Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office Action has been withdrawn pursuant to 37 CFR 1.114.
Detailed Action
3. This Non-Final Office Action is responsive to Applicants’ Reply dated 4/24/65, which reasserts the claims submitted on 3/30/26. Hence, claims 1-20 remain pending, of which claims 1, 9, and 17 are independent.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. Claims 1, 3-5, 9, 12-14, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Non-Patent Literature “Radio Frequency Interference Best Practices Guidebook” (“CISA”, previously relied upon in prior Office Actions) in view of U.S. Patent No. 10904050 (“Marcoux” , previously relied upon in prior Office Actions) and further in view of Non-Patent Literature “Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles” (“Lee”, NEWLY CITED) and Non-Patent Literature “Multimodal Intelligence: Representation Learning, Information Fusion, and Applications” (“Zhang”, previously cited by way of the Advisory Action).
Regarding claim 1, the claim recites a signal classification system, comprising:
a sentence embedding model network trained to convert a body of sentences correlated to different signal modulation schemes into a language-derived latent space learned by the sentence embedding model network;
a convolutional generator network configured to project samples of a measured radio frequency (RF) signal into the language-derived latent space; and
a classifier network configured to classify the measured RF signal from the language-derived latent space responsive to a projection of the samples of the measured RF signal into the language-derived latent space, the classifier network further configured to output a text-based description of a classification of the measured RF signal.
As previously discussed in the prior Office Action, CISA is directed to a best practices guide for identifying and documenting and reporting signal interference, inclusive of manmade signals detected and understood to intentionally jam and disrupt public safety communications. See, e.g., Executive Summary as found on numbered page 1, and Table 2 on numbered page 3 for examples of external RF interferences, and pages 3-4 discussing intentional interference and RF jammers specifically. Hence, CISA establishes a recognized need for recognition, documentation, and reporting of detectable RF signals. See, e.g,. numbered page 6 under Education, and pages 8-9’s discussion of Interference Mitigation Lifecycle, and the further guidance provided in the sections to Recognize (e.g., steps to track reports of disruptions in communication, characterize the interference signal via analysis yielding various descriptive features, etc.), Respond (e.g., reporting of the incident to dispatch and the deployment of trained professionals to analyze and observe the issue, and the further need for the professionals to capture, catalog, and record details of the incident in view of future considerations), and Report (e.g., providing as many details as possible, as enumerated via bullet points on numbered page 10).
Accordingly, based on CISA, one of ordinary skill in the art would understand that in scenarios where RF signals or the like are being received and analyzed and flagged for anomalies, there is an obvious and clear need to analyze, document with detail, and report the incident in terms of characterizing the signal for different personnel in an escalation chain of involved personnel, including a first observer, a dispatcher, trained professionals, and finally FCC, state, and/or local authorities – each of which with perceptible differences in subject matter competency.
CISA merely teaches the importance of analysis and identification and reporting of RF signals once detected, and provides a high level discussion of steps. However, CISA does not teach a more concrete technological implementation in doing so.
More aptly regarding the claim’s concrete limitations, MARCOUX teaches a signal classification system (Abstract: a framework for classifying a modulation scheme of a wireless signal), comprising:
a convolutional generator network configured to project samples of a measured radio frequency (RF) signal ... (signal preprocessor per column 5 line 54 – column 6 line 4, which takes a signal input and extracts features from it (see, e.g., mention of a feature extractor per column 7 line 6), and where the extracted features are understood to serve as a basis for classification, e.g. as discussed in the next mapping); and
a classifier network configured to classify the measured RF signal ... responsive to a projection of the samples of the measured RF signal ... (column 6 lines 15-26 teaching that the framework’s modulation classifier may be a convolutional neural network (CNN), which the Examiner understands to take an input, e.g. a signal input, as characterized in terms of features to arrive at a classification result), the classifier network further configured to classify the measured signal ... (see, e.g., the aforementioned feature-based classification result).
Both CISA and Marcoux generally contemplate the importance and utility in analyzing and identifying detectable RF signals, out of security concerns/considerations. Hence, they are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art to apply Marcoux’s system to more concretely analyze and identify RF signals, pursuant to the concerns and priorities expressed in CISA.
