DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 USC 102 and 103 (or as subject to pre-AIA 35 USC 102 and 103) is incorrect, any correction of the statutory basis 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.
Response to Amendment
It is noted that applicant's amendment to the specification and abstract dated 7-12-2024 has not been entered. According to 37 CFR 1.72(b), an amendment to the abstract must be presented on a separate sheet, apart from any other text. MPEP 714(II)(B) states "Whether supplying a marked-up version of a previous abstract or a clean form new abstract, the abstract must comply with 37 CFR 1.72(b) regarding the length and placement of the abstract on a separate sheet of paper.". Applicant should resubmit an amendment to the specification on one sheet/set of sheets and an amendment to the abstract on another sheet.
Specification Objections
The disclosure is objected to under 37 CFR 1.71(a) because of the following informalities:
In ¶3, line 4, after "interference, --with-- should be inserted.
Appropriate correction is required.
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.
Claim(s) 1-15 is/are rejected under 35 USC 101 because the claimed invention is directed to a judicially recognized exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1-5 and 11-15 is/are directed to a method of, and device for, estimating jamming in a GNSS receiver. Claims 6-10 recites a method for training a machine learning model by a computing device for estimating jamming in a GNSS receiver. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim elements, both individually and in combination, are directed to the mathematical manipulation of data by using a machine learning model to obtain a likelihood value, where the machine learning model is disclosed as operating mathematically (¶51-52), and training a machine learning model to obtain a likelihood value, where the machine learning model is disclosed as operating mathematically (¶51-52), and do not result in an improvement in the functioning of the computer or to another technology. The receiving with a receiver, collecting, and logging steps are all extraneous pre-solution activity/data-gathering. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
ANALYSIS
Patent Ineligible Subject Matter (Claims 1-15)
An invention is patent-eligible if it claims a “new and useful process, machine, manufacture, or composition of matter.” 35 U.S.C. § 101. However, the Supreme Court has long interpreted 35 U.S.C. § 101 to include implicit exceptions: “[l]aws of nature, natural phenomena, and abstract ideas” are not patentable. E.g., Alice Corp. v. CLS Bank lnt’l, 573 U.S. 208, 216 (2014).
In determining whether a claim falls within an excluded category, we are guided by the Supreme Court’s two-step framework, described in Mayo and Alice. Id. at 217—18 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 75—77 (2012)). In accordance with that framework, we first determine what concept the claim is “directed to.” See Alice, 573 U.S. at 219 (“On their face, the claims before us are drawn to the concept of intermediated settlement, i.e., the use of a third party to mitigate settlement risk.”); see also Bilski v. Kappos, 561 U.S. 593, 611 (2010) (“Claims 1 and 4 in petitioners’ application explain the basic concept of hedging, or protecting against risk.”).
Concepts determined to be abstract ideas, and thus patent ineligible, include certain methods of organizing human activity, such as fundamental economic practices (Alice, 573 U.S. at 219—20; Bilski, 561 U.S. at 611); mathematical formulas (Parker v. Flook, 437 U.S. 584, 594—95 (1978)); and mental processes (Gottschalk v. Benson, 409 U.S. 63, 69 (1972)). Concepts determined to be patent eligible include physical and chemical processes, such as “molding rubber products” (Diamond v. Diehr, 450 U.S. 175, 192 (1981)); “tanning, dyeing, making waterproof cloth, vulcanizing India rubber, smelting ores” (id. at 184 n.7 (quoting Corning v. Burden, 56 U.S. 252, 267—68 (1854))); and manufacturing flour (Benson, 409 U.S. at 69 (citing Cochrane v. Deener, 94 U.S. 780, 785 (1876))).
In Diehr, the claim at issue recited a mathematical formula, but the Supreme Court held that “[a] claim drawn to subject matter otherwise statutory does not become nonstatutory simply because it uses a mathematical formula.” Diehr, 450 U.S. at 176; see also id. at 192 (“We view respondents’ claims as nothing more than a process for molding rubber products and not as an attempt to patent a mathematical formula.”). Having said that, the Supreme Court also indicated that a claim “seeking patent protection for that formula in the abstract... is not accorded the protection of our patent laws, . . . and this principle cannot be circumvented by attempting to limit the use of the formula to a particular technological environment.” Id. (citing Benson and Flook); see, e.g., id. at 187 (“It is now commonplace that an application of a law of nature or mathematical formula to a known structure or process may well be deserving of patent protection.”).
