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 .
DETAILED ACTION
This action is in response to the claimed listing filed in the Amendment on 04/14/2026.
Claims 1-3, 5-10, 12-17, 19-20 are pending and addressed in the Action.
Response to Arguments
This is in response to the argument remarks filed in the Amendment on 04/14/2026.
Double Patenting remains applied to the Claims 1-3, 5-10, 12-17, 19-20.
With regards to the amendment and especially, amended with “wherein the file encoder model is trained based on a training set of description sample pairs, wherein each description sample pair in the training set includes a binary code sample and a description text sample describing security risk of the binary code sample.”, Applicant submitted Shanahan and Liu have not been shown to teach or suggest that each description sample pair in the training set includes a binary code sample and a description text sample describing security risk of the binary code sample in the pair, much less the file encoder model that is trained based on these description sample pairs.
Applicant’s submissions are moot in view of new prior art applied to the addition of new limitations.
Claim Objections
Claims 9-10, 12-14 recite “The computer-readable medium…” where the Claims dependent on Claim 8 which recites “A non-transitory computer-readable medium”. Claims 9-10, 12-14 are objected to because of the following informalities: The claims are lack antecedent basis, and they should recite “The non-transitory computer-readable medium…”. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-3, 5-10, 12-17, 19-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. US18/589,818 (reference application: US20250272089A ). Although the claims at issue are not identical, they are not patentably distinct from each other because:
Current Application’s Claims
Application No. US18/589,818 Claims
1. A method, comprising:
processing a binary code by using a file encoder model to obtain a file embedding vector; and
wherein the file encoder model is trained based on a training set of
description sample pairs, wherein each description sample pair in the training set
includes a binary code sample and a description text sample describing security risk of the binary code sample;
selecting one or more natural language description samples based on the file embedding vector and a distance function.
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2. The method of claim 1, wherein the one or more natural language description samples are selected based on a text embedding vector of the one or more natural language description samples, wherein the text embedding vector and the file embedding vector have the same dimension.
3. The method of claim 2, wherein the text embedding vector is generated by using a text
language model.
5. The method of claim 1, wherein the file encoder model comprises a pretrained
embedding model in sequence with a translator model.
6. The method of claim 1, wherein the one or more natural language description samples are
selected by using a k-nearest neighbors algorithm (k-NN).
7. The method of claim 1, further comprising: generating a text description of the binary
code based on the one or more natural language description samples by using a large language
model (LLM).
1. A method, comprising:
processing a binary code by using a file encoder model to obtain a file embedding vector; and
[Claim 4]
4. The method of claim 1, wherein the file encoder model is trained based on a training set of source code sample pairs, wherein each source code sample pair in the training set includes a source code training sample and a binary code sample.
selecting one or more source code samples based on the file embedding vector and a distance function.
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2. The method of claim 1, wherein the one or more source code samples are selected based on a source code embedding vector of the one or more source code samples, wherein the source code embedding vector and the file embedding vector have a same dimension.
3. The method of claim 2, wherein the source code embedding vector is generated by using a text language model.
5. The method of claim 1, wherein the file encoder model comprises a pretrained embedding model and a translator model.
6. The method of claim 1, wherein the one or more source code samples are selected by using a k-nearest neighbors algorithm (k-NN).
7. The method of claim 1, further comprising: generating a text description of the binary code based on the one or more source code samples by using a large language model (LLM).
Claims 8-10, 12-14: The claims are directed to a “medium” having the same functionality recited in the method of Claims 1-3, 5-7. Claims 8-10, 12-14 are not patentably distinct from Claims 8-14 of the copending Application each other by the same comparison above.
Claims 15-17, 19-20: The claims are directed to a computer-implemented system having the same functionality recited in the method of Claims 1-3, 5-6. Claims 15-17, 19-20 are not patentably distinct from Claims 15-20 of the copending Application each other by the same comparison above.
There is no patentably distinct, but the claims present a single structure of the same invention with inputs are selective.
Therefore, it would be obvious to an ordinary of skills before the effective filing of the applications to select a training dataset, for example, select natural language description in the present Application and select the text of a program, the training model would generate the results accordingly, and thus, the selections are the input choices of a user into the same system.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 1-3, 5-10, 12-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Shanahan, US PAP Pub. No. US2024/0152749 A1 (EF: 05/27/2021), in view of
Cui et al., “Binary Code Vulnerability Location Identification with Fine-grained Slicing”, 2023, IEEE, pp. 502-506.
