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 .
DUTY TO DISCLOSE
Applicant has not filed an information disclosure statement (IDS). Applicant is reminded that MPEP 2001 and 37 CFR 1.56 require that any individual associated with the filing and prosecution of the application have a duty of candor with the Office, which includes disclosing all information known to that individual to be material to patentability.
Claim Objections
Claims 65, 67-71, 79-82, and 84 are objected to because of the following informalities:
Claim 65 recites “the treated disorder” without prior mention of a disorder. Please change to “a treated disorder.”
Claims 67 and 78 should read “confidence weighting factor” instead of “confidence weight factor.” Claims 66 and 77 recite “confidence weighting factor” and the disclosure consistently uses “confidence weighting factor.”
Claims refer to “symptoms or disorders” in claims 70, 71, 81, and 82, and “symptoms and disorders” in claims 68, 69, 71, 79, 80, and 82. Applicant should review and address any antecedent basis issues. For example:
Claim 68 recites “…a reliability weighting factor based on mapping of reference circuits for different symptoms and disorders,” and claim 68, which depends form claim 69, recites “using clinical data to map reference circuits for different symptoms and disorders.” It is not clear if the reference circuits in claim 68 are the same reference circuits in claim 69. Claims 79 and 80 have the same issue. Please clarify.
Claim 70 recites “a clinical features weighting factor based on a prominence of symptoms or disorders corresponding to each reference circuit in the subject,” whereas claim 71, which depends from claim 70, recites “determining prominence of symptoms or disorders corresponding to each reference circuit in the subject; and weighting each seed-circuit pair based on the determined prominence of corresponding symptoms or disorders in the subject. Claims 81 and 82 have the same issue. Please clarify.
Claim 71 recites “using clinical data to map reference circuits for different symptoms and disorders… determining prominence of symptoms or disorders corresponding to each reference circuit in the subject.” In the first italicized recitation, the reference circuits have “symptoms and disorders” mapped, but in the second italicized recitation each reference circuit has “symptoms or disorders.” Please clarify.
Claim 84 should read “The system of claim 76, wherein the set of non-transitory computer-readable media...”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 65-84 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.
With respect to claims 65-84, each of the claims either recites or depends from another claim that recites “a set of one or more….” For example, independent claims 65 and 76 recite “a set of one or more seed regions…”; “a set of one or more brain circuits…; “a set of one or more seed-circuit pairs…”; and “a set of one or more weighting factors….” Claim 76 also recites “a set of one or more processors…” and “a set of one or more non-transitory computer readable media….”
Under a broadest reasonable interpretation (BRI), words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. (MPEP 2111.01). In this case, the specification as originally filed does not clearly define the term “set” nor does the specification use the phrase “set of one or more….” Thus, the term must be given its plain meaning, which is the ordinary and customary meaning given to the term by those of ordinary skill in the art at the relevant time. (Id). Examiner is interpreting the meaning of “set” as requiring at least two elements within the set. One relevant definition from Merriam-Webster.com defines set as “a number of things of the same kind that belong or are used together” (“Set.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/set. Accessed 2 Feb. 2026). Examiner believes this is consistent with the specification because one objective of the application is to generate a plurality of seed-circuit pairs (i.e., a set), calculate a weight value for each seed-circuit pair of the plurality of seed-circuit pairs, and then determine the neurostimulation target based on “the seed-circuit pair having the greatest weight value.” ([0010]).
Moreover, if the claims were to be interpreted as the “set” including one or more, then the claim would be anticipated/rendered obvious by any reference that describes one seed region and one brain circuit and, thus, one seed-circuit pair. This does not appear to be what Applicant is attempting to claim. (see, e.g., [0010]).
For the above reasons and pursuant to the principles of a compact prosecution (MPEP 2173.06), Examiner is interpreting independent claim 65 as follows:
selecting at least one seed region within a brain of a subject and at least one brain circuit;
pairing each seed region with each brain circuit to generate a set of two or more seed-circuit pairs;
measuring a connectivity of each seed-circuit pair;
applying at least one weighting factor to the measured connectivity of each of the seed-circuit pairs, wherein the at least one weighting factor comprises a connectivity weighting factor based on a measured connectivity between each seed region and its paired brain circuit and the treated disorder;
calculating a combined weight value for each seed-circuit pair based on the applied weighting factors; and
determining the neurostimulation target based on the calculated combined weight values for the set of seed-circuit pairs.
Claim 76 is interpreted in a similar manner. To be consistent with the independent claims, Examiner is interpreting the dependent claims such that the phrase “set of one or more” is replaced with “at least one,” except for the set of the seed-circuit pairs, which includes two or more seed-circuit pairs.
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 65-84 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite (or similarly recite) the following claim limitations, each of which falls within at least one grouping for an abstract idea as explained below:
selecting a set of one or more seed regions within a brain of a subject and a set of one or more brain circuits – in claims 65 and 76. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mental process. (MPEP 2106.04(a)(2)(III)). Selecting one or more seed regions and one or more brain circuits is an evaluation or judgment that can be practically performed in the human mind. “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” (MPEP 2106.04(a)(2)(III)(B).
pairing each seed region of the set of seed regions with each brain circuit of the set of brain circuits to generate a set of one or more seed-circuit pairs – in claims 65 and 76. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mental process. (MPEP 2106.04(a)(2)(III)). Pairing each seed region with each brain circuit is an evaluation or judgment that can be practically performed in the human mind. (see, e.g., MPEP 2106.04(a)(2)(III)(B).
measuring a connectivity of each seed-circuit pair of the set of seed-circuit pairs – in claims 65 and 76. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mathematical concept. (MPEP 2106.04(a)(2)(I)). Connectivity values can be derived from neuroimaging data using signal-processing techniques and statistical analysis. (see, e.g., one example from MPEP 2106.04(a)(2)(I): “using a formula to convert geospatial coordinates into natural numbers, Burnett v. Panasonic Corp., 741 Fed. Appx. 777, 780 (Fed. Cir. 2018) (non-precedential).”).
applying a set of one or more weighting factors to the measured connectivity of each of the set of seed-circuit pairs, wherein the set of weighting factors comprises a connectivity weighting factor based on a measured connectivity between each seed region and its paired brain circuit and the treated disorder – in claims 65 and 76. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mathematical concept and a mental process. (MPEP 2106.04(a)(2)(I and III)). Applying weighting factors to the seed-circuit pairs can be a mathematical process as it can involve calculating the weighting factors based on various different data. (see, e.g., one example from MPEP 2106.04(a)(2)(I): “using a formula to convert geospatial coordinates into natural numbers, Burnett v. Panasonic Corp., 741 Fed. Appx. 777, 780 (Fed. Cir. 2018) (non-precedential).”). However, applying weighting factors to the seed-circuit pairs can be a mental process as it can involve evaluating different evidence and criteria to determine which factors are more important. “[T]he ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” (MPEP 2106.04(a)(2)(III)).
calculating a combined weight value for each seed-circuit pair based on the applied weighting factors – in claims 65 and 76. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mathematical concept. (MPEP 2106.04(a)(2)(I and III)). Calculating a combined weight value for each seed-circuit pair is an explicit mathematical recitation. (see, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163 (Fed. Cir. 2018) (holding that claims to a “series of mathematical calculations based on selected information” are directed to abstract ideas)).
determining the neurostimulation target based on the calculated combined weight values for the set of seed-circuit pairs – in claims 65 and 76. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mental process. (MPEP 2106.04(a)(2)(III)). With the combined weight values calculated, determining the target involves an evaluation and judgment. “[T]he ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” (MPEP 2106.04(a)(2)(III)).
using clinical data to map reference circuits for different symptoms and disorders – in claims 69, 71, 80, and 82. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mental process. (MPEP 2106.04(a)(2)(III)). Using clinical data to map reference circuits involves an evaluation and judgment of the data. (see, e.g., MPEP 2106.04(a)(2)(III)).
comparing the mapped reference circuits to reference circuits in a database – in claims 69, 71, 80, and 82. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mental process. (MPEP 2106.04(a)(2)(III)). Comparing mapped reference circuits to other circuits in a database involves an evaluation and judgment of the data. (see, e.g., MPEP 2106.04(a)(2)(III)).
