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 .
Status of the Claims
Claims 1, 4-7, 9, 12, 15-17, 19 have been amended. Claims 1-20 are pending.
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.
The claims at a high level recite classifying and marching documents.
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-20 recite a method and a system and therefore, are directed to the statutory class of machine and a product.
The USPTO Guidance recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) (Step 2A, Prong 1); and
(2) additional elements that integrate the judicial exception into a practical application (Step 2A, Prong 2). MPEP §§ 2106.04(a), (d).
Only if the claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look in Step 2B to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field; or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. MPEP § 2106.05(d).
Step 2A, Prong One: Is a Judicial Exception Recited?
First, determine whether the claims recite any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes). MPEP § 2106.04(a).
Claim 1 recites -
▪ generating a multi-sequence vector that contains a plurality of distinct sequences of distinct nodes of a parse tree of source logic text (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — limitation can be performed by the developer mentally analyzing a control flow tree that represents source code and generating a fixed size vector representation of the source code);
▪ training, based on the multi-sequence vector, a logic encoder (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can analyze and encode various vectors to establish patterns and classifications of data and amount to “Apply it” merely using a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using and training encoder merely invoking a computer component to apply the exception. I.e. a “logic encoder” is software);
▪ performing without parsing a new source logic text: a) inferring, by the logic encoder, a fixed-size encoded logic from a new source logic text (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a mathematical evaluation, which performs the determination, thereby further defining the abstract idea. A human being may use this mathematical calculation to facilitate the mental evaluation in order to arrive at the necessary determination. This claim limitation appears to recite both a mathematical formula and mental process);
▪ b) detecting, based on the fixed-size encoded logic, that the new source logic text is anomalous (Abstract Idea of a mental process, see MPEP § 2106.04(a)(2)(III). Under the broadest reasonable interpretation, this limitation is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — imitation can be performed by the developer mentally analyzing encoded logic to detect anomalous code);
These limitations, based on their broadest reasonable interpretation, recite a mental process, i.e. a judicial exception. For these reasons, the independent claim 1, as well as independents claim 12, which include limitations commensurate in scope with claim 1, recite a judicial exception.
A method, like the claimed method, “a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” See Digitech Image Techs, LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014). See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) where collecting information, analyzing it, and displaying results from certain results of the collection and analysis was held to be an abstract idea. See In re Meyer, 688 F.2d 789, 795—96 (CCPA 1982), which held that “a mental process that a neurologist should follow” when testing a patient for nervous system malfunctions was not patentable.
Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
Next determine whether the claims recite additional elements that integrate the judicial exception into a practical application (see MPEP §§ 2106.05(a)-(c), (e)-(h)). To integrate the exception into a practical application, the additional claim elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)).
Additional elements:
▪ training, based on the multi-sequence vector, a logic encoder (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using and training machine learning model amount to merely invoking a computer component to apply the exception);
▪ wherein the method is performed by one or more computers (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: merely invoking a computer components to apply the exception).
The term “additional elements” for claim features, limitations, or steps that the claim recites beyond the identified judicial exception. Claim 12 additionally recites “non-transitory computer-readable media.” However, claims do not recite any improvements to these additional elements, nor does the claims recite any particularly programmed or configured computer system, device, or machine learning. Rather, the additional elements in claims 1 and 12 serve merely to automate the abstract idea. See Int’l Bus. Machs. Corp. v. Zillow Group, Inc., 50 F. 4" 1371, 1382 (Fed. Cir. 2022) (“[A] patent that ‘automate[s] “pen and paper methodologies” to conserve human resources and minimize errors’ is a ‘quintessential “do it on a computer” patent’ directed to an abstract idea.”) (quoting Univ. of Fla. Rsch. Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363, 1367 (Fed. Cir. 2019)). Therefore, none of these recited additional elements, whether considered individually or in combination, integrates the judicial exception into a practical application.
The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating and processing known data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
For these reasons, independent claim 1, as well as independent claim 12, which include similar additional elements as claim 1, are directed to an abstract idea.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, determine whether the claims recite an “inventive concept” that “must be significantly more than the abstract idea itself, and cannot simply be an instruction to implement or apply the abstract idea on a computer.” BASCOM Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016); see MPEP § 2106.05(d). There must be more than “computer functions [that] are “well-understood, routine, conventional activit[ies]’ previously known to the industry.” Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 225 (2014) (second alteration in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 73 (2012)); see MPEP § 2106.05(d).