While Marcoux can be understood to be sufficient for receiving a RF signal, analyzing its features, and classifying it, e.g., identifying it, Marcoux does not explicitly do so in a manner that is text or language-based. While Marcoux alone can be understood to classify and perhaps flag a RF signal once detected and analyzed, it does not explicitly provide for any further descriptive result that would be further useful in CISA’s framework, which as discussed above, involves reporting and alerting in a manner that communicates the RF signal to users, experts, personnel, etc. In that sense, CISA as implemented using Marcoux’s framework does not sufficiently teach the recited a sentence embedding model network trained to convert a body of sentences correlated to different signal modulation schemes into a latent space, and the related limitations of a latent space that (paraphrasing by the Examiner) essentially represents a correlation between different signal modulation schemes and sentences based on the projection of measured signal samples into the latent space.
In regard to the above, and based on Applicants’ prior arguments, the Examiner understands the recited latent space to correlate measured signal projections with sentence/language data that is descriptive of the signal. As noted, CISA and Marcoux are not found to teach this. At best, they teach classification of signals with the aim of identifying and reporting them. Rather, to teach what CISA and Marcoux lack, the Examiner further relies upon LEE and ZHANG:
LEE is similarly directed to network intrusion/threat detection (Abstract, Introduction), specifically through the use of a developed security information and event management system that actively collets and manages information relating to intrusions. A reporting aspect, responsive to intrusion detection, is emphasized, see e.g., Introduction’s 2nd full paragraph on page 165607, and again on page 165609 under sub-heading A. The latter section reinforces the motivation to further automate threat detection using a machine-learned approach, with the expressed aims to reduce human labor, effort, and time. Hence, the Examiner reasons Lee provides ample motivation to automate the teachings of CISA for example, and moreover, a concrete technological framework for doing so: specifically, one that correlates “raw security events” with a threat classification in implementing its learning data, per subheading A found on pages 165612-165613, with the understanding being that an inference case for the developed system can make that same correlation to detect live and emerging threats. The Examiner understands that a basis for this labeling and classification is a consideration of the text/language associated with the raw security events, e.g. as noted in subheading B and Table 1 per pages 165613-165614. Based on this, the Examiner understands Lee’s framework to correlate raw security events, as detected/logged, with text/language data to make a threat/intrusion classification.
The Examiner reasons that such a correlation of different modes of information is what one of ordinary skill in the art would understand to be multi-modal. See Zhang as discussed just below.
ZHANG is directed to the correlation of multi-modal information, generally. It focuses on correlation image/vision with language (page 478, 2nd column’s full paragraph), but also considers further processing that also correlates speech/audio (page 480, 1st column’s item 4 under subheading A). Relatedly, the Examiner understands Zhang more explicitly teaches a latent embedding space that combined diverse modalities, see e.g., the discussion under subheading B beginning on page 480’s 1st column. Accordingly, the Examiner reasons that it would be obvious to one of ordinary skill in the art to implement such a multi-modal embedding space, as Zhang contemplates, to express the correlation considered by Lee in fulfilling CISA’s objectives.
Regarding claim 3, CISA in view of Marcoux and further in view of Lee and Zhang teach the signal classification system of claim 1, as discussed above. The aforementioned references further teach the additional limitation wherein the classifier network is configured to classify the measured RF signal by indicating one of the different signal modulation schemes (Marcoux’s column 10 lines 4-8 discussing “In some embodiments, modulation classifier 208 can be a supervised (or trained) classifier configured to classify equalized signal 220C to a modulation scheme from a plurality of predetermined constant-modulus modulation schemes.”). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 4, CISA in view of Marcoux and further in view of Lee and Zhang teach the signal classification system of claim 3, as discussed above. The aforementioned references further teach the additional limitation wherein the classifier network is further configured to classify the measured RF signal by indicating one or more words taken from the body of sentences that are proximate to the projection of the samples of the measured RF signal in the language-derived latent space (Marcoux’s column 6 lines 15-26 generally teaching that signal modulation classification can make use of clustering approaches, such as k-NN or SVMs, which are generally understood to associate an instance with other embedded instances based on proximity, distance, and the like, and that such an approach is extensible to a latent space that is directed toward the expression/representation of the same data into text, language, written, etc. format as Lee and Zhang teach. The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 5, CISA in view of Marcoux and further in view of Lee and Zhang teach the signal classification system of claim 1, as discussed above. The aforementioned references further teach the additional limitation wherein the classifier network is configured to classify the measured RF signal by providing a caption including a plurality of words taken from the body of sentences (Marcoux’s column 6 lines 15-26 generally teaching that signal modulation classification can make use of clustering approaches, such as k-NN or SVMs, which are generally understood to associate an instance with other embedded instances based on proximity, distance, and the like, and that such an approach is extensible to a latent space that is directed toward the expression/representation of the same data into text, language, written, etc. format as Lee and Zhang teach. The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 9, CISA in view of Marcoux and further in view of Lee and Zhang teach A method of operating a signal classification system, the method comprising:
training a sentence embedding model network to convert descriptive sentences to a language-derived latent space learned by the sentence embedding model network, the descriptive sentences correlated to different signal modulation schemes; and
... a convolutional generator network to project samples of a measured radio frequency (RF) signal into the language-derived latent space.