weighting each seed-circuit pair based on reliability of the mapping for the corresponding reference circuit – in claims 69 and 80. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mathematical concept. (MPEP 2106.04(a)(2)(I)). Weighting each seed-circuit pair based on reliability of the mapping is a mathematical process that can involve applying one or more formulas to determine how likely the mapping is correct. (see, e.g., MPEP 2106.04(a)(2)(I)).
determining prominence of symptoms or disorders corresponding to each reference circuit in the subject – in claims 71 and 82. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mental process. (MPEP 2106.04(a)(2)(III)). Using clinical data to map reference circuits involves an evaluation and judgment of the data. (see, e.g., MPEP 2106.04(a)(2)(III)).
weighting each seed-circuit pair based on the determined prominence of corresponding symptoms or disorders in the subject – in claims 71 and 82. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mathematical concept and a mental process. (MPEP 2106.04(a)(2)(I and III)). Weighting each seed-circuit pair based on determined prominence can be a mathematical process as it can involve applying one or more formulas to determine how prominent a symptom/disorder is with respect to data (not specific to the subject) representing other symptoms or disorders. (see, e.g., one example from MPEP 2106.04(a)(2)(I): “using a formula to convert geospatial coordinates into natural numbers, Burnett v. Panasonic Corp., 741 Fed. Appx. 777, 780 (Fed. Cir. 2018) (non-precedential).”). However, weighting each seed-circuit pair based on determined prominence can also involve evaluating different evidence to judge a weight factor. “[T]he ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” (MPEP 2106.04(a)(2)(III)).
adjusting the measured connectivity of each of the set of seed-circuit pairs based on how close the measured connectivity is to a pre-determined connectivity value – in claims 73 and 83. This claim limitation, as drafted and under its broadest reasonable interpretation, recites a mathematical concept and a mental process. (MPEP 2106.04(a)(2)(I and III)). Applying weighting factors to the seed-circuit pairs is a mathematical process as it can involve calculating the weighting factors based on various different data. (see, e.g., MPEP 2106.04(a)(2)(I)). However, applying weighting factors to the seed-circuit pairs is a mathematical process as it can involve evaluating different evidence and criteria to determine which factors are more important. (see, e.g., MPEP 2106.04(a)(2)(III)).
Once it is established that the claims recite a judicial exception (i.e., an abstract idea), the next question to consider is whether the claims integrate the judicial exception into a practical application. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. (MPEP 2106.04(d)).
Additional elements should be considered to determine if they integrate the judicial exception into a practical application. Independent claim 65 does not recite an “additional element.” With respect to claim 76, the additional elements include one or more processors and one or more non-transitory computer-readable media storing instructions. However, processors and non-statutory computer-readable media are merely using a computer as a tool to perform an abstract idea. (discussed in MPEP § 2106.05(f)).
With respect to the independent claims in this case, the judicial exception is not integrated into a practical application as the additional elements do not meaningfully limit the judicial exception. For example, the additional element of processors and media are merely using a computer as a tool to perform an abstract idea. (discussed in MPEP § 2106.05(f)).
Dependent claims 66-75 and 76-85 also do not integrate the judicial exception into a practical application. For the most part, the dependent claims further characterize the weight factors (see, e.g., claims 66 and 77: “a confidence weighting factor based on predictive values for each seed-circuit pair determined from clinical and normative data”; claims 67 and 78: “determined based on a sample size of existing data for the particular seed-circuit pair”; claims 68 and 79: “a reliability weighting factor based on mapping of reference circuits for different symptoms and disorders”; claims 70 and 80: “a clinical features weighting factor based on a prominence of symptoms or disorders corresponding to each reference circuit in the subject”). However, these limitations are examples of insignificant pre-solution activity (i.e., data-gathering, see MPEP 2106.05(g)) and/or only generally link the judicial exception to a field-of-use (MPEP 2106.04(d)).
Furthermore, dependent claims 72, 74, and 83 only generally link the judicial exception to a certain field-of-use without more (see, e.g., claim 72: “wherein the disorders comprise at least one selected from the group consisting of depression and addiction”; claims 74 and 83: “the pre-determined connectivity value is determined based on clinical and normative data”). (MPEP 2106.04(d))). Dependent claims 75 and 84 merely perform insignificant extra-solution activity (see, e.g., claims 75 and 84: “produce a ranked list of the seed-circuit pairs based on their calculated combined weight values; and select a highest ranked seed-circuit pair as the neurostimulation target”). (MPEP 2106.05(g)).
Accordingly, claims 65-84 do not recite patent-eligible subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 65, 70, 75, 76, 81, and 84 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2019/0217116 A1 (hereinafter “WILLIAMS”) and a translation of CN 112546446 A (hereinafter “SIR RUN”).
WILLIAMS teaches methods and systems for treating clinical neurological conditions using accelerated Theta-Burst Stimulation (aTBS).([0002]). “Theta-burst stimulation (TBS) is a patterned form of rTMS [repeated applications of Transcranial Magnetic Stimulation].” ([0003]). WILLIAMS teaches “methods for performing aTBS [that] can include generating personalized aTBS targets for TBS stimulation which take in to account the idiosyncrasies of a patient.” ([0054]). “Because TMS coils are currently incapable of targeting structures deep within the brain, personalized aTBS targets can be generated to stimulate surface regions that are linked to deep regions, bypassing the need for deep brain stimulation.” ([0054]). WILLIAMS teaches “aTBS systems..[that use] neuroimaging data of a patient's brain and generate personalized aTBS targets for treatment with aTBS devices.”([0056]).
With respect to claim 65 (and in light of the Section 112(b) rejection above), WILLIAMS teaches a method for determining a neurostimulation target (see, e.g., [0056]: “…embodiments of the invention can acquire neuroimaging data of a patient's brain and generate personalized aTBS targets for treatment with aTBS devices.”), comprising:
selecting a set of one or more seed regions within a brain of a subject and a set of one or more brain circuits and pairing each seed region of the set of seed regions with each brain circuit of the set of brain circuits to generate a set of one or more seed-circuit pairs. WILLIAMS calculates correlation coefficients between subregions in one location and subregions in another location. See [0079]: “The process of reducing each functional subregion identified through parcellation to a single time course further allows for the calculation of correlation coefficients between all functional subregions discovered across multiple ROIs across the brain. For example, multiple subregions within the left dorsolateral prefrontal cortex can be correlated with multiple subregions within the cingulate cortex, although any number of different subregions within any number of different brain structures can be correlated.” NOTE 1: Examiner is interpreting seed regions to include “multiple subregions” in one location and brain circuits to include “any number of different subregions within any number of different brain structures can be correlated.” Moreover, WILLIAMS’s example of subregions in the DLPFC being correlated to subregions in the cingulate cortex is consistent with Applicant’s examples of different networks at, e.g., [0044]-[0049] and seed regions at, e.g., [0071]. NOTE 2: Applicant does not define the meaning of “selecting” or the meaning of “pairing.” Based on Applicant’s disclosure, it appears the system automatically selects and pairs the seed regions and brain circuits. (see, e.g., [0105]). However, there do not appear to be any conditions or criteria for the selecting or pairing processors. Examiner is interpreting WILLIAMS as inherently selecting and pairing some subregions (i.e., seed regions) with other subregions (i.e., circuits). (see, e.g., [0079]).
