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AN OVERBROAD PATENT ON pitch detection - This issued patent from Overtone Labs seeks to patent the idea of...pitch detection for resonance tuning of a musical instrument. 10 minutes of your time can help narrow US patent applications before they become patents. Follow @askpatents on twitter to help.

A new method patent was granted in August of 2013 which is extremely dangerous. The patent is being asserted against a small veteran-owned company which released an app. Other apps have fearfully withdrawn from global app markets as a result of this patent.

QUESTION - Have you seen anything that was published before 11/30/2011 that discusses:

  • Using power spectral analysis to tune musical instruments.

Please review Patent US8502060 The app that is being sued is being forced into cease and desist and black out social media communities 60,000 musicians strong.

Claims 1 & 13 are particularly concerning.

1: A method for resonance tuning, comprising:

  • Receiving a signal in response to a resonance of a structure;
  • Determining a frequency or musical note related to an overtone from the signal;
  • Selecting the frequency or musical note related to the overtone as a filter mode reference frequency or musical note; and
  • Suppressing a display of frequencies or musical notes from a subsequent signal that deviate from the filter mode reference frequency or musical note by a predetermined threshold.

.

13: A method for pitch detection, comprising:

  • Providing one or more power spectrum frequency samples;

  • Selecting a frequency in a frequency band having a largest power spectrum magnitude from the one or more power spectrum frequency samples, the frequency band having an upper frequency limit and a lower frequency limit.


"A schematic view of an embodiment of the Pitch Estimator"

FIG. 16 cited by applicant in Supplemental Examination Support Doc as providing support for Claim 13. (annotation emphasis added)

What is good prior art? Please see our FAQ.

Want to help? Please vote or comment on submissions below. We welcome you to post your own request for prior art on other questionable US Patent Applications.


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    I'm not a subject matter expert here but isn't it strange that the applications was filed on 2/15/13 and allowed three months later (5/28/13)? No initial rejection, no changes to the claims. It's not plausible to me that, for example, claim 13 should have been allowed over prior art. I can see why OP posted this prior art request. Any indication in the file history how this could have happened? – Micah Siegel Dec 11 '13 at 4:57
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    It's an expedited prosecution where the Patent office relies on a prior art search by applicant where the applicant supplies the PTO with the best art they could find and says why their claims are patentable over the prior art. In this case, they were UNABLE TO FIND PRIOR ART FOR PEAK DETECTION which strains credulity!!! – Frank-n'Grind Dec 11 '13 at 16:47
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    Luckily, the prosecution history is public record and available at USPTO.gov under public pair. The most recent Examination Support Document (starting at page 5) is the most fun... tied by the Examiner's reasons for allowance in the Notice of Allowance. HOW IS PEAK DETECTION NOVEL, let alone not obvious??!? – Frank-n'Grind Dec 11 '13 at 18:51
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    It's inequitable that there's NO means to respond to a patent suit without a small business paying on the order of $30,000??? Patent attorneys + litigation counsel aren't cheap and neither is instigating a Reexam proceeding (another $6000; micro-entity doesn't apply for 3rd party requesters). – Frank-n'Grind Dec 11 '13 at 18:55
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    A change.org petition was started if anyone is interested. I find this a compelling story. tinyurl.com/kyh4gqx – Robert Tesla III Dec 24 '13 at 19:44

44 Answers 44

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Programming Electronic Music in Pd Johannes Kreidler 27-01-2009 http://www.pd-tutorial.com/english/ch03s08.html This seems a good primer for understanding what's going on with FFT, selection, filtering, displaying, and the like**

3.8.1.1 Analyzing partials

Let's return to a basic concept of additive synthesis: a sound comprises partials. If you want to find out what the component parts of a sound are, you could employ a set of band-pass filters for every partial: ... 3.8.2.1 Filters ... 3.8.3.2 Tuner

Here's one way to build a tuner: patches/3-8-3-2-tuner.pd

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    3.8.2.1 Filters What's useful about FFT, of course, is that the values it determines can be changed before you resynthesize the components into a sounding result. For example, you could set certain bins to be louder or quieter; you could build filters like high-pass, low-pass, etc., or 'draw' one yourself. patches/3-8-2-1-fft-filter.pd – Robert Tesla III Dec 20 '13 at 15:10
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+200

Non-Cited Prior Art

US4434697

Indicator apparatus for indicating notes emitted by means of a musical instrument

Claim 1

Apparatus for indicating the presence of musical notes and for identifying the musical notes detected comprising:

means for amplifying input signals corresponding to musical notes to be identified; filter means connected to said means for amplifying for eliminating harmonics from said input signals;

energy detecting means connected to said means for amplifying for detecting input signals exceeding a predetermined threshhold;

memory means including at least one memory for storing items of information representing a table of musical notes;

means for calculating the frequency and octive of said input signal, said means for calculating including microprocessor means and being connected to said filter means, said energy detecting means and said memory means, said means for calculating being responsive to input signals received from said filter means exceeding said predetermined threshhold determined by said energy detecting means to calculate the frequency thereof, said frequency calculated being employed to read from said memory for storing items of information representing a table of musical notes items of information representing the closest corresponding musical note for said frequency calculated; and

means for displaying, in alphanumeric form, said musical note read closest to each successive musical note in said input signals to be indentified and the octive in which said musical note resides.