Step 2B: The additional elements are not sufficient to amount to significantly more than the judicial exception (see MPEP 2106.05(d)(Il). Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. Using a computer and associated computer network to obtain data, use data to identify other data, and comparing data, are some of the most basic functions of a computer. All of these computer functions are well-understood, routine, conventional activities previously known to the industry. The method claims do not, for example, purport to improve the functioning of the computer itself. Nor do they effect an improvement in any other technology or technical field. Instead, the claims at issue amount to nothing significantly more than an instruction to apply the abstract idea of displaying, processing and storing data using some unspecified, generic computer).
Note, that in similar case, such as Collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), the Courts have identified that the additional elements of displaying and analyzing data, as shown in the independent claims 1, 12 do not amount to significantly more than the judicial exception. Consequently, that is not enough to transform an abstract idea into a patent-eligible invention.
No “inventive concept” sufficient to transform the abstract method of organizing human activity into a patent-eligible application. See MPEP § 2106.05. Rather, the additional elements identified above are merely well-understood, conventional computer components, as confirmed by the Specification. See MPEP § 2106.05(d)(1). For example, the Specification refers to the additional elements in generic terms.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate and process known data.
Additionally, the computer components are used for performing insignificant extra-solution activity and well understood, routine, and conventional functions. Activities such as these are insignificant extra-solution activity and, therefore, well understood, routine, and conventional. See MPEP 2106.05(d); see also, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price); CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (Obtaining information about transactions using the Internet to verify credit card transactions); Ultramercial, Inc. v. Hulu, LLC, 772 F.3d at 715, 112 USPQ2d at 1754 (Consulting and updating an activity log); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display); Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016) (Recording a customer’s order); Return Mail, Inc. v. U.S. Postal Service, -- F.3d --, -- USPQ2d --, slip op. at 32 (Fed. Cir. August 28, 2017) (Identifying undeliverable mail items, decoding data on those mail items, and creating output data); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015) (Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price). Furthermore, limitations such as integrating account details are well-understood, routine, and conventional activity. See Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log).
Independent system claim 1 and 12 contain the identified abstract ideas, with the additional elements of the media, which is a generic computer component, and thus not significantly more for the same reasons and rationale above.
Accordingly, independent claims 1, 10 and 19 are patent ineligible because they are directed to an abstract idea that does not recite an inventive concept that amounts to significantly more than the abstract idea.
Dependent claims further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible. As such, the claims are not patent eligible.
With respect to claims 2-3, 7-8, 10 13-14, 18:
Step 2A Prong 1: the claims recite a judicial exception (an abstract idea)
▪ generating each sequence of the plurality of distinct sequences of distinct nodes of the parse tree by a respective distinct traversal of the parse tree; the plurality of distinct sequences of distinct nodes of the parse tree contains at least three distinct sequences; wherein said at least one sequence in said plurality of distinct sequences is said plurality of distinct sequences; said distinct nodes of the parse tree consists of a plurality of terminal nodes and a plurality of non-terminal nodes; the plurality of terminal nodes are not entirely contiguous in each sequence of the plurality of distinct sequences; the parse tree of the source logic text contains a plurality of edges that interconnect said distinct nodes of the parse tree; for each edge in the plurality of edges: the edge connects two distinct nodes of the parse tree, and the two distinct nodes are adjacent in at least one sequence in said plurality of distinct sequences (Abstract Idea of a mental process. Under the broadest reasonable interpretation, the obtaining/determining probability distribution and divergence, as drafted, is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user can manually determine sequences, analyze and evaluate the tree, determine nodes, edges etc.).
Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application. Additional elements: no additional elements are recited. Step 2B: the additional element is not sufficient to amount to significantly more than the judicial exception.