If the claim is “directed to” an abstract idea, we turn to the second step of the Alice and Mayo framework, where “we must examine the elements of the claim to determine whether it contains an ‘inventive concept’ sufficient to ‘transform’ the claimed abstract idea into a patent-eligible application.” Alice, 573 U.S. at 221 (quotation marks omitted). “A claim that recites an abstract idea must include ‘additional features’ to ensure ‘that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].”’ Id. ((alteration in the original) quoting Mayo, 566 U.S. at 77). “[M]erely requiring] generic computer implementation fail[s] to transform that abstract idea into a patent-eligible invention.” Id.
Under Step 2A of that guidance, we first look to whether the claim recites:
(1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes); and
(2) additional elements that integrate the judicial exception into a practical application (see MPEP § 2106.05(a)-(c), (e)-(h)).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field (see MPEP § 2106.05(d)); or
(4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Step 1 — Statutory Category
Claim(s) 1-10 recite(s) a series of steps, and, therefore, is a process.
Claims(s) 11-15 recite(s) a device, which is a machine.
Step 2A, Prong One — Recitation of Judicial Exception
Step 2A is a two-prong inquiry. In Prong One, we evaluate whether the claim recites a judicial exception. For abstract ideas, Prong One represents a change as compared to prior guidance because we here determine whether the claim recites mathematical concepts, certain methods of organizing human activity, or mental processes.
It is determined that claims 1 and 11 are directed to an abstract idea, and, particularly, are directed to the mathematical manipulation of data by using a machine learning model to obtain a likelihood value, where the machine learning model is disclosed as operating mathematically (¶51-52). Claim 6 is directed to an abstract idea, and, particularly, are directed to the mathematical manipulation of data by training a machine learning model to obtain a likelihood value, where the machine learning model is disclosed as operating mathematically (¶51-52), and do not result in an improvement in the functioning of the computer or to another technology. Dependent claims 2, 5, 9-10, and 12 simply add more calculations, or more detail to the calculation in claim(s) 1, 6, and 11.
Mathematical formulas, mathematical relationships, mathematical calculations, and computational operations fall within the “mathematical concepts” grouping. Accordingly, the subject matter of claim(s) 1-15 falls within this grouping. Accordingly, claim(s) 1-15 recite(s) an abstract idea. We proceed to Prong Two to determine whether the claim is “directed to” the judicial exception.
Step 2A, Prong Two — Practical Application
If a claim recites a judicial exception, in Prong Two we next determine whether the recited judicial exception is integrated into a practical application of that exception by: (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (b) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
If the recited judicial exception is integrated into a practical application, the claim is not directed to the judicial exception. This evaluation requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. If the recited judicial exception is integrated into a practical application, the claim is not directed to the judicial exception.
Here, apart from the mathematical operations limitations (claims 1 and 11 are directed to the mathematical manipulation of data by using a machine learning model to obtain a likelihood value, where the machine learning model is disclosed as operating mathematically (¶51-52); claim 6 is directed to the mathematical manipulation of data by training a machine learning model to obtain a likelihood value, where the machine learning model is disclosed as operating mathematically (¶51-52)), the only additional element that is/are recited in claim(s) 1, 6, and 11 is/are receiving data with a receiver, collecting data, and logging data, i.e., data-gathering. Dependent claims 3-4, 7-8, and 13-15 merely add more extrasolution activity/data gathering. As such, these additional limitations are insignificant extra-solution activity to the judicial exception. Accordingly, these element(s) do not integrate the judicial exception into a practical application of the exception.
Since the additional element(s) in claim(s) 1-15 fails to integrate the judicial exception into a practical application, we proceed to Step 2B to determine whether the claim recites an “inventive concept.”
Step 2B — Inventive Concept
As noted, for Step 2B of the analysis, we determine whether the claim adds a specific limitation beyond the judicial exception that is not “well-understood, routine, conventional” in the field. See Memorandum.
As set forth above it has been concluded that claim(s) 1-15 do/does not include additional elements that are sufficient to amount to significantly more than the abstract idea itself, and thus, the additional elements do not transform the abstract idea into a patent eligible application of the abstract idea.
