CTFR 17/962,330 CTFR 94377 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-20 are presented for examination. Response to Amendment Applicant’s amendment appears to have obviated the rejections under 35 USC § 101. Specifically, the independent claims’ new limitation reciting preventing devices from executing software in response to determining that the software contains malware renders the claims analogous to claim 3 of Example 47, thereby integrating any judicial exception recited into a practical application. Therefore, those rejections are withdrawn. Claim Rejections - 35 USC § 103 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-21-aia AIA Claim s 1, 4, 7-8, 11-13, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Devlin, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding ("Devlin") in view of Burke et al. (US 20210194891) ("Burke") . Regarding Claim 1 , Devlin teaches: A … method, comprising: training a bidirectional encoder representations from transformers (BERT) model to generate a software representation; (Devlin teaches Figure 1 provided below, which shows steps for training a BERT. Devlin also teaches: “At the output, the token representations ( reads on software representation ) are fed into an output layer for token level tasks, such as sequence tagging or question answering, and the [CLS] representation is fed into an output layer for classification, such as entailment or sentiment analysis.” [Page 5] which teaches generation a software representation, in this case a sentiment analysis (representation of data by the software.)) PNG media_image1.png 689 1230 media_image1.png Greyscale inputting an intermediate representation (IR) of a software package to the trained BERT model, (Devlin teaches: “our input representation is able to unambiguously represent both a single sentence and a pair of sentences (e.g., <Question, Answer>) in one token sequence. […] A “sequence” refers to the input token sequence to BERT, which may be a single sentence or two sentences packed together.” [Page 4] which teaches inputting an IR (vector input) that represents a package of information (reads on software package).) wherein the trained BERT model analyzes the IR of the software package … (Devlin sec. 3, subsection entitled “Input/Output Representation” discloses that the first token of every sequence is the classification token [CLS]; sec. 3.2, penultimate paragraph discloses that the [CLS] representation [part of the IR] is fed to an output layer for classification [analysis]) receiving a software representation corresponding to the software package as output from the trained BERT model, (Devlin teaches: “At the output, the token representations ( reads on software representation ) are fed into an output layer for token level tasks, such as sequence tagging or question answering, and the [CLS] representation is fed into an output layer for classification, such as entailment or sentiment analysis.” [Page 5] which teaches an output from the BERT is a software representation that is tagged. As taught in Page 4 and seen in the quotation above, it also corresponds to a software package.) Devlin appears not to disclose explicitly the further limitations of the claim. However, Burke discloses [a] computer-implemented method, comprising: … (Burke Fig. 5 discloses that the method is executed on a computer comprising a processor and memory and that the computer executes coded instructions) determin[ing] whether the software package includes any malicious malware functions; … (Burke Fig. 3 discloses determining whether a classification score of network-traffic samples [software package] satisfies a malware confirmation threshold; if so, a potential malware detection is reported and a malware scan is performed [i.e., the system determines whether the traffic contains malware]) wherein the software representation indicates whether the software package includes any of the malicious malware functions; (Burke Fig. 3 discloses determining whether a classification score [software representation] of network-traffic samples [software package] satisfies a malware confirmation threshold; if so, a potential malware detection is reported and a malware scan is performed [i.e., the system determines whether the traffic contains malware]) and in response to a determination that the software representation indicates that the software package includes at least one of the malicious malware functions, preventing devices from executing the software package. (Burke Fig. 3 discloses that, if the scan identifies malware, remediation actions are taken, and paragraph 76 discloses that those remediation actions may include quarantining files corresponding to a process, program, and/or an application that initiated potentially malicious network traffic [i.e., prevents devices from executing the software contained in the traffic]) Burke and the instant application both relate to malware detection and are analogous. 