With respect to claims 4, 9, 15, 19:
Step 2A Prong 1: the claims recite a judicial exception (an abstract idea)
▪ the source logic text comprises one or more database statements; the method further comprises based on said detecting that the new source logic text is anomalous, not execution planning for the one or more database statements and not executing the one or more database statements; based on said detecting that the new source logic is anomalous, not parsing the new source logic text (Abstract Idea of a mental process. Under the broadest reasonable interpretation, the obtaining/determining probability distribution and divergence, as drafted, is an abstract idea of “a mental process” because it recites a process that can be performed in the human mind (i.e., observation, determination, evaluation, judgment, and opinion) — a user/developer can logically decide whether to proceed examining or not examining malicious code).
Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application. Additional elements: no additional elements are recited. Step 2B: the additional element is not sufficient to amount to significantly more than the judicial exception.
With respect to claims 5, 16:
Step 2A Prong 1: the claims recite a judicial exception (an abstract idea)
▪ said training the logic encoder comprises self-supervised training; copying, after said training the logic encoder, the logic encoder into a new neural network; training the new neural network to detect whether source logic text is anomalous, wherein the training the new neural network is not self-supervised (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using and training machine learning model amount to merely invoking a computer component to apply the exception).
Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application.
Additional elements:
self-supervised training; (Amount to mere instruction to apply the abstract idea using a generic computer component. A mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). and Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using and training machine learning model amount to merely invoking a computer component to apply the exception).
Step 2B: the additional element is not sufficient to amount to significantly more than the judicial exception. Additional elements: listed above in step 2A prong 2.
With respect to claims 6, 17:
Step 2A Prong 1: the claims recite a judicial exception (an abstract idea)
▪ generating a neural network that can accept the source logic text and the plurality of distinct sequences of distinct nodes of the parse tree as input, wherein the neural network contains said logic encoder; self-supervised training the neural network to predict skipped tokens, wherein the self-supervised training the neural network comprises training the logic encoder; deploying, after the training the logic encoder, the logic encoder without the neural network (Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using and training machine learning model amount to merely invoking a computer component to apply the exception).
Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application. Additional elements:
a neural network; self-supervised training; deploying, after the training the logic encoder, the logic encoder without the neural network (Amount to mere instruction to apply the abstract idea using a generic computer component. A mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). and Amount to “Apply it”. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f). Examiner’s note: high level application of using and training machine learning model amount to merely invoking a computer component to apply the exception).
Step 2B: the additional element is not sufficient to amount to significantly more than the judicial exception. Additional elements: listed above in step 2A prong 2.
With respect to claims 10, 20:
Step 2A Prong 1: the claims recite a judicial exception (an abstract idea)
▪ wherein the logic encoder does not accept the entire multi- sequence vector as a single input (is an abstract idea of “a mental process” because it recites a process using an algorithm of language processing (software). A developer is able to evaluate what vector input is desired).
Step 2A Prong 2: the additional elements that are not sufficient to integrate the judicial exception into a practical application. Additional elements: no additional elements are recited. Step 2B: the additional element is not sufficient to amount to significantly more than the judicial exception.
Dependent claims 2-11, 13-20 are thus, also patent ineligible for the reasons discussed 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, 7-15, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over SCHNEUWLY et al. (US 2022/0198294) in view of Van-Hoang Le and Hongyu Zhang “Log-based Anomaly Detection Without Log Parsing,” hereafter Zhang.
Regarding claims 1 and 12, SCHNEUWLY teaches a method and one or more non-transitory computer-readable media storing instructions that, when executed by one or more processors comprising:
generating a multi-sequence vector that contains a plurality of distinct sequences of distinct nodes of a parse tree of source logic text ([0032]-[0034], [0042] “tree nodes are in some ways distinct” [0044], [0051] “contain tree paths of lengths one and three that are based on distinct sequences”, [0052], [0058] “parsing a text such as … logic statement, or script that contains logic statements”);
training, based on the multi-sequence vector, a logic encoder ([0051], [0055], [0064]-[0066]); and
performing without parsing a new source logic text ([0030], [0036] “ML model should analyze a summary of parse tree such as bag of rules in respective embodiments instead of directly analyzing parse tree itself”, [0040] “may recognize syntactic patterns within parse tree without directly analyzing parse tree by instead analyzing bag of rules”, [0019] “a fixed size bag of rules can summarize any parse tree”) (see NOTE):
inferring, by the logic encoder, a fixed-size ([0046]-[0048], [0049] “bag of rules summarizes only paths of predefined lengths”, [0051]-[0052], [0055], [0057], F2) encoded logic from a new source logic text ([0060], [0062], [0072]; C1 “each sequence of production rules of the plurality of sequences of production rules consists of respective production rules of a sequence of tree nodes in a respective directed tree path of the parse tree having a path length that satisfies same said length constraint”); and
detecting, based on the fixed-size encoded logic, that the new source text is anomalous ([0012] “detection of anomalous logic syntax”, [0062]-[0063]);
wherein the method is performed by one or more computers (F4, [0088]).
