# US 8,739,955 B1 · Discriminant verification systems and methods for use in coin discrimination > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,739,955 B1 - Document ID: US8739955B1 - Status: Granted - Kind: B1 - Application number: 13/793,827 - Filed: 2013-03-11 - Granted: 2014-06-03 - Earliest claimed: 2013-03-11 - Assignees: Outerwall Inc. - Applicants: Coinstar, Inc. - Inventors: Daniel D. Everhart - Web page: https://patentgrep.com/patent/US8739955B1 ## CPC - G07D 5/00: Testing specially adapted to determine the identity or genuineness of coins, e.g. for segregating coins which are unacceptable or alien to a currency - G07D: HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING ## Abstract Systems and associated methods for coin discrimination are disclosed herein. In one embodiment, a method for discriminating coins includes obtaining an electromagnetic sensor signal of a coin, sampling the sensor signal, generating a fingerprint of the coin from the sampled sensor signal, and calculating an appraisal using the fingerprint and a linear discriminant vector. The appraisal can be compared to a threshold to determine whether the coin is valued or impostor. In some embodiments, the linear discriminant vector can be calculated using the valued and impostor coin populations' covariance and means. ## Claims ### Claim 1 A computer-implemented method for discriminating coins, the method comprising: acquiring a sensor signal of a coin; generating a fingerprint having a plurality of sampled sensor points from the sensor signal; calculating an appraisal from the fingerprint and a linear discriminant vector, wherein the linear discriminant vector is an inverse of a covariance matrix, and wherein the covariance matrix includes a valued training matrix from a valued coin population and an impostor training matrix from an impostor coin population; and comparing the appraisal to a threshold to discriminate the coin. ### Claim 2 (depends on 1) The method of claim 1 wherein generating the fingerprint includes generating a set of sampled signal points from one or more sensor signals. ### Claim 3 (depends on 1) The method of claim 1 wherein generating a fingerprint further comprises: selecting at least one feature from the sensor signal, determining a sampled sensor signal that corresponds to the at least one feature, and assigning the sampled sensor signal to the fingerprint. ### Claim 4 (depends on 3) The method of claim 3 wherein the sampled sensor signal is a first sampled sensor signal, and wherein the method further comprises: selecting at least one marker from the sensor signal, determining a second sampled sensor signal that corresponds to the at least one marker, and assigning the second sampled sensor signal to the fingerprint. ### Claim 5 (depends on 2) The method of claim 2 wherein calculating the appraisal includes a scalar multiplication of the transpose of the linear discriminant vector and the appraisal. ### Claim 6 (depends on 1) The method of claim 1 wherein the threshold is an optimized threshold, and wherein the method further comprises determining the threshold using one or more iterative numerical methods. ### Claim 7 (depends on 1) The method of claim 1, further comprising: determining a desired rate of spoofs; and calculating the threshold from a density probability function of an impostor coin population and the desired rate of spoofs. ### Claim 8 (depends on 1) The method of claim 1, further comprising: determining a desired rate of forfeits; and calculating the threshold from a density probability function of a valued coin population and the desired rate of forfeits. ### Claim 9 (depends on 1) The method of claim 1, further comprising filtering the sensor signal using a digital filter. ### Claim 10 A consumer operated coin counting apparatus comprising: a coin input region configured to receive a plurality of coins; a coin sensor configured to generate one or more sensor signals corresponding to coin properties; means for generating fingerprints having a plurality of sampled points from the sensor signals; means for determining appraisals from the fingerprints and a linear discrimination discriminant vector, wherein the linear discriminant vector is an inverse of a covariance matrix, and wherein the covariance matrix includes a valued training matrix from a valued coin population and an impostor training matrix from an impostor coin population; and means for discriminating the coins by comparing the appraisals to a threshold. ### Claim 11 (depends on 10) The apparatus of claim 10 wherein the plurality of coins comprises a plurality of valued coins