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 Status
Claims 1-18 are currently pending and are herein under examination.
Claims 1-18 are rejected.
Claims 5, 12 and 14 are objected.
Priority
The instant application claims domestic benefit as a 371 filing of international application PCT/IB2020/061175 filed 11/26/2020, which claims foreign priority to Italian Application No. IT102019000022419 filed 11/28/2019. The claims to domestic benefit and foreign priority are acknowledged for claims 1-18. As such, the effective filing date for claims 1-18 is 11/28/2019.
Information Disclosure Statement
The IDSs filed 05/20/2022, 10/15/2024, and 05/23/2025 follow the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of the list of references cited from these IDSs is included with this Office Action.
Drawings
The drawings filed 05/20/2022 are accepted.
Claim Objections
Claims 5, 12 and 14 are objected to because of the following informalities:
Claim 5, line 8, should have a “;” before the word “units”.
Claim 5, lines 10-12, recites twice the phrase “white or grey matter” which should be “white matter or grey matter” to maintain consistent claim terminology, as similarly recited in claim 2, line 2.
Claim 12, step F), recites the phrase “of cerebral temperature data” which should be “of said cerebral temperature data” to properly refer to the phrase in claim 1, step B1), of “cerebral temperature data.”
Claim 14, line 8, should have the word “and” after the “;”.
Appropriate correction is required.
Claim Interpretation
35 USC 112(f)
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 the 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 the 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(s) 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.
Claims 16-18 recite a “medical apparatus arranged to implement the method of claim 1”. The phrase “medical apparatus” is a nonce word for means. The phrase “arranged to implement the method of claim 1” is functional language performed by the medical apparatus. Thus, the medical apparatus performs steps A1), B1), A2) and C) in claim 1.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. However, neither the specification nor the drawings describe a medical apparatus or any other device arranged to perform steps A1), B1), A2) and C). As such, any structure capable of performing the steps in claim 1 will read on the structure of the claimed “medical apparatus”.
If applicant does not intend to have this/these limitation(s) 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(s) to avoid it/them 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(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
35 USC 112(a)
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 16-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), 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.
Claims 16-18 fail to comply with the written description requirement because they do not adequately link or associate adequately described particular structure, material, or acts to perform the function recited in the claims identified to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Specifically, claims 16-18 recite a “medical apparatus arranged to implement the method of claim 1”, which has been interpreted to invoke 35 U.S.C. 112(f) as discussed above in Claim Interpretation. However, the specification does not provide a structure for the medical apparatus that performs steps A1), B1), A2), or C) in claim 1. Therefore, in accordance with MPEP § 2181.IV, the instant specification does not provide written description support for the medical apparatus because there is no corresponding structure disclosed in the specification.
35 USC 112(b)
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 5-11 and 13-18 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 5, line 7, recites the phrase “each second volumetric unit containing a plurality of first volumetric units” which renders the claim indefinite. It is unclear if the phrase means that each second volumetric unit contains a plurality of the first volumetric units associated with the first mesh in step A0, or if it means that each second volumetric unit contains a plurality of smaller volumetric units that are not associated with the first volumetric units in the first mesh in step A0. To overcome this rejection, clarify what the phrase refers to. For example, if it refers to the first volumetric units in step A0, then amend the phrase to something similar to “a plurality of first volumetric units in the first mesh”. For examination purposes, this phrase is being interpreted to mean that each second volumetric unit contains a plurality of smaller volumetric units that are not associated with the first volumetric units of the first mesh. To overcome this rejection, clarify how the phrase should be interpreted.
Claim 5, line 8, recites the phrase “with respective first volumetric units” which renders the claim indefinite. It is unclear if the phrase refers to the first volumetric units in step A0, or if the phrase refers to specific first volumetric units in a plurality of first volumetric units for a specific second volumetric unit in step B0. To overcome this rejection, clarify what the phrase refers to.
Furthermore, claims 6-11 and 15 are also rejected because they depend on claim 5, which is rejected, and because they do not resolve the issue of indefiniteness.
Claim 7, lines 2-3, recites the phrase “said first volumetric units” which renders the claim indefinite. It is unclear if the phrase refers to the first volumetric units of the first mesh in claim 5 step A0, or if the phrase refers to specific first volumetric units in the plurality of first volumetric units associated with a second volumetric unit in claim 5 step B0. To overcome this rejection, clarify what the phrase refers to.
Claim 7, lines 2-3, recites the phrase “said first volumetric units correspond to voxel of said magnetic resonance images” which renders the claim indefinite. Because of the grammar of the phrase, it is unclear if all the first volumetric units correspond to one voxel, or if each first volumetric unit corresponds to a voxel in a plurality of voxels. To overcome this rejection, clarify how the units correspond to voxels.
Claim 7, lines 5-6, recites the phrase “said second volumetric units correspond to voxel of said magnetic resonance spectroscopy” which renders the claim indefinite. Because of the grammar of the phrase, it is unclear if all the second volumetric units correspond to one voxel, or if each second volumetric unit corresponds to a voxel in a plurality of voxels. To overcome this rejection, clarify how the units correspond to voxels.
Claim 7, lines 5-6, recites the phrase “said magnetic resonance spectroscopy” which renders the claim indefinite. It is unclear if the phrase refers to a specific datapoint in the “magnetic resonance spectroscopy data”, or if it refers to all of the data that composes the “magnetic resonance spectroscopy data”. To overcome this rejection, clarify what the phrase refers to.
Claim 13 recites the phrase “the acquisition of perfusion magnetic resonance images of the encephalon” which lacks antecedent basis. To overcome this rejection, change the phrase to “an acquisition of perfusion magnetic resonance images of the encephalon.”
Claim 14, lines 1-2, recites the phrase “The method of claim 12, when dependent on claim 6” which renders the claim indefinite. Claim 14 depends on claim 12, but this phrase makes it unclear whether claim 14 should rather depend on claim 6.
Claim 14, lines 4-5, recites the phrase “through a respective second volumetric unit” and in lines 6-7 and 9-10 recites the phrase “each second volumetric unit”, which render the claim indefinite. Claim 14 depends on claims 1 and 12, both of which do not recite second volumetric units. Rather, claim 1 recites several instances of “volumetric units”. Thus, it is unclear whether these phrases intend to introduce second volumetric units for the first time, or if they refer to specific volumetric units in claim 1. To overcome this rejection, clarify what the phrases refer to.
