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 .
Detailed Action
2. This Final Office Action is responsive to Applicants’ Reply dated 10/15/25, with amendments and arguments. Claims 1-20 remain pending, of which claims 1, 9, and 17 are independent.
3. Based on Applicants’ Reply, the Examiner withdraws the previously-presented rejection to claims 1-20 under 35 U.S.C. 101.
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”) in view of U.S. Patent No. 10904050 (“Marcoux”) and further in view of Non-Patent Literature “Deep Learning” (“LeCun”).
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. However, CISA does not teach an 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 into the ... latent space (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 from the ... latent space responsive to a projection of the samples of the measured RF signal into the ... latent space (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 output a ... description of a classification of the measured RF signal (see, e.g., the aforementioned feature-based classification result).
One of ordinary skill in the art would understand the CNN as classifier, as Marcoux teaches per column 6 lines 15-26, to implicitly feature a latent space or its equivalent as recited, e.g. somewhere where the extracted features can be mapped to in comparison (e.g., in terms of similarity, distance, and so forth) with predetermined/known modulation schemes (column 10 lines 4-8) to arrive at the classification result as taught such that the received signal is identified as one of those modulation schemes. Marcoux does not spell out the existence of the latent space with any specificity, but the Examiner reasons that such a latent space must exist for a CNN classifier as taught to function as intended. Said another way, there must be a feature space for the known/predetermined modulation schemes to serve as the basis for comparison with the extracted features of the received signal being classified.
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 language-derived latent space and the further limitation that its classifier network is configured to output a text-based description of a classification of the measured RF signal where these limitations are used in accordance with 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. Rather, the Examiner relies upon LeCUN to teach what CISA as implemented using Marcoux’s framework otherwise lacks:
LeCun teaches the use of deep learning for many purposes, including recognition/detection and captioning in the space of computer vision, for example. See, e.g., Figure 3 on its numbered page 440 (discussing the extraction of features from image data by a CNN, which are in turn translated by a different model to arrive at higher level representations of the image in terms of captions (i.e., text description)). Hence, a received image signal can be subject to convolutional processing to arrive at features, which are then processed to then arrive at text descriptions corresponding to the received images. While the example of image data/signals is specifically explored in that citation, the application of these deep learning architectures as contemplated is not limited to that, see e.g., the first five paragraphs on page 436 extending the applicability of such architectures to other types of signal data. Said another way, a change in the character or type of the data for the signal does not preclude applicability of a deep learning framework as considered by LeCun to extract, identify, and provide an output as it teaches.
In the generation of its captioning, LeCun contemplates training its language model with “word sequences [that] come from a large corpus of real text” (equivalent to the recitation for “a body of sentences” correlated to features as might be used to create an embedded space/network). See the paragraph starting at the end of page 440 and continuing onto page 441. The result of that is the creation of a learned word vector space as shown in Figure 4 on page 441. This learned space is clearly correlated with the features as extracted by way of convolution, such that an unlabeled image can be interpreted to have features and the features as understood are correlated to text to realize a caption output.
Regarding LeCun’s mention of “real text”, as noted above by the Examiner, the Examiner notes elsewhere in LeCun how the processing of image data in terms of lower and higher level features by a neural network addresses a compositional hierarchy that is present in image data, but also one present in text data too in terms of phones, phonemes, syllables, words, and sentences. See LeCun’s numbered page 439, 4th paragraph under the heading Convolutional neural networks. Hence, the Examiner understands the training aspect involving real text as mentioned to be capable of handling a compositional hierarchy that is capable of converting words and sentences.
The cited prior art references all relate to the receipt and analysis of signal data. Hence, they are similarly directed in terms of subject matter and hence analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply a signal modulation classification method as Marcoux teaches in a regulatory space discussed per CISA, with a reasonable expectation of success, to better understand interfering signals as detected for purposes of reporting and resolution and general compliance with best practices in the public safety communications (as CISA discusses). Moreover, to implement the communication and reporting aspects of those best practices per CISA, it would have been obvious to use a language model as LeCun contemplates to automatically provide a legible and easily consumable representation of the detected signal in a manner that could be calibrated to address the needs of any in the chain of involved personnel that would be involved in the CISA best practices paradigm (e.g., as the Examiner has noted above in discussing the CISA reference). The Examiner reasons that such a combination would reasonable lend itself to the characterization of a signal in terms of its features, inclusive of features relating to a modulation scheme as Marcoux teaches the classification thereof, and then a written/language/text description thereof, as LeCun would provide, for purposes of complying with CISA’s framework of best practices.
Regarding claim 3, CISA in view of Marcoux and further in view of LeCun 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 LeCun 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 LeCun teaches (see, e.g., LeCun’s discussion under the heading Distributed representation and language processing starting on numbered page 440, including a discussion of word vector embeddings as part of its learned language model used to generate captions for example)). 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 LeCun 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 LeCun teaches (see, e.g., LeCun’s discussion under the heading Distributed representation and language processing starting on numbered page 440, including a discussion of word vector embeddings as part of its learned language model used to generate captions for example)). 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 LeCun 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 LeCun, 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, and also LeCun extensively discusses training aspects for each of its deep learning framewotk instances (see the titled sections Convolutional neural networks and Image understanding with deep convolutional networks)). 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 LeCun 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 LeCun teaches (see, e.g., LeCun’s discussion under the heading Distributed representation and language processing starting on numbered page 440, including a discussion of word vector embeddings as part of its learned language model used to generate captions for example)). 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 LeCun 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 (LeCun’s numbered pages 440-441, under the title Distributed representations and language processing, discussing word prediction as a part of the language model used to generate captions for received signals). 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 LeCun, 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 LeCun 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 LeCun 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 LeCun 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 LeCun 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 LeCun 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 LeCun 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 LeCun 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 LeCun 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 LeCun 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 (LeCun’s numbered pages 440-441, under the title Distributed representations and language processing, discussing word prediction as a part of the language model used to generate captions for received signals). 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 LeCun 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 LeCun 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 LeCun teach the signal classification system of claim 1, as discussed above. The aforementioned references contemplate that the feature extraction and embedding may be in the form of a high dimensional space, see e.g., LeCun’s numbered page 436, fourth paragraph, teaches the application of deep learning as discussed therein to “high-dimensional data.” That said, LeCun does 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.
Response to Arguments
8. Applicants’ arguments filed by way of the recent reply have been fully considered but are respectfully not found persuasive. While the same references as previously cited have been asserted again, despite Applicants’ amendments and arguments, the Examiner believes they are apt in rejecting the amended claims as ordered differently as presented again in this instant Action. Most meaningfully, the Examiner believes CISA provides a framework for receiving, analyzing, identifying, and reporting RF signals for security purposes. CISA is at best a policy or scheme and is not detailed in any sort of implementation. That said, it provides with its breadth and plan for the appropriate problem in the appropriate art space a roadmap for actions that would need to be performed in the same instance that Applicants’ claims contemplate. The further references, such as Marcoux and LeCUN, provide frameworks that are related to signal processing, and specifically to classifying signals and providing a classification result. Like CISA, Marcoux receives, analyzes, and identifies a signal, but is thin on any reporting aspect beyond a mere identification result. However, as CISA provides, there is a recognized need in the state of the art to report an identified signal, which LeCUN provides with its more text/language-based aspect.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure:
Non-Patent Literature “I/Q Signals 101: Neither Complex Nor Complicated”
Non-Patent Literature “What's Your IQ - About Quadrature Signals ...”
10. Applicants’ amendment necessitated the new ground(s) of rejection presented in this Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicants are 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.
11. 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
Prosecution Insights