DETAILED ACTION
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 .
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.
Claims 1-6, 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over WO2020/218179, hereinafter WO in view of BR112014008842, hereinafter BR and Clewell (U.S. Patent No. 2,653,471, hereinafter Clewell).
With respect to Claim 1, WO discloses a gas identification method using a sensor [30; page 4, para 5] that outputs a signal according to an adsorption concentration [see abstract] of a gas, the gas identification method comprising:
(a) obtaining a signal outputted from the sensor exposed to a sample gas during a predetermined measurement period; [See page 4, para 5]
(b) extracting a feature of the signal obtained in (a), the feature being responsive to humidity; [resonance frequency; page 5, para 2]
(c) obtaining humidity data indicating a humidity of the sample gas; [30 measures humidity]
(d) correcting the feature extracted in (b), based on the humidity data obtained in (c) [page 4, para 6]; and
(e) identifying the sample gas by using a trained model for identifying the sample gas, based on the feature corrected in (d), and outputting an identification result [page 7; para 4].
While WO shows using the resonant frequency, including a change in frequency to identify gas [page 5, para 2], WO doesn’t specifically mention using a maximum value of a value of a signal or a gradient of the signal, the gradient being an amount of change in the value of the signal per unit time.
Clewell shows another resonant frequency gas identification method [column 1, lines 1-5] that specifically uses the maximum value of the signal to identify the gas [column 4, lines 21-45].
BR discloses identification of gas by analyzing signal gradients, see para 56 and 57.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify WO such that the feature includes a maximum value of a value of a signal and a gradient of the signal that has changed due to the sensor being exposed to the sample gas for the benefit of increased accuracy.
With respect to Claim 2, WO discloses that in (d), the feature extracted in (b) is corrected by using a correction function representing a relationship between the feature extracted in (b), the humidity data obtained in (c), and a reference humidity that is a humidity of the sample gas learned by the trained model. See page 7, para 4 for machine learning. Page 6 last para shows correcting resonance frequency by comparing humidity to a zero point, which is a reference humidity.
With respect to Claim 3, WO discloses that the correction function is represented by Y = X + A(Ho-H) where A represents a correction coefficient calculated from a gradient of a linear function by which a function representing a relationship between the humidity of the sample gas and the feature of the sample gas is approximated, X represents the feature extracted in (b), H represents the humidity data obtained in (c), Ho represents the reference humidity, and Y represents a correction value of the feature extracted in (b). Page 11, para 8 shows that the scaling is linear. Page 6 last para shows correcting resonance frequency by comparing humidity to a zero point, which is a reference humidity.
With respect to Claim 4, WO discloses that the predetermined measurement period includes at least a first period and a second period, the correction function includes at least a first correction function specific to the first period and a second correction function specific to the second period, in (b), a first feature of the signal outputted from the sensor exposed to the sample gas during the first period and a second feature of the signal outputted from the sensor exposed to the sample gas during the second period are extracted, and in (d), the first feature extracted in (b) is corrected by using the first correction function and the second feature extracted in (b) is corrected by using the second correction function. Page 8, para 11 shows finding a correction coefficient each month. A first month is the first period with a first correction function, and the process is repeated in a second month, which is a second period with a second correction function.
With respect to Claim 5, WO discloses that the sensor includes at least a first sensor and a second sensor, the correction function includes at least a first correction function specific to the first sensor and a second correction function specific to the second sensor, in (a), a first signal outputted from the first sensor exposed to the sample gas during the predetermined measurement period and a second signal outputted from the second sensor exposed to the sample gas during the predetermined measurement period are obtained, in (b), a first feature of the first signal obtained in (a) and a second feature of the second signal obtained in (a) are extracted, and in (d), the first feature extracted in (b) is corrected by using the first correction function and the second feature extracted in (b) is corrected by using the second correction function. See page 4, para 6 and 7, which describe a temperature sensor and a humidity sensor and correcting the resonance frequency based on those sensor outputs.
With respect to Claim 6, WO discloses that the feature includes a value of the signal that has changed due to the sensor being exposed to the sample gas, the correction function includes a first correction function specific to the value of the signal in (b), the value of the signal is extracted as the feature of the signal, and in (d), the value of the signal is corrected by using the first correction function. The resonance frequency correction is a value of the signal, see page 4, paras 6 and 7. Clewell and BR were shown in the rejection of claim 1 to correct using both a maximum value of the signal and a gradient of the signal.
With respect to Claim 10, WO discloses further comprising:(f) generating a training data set to be used in the trained model based on the feature extracted in (b), and outputting the training data set generated. The machine learning in page 7, para 4 necessarily was trained on a generated set of training data.
