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 response to the communication filed on March 13, 2026. Claims 1-10, 12-21 are pending.
Response to Arguments
Applicant's arguments filed on March 13, 2026 have been fully considered but they are not persuasive. Applicant argument regarding art rejection has been addressed in the new rejection.
Regarding 101 rejection applicant argues the amended claim as a whole integrates a practical application at least because the additional elements of the claim reflects an improvement in the functioning of a computer, or an improvement of other technology or technical field.
In response examiner respectfully disagree. 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. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are amounts to no more than mere instructions to apply the exception using a generic computer component. The courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
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 1-10, 12-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding the claim 1, it recites receive software logs identifying raw data; pre-process the raw data to obtain a set of pre-processed log data; detect one or more outlier data sets from the set of pre-processed log data based on one or more of a length or size of the one or more outlier data sets; remove the one or more outlier data sets from the set of pre-processed log data; concatenate the set of pre-processed log data after the one or more outlier data sets are removed to form a software log corpus, wherein the software log corpus includes log data from a test device, and wherein the log data includes alphanumeric formatted measurement data and computer code; identify blocks in the software log corpus, a block being an alphanumeric formatted section of the software log corpus representing a configured amount of information content of the software log corpus; encode the blocks to generate encoded blocks using a set of vocabulary tokens that are based on alphanumeric characters included in the software log corpus, wherein the encoded blocks are associated with a numeric format; generate a set of input sequences and a set of target sequences based on the encoded blocks and a statistical block length associated with the blocks, wherein the set of target sequences are shifted versions of the set of input sequences; generate a training dataset for embedding computation based on combining the set of input sequences and the set of target sequences into a tuple, partitioning the tuple into batches, and shuffling the batches to obtain the training dataset; train a recurrent neural network (RNN) to learn a set of dense embedding tensors using a set of shuffled data tensors associated with the training dataset and the encoded blocks, the set of dense embedding tensors being based on the training dataset; and output information associated with the set of dense embedding tensors.
The claim recited the limitation of “detect one or more outlier data sets from the set of pre-processed log data based on one or more of a length or size of the one or more outlier data sets” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User can mentally detect outlier data from the received data by reading. Therefore, the detecting limitation is a mental process. Similarly, the limitation “identify blocks in the software log corpus, a block being an alphanumeric formatted section of the software log corpus representing a configured amount of information content of the software log corpus” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User can mentally identify a block from the received software log by reading it, wherein the reading can be performed in the user (i.e. human) mind using his/her eye to identify the block. Hence, the identifying limitation is mental process. Similarly, the limitation “encode the blocks to generate encoded blocks using a set of vocabulary tokens that are based on alphanumeric characters included in the software log corpus, wherein the encoded blocks are associated with a numeric format” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User can mentally the encode the data block as describe in the limitation memorize the block in numeric number format which as mental process. The generating limitations as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User can mentally generate input data, target data, and training data as recited in the limitation with or without the help of physical aid (e.g., pen and paper). Hence, these limitations are a mental process. See MPEP 2106.04(a)(2) III, B, If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand.").
The claim recites additional elements: receive software logs identifying raw data; pre-process the raw data to obtain a set of pre-processed log data; remove the one or more outlier data sets from the set of pre-processed log data; concatenate the set of pre-processed log data after the one or more outlier data sets are removed to form a software log corpus, wherein the software log corpus includes log data from a test device, and wherein the log data includes alphanumeric formatted measurement data and computer code, train a recurrent neural network (RNN) to learn a set of dense embedding tensors using a set of shuffled data tensors associated with the training dataset and the encoded blocks, the set of dense embedding tensors being based on the training dataset, and output information associated with the set of dense embedding tensors. The receiving step as recited amounts to mere data gathering for use in the detection step, which is a form of insignificant extra-solution activity, (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Similarly, preprocess the raw data, remove one or more outlier, and concatenate the set of pro-processed log data also insignificant extra-solution action as these are nothing but data manipulation. Further, the additional limitation training step as recited can be insignificant extra-solution activity. The recited Neural network to learn a set of dense embedding tensors is well-known technique can be with generic computer (Flook, 437 U.S. at 593-95, 198 USPQ at 197 (a formula would not be patentable by only indicating that is could be usefully applied to existing surveying techniques)). Similarly, the outputting information limitation as recited is nothing more than data gathering and outputting which is a form of insignificant extra-solution activity. Accordingly, even in combination, these 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. The claim is directed to the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receive, train, and output steps amounts to no more than mere instructions to apply the exception using a generic computer component. The courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of generating a set of dense embedding tensors. The claim recites the limitations of train the RNN, are configured to: select an embedding dimension, wherein the embedding dimension is associated with a length of a set of features captured for the set of multi-dimensional dense embedding tensors, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 3 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 3 recites the same abstract idea of generating a set of dense embedding tensors. The claim recites the limitations of wherein the embedding dimension is less than a threshold value, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of generating a set of dense embedding tensors. The claim recites the limitations of wherein the one or more processors, to generate the set of dense embedding tensors, are configured to: generate an embedding layer as a first layer within a deep neural network (DNN); generate an RNN layer as a second layer within the DNN; and generate a dense neural network layer, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process