Applicant’s disclosure does not provide evidence that the additional element(s) recited in claim(s) 1-15 (i.e., the claim element in addition to the claim elements that recite an abstract idea) is sufficient to amount to significantly more than the abstract idea itself. This issue is explained by the Federal Circuit, as follows:
It has been clear since Alice that a claimed invention’s use of the ineligible concept to which it is directed cannot supply the inventive concept that renders the invention “significantly more” than that ineligible concept. In Alice, the Supreme Court held that claims directed to a computer-implemented scheme for mitigating settlement risks claimed a patent-ineligible abstract idea. 134 S.Ct. at 2352, 2355—56. Some of the claims at issue covered computer systems configured to mitigate risks through various financial transactions. Id. After determining that those claims were directed to the abstract idea of intermediated settlement, the Court considered whether the recitation of a generic computer added “significantly more” to the claims. Id. at 2357. Critically, the Court did not consider whether it was well-understood, routine, and conventional to execute the claimed intermediated settlement method on a generic computer. Instead, the Court only assessed whether the claim limitations other than the invention’s use of the ineligible concept to which it was directed were well-understood, routine and conventional. Id. at 2359-60. BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1290 (2018) (emphases added).
Apart from the limitations that recite an abstract idea, the additional element(s) in claim(s) 1-15 is/are insert additional element limitations, which merely recites insignificant extra-solution activity to the judicial exception. Also, the method/device recited in claim(s) 1-15 merely uses a computer system including generic components as a tool to perform the abstract idea. The application of the abstract idea using generic computer components does not transform the claim into a patent-eligible application of the abstract idea. Id.
Accordingly, claim(s) 1-15 fails to recite an inventive concept that transforms the claim into a patent-eligible application of the abstract idea.
The claims take some measurements, do some mathematics, and do not use the result(s) in any way. Taking the measurements is extra-solution activity. There is no application of the mathematical values that are calculated to solve any particular problem or for any particular purpose, e.g. to exclude a particular signal from being used in a GNSS positioning determination. Thus, the claimed invention does not integrate the identified judicial exception into a practical application. The invention is not performed by a particular machine.
See also https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility , particularly the link to "The 2024 Patent Subject Matter Eligibility Guidance Update Including on Artificial Intelligence (2024 AI SME Update)" and the link to "2024 AI Examples 47 through 49".
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.
Claim(s) 1-2, 5, and 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu (CN 113376659 A) in view of Pramoditha (Logistic Regression As a Very Simple Neural Network Model) [where all references to Zhu are to the English Translation attached to this Office Action].
In regard to claim 1, Zhu discloses a method for estimating jamming in a global navigation satellite system (GNSS) receiver (¶6; ¶30), the method being performed in the receiver and comprising:
receiving GNSS signals at a radio frequency band and processing the received signals at an intermediate frequency band (¶4; ¶6; ¶49);
collecting a set of parameters at the receiver based on the received GNSS signals (¶6; ¶32-33; ¶49) [the feature vector]; and
determining whether or not there is jamming using a machine learning model/neural network trained for the receiver (¶6; ¶35; ¶47; ¶49),
wherein the set of parameters are inputs to the machine learning model, the determining is an output of the machine learning model, and the determining corresponds to a determination of the receiver being jammed in the intermediate frequency band (¶49) [where a spoofing signal is a form a jamming signal].
Zhu fails to explicitly disclose determining whether or not there is jamming encompasses obtaining a likelihood value, the likelihood value corresponds to a likelihood of the receiver being jammed in the intermediate frequency band.
Pramoditha teaches a machine learning model/neural network that determines when a system is in one state or an other state by obtaining a likelihood value, the likelihood value corresponds to a likelihood that the system is in one state or the other state (p. 1).
Replacing one neural network for determining which of two states a system is in with another neural network for determining which of two states a system is in is a simple substitution of one known, equivalent element for another to perform the same function and obtain predictable results. Because both elements are known machine learning models for determining which of two states a system is in, it would have been obvious before the effective filing date of the invention to one of ordinary skill in the art to substitute one for the other to achieve the predictable result of determining which of the two states the system is in.
