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 .
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, image data processing means and selecting means claimed in claim 6 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, a training unit claimed in claim 9 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 1005.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1-5 and 12 are objected to because of the following informalities:
1. (Proposed Amendments) A computer implemented method for calculating and displaying, within a computer-generated display of a display device, a radiation dose distribution of a radiation beam using Monte Carlo simulations passing through a region of interest within a target object, the computer implemented method comprising at least the steps of:
i) obtaining a set of three-dimensional image data representing the region of interest [[in]] within (recited in preamble) the target object;
ii) generating a display signal causing the display device to display the set of three-dimensional image data [[set]] of the region of interest within the target object on the display device;
iii) receiving a user selection, based on [[user]] a user interaction with the set of three-dimensional image data of the region of interest within the target object [[set]] on the display device, the user selection identifying one or more selected locations of interest within the region of interest in the displayed set of three-dimensional image [[set]] data of the region of interest within the target object;
iv) calculating, using radiation dose calculation means, a radiation dose distribution using Monte Carlo simulations of a radiation beam passing through the region of interest within the target object taking the one or more selected locations of interest within the region of interest into account, the radiation dose distribution being composed of noisy dose data and associated uncertainty data;
v) denoising the radiation dose data distribution for the associated uncertainty data with one or more trained machine learning algorithms, thereby generating a denoised radiation dose distribution.
Appropriate correction is required.
Claim 2 is objected to because of the following informalities:
2. (Proposed Amendments) The computer-implemented method according to The computer-implemented method according to wherein the computer implemented method further comprises the step of:
vi) displaying, within the set of three-dimensional image data [[set]] of the region of interest within the target object displayed the display device, the generated denoised radiation dose [[data]] distribution (a previously recited limitation in claim 1).
Appropriate correction is required.
Claims 3 and 4 are objected to because of the following informalities:
3. (Proposed Amendments) The computer-implemented method according to claim 1, wherein the one or more trained machine learning algorithms are selected from [[the]] a group exemplified by but not limited to an artificial neural network, a decision tree, a regression model, a k-nearest neighbour model, a partial least squares model, a support vector machine, or an ensemble of the models that are integrated to define an algorithm.
Appropriate correction is required.
Claim 4 is objected to because of the following informalities:
4. (Proposed Amendments) The computer implemented method according to claim 3, wherein the one or more trained machine learning algorithms [[is]] are a computer-implemented artificial neural network, and whereas, for training the computer-implemented artificial neural network, the computer implemented method further comprises the steps of:
A) inputting, to the computer-implemented artificial neural network, training region of interest data, training noisy dose data, training associated uncertainty [[data]] data, and training denoised dose data characterizing at least one training radiation dose distribution of a radiation beam passing through the training region of interest as well as one or more known selected locations of interest in the training region of interest;
B) applying, to the computer-implemented artificial neural network, test region of interest data, test noisy dose [[data]] data, and test associated uncertainty data characterizing a test radiation dose distribution of a radiation beam passing through a test region of interest and one or more selected test locations of interest in the test region of interest; and
C) analyzing each applied test radiation dose distribution using the at least one training radiation dose distribution of the radiation beam passing through the training region of interest to generate a denoised radiation dose distribution for each test noisy dose data and test associated uncertainty data.
Appropriate correction is required.
Claim 5 is objected to because of the following informalities:
5. (Proposed Amendments) The computer-implemented method according to2D and/or 3D convolutional layers, and/or recurrent layers.
Appropriate correction is required.
Claims 6-10 are objected to because of the following informalities:
6. (Proposed Amendments) An apparatus for calculating and displaying a radiation dose distribution of a radiation beam using Monte Carlo simulations passing through a region of interest within a target object, the apparatus comprising:
[[-]] image data processing means for obtaining a set of image data representing the region of interest within the target object;
[[-]] selecting means for selecting one or more selected locations of interest within the three-dimensional image set;
[[-]] radiation dose calculation means for calculating a radiation dose data distribution using Monte Carlo simulations of a simulated radiation beam passing through the region of interest within the target object,
wherein the calculated radiation dose data distribution being composed of noisy dose data and associated uncertainty [[data]] data, and
wherein the radiation dose calculation means are further configured in denoising the radiation dose data distribution for the associated uncertainty data with one or more trained machine learning algorithms, thereby generating a denoised radiation dose distribution.
