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 .
IDS
The information disclosure statement (IDS) submitted on November 20, 2023 is being considered by the Examiner.
Drawing
The drawing filed on November 20, 2023 is objected to because of the following minor update: in Fig. 2, labeling the respective individual units to their function would provide a better representation.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim rejection – 35 U.S.C. §112
Claims 2-14, 16-17 and 19-20 are rejected under 35 U.S.C. §112(b) or 35 U.S.C. §112 (pre-AIA ), second paragraph, as being indefinite:
With regard to claim 2: the instant claim also includes the terms “the samples of the sequence” and “the samples”.
With regard to claim 3: the instant claim recites the limitation "the samples”, page 1, three lines into the claim, page 2, two lines into the claim, “the weights”, page 2, first line into the claim, and “the respective permutation”, page 1, last line into the claim. There is insufficient antecedent basis for these limitations in the claim. One suggestion for “the respective permutation” would be changing the word “each” in page 1, four lines into the claim to “respective”.
With regard to claim 4: the instant claim recites the limitation "the samples”, page 2, second and six line into the claim, “the respective sample" in page 2, three lines into the clam; and “the respective permutation”, page 2, six lines into the claim. There is insufficient antecedent basis for these limitations in the claim.
With regard to claim 5: the instant claim recites the limitation "the sample" in page 2, second lines into the clam; and “the third vectors”, last lines into the claim. There is insufficient antecedent basis for these limitations in the claim.
With regard to claim 6: the instant claim recites the limitation "the weights" in page 2, second lines into the clam; and “the third vectors”, page 3, first line into the claim. There is insufficient antecedent basis for these limitations in the claim.
With regard to claims 7-14: the instant claims depend on their respective base claims and include similar antecedent issues and/or inherits the attributes of their base claims.
With regard to claims 16-17 and 19-20: the instant claims depend on their respective base claims and include similar antecedent issues and/or inherit the attributes of their respective base claims.
Claims 3-9 and 17 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims because none of the references considered in the prosecution of the instant application discloses “..determining, for respective permutation of two of the samples, a respective weight based on the sets of input features associated with the two samples of the respective permutation; determining a plurality of output features of the attention layer by using the weights for weighting contributions of the input features associated with the samples to the output features; and wherein the processing module is configured for determining the estimation based on the output features of the attention layer.”
Claim rejection – 35 U.S.C. §102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. §102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 10-16, and 18-20 are rejected under 35 U.S.C. §102 (a)(1) as being anticipated by Pan et al. (ELSEVIER Publication, “A fast and robust mixture gases identification and concentration detection algorithm based on attention mechanism equipped recurrent neural network with double loss function”, hereon Pan).
In reference to claim 1: Pan discloses a gas sensing device for sensing a target gas in a gas mixture (see Pan, page 1, introduction), the gas sensing device comprising:
a measurement module (cross-sensitive gas sensors) configured for obtaining a measurement signal, the measurement signal being responsive to a concentration of the target gas in the gas mixture (see Pan, Abstract and page 2, proposed algorithm for mixture of gases);
a processing module configured for determining (see Pan, Fig. 1, and recurrent layer, section 2.2.1), for each of a sequence of samples of the measurement signal, a set of features, the features representing respective characteristics of the measurement signal (see Pan, page 2, first column, towards the middle, CH4 and CO nonlinear characteristics for instance);
using a neural network for determining an estimation of the concentration of the target gas based on the sets of features determined for the samples of the sequence of samples (see Pan, page 2, section 2, 2.1, using recurrent layer of the neural network); and
wherein the neural network comprises an attention layer to weight respective contributions of the samples to the estimation (see Pan, Fig. 1 and page 2, 2.2.2, attention module).
With regard to claim 2: Pan further discloses that the attention layer is configured for determining weights for weighting the contribution of one of the samples to the estimation based on the sets of features determined for the samples of the sequence (see Pan, page 4, fist column, second paragraph).
With regard to claim 10: Pan further discloses that the attention layer is configured for determining a plurality of output features of the attention layer (it is a multilayer system) based on a plurality of input features of the attention layer (see Pan, Fig. 1), and wherein the neural network is configured for combining the input features of the attention layer with the plurality of output features of the attention layer to obtain a plurality of combined features (see Pan, Figs. 1 and 2, illustrate RNN and attention).
With regard to claim 11: Pan further discloses that the neural network is configured for normalizing the combined features (see Pan, page 5, second column, second paragraph, the input data is standardized, which is a normalized sensor data).
