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 .
Summary
This action is responsive to the Request for Continued Examination filed on 05/18/2026. Applicant has submitted Claims 1-8 & 10-14 for examination.
Examiner finds the following: 1) Claims 1-8 & 10-14 are rejected; 2) no claims objected to; and 3) no claims allowable.
Request for Continued Examination
Receipt is acknowledged of a request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e) and a submission, filed on 05/18/2026.
Foreign Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of Application No. KR10-2023-0005335, filed on 01/13/2023, has been filed in this matter.
Response to Arguments and Remarks
Examiner respectfully acknowledges Applicant's arguments, remarks, and amendments.
Applicant’s arguments with respect to direct detection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding Applicant’s arguments regarding mapping Ling and McQuilkin to the matrix and vector sum value, Examiner is not persuaded.
Examiner does not find the claimed application of known standard mathematical concepts to carry patentable weight. The analytical process noted by Applicant does not appear to rise to anything more than the use of known standard mathematical concepts that any PHOSITA would be aware of.
The prior art discusses these known standard mathematical concepts. For example, Deliwala, FIGs. 4A-D, [0109], discusses how the scattering intensity is based on:
As discussed, scattering intensity is dependent on scattering angle, λ, size & shape of the particle, and also the distance.
Additionally, Ling and McQuillan discuss the use of matrixes, which are well known for sorting and collecting data, and Eigenvalues, which are well known in the art for their qualitative value analysis applications.
As stated by Applicant in their remarks on page 9:
This specific mathematical relationship is tailored to the unique dual-detection (scattered + direct transmitted) hardware disclosed in FIG. 4.
If that is the case, Examiner understands that the hardware is the inventive aspect of the claims, not the mathematics. The mathematics appear to be the application of the known standard mathematical concepts as they relate to the claimed hardware. The issues then is not whether the applied known standard mathematical concepts are novel, which they are not, but instead the apparatus which those known standard mathematical concepts are being applied. As such, Examiner does not find these limitations to carry patentable weight.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-5, 8, and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Deliwala (US 20200363312 A1), in view of Kadwell (US6225910 B1), and in view of Ling (US 20240044802 A1).
Regarding Claim 1, Deliwala discloses:
A fire detection method (Deliwala, FIG. 1, [0085], optical smoke detector 100) comprising:
a step of detecting (Deliwala, FIG. 1, [0085], photodetector 150) scattered light generated by smoke-based scattering (Deliwala, FIG. 1, [0087], “Distance r.sub.K 170 is the distance to the smoke particle. Smoke particles 160 that are very close to the module scatter rays 190 that make very low scattering angle θ182 and very large collection solid angle dΩ to the detector”) of multi-wavelength light having n (where n is a natural number of 3 or more) number of wavelengths to obtain n number of measurement values of the scattered light by using a first light detector (Deliwala, [0098], “In some embodiments where light emitting diodes emit different wavelengths, photodetectors can be modified to accommodate the detection thereof,” and FIGS. 2A-2D, [0105], “FIGS. 2A-2D are exemplary graphs representing scattered light as a function of angle, theta, and the resultant light receive at a photodetector”) and detecting transmitted light, generated as the multi-wavelength light passes through the smoke, to obtain n number of measurement values of the transmitted light by using a second light detector, the n number of measurement values corresponding to the n wavelengths (Deliwala, FIG. 1, [0101], “In other embodiments, a plurality of detectors is implemented, e.g., at least two for wavelength such that each of the pair of the plurality is wavelength specific. For example, there are at least two detectors (PD1, PD2) for every light emitting diode for a particular lambda”) …
… a step of normalizing the n number of measurement values of the scattered light to generate n normalized values of scattered light and normalizing the n number of measurement values of the transmitted light to generate n normalized values of the transmitted light ([0094], “One can readily carryout these integrals for spherical particles using Mie scattering theory and calculate the average scattering angles and effect of changing the light source distribution. The light source distribution is varied by changing the geometry of the barriers while making sure that no direct light rays from the light emitting diode reach the photodetector. The average scattering angle is computed by using equations (1) and (2), ” and Deliwala, Equation 2, and FIGS. 4A-D, [0110], “Scattering intensities for particle sizes 3-9 μm for FIGS. 4A-4D, respectively, are depicted. Again, one of ordinary skill in the art will observe that scattering intensities are also dependent on wavelength λ. In the present embodiment, two colors, blue and infrared, are used to demonstrate this dependency. Other colors are not beyond the scope of the present invention. One skilled in the art will appreciate that the forward scattering begins to dominate as the particle size grows”) by using a processor (Deliwala, [0151], “One or more aspects and embodiments of the present application involving the performance of processes or methods may utilize program instructions executable by a device (e.g., a computer, a processor, or other device) to perform, or control performance of, the processes or methods”); and
