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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in India on July 5, 2023. It is noted, however, that applicant has not filed a certified copy of the IN 202311045094 application as required by 37 CFR 1.55.
Specification
The abstract of the disclosure is objected to because the bottom of the abstract contains the extra line stating "FIG. 3" and line 4 of the abstract reads “embodiments herein” but should read “Embodiments herein”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more to the abstract idea itself.
First, it is determined whether the claims are directed to a statutory category of invention. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Claim 1 is directed to a process. Claim 8 is directed to a machine. (Step 1: YES).
Next, it is determined whether the claims are directed to a judicial exception. Step 2(A): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 recites:
` A method (300) for verifying a received navigation signal, the method comprising:
pre-processing (302), by a receiver (101), a received navigation signal; and
verifying (303), by the receiver (101), if the pre-processed navigation signal is a genuine navigation signal or a spoofed navigation signal by verifying patterns in the pre-processed navigation signal using a trained time series neural network.
Verifying patterns in a signal can be performed in the human mind, which is an abstract idea because it is a mental process. Accordingly, claim 1 recites abstract ideas (Step 2A, Prong One: YES).
Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? This judicial exception is not integrated into a practical application.
Claim 1 recites:
` A method (300) for verifying a received navigation signal, the method comprising:
pre-processing (302), by a receiver (101), a received navigation signal; and
verifying (303), by the receiver (101), if the pre-processed navigation signal is a genuine navigation signal or a spoofed navigation signal by verifying patterns in the pre-processed navigation signal using a trained time series neural network.
These additional elements do not integrate the judicial exception into a practical application. The additional element of pre-processing a received navigation signal with a receiver is a generic signal processing process carried out with a generic signal processing tool. The additional element of using a trained time series neural network is a generic technology used to perform an abstract idea. Using technology to carry out an abstract idea is not sufficient to qualify as integration of mental processes into a practical application. (Step 2A, Prong Two: NO).
Because the claim recites an abstract idea and does not recite additional elements that integrate the judicial exception into a practical application, the claim is directed towards an abstract idea (Step 2A: YES).
Step 2(B): Does the claim recite additional elements that amount to significantly more than the judicial exception? The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The intended use in the preamble of the claim, pre-processing of signals, and use of a neural network do not amount to significantly more than the abstract idea represented in the body of the claim. When the additional elements of claim 1 are considered separately and in combination they do not add significantly more to the exception nor amount to significantly more than a mental process. There are no meaningful limitations in the claim that allow the judicial exception to amount to anything significantly more than itself (Step 2B: NO).
Claims 2-7 are rejected under the same rationale as claim 1.
Claim 8 recites the same abstract idea as claim 1. Claim 8 recites the following additional elements: a receiver. This additional element is generic on its own and in combination. This generic computer part does not add more to the judicial exception nor integrate the exception into a practical application. Therefore, claim 9 is rejected under the same rationale as claim 1.
Claims 9-14 are rejected under the same rationale as claim 8.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 4, 10, and 11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 3 and 10, no further explanation in the claims or the specification explain the meaning of the various acronyms/variables. Claims 4 and 11 are rejected under the same rationale because they are dependent on claims 3 and 10, respectively.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6, 8, and 13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shattil et al. (US 20210314081 A1).
