DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 11 is rejected under 35 U.S.C 101 for not falling within one of the four statutory categories of invention. Claim 11 is directed to a computer-readable storage medium comprising instructions. A “computer-readable storage medium” is not defined in the specification and is therefore interpreted to include a signal. “A transitory, propagating signal like Nuitjen’s is not a ‘process, machine, manufacture, or composition of matter.’ . . . .Thus, such a signal cannot be patentable subject matter.” In re Nuitjen, 84 USPQ2d 1495 (CAFC 2007). Thus, as currently drafted claim 11 is directed to a signal and is non-statutory subject matter.
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 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 claims, “analyzing the input computer program to determine a dependency graph describing dependencies between mathematical functions calls,
wherein the method further comprises processing the mathematical functions according to the dependency graph, wherein said processing comprises, for each processed mathematical function:
using a value range determination tool to produce a value range for each variable of the processed mathematical function, based on each synthesized mathematical function obtained for a mathematical function on which the processed mathematical function depends according to the dependency graph,
using a synthesis tool to produce a synthesized mathematical function having the target internal accuracy for the processed mathematical function, based on the value range for each variable of the processed mathematical function”
However, this limitation is not clear to the examiner. It is unclear to the examiner how a value range is produced for a variable based on each synthesized mathematical function obtained for a mathematical function on which the processed mathematical depends according to the dependency graph. Its unclear to the examiner how the mathematical functions are processed according to the dependency graph. Does every mathematical function have a dependency, are the mathematical functions traversed in a top down or bottom up fashion based on the dependency graph. If the first mathematical function being processed has a dependency on another mathematical function, how will a value range of a variable of the processed mathematical function be determined based on a synthesized mathematical function which it depends if that mathematical function which it depends has not been synthesized yet. As claimed it is unclear to the examiner how this process is performed.
Claims 2-12, are either dependent on claim 1 or contain similar limitations and are therefore rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Darulova et al. (“Sound Approximation of Programs with Elementary Functions”, 2018), teaches an automated approach and tool which approximates elementary function calls inside small programs while guaranteeing user provided error bounds (abstract). Elementary function call are replaced by coarser approximations. Given a user-provided target error specification, their tool automatically distributes the error budget among the floating point implementation of arithmetic operations and the elementary functions, and selects a suitable polynomial degree for Metalibm to use (1 Introduction). The tool computes how much of the total error budget can be used to approximate each elementary function call and calls Metalibm to generate a approximation for each elementary function call (2 Overview). Function calls are replaced by polynomial approximations (4.2 Approximation Phase). However, it does not teach the generation of dependency graphs, determining the value range for each variable of a process mathematical function based on synthesized mathematical functions obtained according to the dependency graph and producing a synthesized mathematical function having a target internal accuracy for the processed mathematical function, based on the value range for each variable in the process mathematical function as claimed.
Sazegari et al (US 2010/0125836 A1), teaches a compiler that saves resources by replacing full accuracy match functions with simpler routines. A programmer determines the accuracy requirements for a particular application (0005). However, Sazegari et al does not teach the generation of dependency graphs, determining the value range for each variable of a process mathematical function based on synthesized mathematical functions obtained according to the dependency graph and producing a synthesized mathematical function having a target internal accuracy for the processed mathematical function, based on the value range for each variable in the process mathematical function as claimed.
Brunie et al. (“Code generators for mathematical fucntions”, IEE 2105) teaches, Polynomial approximation (“II Background on Elementary Function Implementation”, A). A first level approximates a function f by polynomial p with a error bounded b and generates C code (III. Approximations of Black-box functions”). However, Brunie et al. does not teach the generation of dependency graphs, determining the value range for each variable of a process mathematical function based on synthesized mathematical functions obtained according to the dependency graph and producing a synthesized mathematical function having a target internal accuracy for the processed mathematical function, based on the value range for each variable in the process mathematical function as claimed.
Darulova et al. (“Daisy-Framework for Analysis and Optimization of Numerical Programs”, 2018), teaches automated techniques for analysis and optimization of finite-precision computations (abstract). A source program is input into Daisy, A user can specify the range of all input variables along with a input error. Daisy supports roundoff error analysis. Choosing different precisions for different variables, is supported by providing a mapping from variables to precisions in a separate file (“2 User’s Guide: An overview of Daisy”). Darulova et al. teaches round off error estimations, however it does not teach the generation of dependency graphs, determining the value range for each variable of a process mathematical function based on synthesized mathematical functions obtained according to the dependency graph and producing a synthesized mathematical function having a target internal accuracy for the processed mathematical function, based on the value range for each variable in the process mathematical function as claimed.
Zhang et al. (US 10,740,514 B1), teaches a model that includes first variables that contribute to a first calculation and second variables that contribute to a second calculation. Dependencies of the variables are determined and a first execution based on the dependencies is determined. (abstract). The generated execution function enable the TCE to execution or simulate the model in more efficient manner (column 2, lines 6-37). However, Zhang et al does not teach the determining the value range for each variable of a process mathematical function based on synthesized mathematical functions obtained according to the dependency graph and producing a synthesized mathematical function having a target internal accuracy for the processed mathematical function, based on the value range for each variable in the process mathematical function as claimed.
Examiner Note:
Regarding 35 U.S.C 101
Claim 1, receives a target internal accuracy, analyzes a computer program to determine a dependency describing dependencies between the mathematical function, produces a value range for each variable of the processed mathematical function, generates a synthesized mathematical function having the target internal accuracy and lastly, replaces each mathematical function call with its corresponding synthesized mathematical function. Therefore, the invention is generating synthesized mathematical functions that meet a specific target internal accuracy and replaces calls in the code to calls to their respective synthesized mathematical function. This is significantly more and improves the computer system by generating code that meets the target internal accuracy. Therefore, there is no 101 rejection.
The examiner states if all independent claims are amended as shown below, the application would be in condition for allowance.
“1. (Original) A computer implemented method for converting an input computer program into an output computer program achieving a target global accuracy, said input computer program including a plurality of mathematical functions applied to floating-point variables and the target global accuracy corresponding to a bound on a global error due to mathematical function
- receiving a target internal accuracy for each mathematical function of the plurality of mathematical functions included in the input computer program, wherein the target internal accuracies correspond to respective bounds on internal errors of the mathematical functions, required to achieve the target global accuracy,
- analyzing the input computer program to determine a dependency graph describing dependencies between the plurality of mathematical functions calls,
determining according to the dependency graph, that a first mathematical function is a origin,
in response to the first mathematical function being a origin, processing the first mathematical function to produce a first value range for each variable of the processed first mathematical function,
producing a first synthesized mathematical function having a respective target internal accuracy based on the produced first value range for each variable of the processed first mathematical function,
determining according to the dependency graph, that a second mathematical function depends on the first mathematical function.
in response to the second mathematical function depending on the first mathematical function, processing the second mathematical function using the first synthesized mathematical function to determine a second value range for each variable of the processed second mathematical function,
producing a second synthesized mathematical function having a respective target internal accuracy based on the produced second value range for each variable of the processed mathematical function. and
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK A GOORAY whose telephone number is (571)270-7805. The examiner can normally be reached Monday - Friday 10:00am - 6:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lewis Bullock can be reached at 571-272-3759. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARK A GOORAY/ Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/ Supervisory Patent Examiner, Art Unit 2199
Prosecution Insights