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 communication is in response to the application filed on 03/12/2024 in which Claims 1-31 are presented for examination.
Drawings
The applicant’s drawings submitted on 03/12/2024 are acceptable for examination purposes.
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 1-31 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.
The term “AI” in claims 1, 3-6, 9-14, 16, 18-20, 22-27, 29 and 31 are vague and unclear which renders the claim indefinite. The term “AI” is not defined by the claim. It’s unclear what it stands for, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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, 8-12, 14-15, 20-22, 24-28, 31 are rejected under 35 U.S.C. 103 as being unpatentable over LU U.S. Patent Application Publication No. 20240419795 A1 (hereinafter "LU") (family with attached CN 116737526 A), in view of SECK US 20250156531 A1.
As to claim 1, LU teaches a system comprising: a microprocessor; and a computer readable medium, coupled with the microprocessor and comprising microprocessor readable and executable instructions that, when executed by the microprocessor, cause the microprocessor to (LU Pa. [0017]) [at least one storage, configured to store a program; and at least one processor, configured to execute the program stored in the storage. When the program stored in the storage is executed, the processor is configured to perform the method provided]: retrieve baseline structure information and/or baseline weight information associated with an AI algorithm (LU Pa. [0012]) [obtaining static baseline data of the user-mode process]; retrieve current structure information and/or current weight information associated with the AI algorithm (LU Pa. [0063]) [the DIM core module can verify signatures of pre-constructed static baseline data and import the static baseline data…DIM core module may perform code segment measurement on each process that is specified in the whitelist data and that requires code segment measurement. A measurement result may be stored in a memory in a form of a dynamic baseline, to obtain ”dynamic baseline data” (read current structure information)]; compare the baseline structure information and/or the baseline weight information to the current structure information and/or the current weight information (LU Pa. [0063]) [the DIM core module may compare the dynamic baseline data with the static baseline data, and record a comparison result as a measurement log]; in response to the baseline structure information and/or the baseline weight information being different from the current structure information and/or the current weight information (LU Pa. [0008]) [when the first measurement data is “inconsistent” (read different) with the target baseline data], determining that the AI algorithm has been compromised (LU Pa. [0008]) [determining that the code segment in the memory of the user-mode process and/or the kernel is tampered with]; and in response to the baseline structure information and/or the baseline weight information being the same as the current structure information and/or the current weight information (LU Pa. [0107]) [Determine whether the first measurement data is “consistent’ (read same) with target baseline data], determining that the AI algorithm has not been compromised (LU Pa. [0066]) [If the comparison is consistent, it indicates that the code segment of each process that requires code segment measurement and that is specified in the list data is not tampered with]
It is noted that LU does not explicitly disclose the aspect of “Artificial Intelligence (AI) algorithm”
However, SECK paragraph [0004] discloses (An initial corpus of source code is received. The initial corpus of source code is for training an Artificial Intelligence (AI) algorithm that generates source code. The initial corpus of source code is scanned, using a test suite, to identify one or more potential vulnerabilities in the initial corpus of the source code. The identified one or more potential vulnerabilities in the initial corpus of the source code are mitigated to produce a training corpus of source code. For example, the mitigation may comprise removing vulnerabilities from the initial corpus. The mitigation, to remove the vulnerabilities, and/or the like, would reduce the likelihood that the vulnerabilities would show up in source code generated by the AI algorithm. The AI algorithm is then trained using the training corpus of source code. The trained AI algorithm is executed to produce generated source code)
Thus, it would have been recognized by one of ordinary skill in the art before the effective filing date of the claimed invention, that applying the known technique taught by SECK to the intrusion detection system of LU would have yield predictable results and resulted in an improved system, namely, a system that would provide a software testing and to scanning source code used to train an Artificial Intelligence (AI) algorithm. (SECK Pa. [0001])
As to claim 2, LU teaches wherein comparing the baseline structure information and/or the baseline weight information to the current structure information and/or the current weight information comprises comparing the baseline structure information to the current structure information (LU Pa. [0063]) [the DIM core module may compare the dynamic baseline data with the static baseline data, and record a comparison result as a measurement log]
As to claim 3, LU teaches wherein the baseline structure information comprises one or more of: a hash of source code of a node in the AI algorithm, a pointer to a next node in the AI algorithm, a length of the source code of the node in the AI algorithm, and a location of the node in the AI algorithm (LU Pa. [0011]) [ code segment in the memory of the user-mode process and/or the kernel, to obtain the target baseline data; performing hash calculation on the memory area occupied by the target baseline data]
