DETAILED ACTION
Claims 1-9 are pending and have been examined.
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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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/02/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 1-9 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1 and 8-9 recite the limitation “model… belongs to each of the small regions.” There is insufficient antecedent basis for the limitation “the small regions” in the claim. For examination purposes examiner has interpreted “the small regions” to be “the plurality of small regions.”
Claims 1 and 8-9 recite the limitation “use the learning data to model, for each of the small regions…” There is insufficient antecedent basis for the limitation “the small regions” in the claim. For examination purposes examiner has interpreted “the small regions” to be “the plurality of small regions.”
Claims 1 and 8-9 recite the limitation “construct… for each of the small regions.” There is insufficient antecedent basis for the limitation “the small regions” in the claim. For examination purposes examiner has interpreted “the small regions” to be “the plurality of small regions.”
Claims 1 and 8-9 recite the limitation “use the learning data to model… in the small region.” There is insufficient antecedent basis for the limitation “the small region” in the claim. For examination purposes examiner has interpreted “the small region” to be “a small region.”
Claims 1 and 8-9 recite the limitation “construct… the modeled probability distribution.” There is insufficient antecedent basis for the limitation “the modeled probability distribution” in the claim. The claimed element “a probability distribution” is recited in the both “divide” and “use” steps, resulting in ambiguity as to which probability distribution is being referred to. For examination purposes examiner has interpreted “the probability distribution” to be “the probability distribution” recited in the “use” step.
Claim 5-7 recite the limitation “model the probability distribution.” The claimed element “a probability distribution” is recited in the both “divide” and “use” steps, resulting in ambiguity as to which probability distribution is being referred to. For examination purposes examiner has interpreted “the probability distribution” to be “the probability distribution” recited in the “divide” step.
Claims 2-4 are also rejected due to their dependency on a rejected claim.
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.
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Claims 1-9 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more
Step 1: Claims 1-7 recite an apparatus. Claim 8 recite a method. Claim 9 recite a non-transitory medium. Therefore, claims 1-7 are directed to a machine, claim 8 is directed to a process, and claims 9 is directed to a manufacture.
With respect to claims 1 and 8-9:
2A Prong 1: The claim recites a judicial exception.
divide a region in which a probability distribution of an objective variable exists into a plurality of small regions according to a property of the objective variable for learning data including the objective variable; (mental process – evaluation or judgement, dividing a region into small regions)
model an existence probability that the objective variable belongs to each of the small regions; (a mental process or mathematical concept, in light of specification [0016] – [0019])
use the learning data to model, for each of the small regions, a probability distribution related to a possible value of the objective variable in the small region under a condition that the objective variable belongs to the small region; and (a mental process or mathematical concept, in light of specification [0016] – [0019])
construct a prediction model of the objective variable by integrating the modeled probability distribution for each of the small regions using the existence probability (a mental process or mathematical concept, in light of specification [0016] – [0019])
2A Prong 2: The judicial exception is not integrated into a practical application.
(claim 1) at least one memory storing instructions; and at least one processor configured to execute the instructions to (claim 8) A prediction model generation method executed by a prediction model generation apparatus, the method comprising (claim 9) A non-transitory computer-readable medium storing a program for causing a computer to perform: (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
(claim 1) at least one memory storing instructions; and at least one processor configured to execute the instructions to (claim 8) A prediction model generation method executed by a prediction model generation apparatus, the method comprising (claim 9) A non-transitory computer-readable medium storing a program for causing a computer to perform: (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 2:
2A Prong 1: The claim recites a judicial exception.
construct a prediction model of the objective variable for each possible value of the initial value. (a mental process or mathematical concept, in light of specification [0016] – [0019])
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the learning data has an initial value of the objective variable, and (whether additional elements meaningfully limit the judicial exception – MPEP 2106.05(e); no actual steps, merely additional details of the claim elements)
the at least one processor is further configured to: (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the learning data has an initial value of the objective variable, and (whether additional elements meaningfully limit the judicial exception – MPEP 2106.05(e); no actual steps, merely additional details of the claim elements)
the at least one processor is further configured to: (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 3:
2A Prong 1: The claim recites a judicial exception.
