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 .
This Office Action is in response to the filing date of 4/12/2024.
Claims 1-20 are pending and have been considered below.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Per claim 1:
Under Prong 1 Step 2A, the claim recites limitations “predicting one or more outputs which can be generated by the at least one software program, in response to at least a portion of the input data”, “generating one or more items of supporting information attributed to at least a portion of the one or more predicted outputs”, and “reverse engineering at least a portion of the at least one software program using at least one of the one or more predicted outputs and the one or more items of supporting information” as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. Thus, these limitations recite and fall within the “Mental Processes.”
Under Prong 2, Step 2A, the judicial exception is not integrated into a practical application. The claim recites the following additional elements “least one processing device comprising a processor coupled to a memory”, “one or more artificial intelligence techniques”, and “automatically” are merely instructions to implement the abstract idea on a computer, or merely uses a computer, with instructions, as a tool to perform the abstract idea according to MPEP 2106.05(f), thus, not indicative of an integration into a practical application. The additional element of “obtain input data associated with at least one software program” which is merely insignificant extra solution activity of gathering data which does not integrate the judicial exception into a practical application. Accordingly, the additional element does not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Under Step 2B, the claims do 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 elements “least one processing device comprising a processor coupled to a memory”, “one or more artificial intelligence techniques”, and “automatically” are the mere use of
generic computer to implement the abstract idea, as discussed above, which does not amount to significantly more, thus, not an inventive concept, and the additional element “obtain input data associated with at least one software program” merely insignificant extra solution activity of gathering data and the courts have identified gathering data, storing data, and outputting the result is well-understood, routine and conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018)), thus, cannot amount to an inventive concept. Accordingly, the claim does not appear to be patent eligible under 35 USC 101. See MPEP 2106.05(d).
Per claim 2:
The claims recite limitations that under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. Thus, these limitations recite and fall within the “Mental Processes.”
Per claim Claims 6 and 7:
The claims recite additional limitations which are merely insignificant extra solution activity of gathering data which does not integrate the judicial exception into a practical application. Accordingly, the additional element does not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Per claim 8 and 9:
The claims recite additional limitations fail to meaningfully limit the claim because it does not require any particular application of the judicial exception and is, at best, the equivalent of merely adding the words “apply it” (or an equivalent) to the judicial exception. See MPEP § 2106.05(f). The additional element recites only the idea of “training/retraining one or more artificial intelligence techniques” without details on how this is accomplished. The claim omits any details as to how the training/retraining one or more artificial intelligence techniques solves a technical problem, and instead recites only the idea of a solution or outcome.
Per claim 10:
Under Prong 1 Step 2A, the claim recites limitations “predicting one or more outputs which can be generated by the at least one software program, in response to at least a portion of the input data”, “generating one or more items of supporting information attributed to at least a portion of the one or more predicted outputs”, and “reverse engineering at least a portion of the at least one software program using at least one of the one or more predicted outputs and the one or more items of supporting information” as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. Thus, these limitations recite and fall within the “Mental Processes.”
Under Prong 2, Step 2A, the judicial exception is not integrated into a practical application. The claim recites the following additional elements “a non-transitory processor-readable storage medium stored therein program code of one or more software program”, “one or more artificial intelligence techniques”, and “automatically” are merely instructions to implement the abstract idea on a computer, or merely uses a computer, with instructions, as a tool to perform the abstract idea according to MPEP 2106.05(f), thus, not indicative of an integration into a practical application. The additional element of “obtain input data associated with at least one software program” which is merely insignificant extra solution activity of gathering data which does not integrate the judicial exception into a practical application. Accordingly, the additional element does not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Under Step 2B, the claims do 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 elements “a non-transitory processor-readable storage medium stored therein program code of one or more software program”, and “automatically” are the mere use of
generic computer to implement the abstract idea, as discussed above, which does not amount to significantly more, thus, not an inventive concept, and the additional element “obtain input data associated with at least one software program” merely insignificant extra solution activity of gathering data and the courts have identified gathering data, storing data, and outputting the result is well-understood, routine and conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018)), thus, cannot amount to an inventive concept. Accordingly, the claim does not appear to be patent eligible under 35 USC 101. See MPEP 2106.05(d).
Per claim 16:
Under Prong 1 Step 2A, the claim recites limitations “predicting one or more outputs which can be generated by the at least one software program, in response to at least a portion of the input data”, “generating one or more items of supporting information attributed to at least a portion of the one or more predicted outputs”, and “reverse engineering at least a portion of the at least one software program using at least one of the one or more predicted outputs and the one or more items of supporting information” as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. Thus, these limitations recite and fall within the “Mental Processes.”
