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 .
Detail action
Claims 1-7 are pending and being considered.
Claims 1-7 have been amended.
Specification
The specification filed on March 14, 2023 is accepted.
Drawings
The drawings filed on March 14, 2023 are accepted.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/14/2023 and 09/03/2024 was filed after the mailing date of the application no. 18/245195. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 5 and 6 recites “ a feature amount of an object to be observed at the point of time and a state of the object to be observed at the point of time” it appears that the above limitation should read as “a feature amount of an object [[to be]] observed at the point of time and a state of the object [[to be]] observed at the point of time” because the record that is stored in the data storage contains point of time when event was observed, amount of objects that were observed and state of each object that were observed.
Claim 7 is neither proper dependent claim nor proper independent claim. The claim is directed towards non-transitory computer readable recording medium being dependent on apparatus claim 1. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1, 5 and 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites storing record with respect to object including time of event observed, amount of an object to be observed and state of the object, calculating an estimated parameter, sorting vector, generating replacement table, totalizing feature amount and outputting the estimated parameter.
The limitations storing record with respect to object including time of event observed, amount of an object to be observed and state of the object, calculating an estimated parameter, sorting vector, generating replacement table, totalizing feature amount and outputting the estimated parameter is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind mentally or physically nothing in the claim precludes the steps from practically being performed in the mind or using paper and pencil. Storing record with respect to object including time of event observed, amount of an object to be observed and state of the object, calculating an estimated parameter, sorting vector, generating replacement table, totalizing feature amount and outputting the estimated parameter. as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the claim recites additional element such as cox proportional hazard model and apparatus comprising memory and processor. These elements in the claim are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of devices to storing record with respect to object including time of event observed, amount of an object to be observed and state of the object, calculating an estimated parameter, sorting vector, generating replacement table, totalizing feature amount and outputting the estimated parameter, steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Further recited elements within dependent claims 2-4 and 7 taken individually do not amount to “significantly more” than just the abstract idea as previously identified above. Therefore, the claims do not amount to significantly more than the previously defined abstract idea. Some of the evidences of “significantly more” are a) improvement to another technology or field; b) applying judicial exception with or by a “particular machine’; c) transforming particular article/data into different state or thing; d) adding unconventional or non-routine steps, producing useful application; and e) other meaningful limitations beyond generic link to particular technological environment.
As a result, the claims are directed to non-statutory subject matter. See Also Alice, 134 S. Ct. at 2360. Under Alice, that is not sufficient "to transform an abstract idea into a patent-eligible invention." See Alice Corporation v. CLS Bank International, (S.Ct.2014) and Ultramercial, Inc. v. Hulu, LLC. (Fed.)
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, 5 and 6 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. The claims recite “storing, in a data storage, database having, with respect to each object to be observed, a record including a point of time at which an event was observed” the above underlined portion of the limitation is ambiguous. First, it is unclear what is being stored in data storage i.e., database or record. Second, it is unclear how should the term “having, with” be interpreted. Third, clarify whether the term “data storage, database” be treated as “data storage database” Appropriate correction is required.
Claims 1, 5 and 6 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. The claims recite “calculating an estimated parameter, by reading a vector comprising points of time from the database, and sorting the vector, to generate a replacement table and a flag indicating a boundary between the points of time, by using the replacement table and the flag, totalize the feature amounts at the respective points of time while concealing values at the points of time, and perform the parameter estimation on the basis of a result of the totalization” The examiner notes various issues with the above limitation which makes the above limitation ambiguous. For example, It’s unclear whether the estimated parameter is calculated based on reading vector from database OR whether the parameter estimation is calculated based on “sorting the vector to generated replacement table, using replacement table and flag……… totalize the feature amount and parameter estimation on result”.
Claim 2 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. The claim recites “wherein calculating includes executing a calculation of a plurality of exps in a calculation equation used in an iterative calculation for the parameter estimation by the calculation of exp once per iteration and a calculation using the calculation result” the above limitation is ambiguous because:
“plurality of exps” and “calculation of exp” the terms “exp” and “exps” are vague/indefinite should be clarified whether these terms refer to “exponent”
It’s unclear what is a calculation equation and iterative calculation. i.e., iterative calculation of what?
The phrase “….an iterative calculation for the parameter estimation by the calculation of exp once per iteration and a calculation using the calculation result” is ambiguous.
Claim 3 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. The claim recites “wherein the calculation unit calculating includes executes executing a calculation of a plurality of reciprocals in a calculation equation used in an iterative calculation for the parameter estimation by a calculation of a reciprocal once per iteration and a calculation using the calculation result” the above underlined portion of the limitation is ambiguous. See remarks claim 2 above.
