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 .
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 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.
DETAILED ACTION
Preliminary Amendment
Preliminary Amendment filed on 01/29/2024 noted by the examiner, claims 1-10 are cancelled, 11-20 are pending.
Citation of Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See MPEP 707.05. Although the prior art discloses several unclaimed, some claimed limitation. The closest Prior Art of record are considered to be defined by:
Naden (US 6639939 B1) described a method, apparatus, computer-based product and system employs a digital receiver (or transceiver) to receive, digitize and process a direct sequence spread spectrum signal using efficient, low-cost digital signal processing components. A radio front end portion of the receiver receives and digitizes the signal, and a digital signal processing portion down converts and spreads the signal by applying a pseudorandom noise (PN) code, used at a transmitter to spread a data signal contained in the direct sequence spread spectrum signal, to the received signal. In order to initially align, and maintain alignment of, the PN code with the direct sequence spread spectrum signal, a timing and state control mechanism is included that provides time reference correction information to the signal processing components of the receiver. This time reference correction information allows the receiver to be compatible with transmitters using inaccurate frequency references which impart a significant frequency ambiguity in the received signal. Additional features include computer-based synchronization methods and mechanisms suitable for use for low performance digital signal processors and employ power management mechanisms that enable long-term operation using battery power. The power management mechanisms enable the receiver to operate in a network setting, over the course of multiple years off battery power, with similar receivers, transmitters and transceiver that communicate with one another using direct sequence spread spectrum signals.
A method for suppressing signal loss caused by decimation of a direct sequence spread spectrum signal transmitted from a transmitter, said decimation having associated therewith a predetermined bandwidth and response characteristic that imparts a greater amount of attenuation in a first part of said predetermined bandwidth than in a second part of said predetermined bandwidth, comprising the steps of:
receiving said direct sequence spread spectrum signal with said receiver, said receiver employing a receiver frequency reference and said transmitter employing a transmitter frequency reference where tolerances of the respective references combine to create a predetermined signal position uncertainty range in which said direct sequence spread spectrum signal is positioned prior to signal demodulation; digitizing said direct sequence spread spectrum to form a digital signal; decimating said digital signal with said decimation operator to reduce a sample rate of said digital signal; subdividing said predetermined signal position uncertainty range bandwidth into a plurality of candidate down conversion frequency bands; changing an amount by which a down conversion operator translates the signal toward the second part of said bandwidth, said amount being set under a hypothesis that the digital signal is in one of the plurality of candidate down conversion frequency bands; attempting to correlate said digital signal with a spreading code used to spread the direct sequence spread spectrum signal at the transmitter and determining whether correlation is achieved; and repeating sequentially said changing step for the other channels and stopping said repeating step when said attempting step determines that correlation is achieved.
Foulard (US 2024/0219440 A1) described a method for determining feature signal filters for preparing signal measurement data series of a plurality of measurement variables for experimentally determining a mathematical model (16) that maps model measurement data for at least one target signal sensor (7) based on detected measurement data of a plurality of feature signal sensors (5), comprising: recording feature signal measurement raw data series (1) with the feature signal sensors (5) using a data processing system; ascertaining feature signal measurement data series (2) from the feature signal measurement raw data series (1) using the data processing system; recording at least one target signal measurement raw data series (6) with the at least one target signal sensor (7) using the data processing system; ascertaining at least one target signal measurement data series (8) from the at least one target signal measurement raw data series (6) using the data processing system; ascertaining, in a frequency analysis step (3), a feature amplitude spectrum (4) by the data processing system for each feature signal measurement data series (2) by a frequency analysis method and ascertaining a target amplitude spectrum (9) for the at least one target signal measurement data series (8) by the frequency analysis method; dividing each feature amplitude spectrum (4) into a plurality of mutually adjacent or partially overlapping feature amplitude spectrum sections (10), wherein the feature amplitude spectrum sections (10) each comprise a manually or automatically predetermined feature frequency range; dividing the target amplitude spectrum (9) into target amplitude spectrum sections (11), wherein target frequency ranges of the target amplitude spectrum sections (11) correspond to the feature frequency ranges; ascertaining, in a match checking process (12) in a plurality of repetitive match checking steps, in each case a match measure for each feature amplitude spectrum section (10) by the data processing system, wherein the match measure is a measure for the matching of the amplitude spectrum of the respective feature amplitude spectrum section (10) and an associated target amplitude spectrum section (11); selecting the feature frequency ranges whose match measure exceeds a predetermined match measure number as selection signal frequency ranges (13) by the data processing system; and designing, in a subsequent determination step (14) for the selection signal frequency ranges (13), in each case a respective selection band pass filter (15) by the data processing system, such that signal measurement data series filtered with the respective selection band pass filter (15) have signal components lying within the respective selection signal frequency range (13) and signal components lying outside the respective selection signal frequency range (13) are filtered out of the filtered signal measurement data series, wherein each selection band pass filter (13) forms a feature signal filter for each feature signal sensor (5), with which the match measure between the feature amplitude spectrum section (10) of the feature signal measurement data series (2) recorded by the respective feature signal sensor (5) and the target amplitude spectrum section (11) in the feature frequency range associated with the respective selection band pass filter (13) exceeds the match measure number.