Appropriate correction is required.
Claims 7 and 10 are objected to because of the following informalities:
7. (Proposed Amendments) The apparatus according to claim 6, further comprising:
a display device for displaying the three-dimensional image data set of the region of interest within the target object and the denoised radiation dose data distribution in the three-dimensional image data set.
Appropriate correction is required.
Claim 10 is objected to because of the following informalities:
10. (Proposed Amendments) The apparatus according to claim 7, wherein the computer-implemented artificial neural network is a deep neural network comprising [[2D,]] 2D and/or 3D convolutional layers, and/or recurrent layers.
Appropriate correction is required.
Claim 8 is objected to because of the following informalities:
8. (Proposed Amendments) The apparatus according to claim 6, wherein the one or more machine learning algorithms are selected from [[the]] a group (a lack of an antecedent basis) exemplified by but not limited to an artificial neural network, a decision tree, a regression model, a k-nearest neighbour model, a partial least squares model, a support vector machine, or an ensemble of the models that are integrated to define an algorithm.
Appropriate correction is required.
Claim 9 is objected to because of the following informalities:
9. (Proposed Amendments) The apparatus according to claim 8, wherein the one or more trained machine learning algorithms [[is]] are a computer-implemented artificial neural network, and wherein the radiation dose calculation means comprises a training unit to train the computer-implemented artificial neural network, the training unit being configured to:
A) input, to the computer-implemented artificial neural network, training region of interest data, training noisy dose data, training associated uncertainty [[data]] data, and training denoised dose data characterizing at least one training radiation dose distribution of a radiation beam passing through the training region of interest and one or more known selected training locations of interest in the training region of interest;
B) apply, to the computer-implemented artificial neural network, test region of interest data, test noisy dose [[data]] data, and test associated uncertainty data characterizing a test radiation dose distribution of a radiation beam passing through a test region of interest and one or more selected test of interest (claim 4) in the test region of interest; and
C) analyze each applied test radiation dose distribution using the at least one training radiation dose distribution of the radiation beam passing through the training region of interest to generate a denoised radiation dose distribution for each test noisy dose data and test associated uncertainty data.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover a corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover a corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is: a training unit in claim 9.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover a corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6-10 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 6 recites a limitation “select means for selecting means for selecting one or more selected locations of interest within the three-dimensional image set” in lines 6-7. However, the written description fails to disclose a corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure is devoid of any structure that performs the function in the claim. Therefore, the claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 9 recites a limitation “a training unit to train the computer-implemented artificial neural network” in lines 3-4. However, the written description fails to disclose a corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure is devoid of any structure that performs the function in the claim. Therefore, the claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
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 pre-AIA 35 U.S.C. 112, 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 3-10 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, 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.
With respect to claim 3, the phrase "a group exemplified by but not limited to" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 3 recites a limitation “the models that are integrated to define an algorithm” in line 5, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim 4 recites a limitation “the training region of interest” in line 8, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim 4 recites a limitation “the training region of interest” in line 9, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim 5 recites a limitation “the computer-implemented artificial neural network” in lines 2-3, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim. Claim 4 previously recites a limitation “a computer-implemented artificial neural network” in lines 2-3.
Claim 6 recites a limitation “the three-dimensional image set” in lines 6-7, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim 7 recites a limitation “the three-dimensional image data set” in line 2, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim 7 recites a limitation “the three-dimensional image data set” in line 3, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim 10 recites a limitation “the computer-implemented artificial neural network” in line 2, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim 8 recites a limitation “the one or more machine learning algorithms” in line 2, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
With respect to claim 8, the phrase "a group exemplified by but not limited to" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 8 recites a limitation “the models that are integrated to define an algorithm” in lines 4-5, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim 8 recites a functional limitation “the one or more machine learning algorithms are selected from a group exemplified by but not limited to an artificial neural network, a decision tree, a regression model, a k-nearest neighbour model, a partial least squares model, a support vector machine, or an ensemble of the models that are integrated to define an algorithm” in lines 2-5, which renders the claim indefinite because boundaries of the functional language are unclear.