Claim 4 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. The claim recites wherein the calculation unit calculating includes collectively executes executing a calculation for each point of time in the calculation equation used in an iterative calculation for the parameter estimation for all points of time using a vector, a matrix, or a tensor. the above underlined portion of the limitation is ambiguous. See remarks claim 2 above.
Claims 1, 5 and 6 recites the limitation “the parameter estimated” and “the calculating”. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation " the calculation equation". There is insufficient antecedent basis for this limitation in the claim.
Dependent claims 2-4 and 7 are also rejected under the same rationales as set forth above.
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 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Shipeng et al (hereinafter Yu) “Privacy-Preserving Cox Regression for Survival Analysis” NPL “2008” in view of Cox “Regression Models and life-tables” NPL “1972”
Regarding claim 1, 5 and 6 Yu teaches a parameter estimation apparatus, which executes a parameter estimation of a cox proportional hazard model by secure computation,
A parameter estimation system, which executes a parameter estimation of a cox proportional hazard model by secure computation
A parameter estimation method executed by a parameter estimation apparatus that executes a cox proportional hazard model by secure computation comprising (Yu on [page 1034 col 2. INTRODUCTION] discloses privacy-preserving cox model for survival analysis. See on [section 6.2] discloses we trained the following 5 models under each configuration:
• HPPCox learn: Apply HPPCox with learned mapping
matrix.
• HPPCox rand: Apply HPPCox with random projection.
• MAASTRO: Only use MAASTRO training patients.
• Gent: Only use Gent training patients. See on [section 3.1 and 3.2] discloses the task is to develop a horizontal privacy-preserving Cox model (HPPCox) which is able to use all the data across different parties. Cox also developed the partial likelihood for parameter estimation. For this we need to distinguish individuals who have the actual survival time observed (e.g., observed death or cancer relapse after treatment), from individuals who are (right-)censored (e.g., still alive or cancer-free);
a memory; and a processor configured to execute: (Yu on [page 1034, col 2 section 1] discloses computer i.e., computer device known to have memory and processor);
storing, in a data storage, database having, (Yu on [1. INTRODUCTION] storing large number of medical data of an individual in database) with respect to each object to be observed, a record including a point of time at which an event was observed (YU on [section 3.1] record of individual observed at time t) a feature amount of an object to be observed at the point of time, (Yu on [section 3.1] discloses related function is the hazard function, which assesses the instantaneous risk of demise at time t, conditional on survival to that time:
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It is of practical interest to relate the hazard function not only to the time t, but also to a set of covariates (explanatory variables), xi 2 Rd, of each individual i. In clinical studies, the covariates typically include demographic variables such as age and gender, and diagnosis information like the tumor size. See also [section 3.1 and col 2]
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where Ri = {j : tj _ ti} is the risk set containing the individuals who are at risk (of failing) at time ti. The key idea here is to compare at each failure time, the risk for the failed individual to the risk for all the other individuals at risk at that time) and a state of the object to be observed at the point of time (Yu on [section 3.1] discloses the probability that the individual is still alive at time t. Further teaches Cox also developed the partial likelihood for parameter estimation. For this we need to distinguish individuals who have the actual survival time observed (e.g., observed death or cancer relapse after treatment), from individuals who are (right-)censored (e.g., still alive or cancer-free at the end of the study) i.e., state of object);
calculating an estimated parameter, by reading a vector comprising points of time from the database (Yu on [page 1037, col 1 lines 1-10]
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where the weight vector w is only of length m (instead of d). When an optimal ˆw and baseline hazard ˆ_0(t) is found using the HPPCox model, for a test individual x_ the hazard function is calculated as
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See on [page 1037, col 2 Algorithm 1] teaches every party calculates zi = B>xi for every individual xi, and shares a predictor profile {zi} and a survival outcome profile {ti, _i} for its population. See also on [section 3.1]
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Please note that
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is estimated parameter being calculated).