Claim 2

The apparatus according to claim 1 wherein said means for calculating additionally comprises:

means for determining any difference between said frequency of said input signals calculated, and said closest corresponding musical note read;

means for providing indicia representative of any difference determined; and

means for supplying said indicia to said means for displaying to cause said indicia to be displayed.

Claim 3

The apparatus according to claim 2 wherein said indicia take the form of a plurality of signs, and selected ones of said plurality of signs respectively indicate that a difference between a calculated frequency of an input signal and a frequency of a displayed musical note is a positive value exceeding a predetermined limit, a negative value exceeding a selected limit and a value within a limit.

Claim 4

The apparatus according to any one of claims 1, 2 or 3 wherein said filter means comprises:

a plurality of low pass filter means for receiving said input signals representing said musical notes to be identified;

a plurality of threshhold detector means for indicating that input signals applied thereto exceed a predetermined threshhold, each of said plurality of threshhold detector means being connected to an associated one of said plurality of low pass filter means;

means for determining a one of said plurality of low pass filter means having the lowest cut-off frequency characteristic and at least a portion of said input signals having a predetermined threshhold level passing therethrough; and means for inhibiting outputs from remaining ones of said plurality of low pass filter means having cut-off frequency characteristics higher than that of said one of said plurality of low pass filter means.

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  • "said means for calculating including microprocessor means and being connected to said filter means, said energy detecting means and said memory means, said means for calculating being responsive to input signals received from said filter means exceeding said predetermined threshhold determined by said energy detecting means to calculate the frequency thereof" == YEP – Fullsail SoundDesign Dec 21 '13 at 20:24
4

Non Cited Prior Art

PITCH DETECTION USING THE SHORT—TERM PHASE SPECTRUM

F.J. CHARPENTIER (Tokyo, 1986)

http://www.ee.columbia.edu/~dpwe/papers/Charp86-pitchphase.pdf

Abstract

A new frequency domain method for determining the fundamental frequency of speech is presented in this paper. This method uses the information contained in short-term phase spectrum whereas the previous methods were limited to the amplitude spectrum. The short-term spectrum is computed by DFT and is interpreted as the output of a bank of band-pass filters. Harmonic components are detected by searching for sets of three continuos filters having the same instantaneous frequency. The frequency of a detected harmonic is given by the instantaneous frequency itself. A conventional harmonic numbering algorithm is used to confer the set of detected harmonics to a value of the fundamental frequency. Preliminary results show the validity of the method.

This document goes into detail about detecting a frequency and automatically band-passing the signal to eliminate other harmonics. It was discusses it being used in a pitch detection application and eliminating other noise and computation below a threshold.

enter image description here enter image description here enter image description here

To further clarify, bandpass a frequency detected, and when applicable, reduce the onboard computation by only computing frequencies above a threshold. enter image description here

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  • "...simultaneously using the information of the amplitude spectrum to restrict the calculations to the neighborhood of the spectral peaks" == 060: above a predetermined threshold with an upper frequency limit and a lower frequency limit. Seems Legit. – Tyler Ulrich Dec 23 '13 at 14:56
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I also find this US Patent US4688464 particularly fascinating. Filed January 16, 1986.

In it's abstract, it states:

A pitch detector is disclosed that automatically recognizes the pitch of musical notes quickly and outputs the pitch information in a variety of formats. The detector employs a microprocessor that samples the signal from a musical instrument or voice at regular intervals using an analog-to-digital converter and then utilizes both amplitude and time information from the waveform to determine the fundamental period of the note, while rejecting the harmonic components. The microprocessor analyzes the waveform looking for peaks that are approximately equal in amplitude separated by opposite polarity peaks. The time intervals between the peaks must be approximately equal too. Timing information is measured using more than one point on the waveform to avoid inaccuracies caused by temporary distortions of the waveform. The timing points are chosen at points where the slope of the waveform is high for substantially optimal accuracy. To filter out erroneous readings caused by pitch detection during note transition or noise, additional processing of the data is performed to cause a second corroborating reading to be taken when a note transition that is uncommon musically is detected.

Here's Claim 1, other claims can invalidate one or more of the '060 Patent claims easily.

  1. An apparatus, for determining the pitch of a substantially periodic audio input signal having one or more sinusoidal components forming a series of peaks of a given polarity that are separated by at least one peak of opposite polarity and that define at least one cycle of said signal, comprising: a first means for producing a plurality of overlapping, sample timing intervals from a portion of said input signal; a second means for producing an indication of the period of said input signal from said plurality of sample timing intervals; and a third means for converting said indication of said period of said input signal into a determination of the pitch of said input signal.
  2. The apparatus of claim 1, wherein said second means comprises: means for determining an average sample timing interval from selected ones of said sample timing intervals; and means for dividing said average sample timing interval by the number of cycles of said portion of said input signal that said sample timing intervals were produced from, to obtain an indication of the period of said input signal.
  3. The apparatus of claim 2, further comprising: means for identifying a substantially recurrent reference peak on said input signal, said reference peak separated in occurrence by a peak of opposite polarity and having an amplitude at least a predetermined percentage of the maximum peak amplitude of said input signal, said means for producing a plurality of sample timing intervals being responsive to the occurrence of said reference peak.