Heath (US 11558133 B2) described an environmental frequency sensing device comprising logic operative to: perform signal strength (SS) level separation on a received band of radio frequency (RF) frequencies to produce SS level separated frequencies based on a signal strength of each RF frequency; perform frequency grouping on the SS level separated frequencies for each signal strength level of the SS level separated frequencies to produce respective magnitude information for each grouping; generate peak data by detecting peaks of the produced respective magnitude information; generate, using the peak data, edge events, each edge event indicating a signal edge based on arrival or departure of a given peak; compare, on a frequency basis, the generated edge events to stored fingerprint data of a signal of interest, the signal of interest associated with a known source of RF frequencies; based on the comparison, determine that at least a portion of the SS level separated frequencies are generated by the known source associated with the signal of interest; and based on the determination, provide detected signal data indicating current use of a range of frequencies by the known source.
Sullivan (US 7046964 B1) described a method of determining stability of frequency and frequency of a frequency-stable signal received from the ambient electromagnetic environment, the method comprising: receiving a dominant signal from ambient electromagnetic signals: counting amplitude transitions of the dominant signal during a primary sampling period; counting the amplitude transitions of the dominant signal during each of at least two secondary sampling periods, wherein the secondary sampling periods are shorter than the primary sampling period and at least one of the secondary sampling periods partially overlaps, in time, the primary sampling period; comparing counts of the amplitude transitions of two of the secondary sampling periods to each other to produce a difference; comparing the difference to a threshold and determining that the frequency of the dominant signal is unstable if the difference exceeds the threshold; and if the difference does not exceed the threshold, determining the frequency of the dominant signal from an accumulated count of the amplitude transitions accumulated during the primary sampling period.
Carbajal (US 2014/0274177 A1) described a system for identifying open space in a wireless communications spectrum comprising: at least one apparatus, wherein the at least one apparatus is operable for network-based communication with at least one server computer including a database, and/or with at least one other apparatus, for identifying signal emitting devices; wherein each apparatus is operable for identifying open space in a spectrum associated with wireless communications including: a housing, at least one processor and memory, and sensors constructed and configured for sensing and measuring wireless communications signals from signal emitting devices in the spectrum associated with wireless communications, for automatically searching the historical and/or reference data stored in a database in memory on the apparatus for predetermined times and frequencies, for automatically analyzing the sensed and measured data to identify the open space in near real-time, and for automatically calculating in near real-time a percent activity associated with the identified open space on predetermined frequencies and/or ISM bands
Lewis (US 5245665 A) described an apparatus for eliminating acoustical feedback in a system which includes a microphone for converting audible acoustic signals into electrical signals, an amplifier for amplifying the electrical signals from the microphone, and a speaker for converting the amplified electrical signals into amplified audible acoustic signals and for broadcasting the amplified acoustic signals in the vicinity of the microphone, the apparatus comprising
analog-to-digital convertor means for digitizing the electrical signals and for periodically producing a predetermined series of digital signals corresponding to a predetermined time segment of the electrical signals;
computer means including fast Fourier transform means for converting each series of digital signals into a frequency spectrum, means for examining successive frequency spectrums to determine the presence of an undesirable acoustic feedback, and means for generating frequency specific filter control signals in response to the determination of the presence of the undesirable acoustic feedback;
the frequency spectrum examining means including means for determining a maximum magnitude frequency, mean for determining whether a magnitude of the maximum magnitude frequency is greater than a magnitude of a selected harmonic of the maximum magnitude frequency by at least a predetermined factor to indicate a candidate resonant frequency, and means for determining the presence of a candidate resonant frequency in a plurality of a predetermined number of successive spectrums to indicate the candidate resonant frequency as the undesirable acoustic feedback; and filter means controlled by the filter control signals form the computer means for attenuating one or more narrow frequency bands in the electrical signal to eliminate the undesirable acoustic feedback.