With respect to Claim 12, WO discloses a gas identification system comprising:
a sensor that outputs a signal according to an adsorption concentration of a gas; multi-array sensor 6, [see final two paragraphs of page 4].
an exposer [intake port 21; fig 1] that exposes the sensor to a sample gas during a predetermined measurement period;
a signal obtainer that obtains a signal outputted from the sensor during the predetermined measurement period; [See page 6, para 1, frequency counter circuit measures resonance frequency and outputs that to unit 4]
an extractor that extracts a feature of the signal obtained by the signal obtainer, the feature being responsive to humidity [41; page 7, para 5];
a humidity data obtainer that obtains humidity data indicating a humidity of the sample gas [41; page 7, para 5];
a corrector that corrects the feature extracted by the extractor based on the humidity data obtained by the humidity data obtainer [42; page 7, para 2]; and
an identifier that identifies the sample gas by using a trained model for identifying the sample gas, based on the feature corrected by the corrector, and outputs an identification result [46; final para of page 9 identifies and outputs to display 5].
While WO shows using the resonant frequency, including a change in frequency to identify gas [page 5, para 2], WO doesn’t specifically mention using a maximum value of a value of a signal or a gradient of the signal, the gradient being an amount of change in the value of the signal per unit time.
Clewell shows another resonant frequency gas identification method [column 1, lines 1-5] that specifically uses the maximum value of the signal to identify the gas [column 4, lines 21-45].
BR discloses identification of gas by analyzing signal gradients, see para 56 and 57.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify WO such that the feature includes a maximum value of a value of a signal and a gradient of the signal that has changed due to the sensor being exposed to the sample gas for the benefit of increased accuracy.
Claims 9, 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over WO, BR and Clewell in further view of CN102095782, hereinafter CN.
With respect to Claim 9, WO does not disclose that in (a), the signal outputted from the sensor is obtained via a network.
CN discloses a similar device that determines gas types [para 11] that is online [para 45] and has a computer [4] networked to a detector [3]. See figs 1 and 2.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify WO such that in (a), the signal outputted from the sensor is obtained via a network for the benefit of being able to do remote analysis.
With respect to Claim 11, WO discloses correcting a measured frequency by machine learning, which necessarily has a training set, but gives no further details on the learning algorithm. See page 7, para 4.
CN shows a similar gas identification device that corrects for measured humidity that uses a plurality of training data sets each of which corresponds to a different one of a plurality of humidities of the sample gas are generated, and the plurality of training data sets generated are outputted. See para 51.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify WO such that a plurality of training data sets each of which corresponds to a different one of a plurality of humidities of the sample gas are generated, and the plurality of training data sets generated are outputted for the benefit of increased accuracy.
With respect to Claim 13, WO discloses a gas identification method using a sensor [6] that outputs a signal according to an adsorption concentration of a gas [see abstract], the gas identification method comprising:(a) obtaining a signal outputted from the sensor exposed to a sample gas having a certain humidity [Page 6 last para shows correcting resonance frequency by comparing humidity to a zero point, which is a reference humidity.]; (b) extracting a feature [resonance frequency] of the signal obtained in (a).
Page 7, para 4 discloses machine learning and Page 11, para 8 shows linear scaling between resonance frequency and humidity data.
WO does not disclose (c) generating, from the feature extracted in (b), a pseudo data set indicating a feature corresponding to the sample gas having a humidity other than the certain humidity, based on a predetermined correction coefficient; and (d) outputting, as a training data set to be used in a trained model for identifying the sample gas, the pseudo data set generated in (c).
CN discloses generating pseudo data sets [interpolated data] as training data to improve a learning algorithms output correlating humidity to correct gas sensor identification.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify WO by (c) generating, from the feature extracted in (b), a pseudo data set indicating a feature corresponding to the sample gas having a humidity other than the certain humidity, based on a predetermined correction coefficient; and (d) outputting, as a training data set to be used in a trained model for identifying the sample gas, the pseudo data set generated in (c) for the benefit of increased gas identification.
While WO shows using the resonant frequency, including a change in frequency to identify gas [page 5, para 2], WO doesn’t specifically mention using a maximum value of a value of a signal or a gradient of the signal, the gradient being an amount of change in the value of the signal per unit time.
Clewell shows another resonant frequency gas identification method [column 1, lines 1-5] that specifically uses the maximum value of the signal to identify the gas [column 4, lines 21-45].
BR discloses identification of gas by analyzing signal gradients, see para 56 and 57.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify WO such that the feature includes a maximum value of a value of a signal and a gradient of the signal that has changed due to the sensor being exposed to the sample gas for the benefit of increased accuracy.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot. The new limitations regarding the “maximum value” are taught by a newly applied reference. The limitations regarding the signal gradient were previously taught by BR, and that been incorporated into the rejection of the independent claims. While the examiner agrees that WO does teach the signal gradient as claimed, the applicant provided no argument against the examiner’s previous rejection using BR to teach this limitation.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX T DEVITO whose telephone number is (571)270-7551. The examiner can normally be reached 12pm- 8 pm EST M-S.
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/ALEX T DEVITO/Examiner, Art Unit 2855 /JOHN E BREENE/Supervisory Patent Examiner, Art Unit 2855