In regard to claim 11, Zhu discloses a device comprising a global navigation satellite system (GNSS) receiving unit and a processing unit (¶6; ¶30) [where it is inherent for a GNSS receiver to include a processing unit to perform required signal and mathematical processing], the device being configured to:
receive GNSS signals at a radio frequency band and process the received signals at an intermediate frequency band (¶4; ¶6; ¶49);
collect a set of parameters at the device (¶6; ¶32-33; ¶49) [the feature vector]; and
determine whether or not there is jamming using a machine learning model/neural network trained for the device (¶6; ¶35; ¶47; ¶49),
wherein the set of parameters are inputs to the machine learning model, the determining is an output of the machine learning model, and the determining corresponds to a determination of the receiver being jammed in the intermediate frequency band (¶49) [where a spoofing signal is a form a jamming signal].
Zhu fails to explicitly disclose determining whether or not there is jamming encompasses obtaining a likelihood value, the likelihood value corresponds to a likelihood of the receiver being jammed in the intermediate frequency band.
Pramoditha teaches a machine learning model/neural network that determines when a system is in one state or an other state by obtaining a likelihood value, the likelihood value corresponds to a likelihood that the system is in one state or the other state (p. 1).
Replacing one neural network for determining which of two states a system is in with another neural network for determining which of two states a system is in is a simple substitution of one known, equivalent element for another to perform the same function and obtain predictable results. Because both elements are known machine learning models for determining which of two states a system is in, it would have been obvious before the effective filing date of the invention to one of ordinary skill in the art to substitute one for the other to achieve the predictable result of determining which of the two states the system is in.
In regard to claims 2 and 12, Pramoditha further teaches the machine learning model is a logistic regression model (p. 1).
In regard to claim 5, Pramoditha further teaches determining a threshold; determining a state to be a first state when the likelihood value is greater than the threshold; and determining the state to be a second state when the likelihood value is smaller than or equal to the threshold (p. 1).
In the combination, the first state is a jamming status to be a status of being jammed and the second state is a jamming status to be a status of not being jammed.
Claim(s) 6-7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu (CN 113376659 A) in view of Dubash (US 2014/0369452 A1) [where all references to Zhu are to the English Translation attached to this Office Action].
In regard to claim 6, Zhu discloses a method for training a machine learning model by a computing device for estimating jamming in a global navigation satellite system (GNSS) receiver (¶6; ¶30) [where it is inherent for a GNSS receiver to include a computing device to perform required signal and mathematical processing], the method comprising:
receiving by the receiver GNSS signals at a radio frequency band and processing by the receiver the received signals at a respective intermediate frequency band, wherein the received GNSS signals comprise GNSS signals being jammed and GNSS signals not being jammed (¶4; ¶6; ¶49) [where a spoofing signal is a form a jamming signal];
logging by the receiver a set of parameters corresponding to the GNSS signals being jammed and the GNSS signals not being jammed at the respective intermediate frequency band (¶6; ¶32-33; ¶49) [where the feature vector is the set of parameters]; and
training by the computing device the machine learning model based on the set of logged parameters and the corresponding GNSS signals at the respective intermediate frequency band (¶49).
Zhu fails to disclose the use of a plurality of radio frequency bands and intermediate frequency bands.
Dubash teaches estimating jamming of a plurality of radio frequency bands at a respective plurality of intermediate frequency bands in order to select and use an unjammed band when another band is experiences jamming (Fig. 2B; Fig. 3B; ¶2; ¶15-16; ¶23-24).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include this feature into the combination with a reasonable expectation of success in order to select an unjammed band when another of the bands is jammed. If only a single band is used, if jamming occurs in that band, no position would be able to be determined. By using and testing for jamming in a plurality of bands, and unjammed band can be selected when jamming occurs in one/some but not all bands.
Additionally, this is a combining of prior art elements according to known methods to yield predictable results, the predictable result being that positioning would still occur despite jamming in one or some bands.
In regard to claim 7, Zhu further discloses logging by the receiver the set of parameters reflecting information of the received GNSS signals within the respective intermediate frequency band being jammed or not being jammed (¶49).
In the combination, where a plurality of bands are being used, the logging by the receiver logs the set of parameters reflecting information of the received GNSS signals within the respective intermediate frequency bands being jammed or not being jammed.