Claim 14, lines 7-8, recites the phrase “the cerebral temperature value related to said second volumetric unit” which lacks antecedent basis. To overcome this rejection, provide antecedent basis for the phrase.
Claim 14, lines 10-11, recites the phrase “the value of conductive heat flow … of said second volumetric unit” which lacks antecedent basis. To overcome this rejection, provide antecedent basis for the phrase.
Claim 15, line 4, recites the phrase “said map of metabolic heat generation”, which lacks antecedent basis. To overcome this rejection, provide antecedent basis for the phrase.
Claims 16-18 do not recite a transitional phrase such as “comprising”, “consisting of”, or “consisting essentially of” which renders the claims indefinite. It is unclear what unrecited elements or steps, if any, are excluded from the scope of the claimed medical apparatus. See MPEP 2111.03 regarding transitional phrases.
Claims 16-18 recite the following limitations that invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: “A medical apparatus arranged to implemented the method of claim 1”, “The medical apparatus of claim 16 being adapted for therapeutic purposes”, and “The medical apparatus of claim 16, being adapted for surgical purposes”. It is also noted that claims 16-17 recite an intended use of the medical apparatus and thus do not provide further structural limitations of the medical apparatus. As such, claims 17-18 are being interpreted as failing to further limit claim 16. See the rejection below under 35 USC 112(d). However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function for each of these limitations, as discussed in Claim Interpretation. Therefore, claims 16-18 are indefinite and are 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 the 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 the 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 the 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.
35 USC 112(d)
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 17-18 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 17-18 fail to further limit claim 16. The recitations of “being adapted for therapeutic purposes” and “being adapted for surgical purposes” equate to an intended use, and thus do not provide further structural limitations of the medical apparatus in claim 16.
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.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 1:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomena (Step 2A, Prong 1). In the instant application, claims 1-15 recite a method and claims 16-18 recite an apparatus. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claims 1 and 16-18 recite “A2) calculating a thermal conductivity distribution in the encephalon as a function of said composition data, said thermal conductivity distribution being discretized into volumetric units; C) calculating a distribution of conductive heat flows in the encephalon as a function of said cerebral temperature data and of said thermal conductivity distribution, said conductive heat flow distribution being calculated through a finite volume calculation of a general heat conduction equation.”
Claim 5 recites “A0) creating a first mesh representative of at least a part of the encephalon in which said encephalon is split into first volumetric units; B0) creating a second mesh representative of at least a part of the encephalon in which said encephalon is split into second more extensive volumetric units with respect to said first volumetric units, each second volumetric unit containing a plurality of first volumetric units; said step Al comprising associating said composition data with respective first volumetric units and said step A2 comprising calculating, for each second volumetric unit, a quantity of white or grey matter or cerebrospinal fluid contained in said second volumetric unit, said quantity of white or grey matter or cerebrospinal fluid being extrapolated from the composition data associated with first volumetric units contained in said second volumetric unit.”
Claim 6 recites “wherein said step B 1 comprises associating with each second volumetric unit a cerebral temperature value on the basis of said cerebral temperature data.”
Claim 8 recites “wherein said thermal conductivity distribution comprises a plurality of thermal conductivity values, said step A2 comprising associating each thermal conductivity value with a second volumetric unit as a function of the respective quantity of white matter or grey matter or cerebrospinal fluid.”
Claim 9 recites “wherein each thermal conductivity value is calculated as a linear combination of the thermal conductivity values of the grey matter and of the white matter weighted according to a coefficient dependent on the respective quantities of matter.”
Claim 10 recites “wherein the calculation of the general heat conduction equation of said step C is performed on said second mesh using the thermal conductivity values of said thermal conductivity distribution.”
Claim 11 recites “aid step C comprising overlooking the second volumetric units containing a quantity of cerebrospinal fluid greater than a predetermined threshold.”
Claim 12 recites “F) calculating a distribution of convective heat flows between the encephalon and said blood flows as a function of said flow rate data, of said blood temperature data and of cerebral temperature data; G) calculating a map of metabolic heat generation of the encephalon through an energy balance equation between: said distribution of conductive heat flows, said distribution of convective heat flows and said map of metabolic heat generation.”
Claim 14 recites “D2) determining a distribution of blood flow rate values as a function of said flow rate data, each flow rate value being representative of blood flow rate through a respective second volumetric unit; said step F comprising calculating a convective heat flow value for each second volumetric unit as a function of the blood flow rate value and the cerebral temperature value related to said second volumetric unit and the blood temperature data; said step G comprising calculating a rate of metabolic heat generation for each second volumetric unit as a function of the value of conductive heat flow and of the value of convective heat flow of said second volumetric unit.”
Claim 15 recites “H) calculating a distribution of cerebral oxygen consumption rates as a function of at least said map of metabolic heat generation, of a reaction enthalpy between glucose and oxygen and of an energy required for separation between oxygen and haemoglobin.”
Limitations reciting a mental process.
Claims 1, 5-6, 8-11 and 14-18 contain limitations recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind. The sentences below discuss the limitations in these claims that recite a mental process under their broadest reasonable interpretation (BRI) in light of the specification:
The BRI of steps A2) and C) in claims 1 and 16-18 include a human using pen and paper to calculate a distribution as a function of given data.
The BRI of steps A0) and B0) in claim 5 includes a human using pen and paper to draw volumetric units on an image of the encephalon.
The BRI of “associating said composition data with respective first volumetric units” in claim 5 includes drawing the volumetric units on the MRI image
The BRI of “associating with each second volumetric unit a cerebral temperature value” in claim 6 includes a human correlating data.
The BRI of claims 8-9 includes calculating each thermal conductivity value using the equation recited on specification pg. 6, lines 7-19. A human is capable of performing such calculations on pen and paper.
The BRI of claims 10-11 include a human performing on pen and paper the calculations of the general heat conduction equation recited on specification pg. 7, lines 11-24.
The BRI of steps F) and G) in claim 12 include a human performing calculations on pen and paper such as those recited on specification pg. 8, line 28 – pg. 9, line 27.
The BRI of step D2) in claim 14 includes a human performing calculations on pep and paper to derive the distribution.
The BRI of “calculating a convective heat flow value for each second volumetric unit” and “calculating a rate of metabolic heat generation for each second volumetric unit” in claim 14 includes a human using pen and paper to perform calculations using the equations recited on specification pg. 8, line 32 – pg. 9, line 27.
