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 .
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.
1. Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1 recites “a processor configured to:… automatically learn a global common behavior.” The claim is unclear as to how a processor “learns.” A processor can be configured or programmed, but it would not ordinarily be considered to “learn.” The claim is therefore unclear as to the specific meaning intended with the usage of the term “learn.”
Claims 2-20 are rejected for the same reasons as Claim 1.
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.
2. Claims 1-5 and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Evans et al. (US 20170054810 A1) in view of Cohen et al. (US 20240070141 A1).
Claim 1 Evans teaches a system, comprising:
a processor (Evans, FIG. 2A, Processor 201) configured to:
receive information associated with network communications of a plurality of Internet of Things (IoT) devices; (Evans, FIG. 5, step 504, ¶0061, referencing, i.e. receiving the manifest data, wherein the data is associated with IoT elements or devices)
perform IoT device identification from the information associated with the network communications of the plurality of loT devices; (Evans, ¶0061, wherein the referencing comprises a device identification for the IoT elements associated with the network)
automatically learn a global common behavior for the plurality of IoT devices to generate a plurality of recommended rules; (Evans, FIG. 5, step 504, ¶0061, creating a rule for one or more IoT elements or devices, the rule comprising a global common behavior)
validate results of the plurality of recommended rules; (Evans, FIG. 5, step 510, ¶0065, storing the rule data, wherein storing the rule data is a form of validating the results) and
apply a policy to at least one of the plurality of IoT devices based on one or more of the plurality of recommended rules; (Evans, FIG. 5, step 510, ¶0065, storing the rule data, wherein storing the rule data applies the rule to the one or more IoT devices) and
a memory (Evans, FIG. 2A, Memory 204) coupled to the processor and configured to provide the processor with instructions.
However, Evans does not explicitly teach using a Large Language Model (LLM) classifier.
From a related technology, Cohen teach using a Large Language Model (LLM) classifier. (¶0017, using a large language model for computational models; ¶0025, wherein the system automatically detect and computes data)
It would be obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Evan to incorporate the teachings of Cohen to better utilized available computational techniques in order to better assess data to determine IoT rules.
Claim 2 Evans in view of Cohen teaches Claim 1, and further teaches wherein the plurality of IoT devices is associated with a plurality of tenants of a security service. (Evans, ¶0041, wherein the IoT elements are associated with security service)
Claim 3 Evans in view of Cohen teaches Claim 1, and further teaches wherein the plurality of recommended rules includes a security policy rule. (Evans, ¶0041, wherein the rules are related to security policy, and therefore security rules)
Claim 4 Evans in view of Cohen teaches Claim 1, and further teaches wherein the plurality of recommended rules includes a security policy rule that is based on applications (apps) (Evans, ¶0026, wherein there are a plurality of applications to established the rules based on) and/or destinations learned from the global common behavior for the plurality of loT devices.
Claim 5 Evans in view of Cohen teaches Claim 1, and further teaches wherein the LLM classifier is applied to identify common behavior patterns of IoT devices from the information associated with the network communications of the plurality of IoT devices for generating the plurality of recommended rules. (Evans, FIG. 5, step 504, ¶0061, creating a rule for one or more IoT elements or devices, the rule comprising a global common behavior; Cohen, wherein a LLM classifier is used as a computational model)
Claim 9 Evans in view of Cohen teaches Claim 1, and further teaches wherein the processor is further configured to train the LLM classifier. (Cohen, ¶0017, training the LLM classifier)
Claim 10 Evans in view of Cohen teaches Claim 1, and further teaches wherein the processor is further configured to train the LLM classifier using unsupervised machine learning (ML) training without any labels. (Cohen, ¶0017, training the LLM classifier; Examiner notes that unsupervised ML training without any labels comprises a negative limitation, and is taught by reference as Cohen does not incorporate labels nor supervision)
Claim 11 Evans in view of Cohen teaches Claim 1, and further teaches wherein the processor is further configured to periodically update a training of the LLM classifier. (Cohen, ¶0017, training the LLM classifier; wherein training comprises updating a training)
Claim 12 Evans in view of Cohen teaches Claim 1, and further teaches wherein the processor is further configured to publish the plurality of recommended rules. (Evans, FIG. 5, step 510, ¶0065, wherein the rules are published as a notification)
Claim 13 Evans in view of Cohen teaches Claim 1, and further teaches wherein the processor is further configured to publish the plurality of recommended rules to a dashboard, wherein the dashboard is accessible to a plurality of tenants of a security service. (Evans, FIG. 5, step 510, ¶0065, wherein the rules are published as a notification and into the rule data store, wherein the rule data store comprises a dashboard accessible to users)
Claims 14-18 are taught by Evans in view of Cohen as described for Claims 1-5 respectively.
Claims 19-20 are taught by Evans in view of Cohen as described for Claims 1-2 respectively.
3. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Evans et al. (US 20170054810 A1) in view of Cohen et al. (US 20240070141 A1) and in further view of Honkala (US 20220027709 A1)
Claim 6 Evans in view of Cohen teaches Claim 1, and but does not explicitly teach wherein the processor is further configured to remove noisy device samples from the network communications of the plurality of IoT devices.
From a related technology, Honkala teaches wherein the processor is further configured to remove noisy device samples from the network communications of the plurality of IoT devices. (FIG. 3A, ¶0035, wherein removing noisy data samples)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Evans in view of Cohen to incorporate the techniques utilized by Honkala to remove noisy data to improve processing.
4. Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Evans et al. (US 20170054810 A1) in view of Cohen et al. (US 20240070141 A1) and in further view of Sehanobish et al. (US 20240029864 A1).
Claim 7 Evans in view of Cohen teaches Claim 1, but does not explicitly teach wherein the processor is further configured to perform text embeddings based on a plurality of features extracted from the information associated with the network communications of the plurality of IoT devices.
From a related technology, Sehanobish teaches wherein the processor is further configured to perform text embeddings based on a plurality of features extracted from the information associated with the network communications of the plurality of IoT devices. (FIG. 4, ¶0094, performing text embedding based on the features)
It would be obvious to modify the teachings of Evans in view of Cohen to further analyze the data by performing text embedding as described in Sehanobish in order to more effectively utilize network resources.
Claim 8 Evans in view of Cohen teaches Claim 1, but does not explicitly teach wherein the processor is further configured to: perform text embeddings based on a plurality of features extracted from the information associated with the network communications of the plurality of IoT devices; and perform clustering based on the text embeddings.
From a related technology, Sehanobish teaches perform text embeddings based on a plurality of features; (FIG. 4, ¶0094, performing text embedding based on the features) and perform clustering based on the text embeddings. (FIG. 4, ¶0094, performing a clustering based on the text embedding)
` It would be obvious to modify the teachings of Evans in view of Cohen to further analyze the data by performing text embedding as described in Sehanobish in order to more effectively utilize network resources.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER PALACA CADORNA whose telephone number is (571)270-0584. The examiner can normally be reached M-F 10:00-7:00.
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/CHRISTOPHER P CADORNA/Examiner, Art Unit 2444
/JOHN A FOLLANSBEE/Supervisory Patent Examiner, Art Unit 2444
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