As per Claim 1: Shanahan discloses the limitation in bold as below:
1. A method, comprising:
processing [a binary code] by using a file encoder model to obtain a file embedding vector;
(Shanahan: See Figure 1, where Figure 1 is processing a “training data item” [See ‘#310’in Figure 3] using model ‘#100” having ‘Encoder #115’ [i.e. a file encoder model] to obtain ‘Encoding #120’ [i.e. a file embedding vector] .
See [0013] “..neural networks may be associated with a respective key. The method may further comprise determining a similarity between the encoding and each respective key; and selecting a subset of neural networks may be based upon the determined similarity.”, [0014 ] “The respective keys may be generated by sampling a probability distribution based upon the embedding space represented by the encoder. That is, the keys may be vectors sampled from the embedding space represented by the encoder. In one example, the embedding space has dimensionality 512. In another example, the embedding space has dimensionality 2048…”, in [0041] “…each of the plurality of neural networks may be associated with a respective key and the selection of a subset of neural networks 130….
… The respective keys and the encoding 120 may therefore reside in the same latent embedding space.”
Thus, ‘embedding space represented by the encoder’ appears being Encoding #120, ‘Keys” are Neural Network 1….N; are embedding vectors, and herein ‘Encoding # 120’ reads on ‘file embedding vector’ since it is obtained from Encoder 115 in Figure 1.
Furthermore, see in [0049] “The encoder may be represented as: z=f(x)∈ Rd where x is the input data item provided to the encoder which implements function f to provide the encoding z.”: It shows z is a vector in d dimension),
wherein the file encoder model is trained based on a training set of description sample pairs, wherein each description sample pair in the training set includes [a binary code] sample and a description text sample [[describing security risk of the binary code sample]];
(Shanahan: Figure 1, Encoder #115, training data item 105, Encoding #120 is based on a selection of subset network 130. See Figure 4, #420, process the Encoding, and [0051] “The memory 125 may be considered to comprise n pairs of keys and neural networks. For example, the memory 125 may be represented as M=(Mkey' , Mcfier)”. With recitations, “wherein each description sample pair”, is in #130, and in [0051] “the memory 125 may be represented as M=(Mkey , Mcfier)”; with recitations, “in the training set includes a description text sample”: i.e. a selection of Neural Network and See in [0051]: Referred to ‘Mkey’ corresponds to n-Keys ‘n pairs of keys’, where ‘Mcfier’ read on ‘sample'. See Figure 4, #430, it shows the output 135 in Figure 1 is of a classification of an aspect of the ‘Data Item’. Thus, Mcfier is a classifier has the aspect of the input item x from the Encoder, where z=f(x)∈ Rd )
In the training sample of the dataset, Shanahan does not address “binary code”, and “describing security risk of the binary code sample”.
Shanahan further discloses,
and selecting one or more natural language description samples based on the file embedding vector and a distance function.
(Shanahan: See Figure 3, ‘#315”, Select a subset of Neural Network, and see in [0083] “…the neural network-based system is a sequence representing a spoken utterance, the output generated by the neural network may be a score for each of a set of pieces of text… As another example, if the input to the neural network-based system is a sequence representing a spoken utterance, the output generated by the neural network-based system can identify the natural language in which the utterance was spoken. …” [compared to the limitations in light of text [0081] in the specification in text [0081] ]. Thus, each of Neural Network 1…N in # 125 in Figure 1 reads on ‘one or more natural language description samples’ , and a ‘Neural Network i’(i.e. i ∈ {1,…,N}) in #125 in Figure 1 is selected based on the Encoding #120 ‘file embedding vector’.
See [0013] “The similarity may be based upon a cosine distance between the encoding and the respective key. Thus, the k-nearest neighbors of keys to the encoding may be selected”. And [0052] “any other suitable distance metric may be used such as Euclidean distance”)
Shanahan does not address “binary code”, and “describing security risk of the binary code sample”.
Cui discloses, “Binary Code” (In p. 504, Fig. 1. “Training binary programs”) and “describing security risk of the binary code sample” (Fig. 1, in “The Training Phase”, the training phase includes Step 1, with added labels, and in step 2 and step 3, it forms a pair of binary and location of binary variability, and in step 4, it concatenates to form the pair HAN and BLSTM, where HAN: See in right column, described, started in “HAN consists...” and BLSTM: see in p. 506, top text portion in left column “… VDSimilar performs similarity vulnerability detection at the function level and inputs binary functions into BLSTM …”).