Under Prong 2, Step 2A, the judicial exception is not integrated into a practical application. The claim recites the following additional elements “an apparatus comprising at least one processing device comprising a processor coupled to a memory”, “one or more artificial intelligence techniques”, and “automatically” are merely instructions to implement the abstract idea on a computer, or merely uses a computer, with instructions, as a tool to perform the abstract idea according to MPEP 2106.05(f), thus, not indicative of an integration into a practical application. The additional element of “obtain input data associated with at least one software program” which is merely insignificant extra solution activity of gathering data which does not integrate the judicial exception into a practical application. Accordingly, the additional element does not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Under Step 2B, the claims do 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 elements “an apparatus comprising at least one processing device comprising a processor coupled to a memory”, and “automatically” are the mere use of generic computer to implement the abstract idea, as discussed above, which does not amount to significantly more, thus, not an inventive concept, and the additional element “obtain input data associated with at least one software program” merely insignificant extra solution activity of gathering data and the courts have identified gathering data, storing data, and outputting the result is well-understood, routine and conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018)), thus, cannot amount to an inventive concept. Accordingly, the claim does not appear to be patent eligible under 35 USC 101. See MPEP 2106.05(d).
Per claims 11-15 and 17-20 recite similar limitations as in claims 2-9 and therefore are rejected for the same reasons above for claims 1-9 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, 4, 10, 13, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20180081681 to Sethu in view of U.S. Pub. No. 20240427593 to Yuki.
Per claims 1, 10, and 16, Sethu teaches a computer-implemented method comprising:
obtaining input data associated with at least one software program (see at least paragraphs [0081-0082] “The initial code 301 is input, more particularly, to the task-slicing sub-module 212 of the function-extraction module 210, as shown in FIG. 3. Another of the multiple inputs to the function-extraction module 210 includes a variable list, or list of variables of interest, 302. The list in some implementations includes variables of interest from the input code 301. The variables include some or all of the input variables occurring in various functions called by the tasks in the input…”);
predicting one or more outputs which can be generated by the at least one software program, in response to at least a portion of the input data, FIG. 3; see at least paragraph [0089] “…the symbolic-execution-and-simplification sub-module 214, generates a list of output and state transition function(s) per task 315…”);
generating one or more items of supporting information attributed to at least a portion of the one or more predicted outputs (see at least paragraph [0096] “… With continued reference to the function-extraction module 210, the task-scheduling sub-module 216, when executed by the hardware-based processing unit 104, receives the task table 303 and, using the task table 303, constructs the scheduler automaton 319.…”); and
automatically reverse engineering at least a portion of the at least one software program using at least one of the one or more predicted outputs and the one or more items of supporting information (see at least FIG. 3; see paragraphs [0105-0106] “The system-composition sub-module 226, when executed, composes the data-flow blocks 323, and the scheduler automaton 318 generates the control-flow triggers 325, to produce a single model 327. This model 327 is in a modeling language chosen in the template-based translation and is equivalent in behavior to the initial task code 301. The high-level model 327 is in some implementations referred to as being equivalent, because the model 327 is fully or generally equivalent in behavior to the tasks in the input initial code. As referenced, Mathworks Simulink/SF is just one example modeling language that can be used in connection with the reverse engineering. The template-based translation depends upon the chosen modeling language”; see also at least paragraph [0077] “One of multiple inputs to the function-extraction module 210 includes present, subject, or initial computer code 301 to be automatically reverse engineered…”);
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (see at least FIG. 1).
Sethu does not explicitly teach processing input data using one or more artificial intelligence techniques.
Yuki teaches an analogous art relates to software reverse engineering, comprising:
processing input data using one or more artificial intelligence techniques (see at least paragraph [0004] “…A valuable tool for reverse engineering is code summarization. It is also known as code comment and concerns generating a concise and informative summary of a software code's functionality or behavior. Code summarization techniques may use natural language processing and machine learning algorithms to analyze the code's syntax, structure, and comments to generate a human-readable and easy to understand summary…”).
It would have been obvious for a person of an ordinary skilled in the art as of the effective filing date of the claimed invention to modify the teaching of Sethu to incorporate the teaching of Yuki to use a machine learning algorithm for processing input code. One would have been motivated to use one or more machine learning algorithms for processing input code in order to generate a easy to understand output (see paragraph [0004]).
Per claim 4, 13, and 19, Yuki further teaches
wherein automatically reverse engineering at least a portion of the at least one software program comprises generating, using at least a portion of the one or more predicted outputs and at least a portion of the one or more items of supporting information, at least one artificial intelligence model that mimics at least a portion of the at least one software program (see at least paragraph [0012] “…a method for creating a model to add a code summary to functions of assembly language code…”).
Claims 2, 3, 11, 12, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20180081681 to Sethu in view of U.S. Pub. No. 20240427593 to Yuki and in further view of CN115623207A to Ding.
Per claims 2, 11, and 17, neither Sethu nor Yuki teaches
wherein predicting one or more outputs comprises processing the input data using at least one multi-input multi-output (MIMO) neural network.
Ding teaches an analogous art relates to neural network, comprising:
processing input data using at least one multi-input multi-output (MIMO) neural network (see at least page 3, paragraph 15 “…the data transmission method based on multi-input multi-output technology, wherein the coding network and the decoding network is neural network…”).
It would have been obvious for a person of an ordinary skilled in the art as of the effective filing date of the claimed invention to modify the teachings of Sethu and Yuki to incorporate the teaching of Ding to use multi-input multi-output technology for processing input data. One would have been motivated use multi-input and multi-output technology in order to allow multiple inputs to be processed.