NOTE - SCHNEUWLY teaches the ML model such as an artificial neural network (ANN) (aka logic encoder as shown in [0139]). SCHNEUWLY shows that ML model is trained on a parse tree of source logic (F3:302). In order to detect an anomaly in a new parse tree (new source code) a bag of rules is analyzed instead of directly analyzing parse tree itself – “recognize syntactic patterns within parse tree without directly analyzing parse tree” [0040]. Not analyzing the parse tree obviously indicates that no parsing is performed on the AST (source code).
However, to further obviate such reasoning, Zhang teaches performing without parsing a new source logic text (p.492 C2 last para, p.497 ¶IV, NOTE Zhang specifically teaches that BERT encodes each log message into a vector with a fixed dimension and the resulting vectors are used in an anomaly detection p.497 C2 last para).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of SCHNEUWLY to not parse the source code as disclosed by Zhang. Doing so allows for effective understanding of the semantic meaning of log messages and achieve accurate anomaly detection results (Zhang Abstract).
Regarding claims 2 and 13, SCHNEUWLY as modified teaches the method and the media further comprising generating each sequence of the plurality of distinct sequences of distinct nodes of the parse tree by a respective distinct traversal of the parse tree (SCHNEUWLY [0022], [0033]-[0034]).
Regarding claims 3 and 14, SCHNEUWLY as modified teaches the method and the media wherein the plurality of distinct sequences of distinct nodes of the parse tree contains at least three distinct sequences (SCHNEUWLY [0034], [0051]-[0052], [0072]).
Regarding claims 4 and 15, SCHNEUWLY as modified teaches the method and the media wherein:
the source logic text comprises one or more database statements (SCHNEUWLY [0058], [0079]); the method further comprises based on said detecting that the new source logic text is anomalous, not execution planning for the one or more database statements and not executing the one or more database statements (SCHNEUWLY [0063]).
Regarding claims 7 and 18, SCHNEUWLY as modified teaches the method and the media wherein:
the parse tree of the source logic text contains a plurality of edges that interconnect said distinct nodes of the parse tree (SCHNEUWLY [0119]); for each edge in the plurality of edges: the edge connects two distinct nodes of the parse tree, and the two distinct nodes are adjacent in at least one sequence in said plurality of distinct sequences (SCHNEUWLY [0032], [0137]).
Regarding claim 8, SCHNEUWLY as modified teaches the method of Claim 7 wherein said at least one sequence in said plurality of distinct sequences is said plurality of distinct sequences (SCHNEUWLY [0034], [0051]-[0052], [0072]).
Regarding claims 9 and 19, SCHNEUWLY as modified teaches the method and the media further comprising based on said detecting that the new source logic text is anomalous, not parsing the new source logic text (SCHNEUWLY [0063], Zhang p.492 C2 last para, p.497 IV).
Schneuwly teaches parsing the code into AST and analyzing pattens "without directly analyzing the parse tree" and Zhang further discloses not parsing both anomalous and non-anomalous logic. Thus, it would have been obvious to those skill in the art not further parse anomalous logic already determined to be anomalous.
Regarding claim 10, SCHNEUWLY as modified teaches the method of Claim 1 wherein:
said distinct nodes of the parse tree consists of a plurality of terminal nodes and a plurality of non-terminal nodes ([0024]-[0026]); the plurality of terminal nodes are not entirely contiguous in each sequence of the plurality of distinct sequences (SCHNEUWLY [0013], [0032]-[0034], [0039]).