The BRI of step H in claim 15 includes a human using pen and paper to perform calculations using the equation recited on pg. 9, lines 32 – pg. 10, line 7.
Limitations reciting a mathematical concept.
Claims 1, 5, 8-12 and 14-18 recite mathematical concepts similar to the concepts of organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. The sentences below discuss the limitations in these claims that recite a mathematical concept under their BRI in light of the specification:
The BRI of step A2) in claims 1 and 16-18 includes a mathematical calculation and equation.
The BRI of step C) in claims 1 and 16-18 includes performing a calculation using a general heat conduction equation, such as that recited in specification pg. 7, lines 1-24.
The BRI of “calculating, for each second volumetric unit, a quantify of white or grey matter or cerebrospinal fluid” in claim 5 includes performing a calculation.
The BRI of claims 8-9 includes calculating each thermal conductivity value using the equation recited on specification pg. 6, lines 7-19.
The BRI of claims 10-11 include performing the calculations of the general heat conduction equation recited on specification pg. 7, lines 11-24.
The BRI of steps F) and G) in claim 12 include performing a calculation using an equation to derive the distribution and map.
The BRI of step D2) in claim 14 includes performing a calculation using an equation to derive the distribution.
The BRI of “calculating a convective heat flow value for each second volumetric unit” and “calculating a rate of metabolic heat generation for each second volumetric unit” includes performing calculations using the equations recited on specification pg. 8, line 32 – pg. 9, line 27.
The BRI of step H in claim 15 includes performing calculations using the equation recited on pg. 9, line 32 – pg. 10, line 7.
As such, claims 1-18 recite an abstract idea and a natural phenomenon (Step 2A, Prong 1: Yes).
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)) and equate to insignificant extra-solution activity (MPEP § 2106.05(g)). The instant claims recite the following additional elements:
Claims 1 and 16-18 recite “A1) acquiring composition data regarding matter distribution in the encephalon, said composition data being discretized into volumetric units; B1) acquiring cerebral temperature data regarding a temperature distribution in the encephalon, said temperature data being discretized into volumetric units;”
Claim 2 recites “wherein said composition data acquired correspond to a distribution of white matter or grey matter or cerebrospinal fluid.”
Claim 3 recites “wherein said step Al comprises performing an acquisition of magnetic resonance images of the encephalon.”
Claim 4 recites “wherein said step B1 comprises acquiring magnetic resonance spectroscopy data of the encephalon.”
Claim 7 recites “wherein said step A1 comprises performing an acquisition of magnetic resonance images of the encephalon and said first volumetric units correspond to voxel of said magnetic resonance images, and wherein said step B1 comprises acquiring magnetic resonance spectroscopy data of the encephalon and said second volumetric units correspond to voxel of said magnetic resonance spectroscopy.”
Claim 12 recites “D1) acquiring flow rate data related to blood flows in the encephalon; E1) acquiring blood temperature data related to the encephalon;”
Claim 13 recites “wherein said step D1 is performed through the acquisition of perfusion magnetic resonance images of the encephalon.”
Claim 16 recites “A medical apparatus arranged to implement the method of claim 1”
Claim 17 recites “The medical device of claim 16, being adapted for therapeutic purposes.”
Claim 18 recites “The medical device of claim 16, being adapted for surgical purposes.”
Regarding the above cited limitations in claims 16-18, the medical apparatus is being interpreted as a generic computer configured to perform the steps in claim 1. As such, requiring the medical apparatus (i.e. generic computer) to perform the abstract ideas in claim 1 equates to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983. Furthermore, they also equate to invoking a computer as a tool to perform an existing process (e.g. to receive, store, or transmit data) (MPEP 2106.05(f)(2)).
Regarding the above cited limitations in claims 17-18 of “being adapted for therapeutic purposes” and “being adapted for surgical purposes”, these limitations are being interpreted as an intended use. Thus, they are not required to be performed by the claim.
Regarding the above cited limitations in claims 1-4, 7, 12-13 and 16-18, these limitations equate to insignificant, extra-solution activity of necessary data gathering. These limitations gather the necessary data to perform the abstract idea of steps A2) and C) in claim 1 as well as steps F) and G) in claim 12.
As such, claims 1-18 are directed to an abstract idea (Step 2A, Prong 2: No).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite the following additional elements:
Claims 1 and 16-18 recite “A1) acquiring composition data regarding matter distribution in the encephalon, said composition data being discretized into volumetric units; B1) acquiring cerebral temperature data regarding a temperature distribution in the encephalon, said temperature data being discretized into volumetric units;”
Claim 2 recites “wherein said composition data acquired correspond to a distribution of white matter or grey matter or cerebrospinal fluid.”
Claim 3 recites “wherein said step Al comprises performing an acquisition of magnetic resonance images of the encephalon.”
Claim 4 recites “wherein said step B1 comprises acquiring magnetic resonance spectroscopy data of the encephalon.”
Claim 7 recites “wherein said step A1 comprises performing an acquisition of magnetic resonance images of the encephalon and said first volumetric units correspond to voxel of said magnetic resonance images, and wherein said step B1 comprises acquiring magnetic resonance spectroscopy data of the encephalon and said second volumetric units correspond to voxel of said magnetic resonance spectroscopy.”
Claim 12 recites “D1) acquiring flow rate data related to blood flows in the encephalon; E1) acquiring blood temperature data related to the encephalon.”
Claim 13 recites “wherein said step D1 is performed through the acquisition of perfusion magnetic resonance images of the encephalon.”
Claim 16 recites “A medical apparatus arranged to implement the method of claim 1”
Claim 17 recites “The medical device of claim 16, being adapted for therapeutic purposes.”
Claim 18 recites “The medical device of claim 16, being adapted for surgical purposes.”
Regarding the above cited limitations in claims 16-18, the medical apparatus is being interpreted as a generic computer configured to perform the steps in claim 1. As such, requiring the medical apparatus (i.e. generic computer) to perform the abstract ideas in claim 1 equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
The BRI of claims 1-4, 7 and 12-13 includes that they are computer-implemented. The BRI of claims 1, 3-4, 7 and 12-13 of “A1) acquiring composition data”, “B1) acquiring cerebral temperature data”, “step A1 comprises performing an acquisition of magnetic resonance images”, “step B1 comprises acquiring magnetic resonance spectroscopy data”, “step A1 comprises performing an acquisition of magnetic resonance images”, “step B1 comprises acquiring magnetic resonance spectroscopy data”, “D1) acquiring flow rate data; E1) acquiring blood temperature data related to the encephalon”, and “step D1 is performed through the acquisition of perfusion magnetic resonance images” includes a computer receiving previously generated data over a network. This is because the BRI of acquiring data includes receiving it rather than actively generating data to obtain it. As such, these limitations equate to receiving data over a network, which the courts have established as WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Regarding the above cited limitations in claim 2, this limitation also equates to receiving data over a network because it limits the type of data being received but do not change the fact that data is being received.