During examination, claims are given their broadest reasonable interpretation (BRI) in light of the specification as it would be interpreted by one of ordinary skill in the art. It is a best practice to make the record clear during prosecution by explaining the BRI of claim terms, as necessary, including explaining the BRI of any functional language. When 35 U.S.C. 112(f) is invoked, the BRI of the “means-plus-function” limitation is restricted to a corresponding structure in the supporting disclosure, and its equivalents (a corresponding specification that identifies and links a structure, material, or act to the function recited in the claim is considered to be part of the claim limitation). When 35 U.S.C. 112(f) is not invoked and an element is recited along with a function, that element is construed as being capable of performing the function – in other words, the BRI of that element is limited by the function.
It should be kept in mind, however, that there is a distinction between reciting a function compared to reciting an intended use or result. A functional limitation can provide a patentable distinction (limit the claim scope) by imposing limits on the function of a structure, material, or action. Typically, no patentable distinction (no limit on the claim scope) is made by an intended use or result unless some structural difference is imposed by the use or result on the structure or material recited in the claim, or some manipulative difference is imposed by the use or result on the action recited in the claim.
While functional limitations may be properly used in claims, the boundaries imposed by a functional limitation must be clearly defined to be definite under 35 U.S.C. 112(b). Claim language that merely states a result to be obtained without providing boundaries on the claim scope (e.g., by not specifying any way to achieve those results) is unclear. Consider the following to determine whether a claim limitation expressed in functional language has clear boundaries: whether one of ordinary skill in the art can determine what structure, material, or act in the claim performs this function; whether the limitation has well defined boundaries or only expresses a problem solved or intended result; and what an anticipatory reference would need to disclose in order to satisfy this claim limitation. These considerations are not all-inclusive or limiting.
When 35 U.S.C. 112(f) is invoked, the specification must adequately disclose a corresponding structure, material, or act that performs the function. For “means”-type claims, an adequate disclosure requires that a corresponding structure or material is: (a) disclosed in a way that one of ordinary skill in the art will understand what specific structure or material the inventor has identified to perform the recited function; (b) sufficient to perform the entire function recited in the claim limitation; and (c) clearly linked to the function in the written description.
When the examiner determines that the boundaries of a claim are not reasonably clear, a rejection under 35 U.S.C. 112(b) should be made. Such a rejection puts the applicant on notice that it must fulfill its statutory duty under 35 U.S.C. 112(b) to ensure that claim language clearly defines the boundaries of the claim scope sought. In making a rejection, the examiner must identify the specific claim language that is indefinite, and explain why that language renders the boundaries of the claim unclear. When possible, the examiner should suggest how the indefiniteness issues may be resolved.
The boundaries of the functional language are unclear because the claim does not provide a discernable boundary on what performs the function. The recited function does not follow from the structure recited in the claim, i.e., image data processing means, selecting means, and radiation dose calculation mean, so it is unclear whether the function requires some other structure or is simply a result of operating the apparatus in a certain manner. Thus, one of ordinary skill in the art would not be able to draw a clear boundary between what is and is not covered by the claim. See MPEP 2173.05(g) for more information.
The limitation is unclear because it merely states a function (“the one or more machine learning algorithms are selected from a group exemplified by but not limited to an artificial neural network, a decision tree, a regression model, a k-nearest neighbour model, a partial least squares model, a support vector machine, or an ensemble of the models that are integrated to define an algorithm”) without providing any indication about how the function is performed. The recited function does not follow from the structure recited in the claim, i.e., image data processing means, selecting means, and radiation dose calculation mean, so it is unclear whether the function requires some other structure or is simply a result of operating the apparatus in a certain manner.
Claim 9 recites a limitation “the one or more machine learning algorithms” in lines 1-2, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim 9 recites a limitation “the training region of interest” in line 8, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim 9 recites a limitation “the training region of interest” in line 9, which renders the claim indefinite. There is insufficient antecedent basis for the limitation in the claim.