select, when prediction target data having an initial value of an objective variable to be predicted is input, a prediction model corresponding to the initial value of the objective variable included in the prediction target data from among the prediction models of the constructed objective variable, and (mental process – evaluation or judgement, selecting a model)
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the at least one processor is further configured to: (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
predict the objective variable in the prediction target data using the selected prediction model. (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using a model to predict the variable)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the at least one processor is further configured to: (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
predict the objective variable in the prediction target data using the selected prediction model. (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using a model to predict the variable)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 4:
2A Prong 1: The claim recites a judicial exception.
divide a region in which the degree of recovery at discharge from hospital exists into two, by using a value of the degree of recovery upon hospitalization of the patient as a boundary. (mental process – evaluation or judgement, dividing a region into two)
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the objective variable is a degree of recovery of a patient at discharge from hospital, and (whether additional elements meaningfully limit the judicial exception – MPEP 2106.05(e); no actual steps, merely additional details of the claim elements)
the at least one processor is further configured to: (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the objective variable is a degree of recovery of a patient at discharge from hospital, and (whether additional elements meaningfully limit the judicial exception – MPEP 2106.05(e); no actual steps, merely additional details of the claim elements)
the at least one processor is further configured to: (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 5:
2A Prong 1: The claim recites a judicial exception.
model the probability distribution so as to depend on the patient information (a mental process or mathematical concept, in light of specification [0016] – [0019])
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the learning data includes patient information of the patient, and (whether additional elements meaningfully limit the judicial exception – MPEP 2106.05(e); no actual steps, merely additional details of the claim elements)
the at least one processor is further configured to: (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the learning data includes patient information of the patient, and (whether additional elements meaningfully limit the judicial exception – MPEP 2106.05(e); no actual steps, merely additional details of the claim elements)
the at least one processor is further configured to: (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 6:
2A Prong 1: The claim recites a judicial exception.
model the probability distribution that has been subjected to generalized linear modeling. (a mental process or mathematical concept, in light of specification [0016] – [0019])
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the at least one processor is further configured to (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the at least one processor is further configured to (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
With respect to claim 7:
2A Prong 1: The claim recites a judicial exception.
model the probability distribution represented by a binomial distribution. (a mental process or mathematical concept, in light of specification [0016] – [0019])
2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the at least one processor is further configured to (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the at least one processor is further configured to (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components)
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is 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 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.
Claims 1-2 and 6-9 rejected under 35 U.S.C. 103 as being unpatentable over Hobbs ("Bayesian basket trial design with exchangeability monitoring" 2018) in view of McNair (US 12020820 B1)