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 Devlin to prevent devices from executing malicious software packages, as disclosed by Burke, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would protect the computer from malware, thereby enhancing its performance. See Burke, paragraph 76. Claim 11 is a computer program product claim corresponding to method claim 1 and is rejected for the same reasons as given in the rejection of that claim. Similarly, claim 20 is a system claim corresponding to method claim 1 and is rejected for the same reasons as given in the rejection of that claim. Regarding Claim 4 , Devlin/Burke teaches all the limitations of Claim 1, and further teaches: The computer-implemented method of claim 1, wherein the BERT model is trained with a pre-training objective that includes a first model, wherein training with the first model includes: (Devlin teaches all the steps below, in section 3.1 on page 4, which details training the BERT model with the effectiveness of training tested by pre-training objectives: “evaluating two pretraining objectives using exactly the same pretraining data, fine-tuning scheme, and hyperparameters” [Page 8] which is performed for the first BERT model.) randomly selecting a first predetermined percentage of tokens of a training IR based on a source code, (First, Devin teaches: “The training data generator chooses 15% of the token positions at random for prediction.” [Page 4] which teaches randomly selecting a predetermined percentage of tokens of the input training Intermediate representation (IR) based on the training data generator (reads on source code). See Figure 1 above for the Sentence Pairs Tok 1 – Tok N which are inputted into training the model. This is understood to be a set of data created for training by the “training data generator” which has the same format as the IR to the BERT (as they both serve as inputs).) masking a second predetermined percentage of the randomly selected tokens, (Devlin teaches: “If the i-th token is chosen, we replace the i-th token with (1) the [MASK] token 80% of the time “[Page 4] which teaches choosing 80% (predetermined percentage) of the selected tokens and masking them with a mask tag.) replacing a third predetermined percentage of the randomly selected tokens with random tokens, (Devlin teaches: “If the i-th token is chosen, we replace the i-th token with [..] (2) a random token 10% of the time” [Page 4] which directly follows the step above and replacing 10% (predetermined percentage) of the random selected tokens with random tokens.) and not modifying a remainder of the randomly selected tokens. (Devlin finishes teaching: “If the i-th token is chosen, we replace the i-th token with [..] (3) the unchanged i-th token 10% of the time.”[Page 4] which teaches not modifying the remainder 10% of tokens.) Claim 13 is a computer program product claim corresponding to method claim 4 and is rejected for the same reasons as given in the rejection of that claim. Regarding Claim 7 , Devlin/Burke teaches all the limitations of Claim 1, and further teaches: The computer-implemented method of claim 1, wherein the BERT model is trained with a pre-training objective that includes a first model (Devlin sec. 3.1 discloses that the pre-training of BERT may be for tasks [objectives] including masked LM and next-sentence prediction) , wherein training with the first model includes: selecting a value sentence of a first sequence of a training IR based on a source code, wherein the selected value sentence corresponds to an IR sentence of a second sequence of the training IR in a first predetermined percentage of cases of the first model, (Devlin teaches: “The first sentence receives the A embedding and the second receives the B embedding. 50% of the time B is the actual next sentence that follows A and 50% of the time it is a random sentence, which is done for the “next sentence prediction” task.” [Page 13] which teaches selecting a value sentence (embedding) for training (therefore based on a training IR) ran on the code of the machine learning model, where the sentence (when chosen to be B) is the second sequence (next sentence) in 50% (predetermined percentage) of cases.) and selecting value sentences randomly in a second predetermined percentage of the cases of the first model. (Devlin teaches the quotation of page 13 provided above, which teaches that 50% of the time the value sentence B is randomly selected rather than a next sentence.) Claim 16 is a computer program product claim corresponding to method claim 7 and is rejected for the same reasons as given in the rejection of that claim. Regarding Claim 8 , Devlin/Burke teaches all the limitations of Claim 1, and further teaches: The computer-implemented method of claim 1, wherein the BERT model is trained with a pre-training objective that includes a first model (Devlin sec. 3.1 discloses that the pre-training of BERT may be for tasks [objectives] including masked LM and next-sentence prediction) , wherein training with the first model includes: selecting a pair that includes a first sequence and a second sequence from a same basic block of a training IR in a first predetermined percentage of cases of the first model, (Devlin teaches: “The first sentence receives the A embedding and the second receives the B embedding. 