In regard to claim 9, Zhu further discloses setting a jamming status of the GNSS signals at the respective intermediate frequency, wherein the jamming status is a status of the GNSS signals being jammed or a status of the GNSS signals not being jammed; and training the machine learning model based on the logged parameters and the corresponding jamming status of the GNSS signals at the respective intermediate frequency (¶49).
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu and Pramoditha, as applied to claims 1 and 11, above, and further in view of Kaufmann (US 2021/0226346 A1).
Zhu and Pramoditha fail to teach, before collecting the set of parameters, applying jamming mitigation on the received GNSS signals.
Kaufmann teaches applying jamming mitigation on received GNSS signals (¶3; ¶15; ¶43; ¶46).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include this feature into the combination with a reasonable expectation of success in order to eliminate jamming using standard methods and then see if any jamming remains that cannot be eliminated by standard methods before deciding to not use a GNSS signal for positioning.
Additionally, this is a combining of prior art elements according to known methods to yield predictable results, the predictable result being that signals that can be used using standard jamming mitigation to mitigate any jamming that may be occurring are used in determining the position of the receiver.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu and Dubash, as applied to claim 6, above, and further in view of Shimon (US 2025/0060487 A1).
Zhu and Dubash fail to teach the training involves simulating jamming signals by the computing device; and applying the simulated jamming signals to at least part of the GNSS signals to be received by the receiver; wherein the simulated jamming signals: are different types of signals including continuous wave signals, narrowband signals and broadband signals; have different power levels; have different frequencies; and have different durations.
Shimon teaches simulating jamming signals by the computing device and applying the simulated jamming signals to at least part of the GNSS signals to be received by the receiver (¶234).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include this feature into the combination with a reasonable expectation of success in order to train the neural network/machine learning model without having to produce real jamming/spoofing signals, which could risk interfering with nearby GNSS receivers not under test.
Additionally, this is a combining of prior art elements according to known methods to yield predictable results, the predictable result being that the training occurs without the need to produce real/jamming/spoofing signals.
The Office takes Official Notice that one of ordinary skill in the art would have found it well known before the effective filing date of the invention to simulate jamming signals with the properties or real jamming signals that are known to be used in practice, such as different types of signals including continuous wave signals, narrowband signals and broadband signals; signals having different power levels; signals having different frequencies; and signals having different durations.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu and Pramoditha, as applied to claim 11, above, and further in view of Dubash (US 2014/0369452 A1).
Zhu further discloses receive further GNSS signals at a radio frequency band and process the further signals at an intermediate frequency band, wherein the further GNSS signals comprise GNSS signals being jammed and GNSS signals not being jammed; and log a further set of parameters for training the machine learning model, wherein the further set of parameters corresponds to the further GNSS signals being jammed and not being jammed at a respective intermediate frequency band (¶49).
Zhu fails to disclose the use of a plurality of radio frequency bands and intermediate frequency bands.
Dubash teaches estimating jamming of a plurality of radio frequency bands at a respective plurality of intermediate frequency bands in order to select and use an unjammed band when another band is experiences jamming (Fig. 2B; Fig. 3B; ¶2; ¶15-16; ¶23-24).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include this feature into the combination with a reasonable expectation of success in order to select an unjammed band when another of the bands is jammed. If only a single band is used, if jamming occurs in that band, no position would be able to be determined. By using and testing for jamming in a plurality of bands, and unjammed band can be selected when jamming occurs in one/some but not all bands.
Additionally, this is a combining of prior art elements according to known methods to yield predictable results, the predictable result being that positioning would still occur despite jamming in one or some bands.
The following reference(s) is/are also found relevant:
The SAGE Encyclopedia of Research Design (Logistic Regression), which defines the term "logistic regression", and makes clear that it is a mathematical concept.
Vosburgh (US 2014/0152499 A1), which teaches that GNSS spoofing a form of jamming (¶4).
The Penguin Dictionary of Mathematics (sigmoid curve), which teaches a sigmoid curve is sometimes called S-shaped, and sometimes called a logistic curve (p. 1) [where Zhu (p. 3, section 3.1, final sentence; table in ¶97) and Pramoditha (p. 2) both teach a sigmoid function]. To be clear, while Zhu does not explicitly refer to logistic regression, Zhu refers to a sigmoid function which is also called a logistic function.