and sorting the vector, to generate a replacement table (Yu on [page 1038, col 2 last 5 lines and 1039 col 1 lines 1-10] teaches we can find the optimal d0 × m matrix B as follows. We first remove the zero rows/columns of A, which results in a d0×d0 matrix ˆA (i.e., the rest d−d0 features are irrelevant features). We then perform an eigenvalue decomposition for i.e., ˆA = VDV>, with V an orthogonal matrix and D a diagonal matrix with non-negative diagonal entries sorted in a non-increasing order. The diagonal entries of D are all non-negative because ˆA is positive semidefinite);
(Yu on [algorithm 1] teaches every party calculates zi = B>xi for every individual
xi, and shares a predictor profile {zi} and a survival outcome profile {ti, i} for its population. All the data are combined, and a standard Cox model is learned (specifically the weight vector w and the baseline hazard _0(t)) with zi’s being the predictive variables. See on [1036, col 2 last 5 lines] teaches Since there is an information loss when applying the mapping B, it is not possible to recover the exact xi given zi, even when B is known. Therefore, in the privacy-preserving setting, we can use this technique to “hide” the sensitive data xi, and only share with others the projected data zi. All the data from different parties are then combined, and a standard Cox model can be learned using zi as the (reduced) predictors. See on [page 1038, col 2] teaches each party p makes the following information available for (i, j, k) 2 T (p):
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Note that C(p) ijk does not reveal the original records xi, xj , xk because it combines data from these three records in a way that it is not possible to recover them back since the user does not know at any moment which three records are linearly combined. The resulting set of constrains provided by party p is incorporated into a large (combined) problem by any untrusted party given all the vectors C(p) ijk as follows:
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);
and perform the parameter estimation on the basis of a result of the totalization (Yu on [page 1038, col 2] teaches Thus, each party p makes the following information available for (i, j, k) 2 T (p):
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Note that C(p) ijk does not reveal the original records xi, xj , xk because it combines data from these three records in a way that it is not possible to recover them back since the user does not know at any moment which three records are linearly combined. The resulting set of constrains provided by party p is incorporated into a large (combined) problem by any untrusted party given all the vectors C(p) ijk as follows:
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);
outputting the parameter estimated at the calculating (Yu on [page 1038, col 2] teaches Thus, each party p makes the following information available for (i, j, k) 2 T (p):
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Note that C(p) ijk does not reveal the original records xi, xj , xk because it combines data from these three records in a way that it is not possible to recover them back since the user does not know at any moment which three records are linearly combined. The resulting set of constrains provided by party p is incorporated into a large (combined) problem by any untrusted party given all the vectors C(p) ijk as follows:
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).
Yu fails to explicitly teach indication of time boundary at which feature amounts are totalized, however Cox from analogous art teaches a flag indicating a boundary between the points of time, by using the replacement table and the flag, totalize the feature amounts at the respective points of time (Cox on [page 192 section 6] discloses
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where s(i) is the sum of z over the individuals failing at t(i) and the notation in the denominator means that the sum is taken over all distinct sets of mi individuals drawn from
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i.e., Note that the term “falling at ti” is the flag indicating time boundary)
Thus, it would have been obvious to one ordinary skill in the art before the effective filing date to implement the teaching of Cox into the teaching of YU by totalize the feature amounts at the respective points of time. One would be motivated to do so in order to obtain maximum-likelihood estimate (Cox on [page 192 section 6).
Regarding claim 2 the combination of Yu and Cox teaches all the limitations of claim 1 above, Yu further teaches wherein calculating includes executing a calculation of a plurality of exps in a calculation equation used in an (Yu [section 3.1] teaches One of the first and the most popular survival models is the Cox model, in which the hazard function takes, in its most general form, the following proportional-hazard form:
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where Ri = {j : tj _ ti} is the risk set containing the individuals who are at risk (of failing) at time ti. The key idea here is to compare at each failure time, the risk for the failed individual to the risk for all the other individuals at risk at that time).
Cox teaches a plurality of exps in a calculation equation used in an iterative calculation for the parameter estimation by the calculation of exp once per iteration (Cox [equation 37 and 38]
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)
Regarding claim 3 the combination of Yu and Cox teaches all the limitations of claim 1 above, Cox further teaches wherein the calculation unit calculating includes executes executing a calculation of a plurality of reciprocals in a calculation equation used in an iterative calculation for the parameter estimation by a calculation of a reciprocal once per iteration and a calculation using the calculation result (Cox on [201 lines 5-15] teaches then by the theory of rate processes (47) can be used with ,\0(.) = 1 and z equal to the reciprocal of absolute temperature. See on [page 204]
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)
The motivation for combining is same as claim 1 set forth above.
Regarding claim 4 the combination of Yu and Cox teaches all the limitations of claim 1 above, Yu further teaches wherein the calculation unit calculating includes collectively executes executing a calculation for each point of time in the calculation equation used in an iterative calculation for the parameter estimation for all points of time using a vector, a matrix, or a tensor (Yu on [page 1037, Algorithm 1] teaches using matrix B and vector w).
Regarding claim 7 the combination of Yu and Cox teaches all the limitations of claim 1 above, Cox further teaches A non-transitory computer-readable recording medium having computer-readable instructions stored thereon, which when executed, cause a computer to execute each process performed by the parameter estimation apparatus (Cox on [page 213] computer executable instructions).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
HODGSON et al (US 20200387810) is directed towards systems and methods for modeling complex outcomes using clustering and machine learning algorithms. Machine learning algorithms and models can be implemented on platforms comprising one or more user interfaces and an insight engine.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOEEN KHAN whose telephone number is (571)272-3522. The examiner can normally be reached 7AM-5PM EST M-TH Alternate Fridays.
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/MOEEN KHAN/Primary Examiner, Art Unit 2436
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