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measuring a connectivity of each seed-circuit pair of the set of seed-circuit pairs. “The relationships between the functional subregions can be determined (650) using a variety of techniques.” ([0078]). “The process of reducing each functional subregion identified through parcellation to a single time course further allows for the calculation of correlation coefficients between all functional subregions discovered across multiple ROIs across the brain. For example, multiple subregions within the left dorsolateral prefrontal cortex can be correlated with multiple subregions within the cingulate cortex….” ([0079]; see also FIG. 8A shown here).
applying a set of one or more weighting factors to the measured connectivity of each of the set of seed-circuit pairs, wherein the set of weighting factors comprises a connectivity weighting factor based on a measured connectivity between each seed region and its paired brain circuit and the treated disorder. WILLIAMS calculates a “target quality score” for each functional subregion. “Functional subregion parameters can be used to calculate (670) target quality scores for each the functional subregions. In numerous embodiments, the target quality score for each subregion is a function of weighted combinations of subregion parameters. In many embodiments, the target quality scores are generated by determining the surface influence (voxel size-weighted correlation coefficients) for a set of subregions. In many embodiments, the surface influence for a given subregion is the sum of a two-dimensional matrix of Spearman's or Pearson's correlation coefficients derived from a hierarchical clustering algorithm describing the correlation coefficients between a surface ROI subregion and all of the deep ROI subregions.” ([0081]).
calculating a combined weight value for each seed-circuit pair based on the applied weighting factors. “In numerous embodiments, the target quality score for each subregion is a function of weighted combinations of subregion parameters.” ([0081]).
determining the neurostimulation target based on the calculated combined weight values for the set of seed-circuit pairs. WILLIAMS selects a personalized target based on the target quality score. ([0082]). “In numerous embodiments, the target quality score for each subregion is a function of weighted combinations of subregion parameters.” ([0081]).
However, it is not clear if WILLIAMS describes wherein the set of weighting factors comprises a connectivity weighting factor based on a measured connectivity between each seed region and its paired brain circuit and the treated disorder. Nonetheless, WILLIAMS does teach measuring the connectivity between each seed region and it paired brain circuit. (see, e.g., [0079]: “The process of reducing each functional subregion identified through parcellation to a single time course further allows for the calculation of correlation coefficients between all functional subregions discovered across multiple ROIs across the brain.”). WILLIAMS also teaches that the ROI from which functional subregions are determined is based on the condition or disorder of the patient. “Preprocessing steps can include, but are not limited to, physiological noise regression, slice-time correction, motion correction, co-registration, band-pass filtering, de-trending, and/or any other preprocessing step as appropriate to the requirements of a given application.” ([0069]). As such, the connectivity value would not be considered if not for the selected ROI. See also [0078]: “Clinical evaluation as determined that aTBS can be used to treat a variety of different medical conditions, both mental and physical. For example, aiTBS is effective at reducing suicide ideation and lessening symptoms of depression when performed over the left dorsolateral prefrontal cortex (L-DLPFC).”
In the same field of endeavor, SIR RUN relates to “individualized target location method based on weighted functional connection.” (p.1, line 18). “[Repetitive transcranial magnetic stimulation] rTMS can not only regulate the nerve activity of the stimulation site, but also remotely regulate other brain regions or networks with strong activity correlation.” (p.1, lines 40-42). While TMS can directly stimulate superficial brain areas, “[i]n order to make the TMS effect accurately act on the deep brain area of the treatment target, the main method currently used is the indirect functional connection algorithm.” (p.2, lines 12-14). According to SIR RUN, “[a]lthough the current target location methods can effectively locate deep target brain regions, these methods can only stimulate a single brain region and are lacking in improving the effectiveness of treatment.” (p.2, lines 31-33).
SIR RUN teaches “an individualized target location method based on weighted functional connection….” (p.3, lines 1-3). In addition to obtaining structural and functional MRI data, SIR RUN teaches additional steps to “evaluate the patient with a mild cognitive impairment scale, and determine the scores of different dimensions of the scale” (p.3, lines 9-11), “[d]etermine the deep brain area and superficial contact brain area that need intervention” (p.3, lines 15-16), and “[c]alculate the maximum weighted functional connection between the cognitively related deep brain area and the brain area of interest based on the score of the cognitive assessment scale, and establish the point of the brain area of interest corresponding to the maximum weighted functional connection as the transcranial magnetic field [i]ndividualized stimulation targets for stimulation.” (p.3, lines 18-22). SIR RUN describes one example in which the brain regions that correspond to different cognitive functions are weighted based on a cognitive evaluation. (p.5, lines 23-29). “In this example, the main manifestations are the impairment of memory, the impairment of attention, and the change of language ability. According to the hippocampus is the core structure in the memory system, it is also the main damaged structure of Alzheimer’s disease, so we choose the left side Hippocampus; the corresponding brain area of attention is usually the anterior cingulate gyrus; language is the temporal lobe.” (p.5, lines 34-38). The functional connectivity between these three areas and a superficial area of interest in the cerebral cortex is then calculated. (p.5, lines 42-44). Regions of the brain associated with more severe impairment are weighted stronger. (p.6, lines 32-36).
SIR RUN teaches that the “beneficial effects” of the invention include finding “the most weighted functionally connected points in the superficial brain regions of the brain as stimulus targets.” (p.4, lines 39-41). “It can not only intervene in the deep brain activity of the target at a long distance, but also combine the weights of different dimensions of cognitive impairment to find precise and individualized treatment targets….” (p.4, lines 42-43).
It would have been obvious to one having ordinary skill in the art at the time of filing to determine the deep brain regions and superficial brain areas that need intervention prior to calculating correlation coefficient values, as taught in SIR RUN, and to use a connectivity weighting factor that is based on a measured connectivity. One would be motivated to determine the deep brain regions and superficial brain areas that need intervention and to calculate weighted functional connections in order to find “the most weighted functionally connected points in the superficial brain regions of the brain as stimulus targets,” as taught in SIR RUN. Any confidence weighting factor determined from the correlation coefficients would necessarily be based on the disorder that needs intervention. There would be a reasonable expectation of success as SIR RUN teaches that one can identify the regions and areas for intervention and calculate the weighted functional connections.
With respect to claim 70, WILLIAMS does not explicitly teach wherein the set of one or more weighting factors further comprises a clinical features weighting factor based on a prominence of symptoms or disorders corresponding to each reference circuit in the subject. Nonetheless, WILLIAMS is clearly considers the prominence of symptoms as an important factor. “aTBS protocols can vary depending on numerous factors, including, but not limited to, the severity of condition, whether or not aiTBS or acTBS is used, or any other factors as appropriate to the requirements of specific applications of embodiments of the invention.” ([0067]).
SIR RUN’s method addresses “[t]he problem of inability to stimulate deep brain tissue, and at the same time, combined with the different dimensions of clinical cognitive impairment, it provides accurate transcranial magnetic stimulation for neuropsychiatric diseases with cognitive impairment to find precise and individualized treatment targets.” (p.2, lines 38-42).
Step S5 considers different brain regions in addition to scores from the cognitive assessment to “[c]alculate the maximum weighted function connection.” (p.3, line 18) SIR RUN further explains operations within step S5, which teach a clinical features weighting factor based on a prominence of symptoms or disorders corresponding to each reference circuit in the subject:
Step S51, Confirm a number of deep brain regions as seed point regions, and each target brain region corresponds to a different cognitive function;
S52, Perform weight estimation based on scale scores related to cognitive functions, and obtain the weight values of brain regions corresponding to different cognitive functions; Step S53: Set the cortical target area stimulated by TMS to further determine the stimulation target in the region;
Step S54, Calculate the functional connectivity of each voxel in the different seed point area and the designated cortical target area; the obtained functional connection strength of the various sub-points and each target area voxel;
Step S55, Combining the cognitive function weights of different seed points, recalculate the weight function connection strength of each voxel in the cortical target area. (p.3, line 45 to p.4, line 9).
Notably, step S55 uses an “exponential weighting method” that is based on Si, which represents a cognitive impairment score “and the lower the score, the more severe the cognitive impairment….” (p.4, line 26 and lines 30-31).
It would have been obvious to one having ordinary skill in the art at the time of filing to include, among the weighting factors, a clinical features weighting factor that is based on a prominence of symptoms or disorders corresponding to each reference circuit in the subject. One would be motivated to consider the symptoms or disorders corresponding to each reference circuit in order to find “the most weighted functionally connected points in the superficial brain regions of the brain as stimulus targets…[which includes combining] the weights of different dimensions of cognitive impairment to find precise and individualized treatment targets,” (p.4, lines 40-42) as taught in SIR RUN. There would be a reasonable expectation of success as SIR RUN teaches that one can identify the regions and areas for intervention and calculate the weighted functional connections.