and a plurality of impostor coins, and wherein the apparatus further comprises means for determining the linear discriminant vector from the fingerprints belonging to the plurality of the valued coins and the plurality of the impostor coins. ### Claim 12 (depends on 10) The apparatus of claim 10 wherein a consumer operated coin counting apparatus is a first consumer operated coin counting apparatus, wherein the linear discriminant vector is obtained by a second consumer operated coin counting apparatus. ### Claim 13 (depends on 11) The apparatus of claim 11, further comprising: means for generating sampled sensor signals from the sensor signals; means for determining at least one feature of the sensor signal, means for determining at least one sampled sensor signal that corresponds to the at least one feature, and assigning the at least one sampled sensor signal to the fingerprint. ### Claim 14 (depends on 13) The apparatus of claim 13 wherein the at least one feature of the sensor signal is an approach point. ### Claim 15 (depends on 13) The apparatus of claim 13 wherein the at least one feature of the sensor signal is a departure point. ### Claim 16 (depends on 13) The apparatus of claim 13 wherein the at least one sampled sensor signal is a first sampled sensor signal, further comprising: means for determining at least one marker of the sensor signal, means for determining a second sampled sensor signal that corresponds to the at least one marker, and means for assigning at least one sampled sensor signal to the fingerprint. ### Claim 17 (depends on 16) The apparatus of claim 16, further comprising means for determining a plurality of non-uniformly spaced markers. ### Claim 18 (depends on 13) The apparatus of claim 13 wherein the means for determining at least one feature of the sensor signal comprise means for determining a minimum voltage of the sensor signal. ### Claim 19 (depends on 10) The apparatus of claim 10 wherein the fingerprint comprises sampled points from low frequency inductance (LD), low frequency resistance/conductance (LQ), high frequency inductance (HD) and high frequency resistance/conductance (HQ) sensor signals. ### Claim 20 A computer-readable medium whose contents cause a computer to discriminate coins, the coins being discriminated by a method comprising: receiving multiple coins; obtaining a sensor signal of one of the coins; detecting a coin feature in the sensor signal; generating a fingerprint at least in part from the coin feature; calculating an appraisal from the fingerprint and a linear discriminant vector, wherein the linear discriminant vector is an inverse of a covariance matrix, and wherein the covariance matrix includes a valued training matrix from a valued coin population and an impostor training matrix from an impostor coin population; and comparing the appraisal to a threshold. ### Claim 21 (depends on 20) The computer readable medium of claim 20 wherein the method further comprises accepting or rejecting the coin based on results of comparing the appraisal to the threshold of known coin denomination. ### Claim 22 (depends on 20) The computer readable medium of claim 20 wherein calculating an appraisal includes determining a dot product of a transpose of the linear discriminant vector and the fingerprint. ### Claim 23 (depends on 22) The computer readable medium of claim 22 wherein the linear discriminant vector is obtained from the sensor signals of a valued coin population and an impostor coin population. ### Claim 24 (depends on 20) The computer readable medium of claim 20 wherein the method further comprises: replacing the coin feature with at least one sampled sensor signal; and assigning the at least one sampled sensor signal to the fingerprint. ### Claim 25 (depends on 20) The computer readable medium of claim 20 wherein the method further comprises determining the threshold based at least in part on a desired ratio of spoofs. ### Claim 26 (depends on 20) The computer readable medium of claim 20, wherein the method further comprises determining the threshold based at least in part on a desired ratio of forfeits. ### Claim 27 A computer-implemented method for discriminating coins, the method comprising: acquiring a sensor signal of a coin; calculating an appraisal from the sensor signal and a linear discriminant vector, wherein the linear discriminant vector is an inverse of a covariance matrix, and wherein the covariance matrix includes a valued training matrix from a valued coin population and an impostor training matrix from an impostor coin population; and comparing the appraisal to a threshold to discriminate the coin.