50% of the time B is the actual next sentence that follows A and 50% of the time it is a random sentence, which is done for the “next sentence prediction” task.” [Page 13] which also reads on selecting a pair (A and B embedding pair for a first sentence – reads on first sequence and a second sentence – reads on second sequence.) based on a first block based on B following A in 50% of cases.) and selecting at least a second pair of sequences from other basic blocks in a second predetermined percentage of the cases of the first model. (Devlin teaches the quotation of page 13 provided above which teaches selecting a new random block based on A and the random B in 50% of cases.) Claim 17 is a computer program product claim corresponding to method claim 8 and is rejected for the same reasons as given in the rejection of that claim . 07-21-aia AIA Claim s 2-3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Devlin in view of Burke and further in view of Ibrahim et al. (US 20230126764) (“Ibrahim”) . Regarding Claim 2 , Devlin teaches all the limitations of Claim 1, and further teaches: The computer-implemented method of claim 1, wherein the software representation received as the output from the trained BERT model is in a form of embedding, (Devlin teaches Figure 1, which shows the output of the BERT (which is the software representation) as a span of data which is a form of embedding (as it is data that is stored or embedded in a span).) …. Devlin/Burke appears not to disclose explicitly the further limitations of the claim. However, Ibrahim discloses storing the software representation corresponding to the software package in qubit form on a storage device, wherein the storage device is a quantum computing storage device. (Ibrahim paragraph 62 discloses that a quantum machine simulator module may be stored in associated computer memory [i.e., the storage device is for quantum computing and is therefore a quantum computing storage device] and that the QISKIT software package may simulate a quantum circuit by computing the wavefunction of the qubit’s state vector as gates [i.e., QISKIT encodes qubits and is therefore in qubit form].) Ibrahim and the instant application both relate to quantum computing and are analogous. 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 Devlin/Burke to store quantum computing-related software packages on a device, as disclosed by Ibrahim, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the system to exploit the quantum property of entanglement, thereby enhancing the system’s computing capabilities. See Ibrahim, abstract. Claim 12 is a computer program product claim corresponding to method claim 2 and is rejected for the same reasons as given in the rejection of that claim. Regarding Claim 3, Devlin/Burke/Ibrahim teaches all the limitations of Claim 2, and further teaches: The computer-implemented method of claim 2, wherein the embedding is selected from the group consisting of: a vector representation of the software package and a tensor representation of the software package. (Devlin teaches: “final hidden vector for the i th input token as Ti ∈ R H” [Page 4] which teaches that the output span consistent of a set of vectors Tn which formulate the embedding. Therefore, the embedding is “selected” and consists of a vector representation of the software package.) 07-21-aia AIA Claim s 5-6 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Devlin in view of Burke and further in view of Xia (US Publication US20210374603A1) . Regarding Claim 5 , Devlin/Burke teaches all the limitations of Claim 4, but does distinctly teach: The computer-implemented method of claim 4, wherein training with the first model includes: extracting symbolic identifiers from a first sequence of the training IR, and creating the tokens from the extracted symbolic identifiers. However, Xia teaches these limitations. Xia teaches: “For example, the intent 406 may include a combination of a domain and an action, denoted by y=(y d ,y a ). Then for a given intent y=(y d ,y a ) and an utterance x=(w 1 , w 2 , . . . , w n ) with n tokens “ [0040] which teaches extracting intent token (symbolic identifiers) from a first sequence of data and Xia further teaches using this extracted intent token being used with training data (and therefore is from a training IR) from the extracted symbolic identifiers: “At subprocess 922 , the CLANG model encodes the in-class training sample with the first intent token and the second intent token ( reads on from the extracted symbolic identifiers) into a first encoded sequence ( reads on token ), and the out-of-class training sample with the first intent token and the second intent token into a second encoded sequence, respectively. “ [0076]. Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify the training IR of Devlin/Burke with the method of training a model by extracting symbolic identifiers from a first sequence and creating tokens from the extracted symbolic identifiers for the improvement of introducing a contrastive regularization loss system to improve learning of the model and lowering the probability of generating negative examples. This improvement is taught by Xia: “Additionally, the training 210 of the CLANG model 130 further adopts a contrastive regularization loss to improve learning. For example, during the training 210 , an in-class utterance (e.g., the utterance that specifically corresponds to a certain intent) from one intent may be contrasted with an out-of-class utterance (e.g., an utterance that does not match with the one intent) from another intent. [..] With the contrastive loss, the CLANG model 130 is regularized to focus on the given domain and intent and the probability of generating negative examples is reduced” [0030]. Xia teaches through this the improvement of creating symbolic identifiers so that the system can then leverage machine learning to improve the performance of the model. Claim 14 is a computer program product claim corresponding to method claim 5 and is rejected for the same reasons as given in the rejection of that claim. Regarding Claim 6 , Devlin teaches all the limitations of Claim 4, and further teaches: The computer-implemented method of claim 4, wherein the BERT model is trained by iteratively considering a plurality of variations of the pre-training objective, (Devlin teaches a plurality of variations of the pre-training objective in section 5.1 on page 8, where there’s variation 1 (No NSP) and variation two (LTR & No NSP).) wherein each iteration is based on different tokens of the training IR. Devlin does not distinctly disclose: - wherein each iteration is based on different tokens of the training IR. However, Xia teaches this improvement. “At subprocess 922 , the CLANG model encodes the in-class training sample with the first intent token and the second intent token into a first encoded sequence ( reads on token ), and the out-of-class training sample with the first intent token and the second intent token into a second encoded sequence, respectively. “ [0076] which teaches generating different tokens (encoded sequences) for different objectives (in-class vs out-of-class). Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify the training of the model by using iterations of objectives as taught by Devlin/Burke by having each iteration being based on different tokens as taught by Xia for the improvement of lowering the probability of generating negative examples. This improvement is taught by Xia: “With the contrastive loss, the CLANG model 130 is regularized to focus on the given domain and intent and the probability of generating negative examples is reduced” [0030]. Xia teaches the model generating different subsets of data for the contrastive loss method to decrease the odds of generating negative examples. Claim 15 is a computer program product claim corresponding to method claim 6 and is rejected for the same reasons as given in the rejection of that claim . 07-21-aia AIA Claim s 9-10 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Devlin in view of Burke and further in view of Satish (U.S. Patent US9412066B1) . Regarding Claim 9 , Devlin/Burke teaches all the limitations of Claim 1, and further teaches: The computer-implemented method of claim 1, comprising: using the software representation received as output from the trained BERT model (Devlin teaches output a representation of data from the BERT model: “At the output, the token representations ( reads on software representation ) are fed into an output layer for token level tasks, such as sequence tagging or question answering, and the [CLS] representation is fed into an output layer for classification, such as entailment or sentiment analysis.” [Page 5] which teaches an output from the BERT is a software representation.) to determine whether any functions of an object of an executable of the IR of the software package have at least a predetermined percentage of similarity with a function determined to have a first characteristic; and in response to a determination that at least one function of the object of the executable has at least the predetermined percentage of similarity with the function determined to have the first characteristic, determining that the executable also has the first characteristic. Devlin/Burke does not distinctly disclose: to determine whether any functions of an object of an executable of the IR of the software package have at least a predetermined percentage of similarity with a function determined to have a first characteristic; and in response to a determination that at least one function of the object of the executable has at least the predetermined percentage of similarity with the function determined to have the first characteristic, determining that the executable also has the first characteristic. However, Satish teaches: to determine whether any functions of an object of an executable of the IR of the software package have at least a predetermined percentage of similarity with a function determined to have a first characteristic; (Satish teaches Figure 3, which teaches identifying a set of characteristics (including a first characteristic) of a software sample (the software sample reads on functions of an object of an executable of the intermediate representation of the software package as it is a sample of executable code that is executed by the software-analysis mechanism and represents a set of software.) in step 302 then teaches determining the level of similarity to another