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
This office action is in response to the application filed on 1/9/2024.
Claims 1-20 are pending.
Claim Objections
Claim 10 is objected to because of the following informalities: Claim 10 recites the limitation of “deep neural network neural work”. It appears “neural network” is duplicated. 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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth 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.
Independent claims 1, 11 and 16 recite the limitation of “HDL” which appears to be an acronym without particularly definition and therefore “HDL” can be interpreted as “High-Definition Language” and render the above claims indefinite. Dependent claims of the above independent claims inherit the limitation and deficiency of respective parent claims and are rejected for similar reason.
Additionally, claim 5 recites acronym “VHSIC” and is rejected for similar reason.
Claims 1-10 are rejected because claim 1 recites the limitation "the module generation request" in “the module generation request are input into the deep neural network;”. There is insufficient antecedent basis for this limitation in the claim. Dependent claims of claim 1 inherit the limitation and deficiency of claim 1 and are rejected for similar reason.
Claim 5 are rejected because claim 5 recites the limitation "the HDL codes” There is insufficient antecedent basis for this limitation in the claim.
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.
Claim(s) 1-5, 7-8, 11-14, and 16-17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ogilvie (US 2008/0066046 A1) in view of Wang et al. (CN 202111142407 A) and further in view of Turek et al. (US 2019/0325108 A1)
Per claim 1,
Ogilvie discloses
receiving a request which includes a natural-language description for a high-level representation of computer hardware behavior;( [0065], discloses generating HDL code based on user input including parameters to define optimization goal [0014], discloses user input of desired processing latency corresponding to functions desired corresponding to natural language description as disclosed in Specification [0028]. Also see [0014])
generating code which includes HDL code for a module,(see above)
generating a testbench for the code, wherein features of the code and the module generation request are input into the deep neural network;
([0129]-[0130] discloses generating testbench to verify functionality corresponding to features of the code using code simulation)
executing a functional simulation of the code using the testbench; (see above, [0129-0130]) and
sending the code to be presented when the function simulation passes tests of the testbench.([0130], discloses testbench verify if the outputs are correct. [0131], discloses generated HDL code as options and presented to user)
Ogilvie does not, however Wang discloses
Discloses using trained deep neural network to generation function codes from natural language. (see. abstract & pp. 3 discloses using deep neural network to generate code)
Therefore, it would have been obvious to a person of ordinary skill before the effective filing date of the invention to incorporate the teachings of Wang into the teachings of Ogilvie to include the limitation disclosed by Wang. The modification would be obvious to one of ordinary skill in the art to want to improve code correctness using deep neural network as suggested by Wang(see Abstract)
Ogilvie/Wang does not, however, Turek discloses
Using deep neural network to a testbench ([0059][0060], using machine learning (neural network) for HDL generator, simulation and performance evaluation/test and Fig. 2,see 214 performance indicator improved, no, then reject, re-apply machine learning model)
Therefore, it would have been obvious to a person of ordinary skill before the effective filing date of the invention to incorporate the teachings of Turek into the teachings of Ogilvie/Wang to include the limitation disclosed by Turek. The modification would be obvious to one of ordinary skill in the art to want to converge to a best configuration via repeated reconfiguration ([0105])
Per claim 2, the rejection of 1 is incorporated.
Ogilvie/Wang /Turek discloses
further comprising re-generating the module when the functional simulation fails, wherein the re-generation uses the deep neural network and the request as input to the deep neural network. (Turek, [0060], Fig. 2, machine learning and simulator to determine if performance indicator improved for rejection (fail) and going back to 208 for re-gen.)
Per claim 3, the rejection of 1 is incorporated.
Ogilvie/Wang /Turek discloses
wherein sending the code and related HDL code further comprises sending the module to a user interface of a development environment using an API (application programming interface) call. (Ogilvie, [0130], discloses MATLAB corresponding to development environment and functions call corresponding to API call. )
Per claim 4, the rejection of 1 is incorporated.
Ogilvie/Wang /Turek discloses
wherein the features of the code include at least one of: a number of inputs, a number of outputs, functional keywords, hardware keywords, or memory keywords. (Ogilvie [0065], discloses define optimization goals, [0073-74] discloses latency corresponding to functional keywords)
Per claim 5, the rejection of 1 is incorporated.
Ogilvie/Wang /Turek discloses
wherein in the HDL codes are in Verilog or VHSIC Hardware Description Language (VHDL). (Ogilvie [0065], discloses Verilog)
Per claim 7, the rejection of 1 is incorporated.
Ogilvie/Wang /Turek discloses
further comprising writing diagnostic information to temporary files that are processed at the end of the simulation to create a list of issues identified within the code. (Turek, Fig. 2, 216, perform simulation, 218, performance indicators in database, 214/222, at the end of simulation determine if issues arise.)
Per claim 8, the rejection of 1 is incorporated.
Ogilvie/Wang /Turek discloses
wherein the deep neural network is trained on a data set of synthesizable HDL code blocks. (Turek, [0073], discloses training data in database including configuration of parameters as part of HDL code.)
Per claims 11-13, see rejections of claims 1-3.
Per claim 14, 19, see rejection of claim 8.
Per claims 16-17, see rejections of claims 1-2.
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ogilvie (US 2008/0066046 A1) in view of Wang et al. (CN 202111142407 A) and further in view of Turek et al. (US 2019/0325108 A1) and Nelson et al. (US 20080288234 A1)
Per claim 6, the rejection of 1 is incorporated.
Ogilvie/Wang /Turek does not, however, Nelson discloses
wherein executing a functional simulation further comprises executing a syntactically correct testbench against the code. ([0075], discloses checking HDL code syntactic correctness.)
Therefore, it would have been obvious to a person of ordinary skill before the effective filing date of the invention to incorporate the teachings of Nelson into the teachings of Ogilvie/Wang /Turek to include the limitation disclosed by Nelson. The modification would be obvious to one of ordinary skill in the art to want to ensure syntax corrected for further processing.
Per claim 18, see rejection of claim 18.
Claim(s) 9-10, 15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ogilvie (US 2008/0066046 A1) in view of Wang et al. (CN 202111142407 A) and further in view of Turek et al. (US 2019/0325108 A1) and Willson et al. (US 20180143965 A1)
Per claim 9, the rejection of 1 is incorporated.
Ogilvie/Wang /Turek does not, however, Wilson discloses
wherein the deep neural network is a language model. ([0021] discloses deep neural network as a language model)
Therefore, it would have been obvious to a person of ordinary skill before the effective filing date of the invention to incorporate the teachings of Wilson into the teachings of Ogilvie/Wang /Turek to include the limitation disclosed by Wilson. The modification would be obvious to one of ordinary skill in the art to want to incorporate language model to process user input.
Per claim 10, the rejection of claim 9 is incorporated.
Ogilvie/Wang /Turek does not, however, Willson discloses
wherein the deep neural network neural network that is a transformer, encoder-decoder transformer, a text-to-text transformer or a generative adversarial network (GAN) (Turek, [0051], disclose machine learning model perform transformation corresponding to function of a transformer.).
Per claims 15 and 20, see rejection of claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
It is noted that any citation [[s]] to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. [[See, MPEP 2123]]
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip Wang whose telephone number is 571-272-5934. The examiner can normally be reached on Monday – Friday 8:00AM -4:00PM. Any inquiry of general nature or relating to the status of this application should be directed to the TC2100 Group receptionist: 571-272-2100.
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.
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/PHILIP WANG/Primary Examiner, Art Unit 2199