Thus, Cui, with the encoder in detecting binary code similarities, labels the code vulnerability used in the training phase, where identifying vulnerability locations in a program is a concern to all code developers, particularly, if the code is in binary, it is time-consuming (discussed in Cui’s Abstract); therefore developers direct the detection using encoder model.
With Shanahan, the Encoder model is to detect the similarity of data items entering the encoder for target data, where the data item would be selected as any type of data, due to the loss of data.
Therefore, it would be obvious to an ordinary of skills in the art before the effective filing of the application to combine the training data item with an encoder in Shanahan, with the teaching of binary code with the encoder model for detecting and labeling the security risk in terms of vulnerability detection of Cui. The combination would yield predictable results because the developers recognized that detecting security risks in binary code is time-consuming. They directed to the encoder model as the best choice and the combination would yield such predictable results.
As per Claim 2: Shanahan and combining Cui, where
Shanahan further discloses,
2. The method of claim 1, wherein the one or more natural language description samples are selected based on a text embedding vector of the one or more natural language description samples, (Referred to “keys’ / or Neural networks I in #125. See [0014] “The respective keys may be generated by sampling a probability distribution based upon the embedding space represented by the encoder. That is, the keys may be vectors sampled from the embedding space represented the encoder.”) wherein the text embedding vector (Referred to ‘keys’) and the file embedding vector (Referred to Encoding #120, : z=f(x)∈ Rd) have the same dimension (in [0041] “…The respective keys and the encoding 120 may therefore reside in the same latent embedding space.”. See [0050] “ For example, the memory 125 may be represented as M=(Mkey, Mcfier) with Mkey ∈ Rnxd (i.e. n keys, each with dimensionality d)” : i.e. vector ‘keys’ and vector ‘z’-Encoding 120- have the same dimension).
As per Claim 3: Shanahan and combining Cui, where
Shanahan further discloses,
3. The method of claim 2, wherein the text embedding vector is generated by using a text language model.
(Referred Keys as text embedding vectors, where see in [0083] “if the input to the neural network-based system is a sequence representing a spoken utterance, the output generated by the neural network-based system can identify the natural language in which the utterance was spoken. Thus in general the network input may comprise audio data for performing an audio processing task and the network output may provide a result of the audio processing task e.g. to identify a word or phrase or to convert the audio to text.” : Thus Neural network bases system using text language model)
As per Claim 5: Shanahan and combining Cui, where
Shanahan further discloses,
5. The method of claim 1, wherein the file encoder model comprises a pretrained embedding model in sequence with a translator model (See [0009] “The encoder may be pre-trained using a dataset different to the dataset that the training data item belongs to”, and [0084] “if the input to the neural network-based system is a sequence of text in one language, the output generated by the neural network may be a score for each of a set of pieces of text in another language, with each score representing an estimated likelihood that the piece of text in the other language is a proper translation of
the input text into the other language”).
As per Claim 6: Shanahan and combining Cui, where
Shanahan further discloses,
6. The method of claim 1, wherein the one or more natural language description samples are selected by using a k-nearest neighbors algorithm (k-NN) (See [0013] “The similarity may be based upon a cosine distance between the encoding and the respective key. Thus, the k-nearest neighbors of keys to the encoding may be selected.”).
As per Claim 7: Shanahan and combining Cui, where
Shanahan further discloses the limitation in bold below:
6. The method of claim 1, further comprising: generating a text description of [the binary code] based on the one or more natural language description samples by using a large language model (LLM) (Figure 1, The Neural Network Training system #100, where neural network is known as LLM, The Neural Network system #100 generating Output #135 of Training data item 105 based on one or more “Neural Network 1..n ” selected in memory #125, the “Neural Network 1..n ” reads on one natural language description samples as given in the rationales above in claim 1.
Cui discloses “the binary code” (See in the claim 1, the same rationale is applied in combination).
As per claims 8-14: The Claims are directed to a computer-readable medium which recites the limitations having functionality corresponding to the method of Claims 1-7 above. The claims are rejected with the same rationales addressed in Claims 1-7.
As per claims 15-20: The Claims are directed to a computer-implemented system which recites the limitations having functionality corresponding to the method of Claims 1-6 above. The claims are rejected with the same rationales addressed in Claims 1-6.
Conclusion
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 Ted T Vo whose telephone number is (571)272-3706. The examiner can normally be reached 8am-4:30pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wei Y Mui 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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TTV
April 30, 2026
/Ted T. Vo/
Primary Examiner, Art Unit 2191
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