Regarding the above cited limitations in claims 17-18 of “being adapted for therapeutic purposes” and “being adapted for surgical purposes, these limitations are being interpreted as an intended use. Thus, they are not required to be performed by the claim.
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they all equate to WURC functions/components of a generic computer and equate to instruction to implement the abstract idea on a computer. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-18 are not patent eligible.
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 12, 14 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Schooneveldt et al. (“Schooneveldt”; Cancers 11, no. 8 (2019): 1183) in view of Bousselham et al. (“Bousselham”; In 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 762-767. IEEE, 2016).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claims 1 and 16-18:
A method for estimating heat transfer energy parameters in an encephalon through discretization and numerical calculation, comprising the steps of:
Schooneveldt teaches hyperthermia treatment planning using connective flow in cerebrospinal fluid (CSF) for brain hyperthermia treating (title). Temperature distributions within the brain of a brain tumor patient were predicted using three CSF models (pg. 6) that use a finite volume thermodynamics model that implements Penne’s bioheat equation (pg. 5, sec. 2.2).
A1) acquiring composition data regarding matter distribution in the encephalon, said composition data being discretized into volumetric units;
Schooneveldt teaches “An MRI scan with a 1 × 1 × 1 mm resolution was obtained from an actual patient, a 13-year-old boy with a large medulloblastoma (92 mL)”. Figure 1 and Table show the tissue types in the MRI (composition data). The MRI image is voxelized (discrete volumetric units) (pg. 4, sec. 2.1).
B1) acquiring cerebral temperature data regarding a temperature distribution in the encephalon, said temperature data being discretized into volumetric units;
Schooneveldt teaches computing temperature distributions in the brain and acquiring an MRI scan with 1 x 1 x 1 mm resolution to generate voxels (discretized into volumetric units) (pg. 4, sec. 2.1—2.2).
However, Schooneveldt does not teach acquiring measured cerebral temperature data (pg. 5, sec. 2.2).
Bousselham models heat distribution in the brain in a patient with a tumor using Pennes BioHeat Transfer Equation (PBHTE) solved by a finite volume method (FVM) (abstract). MRI thermometry is used to acquire a temperature profile of the brain which is then used to estimate initial thermal parameters in the PBHTE by inverse analysis (pg. 4, col. 2, para. 2). Figure 2 shows the temperature profile inputted into the PBHTE along with structural MRI imaging data. A cost function is used to give the difference between measured temperatures of the temperature profile and the simulated temperature using the PBHTE (pg. 4, col. 2, para. 4) (Figure 2).
A2) calculating a thermal conductivity distribution in the encephalon as a function of said composition data, said thermal conductivity distribution being discretized into volumetric units;
Schooneveldt shows in Table 1 calculated thermal conductivity parameters for white matter, grey matter, and CSF (composition data). Thermal conductivity for these tissues is denotated as k[W/(mK)]. The thermal conductivity values of these tissue types are volumetric units which are part of the segmented mesh created from the MRI with 1 x 1 x 1 mm resolution represented as voxels (pg. 4, sec. 2.1) (pg. 5, sec. 2.2).
C) calculating a distribution of conductive heat flows in the encephalon as a function of said cerebral temperature data and of said thermal conductivity distribution, said conductive heat flow distribution being calculated through a finite volume calculation of a general heat conduction equation.
Schooneveldt teaches “An MRI scan with a 1 × 1 × 1 mm resolution was obtained from an actual patient, a 13-year old boy with a large medulloblastoma (92 mL) … The scan was manually segmented by a clinician into thirteen tissue types; the most relevant ones of which are tumour, tumour cyst, CSF, white matter, grey matter, muscle, and bone” (pg. 4, sec. 2.1). Schooneveldt further states “The computation of the temperature distribution uses the same segmentation and is computed based on this electromagnetic field distribution using a finite volume thermodynamic fluid model. This model was built in OpenFOAM and implements Pennes’ bioheat equation in the solid tissue regions, and uses the Boussinesq approximation to the Navier–Stokes equations for the fluid (CSF) regions … Meshes for thermal simulations in OpenFOAM were created and the resolution in the solid regions was kept at 1 × 1 × 1 mm” (pg. 5, sec. 2.2). Equation 2 is used for calculating temperature change (conductive heat flows). Figure 4 shows change of temperature as a distribution in the brain from the models.
However, Schooneveldt does not teach that the simulation using Penne’s bioheat equation, which calculates heat transfer, is calculated based on brain temperature data.
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Schooneveldt for calculating brain temperature distributions with a FVM that implements Penne’s bioheat equation by using measured temperature data of the brain to estimate the initial thermal parameters used in Penne’s bioheat equation, as taught by Bousselham. The measured temperature data of Bousselham would be discretized into voxels, so as to coincide with the mesh in Schooneveldt. The motivation for doing so is taught by Bousselham who states that this optimizes the PBHTE simulation (Figure 3) (pg. 4, col. 2, sec. B and C). One of ordinary skill in the art would have had a reasonable expectation of success for using measured temperature data to fine tune thermal parameters of the bioheat equation because Bousselham demonstrates it in their model (Figure 2), wherein Schooneveldt also uses a FVM using a bioheat equation to predict brain temperature distribution.
Claims 2-3:
Schooneveldt shows in Figure 1 and Table 1 that MRI data was segmented into tissue types such as white matter, grey matter, and CSF (pg. 4, sec. 2.1).
Claim 12:
D1) acquiring flow rate data related to blood flows in the encephalon;
Schooneveldt discloses that Penne’s equation has a blood perfusion heat sink term (pg. 16, last para.). Table 1 also shows volumetric perfusion rate ([kg/(m3s)]).