With respect to claim 75, WILLIAMS teaches producing a ranked list of the seed-circuit pairs based on their calculated combined weight values and selecting a highest ranked seed-circuit pair as the neurostimulation target. “Personalized aTBS target can be generated (680) based on the target quality scores. In numerous embodiments, the highest quality targets are selected as the personalized aTBS target. In a variety of embodiments, more than one target can be selected.” ([0082]). While WILLIAMS does not explicitly use the term “ranked list,” WILLIAMS calculates multiple target quality scores, teaches that the “highest quality targets” are selected, and enables the selection of more than one target. It would have been obvious to present the user with a ranked list so that the user could consider the treatment protocol as a whole and how and in what order to stimulate multiple targets.
With respect to claim 76 (and in light of the Section 112(b) rejection above), WILLIAMS teaches a system for determining a neurostimulation target (see, e.g., [0056]: “…embodiments of the invention can acquire neuroimaging data of a patient's brain and generate personalized aTBS targets for treatment with aTBS devices.”). The system includes a set of one or more processors (see, e.g., “processor 210” in [0061]-[0063]) and a set of one or more non-transitory computer-readable media comprising program instructions that are executable by the one or more processors (“memory 230” stores “aTBS targeting application” ([0063])) such that the system is configured to:
select a set of one or more seed regions within a brain of a subject and a set of one or more brain circuits and pair each seed region of the set of seed regions with each brain circuit of the set of brain circuits to generate a set of one or more seed-circuit pairs. WILLIAMS calculates correlation coefficients between subregions in one location and subregions in another location. See [0079]: “The process of reducing each functional subregion identified through parcellation to a single time course further allows for the calculation of correlation coefficients between all functional subregions discovered across multiple ROIs across the brain. For example, multiple subregions within the left dorsolateral prefrontal cortex can be correlated with multiple subregions within the cingulate cortex, although any number of different subregions within any number of different brain structures can be correlated.” NOTE 1: Examiner is interpreting seed regions to include “multiple subregions” in one location and brain circuits to include “any number of different subregions within any number of different brain structures can be correlated.” Moreover, WILLIAMS’s example of subregions in the DLPFC being correlated to subregions in the cingulate cortex is consistent with Applicant’s examples of different networks at, e.g., [0044]-[0049] and seed regions at, e.g., [0071]. NOTE 2: Applicant does not define the meaning of “selecting” or the meaning of “pairing.” Based on Applicant’s disclosure, it appears the system automatically selects and pairs the seed regions and brain circuits. (see, e.g., [0105]). However, there do not appear to be any conditions or criteria for the selecting or pairing processes. Examiner is interpreting WILLIAMS as inherently selecting and pairing some subregions (i.e., seed regions) with other subregions (i.e., circuits). (see, e.g., [0079]).
measure a connectivity of each seed-circuit pair of the set of seed-circuit pairs. “The relationships between the functional subregions can be determined (650) using a variety of techniques.” ([0078]). “The process of reducing each functional subregion identified through parcellation to a single time course further allows for the calculation of correlation coefficients between all functional subregions discovered across multiple ROIs across the brain. For example, multiple subregions within the left dorsolateral prefrontal cortex can be correlated with multiple subregions within the cingulate cortex….” ([0079]; see also FIG. 8A shown here).
apply a set of one or more weighting factors to the measured connectivity of each of the set of seed-circuit pairs, wherein the set of weighting factors comprises a connectivity weighting factor based on a measured connectivity between each seed region and its paired brain circuit. WILLIAMS calculates a “target quality score” for each functional subregion. “Functional subregion parameters can be used to calculate (670) target quality scores for each the functional subregions. In numerous embodiments, the target quality score for each subregion is a function of weighted combinations of subregion parameters. In many embodiments, the target quality scores are generated by determining the surface influence (voxel size-weighted correlation coefficients) for a set of subregions. In many embodiments, the surface influence for a given subregion is the sum of a two-dimensional matrix of Spearman's or Pearson's correlation coefficients derived from a hierarchical clustering algorithm describing the correlation coefficients between a surface ROI subregion and all of the deep ROI subregions.” ([0081]).
calculate a combined weight value for each seed-circuit pair based on the applied weighting factors. “In numerous embodiments, the target quality score for each subregion is a function of weighted combinations of subregion parameters.” ([0081]).
determine the neurostimulation target based on the calculated combined weight values for the set of seed-circuit pairs. WILLIAMS selects a personalized target based on the target quality score. ([0082]). “In numerous embodiments, the target quality score for each subregion is a function of weighted combinations of subregion parameters.” ([0081]).
However, it is not clear if WILLIAMS describes wherein the set of weighting factors comprises a connectivity weighting factor based on a measured connectivity between each seed region and its paired brain circuit. Nonetheless, WILLIAMS does teach measuring the connectivity between each seed region and it paired brain circuit. (see, e.g., [0079]: “The process of reducing each functional subregion identified through parcellation to a single time course further allows for the calculation of correlation coefficients between all functional subregions discovered across multiple ROIs across the brain.”).
In the same field of endeavor, SIR RUN relates to “individualized target location method based on weighted functional connection.” (p.1, line 18). “[Repetitive transcranial magnetic stimulation] rTMS can not only regulate the nerve activity of the stimulation site, but also remotely regulate other brain regions or networks with strong activity correlation.” (p.1, lines 40-42). While TMS can directly stimulate superficial brain areas, “[i]n order to make the TMS effect accurately act on the deep brain area of the treatment target, the main method currently used is the indirect functional connection algorithm.” (p.2, lines 12-14). According to SIR RUN, “[a]lthough the current target location methods can effectively locate deep target brain regions, these methods can only stimulate a single brain region and are lacking in improving the effectiveness of treatment.” (p.2, lines 31-33).
SIR RUN teaches “an individualized target location method based on weighted functional connection….” (p.3, lines 1-3). In addition to obtaining structural and functional MRI data, SIR RUN teaches additional steps to “evaluate the patient with a mild cognitive impairment scale, and determine the scores of different dimensions of the scale” (p.3, lines 9-11), “[d]etermine the deep brain area and superficial contact brain area that need intervention” (p.3, lines 15-16), and “[c]alculate the maximum weighted functional connection between the cognitively related deep brain area and the brain area of interest based on the score of the cognitive assessment scale, and establish the point of the brain area of interest corresponding to the maximum weighted functional connection as the transcranial magnetic field [i]ndividualized stimulation targets for stimulation.” (p.3, lines 18-22). SIR RUN describes one example in which the brain regions that correspond to different cognitive functions are weighted based on a cognitive evaluation. (p.5, lines 23-29). “In this example, the main manifestations are the impairment of memory, the impairment of attention, and the change of language ability. According to the hippocampus is the core structure in the memory system, it is also the main damaged structure of Alzheimer’s disease, so we choose the left side Hippocampus; the corresponding brain area of attention is usually the anterior cingulate gyrus; language is the temporal lobe.” (p.5, lines 34-38). The functional connectivity between these three areas and a superficial area of interest in the cerebral cortex is then calculated. (p.5, lines 42-44). Regions of the brain associated with more severe impairment are weighted stronger. (p.6, lines 32-36).
SIR RUN teaches that the “beneficial effects” of the invention include finding “the most weighted functionally connected points in the superficial brain regions of the brain as stimulus targets.” (p.4, lines 39-41). “It can not only intervene in the deep brain activity of the target at a long distance, but also combine the weights of different dimensions of cognitive impairment to find precise and individualized treatment targets….” (p.4, lines 42-43).
It would have been obvious to one having ordinary skill in the art at the time of filing to determine the deep brain regions and superficial brain areas that need intervention prior to calculating correlation coefficient values, as taught in SIR RUN, and to use a connectivity weighting factor that is based on a measured connectivity. One would be motivated to determine the deep brain regions and superficial brain areas that need intervention and to calculate weighted functional connections in order to find “the most weighted functionally connected points in the superficial brain regions of the brain as stimulus targets,” as taught in SIR RUN. There would be a reasonable expectation of success as SIR RUN teaches that one can identify the regions and areas for intervention and calculate the weighted functional connections.
