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 .
1. Claims 1 – 20 are currently pending in this application.
Claims 1, 9, and 17 are amended as filed on 05/29/2026.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (Pre-Grant Publication No. US 2024/0430279 A1), hereinafter Huang, in view of Camp et al. (Patent No. US 12,705,353 B2), hereinafter Camp, in view of Ryver (Pre-Grant Publication No. US 2021/0352087 A1), in view of Murdock, IV et al. (Pre-Grant Publication No. US 2024/0098055 A1), hereinafter Murdock, and in further view of Lovegrove (Pre-Grant Publication No. US 2023/0215285 A1).
2. With respect to claims 1, 9, and 17, Huang taught a method comprising: deploying a first machine learning model based on first data associated with a first cybersecurity incident of a plurality of cybersecurity incidents (0005, where the cybersecurity incident monitoring is described in 0002 & 0021); training the first ML model based on actor attribution associated with the first cybersecurity incident to deploy a second ML model (0031); receiving second data associated with a second cybersecurity incident of the plurality of cybersecurity incidents (0031, the multiple modules teaches the second module); and training, by a processing device for the second ML model using the second data, an attribution of the second cybersecurity incident to an actor (0031).
However, Huang did not explicitly state that the models were being generated and that training the models included producing the models; pre-training a large language model through self-supervised learning on a corpus comprising first data associated with a first cybersecurity incident of a plurality of cybersecurity incidents to generate a first machine learning model; fine-tuning the first ML model for actor attribution labels; and checking for consistency in reasoning based on knowledge from the corpus upon which the first ML model was pretrained. On the other hand, Camp did teach that the models were being generated and that training the models included producing the models (4:51-58); pre-training a large language model through self-supervised learning on a corpus comprising first data associated with a first cybersecurity incident of a plurality of cybersecurity incidents to generate a first machine learning model (11:29-45 & 17:56-58); fine-tuning the first ML model for actor attribution labels (17:59-67); and checking for consistency in reasoning based on knowledge from the corpus upon which the first ML model was pretrained (17:18-30, where checking against the corpus teaches consistency of reasoning with respect to the applicant’s explanation of the term, in the applicant’s specification, paragraph 0023). Both of the systems of Huang and Camp are directed towards managing machine learning models and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings of Huang, to utilize generating and training machine learning models, as taught by Camp, as it could be argued that Huang already performed this step in the training and deploying features of the invention. However, it is not explicitly stated.
However, the combination of Huang and Camp did not explicitly state wherein the second ML model is trained using reasoning data comprising historical analytical processes that produced historical associations between historical cybersecurity incidents and historical threat actors. On the other hand, Ryver did teach wherein the second ML model is trained using reasoning data comprising historical analytical processes that produced historical associations between historical cybersecurity incidents and historical threat actors (0054-0058, where the historical malicious actor data trains the model). Both of the systems of Huang and Ryver are directed towards managing machine learning models and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings of Huang, to utilize generating a machine learning models based on historical malicious actor data, as taught by Ryver, in order to maintain a more accurate model for detecting threats.
However, Huang did not explicitly state investigation data, wherein the investigation data comprises analyst notes corresponding to historical attribution investigations and that the output comprised a validation of reasoning information identified in the second data; and wherein the validation of reasoning information comprises comparing an analyst report included in the second data to the historical analytical processes and the investigation data. On the other hand, Murdock did teach investigation data, wherein the investigation data comprises analyst notes corresponding to historical attribution investigations (0031) and that the output comprised a validation of reasoning information identified in the second data (0031, the factor weights used to train/update further models. Accordingly, the weights represent reasoning data under broadest reasonable interpretation); and wherein the validation of reasoning information comprises comparing an analyst report included in the second data to the historical analytical processes and the investigation data (0030-0031, where it can be seen that the user data is utilized to train the model base don reported user solutions such as system faults, which could potentially be a cybersecurity incident. Lastly, Murdock generically teaches dealing with incidents. An incident, specifically being a cybersecurity incident, was previously shown by Huang: 0005). Both of the systems of Huang and Murdock are directed towards security analytics and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings of Huang, to utilize training a model with user notations, as taught by Murdock, in order to look at the totality of relevant data for training a model.
However, the combination of Huang and Camp did not explicitly state that the labels were ground truth labels. On the other hand, Lovegrove did teach that the labels were ground truth labels (claim 1). Both of the systems of Huang and Lovegrove are directed towards managing machine learning models and therefore, it would have been obvious to a person having ordinary skill in the art, to modify the teachings of the combination of Huang and Camp, to utilize ground truth labels, as doing so would provide further accuracy in malicious actor detection.
3. As for claims 2 and 10, they are rejected on the same basis as claims 1 and 9 (respectively). In addition, Huang taught identifying the first cybersecurity incident associated with the first data from at least one of: a data archive, scaped content, or an external report (0032, where the external trigger that’s reported, at least, teaches the external report limitation).
4. As for claims 3, 11, and 18, they are rejected on the same basis as claims 1, 9, and 17 (respectively). In addition, Ryver taught wherein the reasoning data comprises step-by-step demonstrations of the historical analytical process that produced the associations between the historical cybersecurity incidents and the historical threat actors (0054-0058, where the step process is given to build a historical record of actors and associated incidents).
5. As for claims 4 and 12, they are rejected on the same basis as claims 1 and 9 (respectively). In addition, Huang taught wherein the actor attribution corresponds to a ground truth label for a particular incident of the plurality of cybersecurity incidents, and wherein the actor for the particular incident is identifiable with a threshold level of confidence using the ground truth label (0015 & 0017, where the associated historical data is a ground truth label based on the applicant’s definition, located in paragraph 0023, as well as broadest reasonable interpretation).
6. As for claims 5 and 13, they are rejected on the same basis as claims 1 and 9 (respectively). In addition, Huang taught wherein the second data associated with the second cybersecurity incident is a prompt related to the plurality of cybersecurity incidents, the second cybersecurity incident being validated based on the prompt (0028, where the updated events prompt the system under broadest reasonable interpretation).
7. As for claims 6, 14, and 19, they are rejected on the same basis as claims 6, 14, and 19 (respectively). In addition, Huang taught wherein the producing the attribution of the second cybersecurity incident, comprises: outputting, by the second ML model, at least one of a prediction, a textual analysis, or an embedding associated with the second data (0017).
8. As for claims 7, 15, and 20, they are rejected on the same basis as claims 1, 9, and 17 (respectively). In addition, Huang taught wherein the textual analysis comprises at least one of: a hypothesis related to the second cybersecurity incident, the validation of the reasoning information associated with a prompt related to the second cybersecurity incident, or a suggestion for an additional prompt related to discovery procedures associated with the second cybersecurity incident (0017, where the prediction, at least, teaches the hypothesis limitation).
9. As for claims 8 and 16, they are rejected on the same basis as claims 1 and 9 (respectively). In addition, Huang taught storing a record of prior cybersecurity incidents in an indexed database based on an embedding generated from the second data (0021, the historical activity is stored in databases in accordance with 0020 & 0058).
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/JOSEPH L GREENE/Primary Examiner, Art Unit 2443