Because claim 9 includes many of the same limitations included in claim 1 as already addressed, the Examiner’s mappings provided above per claim 1 are reiterated here to address each of the above limitations fully. However, the Examiner’s treatment of claim 1 does not specifically address the training of the recited convolutional generator network, as newly recited here. One of ordinary skill in the art would understand that a neural network, such as the CNN and classifier elements taught by Marcoux and also Lee and Zhang, would necessarily need to be trained for them to converge and reduce error and thereby promote accuracy. Marcoux’s column 1 lines 44-60 actually speaks to the need for training these same elements, and challenges thereof (see also Marcoux’s column 2 lines 3-18 and more concretely column 8 lines 4-35). Hence, the art combination presented above with respect to claim 1 is deemed sufficient to read on each and every limitation presented in this instant claim. The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 12, the claim includes the same or similar limitation as already discussed above in relation to claim 1, and is therefore rejected under the same rationale provided for claim 1.
Regarding claim 13, CISA in view of Marcoux and further in view of Lee and Zhang teach the method of claim 12, as discussed above. The aforementioned references teach the additional limitations wherein classifying the measured signal comprises identifying a predetermined number of closest neighboring points in the language-derived latent space, and converting the predetermined number of closest neighboring points to a text space to provide a plurality of words that are descriptive of the measured RF signal (Marcoux’s column 6 lines 15-26 generally teaching that signal modulation classification can make use of clustering approaches, such as k-NN or SVMs, which are generally understood to associate an instance with other embedded instances based on proximity, distance, and the like, and that such an approach is extensible to a latent space that is directed toward the expression/representation of the same data into text, language, written, etc. format as Lee and Zhang teach. The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 14, the claim includes the same or similar limitation as already discussed above in relation to claim 3, and is therefore rejected under the same rationale provided for claim 1.
Regarding claim 17, the claim includes the same or similar limitation as already discussed above in relation to claim 1, and is therefore rejected under the same rationale provided for claim 1. With the instant claim, A non-transitory computer-readable medium having computer-readable instructions stored thereon is additionally recited, which Marcoux teaches per column 13 lines 44-54 in relation to FIG. 6 (“Software 650 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage 640, that can contain or store programming for use by or in connection with an instruction execution system, apparatus, or device.”). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 19, the claim includes the same or similar limitation as already discussed above in relation to claim 1, and is therefore rejected under the same rationale provided for claim 1.
Regarding claim 20, CISA in view of Marcoux and further in view of Lee and Zhang teach the non-transitory computer-readable medium of claim 17, as discussed above. The aforementioned references teach the additional limitations wherein the computer-readable instructions are configured to instruct the one or more processors to train the sentence embedding model network to convert the body of sentences into the language-derived latent space based, at least in part, on a prediction of a next word in a sentence given a context (see Lee’s discussion of recurrent features, per page 165616’s subheading C). The motivation for combining the references is as discussed above in relation to claim 1.
13. Claims 2, 11, 15-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over CISA in view of Marcoux and further in view of Lee and Zhang, and further yet in view of previously-presented Non-Patent Literature “Unsupervised Image Captioning” (“Feng”).