Regarding claims 11 and 20, SCHNEUWLY as modified teaches the method and the media wherein the logic encoder does not accept the entire multi- sequence vector as a single input (SCHNEUWLY [0044]).
Claims 5, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over SCHNEUWLY as modified and in further view of Nikolic et al. (US 20230376743) or Zhao et al. (US 20180075349).
Regarding claims 5 and 16, SCHNEUWLY does not explicitly teach, however Nikolic and Zhao discloses said training the logic encoder comprises self-supervised training; the method further comprises after said training the logic encoder: including the logic encoder into a new neural network, and training the new neural network to detect whether source logic text is anomalous (Nikolic [0016], [0020], [0063] and [0086]-[0087], Zhao [0031], [0052], [0031], [0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of SCHNEUWLY as modified to include training the logic encoder, the logic encoder into a new neural network as disclosed by Zhao and Nikolic. Doing so may help detect more structurally complex interdependencies between tokens in the instruction sequence (Zhao [0081]).
Claims 6 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over SCHNEUWLY as modified and in further view of Nikolic et al. (US 20230376743) or Liu et al. (US 20230042327).
Regarding claims 6 and 17, SCHNEUWLY does not explicitly teach, however Nikolic discloses self-supervised training the neural network to predict skipped tokens, wherein the self-supervised training the neural network comprises training the logic encoder (Nikolic [0016], [0021], [0031], [0114]-[0115]); deploying, after the training the logic encoder, the logic encoder without the neural network (Nikolic [0120], [0125]). Liu teaches the same in [0023], [0046]-[0047].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of SCHNEUWLY as modified to predict skipped tokens as disclosed by Nikolic or Liu. Doing so may help detect malware in the source code.
Claims 9 and 19 is/are additionally or alternatively rejected under 35 U.S.C. 103 as being unpatentable over SCHNEUWLY as modified and in further view of Gurbani et al. (US 20130054816).
Regarding claims 9 and 19, SCHNEUWLY as modified teaches the method and the media as disclosed above, Gurbani additionally or alternatively discloses further comprising based on said detecting that the new source logic text is anomalous, not parsing the new source logic text ([0017], Title).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of SCHNEUWLY as modified to not parsing anomalous new source logic as disclosed by Gurbani. Doing so may prevent spending resources parsing the message in its entirety before reaching the conclusion that the message is invalid (Gurbani [0004]).
Response to Arguments
Applicant's arguments filed 05/20/2026 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 101, the arguments are not persuasive. The claims are directed to an abstract idea of transforming source-logic into a mathematical representation and classifying the representations to identify anomalous logic. The claimed method of generating multiple traversal sequences, vectors, training encoder and perfuming inference and detection of anomalies without parsing the source code does not itself establish an improvement to a computer technology. While the claims may represent an improvement to the process of anomaly detection, they in no way either claimed or disclosed represent a practical application. I.e. inferring anomaly without parsing source code merely specifies the manner in which the information is processed and does not recite an improvement to a parser, processor or any other computer component and is thus, an improvement of the abstract idea only.
The applicant argues, by pointing out that the specification (0064-0065) showing that the “step 208 accelerates computer 102 itself," “This claimed new way causes acceleration by parse avoidance”, “that "improvement" causes acceleration in a computer and acceleration of a technological process. Claim 1 in its present form is a faster technological process.” (see Remarks Pages 12-13).
The arguments are not persuasive. Under the Step 2A/Prong 2, the present claims do not demonstrate a technological improvement. Once again, not performing the parsing of the source code indeed saves a processing time, however it does not provide an improvement to the logic encoder, computer or any machine leaning techniques and training. Generating a vector, inferring encoding logic – is purely a mathematical reasoning and detecting an anomalies is additional logical reasoning and deduction. While training the encoder is considered to be an additional element, the claim does not provide any novel improvement to such training or the data analysis. The training is a well-known, routine and conventional functionality applicable to any machine learning process and does is not significantly more than the identified abstract idea.