With respect to claim 81, WILLIAMS does not explicitly teach wherein the set of one or more weighting factors further comprises a clinical features weighting factor based on a prominence of symptoms or disorders corresponding to each reference circuit in the subject. Nonetheless, WILLIAMS is clearly considers the prominence of symptoms as an important factor. “aTBS protocols can vary depending on numerous factors, including, but not limited to, the severity of condition, whether or not aiTBS or acTBS is used, or any other factors as appropriate to the requirements of specific applications of embodiments of the invention.” ([0067]).
SIR RUN’s method addresses “[t]he problem of inability to stimulate deep brain tissue, and at the same time, combined with the different dimensions of clinical cognitive impairment, it provides accurate transcranial magnetic stimulation for neuropsychiatric diseases with cognitive impairment to find precise and individualized treatment targets.” (p.2, lines 38-42).
Step S5 considers different brain regions in addition to scores from the cognitive assessment to “[c]alculate the maximum weighted function connection.” (p.3, line 18) SIR RUN further explains operations within step S5, which teach a clinical features weighting factor based on a prominence of symptoms or disorders corresponding to each reference circuit in the subject:
Step S51, Confirm a number of deep brain regions as seed point regions, and each target brain region corresponds to a different cognitive function;
S52, Perform weight estimation based on scale scores related to cognitive functions, and obtain the weight values of brain regions corresponding to different cognitive functions; Step S53: Set the cortical target area stimulated by TMS to further determine the stimulation target in the region;
Step S54, Calculate the functional connectivity of each voxel in the different seed point area and the designated cortical target area; the obtained functional connection strength of the various sub-points and each target area voxel;
Step S55, Combining the cognitive function weights of different seed points, recalculate the weight function connection strength of each voxel in the cortical target area. (p.3, line 45 to p.4, line 9).
Notably, step S55 uses an “exponential weighting method” that is based on Si, which represents a cognitive impairment score “and the lower the score, the more severe the cognitive impairment….” (p.4, line 26 and lines 30-31).
It would have been obvious to one having ordinary skill in the art at the time of filing to include, among the weighting factors, a clinical features weighting factor that is based on a prominence of symptoms or disorders corresponding to each reference circuit in the subject. One would be motivated to consider the symptoms or disorders corresponding to each reference circuit in order to find “the most weighted functionally connected points in the superficial brain regions of the brain as stimulus targets…[which includes combining] the weights of different dimensions of cognitive impairment to find precise and individualized treatment targets,” (p.4, lines 40-42) as taught in SIR RUN. There would be a reasonable expectation of success as SIR RUN teaches that one can identify the regions and areas for intervention and calculate the weighted functional connections.
With respect to claim 84, WILLIAMS teaches that the set of non-transitory computer-readable media further comprises program instructions that are executable by the one or more processors such that the system is configured to: produce a ranked list of the seed-circuit pairs based on their calculated combined weight values and select a highest ranked seed-circuit pair as the neurostimulation target. “Personalized aTBS target can be generated (680) based on the target quality scores. In numerous embodiments, the highest quality targets are selected as the personalized aTBS target. In a variety of embodiments, more than one target can be selected.” ([0082]). While WILLIAMS does not explicitly use the term “ranked list,” WILLIAMS calculates multiple target quality scores, teaches that the “highest quality targets” are selected, and enables the selection of more than one target. It would have been obvious to present the user with a ranked list so that the user could consider the treatment protocol as a whole and how and in what order to stimulate multiple targets prior to selecting the highest seed-circuit pair as the target.
Claims 66, 67, 71, 72, 77, 78, and 82 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2019/0217116 A1 (hereinafter “WILLIAMS”) and a translation of CN 112546446 A (hereinafter “SIR RUN”) as applied to claims 65, 70, and 76 above, and further in view of Siddiqi, Shan H., et al. “Distinct symptom-specific treatment targets for circuit-based neuromodulation.” American Journal of Psychiatry 177.5 (2020): 435-446 (hereinafter “SIDDIQI”).
With respect to claim 66, neither WILLIAMS nor SIR RUN explicitly teach wherein the set of one or more weighting factors further comprises a confidence weighting factor based on predictive values for each seed-circuit pair determined from clinical and normative data. However, WILLIAMS and SIR-RUN, combined as discussed above, teach generating weighted scores of seed-circuit pairs to determine a stimulation target.
SIDDIQI notes that “[t]reatment of different depression symptoms may require different brain stimulation targets with different underlying brain circuits.” (Abstract, Objective). SIDDIQI “sought to identify such targets, which could improve the efficacy of therapeutic brain stimulation and facilitate personalized therapy.” (Id). SIDDIQI concluded that “[d]istinct clusters of depressive symptoms responded better to different TMS targets across independent retrospective data sets.”
For each seed (i.e., stimulation site) to circuit pair, SIDDIQI teaches that one can provide a predictive value (i.e., expected symptom improvement or predictive correlation) that is derived from clinical outcomes that are linked to normative-connectome-defined circuits. SIDDIQI stimulation sites were estimated. “Normative connectivity of this stimulation site was computed using resting-state functional MRI (rsfMRI) data from a large connectome database (N=1,000), as in past work. This method provides a highly reliable estimate of each stimulation site’s expected connectivity profile (Figure 1B), enabling direct comparison of stimulated circuits and clinical changes.” (p.436, right column). SIDDIQI made “system-response maps” based on these stimulation site connectivity maps correlated with change in depression symptoms and then classified similar maps into common clusters. (p.436, right column, second paragraph). “At each voxel, this yielded a value representing the degree to which this voxel’s connectivity to the stimulation site predicted efficacy for any given symptom.” (p.436, right column, second paragraph). “By definition, these maps reflect the symptomatic improvement expected after TMS to each voxel in the DLPFC.” (p.437, right column). The maps were able to predict clinical improvement. (p.443, left column).
It would have been obvious to one having ordinary skill in the art at the time of filing to include, in addition to the connectivity weighting factor, a confidence weighting factor that is based on predictive values for each seed-circuit pair determined from clinical and normative data. One would be motivated to use a system-response map, as taught in SIR-RUN, because the map, at each voxel, yielded a value representing the degree to which the voxel’s connectivity to the stimulation site predicted efficacy for any given symptom. There would be a reasonable expectation of success as SIR RUN teaches that one generate the system-response maps based on clinical and normative data.
With respect to claim 67 (depending from claim 66), SIDDIQI teaches wherein the confidence weight factor for a particular seed-circuit pair is determined based on a sample size of existing data for the particular seed-circuit pair. While SIDDIQI does not explicitly teach that the factor is based on sample size of existing data for a particular seed-circuit pair, the maps are generated based on a number of samples. Data-sets having different numbers of samples for a stimulation site would necessarily provide different levels of confidence in the different sites. SIDDIQI is clearly concerned with such confidence as it describes replication, sham specificity, and cross-dataset prediction. One skilled in the art understands that one reason weights have different values is because of the amount and/or quality of data that supports the weights. (see, e.g., “It is worth noting that not all symptom-response maps were equally consistent across data sets and symptom scales. Some symptoms, such as sadness, decreased interest/activities, suicidality, guilt, and hopelessness/pessimism, were consistently part of the dysphoric cluster. Symptoms such as insomnia and sexual dysfunction were consistently part of the anxiosomatic cluster, with the exception of the HAM-D in the discovery cohort.”) (p.443, right column).
With respect to claim 71 (depending from claim 70), WILLIAMS does not teach the claim limitations of claim 71. However, SIR RUN teaches using clinical data to identify different reference circuits for different symptoms and disorders (see e.g., Steps S51 and S52 at p.3, line 45 to p.4, line 2); determining prominence of symptoms or disorders corresponding to each reference circuit in the subject (S52, Perform weight estimation based on scale scores related to cognitive functions, and obtain the weight values of brain regions corresponding to different cognitive functions (Id).); and weighting each seed-circuit pair based on the determined prominence of corresponding symptoms or disorders in the subject (Step S55 uses an “exponential weighting method” that is based on Si, which represents a cognitive impairment score “and the lower the score, the more severe the cognitive impairment….” (p.4, line 26 and lines 30-31).
However, it is not clear that SIR RUN teaches using clinical data to map reference circuits for different symptoms and disorders and comparing the mapped reference circuits to reference circuits in a database.