Regarding claim 2, CISA in view of Marcoux and further in view of Lee and Zhang teach the signal classification system of claim 1, as discussed above. The aforementioned references do not explicitly teach a discriminator network configured to attempt to distinguish outputs from the convolutional generator network from outputs from the sentence embedding model network. Rather, the Examiner relies upon FENG to teach what CISA etc. otherwise lack, see e.g. Feng’s Figure 2 and related discussion (including the caption for the FIG.) teaching of a discriminator as part of the CNN-based unsupervised image captioning model.
Like the references discussed above in relation to claim 1, Feng is directed to image captioning based on received and processed signal data, e.g. images (as Zhang most similarly contemplates). Hence, the aforementioned references are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the GAN-like features of Feng’s captioning architecture with Marxoux’s modified framework, with a reasonable expectation of success, e.g. to better reach a balance point where the generated information/data is as good as the ground truth / actual data.
Regarding claim 11, CISA in view of Marcoux and further in view of Lee and Zhang teach the method of claim 9, as discussed above. The aforementioned references do not teach wherein training the convolutional generator network comprises training the convolutional generator network as a generator of a generative adversarial network. Rather, the Examiner relies upon FENG to teach what CISA etc. otherwise lack, see e.g. Feng’s section 3.2.1 discussing adversarial caption generation.
Like the references discussed above in relation to claim 1, Feng is directed to image captioning based on received and processed signal data, e.g. images (as Zhang most similarly contemplates). Hence, the aforementioned references are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the GAN-like features of Feng’s captioning architecture with Marxoux’s modified framework, with a reasonable expectation of success, e.g. to better reach a balance point where the generated information/data is as good as the ground truth / actual data.
Regarding claim 15, CISA in view of Marcoux and further in view of LeCun teach the method of claim 9, as discussed above. The aforementioned references further teach generating, with the convolutional generator network, data to mimic the samples of the measured RF signal. Rather, the Examiner relies upon FENG to teach what CISA etc. otherwise lack, see e.g. Feng’s section 3.2.1 discussing adversarial caption generation, where the encoder and decoder compete to produce the best possible generative data that constitutes plausible actual data to the discriminator.
Like the references discussed above in relation to claim 1, Feng is directed to image captioning based on received and processed signal data, e.g. images (as Zhang most similarly contemplates). Hence, the aforementioned references are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the GAN-like features of Feng’s captioning architecture with Marxoux’s modified framework, with a reasonable expectation of success, e.g. to better reach a balance point where the generated information/data is as good as the ground truth / actual data.
Regarding claim 16, CISA in view of Marcoux and further in view of Lee and Zhang and further yet in view of Feng teach the method of claim 15, as discussed above. The aforementioned references further teach the additional limitation further comprising distinguishing between the data provided by the convolutional generator network from outputs originating at the sentence embedding model network (Feng’s section 3.2.1 discussing adversarial caption generation, where the encoder and decoder compete to produce the best possible generative data that constitutes plausible actual data to the discriminator). The motivation for combining the references is as discussed above in relation to claim 15.
Regarding claim 18, the claims include the same or similar limitations as discussed above in relation to claims 15-16, and is therefore rejected under the same rationale.
14. Claims 6-7, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over CISA in view of Marcoux and further in view of Lee and Zhang and further yet in view of previously-presented Non-Patent Literature “SAO2Vec: Development of an algorithm for embedding the subject-action-object (SAO) structure using Doc2Vec” (Kim).
Regarding claim 6, CISA in view of Marcoux and further in view of Lee and Zhang teach the signal classification system of claim 1, as discussed above. The aforementioned references teach the processing of document information to generate sentences and select words but do not explicitly teach wherein the sentence embedding model network is configured to use a paragraph vector algorithm to generate unique vectors for each sentence of the body of sentences and for each word of the body of sentences. Rather, the Examiner relies upon KIM to teach what CISA etc. otherwise lack, see e.g. Kim’s page 5 discussing “Using the Doc2Vec algorithm (which acts as a memory cell that remembers information that is not reflected in the context of Word2Vec [21]), various vectors can be derived for the word vector, the sentence, and the document. This helps to compensate for the traditional method’s limitations (such as its inability to consider the words’ sequence) and allows for words, sentences, paragraphs, and documents to be mapped in the same space.”