E1) acquiring blood temperature data related to the encephalon;
Schooneveldt discloses Penne’s equation:
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(pg. 5, sec. 2.2), which inherently uses temperature of blood. However, Schooneveldt does not explicitly disclose acquiring blood temperature in the brain.
Bousselham calculates PBHTE in the brain, which contains 𝑇𝑎 as temperature of the artery (K,◦ C) (pg. 3, col. 2).
F) calculating a distribution of convective heat flows between the encephalon and said blood flows as a function of said flow rate data, of said blood temperature data and of cerebral temperature data;
Schooneveldt discloses Penne’s equation:
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and calculates convective heat flows of CSF
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with volumetric perfusion rates (Table 1) (pg. 5, sec. 2.2). However, Schooneveldt does not explicitly disclose calculating it using blood temperature and cerebral temperature data.
Bousselham teaches that the PBHTE calculates convective heat flows as show in equation 10 as Ƞ𝑏𝜌𝑏𝐶𝑝𝑏(𝑇𝑎 − 𝑇); where 𝐶𝑃is the specific heat of the tissue (J/kg k), 𝜌𝑏 is density of blood (kg/𝑚3), 𝐶𝑃𝑏is specific heat of the blood (J/kg k), 𝑇𝑎is temperature of the artery (K,◦ C), and T is temperature (K, ◦ C).
G) calculating a map of metabolic heat generation of the encephalon through by means of an energy balance equation between: said distribution of conductive heat flows, said distribution of convective heat flows and said map of metabolic heat generation.
Schooneveldt discloses Penne’s bioheat equation, which inherently calculates metabolic heat generation using conductive heat flow and convective heat flow. However, Schooneveldt does not explicitly recite these variables.
Bousselham teaches calculating a map of metabolic heat generation of the brain in the PBHTE:
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, wherein conductive heat flows are represented as
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, convective heat flows are represented as
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, and metabolic heat generation is represented as Qm.
It would have been prima facie obvious to one of ordinary skill in the art to have modified Penne’s bioheat equation in Bousselham (pg. 5, sec. 2.2) to account for conductive heat flows, convective heat flows, and metabolic heat generation as taught by Bousselham. The motivation for doing so is taught by Bousselham who states that Penne’s equation is used for hyperthermia treatment and because Bousselham states “A brain tissue is a thermal system that has a blood perfusion rate and metabolism heat generation, in the case of abnormality such as tumor in a tissue these properties changes, this changing will affect the temperature distribution of the tissue. By modeling the heat distribution we will estimate thermal properties of the tumor” (pg. 3, col. 1). Schooneveldt is directed to using Penne’s equation for hyperthermia treatment (abstract) (pg. 5, sec. 2.2). Thus, these parameters would calculate a more accurate heat transfer in Schooneveldt.
One of ordinary skill in the art would have had a reasonable expectation of success to use the parameters described in Bousselham in Schooneveldt because both use Penne’s bioheat equation to calculate temperature distribution by modeling heat transfer.
Claim 14:
D2) determining a distribution of blood flow rate values as a function of said flow rate data, each flow rate value being representative of blood flow rate through a respective second volumetric unit;
Schooneveldt teaches calculating Penne’s bioheat equation which implicitly requires calculation of blood flow rate as represented by the volumetric perfusion rate in Table 1. Because Schooneveldt solves the Penne’s bioheat equation using a finite volume method using voxels of an MRI image, each voxel is calculated using the parameters in Table 1 and Penne’s bioheat equation (pg. 4, sec. 2.1) (pg. 5, sec. 2.2).
said step F comprising calculating a convective heat flow value for each second volumetric unit as a function of the blood flow rate value and the cerebral temperature value related to said second volumetric unit and the blood temperature data; said
Schooneveldt teaches calculating convective flow using CSF (Table 1). As stated above, the calculation is performed for each voxel in the MRI image.
Bousselham explicitly shows Penne’s bioheat equation calculating convective heat flows as a function of blood flow rate (pg. 3, col. 2). As discussed above, the combination of Schooneveldt and Bousselham teach using measured brain temperature data to estimate thermal parameters used in Penne’s equation (Figure 2 and 3 and pg. 4, col. 2 of Bousselham).
step G comprising calculating a rate of metabolic heat generation for each second volumetric unit as a function of the value of conductive heat flow and of the value of convective heat flow of said second volumetric unit.
Schooneveldt teaches calculating Penne’s bioheat equation which inherently requires a metabolic heat generation parameter, which results in a rate of metabolic heat generation.
Bousselham explicitly shows Penne’s bioheat equation using a metabolic heat generation parameter used to calculate a rate of metabolic heat generation (pg. 3, col. 2).
Claims 16-18:
Schooneveldt performs the simulations on a high-performance computing platform (medical apparatus) (pg. 6, last para.). The limitations of “being adapted for therapeutic purposes” and “being adapted for surgical purposes” are being interpreted as intended uses of the medical device. As such, they are not required by the claim.
Claims 5-6, 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Schooneveldt et al. (“Schooneveldt”; Cancers 11, no. 8 (2019): 1183) in view of Bousselham et al. (“Bousselham”; In 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 762-767. IEEE, 2016), as applied above to claim 1, and in further view of Park et al. (“Park”; US 2003/0044055 A1).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
The limitations of claim 1 have been taught in the rejection above by Schooneveldt and Bousselham.
Claim 5:
A0) creating a first mesh representative of at least a part of the encephalon in which said encephalon is split into first volumetric units;
Schooneveldt teaches “Meshes for thermal simulations in OpenFOAM were created and the resolution in the solid regions was kept at 1 × 1 × 1 mm” (pg. 5, para. 3).
B0) creating a second mesh representative of at least a part of the encephalon in which said encephalon is split into second more extensive volumetric units with respect to said first volumetric units, each second volumetric unit containing a plurality of first volumetric units;
Schooneveldt teaches generating meshes wherein “in the fluid regions, these (1 mm)3 cubes were subdivided into six identical pyramids by adding a vertex at the center of the cube, in order to increase the computational stability” (pg. 5, sec. 2.2).
said step A1 comprising associating said composition data with respective first volumetric units and
Schooneveldt teaches “An MRI scan with a 1 × 1 × 1 mm resolution was obtained from an actual patient, a 13-year old boy with a large medulloblastoma (92 mL) … The scan was manually segmented by a clinician into thirteen tissue types; the most relevant ones of which are tumour, tumour cyst, CSF, white matter, grey matter, muscle, and bone” (pg. 4, sec. 2.1). Schooneveldt further states “The computation of the temperature distribution uses the same segmentation and is computed based on this electromagnetic field distribution using a finite volume thermodynamic fluid model … Meshes for thermal simulations in OpenFOAM were created and the resolution in the solid regions was kept at 1 × 1 × 1 mm” (pg. 5, sec. 2.2).
said step A2 comprising calculating, for each second volumetric unit, a quantity of white or grey matter or cerebrospinal fluid contained in said second volumetric unit, said quantity of white or grey matter or cerebrospinal fluid being extrapolated from the composition data associated with first volumetric units contained in said second volumetric unit.