US4688464

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  • The peaks mentioned in the abstract are time domain peaks, not frequency domain peaks. – George White Dec 13 '13 at 18:11
  • @GeorgeWhite how is time or frequency domain relevant when not identified in the claim? Either way, both are used to compute frequency analysis across tuners alike. link link – Robert Tesla III Dec 13 '13 at 20:50
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    The claims all refers to frequency, frequency bands, frequency power spectrum peaks, etc. The patent mentioned here says "microprocessor analyzes the waveform looking for peaks that are approximately equal in amplitude separated by opposite polarity peaks." These are waveform peaks.I do not particularly think the patent in question is very good but I am pointing out ways in which the references people have come up with may not actually be 100% on target. – George White Dec 13 '13 at 21:39
  • I have a few patents I found and when I get time later I will post them. Regarding waveform / frequencies. They are contained within a wave. synthesizeracademy.com/harmonics "Different waveforms sound different. A sine wave sounds different than a square wave, which sounds different than the waveform that comes out of an accordion. All these waves have unique timbres because they have different harmonic content. A harmonic is basically a multiple of a fundamental frequency. For example, let’s say you’re playing a note with a frequency of 100Hz. That would be the 1st harmonic." – Robert Tesla III Dec 13 '13 at 21:46
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    Claim 13 of '060 recites "power spectrum frequency samples...selecting a frequency" these indicate a fourier transform to convert from timebased intensity graph to a graph of each component frequency's intensity.To anticipate claim 13 '060, somewhere within a prior art reference (not necessarily in its Claims) it must disclose each and every step claimed. Claim 13 recites freq spectrum(which is generally arrived at with FFT).So finding a ref that recvs a sound,FFT's it,&"selects"the most intense(loudest==highest magnitude==highest amplitude...)frequency anticipates& invalidates'060. – Frank-n'Grind Dec 19 '13 at 18:50
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From my quick look at this, I can see that this is based on FFT Fast Fourier Transforms which is a public domain algorithm.

From "The FFT - an algorithm the whole family can use"

A paper by Cooley and Tukey [5] described a recipe for computing Fouri- er coecients of a time series that used many fewer machine operations than did the straightforward procedure...What lies over the horizon in digital signal processing is anyone's guess, but I think it will surprise us all.

We are surprised.

"It seems almost everyone knows that somehow all the data whizzing over the internet, bustling through our modems or crashing into our cell phones is ultimately just a sequence of 0's and 1's { a digital sequence { that magically makes the world the convenient high speed place it is today. So much of this magic is due to a family of algorithms that collectively go by the name "The Fast Fourier Transform", or "FFT" to its friends, among which the version published by Cooley and Tukey [5] is the most famous. Indeed, the FFT is perhaps the most ubiquitous algorithm used today in the analysis and manipulation of digital or discrete data."

So it's an obvious choice and the most common used data analysis algorithm known to man and it's in the Public Domain.

"My own research experience with various avors of the FFT is evidence of the wide range of applicability: electroacoustic music and audio signal processing, medical imaging, image processing, pattern recognition, computational chem- istry, error correcting codes, spectral methods for PDEs and last but not least, in mathematics, as the starting point of my doctoral dissertation in computa- tional harmonic analysis which investigated group theoretic generalizations of the Cooley-Tukey FFT. Of course many more could be listed, notably those to radar and communications. The book 2 is an excellent place to look, especially pages 2 and 3 which contain a (nonexhaustive) list of seventy-seven applications"

This was written in 1999.

"Despite these early discoveries of an FFT, it wasn't until Cooley and Tukey's article that the algorithm gained any notice. The story of their collaboration is an interesting one. Tukey arrived at the basic reduction while in a meeting of President Kennedy's Science Advisory Committee where among the topics of discussions were techniques for o-shore detection of nuclear tests in the Soviet Union. Ratication of a proposed United States/Soviet Union nuclear test ban depended upon the development of a method for detecting the tests without actually visiting the Soviet nuclear facilities. One idea was to analyze seismo- logical time series obtained from o-shore seismometers, the length and number of which would require fast algorithms for computing the DFT. Other possible applications to national security included the long-range acoustic detection of nuclear submarines."

So obviously, FFT was treated with the highest National Security in mind when it was discovered.

"Richard Garwin of IBM was another of the participants at this meeting and when Tukey showed him this idea he immediately saw a wide range of potential applicability and quickly set to getting this algorithm implemented. He was directed to Cooley, and, needing to hide the national security issues, instead told Cooley that he wanted the code for another problem of interest: the determination of the periodicities of the spin orientations in a 3-D crystal of He3. Cooley had other projects going on, and only after quite a lot of prodding did he sit down to program the "Cooley-Tukey" FFT. In short order, Cooley and Tukey prepared a paper which, for a mathematics/computer science paper, was published almost instantaneously (in six months!) [5]. This publication, as well as Garwin's fervent prosletizing, did a lot to help publicize the existence of this (apparently) new fast algorithm. (See also [4] and the introductory papers of [17] for more historical details."

