for example, be preloaded into the processor or may be stored in a separate memory unit such as a RAM, a ROM, a hard disk, an optical disk, a magnetic medium, a flash memory, other permanent, fixed, or volatile memory, or any other mechanism capable of storing instructions for the processor/module”), cause the at least one processor to:
obtain a first image of a subject (Ben-Haim, [0213] “imaging modality means an imaging modality that is designed to or otherwise configured to produce functional based data and/or images (e.g., of an intrabody organ or a part thereof) for example, a nuclear based modality such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), or other modalities. The images may be based on changes within tissues, for example, chemical composition (e.g., at nerve synapses), released chemicals (e.g., at synapses), metabolism, blood flow, absorption, and/or other changes. The images may provide physiological functional data, for example, activity of nervous system tissue”);
determine a location for simulating a structure within the first image (Ben-Haim, [0220] “a method of processing functional images to identify and/or locate nerves (e.g., GPs) within tissues (e.g., heart, stomach, intestines, kidney, aorta, or other organs or structures). Optionally, anatomical images used to reconstruct the functional image and/or process the functional data are combined with the functional images, the combined image may be used as a basis for locating GPs”);
simulate, according to the location, a shape for the structure (Ben-Haim, [0220] - [0222] “The size and/or shape of the image masks may be defined, for example, by the ability of software to segment the anatomical image, by the resolution of the anatomical and/or functional images, by the resolution of the ablation treatment, by the size of the structure being identified”; Ben-Haim, [0364] The image mask may be a 2D and/or 3D volume with a shape and/or size selected based on tissues and/or organ parts within the anatomical image. The image mask may correspond to anatomical parts believed to contain the neural tissue for imaging (e.g., GPs), for example, corresponding to the walls of the four heart chambers, corresponding to the intestinal wall, bladder wall, renal artery, aortic branch region of the renal artery, kidney, or other structures”; Ben-Haim, [0367] “The templates may define: the location of the innervated organ (or tissue) and/or the location of the GPs within and/or in proximity to the innervated organ, outside of the organ. The templates may be generated, for example based on a predefined anatomical atlas that maps nerve structures to tissues and/or organs of the body”);
generate a mask according to the location and the shape for the structure (Ben-Haim, [0220] “The method may comprise generating image masks corresponding to regions of the anatomical image contain the GPs and/or the innervations of the organ… the selected image masks are applied to corresponding locations on the functional image, for example, by a registration process. GP characteristics within the functional image are reconstructed, instructed by the applied mask”); and
apply the mask to the first image to generate a second image simulating the structure (Ben-Haim, [0364] “The image mask may correspond to anatomical parts believed to contain the neural tissue for imaging (e.g., GPs), for example, corresponding to the walls of the four heart chambers, corresponding to the intestinal wall, bladder wall, renal artery, aortic branch region of the renal artery, kidney, or other structures”; Ben-Haim, [0370] “The image masks are applied to the functional image… the image masks are applied to the functional data…the image masks are applied to combined functional and anatomical images and/or data, for example, overlaid images”).
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 5 – 7 are rejected under 35 U.S.C. 103 as being unpatentable over Ben-Haim (US 20160027342 A1; hereafter referred to as Ben-Haim) in view of Behrooz et al. (US 20180360404 A1; hereafter referred to as Behrooz).
Regarding Claim 5, Ben-Haim teaches the method of claim 1, but does not explicitly recite:
providing a seed to the first image at the location;
growing at least one region around the seed using at least one region-growing algorithm; and
generating the mask based on the grown seed.
In the same field of endeavor, Behrooz teaches:
providing a seed to the first image at the location (Behrooz, [0027] “apply one or more second derivative splitting filters to portions of the image corresponding to the one or more user-identified segmented regions to produce a split bone mask; determine split binary components of the split bone mask; and perform a region growing operation using the split bone mask components as seeds, thereby producing a refined or recalculated segmentation map differentiating individual bones in the image”; Behrooz, [0072] “the connected components of the binary mask can be identified and labeled through connected component analysis, which is a digital image processing technique based on graph traversal. This step produces the seeds for marker-controlled watershed as shown in FIG. 7”);
growing at least one region around the seed using at least one region-growing algorithm (Behrooz, Fig. 7, [0075] “following determining the split binary components of the split bone mask, the method includes performing, by the processor, a region growing operation using the split bone mask components as seeds, thereby producing a refined or recalculated segmentation map differentiating individual bones in the image”); and
generating the mask based on the grown seed (Behrooz, Fig. 7, [0075] “following determining the split binary components of the split bone mask, the method includes performing, by the processor, a region growing operation using the split bone mask components as seeds, thereby producing a refined or recalculated segmentation map differentiating individual bones in the image”).
Ben-Haim and Behrooz are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing/analysis. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Ben-Haim with the method of using the seed to generate a mask as taught by Behrooz to make the invention that uses region growing algorithm to generate a segmented mask of an anatomical region (bone); doing so can result in the system that generates a refined or recalculated segmentation map differentiating individual regions (bones) in an image (Behrooz, [0075]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 6, Ben-Haim in view of Behrooz teaches the method of claim 5, wherein the at least one region-growing algorithm comprises at least one of:
region growing, region merging, split and merge, watershed transform, connected component labeling, or graph-cut segmentation (Behrooz, [0058] “the connected components of the split binary bone mask can be used as seeds (sources) for marker-controlled watershed segmentation”; Behrooz, Fig. 7, [0072] the connected components of the binary mask can be identified and labeled through connected component analysis, which is a digital image processing technique based on graph traversal. This step produces the seeds for marker-controlled watershed as shown in FIG. 7. The split mask used for the labeled seeds shown in FIG. 7 was created by applying a hybrid LAP-HEH splitting filter to the gray-scale images of the dataset shown in FIG. 1”).