Schooneveldt teaches using a finite volume thermodynamic model that implements Penne’s bioheat equation on MRI images that are voxelized (pg. 4, sec. 2.1) (pg. 5, sec. 2.2). This necessitates calculating the equation for each voxel in the meshes. The MRI image is segmented into tissue types and is voxelized, with each voxel representing a tissue type (pg. 4, sec. 2.1).
However, neither Schooneveldt nor Bousselham calculate a quantify of white matter, grey matter, or CSF in the voxels of the meshes.
Park discloses a method for segmentation and volume calculation of white matter and grey matter, and cerebrospinal fluid using MRI images of the human brain (abstract). Park calculates the volume of white matter, grey matter, and cerebrospinal fluid in a given voxel of an MRI image [45].
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Schooneveldt for segmenting brain MRI images by using the segmentation and volume calculation method of Park. The motivation for doing so is taught by Park who states a voxel may contain partial volumes of grey/white matter and CSF giving rise to blurring of an image, preventing the ability to calculate volumes accurately. Park also states that semi-automatic segmentation and of tissue is troublesome, wherein Park’s method resolves these issues [7-8]. This relates to Schooneveldt because Schooneveldt calculates volumetric perfusion rates of tissue types based on voxel representations of tissues (Table 1) as well as performing tissue segmentation in brain MRI images (pg. 6, sec. 3).
There would have been a reasonable expectation of success to apply the segmentation and volume calculation method of Park in Schooneveldt because both methods use voxelized MRI data.
Claim 6:
Schooneveldt shows in Figure 3 temperature distributions in the brain. Schooneveldt also teaches “The computation of the temperature distribution uses the same segmentation and is computed based on this electromagnetic field distribution using a finite volume thermodynamic fluid model” (pg. 5, sec. 2.2), which is associated with voxels from the segmented MRI image of resolution 1 x 1 x 1 mm (pg. 4, sec. 2.1). However, Schooneveldt does not associate a measured cerebral temperature with a predicted temperature distribution per voxel using the finite volume thermodynamics model.
Bousselham teaches acquiring cerebral temperature data which is used to estimate thermal parameters by inverse analysis (pg. 4, col. 2, sec. B). The initial thermal parameters for each pixel (where it would be for each voxel in Schooneveldt) is used to calculate temperature distribution using the bioheat equation. Then a cost function is used to give the difference between measure temperature and the predicted temperature using the bioheat equation, which necessitates a pixel-by-pixel comparison (i.e., voxel-by-voxel comparison in Schooneveldt) (pg. 4, sec. C) (Figure 2).
It would have been prima facie obvious to one of ordinary skill in the art would to have estimated thermal parameters in Schooneveldt by using measured temperature data as taught by Bousselham because Bousselham teaches that this optimizes the thermal parameters used in the bioheat equation (pg. 4, col. 2, sec. C) (Figure 3). One of ordinary skill in the art would have had a reasonable expectation of success because Bousselham demonstrates that this method functions with a finite volume method solution to the bioheat equation using MRI data, wherein Schooneveldt also uses a finite volume thermodynamic model that implements Penne’s bioheat equation on MRI data.
Claim 8:
Schooneveldt shows in Table 1 thermal conductivity values for grey matter, white matter, and cerebrospinal fluid (plurality of thermal conductivity values). These values are associated to their respective tissues in the temperature simulations, which are represented by voxels (second volumetric units) in the MRI image (pg. 5, sec. 2.2). Schooneveldt teaches “Meshes for thermal simulations in OpenFOAM were created and the resolution in the solid regions was kept at 1 × 1 × 1 mm” (pg. 5, sec. 2.2) (associating each thermal conductivity value with a second volumetric unit).
However, neither Schooneveldt nor Bousselham teach associating each thermal conductivity value to a pixel or voxel as a function of a respective quantity of either white matter, grey matter, or CSF.
Park discloses associating partial volumes to a voxel based on fractions of white matter, grey matter, and cerebrospinal fluid using MRI images of the human brain (abstract) [45].
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Schooneveldt for calculating temperature distribution in the brain using thermal conductivity values of white matter, grey matter, and CSG (Table 1) (pg. 5, sec. 2.2) by associating the thermal conductivity of each voxel as a function of respective tissue volumes as taught by Park [45]. The motivation for doing so is taught by Park who states a voxel may contain partial volumes of grey/white matter and CSF giving rise to blurring of an image, preventing the ability to calculate volumes accurately [7]. This would also be advantageous for correctly calculating volumetric perfusion rates in Schooneveldt as it would properly account for volume of tissue types per voxel (Table 1). One of ordinary skill in the art would have had a reasonable expectation of success for associating partial volumes to voxels in order to calculate thermal conductivity parameters of specific tissues because Schooneveldt calculates Penne’s bioheat equation using a finite volume method using voxels (pg. 5, sec. 2.2) (pg. 4, sec. 21.).
Claim 10:
Schooneveldt teaches calculating temperature distributions in the brain using a finite volume thermodynamics model that implements Penne’s bioheat equation (pg. 5, sec. 2.2). The simulation uses a segmentation of an MRI with resolution 1 x 1 x 1 mm, wherein voxels are attributed to tissue types (pg. 4, sec. 2.1). These voxels are used to perform the temperature distribution simulations (pg. 5, sec. 2.2). Meshes for the thermal simulations were performed in OpenFOAM, with meshes representing solid tissues and a finer resolution mesh for CSF (pg. 5, sec. 2.2).
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Schooneveldt et al. (“Schooneveldt”; Cancers 11, no. 8 (2019): 1183) in view of Bousselham et al. (“Bousselham”; In 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 762-767. IEEE, 2016), as applied above to claims 1 and 12, and in further view of Trubel et al. (“Trubel”; Journal of Cerebral Blood Flow & Metabolism 26, no. 1 (2006): 68-78).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
The limitations of claims 1 and 12 have been taught in the rejection above by Schooneveldt and Bousselham.