So this obviously caught the eye of the government, along with IBM.

The timing of the announcement was such that now usage spread quickly. The roughly simultaneous development of analog to digital converters capable of producing digitized samples of a time-varying voltage at rates of 300,000 samples/second had already initiated something of a digital revolution, and was also providing scientists with heretofore unimagined quantities of digital data to analyze and manipulate (just as is the case today!). Even the standard applications of Fourier analysis as an analysis tool for waveforms or solving PDEs, meant that a priority there would be a tremendous interest in the algorithm. But even more, the ability to do this analysis quickly allowed scientists from new areas to try the DFT without having to invest too much time and energy in the exercise. I can do no better than to quote the introduction to the FFT from Numerical Recipes, If you speed up any nontrivial algorithm by a factor of a million or so the world will beat a path towards nding useful applications for it 3."

Indeed, it's powerful, now free, and non-trivial to use.

Even beyond these direct technological applications, the FFT influenced the direction of academic research too.

Academic research. Doesn't the app being sued cost a few dollars on the app store? I'd say it's a fair allocation of an FFT algorithm.

"Ironically, the prominence of the FFT may have also contributed to slow oth- er areas of research. The FFT provided scientists with a big analytic hammer, and for many, the world suddenly looked as though it was full of nails { even if this wasn't always so."

Taken out of context perhaps, but I'd say patenting anything that uses the FFT algorithms and re-wording it, without it being actually examined is a "big analytical hammer".

Even now there are still lessons to be learned from the FFT's development. In this day and age in which almost any new technological idea seems fodder for internet venture capitalists and patent lawyers, it is natural to ask, Why was the FFT not patented by IBM?" As Cooley tells the story, on the one hand,Tukey was not an IBM employee, so IBM had some worry that they might not be able to gain the patent. Consequently, they had great interest in putting the algorithm in the public domain. The eect of this was that no one else would be able to patent the algorithm, and even more, like many computer manufacturers of that time, the thought was that the money was to be made in hardware, not software. In fact, the FFT was designed as a tool for the analysis of huge time series, in theory something only tackled by supercomputers. So by placing in the public domain an algorithm which would make feasible the analysis of large time series, more big companies might have an interest in buying supercomputers (like IBM mainframes) to do their work. Times certainly have changed.

Whether having the FFT in the public domain had the eect IBM hoped for is moot, but it is certain that it did provide many, many scientists with appli- cations to work on and apply the algorithm. The breadth of scientic interests at the Arden workshop (held only two years after publication of the paper) is truly impressive. In fact, the rapid pace of today's technological developments is in many ways a testament to the advantage of this open development. This is a cautionary tale in today's arena of proprietary research, and we can only wonder which of the many recent private technological discoveries might have prospered from a similar announcement

There's a very interesting quote, extracted from above:

"In this day and age in which almost any new technological idea seems fodder for internet venture capitalists and patent lawyers, it is natural to ask, Why was the FFT not patented by IBM?"

An algorithm for the machine calculation of complex Fourier series Authors James W. Cooley and John W. Tukey 1965

Full PDF Free Access of Cooley and Turkey's Paper

The '060 Patent on FFT

The '060 Patent talks about using Fourier and basically claims that all others do it wrong?

FIG. 6 shows an embodiment of the Power-Spectral Estimator 78 as referenced in FIG. 5. The Power-Spectral Estimator 78 receives a series of buffered time samples from the Buffer 70 in FIG. 5 and optionally conditions the samples with Zero-padding 84 and Windowing 86 prior to converting the time samples to the frequency domain with a Fast Fourier Transform (FFT) 88. Zero-padding 84 refers to adding zero-value samples to the predominately non-zero value series of time samples to increase the size of the FFT and hence the resulting frequency resolution

And then...

The FFT is a specific implementation of a Time-To-Frequency-Transform, defined herein to refer to the conversion of time samples to the frequency domain irrespective of the algorithm used. For example, in other embodiments the Time-To-Frequency-Transform uses either a Discrete Fourier Transform (DFT), a Discrete Cosine Transform (DCT), a Fast Cosine Transform, a Discrete Sine Transform (DST) or a Fast Sine Transform (FST).

And then...

In a preferred embodiment Zero-padding 84 is used with Windowing 86. Because the series of time samples, with or without the Zero-padding 84, only represents a finite observation window, the resulting spectral information will be distorted after performing an FFT due to the ringing or sin(f)/f spectral peaks of the rectangular window. This is also referred to as “spectral leakage.” To correct for this, each sample in a series of time samples is multiplied by a sample from a fixed waveform such as a Hanning, Bartlett or Kaiser window. In this embodiment these window functions have the same number of samples as the FFT (e.g. 4096), have symmetry about N/2 and increase in value from close to zero at the beginning and end of the time series to a maximum value at the center of the time series. In a preferred embodiment, a Blackman-Harris window function is used.

And then...

In a preferred embodiment the time samples are preconditioned with Zero-padding 84 and Windowing 86 and are subsequently converting to the frequency domain with an FFT processor. It is envisioned that any Discrete Fourier Transform (DFT) can be used to perform the frequency conversion without being limited to using an FFT. Following the FFT 88, the series of frequency samples forming an estimate of the frequency spectrum is converted into a power-spectral estimate by squaring each of the frequency samples with a Magnitude Squared function 90.