Regarding Claim 7, Ben-Haim in view of Behrooz teaches the method of claim 5, wherein the seed is provided at at least one voxel having a first intensity, and wherein the region around the seed is grown to at least one neighboring voxel having a second intensity, wherein the second intensity is substantially similar to the first intensity (Behrooz, [0052] ““bone seed” refers to a set of voxels (e.g., connected set of voxels) which is a part of a bone in a skeleton, and which can be used to find the complete, discrete bone by expanding it (e.g., by repeatedly adding to it neighboring voxels which are likely to be voxels of the same individual bone)”; Behrooz, [0056] “applying a “second derivative splitting filter” is an image processing operation based on the second derivatives (or approximations thereof) of the intensity of a 3D image, e.g., a gray-scale 3D image, at each of a plurality of voxels”; see Behrooz, [0059]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ben-Haim (US 20160027342 A1; hereafter referred to as Ben-Haim) in view of Shmayahu et al. (US 20190328458 A1; hereafter referred to as Shmayahu).
Regarding Claim 8, Ben-Haim teaches the method of claim 1, but does not explicitly recite: wherein simulating the shape comprises:
generating an elliptical shape according to one or more parameters, the one or more parameters comprising a long axis or a short axis of the first image defining a dimension of the structure; and
applying an elastic distortion to the elliptical shape to simulate the shape of the structure.
In the same field of endeavor, Shmayahu teaches:
generating an elliptical shape according to one or more parameters, the one or more parameters comprising a long axis or a short axis of the first image defining a dimension of the structure (Shmayahu, Fig. 5A, [0250] “V denotes the volume of ablated shape in mm.sup.3. The top view of an exemplary ablation region may be modeled as an approximately elliptical shape”); and
applying an elastic distortion to the elliptical shape to simulate the shape of the structure (Shmayahu, [0127] “the services provided by some prominent graphical game engines are motion physics simulators (e.g., for modeling collisions, accelerations, elastic deformations, object destruction, and the like). In some embodiments, one or more of these motion physics simulators is used to increase the naturalistic impression of a scene”).
Ben-Haim and Shmayahu are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing/analysis. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Ben-Haim with the method of simulating the shape as taught by Shmayahu to make the invention that generates elliptical shape according to one or more parameters and applies an elastic distortion to the elliptical shape to simulate the shape of the structure; doing so the system can identify geometrical appearance of the shape and apply elastic deformations to visually simulate geometrical deformations or change in the shape and increase naturalistic impression of the simulated scene (Shmayahu, [0113], [0127]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Ben-Haim (US 20160027342 A1; hereafter referred to as Ben-Haim) in view of Cecil et al. (US 20210064977 A1; hereafter referred to as Cecil).
Regarding Claim 13, Ben-Haim teaches the method of claim 10, wherein determining the appearance and applying the mask comprises:
updating the third image based on a comparison of the third image to at least one historical image, the at least one historical image having a second mask at the location with a second shape similar to the shape of the mask (Ben-Haim, Fig. 8, [0481] “diagnosis sub-system 806 may include one or more modules which apply processing on the model to extract diagnose…, the diagnosis database is updatable and/or parts thereof are available at different and/or additional cost. The result may be a personalized diagnosis 810 …, the diagnosis database includes a plurality of templates, each one optionally associated with one or more possible diagnoses and/or including instruction for missing data to assist in diagnosis. Optionally or alternatively, at least one dynamic template is used. Such a template may be useful, for example, if a disease is characterized by a temporal pattern of behavior. Such a template may include, for example, multiple snapshots with a time indicator or define a function of change over time and/or in response to a trigger”).
However, Ben-Haim but does not explicitly recite:
providing the generated mask and at least a portion of the first image for applying the mask as inputs to a model trained using a machine learning technique;
generating, using the model, a third image comprising at least the portion of the first image and the generated mask;
In the same field of endeavor, Cecil teaches:
providing the generated mask and at least a portion of the first image for applying the mask as inputs to a model trained using a machine learning technique (Cecil, [0037] The machine learning based approach disclosed herein allows for additional OPC correction on top of the results provided by the machine learning model, thereby allowing the output of the machine learning model to be further improved for generating the final mask solution”);
generating, using the model, a third image comprising at least the portion of the first image and the generated mask (Cecil, [0037] The machine learning based approach disclosed herein allows for additional OPC correction on top of the results provided by the machine learning model, thereby allowing the output of the machine learning model to be further improved for generating the final mask solution. Accordingly, the machine learning based mask generation acts as a shortcut to get the mask closer to final solution and let OPC finish the correction with less effort”);
Ben-Haim and Cecil are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing/analysis. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Ben-Haim with the method of generating third input mask as taught by Cecil to make the invention that applies mask into a machine learning model to generate a third image; doing so can optimize the machine learning based mask generation acts and result in improved runtime efficiency and accuracy (Cecil, [0037]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20160300343 A1 ORGAN DETECTION AND SEGMENTATION A method of segmenting a target organ of the body in medical images, the method comprising: providing a probabilistic atlas with probabilities for a presence of the target organ at different locations relative to a bounding region of the target organ; providing one or more medical test images; for each test image, identifying, at least provisionally, a location of a bounding region for the target organ in the test image; and for each test image, using the probabilities from the probabilistic atlas to segment the target organ in the test image, with the segmenting performed by a data processor.
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VAISALI RAO. KOPPOLU
Examiner
Art Unit 2664
/VAISALI RAO KOPPOLU/Examiner of Art Unit 2664
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