As to claim 4, the combination of LU and SECK discloses wherein the baseline structure information comprises the hash of the source code of the node the node in the AI algorithm and wherein the hash of the node in the AI algorithm comprises individual hashes of the source code (LU Pa. [0011]) [ code segment in the memory of the user-mode process and/or the kernel, to obtain the target baseline data; performing hash calculation on the memory area occupied by the target baseline data] of a plurality of nodes in the AI algorithm (SECK Pa. [0004]) (An initial corpus of source code is received. The initial corpus of source code is for training an Artificial Intelligence (AI) algorithm that generates source code]
Thus, it would have been recognized by one of ordinary skill in the art before the effective filing date of the claimed invention, that applying the known technique taught by SECK to the intrusion detection system of LU would have yield predictable results and resulted in an improved system, namely, a system that would provide a software testing and to scanning source code used to train an Artificial Intelligence (AI) algorithm. (SECK Pa. [0001])
As to claim 8, LU teaches wherein comparing the baseline structure information and/or the baseline weight information to the current structure information and/or the current weight information comprises comparing the baseline weight information to the current weight information (LU Pa. [0063]) [the DIM core module may compare the dynamic baseline data with the static baseline data, and record a comparison result as a measurement log]
As to claim 9, the combination of LU and SECK discloses wherein the baseline weight information comprises individual hashes (LU Pa. [0011]) [ code segment in the memory of the user-mode process and/or the kernel, to obtain the target baseline data; performing hash calculation on the memory area occupied by the target baseline data] of weights used by the AI algorithm (SECK Pa. [0004]) (An initial corpus of source code is received. The initial corpus of source code is for training an Artificial Intelligence (AI) algorithm that generates source code]
Thus, it would have been recognized by one of ordinary skill in the art before the effective filing date of the claimed invention, that applying the known technique taught by SECK to the intrusion detection system of LU would have yield predictable results and resulted in an improved system, namely, a system that would provide a software testing and to scanning source code used to train an Artificial Intelligence (AI) algorithm. (SECK Pa. [0001])
As to claims 10-12, claims 10-12 recite the claimed that respectively contain similar limitations as claim 9; therefore, they are rejected under the same rationale.
As to claim 14 the combination of LU and SECK discloses wherein the current structure information and/or the current weight information are captured in real-time while the AI algorithm is running (SECK Pa. [0033]) [runtime analysis (testing the source code 121 in a real environment)]
Thus, it would have been recognized by one of ordinary skill in the art before the effective filing date of the claimed invention, that applying the known technique taught by SECK to the intrusion detection system of LU would have yield predictable results and resulted in an improved system, namely, a system that would provide a software testing and to scanning source code used to train an Artificial Intelligence (AI) algorithm. (SECK Pa. [0001])
As to claim 15, LU teaches wherein at least a portion of the baseline structure information and/or the baseline weight information, and at least a portion of the current structure information and/or at least a portion of the current weight information (LU Pa. [0063]) [the DIM core module may compare the dynamic baseline data with the static baseline data, and record a comparison result as a measurement log]
are displayed in a graphical user interface at a same time (LU Pa. [0101) [reading a measurement parameter configured by a user, and reporting a measurement result for display]
As to claims 20-22, claims 20-22 recite the claimed that respectively contain similar limitations as claims 1-2; therefore, they are rejected under the same rationale.
As to claims 24-26, claims 24-26 recite the claimed that respectively contain similar limitations as claims 10-12; therefore, they are rejected under the same rationale.
As to claim 27, claim 27 recites the claimed that respectively contain similar limitations as claim 14; therefore, it is rejected under the same rationale.
As to claim 28, claim 28 recites the claimed that respectively contain similar limitations as claim 15; therefore, it is rejected under the same rationale.
As to claim 31, claim 31 recites the claimed that respectively contain similar limitations as claim 1; therefore, it is rejected under the same rationale.
Claims 5-7, 16-19, 23, 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over LU U.S. Patent Application Publication No. 20240419795 A1 (hereinafter "LU") (family with attached CN 116737526 A), in view of SECK US 20250156531 A1, in further view of Fay US 20250173085 A1.
As to claim 5 the combination of LU and SECK fails to disclose wherein the baseline structure information comprises the pointer to the next node in the AI algorithm and wherein a pointer to the next node in the AI algorithm in the current structure information is changed in one of the following ways: the pointer to the next node in the AI algorithm is now pointing to a new node in the AI algorithm, the pointer to the next node in the AI algorithm is now pointing to a different existing node at a same layer in the in the AI algorithm, the pointer to the next node in the AI algorithm is now pointing to a node at a previous layer in the AI algorithm, and the pointer to the next node in the AI algorithm no longer exists.