Claim limitation “a training unit to train the computer-implemented artificial neural network” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose a corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure is devoid of any structure that performs the function in the claim. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses a corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites a corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what a corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS. —Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 8 and 10 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claims 8 and 10 fail to further limit the apparatus by setting forth an additional structural limitation. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
With respect to claims 1 and 2, Tang et al. (U. S. Patent No. 10,949,951 B2) disclosed a computer implemented method for calculating and displaying, within a computer-generated display of a display device, a radiation dose distribution of a radiation beam using Monte Carlo simulations passing through a region of interest within a target object, the computer implemented method comprising at least the steps of:
i) obtaining a set of three-dimensional image data representing the region of interest within the target object (column 3, lines 37-51);
ii) generating a display signal causing the display device to display the set of three-dimensional image data of the region of interest within the target object on the display device (column 3, lines 37-51); and
iii) receiving a user selection, based on a user interaction with the set of three-dimensional image data of the region of interest within the target object on the display device (a medical image visualization software), the user selection identifying one or more selected locations of interest within the region of interest in the displayed set of three-dimensional image data of the region of interest within the target object (column 3, lines 37-51).
However, the prior art failed to disclose or fairly suggested that the computer implemented method for calculating and displaying, within a computer-generated display of a display device, a radiation dose distribution of a radiation beam using Monte Carlo simulations passing through a region of interest within a target object, the computer implemented method further comprising at least the steps of:
iv) calculating, using radiation dose calculation means, a radiation dose distribution using Monte Carlo simulations of a radiation beam passing through the region of interest within the target object taking the one or more selected locations of interest within the region of interest into account, the radiation dose distribution being composed of noisy dose data and associated uncertainty data;
v) denoising the radiation dose data distribution for the associated uncertainty data with one or more trained machine learning algorithms, thereby generating a denoised radiation dose distribution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. (U. S. Patent No. 12,420,112 B2) disclosed automatic beam modeling based on deep-learning.
Duan et al. (U. S. Patent No. 12,377,288 B2) disclosed an evaluation and a presentation of a robustness of a treatment plan.
Li et al. (U. S. Patent No. 11,964,170 B2) disclosed a system and a method of automatic radiation therapy planning by standardized artificial intelligence.
Hibbard (U. S. Patent No. 11,850,445 B2) disclosed a system and a method for learning models of radiotherapy treatment plans to predict distributions of a radiotherapy dose.
Eriksson et al. (U. S. Patent No. 11,026,615 B2) disclosed computing a distribution of a radiotherapy dose.
Tang et al. (U. S. Patent No. 10,949,951 B2) disclosed patient-specific deep-learning image denoising methods and systems.
Moore et al. (U. S. Patent No. 10,850,121 B2) disclosed a prediction of a three-dimensional dose distribution in a radiotherapy.
Sjolund et al. (U. S. Patent No. 10,765,888 B2) disclosed a system and a method for automatic treatment planning.
Li et al. (U. S. Patent No. 10,737,115 B2) disclosed a method, an apparatus, and a system for simulating a particle transport and determining a dose in a radiotherapy.
Purdie et al. (U. S. Patent No. 10,475,537 B2) disclosed a method and a system for an automated quality assurance and automated treatment planning in a radiation therapy.
Sjölund et al. (U. S. Patent No. 10,046,177 B2) disclosed a system and a method for automatic treatment planning.
Nord et al. (U. S. Patent No. 9,987,504 B2) disclosed portal dosimetry systems, devices, and methods.
Nyholm et al. (U. S. Patent No. 7,542,545 B2) disclosed a method and a device for calculating a distribution of a radiation dose for a radiation treatment system.
Surridge (U. S. Patent No. 6,301,329 B1) disclosed a method of treatment planning and an apparatus for a radiation therapy.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Allen C. Ho, whose telephone number is (571) 272-2491. The examiner can normally be reached Monday - Friday 10AM - 6PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David J. Makiya, can be reached at (571) 272-2273. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at (866) 217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call (800) 786-9199 (IN USA OR CANADA) or (571) 272-1000.
Allen C. Ho, Ph.D.
Primary Examiner
Art Unit 2884
/Allen C. Ho/Primary Examiner, Art Unit 2884 Allen.Ho@uspto.gov
Prosecution Insights