a step of calculating a singular value for determining whether the smoke is caused by a fire or a non-fire by using the processor, … the n normalized values of the scattered light and the n normalized values of the transmitted light (Deliwala, [0094], “One can readily carryout these integrals for spherical particles using Mie scattering theory and calculate the average scattering angles and effect of changing the light source distribution. The light source distribution is varied by changing the geometry of the barriers while making sure that no direct light rays from the light emitting diode reach the photodetector. The average scattering angle is computed by using equations (1) and (2)”) …
Deliwala discloses the above, but does not explicitly disclose:
… wherein the second light detector is disposed to face a light emitter to receive direct light reaching the second light detector after passing through the smoke; …
However, Kadwell, in a similar field of endeavor (Dual Emitter Smoke Detector For Detecting Gray And Black Smoke In E.g. Factories, Offices, Has Scatter Emitter And Obscuration Emitter Within Chamber Admitting Test Atmosphere, So That Light Emitted By Emitters Strikes Receiver), discloses:
… wherein the second light detector is disposed to face a light emitter to receive direct light reaching the second light detector after passing through the smoke (Kadwell, FIG. 1, C3, L20-23, Obscuration emitter 38 is positioned Within housing 22 to generate light 40 that strikes receiver 28 unless obstructed by smoke particles 26 suspended in test atmosphere 24”); …
It would have been obvious to PHOSITA before the effective filing date of the claimed invention to modify Deliwala with the direct detection of Kadwell. PHOSITA would have known about the uses of direct detection as disclosed by Kadwell and how to use it to modify Deliwala. PHOSITA would have been motivated to do this as a use of known technique to improve similar devices in the same way (See MPEP § 2143 (I)(C)), specifically the use of direct detection for normalizing and reference sensing.
The combination of Deliwala and Kadwell discloses the above but does not explicitly disclose:
… by calculating a distance matrix representing a similarity between …
… and calculating an eigen vector of the distance matrix as the singular value.
However, Ling, in a similar field of endeavor (SURFACE-ENHANCED RAMAN SCATTERING (SERS) PLATFORM FOR ANALYSIS), discloses:
… by calculating a distance matrix representing a similarity between (Ling, FIG. 15E, [0253], “To quantitatively evaluate the predictive capability of the SERS taster, confusion matrices using SVM-DA was constructed (FIG. 15E). SVM-DA is a supervised machine learning model that allows us to predict the identity of flavour molecules by examining their SERS super-profiles with a high degree of flexibility and robustness”) …
… and calculating an eigen vector of the distance matrix as the singular value (Ling, FIG. 15E, Examiner notes that due to the design of the shown matrices, the matrices of FIG. 15E would inherently have eigenvectors).
It would have been obvious to PHOSITA before the effective filing date of the claimed invention to modify the combination of Deliwala and Kadwell with the matrices of Ling. PHOSITA would have known about the uses of matrices as disclosed by Ling and how to use them to modify the combination of Deliwala and Kadwell. PHOSITA would have been motivated to do this as a use of known technique to improve similar devices in the same way (See MPEP § 2143 (I)(C)), specifically the use of matrices and matrix principles when analyzing sets of data.
Regarding Claim 2, the combination of Deliwala, Kadwell, and Ling discloses Claim 1, and Deliwala further discloses:
… wherein the step of calculating the singular value comprises:
a step of detecting occurrence of an event estimated as a fire, based on at least one of a sum value of the n normalized values of the scattered light and a sum value of the n normalized values of the transmitted light (Deliwala, Equation 2); and
a step of calculating the singular value when the occurrence of the event is detected (Deliwala, [0094], “One can readily carryout these integrals for spherical particles using Mie scattering theory and calculate the average scattering angles and effect of changing the light source distribution. The light source distribution is varied by changing the geometry of the barriers while making sure that no direct light rays from the light emitting diode reach the photodetector. The average scattering angle is computed by using equations (1) and (2)”).