The description in the '060 talks about FFT in it's digital sense, yet it's claims are overly broad because it's not bound to a specific device by the sense that if you are listening to anything and select the highest frequency, loudest person, loudest sound and focus on it, you are infringing upon '060.

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    The link Patent states in the description: "FIG. 6 shows an embodiment of the Power-Spectral Estimator 78 as referenced in FIG. 5. The Power-Spectral Estimator 78 receives a series of buffered time samples from the Buffer 70 in FIG. 5 and optionally conditions the samples with Zero-padding 84 and Windowing 86 prior to converting the time samples to the frequency domain with a Fast Fourier Transform (FFT)" – Tyler Ulrich Dec 13 '13 at 4:37
  • One has to wonder how you can use a Public Domain algorithm, patent it, then assert a patent on someone else for using the most common algorithms in the world. Let's just add Whip Topping, Sprinkles, on top of someone else's work, patent it, then claim all rights to it and sue everyone, well one small company because it's unlikely a large company would back down. Strange? – Aron Stein Dec 15 '13 at 7:29
  • I find it unethical to patent a method that relies solely upon the hard work of others, then tell others you can't do it because I did it first. The 060 needs an OH-WHAT-OH? Reexamination. – Audio Sniper Dec 17 '13 at 20:07
  • I agree with Aron. Currently there are numerous tuning devices in the Apply Itunes app store. Of note is one that bears the Evans brand which is owned by D'Addario. Another belongs to Toulson. In fact, if you consider a tennis racquet a resonant body - which it is - then the Raquetune app may also be exposed to an infringement lawsuit. – user12023 Dec 6 '14 at 22:30
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Checking some dates here from the original post, I find this to be very concerning:

Asserted Patent in lawsuit: US8502060 also filed as application US20130139672

Original Application: US20130145921:

  • Filing Date: Nov 30, 2011
  • Publication Date: June 13, 2013

This application is specifically for a handheld tuner via the claims, yet still has the methods in it.

An app comes on the market called iDrumTune on 4/20/2012 - the creator cited as prior art in '060 Patent

An app comes on the market called iDrumTech on February 8th, 2013 and one week later the '921 application is revised and stripped of any novel information, filed as '060 (a continuation) and gets granted? Seems fishy!

iDrumTech App (Named in lawsuit) Release date on itunes

Continuation Of US20130145921 Filed As US8502060

  • Filing Date: Feb 15, 2013
  • Publication Date: Aug 6, 2013

All CLAIMS for a tuner device are now obscured by a Method?

So in summary to the above, an original application US20130145921 was filed on Nov 30,2011 claiming an actual tuner device in the claims. It wasn't published until Jun 13, 2013? Two apps are released, one is built on prior art (iDrumTune / Rob Toulson) released 4/20/2012, and another released 2/8/2013. Patent filed, overbroad claims on 2/15/2013 and published 5/2013, granted 8/2013.

WHAT HAPPENED? Expedited Processing. Applicant claims novel over prior art, the patent office doesn't dispute, rubber stamps, law suits filed.

There's a great article I read recently called This simple change could fix the patent system—but it’ll never happen. by James Bessen on slate.com:

Problem No. 2: Gotcha lawsuits

Continued patents are much more likely to be litigated than are other patents—not only because they result in low-quality patents, but also because they permit a particularly nasty legal tactic: A patent applicant with a continued application can rewrite the claim language so that the patent covers technology that is developed after the patent is filed. In the worst cases, so-called “submarine patents” are kept hidden from the public as they mark time in the Patent Office. Innovative companies come along and develop new technology only to find themselves surprised one day with a patent infringement lawsuit, a lawsuit based on a patent application that was rewritten to cover the defendant’s own innovations.

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  • My observations as well. – Aron Stein Dec 15 '13 at 7:26
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Non Cited Prior Art

EP0285238B1 (1987)

Digital bandpass oscilloscope

This patent from 1987 discusses an Oscilloscope with bandpass filters, an accumulator that uses gates (predetermined threshold) and can easily be used to invalidate claims of the '060 patent.

This patent talks about using Time and converting to Frequency Domains, analyzing a signal, applying a bandpass filter to it (high/low frequency limits) and deriving subsequent signals from bandpass above a predetermined threshold (gates). Look under [0047] in the description for gates used in the embondiment.

Background of the Invention

[0001] The present invention relates to digital storage oscilloscopes in general and in particular to a digital oscilloscope for displaying waveforms representing component signals of frequency within selectable passbands of an input signal.

[0002] The behavior of component signals of frequency within selected frequency bands of a wideband analog signal is often of interest, and spectrum analyzers provide researchers with frequency domain plots of signal amplitudes within a band. However, sometimes researchers wish to view a frequency band of interest as a time domain plot. Oscilloscopes plot signal magnitudes as function of time, but when a waveform representing an input signal is displayed by a conventional oscilloscope, signal components having frequencies within a particular frequency band of interest are often difficult to observe due to the presence of higher or lower frequency components. A analog bandpass filter is sometimes utilized to remove the higher and lower frequency components from the analog signal before it is applied to the input of an oscilloscope, but many different bandpass filters would be needed in order to separately view a wide range of selectable passbands.