Claim 4:
Bousselham teaches that the measured temperature profile can be acquired by several means such as by infrared thermal images or MRI thermometry (pg. 4, col. 2, sec. B) (Figure 2).
However, neither Schooneveldt nor Bousselham acquire brain temperature data from magnetic resonance spectroscopy (MRS).
Trubel discusses regional temperature changes in the brain during somatosensory stimulation, combining MRI, temperature sensing, an electrophysiological method to obtain a multimodal measurement during forepaw stimulation (abstract). Trubel teaches “A variety of MRS methods can noninvasively map temperature in different regions of the brain” (pg. 76, col. 1, last para.).
An invention would have been prima facie obvious to one of ordinary skill in the art if there was a finding that the prior art contained a method/system that differed from the instant invention by the substitution of some components with other components, wherein the results of the substitution would have been predictable. Thus, it would have been prima facie obvious to have substituted the measured temperature profile acquired by MRI thermometry of Bousselham with MRS as taught by Trubel (pg. 76, col. 1, last para.). The result of this substitution would have yielded predictable results because MRS can measure brain temperature.
Claim 13:
Schooneveldt discloses that Penne’s equation has a blood perfusion heat sink term (pg. 16, last para.). Table 1 also shows volumetric perfusion rate ([kg/(m3s)]).
Trubel teaches measuring perfusion with MRI using an iron oxide contrast agent (pg. 75, col. 1, last para.).
It would have been prima facie obvious to one of ordinary skill in the art to have calculated perfusion rates in Schooneveldt using perfusion MRI as taught by Trubel because it would have made the perfusion rates patient specific as opposed to being values acquired from literature, as discussed in Schooneveldt (pg. 4, sec. 2.1) (Table 1). Trubel also states that their method can be used in functional studies in humans (abstract), wherein Schooneveldt is directed to a 13-year-old boy with a brain tumor (abstract).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Schooneveldt et al. (“Schooneveldt”; Cancers 11, no. 8 (2019): 1183) in view of Bousselham et al. (“Bousselham”; In 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 762-767. IEEE, 2016) and Park et al. (“Park”; US 2003/0044055 A1), as applied above to claims 1 and 5-6, and in further view of Trubel et al. (“Trubel”; Journal of Cerebral Blood Flow & Metabolism 26, no. 1 (2006): 68-78).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
The limitations of claim 1 have been taught in the rejection above by Schooneveldt and Bousselham.
The limitations of claims 5 and 6 have been taught in the rejection above by Schooneveldt, Bousselham, and Park.
Claim 7:
Schooneveldt teaches computing temperature distributions in the brain and acquiring an MRI scan with 1 x 1 x 1 mm resolution to generate voxels (step A1 comprises performing an acquisition of magnetic resonance images of the encephalon and said first volumetric units correspond to voxel of said magnetic resonance images) (pg. 4, sec. 2.1—2.2).
However, neither Schooneveldt, Bousselham, nor Park acquire brain temperature data from magnetic resonance spectroscopy (MRS) which is then voxelized.
Trubel teaches “A variety of MRS methods can noninvasively map temperature in different regions of the brain” (pg. 76, col. 1, last para.). The combination of Schooneveldt and Trubel would require generating voxels of the MRS data to correspond to the predicted temperatures which are voxel based in Schooneveldt (pg. 4, sec. 2.1).
An invention would have been prima facie obvious to one of ordinary skill in the art if there was a finding that the prior art contained a method/system that differed from the instant invention by the substitution of some components with other components, wherein the results of the substitution would have been predictable. Thus, it would have been prima facie obvious to have substituted the measured temperature profile acquired by MRI thermometry of Bousselham with MRS as taught by Trubel (pg. 76, col. 1, last para.). The result of this substitution would have yielded predictable results because MRS can measure brain temperature.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Schooneveldt et al. (“Schooneveldt”; Cancers 11, no. 8 (2019): 1183) in view of Bousselham et al. (“Bousselham”; In 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 762-767. IEEE, 2016) and Park et al. (“Park”; US 2003/0044055 A1), as applied above to claims 1, 5 and 8, and in further view of Gonçalves et al. (“Gonçalves”; Journal of dairy science 100, no. 5 (2017): 3513-3525).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
The limitations of claim 1 have been taught in the rejection above by Schooneveldt and Bousselham.
The limitations of claims 5 and 8 have been taught in the rejection above by Schooneveldt, Bousselham, and Park.
Claim 9:
As discussed in the rejection above regarding claim 8, the combination of Schooneveldt and Park disclose calculating thermal conductivity values of grey matter and white matter as a function of their respective volumes in a given pixel. However, neither Schooneveldt, Bousselham, nor Park teach calculating a weighted sum of thermal conductivity values using thermal conductivity values and partial volumes of respective tissue types in a voxel.
Gonçalves calculates thermal conductivity as influenced by temperature and viscosity of dairy products (abstract). Gonçalves recites “effective thermal conductivity can be predicted from the intrinsic thermal conductivity combined with a suitable heat transfer model and volumetric fraction of each component. The intrinsic thermal conductivity of each component combined with the respective volume fractions provides the determination of the thermal conductivity of the food as a whole, as shown in equation [5]:
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where ke is the effective thermal conductivity; k1, k2, k3, … are the values of the intrinsic thermal conductivities of the components; and X1, X2, X3, … are the volumetric fractions … The theoretical models used to predict the thermal conductivity of the dairy products were parallel … In the parallel model, thermal conductivity is calculated by multiplying the sum of the thermal conductivity by the volumetric fraction of each component according to Eq. [7]:
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” (pg. 3515, col. 2, para. 2 – last para.).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Schooneveldt, Bousselham, and Park for calculating temperature distributions in the brain using an effective thermal conductivity (abstract of Schooneveldt) by calculating a weighted sum of thermal conductivities using volume fractions of each tissue type as a coefficient taught by Gonçalves. The motivation for doing so is taught by Gonçalves who states that the parallel model is an accurate depiction of the thermal conductivity as a whole, by accounting for each individual component (pg. 3517, col. 2, para. 2).