Regarding Claim 3, the combination of Deliwala, Kadwell, and Ling discloses Claim 2, and Deliwala further discloses:
… wherein the step of detecting the occurrence of the event comprises a step of detecting the occurrence of the event, based on a comparison result obtained by comparing the at least one sum value with a threshold value (Deliwala, [0010], “The received light intensity will be reduced by absorption due to smoke, air-borne dust, or other substances; the circuitry detects the light intensity and generates the alarm if it is below a specified threshold, potentially due to smoke”).
Regarding Claim 4, the combination of Deliwala, Kadwell, and Ling discloses Claim 1, and Deliwala further discloses:
… wherein the step of calculating the singular value comprises:
a step of calculating … including nxn number of elements representing a vector sum of a distance value representing a similarity between the n normalized values of the scattered light and a distance value representing a similarity between the n normalized values of the transmitted light (Deliwala, [0094], “One can readily carryout these integrals for spherical particles using Mie scattering theory and calculate the average scattering angles and effect of changing the light source distribution. The light source distribution is varied by changing the geometry of the barriers while making sure that no direct light rays from the light emitting diode reach the photodetector. The average scattering angle is computed by using equations (1) and (2)”);
a step of calculating … including nxn number of elements for calculating an optimal distribution of elements of the first matrix in each wavelength (Deliwala, [0094], “One can readily carryout these integrals for spherical particles using Mie scattering theory and calculate the average scattering angles and effect of changing the light source distribution. The light source distribution is varied by changing the geometry of the barriers while making sure that no direct light rays from the light emitting diode reach the photodetector. The average scattering angle is computed by using equations (1) and (2)”); and …
Ling further discloses:
… a first matrix (Ling, FIG. 15E, [0253], “To quantitatively evaluate the predictive capability of the SERS taster, confusion matrices using SVM-DA was constructed (FIG. 15E). SVM-DA is a supervised machine learning model that allows us to predict the identity of flavour molecules by examining their SERS super-profiles with a high degree of flexibility and robustness”) …
… a second matrix (Ling, FIG. 15E, [0253], “To quantitatively evaluate the predictive capability of the SERS taster, confusion matrices using SVM-DA was constructed (FIG. 15E). SVM-DA is a supervised machine learning model that allows us to predict the identity of flavour molecules by examining their SERS super-profiles with a high degree of flexibility and robustness”) …
… a step of calculating an eigenvector of the second matrix as the singular value (Ling, FIG. 15E, Examiner notes that due to the design of the shown matrices, the matrices of FIG. 15E would inherently have eigenvectors).
Regarding Claim 5, the combination of Deliwala, Kadwell, and Ling discloses Claim 4, and Deliwala further discloses:
… wherein the first matrix comprises the nxn elements representing a vector sum of a distance value representing the similarity between the n normalized values of the scattered light and a distance value representing the similarity between the n normalized values of the transmitted light (Deliwala, Equation 2, and [0093], “The overall signal received by the detector is of course from all particles at all possible distances”).