Regarding claim 1, Shattil discloses
A method (300) for verifying a received navigation signal (see pg. 2, paragraph 0011, method for classifying radio signals; Fig. 1; pg. 3, paragraph 0034, edge devices 101-110 of Figure 1 may be navigation devices capable of receiving navigation signals), the method comprising:
pre-processing (302), by a receiver (101) (see Fig. 5, radio receiver 501), a received navigation signal (see pg. 11 paragraph 0097, “A spectrum fragmenter 502 partitions IQ data received from the radio receiver 501 into independent groups, referred to herein as pulses. The spectrum fragmenter 502 preprocesses the pulses for cleaning, followed by compression for use in downstream processes”; pg. 10, paragraph 0096, “FIG. 5 and the other figures, can be implemented as apparatus configurations comprising structural features that perform the functions”); and
verifying (303), by the receiver (101), if the pre-processed navigation signal is a genuine navigation signal or a spoofed navigation signal (see pg. 1, paragraph 0010, the data analysis and results can identify spoofed radio signals from signals of interest) by verifying patterns in the pre-processed navigation signal (see pg. 9, paragraph 0085, “machine learning algorithm may be implemented with a patterns and concepts cognitive learning technique”; Fig. 2, machine learning system 210 with collaborative filtering 206 and semantic analysis 202 connected to cognitive graph 228; pg. 6, paragraph 0054, “ Collaborative filtering 206, as used herein, broadly refers to the process of filtering for information or patterns through the collaborative involvement of multiple agents, sensors, data sources, and so forth”; pg. 5, paragraph 0047, “the semantic analysis 202 includes processing an intercepted radio signal to…identify dependencies between individual words or other signal patterns” pg. 7, paragraph 0062, “A cognitive graph 228 refers to a representation of expert knowledge associated with UAVs and communication devices/protocols over a period of time, to depict relationships between radio signals, places, times, device uses, and behavior patterns. As such, it is a machine-readable formalism for knowledge representation that provides a common framework allowing data and knowledge to be shared and reused across devices, applications, and networks”) using a trained time series neural network (see pg. 9, paragraph 0084, the machine learning classifier may be neural network; pg. 1, paragraph 0010, “The analytics engine may pre-trained through supervised machine learning using a set of predefined training data, such as pre-classified signals, data patterns, and the like”).
Regarding claim 6, Shattil further discloses
The method, as claimed in claim 1, wherein on determining that the received navigation signal is spoofed, the method further comprises at least one of:
raising an alert, by the receiver (101) (see pg. 11, paragraph 0097, the receiver can generate an intelligent response such as an alert based on signal information); and
ignoring the received navigation signal, by the receiver (101) (see pg. 9, paragraph 0082, the machine learning process can disqualify signals from being processed further if they’re determined to not be of interest).
Regarding claim 8, the same cited sections and rationale from claim 1 are applied.
Regarding claim 13, the same cited sections and rationale from claim 6 are applied.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Shattil et al. (US 20210314081 A1) in view of Zangvil et al. (US 20220236425 A1).
Regarding claim 2, Shattil further discloses [Note: what Shattil fails to disclose is strike-through]
The method, as claimed in claim 1, wherein pre-processing the received navigation signal comprises:
(see pg. 8, paragraph 0070, the technique includes “detecting, identifying, and characterizing unknown signals via signal features and meta features using machine learning”), such that values of the plurality of features are in a pre-defined range (see pg. 10, paragraph 0095, “Each receiver may employ a predetermined algorithm”)
configuring, by the receiver (101), the normalized and standardized navigation signal into a plurality of channels (see Fig. 4B, step 411 to channelize received signal into multiple components; pg. 10, paragraph 0093, “A received signal is channelized or broken down into data fragments 411”).
Zangvil discloses
normalizing and standardizing (see pg. 10, paragraphs 320-328, various examples of features of a signal being normalized and standardized to fit within a pre-defined range with limited deviation)
with minimal deviation
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Zangvil into the invention of Shattil. Both Shattil and Zangvil are considered analogous arts to the claimed invention as they both disclose navigation signal spoofing detection techniques with neural networks. Shattil discloses a plurality of features of the received signal being in a pre-defined range and configuring the signal in channels; however, Shattil fails to disclose normalizing and standardizing the received signal with minimal deviation. This feature is disclosed by Zangvil where the features of the received signal can be analyzed in various standardized ways with limited deviations. The combination of Shattil and Zangvil would be obvious with a reasonable expectation of success in order to make the components of the received signal easier to analysis, providing a streamlined, more efficient method to determining if the signal is legit or spoofed.
Regarding claim 9, the same cited sections and rationale from claim 2 are applied.
Claims 5, 7, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Shattil et al. (US 20210314081 A1) in view of Kaabouch et al. (US 20200225358 A1).