SIDDIQI notes that “[t]reatment of different depression symptoms may require different brain stimulation targets with different underlying brain circuits.” (Abstract, Objective). SIDDIQI “sought to identify such targets, which could improve the efficacy of therapeutic brain stimulation and facilitate personalized therapy.” (Id). SIDDIQI concluded that “[d]istinct clusters of depressive symptoms responded better to different TMS targets across independent retrospective data sets.”
For each seed (i.e., stimulation site) to circuit pair, SIDDIQI teaches that one can provide a predictive value (i.e., expected symptom improvement or predictive correlation) that is derived from clinical outcomes that are linked to normative-connectome-defined circuits. SIDDIQI stimulation sites were estimated. “Normative connectivity of this stimulation site was computed using resting-state functional MRI (rsfMRI) data from a large connectome database (N=1,000), as in past work. This method provides a highly reliable estimate of each stimulation site’s expected connectivity profile (Figure 1B), enabling direct comparison of stimulated circuits and clinical changes.” (p.436, right column). SIDDIQI made “system-response maps” based on these stimulation site connectivity maps correlated with change in depression symptoms and then classified similar maps into common clusters. (p.436, right column, second paragraph). “At each voxel, this yielded a value representing the degree to which this voxel’s connectivity to the stimulation site predicted efficacy for any given symptom.” (p.436, right column, second paragraph). “By definition, these maps reflect the symptomatic improvement expected after TMS to each voxel in the DLPFC.” (p.437, right column). The maps were able to predict clinical improvement. (p.443, left column).
It would have been obvious to one having ordinary skill in the art at the time of filing to use clinical data to map reference circuits for different symptoms and disorders and compare the mapped reference circuits to reference circuits in a database. More specifically, one would be motivated to compare a functional connectivity map of a patient to a system-response map generated from reference circuits in a database, as taught in SIR-RUN, because the system-response map, at each voxel, yields a value representing the degree to which the voxel’s connectivity to the stimulation site predicted efficacy for any given symptom. There would be a reasonable expectation of success as WILLIAMS teaches that a functional connectivity map can be made and SIDDIQI teaches that the map can be compared to a system-response map.
With respect to claim 72 (depending from claim 71), WILLIAMS the disorders comprise at least one selected from the group consisting of depression and addiction. (see, e.g., [0020] and [0052]).
With respect to claim 77 (depending from claim 76), neither WILLIAMS nor SIR RUN explicitly teach wherein the set of one or more weighting factors further comprises a confidence weighting factor based on predictive values for each seed-circuit pair determined from clinical and normative data. However, WILLIAMS and SIR-RUN, combined as discussed above, teach generating weighted scores of seed-circuit pairs to determine a stimulation target.
SIDDIQI notes that “[t]reatment of different depression symptoms may require different brain stimulation targets with different underlying brain circuits.” (Abstract, Objective). SIDDIQI “sought to identify such targets, which could improve the efficacy of therapeutic brain stimulation and facilitate personalized therapy.” (Id). SIDDIQI concluded that “[d]istinct clusters of depressive symptoms responded better to different TMS targets across independent retrospective data sets.”
For each seed (i.e., stimulation site) to circuit pair, SIDDIQI teaches that one can provide a predictive value (i.e., expected symptom improvement or predictive correlation) that is derived from clinical outcomes that are linked to normative-connectome-defined circuits. SIDDIQI stimulation sites were estimated. “Normative connectivity of this stimulation site was computed using resting-state functional MRI (rsfMRI) data from a large connectome database (N=1,000), as in past work. This method provides a highly reliable estimate of each stimulation site’s expected connectivity profile (Figure 1B), enabling direct comparison of stimulated circuits and clinical changes.” (p.436, right column). SIDDIQI made “system-response maps” based on these stimulation site connectivity maps correlated with change in depression symptoms and then classified similar maps into common clusters. (p.436, right column, second paragraph). “At each voxel, this yielded a value representing the degree to which this voxel’s connectivity to the stimulation site predicted efficacy for any given symptom.” (p.436, right column, second paragraph). “By definition, these maps reflect the symptomatic improvement expected after TMS to each voxel in the DLPFC.” (p.437, right column). The maps were able to predict clinical improvement. (p.443, left column).
It would have been obvious to one having ordinary skill in the art at the time of filing to include, in addition to the connectivity weighting factor, a confidence weighting factor that is based on predictive values for each seed-circuit pair determined from clinical and normative data. One would be motivated to use a system-response map, as taught in SIR-RUN, because the map, at each voxel, yielded a value representing the degree to which the voxel’s connectivity to the stimulation site predicted efficacy for any given symptom. There would be a reasonable expectation of success as SIR RUN teaches that one generate the system-response maps based on clinical and normative data.
With respect to claim 78 (depending from claim 77), SIDDIQI teaches wherein the confidence weight factor for a particular seed-circuit pair is determined based on a sample size of existing data for the particular seed-circuit pair. While SIDDIQI does not explicitly teach that the factor is based on sample size of existing data for a particular seed-circuit pair, the maps are generated based on a number of samples. Data-sets having different numbers of samples for a stimulation site would necessarily provide different levels of confidence in the different sites. SIDDIQI is clearly concerned with such confidence as it describes replication, sham specificity, and cross-dataset prediction. One skilled in the art understands that one reason weights have different values is because of the amount and/or quality of data that supports the weights. (see, e.g., “It is worth noting that not all symptom-response maps were equally consistent across data sets and symptom scales. Some symptoms, such as sadness, decreased interest/activities, suicidality, guilt, and hopelessness/pessimism, were consistently part of the dysphoric cluster. Symptoms such as insomnia and sexual dysfunction were consistently part of the anxiosomatic cluster, with the exception of the HAM-D in the discovery cohort.”) (p.443, right column).
With respect to claim 82 (depending from claim 81), WILLIAMS does not teach the claim limitations of claim 82. However, SIR RUN teaches using clinical data to identify different reference circuits for different symptoms and disorders (see e.g., Steps S51 and S52 at p.3, line 45 to p.4, line 2); determining prominence of symptoms or disorders corresponding to each reference circuit in the subject (S52, Perform weight estimation based on scale scores related to cognitive functions, and obtain the weight values of brain regions corresponding to different cognitive functions (Id).); and weighting each seed-circuit pair based on the determined prominence of corresponding symptoms or disorders in the subject (Step S55 uses an “exponential weighting method” that is based on Si, which represents a cognitive impairment score “and the lower the score, the more severe the cognitive impairment….” (p.4, line 26 and lines 30-31).
However, it is not clear that SIR RUN teaches using clinical data to map reference circuits for different symptoms and disorders and comparing the mapped reference circuits to reference circuits in a database.
SIDDIQI notes that “[t]reatment of different depression symptoms may require different brain stimulation targets with different underlying brain circuits.” (Abstract, Objective). SIDDIQI “sought to identify such targets, which could improve the efficacy of therapeutic brain stimulation and facilitate personalized therapy.” (Id). SIDDIQI concluded that “[d]istinct clusters of depressive symptoms responded better to different TMS targets across independent retrospective data sets.”
For each seed (i.e., stimulation site) to circuit pair, SIDDIQI teaches that one can provide a predictive value (i.e., expected symptom improvement or predictive correlation) that is derived from clinical outcomes that are linked to normative-connectome-defined circuits. SIDDIQI stimulation sites were estimated. “Normative connectivity of this stimulation site was computed using resting-state functional MRI (rsfMRI) data from a large connectome database (N=1,000), as in past work. This method provides a highly reliable estimate of each stimulation site’s expected connectivity profile (Figure 1B), enabling direct comparison of stimulated circuits and clinical changes.” (p.436, right column). SIDDIQI made “system-response maps” based on these stimulation site connectivity maps correlated with change in depression symptoms and then classified similar maps into common clusters. (p.436, right column, second paragraph). “At each voxel, this yielded a value representing the degree to which this voxel’s connectivity to the stimulation site predicted efficacy for any given symptom.” (p.436, right column, second paragraph). “By definition, these maps reflect the symptomatic improvement expected after TMS to each voxel in the DLPFC.” (p.437, right column). The maps were able to predict clinical improvement. (p.443, left column).