Regarding Claim 8, Deliwala discloses:
A fire detection apparatus comprising:
a light emitter disposed in a chamber into which smoke penetrates (Deliwala, FIG. 1, [0085], photodetector 150) and configured to emit multi-wavelength light having n (where n is a natural number of 3 or more) number of wavelengths (Deliwala, [0098], “In some embodiments where light emitting diodes emit different wavelengths, photodetectors can be modified to accommodate the detection thereof”);
a first light detector disposed in the chamber and configured to detect scattered light generated by smoke-based scattering of the multi-wavelength light to obtain n number of measurement values of the scattered light, the n number of measurement values corresponding to the n wavelengths (Deliwala, FIG. 1, [0101], “In other embodiments, a plurality of detectors is implemented, e.g., at least two for wavelength such that each of the pair of the plurality is wavelength specific. For example, there are at least two detectors (PD1, PD2) for every light emitting diode for a particular lambda”);
a second light detector disposed in the chamber and configured to detect transmitted light, generated as the multi-wavelength light passes through the smoke, to obtain n number of measurement values of the transmitted light, the n number of the measurement values corresponding to the n wavelengths (Deliwala, FIG. 1, [0101], “In other embodiments, a plurality of detectors is implemented, e.g., at least two for wavelength such that each of the pair of the plurality is wavelength specific. For example, there are at least two detectors (PD1, PD2) for every light emitting diode for a particular lambda”) …
a processor (Deliwala, [0151], “One or more aspects and embodiments of the present application involving the performance of processes or methods may utilize program instructions executable by a device (e.g., a computer, a processor, or other device) to perform, or control performance of, the processes or methods”) configured to normalize the n number of measurement values of the scattered to generate n normalized values of the scattered light and normalize the n number of measurement values of the transmitted light to generate n normalized values of the transmitted light (Deliwala, [0094], “One can readily carryout these integrals for spherical particles using Mie scattering theory and calculate the average scattering angles and effect of changing the light source distribution. The light source distribution is varied by changing the geometry of the barriers while making sure that no direct light rays from the light emitting diode reach the photodetector. The average scattering angle is computed by using equations (1) and (2),” and Equation 2, and FIGS. 4A-D, [0110], “Scattering intensities for particle sizes 3-9 μm for FIGS. 4A-4D, respectively, are depicted. Again, one of ordinary skill in the art will observe that scattering intensities are also dependent on wavelength λ. In the present embodiment, two colors, blue and infrared, are used to demonstrate this dependency. Other colors are not beyond the scope of the present invention. One skilled in the art will appreciate that the forward scattering begins to dominate as the particle size grows”), and to determine whether the smoke penetrating into the chamber is caused by a fire or a non-fire, … based on the n normalized values of the scattered light and the n normalized values of the transmitted light (Deliwala, [0094], “One can readily carryout these integrals for spherical particles using Mie scattering theory and calculate the average scattering angles and effect of changing the light source distribution. The light source distribution is varied by changing the geometry of the barriers while making sure that no direct light rays from the light emitting diode reach the photodetector. The average scattering angle is computed by using equations (1) and (2)”) …
Deliwala discloses the above, but does not explicitly disclose:
… wherein the second light detector is disposed to face a light emitter to receive direct light reaching the second light detector after passing through the smoke; …
However, Kadwell, in a similar field of endeavor (Dual Emitter Smoke Detector For Detecting Gray And Black Smoke In E.g. Factories, Offices, Has Scatter Emitter And Obscuration Emitter Within Chamber Admitting Test Atmosphere, So That Light Emitted By Emitters Strikes Receiver), discloses:
… wherein the second light detector is disposed to face a light emitter to receive direct light reaching the second light detector after passing through the smoke (Kadwell, FIG. 1, C3, L20-23, Obscuration emitter 38 is positioned Within housing 22 to generate light 40 that strikes receiver 28 unless obstructed by smoke particles 26 suspended in test atmosphere 24”); …
It would have been obvious to PHOSITA before the effective filing date of the claimed invention to modify Deliwala with the direct detection of Kadwell. PHOSITA would have known about the uses of direct detection as disclosed by Kadwell and how to use it to modify Deliwala. PHOSITA would have been motivated to do this as a use of known technique to improve similar devices in the same way (See MPEP § 2143 (I)(C)), specifically the use of direct detection for normalizing and reference sensing.
The combination of Deliwala and Kadwell discloses the above but does not explicitly disclose:
… by calculating a distance matrix …
… and calculating an eigen vector of the distance matrix as the singular value.
However, Ling, in a similar field of endeavor (SURFACE-ENHANCED RAMAN SCATTERING (SERS) PLATFORM FOR ANALYSIS), discloses:
… by calculating a distance matrix representing a similarity between (Ling, FIG. 15E, [0253], “To quantitatively evaluate the predictive capability of the SERS taster, confusion matrices using SVM-DA was constructed (FIG. 15E). SVM-DA is a supervised machine learning model that allows us to predict the identity of flavour molecules by examining their SERS super-profiles with a high degree of flexibility and robustness”) …
… and calculating an eigen vector of the distance matrix as the singular value (Ling, FIG. 15E, Examiner notes that due to the design of the shown matrices, the matrices of FIG. 15E would inherently have eigenvectors).
It would have been obvious to PHOSITA before the effective filing date of the claimed invention to modify the combination of Deliwala and Kadwell with the matrices of Ling. PHOSITA would have known about the uses of matrices as disclosed by Ling and how to use them to modify the combination of Deliwala and Kadwell. PHOSITA would have been motivated to do this as a use of known technique to improve similar devices in the same way (See MPEP § 2143 (I)(C)), specifically the use of matrices and matrix principles when analyzing sets of data.