Per claims 3, 12, and 18, Yuki further teaches
wherein processing the input data using at least one MIMO neural network comprises using at least one MIMO neural network in conjunction with one or more deep learning important features techniques to compute at least one importance score for at least one of the one or more predicted outputs based at least in part on a difference between the at least one of the one or more predicted outputs and at least one reference output in relation to a difference between the at least a portion of the input data and at least one corresponding reference input (see paragraph 0018] “Transformers are a deep learning model in which every output element is connected to every input element with dynamic weightings between them…”; see also at least paragraph [0092] “…Using this setup, the various models were tested. Results of the testing are shown, for example, quantitatively in Table 1 below, which shows the Bilingual Evaluation Understudy (BLEU) score for the tests. In particular, BLEU is a metric used to evaluate the quality of the machine-generated text, such as machine translation or text summarization. It measures the similarity between the generated text and the reference text (usually human-generated) based on n-gram overlap. The higher the BLEU score, the better the machine-generated text matches the reference text. BLEU score ranges from 0 to 1, where a score of 1 indicates that the machine-generated text is identical to the reference text. BLEU score is commonly used in natural language processing research as a standard metric for evaluating the quality of the machine-generated text…”).
Claims 5-8, 14, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20180081681 to Sethu in view of U.S. Pub. No. 20240427593 to Yuki and in further view of CA3174382A1 to Albiston.
Per claims 5, 14, and 20, neither Sethu nor Yuki teaches
wherein generating one or more items of supporting information comprises perturbing one or more data points from the at least a portion of the input data and generating one or more corresponding synthetic data points to be utilized in training at least one glass box model.
Albiston teaches an analogous art relates to training a machine learning, comprising:
perturbing one or more data points from at least a portion of the input data and generating one or more corresponding synthetic data points to be utilized in training at least one glass box model (see least paragraph [0198] “machine learning subsystem for generating synthetic datasets for training machine learning models…”).
It would have been obvious for a person of an ordinary skilled in the art as of the effective filing date of the claimed invention to modify the teachings of Sethu and Yuki to incorporate the teaching of Albiston to generate synthetic dataset to train machine learning models. One would have been motivated to generate synthetic dataset for training models in order to prevent sensitive data from exposing, allow comprehensive model training, avoid privacy violation, etc.,.
Per claims 6 and 15, neither Sethu nor Yuki teaches
obtaining time series data from one or more automated software monitoring logs.
Albiston teaches an analogous art relates to training machine learning models, comprising:
obtaining time series data from one or more automated software monitoring logs (see at least paragraph [0642] “transforming time-series-based sample data set into a frequency-based data set…”).
It would have been obvious for a person of an ordinary skilled in the art as of the effective filing date of the claimed invention to modify the teachings Sethu and Yuki to incorporate the teaching of Albiston to process time series dataset. One would have been motivated to obtain time series data for better understanding how data evolve over time.
Per claim 7, neither Sethu nor Yuki teaches
wherein obtaining input data comprises obtaining JavaScript object notation (JSON) values associated with the at least one software program.
Albiston teaches an analogous art relates to training machine learning models, comprising:
obtaining JavaScript object notation (JSON) values associated with the at least one software program (see at least paragraph [0815] “…The trained machine learning model may then be used to recognize noise-related (hypertext markup language), XML (extensible markup language), and JSON syntax from languages including C, C++, JavaScript, Java, Python, R, etc. transformer (GPT)…”).
It would have been obvious for a person of an ordinary skilled in the art as of the effective filing date of the claimed invention to modify the teachings of Sethu and Yuki to incorporate the teaching of Albiston to use input data in JSON format. One would have been motivated to use input data in JSON format because it is easy to be processed.
Per claim 8, neither Sethu nor Yuki teaches
training at least a portion of the one or more artificial intelligence techniques using one or more of: historical input data associated with the at least one software program, historical output data associated with the at least one software program, and historical database operations data associated with the at least one software program.
Albiston teaches an analogous art relates to machine learning models, comprising:
training at least a portion of the one or more artificial intelligence techniques using one or more of: historical input data associated with at least one software program, historical output data associated with at least one software program, and historical database operations data associated with at least one software program (see at least paragraph [0648] “…In block 1068 (near bottom of FIG. 42B), historical compilations of and assessment data from the person are analyzed to identify sequences of training objective function (i.e., prediction model) has been optimized, the model parameters, with new data. Model training may include, for example, the most recent week's derived daily reports and model training. In the listing below, selected NEPAS Visually inspect time- Overall poor data quality…”).
It would have been obvious for a person of an ordinary skilled in the art as of the effective filing date of the claimed invention to modify the teachings Sethu and Yuki to train the model based on historical data. One would have been motivated to do in order to improve the machine learning model by learning from past data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Becker et al., "Reverse Engineering Component Models for Quality Predictions"
Yaniv et al., "Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs"
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHILLIP H NGUYEN whose telephone number is (571)270-1070. The examiner can normally be reached Monday-Friday 9:00AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wei Zhen can be reached at (571) 272-3708. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHILLIP H NGUYEN/Primary Examiner, Art Unit 2191
Prosecution Insights