[0047] Accumulator 132 includes an adder 134 and a random access memory (RAM) 136. Adder 134 is adapted to add each output term produced by multiplier 130 to an accumulated sum R stored in RAM 136. The sum produced by adder 134 may then be stored in RAM 136, thereby replacing the accumulated sum R with the result of the addition. Addressing of RAM 136 is controlled by an address signal (ADDR) provided by state machine 118 of FIG. 8. Data output terminals of RAM 136 are coupled to an input of adder 134 through a set of AND gates 138, each having another input controlled by a signal NADD supplied by state machine 118 of FIG. 8. When NADD is low, a 0 value, rather than the currently addressed data in RAM 136, is passed to adder 134. The NADD signal may be driven low when the output of multiplier 130 is the first term of a sum to be accumulated in RAM 136 so that adder 134 merely added a 0 to that term and forwards it for storage in RAM 136. The output of adder 134 is coupled to data input terminals of RAM 136 through another set of AND gates 140. A signal NLOAD produced by state machine 118 is applied to an additional input of each AND gate 140 and is driven low when RAM 136 is to store a 0 value rather than the output of adder 134. The NLOAD signal allows the contents of any storage location in RAM 136 to be initialized to 0 when necessary.

(going through a serious of noise gates to determine if the signal is worthy of displaying above a threshold / level).

EP0285238B1

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3

I find pitch refinement an interesting topic.

WO1999059138A2 (May 11, 1998)

Refinement of pitch detection

Abstract

Successive pitch periods/frequencies are accurately determined in an audio equivalent signal. Using a suitable conventional pitch detection technique, an initial value of the pitch frequency/period is determined for so-called pitch detection segments of the audio equivalent signal. Based on the determined initial value, a refined value of the pitch frequency/period is determined. To this end, the signal is divided into a sequence of pitch refinement segments. Each pitch refinement segment is associated with at least one of the pitch detection segments. The pitch refinement segments are filtered to extract a frequency component with a frequency substantially corresponding to an initially determined pitch frequency of an associated pitch detection segment. The successive pitch periods/frequencies are determined in the filtered signal

Description

.... In step 130, each pitch refinement segment is filtered to extract the fundamental frequency component (also referred to as the first harmonic) of that segment. The filtering may, for instance, be performed by using a band-pass filter around the first harmonic. It will be appreciated that if the first harmonic is not present in the signal (e.g. the signal is supplied via a telephone line and the lowest frequencies have been lost) a first higher harmonic which is present may be extracted and used to accurately detect this representation of the pitch. For many applications it is sufficient if one of the harmonics, preferably one of the lower harmonics, is accurately detected. It is not always required that the actually lowest harmonic is detected. Preferably, the filtering is performed by convolution of the input signal with a sine/cosine pair as will be described in more detail below.

I read each pitch refinement segment ('138) as subsequent signal ('060)

In step 140, a concatenation occurs of the filtered pitch refinement segments. The filtered pitch detection segments are concatenated by locating each segment at the original time instant and adding the segments together (the segments may overlap). The concatenation results in obtained a filtered signal. In step 150, an accurate value for the pitch period/frequency is determined from the filtered signal. In principle, the pitch period can be determined as the time interval between maximum and/or minimum amplitudes of the filtered signal. Advantageously, the pitch period is determined based on successive zero crossings of the filtered signal, since it is easier to determine the zero crossings. Normally, the filtered signal is formed by digital samples, sampled at, for instance, 8 or 16 Khz. Preferably, the accuracy of determining the moments at which a desired amplitude (e.g. the maximum amplitude or the zero-crossing) occurs in the signal is increased by interpolation. Any conventional interpolation technique may be used (such as a parabolic interpolation for determining the moment of maximum amplitude or a linear interpolation for determining the moment of zero crossing). In this way accuracy well above the sampling rate can be achieved.

(Extract from above:)

the pitch period is determined based on successive zero crossings of the filtered signal

To determine Zero Crossings, you need to have a predetermined threshold. For it to be successive, it would have been required to exceed a predetermined threshold.

In summary, if this method is employed on a microphone, guitar, keyboard, amplifier, and human voice, detects a pitch/fundamental/harmonics and applies a bandpass filter around them for suppressing further action it would infringe. This could include spectral analysis, visualizers, frequency readouts and any derivative thereof.

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  • If I sing a note, or even speak in a certain tone, then this device concatenates over time when a specific tone is heard by way of amplitude and filters. Displaying a histogram of a repetitive action within a certain criteria (filtered range above a sound level). I suppose this would be the same as tuning a drum, guitar, piano, voice? Repeatedly producing a pitch within a pitch range.. – Robert Tesla III Dec 21 '13 at 19:55
  • Pitch recognition is done by spectral analysis. Nice find. YEP. – Fullsail SoundDesign Dec 21 '13 at 20:25
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I do believe the defendant demonstrated his prior art on Youtube in September 2010 as in this video. From his other videos, he teaches how to use Noise Gates, Filters, etc for drum recording and demonstrates the differences. In this video, the same methods applied, standard audio DAW called Logic Pro by Apple and he's got the spectrum analyzers and frequency readouts right there.

http://www.youtube.com/watch?v=jSTp9jKJpZU&feature=youtu.be&t=1m50s

Ironic isn't it how the patent applied for is for Drum Tuning, specifically in Spectral Analysis yet the gentleman who demonstrated it on YouTube in several videos before Nov 30, 2011 is being shut down and sued out of business for a method that can't technically be patented?