Claim 5 is dependent on claim 4 and includes all the limitations of claim 4. Therefore, claim 5 recites the same abstract idea of generating a set of dense embedding tensors. The claim recites the limitations of wherein the RNN layer includes at least one of a long short-term memory (LSTM) based layer or a gated recurrent unit (GRU) based layer, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process
Claim 6 is dependent on claim 4 and includes all the limitations of claim 4. Therefore, claim 6 recites the same abstract idea of generating a set of dense embedding tensors. The claim recites the limitations of wherein an input to the RNN layer includes at least one of a quantity of neurons or a recurrent initializer, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process
Claim 7 is dependent on claim 4 and includes all the limitations of claim 4. Therefore, claim 7 recites the same abstract idea of generating a set of dense embedding tensors. The claim recites the limitations of wherein the dense neural network layer includes a vocabulary size as an argument to the dense neural network layer, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process
Claim 8 is dependent on claim 4 and includes all the limitations of claim 4. Therefore, claim 8 recites the same abstract idea of generating a set of dense embedding tensors. The claim recites the limitations of wherein the one or more processors, to train the RNN, are configured to: train the RNN to identify an association between a first block at a first position and a second block at a second position, the first position and the second position being within a threshold window size, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process
Claim 9 is dependent on claim 4 and includes all the limitations of claim 4. Therefore, claim 9 recites the same abstract idea of generating a set of dense embedding tensors. The claim recites the limitations of wherein the one or more processors, to generate the set of dense embedding tensors, are configured to: converge a set of numerical optimization equations for at least one of: a one-sided backward set of sequences, a one-sided forward set of sequences, or a two-sided set of sequences, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
As to claims 10-20, they have similar limitations as of claims 1-9 above. Hence, they are rejected under the same rational as of claims 1-9 above.
Claim 21 is dependent on claim 10 and includes all the limitations of claim 10. Therefore, claim 21 recites the same abstract idea of generating a set of dense embedding tensors. The claim recites the limitations of performing an artificial intelligence operation using the set of embedding tensors to obtain information associated with new log data, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
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 (i.e., changing from AIA to pre-AIA ) 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, 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 1-10, 12-21 are rejected under 35 U.S.C. 103 as being unpatentable over Jin Wang et al. (LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things, Published: April 26, 2020) in the view of Smith et al. (Pub. No. : US 20200117446 A1).
As to claim 1 Jin Wang teaches a device, comprising: one or more memories and one or more processors, coupled to the one or more memories, configured to:
receive software logs identifying raw data (section 3 fig. 1: raw log is data is received by the framework);
pre-process the raw data to obtain a set of pre-processed log data (section 3 fig. 1: log parsing and feature extraction);
detect one or more outlier data sets from the set of pre-processed log data based on one or more of a length or size of the one or more outlier data sets (section 3 table 2);
remove the one or more outlier data sets from the set of pre-processed log data (section 3.1: The log parsing is used to remove all specific parameters from log messages and extract all the log events);
concatenate the set of pre-processed log data after the one or more outlier data sets are removed to form a software log corpus, wherein the software log corpus includes log data from a test device, and wherein the log data includes alphanumeric formatted measurement data and computer code (Fig. 2, abstract, section 1 lines 4-9, 15, 21-22, section 5.2, section 3.1 line 6-8, and table 3: receive “raw log” which is associated as claimed);
identify blocks in the software log corpus, a block being an alphanumeric formatted section of the software log corpus representing a configured amount of information content of the software log corpus (Section 3.1: log data are divided into various chunks. A chunk is a log sequence);
encode the blocks to generate encoded blocks using a set of vocabulary tokens that are based on alphanumeric characters included in the software log corpus, wherein the encoded blocks are associated with a numeric format (section 3.2: the log event should be numerically encoded for further anomaly detection. Text of log events can be encoded by NLP models);
generate a set of input sequences and a set of target sequences based on the encoded blocks and a statistical block length associated with the blocks, wherein the set of target sequences are shifted versions of the set of input sequences (Fig. 2 and Fig. 3, section 3.1, sections 4 and 4.2.1, section 5.2: LogEvent2vec takes the log event as input of the word2vec model, and then transforms the log event vector to the log sequence vector);
generate a training dataset for embedding computation based on combining the set of input sequences and the set of target sequences into a tuple, partitioning the tuple into batches, and shuffling the batches to obtain the training dataset (section 4.2.1, second 4.3: After training the model, we can get the embedding of a log event by multiplying its one-hot vector and the weight matrix Wl'v1 E RiElxdimT).