With regard to claim 12: Pan further discloses that the neural network comprises a positional encoding layer configured for determining (see Pan, Fig. 1, encoder), for each of the samples, a set of positional coded features based on the set of features associated with the respective sample by coding, into the set of positional coded features, information about a position of the respective sample within the sequence of samples; and using the positional coded features for input features of the attention layer (see Pan, page 3, second column, last paragraph and continues to page 4, first column, first and second paragraphs).
With regard to claim 13. Pan further discloses that the neural network comprises a feed forward layer configured for applying a feed forward transformation to each of the sets of features associated with the samples, wherein input features of the attention layer are based on output features of the feed forward layer (or multi-layer perceptron) (see Pan, page 4, first column, second paragraph).
With regard to claim 14. Pan further discloses that the measurement module comprises one or more chemo-resistive gas sensing units to provide the measurement signal because L-ARNN is not affected by high sensitivity of the sensors of various gases (see Pan, page 12, second column, first paragraph).
In reference to claim 15: Pan discloses a method for sensing a target gas in a gas mixture (see Pan, page 1, introduction), the method comprising:
obtaining a measurement signal, the measurement signal being responsive to a concentration of the target gas in the gas mixture (see Pan, Abstract and page 2, proposed algorithm for mixture of gases and obtaining measurements using cross-sensitive gas sensors);
determining, for each of a sequence of samples of the measurement signal, a set of features, the features representing respective characteristics of the measurement signal (see Pan, page 2, first column, towards the middle, CH4 and CO nonlinear characteristic of these gases for instance);
using a neural network for determining an estimation of the concentration of the target gas based on the sets of features determined for the samples of the sequence see Pan, page 2, section 2, 2.1, using current layer of the neural network); and
wherein the neural network comprises an attention layer to weight respective contributions of the samples to the estimation (see Pan, Fig. 1 and page 2, 2.2.2., attention module).
With regard to claim 16: Pan further discloses that the attention layer is configured for determining weights for weighting the contribution of one of the samples to the estimation based on the sets of features determined for the samples of the sequence (see Pan, page 4, fist column, second paragraph).
In reference to claim 18: Pan discloses a gas sensing device for measuring a target gas in a gas mixture (see Pan, Abstract and page 2, proposed algorithm for mixture of gases), the gas sensing device comprising a processor having access to memory media storing instructions executable by the processor (since Pan is using a computerized system to implement those modules, these features would be considered inherently present in the set up) for:
obtaining a measurement signal, the measurement signal being responsive to a concentration of the target gas in the gas mixture (see Pan, Abstract and page 2, proposed algorithm for mixture of gases);
determining, for each of a sequence of samples of the measurement signal, a set of features, the features representing respective characteristics of the measurement signal (see Pan, page 2, first column, towards the middle, CH4 and CO nonlinear characteristics for instance);
using a neural network for determining an estimation of the concentration of the target gas based on the sets of features determined for the samples of the sequence (see Pan, page 2, section 2, 2.1, using recurrent layer of the neural network); and
wherein the neural network comprises an attention layer to weight respective contributions of the samples to the estimation (see Pan, Fig. 1 and page 2, 2.2.2, attention module).
With regard to claim 19: Pan further discloses that the neural network comprises a positional encoding layer (see Pan, Fig. 1, encoder) configured for determining, for each of the samples, a set of positional coded features based on the set of features associated with the respective sample by coding, into the set of positional coded features, information about a position of the respective sample within the sequence of samples; and using the positional coded features for input features of the attention layer (see Pan, page 3, second column, last paragraph and continues to page 4, first column, first and second paragraphs).
With regard to claim 20: Pan further discloses that the neural network comprises a feed forward layer configured for applying a feed forward transformation to each of the sets of features associated with the samples, wherein input features of the attention layer are based on output features of the feed forward layer (or multi-layer perceptron) (see Pan, page 4, first column, second paragraph).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Nottrott et al. (U.S. Patent No. 10,962,437) discloses a vehicle-borne gas concentration measurement device configured to conduct a gas leak detection survey by performing a sequence of geospatially-referenced mobile gas concentration measurements along one or more survey paths to collect data including gas concentration and location.
Blackley (U.S. Patent No. 10,564,655) discloses a method for drawing air into a robotic vapor device, exposing the drawn air to a sensor to detect one or more constituents in the drawn air, determining first measurement data for the one or more constituents of the drawn air via the sensor, transmitting the first measurement data to a one or more of a plurality of vapor devices via a peer-to-peer network, receiving second measurement data from the one or more of the plurality of vapor devices via the peer-to-peer network, determining one or more vaporizable materials to vaporize based on the first measurement data and the second measurement data, and dispensing a vapor comprised of the one or more vaporizable materials.
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/ELIAS DESTA/
Primary Examiner, Art Unit 2857