Concerned.

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  • Here's a video from a Book/DVD from 2004 called Sound Design. Cited as prior art on the patent, but interesting to say the least. Bob talks about sound being a sense, and the Attack and Fundamental of a drum. 31:30 just give that man 2 minutes. youtu.be/kM19VkXXJp4?t=31m30s – Aron Stein Dec 13 '13 at 0:28
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    Let's focus on the claims. That seems to be a more direct route. – Tyler Ulrich Dec 16 '13 at 21:45
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I will respectfully revert to a couple of fart jokes if you don't mind.

Fart Joke 1

1: A method for resonance tuning, comprising:

Receiving a signal in response to a resonance of a structure; Determining a frequency or musical note related to an overtone from the signal; Selecting the frequency or musical note related to the overtone as a filter mode reference frequency or musical note; and

Suppressing a display of frequencies or musical notes from a subsequent signal that deviate from the filter mode reference frequency or musical note by a predetermined threshold.

Jim is a man who is of normal stature. He's clean and respectable and while riding on a subway, he passes gas. The resonant result of this action did not result in a sound above a certain level. Therefore, he suppresses his facial expressions and does not display a result of the frequency in sound because it was below a certain level. Therefore his high/low limits based on a target frequency of his natural bandpass filtering capabilities helped him avoid humiliation more than it was due. This is normal behavior, and obvious to most. His retraction from displaying the result therefore suppressed naturally.

filter mode reference frequency

If I'm not mistaken, every filter has a center point (a reference frequency), therefore you simply cannot apply a high/low limit because it will have a reference frequency, so that covers A) a common mode of usage, not novel, obvious and B) every digital device in history.

A "Mode" in simpler terms is a means of on/off.

I believe this simple diagram explains what happens in each human mind when they pass gas:

"A schematic view of an embodiment of the Pitch Estimator"

FIG. 16 cited by applicant in Supplemental Examination Support Doc as providing support for Claim 13. (annotation emphasis added)

Fart Joke 2

13: A method for pitch detection, comprising:

Providing one or more power spectrum frequency samples;

Selecting a frequency in a frequency band having a largest power spectrum magnitude from the one or more power spectrum frequency samples, the frequency band having an upper frequency limit and a lower frequency limit.

Jim accidentally passes a very loud percussive, resonant fart on the subway surrounded by people. His natural reaction is to either say Excuse ME!! or point at someone else and shift the blame. Therefore, he reacted above a certain sound threshold and indicated his result until the sound threshold was later lowered. A noise gate is all that is really needed to understand any of these claims.

A method for pitch detection

There are thousands of pitch detection methods to include telephones that recognize pitch and many others to include natural instinct. Red Flag #1

Providing one or more power spectrum frequency samples;

A person listening to anything that is in their environment

Selecting a frequency in a frequency band having a largest power spectrum magnitude from the one or more power spectrum frequency samples, the frequency band having an upper frequency limit and a lower frequency limit.

Listening to whichever source you desire by naturally filtering out other sounds. Imagine if your Mother was talking to you while you were watching television. You choose to listen to either your mother, or the television and you've infringed upon this method. Danger!

I believe this simple diagram explains what happens in each human mind when they pass gas:

"A schematic view of an embodiment of the Pitch Estimator"

FIG. 16 cited by applicant in Supplemental Examination Support Doc as providing support for Claim 13. (annotation emphasis added)

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  • I believe there is something called the but trumpet, and other musical instruments have been named after such. I would agree with this assessment and way of putting it. Well done. – Tyler Ulrich Dec 14 '13 at 22:36
  • Nice ASSessment. I do agree that the pitch of the resonance of the fart would affect the manner of the result of his expression and reaction, especially since certain pitches produce different facial recognition in relation to the resonance of the structure of thy anus. I think this is fantastic. – Robert Tesla III Dec 14 '13 at 22:48
  • Well, I wasn't expecting that! Oops, I reacted. – Aron Stein Dec 15 '13 at 7:28
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This could be considered prior art, but it's an application called Drum-Tuner on both iTunes and Google Play. The iphone version was published Jul 25, 2012

enter image description here

The proof here is that the '060 is not novel, and obvious.

The '060 Application filing date is 02/15/2013, published Aug 6, 2013 The '060 patent was an extension of Application US20130139672 Published Jun 6, 2013

My thoughts are leaning towards how is an idea novel and unobvious if the application or patent was not published yet so many very close ideas such as Drum-Tuner and apparently a few other software apps like Toulson's iDrumTune came out which now technically infringe upon 060 due to its broad claims.

Anyone?

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  • I wouldn't consider this prior art, but it states obviousness. – Aron Stein Dec 17 '13 at 21:16
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Can You Patent Human Auditory Senses?