One of ordinary skill in the art would have had a reasonable expectation of success for calculating effective thermal conductivity of the brain in Schooneveldt, Bousselham, and Park using the weighted sum of Gonçalves. This is because Schooneveldt already calculates effective thermal conductivity and has thermal conductivity values of each tissue type (Table 1) (abstract), and Park discloses the partial volume fractions of white matter, grey matter, and CSF in a given voxel [45]. Gonçalves also states that their method is used with a suitable heat transfer model (pg. 3515, col. 2). Schooneveldt teaches a heat transfer model (pg. 5, sec. 2.2).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Schooneveldt et al. (“Schooneveldt”; Cancers 11, no. 8 (2019): 1183) in view of Bousselham et al. (“Bousselham”; In 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 762-767. IEEE, 2016) and Park et al. (“Park”; US 2003/0044055 A1), as applied above to claims 1 and 5, and in further view of Mossahebi et al. (“Mossahebi”; Magnetic resonance in medicine 74, no. 5 (2015): 1317-1326).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
The limitations of claim 1 have been taught in the rejection above by Schooneveldt and Bousselham.
The limitations of claim 5 have been taught in the rejection above by Schooneveldt, Bousselham, and Park.
Claim 11:
Schooneveldt calculates temperature distribution in the brain using meshes, including the mesh for CSF, using a finite volume thermodynamic model that implements Penne’s bioheat equation (pg. 5, sec. 2.2).
Mossahebi teaches removing CSF partial volume effects in quantitative magnetization transfer imaging (abstract). Mossahebi recites “Imaging anatomical structures with sizes on the order of clinical MRI resolution often has to contend with partial volume (PV) averaging with surrounding tissues. The PV effects may be especially pronounced in quantitative techniques if the relevant MR characteristics of the tissues differ significantly. A well-known source of such errors is a bias induced by PV averaging with cerebrospinal fluid (CSF). For example, even a small amount of CSF in a brain-containing voxel may strongly affect quantitative indices of diffusion-weighted imaging (DWI) (20,21) and multi-component T1/T2 relaxometry (22) owing to its distinct diffusion and relaxation properties, respectively” (pg. 2, para. 2).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Schooneveldt, Bousselham, and Park for calculating heat transfer in a brain using a bioheat equation and calculating partial volumes of each tissue type in a voxel by not calculating voxels with a partial volume of CSF that exceeds a threshold, as taught by Mossahebi. The motivation for doing so is taught by Mossahebi who states that even a small portion of CSF in a voxel can produce quantitative errors (pg. 2, para. 2).
One of ordinary skill in the art would have had a reasonable expectation of success for the combination because Schooneveldt and Park disclose calculating thermal properties and tissue fraction in individual voxels.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Schooneveldt et al. (“Schooneveldt”; Cancers 11, no. 8 (2019): 1183) in view of Bousselham et al. (“Bousselham”; In 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 762-767. IEEE, 2016), Park et al. (“Park”; US 2003/0044055 A1), and Mossahebi et al. (“Mossahebi”; Magnetic resonance in medicine 74, no. 5 (2015): 1317-1326), as applied above to claims 1 and 5, and in further view of Trubel et al. (“Trubel”; Journal of Cerebral Blood Flow & Metabolism 26, no. 1 (2006): 68-78).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
The limitations of claim 1 have been taught in the rejection above by Schooneveldt and Bousselham.
The limitations of claim 5 have been taught in the rejection above by Schooneveldt, Bousselham, and Park.
The limitations of claim 11 have been taught in the rejection above by Schooneveldt, Bousselham, Park, and Mossahebi.
Claim 15:
Schooneveldt uses Penne’s bioheat equation to calculate metabolic rate, which is an inherent parameter in equation 2 (pg. 5, sec. 2.2). However, neither Schooneveldt, Bousselham, Park, nor Mossahebi disclose calculating cerebral oxygen consumption rates as a function of metabolic heat generation, reaction enthalpy between glucose and oxygen, and energy required to separate oxygen from hemoglobin.
Trubel uses Penne’s bioheat equation (pg. 69, col. 1, last para. – col. 2). Trubel uses equation:
PNG
media_image8.png
262
838
media_image8.png
Greyscale
, wherein ΔHo and ΔHb are the enthalpies of oxidative phosphorylation and oxygen release from hemoglobin (pg. 69, col. 2, para. 1).
One of ordinary skill in the art would have had been motivated to modify the Penne’s bioheat equation in Schooneveldt to calculate distribution of cerebral oxygen consumption rates in the brain by using the modified Penne’s equation in Trubel that accounts for enthalpies of oxidative phosphorylation and oxygen release from hemoglobin. The motivation for doing so is taught by Trubel who states that time-dependent variations in brain temperature are likely caused by fluctuations of cerebral blood flow and cerebral metabolic rate of oxidative consumption (abstract). Thus, the temperature distributions of the brain Schooneveldt would be more accurate by accounting for oxygen consumption rates.
One of ordinary skill in the art would have had a reasonable expectation of success because Penne’s bioheat equation, which is used in both Schooneveldt and Trubel, can be modified to account for additional parameters, such as cerebral oxygen consumption as taught by Trubel.
Conclusion
No claims are allowed.
Notable, but not relied upon, prior art includes: Van Leeuwen et al. (Pediatric Research 48, no. 3 (2000): 351-356). Amare et al. (American Society of Mechanical Engineers, 2018) who teaches modeling heat regulation with a structure mesh using a finite volume approach in a voxelized domain. Khundrakpam et al. (NPL ref. 2 on IDS filed 10/15/2024; Annals of biomedical engineering 38, no. 10 (2010): 3070-3083) who teaches thermal conduction tensor imaging and energy flow of brains. Bousselham et al. (Procedia Computer Science 127 (2018): 336-343) who teaches brain tumor temperature effect on extraction from MRI imaging using bioheat equation. Chewpanyanun et al. (In 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST), pp. 1-4. IEEE, 2019) who teaches visualizing temperature and heat transfer in the brain based on MRI. Dillion et al. (NMR in Biomedicine 28, no. 7 (2015): 840-851) who teaches calculating Penne’s bioheat equation for each voxel in an MRI image. Curran et al. ("3-D Voxel-Based Bio-Heat Transfer Code." (2001)) who teaches predicting properties of tissue in each voxel and using SAR data as input into a thermal model to predict tissue temperatures (pg. 4, para. 3). Aburano et al. (US 2011/0002521 A1). Abreu (US 8,849,379 B2). Raghavan et al. (US 2003/0028090 A1).
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685