In regard to claims 1 and 8-9, Hobbs teaches: divide a region in which a probability distribution of an objective variable exists into a plurality of small regions according to a property of the objective variable for learning data including the objective variable; (Hobbs, p. 3557, Abstract "This article presents novel methodology for sequential basket trial design formulated with Bayesian monitoring rules."; p. 3559, 2 Methodology "Using i to index patient, let Y_i,j = 1 indicate the occurrence of a successful response for the ith patient enrolled in basket j [a plurality of small regions], whereas = 0 indicates treatment failure. Let nj denote the number of patients observed in basket j and denote the total number of responses in basket j by Sj = Σ i=1:nj Yi,j"; p. 3560, 2.2 Multisource exchangeability model specification "We formulate the Bayesian model such that the number of unique parameters, J∗, is determined by the pairwise exchangeability groupings. [according to a property of the objective variable] The likelihood function for parameter πj is defined as the probability mass function of Sj|πj ~ Bin(Sj|πj, nj) [likelihood: a probability distribution of an objective variable (πj) exists in each of the small regions (basket j)] with prior distribution for π = (π1,…, πj*) defined conditionally on the set S(-j) = S\Sj as the likelihood of S(-j) and an initial prior distribution for πj."; respective PMFs for respective baskets [divide a region in which a probability distribution of an objective variable exists into a plurality of small regions]; the baskets are determined based on pairwise exchangeability relationships of πj [according to a property of the objective variable]; Sj|πj, the observed data Sj given the parameter πj [learning data including the objective variable]; objective variable (πj) is the primary parameter being estimated)
model an existence probability that the objective variable belongs to each of the small regions; (Hobbs, p. 3560, 2.2 Multisource exchangeability model specification "In the presence of fully exchangeable subtypes, Ω =1, the prior p(π|S(-j) = (∏ Bin i∈S(-j) (Si|πj, ni)) p(πj) follows the product j-1 binomial likelihoods each with single common parameters πj and an initial prior for πj, p(πj) [an existence probability]... Assuming that the intial priors are beta with common hyperparameters a and b, [beta distribution is used to model the priors p(πj)]"; the objective variable (πj) belonging to each of the small regions (basket j))
use the learning data to model, for each of the small regions, a probability distribution related to a possible value of the objective variable in the small region under a condition that the objective variable belongs to the small region; and (Hobbs, p. 3560-3561, 2.2 Multisource exchangeability model specification "upon having observed successes S = {S1,… , Sj} [using the learning data], Bayes' theorem yields the folllowing conjugate conditional posterior distribution for the response probability of basket j representing the Bayesian update of p(π|S(-j)) with likelihood Bin(Sj|πj, nj): q(πj|S, Ωj) ∝ Beta (S..., S) (1) [a probability distribution related to a possible value of the objective variable p(πj) in the small region j]"; in Bayesian inference, the beta distribution is the conjugate prior probability distribution for the binomial distributions; p(πj) is a probability distribution over the possible values of the parameter πj, which belongs to a small region (basket j))
construct a prediction model of the objective variable by integrating the modeled probability distribution for each of the small regions using the existence probability. (Hobbs, p. 3560-3561, 2.2 Multisource exchangeability model specification "The marginal posterior distribution [construct a prediction model of the objective variable] can be represented by a finite mixture density q (πj|S) ∝ Σ q(πj|S, Ωj = wg) Pr(Ωj = wg|S), (2) [integrating the modeled probability distribution (q(πj|S, Ωj)) for each of the small regions (j) using the existence probability (Pr(...), which is based on p(πj))]... and its unconditional prior probability Pr(Ωj = wg|S)... (3)... Let B() denote the beta function... "; Pr(...) ∝ m, and m ∝ B(), the beta function, which is used to model the initial prior for πj, p(πj) [the existence probability])
Hobbs does not teach, but McNair teaches: A prediction model generation apparatus comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to: (McNair, (42) "One or more CPUs such as 901, have internal memory for storage and couple to the north bridge device 902, allowing CPU 901 to store instructions and data elements in system memory 915...")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hobbs to incorporate the teachings of McNair by including the hardware implementation. A person of ordinary skill in the art would find it obvious to implement the Bayesian inference on a programmable processing device, such as a general-purpose processor or computer.
Claims 8-9 recite substantially the same limitation as claim 1, therefore the rejection applied to claim 1 also apply to claims 8-9. In addition, McNair teaches: (claim 8) A prediction model generation method executed by a prediction model generation apparatus, the method comprising (claim 9) A non-transitory computer-readable medium storing a program for causing a computer to perform: (McNair, (42) "One or more CPUs such as 901, have internal memory for storage and couple to the north bridge device 902, allowing CPU 901 to store instructions and data elements in system memory 915...")