It would have been obvious to one having ordinary skill in the art at the time of filing to use clinical data to map reference circuits for different symptoms and disorders and compare the mapped reference circuits to reference circuits in a database. More specifically, one would be motivated to compare a functional connectivity map of a patient to a system-response map generated from reference circuits in a database, as taught in SIR-RUN, because the system-response map, at each voxel, yields a value representing the degree to which the voxel’s connectivity to the stimulation site predicted efficacy for any given symptom. There would be a reasonable expectation of success as WILLIAMS teaches that a functional connectivity map can be made and SIDDIQI teaches that the map can be compared to a system-response map.
Claims 73, 74, and 83 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2019/0217116 A1 (hereinafter “WILLIAMS”) and a translation of CN 112546446 A (hereinafter “SIR RUN”) as applied to claims 65 and 76 above, and further in view of Horn, Andreas, et al. “Connectivity predicts deep brain stimulation outcome in Parkinson disease.” Annals of neurology 82.1 (2017): 67-78 (hereinafter “HORN”).
With respect to claim 73, WILLIAMS does not explicitly teach that applying the connectivity weighting factor comprises adjusting the measured connectivity of each of the set of seed-circuit pairs based on how close the measured connectivity is to a pre-determined connectivity value.
However, HORN teaches that “[e]ffective [subthalamic nucleus deep brain stimulation for Parkinson disease] is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts.” (Abstract, Interpretation). To generate the specific connectivity profile, HORN teaches using “high-quality connectome datasets of both diffusion tractography and functional connectivity to compute the connectivity profile of effective STN stimulation for PD.” (p.68, left column). HORN teaches that “[b]y showing that connectivity predicts the response of individual PD patients to DBS, the current results support the notion that targets of therapeutic brain stimulation may be brain networks and not individual brain regions. As such, different network nodes may potentially be targeted by different stimulation modalities with similar therapeutic benefit.”
It would have been obvious to one having ordinary skill in the art at the time of filing to apply the connectivity weighting factor by adjusting the measured connectivity of each of the set of seed-circuit pairs based on how close the measured connectivity is to a pre-determined connectivity value. One having ordinary skill in the art would have been motivated to compare the patient’s measured connectivity to a predetermined connectivity profile, as taught in HORN, and adjust the weighting factor based on how close the measured connectivity to a predetermined connectivity profile. There would have been a reasonable expectation of success as HORN teaches that a measured connectivity can be compared to a predetermined connectivity profile.
With respect to claim 74, as discussed above with respect to claim 73, HORN teaches that wherein the pre-determined connectivity value is determined based on clinical and normative data (In HORN, the connectivity profile is derived from “[a] training dataset of 51 PD patients with STN DBS…combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) [that identifies] connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]).” (Abstract)).
With respect to claim 83, WILLIAMS does not explicitly teach that applying the connectivity weighting factor comprises adjusting the measured connectivity of each of the set of seed-circuit pairs based on how close the measured connectivity is to a pre-determined connectivity value in which the pre-determined connectivity value is determined based on clinical and normative data.
However, HORN teaches that “[e]ffective [subthalamic nucleus deep brain stimulation for Parkinson disease] is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts.” (Abstract, Interpretation). To generate the specific connectivity profile, HORN teaches using “high-quality connectome datasets of both diffusion tractography and functional connectivity to compute the connectivity profile of effective STN stimulation for PD.” (p.68, left column). HORN teaches that “[b]y showing that connectivity predicts the response of individual PD patients to DBS, the current results support the notion that targets of therapeutic brain stimulation may be brain networks and not individual brain regions. As such, different network nodes may potentially be targeted by different stimulation modalities with similar therapeutic benefit.”
It would have been obvious to one having ordinary skill in the art at the time of filing to apply the connectivity weighting factor by adjusting the measured connectivity of each of the set of seed-circuit pairs based on how close the measured connectivity is to a pre-determined connectivity value. One having ordinary skill in the art would have been motivated to compare the patient’s measured connectivity to a predetermined connectivity profile, as taught in HORN, and adjust the weighting factor based on how close the measured connectivity to a predetermined connectivity profile. There would have been a reasonable expectation of success as HORN teaches that a measured connectivity can be compared to a predetermined connectivity profile.
Note: Using HORN’s teachings, the predetermined connectivity profile would necessarily be determined based on clinical and normative data. In HORN, the connectivity profile is derived from “[a] training dataset of 51 PD patients with STN DBS…combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) [that identifies] connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]).” (Abstract).
Claims 68, 69, 79, and 80 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2019/0217116 A1 (hereinafter “WILLIAMS”) and a translation of CN 112546446 A (hereinafter “SIR RUN”) as applied to claims 65 and 76 above, and further in view of Siddiqi, Shan H., et al. “Distinct symptom-specific treatment targets for circuit-based neuromodulation.” American Journal of Psychiatry 177.5 (2020): 435-446 (hereinafter “SIDDIQI”) and Ning, Lipeng, et al. “Limits and reproducibility of resting-state functional MRI definition of DLPFC targets for neuromodulation.” Brain stimulation 12.1 (2019): 129-138 (hereinafter “NING”).
With respect to claim 68, neither WILLIAMS nor SIR RUN explicitly teach that the set of one or more weighting factors further comprises a reliability weighting factor based on mapping of reference circuits for different symptoms and disorders. However, WILLIAMS is clearly concerned with data reliability when it discusses preprocessing. “Preprocessing steps can include, but are not limited to, physiological noise regression, slice-time correction, motion correction, co-registration, band-pass filtering, de-trending, and/or any other preprocessing step as appropriate to the requirements of a given application.” Also, when discussing selection of the voxel time course that reflect the activity of the subregion, WILLIAMS notes that “taking a simple average of all time courses for voxels within the subregion can be used, although this tends to be less robust. By reducing each functional subregion to a single time course, an accurate representation of the typical time course of brain activity that occurs within groups of homogenous voxels can be determined.” ([0078]). SIR RUN is also concerned with data reliability
SIDDIQI teaches that “[t]reatment of different depression symptoms may require different brain stimulation targets with different underlying brain circuits.” (Abstract, Objective). SIDDIQI “sought to identify such targets, which could improve the efficacy of therapeutic brain stimulation and facilitate personalized therapy.” (Id). SIDDIQI concluded that “[d]istinct clusters of depressive symptoms responded better to different TMS targets across independent retrospective data sets.”
For each seed (i.e., stimulation site) to circuit pair, SIDDIQI teaches that one can provide a predictive value (i.e., expected symptom improvement or predictive correlation) that is derived from clinical outcomes that are linked to normative-connectome-defined circuits. SIDDIQI stimulation sites were estimated. “Normative connectivity of this stimulation site was computed using resting-state functional MRI (rsfMRI) data from a large connectome database (N=1,000), as in past work. This method provides a highly reliable estimate of each stimulation site’s expected connectivity profile (Figure 1B), enabling direct comparison of stimulated circuits and clinical changes.” (p.436, right column). SIDDIQI made “system-response maps” based on these stimulation site connectivity maps correlated with change in depression symptoms and then classified similar maps into common clusters. (p.436, right column, second paragraph). “At each voxel, this yielded a value representing the degree to which this voxel’s connectivity to the stimulation site predicted efficacy for any given symptom.” (p.436, right column, second paragraph). “By definition, these maps reflect the symptomatic improvement expected after TMS to each voxel in the DLPFC.” (p.437, right column). The maps were able to predict clinical improvement. (p.443, left column).
While SIDDIQI teaches the importance of mapping sites to system-response maps (i.e., reference circuits) for different symptoms and disorders, SIDDIQI does not explicitly teach considering the reliability of the data.
NING, on the other hand, teaches that “[q]uantifying the spatial variability of the subject-specific rsfMRI-guided target is important for understanding the reliability and reproducibility of target definition strategies. A reliable localization methodology is expected to produce nearly the same brain stimulation target from data acquired in different sessions. Excessive intra-subject variability in the identification of the target, particularly if it is beyond the spatial resolution of the stimulation technique, could lead to highly variable therapeutic results. Thus, high reproducibility is critical for the clinical efficacy of rTMS in depression.” (p.130, end of first paragraph).