Syrjarinne (US 2022/0137234 A1), which teaches checking for jamming on a plurality of GNSS bands in order to use bands where jamming is not present (¶16; ¶28).
Mehr (Detection and Classification of GNSS Jammers Using Convolutional Neural Networks), which teaches detecting RF jamming in IF using trained neural networks including through simulated jamming signals (p. 1, final full ¶; section III).
Ferre (Jammer Classification in GNSS Bands Via Machine Learning Algorithms), which teaches jammer classification in GNSS bands via machine learning algorithms (p. 1+).
Elango (Disruptive GNSS Signal detection and classification at different Power levels Using Advanced Deep-Learning Approach), which teaches GNSS Signal detection and classification at different Power levels Using Advanced Deep-Learning Approach (abstract; p. 4, section C).
Qin (Situational Awareness of Chirp Jamming Threats to GNSS Based on Supervised Machine Learning), which teaches situational awareness of chirp jamming threats to GNSS based on supervised machine learning (p. 1707+).
Applicant is encouraged to consider these documents in formulating their response (if one is required) to this Office Action, in order to expedite prosecution of this application.
Allowable Subject Matter
Claim(s) 3, 8, and 13 would be allowable if amended to overcome the rejection(s) under 35 USC 101, set forth in this Office Action, without the addition of new matter, and if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Reasons for Allowance/Allowable Subject Matter
The following is an examiner's statement of reasons for allowance/allowable subject matter:
The references cited, alone or in combination, do not teach or make obvious the following limitation(s):
quoted from claim 3, in combination with the claim as a whole:
"the set of parameters comprises: skewness of a radio frequency spectrum at the radio frequency band; gain of a radio frequency amplifier in the receiver; skewness of an intermediate frequency spectrum at the intermediate frequency band; variance of an intermediate frequency spectrum at the intermediate frequency band; variance of an intermediate frequency histogram at the intermediate frequency band; mean of a collapsed modulo-1 kHz spectrum at the intermediate frequency band, wherein the collapsed modulo-1 kHz spectrum is chosen from frequency components with modulo-1 kHz value in a frequency spectrum around the intermediate frequency; and a number of GNSS signals whose tracking is aborted by the receiver due to high correlation with noise".
quoted from claim 8, in combination with the claim as a whole:
"the set of parameters comprise: skewness of radio frequency spectrums at the plurality of radio frequency bands; gain of a radio frequency amplifier in the receiver; skewness of intermediate frequency spectrums at the plurality of intermediate frequency bands; variance of intermediate frequency spectrums at the plurality of intermediate frequency bands; variance of intermediate frequency histograms at the plurality of intermediate frequency bands; mean of collapsed modulo-1 kHz spectrums at the plurality of intermediate frequency bands, wherein the collapsed modulo-1 kHz spectrum is chosen from frequency components with modulo-1 kHz value in a frequency spectrum at the intermediate frequency; and a number of GNSS channels of which tracking is aborted by the receiver due to high correlation with noises".
quoted from claim 13, in combination with the claim as a whole:
"the set of parameters comprise: skewness of a radio frequency spectrum at the radio frequency band; gain of a radio frequency amplifier in the device; skewness of an intermediate frequency spectrum at the intermediate frequency band; variance of an intermediate frequency spectrum at the intermediate frequency band; variance of an intermediate frequency histogram at the intermediate frequency band; mean of a collapsed modulo-1 kHz spectrum at the intermediate frequency band, wherein the collapsed modulo-1 kHz spectrum is chosen from frequency components with modulo-1 kHz value in a frequency spectrum at the intermediate frequency; and a number of GNSS signals whose tracking is aborted by the device due to high correlation with noise".
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled "Comments on Statement of Reasons for Allowance".
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fred H. Mull whose telephone number is 571-272-6975. The examiner can normally be reached on Monday through Friday from approximately 9-5:30 Eastern Time.
Examiner interviews are available via telephone and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at https://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha Desai, can be reached at 571-270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Fred H. Mull
Examiner
Art Unit 3648
/F. H. M./
Examiner, Art Unit 3648
/BERNARR E GREGORY/Primary Examiner, Art Unit 3648
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