Moshier (US 4227177 A) described In a speech analysis system for recognizing at least one predetermined keyword in an audio signal, each said keyword being characterized by a template having at least one target pattern, said target patterns having an ordered sequence and each target pattern representing at least one short term power spectrum, an analysis method comprising the steps of
repeatedly evaluating electrical signals representing a set of parameters determining a short-term power spectrum of said audio signal within each of a plurality of equal duration sampling intervals, thereby to generate a continuous time ordered sequence of short-term audio power spectrum frames,
repeatedly generating electrical signals representing a peak spectrum corresponding to said short-term power spectrum frames by a fast attack, slow decay peak detecting function, and
for each short-term power spectrum frame, dividing the amplitude of each frequency band by the corresponding intensity value in the corresponding peak spectrum, thereby to generate a frequency band equalized spectrum frame corresponding to a compensated audio signal having the same maximum short-term energy content in each of the frequency bands comprising the frame, and
identifying electrical signals representing a candidate keyword template when said selected multi-frame patterns correspond respectively to the target patterns of a said keyword template.
Giust (US 10802074 B2) described an apparatus, comprising: a) an input unit to receive a signal-under-test (SUT); b) a noise-measurement unit to convert the SUT into signal data; c) a processing unit; d) memory connected to the processing unit with sufficient storage for the processing unit to perform its operations; and e) programming instructions for the processing unit that causes the processing unit to: i) receive the signal data, ii) derive a plurality of signal samples from the signal data, each signal sample representing a value of phase noise in the SUT at a corresponding offset frequency, iii) use filter data that represents filter characteristics of an application system on a first side of a spectrum boundary to filter a first set of signal samples representing phase noise located on the first side of the spectrum boundary in accordance with the filter data, and iv) filter a second set of signal samples representing phase noise located on a second side of the spectrum boundary by performing an analysis that results in one of: (A) folding the filter characteristics across the spectrum boundary and using the folded filter characteristics to filter signal samples representing phase noise located on a second side of the spectrum boundary, and (B) folding signal samples representing phase noise located on the second side of the spectrum boundary across the spectrum boundary and filtering the folded signal samples in accordance with the filter data.
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 11-20 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 11, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a method for determining feature signal filters for preparing signal measurement data series of a plurality of measurement variables for experimentally determining a mathematical model (16) that maps model measurement data for at least one target signal sensor (7) based on detected measurement data of a plurality of feature signal sensors (5), comprising:
recording feature signal measurement raw data series (1) with the feature signal sensors (5) using a data processing system; ascertaining feature signal measurement data series (2) from the feature signal measurement raw data series (1) using the data processing system; recording at least one target signal measurement raw data series (6) with the at least one target signal sensor (7) using the data processing system; ascertaining at least one target signal measurement data series (8) from the at least one target signal measurement raw data series (6) using the data processing system; ascertaining, in a frequency analysis step (3), a feature amplitude spectrum (4) by the data processing system for each feature signal measurement data series (2) by a frequency analysis method and ascertaining a target amplitude spectrum (9) for the at least one target signal measurement data series (8) by the frequency analysis method; dividing each feature amplitude spectrum (4) into a plurality of mutually adjacent or partially overlapping feature amplitude spectrum sections (10), wherein the feature amplitude spectrum sections (10) each comprise a manually or automatically predetermined feature frequency range; dividing the target amplitude spectrum (9) into target amplitude spectrum sections (11), wherein target frequency ranges of the target amplitude spectrum sections (11) correspond to the feature frequency ranges; ascertaining, in a match checking process (12) in a plurality of repetitive match checking steps, in each case a match measure for each feature amplitude spectrum section (10) by the data processing system, wherein the match measure is a measure for the matching of the amplitude spectrum of the respective feature amplitude spectrum section (10) and an associated target amplitude spectrum section (11); selecting the feature frequency ranges whose match measure exceeds a predetermined match measure number as selection signal frequency ranges (13) by the data processing system which is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes)
Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of designing, in a subsequent determination step (14) for the selection signal frequency ranges (13), in each case a respective selection band pass filter (15) by the data processing system, such that signal measurement data series filtered with the respective selection band pass filter (15) have signal components lying within the respective selection signal frequency range (13) and signal components lying outside the respective selection signal frequency range (13) are filtered out of the filtered signal measurement data series are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional element wherein each selection band pass filter (13) forms a feature signal filter for each feature signal sensor (5), with which the match measure between the feature amplitude spectrum section (10) of the feature signal measurement data series (2) recorded by the respective feature signal sensor (5) and the target amplitude spectrum section (11) in the feature frequency range associated with the respective selection band pass filter (13) exceeds the match measure number appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 11 not eligible.