Jin Wang does not explicitly disclose but Smith teaches train a recurrent neural network (RNN) to learn a set of dense embedding tensors using a set of shuffled data tensors associated with the training dataset and the encoded blocks, the set of dense embedding tensors being based on the training dataset (paragraph [0090]-[0091], [0047]: textual sources are pre-processed at step 612 to produce a training dataset and at step 613, a code entity is provided to the code entity neural network encoder and training data associated with the code entity is provided to a natural language neural network encoder, where the neural network may be densely connected and have a plurality of recurrent neural network layers); and
output information associated with the set of dense embedding tensors (paragraph [0091]: The output of each encoder is a tensor embedding in a joint tensor space).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Jin Wang by adding above limitation as taught by Smith to reduce the number of routine tasks (Smith, abstract).
As to claim 2 Jin Wang together with Smith teaches a device according to claim 1. Smith teaches wherein the one or more processors, to train the RNN, are configured to: select an embedding dimension, wherein the embedding dimension is associated with a length of a set of features captured for the set of multi-dimensional dense embedding tensors (paragraphs [0038], [0087], [0091]).
As to claim 3 Jin Wang together with Smith teaches a device according to claim 2. Smith teaches wherein the embedding dimension is less than a threshold value (paragraph [0127]).
As to claim 4 Jin Wang together with Smith teaches a device according to claim 1. Smith teaches wherein the one or more processors, to generate the set of dense embedding tensors, are configured to: generate an embedding layer as a first layer within a deep neural network (DNN), generate an RNN layer as a second layer within the DNN, and generate a dense neural network layer (paragraphs [0045], [0047], [0091]).
As to claim 5 Jin Wang together with Smith teaches a device according to claim 4. Smith teaches wherein the RNN layer includes at least one of a long short-term memory (LSTM) based layer or a gated recurrent unit (GRU) based layer (paragraphs [0046], [0048]).
As to claim 6 Jin Wang together with Smith teaches a device according to claim 4. Smith teaches wherein an input to the RNN layer includes at least one of a quantity of neurons or a recurrent initializer (paragraph [0045]).
As to claim 7 Jin Wang together with Smith teaches a device according to claim 4. Smith teaches wherein the dense neural network layer includes a vocabulary size as an argument to the dense neural network layer (paragraph [0089]).
As to claim 8 Jin Wang together with Smith teaches a device according to claim 4. Smith teaches wherein the one or more processors, to train the RNN, are configured to: train the RNN to identify an association between a first block at a first position and a second block at a second position, the first position and the second position being within a threshold window size (paragraphs [0044], [0091]-[0092]).
As to claim 9 Jin Wang together with Smith teaches a device according to claim 4. Jin Wang teaches wherein the one or more processors, to generate the set of dense embedding tensors, are configured to: converge a set of numerical optimization equations for at least one of: a one-sided backward set of sequences, a one-sided forward set of sequences, or a two-sided set of sequences (section 3.1).
As to claims 10-20, they have similar limitations as of claims 1-9 above. Hence, they are rejected under the same rational as of claims 1-9 above.
As to claim 21 Jin Wang together with Smith teaches a method according to claim 10. Jin Wang teaches performing an artificial intelligence operation using the set of embedding tensors to obtain information associated with new log data (section 2.3.).
Examiner's Note: Examiner has cited particular columns and line numbers or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in its entirety as potentially teaching of all or part of the claimed invention, as well as the context.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
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/MD I UDDIN/Primary Examiner, Art Unit 2169
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