However, Fay discloses wherein the baseline structure information comprises the pointer to the next node in the AI algorithm and wherein a pointer to the next node in the AI algorithm in the current structure information is changed in one of the following ways (Fay Pa. [0255]) [metadata representation (451-54) to represent a snapshot (451-56), the storage system (451-06) may receive an I/O operation that writes to data that is ultimately stored within a particular data object (451-53B), where the data object (451-53B) is pointed to by a leaf node pointer (451-52B), and where the leaf node pointer (451-52B)]: the pointer to the next node in the AI algorithm is now pointing to a new node in the AI algorithm, the pointer to the next node in the AI algorithm is now pointing to a 56different existing node at a same layer in the in the AI algorithm, the pointer to the next node in the AI algorithm is now pointing to a node at a previous layer in the AI algorithm, and the pointer to the next node in the AI algorithm no longer exists (Fay Pa. [0094]) [the authority is transferred to a new storage node]
Thus, it would have been recognized by one of ordinary skill in the art before the effective filing date of the claimed invention, that applying the known technique taught by Fay to the intrusion detection system of LU and SECK would have yield predictable results and resulted in an improved system, namely, a system that would provide managing data configuration information across data repositories (Fay Abstract)
As to claim 6 the combination of LU, SECK and Fay discloses wherein the hash of the source code of the node in the AI algorithm (LU Pa. [0011]) [ code segment in the memory of the user-mode process and/or the kernel, to obtain the target baseline data; performing hash calculation on the memory area occupied by the target baseline data], the pointer to a next node in the AI algorithm (Fay Pa. [0094]) [the authority is transferred to a new storage node], the length of the source code of the node of the AI algorithm, and the location of the node in the AI algorithm are stored in a blockchain (Fay Pa. [0179]) [. While conventional blockchains store every transaction to achieve validation, a blockweave permits secure decentralization without the usage of the entire chain, thereby enabling low cost on-chain storage of data.]
Thus, it would have been recognized by one of ordinary skill in the art before the effective filing date of the claimed invention, that applying the known technique taught by Fay to the intrusion detection system of LU and SECK would have yield predictable results and resulted in an improved system, namely, a system that would provide managing data configuration information across data repositories (Fay Abstract)
As to claim 7 the combination of LU, SECK and Fay disclose wherein the current structure information comprises at least one of: a new node, a pointer from the new node to an existing node at a next layer, a pointer from the new node to a node at a same layer, a removed pointer, and a removed node (Fay Pa. [0256]) [o remove an existing metadata object pointer]
Thus, it would have been recognized by one of ordinary skill in the art before the effective filing date of the claimed invention, that applying the known technique taught by Fay to the intrusion detection system of LU and SECK would have yield predictable results and resulted in an improved system, namely, a system that would provide managing data configuration information across data repositories (Fay Abstract)
As to claim 13, claim 13 recites the claimed that respectively contain similar limitations as claim 6; therefore, it is rejected under the same rationale.
As to claim 16 the combination of LU, SECK and Fay disclose wherein the graphical user interface displays the at least a portion of the baseline structure information and the at least a portion of the current structure information and wherein the at least a portion of the current structure information shows at least one of: a new node, a removed node, a failed hash of source code of a node of the AI algorithm, a new pointer, and a removed pointer (Fay Pa. [0256]) [remove an existing metadata object pointer]
Thus, it would have been recognized by one of ordinary skill in the art before the effective filing date of the claimed invention, that applying the known technique taught by Fay to the intrusion detection system of LU and SECK would have yield predictable results and resulted in an improved system, namely, a system that would provide managing data configuration information across data repositories (Fay Abstract)
As to claim 17, claim 17 recites the claimed that respectively contain similar limitations as claim 15; therefore, it is rejected under the same rationale.
As to claims 18-19 the combination of LU, SECK and Fay disclose wherein microprocessor readable and executable instructions further cause the microprocessor to: in response to determining that the AI algorithm has been compromised reload the AI algorithm from a first block in blockchain, wherein reloading the AI algorithm from the first block in the blockchain comprises getting weights associated with the AI algorithm from a second block in the blockchain; wherein microprocessor readable and executable instructions further cause the microprocessor to: add compromise block to the blockchain, wherein the compromise block includes information about how the AI algorithm was compromised (Fay Pa. [0177]) [Every blockchain transaction may be digitally signed by one or more parties, replicated to a plurality of nodes, ordered and timestamped by the chain's consensus algorithm, and stored permanently in a tamper-proof way. Any data within the transaction will therefore be stored identically but independently by every node, along with a proof of who wrote it and when]
Thus, it would have been recognized by one of ordinary skill in the art before the effective filing date of the claimed invention, that applying the known technique taught by Fay to the intrusion detection system of LU and SECK would have yield predictable results and resulted in an improved system, namely, a system that would provide managing data configuration information across data repositories (Fay Abstract)
As to claim 23, claim 23 recites the claimed that respectively contain similar limitations as claim 7; therefore, it is rejected under the same rationale.
As to claim 29, claim 29 recites the claimed that respectively contain similar limitations as claim 16; therefore, it is rejected under the same rationale.
As to claim 30, claim 30 recites the claimed that respectively contain similar limitations as claim 5; therefore, it is rejected under the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EVANS DESROSIERS whose telephone number is (571)270-5438. The examiner can normally be reached Monday -Friday 8:00 am - 5:30 pm.
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/EVANS DESROSIERS/Primary Examiner, Art Unit 2491
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