NING “[o]ur results provide a quantitative assessment of the topographic precision and variability of rsfMRI functional connectivity to identify DLPFC targets for neuromodulation based on SGC and NAc seeds… Our data identified a number of methodological variables (data quality and pre-processing strategies) that impact the reliability of this targeting strategy.” (p.137, Conclusions). For example, NING found that inter-day targets were less variable than inter-scan targets. More specifically, more resting data per person improved the reproducibility of the target. With respect to pre-processing, NING found that a particular smoothing kernel produced more stable and reliable targets. “All analyses show consistently that the 12 mm FWHM smoothing kernel of the FC maps leads to more stable and least variable DLPFC targets.” (p.136, left column). NING emphasizes that a subject’s target variability should be less than the spatial resolution of TMS. “Individualized modeling, or empirical assessments, of the TMS-induced electric field may be an important additional step to maximize the efficacy of target and network modulation with TMS.” (p.137, Conclusions).
It would have been obvious to one having ordinary skill in the art at the time of filing to include a reliability weighting factor based on mapping of reference circuits for different symptoms and disorders. One having ordinary skill in the art would have been motivated to add another weighting factor to the calculation in which the other weighting factor is a function of the reliability of the data that is mapped to the reference circuit. There would have been a reasonable expectation of success as SIDDIQI teaches one can map sites to reference circuits and NING teaches that one can consider different factors that increase or decrease the reliability of the data.
With respect to claim 69, as discussed above with respect to claim 68, SIDDIQI teaches using clinical data to map reference circuits for different symptoms and disorders and comparing the mapped reference circuits to reference circuits in a database (p.436, right column).
While SIDDIQI does not explicitly teach weighting each seed-circuit pair based on reliability of the mapping for the corresponding reference circuit, NING teaches that high reproducibility is critical for the clinical efficacy of rTMS in depression and that various factors lead to more reliable data.
It would have been obvious to one having ordinary skill in the art at the time of filing to weight each seed-circuit pair based on reliability of the mapping for the corresponding reference circuit. One having ordinary skill in the art would have been motivated to add another weighting factor to the calculation in which the other weighting factor was a function of the reliability of the data that is mapped to the reference circuit. There would have been a reasonable expectation of success as SIDDIQI teaches one can map sites to reference circuits and NING teaches that one can consider different factors that increase or decrease the reliability of the data.
With respect to claim 79, neither WILLIAMS nor SIR RUN explicitly teach that the set of one or more weighting factors further comprises a reliability weighting factor based on mapping of reference circuits for different symptoms and disorders. However, WILLIAMS is clearly concerned with data reliability when it discusses preprocessing. “Preprocessing steps can include, but are not limited to, physiological noise regression, slice-time correction, motion correction, co-registration, band-pass filtering, de-trending, and/or any other preprocessing step as appropriate to the requirements of a given application.” Also, when discussing selection of the voxel time course that reflect the activity of the subregion, WILLIAMS notes that “taking a simple average of all time courses for voxels within the subregion can be used, although this tends to be less robust. By reducing each functional subregion to a single time course, an accurate representation of the typical time course of brain activity that occurs within groups of homogenous voxels can be determined.” ([0078]). SIR RUN is also concerned with data reliability
SIDDIQI teaches that “[t]reatment of different depression symptoms may require different brain stimulation targets with different underlying brain circuits.” (Abstract, Objective). SIDDIQI “sought to identify such targets, which could improve the efficacy of therapeutic brain stimulation and facilitate personalized therapy.” (Id). SIDDIQI concluded that “[d]istinct clusters of depressive symptoms responded better to different TMS targets across independent retrospective data sets.”
For each seed (i.e., stimulation site) to circuit pair, SIDDIQI teaches that one can provide a predictive value (i.e., expected symptom improvement or predictive correlation) that is derived from clinical outcomes that are linked to normative-connectome-defined circuits. SIDDIQI stimulation sites were estimated. “Normative connectivity of this stimulation site was computed using resting-state functional MRI (rsfMRI) data from a large connectome database (N=1,000), as in past work. This method provides a highly reliable estimate of each stimulation site’s expected connectivity profile (Figure 1B), enabling direct comparison of stimulated circuits and clinical changes.” (p.436, right column). SIDDIQI made “system-response maps” based on these stimulation site connectivity maps correlated with change in depression symptoms and then classified similar maps into common clusters. (p.436, right column, second paragraph). “At each voxel, this yielded a value representing the degree to which this voxel’s connectivity to the stimulation site predicted efficacy for any given symptom.” (p.436, right column, second paragraph). “By definition, these maps reflect the symptomatic improvement expected after TMS to each voxel in the DLPFC.” (p.437, right column). The maps were able to predict clinical improvement. (p.443, left column).
While SIDDIQI teaches the importance of mapping sites to system-response maps (i.e., reference circuits) for different symptoms and disorders, SIDDIQI does not explicitly teach considering the reliability of the data.
NING, on the other hand, teaches that “[q]uantifying the spatial variability of the subject-specific rsfMRI-guided target is important for understanding the reliability and reproducibility of target definition strategies. A reliable localization methodology is expected to produce nearly the same brain stimulation target from data acquired in different sessions. Excessive intra-subject variability in the identification of the target, particularly if it is beyond the spatial resolution of the stimulation technique, could lead to highly variable therapeutic results. Thus, high reproducibility is critical for the clinical efficacy of rTMS in depression.” (p.130, end of first paragraph).
NING “[o]ur results provide a quantitative assessment of the topographic precision and variability of rsfMRI functional connectivity to identify DLPFC targets for neuromodulation based on SGC and NAc seeds… Our data identified a number of methodological variables (data quality and pre-processing strategies) that impact the reliability of this targeting strategy.” (p.137, Conclusions). For example, NING found that inter-day targets were less variable than inter-scan targets. More specifically, more resting data per person improved the reproducibility of the target. With respect to pre-processing, NING found that a particular smoothing kernel produced more stable and reliable targets. “All analyses show consistently that the 12 mm FWHM smoothing kernel of the FC maps leads to more stable and least variable DLPFC targets.” (p.136, left column). NING emphasizes that a subject’s target variability should be less than the spatial resolution of TMS. “Individualized modeling, or empirical assessments, of the TMS-induced electric field may be an important additional step to maximize the efficacy of target and network modulation with TMS.” (p.137, Conclusions).
It would have been obvious to one having ordinary skill in the art at the time of filing to include a reliability weighting factor based on mapping of reference circuits for different symptoms and disorders. One having ordinary skill in the art would have been motivated to add another weighting factor to the calculation in which the other weighting factor is a function of the reliability of the data that is mapped to the reference circuit. There would have been a reasonable expectation of success as SIDDIQI teaches one can map sites to reference circuits and NING teaches that one can consider different factors that increase or decrease the reliability of the data.
With respect to claim 80, as discussed above with respect to claim 68, SIDDIQI teaches using clinical data to map reference circuits for different symptoms and disorders and comparing the mapped reference circuits to reference circuits in a database (p.436, right column).
While SIDDIQI does not explicitly teach weighting each seed-circuit pair based on reliability of the mapping for the corresponding reference circuit, NING teaches that high reproducibility is critical for the clinical efficacy of rTMS in depression and that various factors lead to more reliable data.
It would have been obvious to one having ordinary skill in the art at the time of filing to weight each seed-circuit pair based on reliability of the mapping for the corresponding reference circuit. One having ordinary skill in the art would have been motivated to add another weighting factor to the calculation in which the other weighting factor was a function of the reliability of the data that is mapped to the reference circuit. There would have been a reasonable expectation of success as SIDDIQI teaches one can map sites to reference circuits and NING teaches that one can consider different factors that increase or decrease the reliability of the data.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20150119689A1 teaches techniques for identifying individual target sites for application of transcranial magnetic stimulation (TMS) to a brain of a patient for treatment of neurological and psychiatric disorders. The document also teaches computing a weighted average for seed maps.
US20230419484A1 teaches targeted and individualized methods for determining specific treatment sites based on an individual patient. It relies upon on provisional application filed prior to Applicant’s.
Conclusion
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/JASON P GROSS/ Examiner, Art Unit 3797
/SERKAN AKAR/ Primary Examiner, Art Unit 3797