Claim 12 related to determining the match measure by a correlation analysis by the data processing system appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 2 not eligible.
Claim 13 related to the band pass filter has a filter order of at least eight appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 13 not eligible.
Claim 14 related to wherein the feature amplitude spectrum sections (10) have a predetermined amplitude spectrum width appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 14 not eligible.
Claim 15 related to ascertaining the feature amplitude spectrum sections (10) by the data processing system by dividing the feature amplitude spectrum (4) into two feature amplitude spectrum sections (10) in a first sub-step and determining a first match measure for each feature amplitude spectrum section (10); subsequently further dividing the feature amplitude spectrum sections (10) in each case into smaller feature amplitude spectrum sections (10) in further sub- steps and determining the match measure in each case; and dividing each feature amplitude spectrum section (10) into new feature amplitude spectrum sections (10) in the further sub-steps until an improvement of the match measure between a preceding sub-step and a current sub-step is smaller than a predetermined improvement value appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 15 not eligible.
Claim 16 related to wherein adjacent feature amplitude spectrum sections (10) overlap by a predetermined amplitude spectrum overlap width appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 16 not eligible.
Claim 17 related to mapping training input measurement data series (19) ascertained by the feature signal sensors (5) onto at least one training output measurement data series (18) ascertained by at least one target signal sensor (7); and filtering, by feature signal filters designed the training input measurement data series (19) using a data processing system and thereby forming training input data series (17), wherein a training input measurement data series (19) can be filtered with differently designed feature signal filters, such that a plurality of training input data series (17) are formed from a training input measurement data series (19), and wherein the mathematical model (16) is ascertained using the data processing system by a data-based model determination method (20) starting from the training input data series (17) as model input variables and the at least one training output measurement data series (18) as a model output variable appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 17 not eligible.
Claim 18 related to wherein the training input measurement data series (19) are formed by the feature signal measurement data series (2) ppears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 18 not eligible.
Claim 19 related to wherein the feature signal measurement data series (2) have feature signal measurement data points directly following one another in time and thus in each case form a section of the associated feature signal measurement raw data series (1), wherein the sections of the feature signal measurement data series (2) have at least one predetermined minimum number of data points and wherein the section is selected by the data processing system such that a target signal power is maximum in the selected section appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 19 not eligible.
Claim 20 related to ascertaining, in order to determine the target signal power for a target signal measurement raw data series (6), a short-term frequency amplitude spectrum by the data processing system for each target signal measurement raw data point; determining a short-term frequency amplitude power for each short-term frequency amplitude spectrum; combining target signal measurement raw data points following one another in time into target signal measurement raw data sections by the data processing system in such a way that a change in the short-term frequency amplitude power of target signal raw data points directly following one another in time is below a predetermined change power; and subsequently forming in each case a target signal power by the data processing system for all combinations of target signal raw data sections following one another in time that have the predetermined minimum number of data points; and selecting the combination as section by the data processing system whose target signal power is maximum appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 20 not eligible.
Contact information
4. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST.
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/TUNG S LAU/Primary Examiner, Art Unit 2857
Technology Center 2800
February 24, 2026