In regard to claim 2, Hobbs teaches: wherein the learning data has an initial value of the objective variable, and the at least one processor is further configured to: construct a prediction model of the objective variable for each possible value of the initial value. (Hobbs, p. 3560, 2.2 Multisource exchangeability model specification "... an initial prior for πj, p(πj)... Assuming that the initial priors are beta with common hyperparameters a and b, [initial value]"; the objective variable (πj) belonging to each of the small regions (basket j); "initial value" refers to the prior distribution assigned to this variable before observing any patient data; Beta(a, b) [a prediction model] represents possible probability values, i.e. every possible value between 0 and 1 [each possible value of the initial value])
In regard to claim 6, Hobbs teaches: wherein the at least one processor is further configured to: model the probability distribution that has been subjected to generalized linear modeling. (Hobbs, p. 3560, 2.2 Multisource exchangeability model specification "The likelihood function for parameter πj is defined as the probability mass function of Sj|πj ~ Bin(Sj|πj, nj) [a binomial distribution is generalized linear modeling] with prior distribution for π = (π1,…, πj*)... "; in light of specification [0052] "... generalized linear modeling (in particular, a probability distribution represented by a binomial distribution)")
In regard to claim 7, Hobbs teaches: wherein the at least one processor is further configured to: model the probability distribution represented by a binomial distribution. (Hobbs, p. 3559, 2 Methodology "Using i to index patient, let Y_i,j = 1 indicate the occurrence of a successful response for the ith patient enrolled in basket j, whereas = 0 indicates treatment failure. Let nj denote the number of patients observed in basket j and denote the total number of responses in basket j by Sj = Σ i=1:nj Yi,j" p. 3560, 2.2 Multisource exchangeability model specification "The likelihood function for parameter πj is defined as the probability mass function of Sj|πj ~ Bin(Sj|πj, nj) [a binomial distribution] with prior distribution for π = (π1,…, πj*)... "; Sj|πj ~ Bin(Sj|πj, nj) a binomial distribution modeling the count of successes sj out of a number of trials nj)
Claim 3 rejected under 35 U.S.C. 103 as being unpatentable over Hobbs in view of McNair, as applied to claim 1, and in further view of Psioda ("Bayesian adaptive basket trial design using model averaging" 2019)
In regard to claim 3, Hobbs and McNair not teach, but Psioda teaches: wherein the at least one processor is further configured to: select, when prediction target data having an initial value of an objective variable to be predicted is input, a prediction model corresponding to the initial value of the objective variable included in the prediction target data from among the prediction models of the constructed objective variable, and (Psioda, p. 24 , 3.2.2. Prior elicitation "To elicit a prior in a scenario with multiple competing models, one must elicit a prior probability for each model Mj and a prior for the distinct response rates π(j) under model Mj. [eliciting/selecting a prior (a prediction model)] We propose the following default prior over the model space: p(Mj) [e.g. a prediction model] ∝ Pj_α... We We propose taking π(j,p) Mj ~ Beta (a0, b0) for each j = 1, ..., J and p = 1, ..., Pj... As a default choice, we propose choosing a0 and b0 such that a0/a0+b0 = πA and a0 +b0 = 1.0 to obtain a weakly informative prior with mean equal to the hypothesized response rate associated with activity πA. [selecting a weakly informative prior with a0 and b0 (e.g. a prediction model), when having hypothesized response rate with πA as a0/a0+b0 (target data having an initial value of an objective variable as input)]")
predict the objective variable in the prediction target data using the selected prediction model. (Psioda, p. 24 , 3.2.2. Prior elicitation "Given the above formulation of the prior, it follows that π…|D, Mj ~ Beta (a(jp), b (jp), where a(jp)… and b(jp)... [predict π using Beta (a0, b0), the selected prediction model]")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hobbs and McNair to incorporate the teachings of Psioda by including prior configurations for subsets of baskets. Doing so would allow information borrowing over any subset of baskets that have similar activity. (Psioda, p. 20, 1. Introduction "The key benefit of the BMA approach over many existing methods is that it naturally allows for information borrowing over any subset of baskets that have similar activity.")