The references discussed above in relation to claim 1 along with Kim are directed to frameworks relating to embedding data as associated with language/text subject matter and using that embedded data in a machine-learned manner to generate a useful result. Hence, they are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to consider the algorithm as explicitly taught by Kim for use in ... modified framework, with a reasonable expectation of success, such that language/text subject matter of all scope/scale, as is generally and widely found whenever text information is presented, provided, used, etc., can be subject to processing by a machine-learning framework for the sort of useful purposes contemplated by CISA etc. for example as discussed above per claim 1.
Regarding claim 7, CISA in view of Marcoux and further in view of Lee and Zhang and further yet in view of Kim teach the signal classification system of claim 6, as discussed above. The aforementioned references further teach the additional limitation wherein the sentence embedding model network is configured to use the unique vectors as features to predict a next word in a context (see Lee’s discussion of recurrent features, per page 165616’s subheading C). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 10, CISA in view of Marcoux and further in view of Lee and Zhang teach the method of claim 9, as discussed above. The aforementioned references do not teach the additional limitation wherein training the sentence embedding model network comprises: parsing a body of documents into the descriptive sentences; and segmenting the descriptive sentences into lists of word tokens; and training neural network weight matrices used for predicting a next word in a sentence based, at least in part, on a fixed-length context sample from a random document of the body of documents. Rather, the Examiner relies upon KIM to teach what CISA etc. otherwise lack, see e.g. Kim’s page 5 discussing “Using the Doc2Vec algorithm (which acts as a memory cell that remembers information that is not reflected in the context of Word2Vec [21]), various vectors can be derived for the word vector, the sentence, and the document. This helps to compensate for the traditional method’s limitations (such as its inability to consider the words’ sequence) and allows for words, sentences, paragraphs, and documents to be mapped in the same space.”
The references discussed above in relation to claim 1 along with Kim are directed to frameworks relating to embedding data as associated with language/text subject matter and using that embedded data in a machine-learned manner to generate a useful result. Hence, they are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to consider the algorithm as explicitly taught by Kim for use in ... modified framework, with a reasonable expectation of success, such that language/text subject matter of all scope/scale, as is generally and widely found whenever text information is presented, provided, used, etc., can be subject to processing by a machine-learning framework for the sort of useful purposes contemplated by CISA etc. as discussed above per claim 1.
15. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over CISA in view of Marcoux and further in view of Lee and Zhang and further yet in view of previously-presented Non-Patent Literature “NLP-guidance - Neural Network models” (Tazzyman).
Regarding claim 8, CISA in view of Marcoux and further in view of Lee and Zhang teach the signal classification system of claim 1, as discussed above. The aforementioned references do not put a number to what it considers to be high dimensional, and therefore the aforementioned references do not fully teach the additional limitation wherein the language-derived latent space includes a one hundred dimensional embedding space. Rather, the Examiner relies upon TAZZYMAN to teach what CISA etc. otherwise lack, see e.g. Tazzyman’s third and fourth bullet points on its first page, discussing that dimensions may be between 100 and 1000, thereby more concretely reading on the aforementioned limitation.
The references as discussed above in relation to claim 1 along with Tazzyman are directed to frameworks relating to embedding data as associated with language/text subject matter and using that embedded data in a machine-learned manner to generate a useful result. Hence, they are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to consider the range and limits expressed by Tazzyman as discussed here in defining a modified framework for CISA etc., with a reasonable expectation of success, since such a range or limit would be within the typical size or scale as known in the state of the art as Tazzyman establishes.
Conclusion
16. The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure:
Non-Patent Literature “Big Data in Intrusion Detection Systems and Intrusion Prevention Systems” (Wang)
Non-Patent Literature “A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning” (Qaddoura)
Non-Patent Literature “Deep Learning-Based Intrusion Detection Systems: A Systematic Review” (Lansky)
Non-Patent Literature “Guide to Intrusion Detection and Prevention Systems (Draft)” (Scarfone)
See sections 3.3.2.1, 3.4, 4.3.2-4.3.3.1, and 5.1.3-5.3.3.1
17. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHOURJO DASGUPTA whose telephone number is (571)272-7207. The examiner can normally be reached M-F 8am-5pm CST.
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/SHOURJO DASGUPTA/Primary Examiner, Art Unit 2144
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