The claims do not integrate the abstract idea into a practical application. The source code merely supplies the information being analyzed, and the determination not to execute anomalous statements merely applies the classification result. The claims do not recite a specific improvement to database execution, parsing, processor operation or memory utilization. Nor do the additional limitations supply an inventive concept. The claimed vector generations, tree traversal, encoder training and anomaly classification are conventional categories of mathematical and machine learning operations performed on generic computer components. The claimed combination therefore amounts to applying an abstract mathematical / logical data analysis techniques to source logic without reciting a sufficiently specific technological improvement.
The rejection is maintained.
◊ With respect to the rejection under 35 USC 103 and the SCHNEUWLY reference, the arguments are not persuasive.
The applicant argues -
“Claim 1 in its present form recites "text" (published 0021) and "new text" (published 0073). The Office action instead says "SCHNEUWLY new source logic ([0030], [0036] a summary of parse tree such as bag of rules [0046]-[0048], [0049] summarizes only paths". Here, cited "summary summarizes only" is not the "new source logic text" recited by Claim 1 in its present form. Here, cited "bag" and cited "paths" are not the claimed "new source logic text". Schneuwly teaches "the bag of rules only stores tallies" (Overview) and "In a bag of rules data structure, the extracted sequences of production rules are aggregated by distinct sequence and duplicates are counted" (Abstract). Here, "production rules" are a language grammar, not source logic. Here, "bag of tallies" is a histogram, not text. Schneuwly is not analogous to Claim 1 in its present form.”
The arguments are not persuasive. Schneuwly is directed to analyzing source logic that takes database statement (SQL or similar). The database statements / scripts are clearly text. The reference repeatedly refers to this input as “source logic”, “source statement” and is fully analogous to the claimed “source logic text” and “a new source logic text.” Calling the input “source logic” rather than the longer phrase “source logic text” does not create discontinuity, the statements themselves are textual.
Schneuwly teaches “a summary of parse tree such as bag of rule” [0036], “summarize the structure of same parse tree based on paths” [0049]; “bag of rules stores the single path and a Boolean” [0044], etc. All the paragraphs with respect to the bag of rules describe an intermediate feature representation that is generated from the parse tree of the source logic text. I.e. the spruce logic is parsed into a parse tree. Distinct sequences /paths rules are extracted by traversal of that tree. Those sequences are aggregated into a bag of rules, which function as a compact vector usable by a machine learning. The bag of rules is not offered as a replacement for the original source logic text, it is the encoding derived from that text.
Schneuwly clearly teaches –
“Structured content such as text are represented by parse tree”; “parse tree represents a logic statement such as a database query such as structured query language (SQL) or a statement in a programing language such as a code source language such as a scripting language” [0021].
Once again, Schneuwly does not teach that the bag of rules is the source logic. Schneuwly teaches that the parse tree represents the source logic and the bag of rules is a compact representation derived from that parse tree. See specifically – “production rule C matched a portion of text to create leaf node” [0025].
Thus, the bag of rules sequence is not some unrelated grammar. It is a structural representation of nodes generated from portions of the source logic text, see “the bag of rules is encoded into a feature vector that the ML model accepts as input” [0017] and C1 “each sequence of production rules of the plurality of sequences of production rules consists of respective production rules of a sequence of tree nodes in a respective directed tree path of the parse tree having a path length that satisfies same said length constraint.”
Thus, Schneuwly is not merely analogous, Schneuwly addresses substantially the same technological problem and substantially the same type of input (i.e. structurally representing source logic and using ML to identify anomalies).
The applicant improperly combines the claimed source logic text with the feature representation generated from that source logic. The limitations does not require the multi-sequence vector, parse tree or encoded representation to be text. Schneuwly explicitly show - “a text such as … logic statement” [0058] to generate parse tree and further clearly states that “parse tree represents a logic statement such as a database query such as structured query language (SQL) or a statement” [0021]. Schneuwly explicitly show that rules match portions of the text to create the corresponding tree nodes. Although Schneuwly subsequently compresses the parse tree into a bag of rules feature vector does not negate disclosure of the source logic text.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure –
US 20220107828 discloses in [0023] construct embeddings from code text include techniques that construct code embeddings directly from the code text without using any intermediate structural representation.
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/POLINA G PEACH/ Primary Examiner, Art Unit 2165 August 13, 2026
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