I believe they just patented basic human auditory senses. The Claims in '060 discuss selecting frequencies above a predetermined threshold, and suppressing the rest. Likewise, Claim 1 discusses bandpass filtering around a target frequency. This is exactly what the natural auditory senses of the human ear and brain do.

Auditory Masking - Wikipedia

http://en.wikipedia.org/wiki/Auditory_masking

"Off frequency listening is when a listener chooses a filter just lower than the signal frequency to improve their auditory performance. This “off frequency” filter reduces the level of the masker more than the signal at the output level of the filter, which means they can hear the signal more clearly hence causing an improvement of auditory performance."

Am I to assume that anyone recognizing a pitch would be infringing?

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The more I look into this I find that it's obvious the patent covers basic human auditory senses.

Psychoacoustics:

http://en.wikipedia.org/wiki/Psychoacoustics

Hearing is not a purely mechanical phenomenon of wave propagation, but is also a sensory and perceptual event; in other words, when a person hears something, that something arrives at the ear as a mechanical sound wave traveling through the air, but within the ear it is transformed into neural action potentials. These nerve pulses then travel to the brain where they are perceived. Hence, in many problems in acoustics, such as for audio processing, it is advantageous to take into account not just the mechanics of the environment, but also the fact that both the ear and the brain are involved in a person’s listening experience.

The inner ear, for example, does significant signal processing in converting sound waveforms into neural stimuli, so certain differences between waveforms may be imperceptible.1 Data compression techniques, such as MP3, make use of this fact.2 In addition, the ear has a nonlinear response to sounds of different intensity levels; this nonlinear response is called loudness. Telephone networks and audio noise reduction systems make use of this fact by nonlinearly compressing data samples before transmission, and then expanding them for playback.3 Another effect of the ear's nonlinear response is that sounds that are close in frequency produce phantom beat notes, or intermodulation distortion products.4

Limits of perception

The human ear can nominally hear sounds in the range 20 Hz (0.02 kHz) to 20,000 Hz (20 kHz). The upper limit tends to decrease with age; most adults are unable to hear above 16 kHz. The lowest frequency that has been identified as a musical tone is 12 Hz under ideal laboratory conditions.5 Tones between 4 and 16 Hz can be perceived via the body's sense of touch. Frequency resolution of the ear is 3.6 Hz within the octave of 1000 – 2000 Hz. That is, changes in pitch larger than 3.6 Hz can be perceived in a clinical setting.5 However, even smaller pitch differences can be perceived through other means. For example, the interference of two pitches can often be heard as a (low-)frequency difference pitch. This effect of phase variance upon the resultant sound is known as beating.

The semitone scale used in Western musical notation is not a linear frequency scale but logarithmic. Other scales have been derived directly from experiments on human hearing perception, such as the mel scale and Bark scale (these are used in studying perception, but not usually in musical composition), and these are approximately logarithmic in frequency at the high-frequency end, but nearly linear at the low-frequency end.

The psychoacoustic model provides for high quality lossy signal compression by describing which parts of a given digital audio signal can be removed (or aggressively compressed) safely — that is, without significant losses in the (consciously) perceived quality of the sound.

It can explain how a sharp clap of the hands might seem painfully loud in a quiet library, but is hardly noticeable after a car backfires on a busy, urban street. This provides great benefit to the overall compression ratio, and psychoacoustic analysis routinely leads to compressed music files that are 1/10th to 1/12th the size of high quality masters, but with discernibly less proportional quality loss. Such compression is a feature of nearly all modern lossy audio compression formats. Some of these formats include Dolby Digital (AC-3), MP3, Ogg Vorbis, AAC, WMA, MPEG-1 Layer II (used for digital audio broadcasting in several countries) and ATRAC, the compression used in MiniDisc and some Walkman models.

Psychoacoustics is based heavily on human anatomy, especially the ear's limitations in perceiving sound as outlined previously. To summarize, these limitations are:

Psychoacoustics Model (from: http://en.wikipedia.org/wiki/File:Psychoacoustic_Model.svg)

Given that the ear will not be at peak perceptive capacity when dealing with these limitations, a compression algorithm can assign a lower priority to sounds outside the range of human hearing. By carefully shifting bits away from the unimportant components and toward the important ones, the algorithm ensures that the sounds a listener is most likely to perceive are of the highest quality.

How did they patent natural human instinct of auditory senses?

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(The point of this post is to prove obviousness and lack of novelty in the '060 Patent)

WikiPedia: Nyquist Frequency

The Nyquist frequency, named after electronic engineer Harry Nyquist, is ½ of the sampling rate of a discrete signal processing system. It is sometimes known as the folding frequency of a sampling system.

Nyquist Frequency Aliasing

This describes common Nyquist anti-aliasing (filtering) which is also includes bandpass.

In a typical application of sampling, one first chooses the highest frequency to be preserved and recreated, based on the expected content (voice, music, etc.) and desired fidelity. Then one inserts an anti-aliasing filter ahead of the sampler. Its job is to attenuate the frequencies above that limit. Finally, based on the characteristics of the filter, one chooses a sample-rate (and corresponding Nyquist frequency) that will provide an acceptably small amount of aliasing.

Also read:

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