Claims 4-5 rejected under 35 U.S.C. 103 as being unpatentable over Hobbs in view of McNair, as applied to claim 1, and in view of Kahn ("Discharge Rates of Medicare Stroke Patients to Skilled Nursing Facilities: Bayesian Logistic Regression With Unobserved Heterogeneity" 1996) in further view of Chen ("Bayesian hierarchical classification and information sharing for clinical trials with subgroups and binary outcomes" 20200901)
In regard to claim 4, Hobbs and McNair not teach, but Kahn teaches: wherein the objective variable is a degree of recovery of a patient at discharge from hospital, and the at least one processor is further configured to: (Kahn, p. 31, 1 Introduction "The model described in this article is motivated by, and applied to, the analysis of rates at which urban hospitals discharge Medicare stroke patients [a degree of recovery of a patient at discharge from hospital] to nursing homes")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hobbs and McNair to incorporate the teachings of Kahn by estimating rates of patients at discharge with a betabinomial model. Doing so would allow us to test for overdispersion in a natural way. (Kahn, p. 31, 1 Introduction "We develop fully Bayesian inference that takes into account uncertainty about the hyperparameters, and we find that this also allows us to test for overdispersion in a natural way. The number of observed zeros (i.e., hospitals that sent no stroke patients to a SNF) is excessive compared to the number expected from a standard iogistic regression model and is fit better by the hierarchical betabinomial model.")
Hobbs, McNair and Kahn not teach, but Chen teaches: divide a region in which the degree of recovery at discharge from hospital exists into two, by using a value of the degree of recovery upon hospitalization of the patient as a boundary. (Chen, p. 4-5, 2. Hierarchical Bayesian subgroup classification and information sharing (BaCIS) model "We assume that a clinical trial includes k subgroups of patients with a binary outcome as the primary endpoint. These subgroups can be considered as different disease entities or different drugs. After the trial is completed, each of the k subgroups is classified into one of two response clusters (high or low-response clusters) [two clusters/regions]... pi denote the response rate in subgroup i (i=1,…,k)... Subgroup i is classified into the cluster 1 (low-response) if Prob(θi > 0) > θc or the cluster 2 (high-response) otherwise... We can determine the classification threshold value θc adaptively based on the response outcome [objective response rate is used for setting the boundary/threshold]:... For example, when the mean response rate of all subgroups is large, Δr is large, and the threshold value θc is small."; in light of specification [0011] "a region where the objective variable is equal to or greater than the threshold value… less than the threshold...")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Hobbs, McNair and Kahn to incorporate the teachings of Chen
by including subgroup classification. Doing so would allow information sharing taking place within subgroups in the same cluster, and further obtain better operating characteristics with the BaCIS model. (Chen, p. 1, Abstract "We introduce subgroup classification into the hierarchical model. Subgroups are classified into two clusters on the basis of their outcomes mimicking the hypothesis testing framework. Subsequently, information sharing takes place within subgroups in the same cluster, rather than across all subgroups... Compared to the traditional hierarchical models, better operating characteristics are obtained with the BaCIS model under various scenarios.")
In regard to claim 5, Hobbs teaches: wherein the learning data includes patient information of the patient, and the at least one processor is further configured to: (Hobbs, 2 Methodology "Using i to index patient, let Y_i,j = 1 indicate the occurrence of a successful response for the ith patient [patient information of the patient] enrolled in basket j, whereas = 0 indicates treatment failure. Let nj denote the number of patients observed in basket j and denote the total number of responses in basket j by Sj [learning data] = Σ i=1:nj Yi,j")
model the probability distribution so as to depend on the patient information. (Hobbs, p. 3560, 2.2 Multisource exchangeability model specification "The likelihood function for parameter πj is defined as the probability mass function of Sj|πj ~ Bin(Sj|πj, nj) [the probability distribution on the patient information (Sj)] with prior distribution for π = (π1,…, πj*)... ")
Conclusion
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/S. C./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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