Regarding claim 5, Kaabouch discloses
The method, as claimed in claim 1, wherein the time series neural network is at least one of a Long Short-Term Memory (LSTM); and a Recurrent Neural Network (RNN) (see pg. 4, paragraph 0042, “In some example embodiments, the neural network 204 (e.g., deep learning, deep convolutional, or recurrent neural network) comprises a series of neurons 208, such as Long Short Term Memory (LSTM) nodes, arranged into a network”).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Kaabouch into the invention of Shattil. Both Shattil and Kaabouch are considered analogous arts to the claimed invention as they both disclose methods of detecting spoofed position signals with neural networks. Shattil discloses the limitations of claim 1; however, Shattil fails to disclose if the neural network is specifically a Long Short-Term Memory (LSTM) or a Recurrent Neural Network (RNN). This feature is disclosed by Kaabouch where the neural network may be a recurrent neural network or a long short term memory in addition to other types of neural networks. The combination of Shattil and Kaabouch would be obvious with a reasonable expectation of success in order to process sequential data effectively with a neural network well known in the art (see Kaabouch pg. 4, paragraph 0043 for example structure of an LSTM), reducing production costs by preventing the manufacturing of a new neural network in favor of an established LSTM neural network.
Regarding claim 7, Kaabouch discloses
The method, as claimed in claim 1, wherein on verifying the received navigation signal, the method further comprises determining, by the receiver (101), geo- location of the receiver (101) using the received navigation signal (see pg. 9, paragraph 0105, “a computing machine, which includes processing circuitry, memory, and a GPR, accesses an incoming geolocation positioning signal 601. The geolocation positioning signal may be … used to determine a geographic location of the computing machine”).
It would have been obvious to someone with ordinary skill in the art prior to the effective filing date of the claimed invention to incorporate the features as disclosed by Kaabouch into the invention of Shattil. Shattil discloses the limitations of claim 1; however, Shattil fails to disclose the receiver using the authentic signal to determine the location of itself. This feature is disclosed by Kaabouch where the computing machine can use the incoming positioning signal to determine a geographic location of the computing machine. The combination of Shattil and Kaabouch would be obvious with a reasonable expectation of success in order to further improve communication and security between the signal transmitter and receiver by confirming the correct receiver location with verified positioning information.
Regarding claim 12, the same cited sections and rationale from claim 5 are applied.
Regarding claim 14, the same cited sections and rationale from claim 7 are applied.
Allowable Subject Matter
Claims 3, 4, 10, and 11 are objected to as being dependent upon a rejected base claim, but would be allowable based on the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 3, 4, 10, and 11 would be allowable if rewritten to overcome the rejections under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Although claims 4 and 11 are marked allowable, they would be rejected over the prior art should the deficiencies of claims 3 and 10 be remedied. An example rejection is provided below. Regarding claim 11, the same cited sections and rationale from claim 4 would be applied.
Regarding claim 4, Shattil further discloses
The method, as claimed in claim 3, wherein verifying patterns in the pre- processed navigation signal comprises comparing patterns in the plurality of features in the pre-processed navigation signal with patterns in corresponding features of previously recorded navigation signals (see pg. 9, paragraph 0081, “The spatial signature may be compared with radar data and/or other remote-sensing data, such as to assist in associating transmissions with a particular target 313. This signature can be used to discriminate between signals of interest and other signals”) wherein the time series based neural network has been trained using the previously recorded navigation signals recorded at a plurality of locations (see pg. 2, paragraph 0016, “The method comprises receiving training data based upon sensor measurements”; pg. 1, paragraph 0010, “The analytics engine may pre-trained through supervised machine learning using a set of predefined training data, such as pre-classified signals, data patterns, and the like”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISABELLA AMEYALI EDRADA whose telephone number is (571)272-4859. The examiner can normally be reached Mon - Fri 9am-5pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Kelleher can be reached at (571) 272-7753. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISABELLA AMEYALI EDRADA/Examiner, Art Unit 3648
/William Kelleher/Supervisory Patent Examiner, Art Unit 3648