software sample in 306 on the basis of the characteristics (including at least a first characteristic).) and in response to a determination that at least one function of the object of the executable has at least the predetermined percentage of similarity with the function determined to have the first characteristic, (Satish teaches “For example, at step 306 determination module 106 may, as part of server 206 in FIG. 2, determine that the static characteristics of software sample 126 and the static characteristics of software sample 124 exceed a threshold level of similarity. “ [Page 16, Column 13, last paragraph] which teaches determining that the software sample (which reads on the function of the object of the executable) exceeds (has at least) a threshold of similarity with the function with the first characteristic.) determining that the executable also has the first characteristic. (Satish teaches “For example, determination module 106 may compare the number of executable sections included in software samples 126 and 124 , the respective sizes of the executable sections included in software samples 126 and 124 , the number of functions imported and/or called by software samples 126 and 124 , and whether software samples 126 and 124 each have a multimedia library dependency” [Page 16, Column 14] which teaches the system determining that each set of software has a certain characteristic, in this case: “multimedia library dependency”.) Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify the output step as taught by Devlin/Burke by introducing the comparison step of comparing the software samples as taught by Satish for the improvement of being able to predict the runtime of the new executable based on the old executable. This improvement is taught by Satish: “fairly accurately predict an optimum run time for the soon-to-be executed software sample based at least in part on the amount of time needed to observe the interesting run-time behaviors exhibited by the previously executed software samples” [Page 12, Column 5, first paragraph] which teaches predicting an optimum run time for the executed sample based on a previous sample. Claim 18 is a computer program product claim corresponding to method claim 9 and is rejected for the same reasons as given in the rejection of that claim. Regarding Claim 10, Devlin as modified by Burke and Satish teaches all the limitations of Claim 9, and Satish further teaches: The computer-implemented method of claim 9, wherein the first characteristic is selected from the group consisting of: trustworthy and untrustworthy. (For example, identification module 104 may apply a run time of “00:20:45” to the “Low Risk” sample class since software sample 124 did not exhibit any run-time behaviors after running for “00:20:45.” Additionally or alternatively, identification module 104 may apply a run time of “05:30:00” to the “High Risk” sample class since the other software sample represents a “High Risk” of malware” [Page 16, Column 13, first paragraph] which teaches the characteristic of “Low Risk” (which reads on trustworthy) and “High Risk” which reads on untrustworthy.) The motivation for this combination is the same as claim 9. Claim 19 is a computer program product claim corresponding to method claim 10 and is rejected for the same reasons as given in the rejection of that claim . Response to Arguments Applicant’s arguments with respect to the art rejection of the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Specifically, Applicant’s argument that the existing references do not teach preventing devices from executing malicious software packages is moot by virtue of the use of newly cited reference Burke to teach this element. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Rahali et al., “MalBERT: Using Transformers for Cybersecurity and Malicious Software Detection,” in arXiv preprint arXiv:2103.03806 (2021) (disclosing the use of transformers, including BERT, in malicious software detection) . Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN C VAUGHN whose telephone number is (571)272-4849. The examiner can normally be reached M-R 7:00a-5:00p ET. 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, Kamran Afshar can be reached at 571-272-7796. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /RYAN C VAUGHN/Primary Examiner, Art Unit 2125 Application/Control Number: 17/962,330 Page 2 Art Unit: 2125 Application/Control Number: 17/962,330 Page 3 Art Unit: 2125 Application/Control Number: 17/962,330 Page 4 Art Unit: 2125 Application/Control Number: 17/962,330 Page 5 Art Unit: 2125 Application/Control Number: 17/962,330 Page 6 Art Unit: 2125 Application/Control Number: 17/962,330 Page 7 Art Unit: 2125 Application/Control Number: 17/962,330 Page 8 Art Unit: 2125 Application/Control Number: 17/962,330 Page 9 Art Unit: 2125 Application/Control Number: 17/962,330 Page 10 Art Unit: 2125 Application/Control Number: 17/962,330 Page 11 Art Unit: 2125 Application/Control Number: 17/962,330 Page 12 Art Unit: 2125 Application/Control Number: 17/962,330 Page 13 Art Unit: 2125 Application/Control Number: 17/962,330 Page 14 Art Unit: 2125
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