Regarding Claim 10, the combination of Deliwala, Kadwell, and Ling discloses Claim 8, and Deliwala further discloses:
… detecting occurrence of an event estimated as a fire, based on at least one sum value of the n normalized values of the scattered light and a sum value of the n normalized values of the transmitted light (Deliwala, [0010], “The received light intensity will be reduced by absorption due to smoke, air-borne dust, or other substances; the circuitry detects the light intensity and generates the alarm if it is below a specified threshold, potentially due to smoke”); and
calculating the singular value when the occurrence of the event is detected (Deliwala, [0010], “The received light intensity will be reduced by absorption due to smoke, air-borne dust, or other substances; the circuitry detects the light intensity and generates the alarm if it is below a specified threshold, potentially due to smoke”).
Regarding Claim 11, the combination of Deliwala, Kadwell, and Ling discloses Claim 8, and Ling further discloses:
… calculating a first matrix including nxn number of elements representing a vector sum of a distance value representing a similarity between the n normalized values of the scattered light and a distance value representing a similarity between the n normalized values of the transmitted light (Ling, FIG. 15E, [0253], “To quantitatively evaluate the predictive capability of the SERS taster, confusion matrices using SVM-DA was constructed (FIG. 15E). SVM-DA is a supervised machine learning model that allows us to predict the identity of flavour molecules by examining their SERS super-profiles with a high degree of flexibility and robustness”),
calculating a second matrix including nxn number of elements for calculating an optimal distribution of elements of the first matrix in each wavelength (Ling, FIG. 15E, [0253], “To quantitatively evaluate the predictive capability of the SERS taster, confusion matrices using SVM-DA was constructed (FIG. 15E). SVM-DA is a supervised machine learning model that allows us to predict the identity of flavour molecules by examining their SERS super-profiles with a high degree of flexibility and robustness”), and
calculating an eigenvector of the second matrix as the singular value (Ling, FIG. 15E, Examiner notes that due to the design of the shown matrices, the matrices of FIG. 15E would inherently have eigenvectors).
Regarding Claim 12, the combination of Deliwala, Kadwell, and Ling discloses Claim 11, and Deliwala further discloses:
… wherein the processor calculates the first matrix including the nxn elements representing a vector sum of a distance value representing the similarity between the n normalized values of the scattered light and a distance value representing the similarity between the n normalized values of the transmitted light (Deliwala, Equation 2, and [0093], “The overall signal received by the detector is of course from all particles at all possible distances”).
Claims 6-7 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Deliwala (US 20200363312 A1), in view of Kadwell (US6225910 B1), in further view of Ling (US 20240044802 A1), and in further view of McQuilkin (US 20160069743 A1).
Regarding Claim 6, the combination of Deliwala, Kadwell, and Ling discloses Claim 4, and further discloses:
… wherein the eigenvector comprises n number of eigenvectors (Examiner notes that one or more eigenvectors is inherent from Claim 4), and …
However, McQuilkin, in a similar field of endeavor (SPECTRAL IMAGING SYSTEM FOR REMOTE AND NONINVASIVE DETECTION OF TARGET SUBSTANCES USING SPECTRAL FILTER ARRAYS AND IMAGE CAPTURE ARRAYS), discloses:
… the fire detection method further comprises a step of analyzing a ratio of the n eigenvectors to determine whether the smoke is caused by a fire or a non-fire (McQuilkin, [0170], “Examples of algorithm methods include, but are not limited to, eigenvector, basis function, least squares, principle component analysis, ratios, differences, matched filter, neural networks, cross-correlation, multivariate analysis, and numerous classification methods”).
It would have been obvious to PHOSITA before the effective filing date of the claimed invention to modify the combination of Deliwala and Ling with the analysis methods of McQuilkin. PHOSITA would have known about the uses of the analysis methods as disclosed by McQuilkin and how to use them to modify the combination of Deliwala and Ling. PHOSITA would have been motivated to do this as a use of known technique to improve similar devices in the same way (See MPEP § 2143 (I)(C)), specifically the use of known analysis methods in similar systems.
Regarding Claim 7, the combination of Deliwala, Kadwell, and Ling discloses Claim 4, and further discloses:
… wherein the eigenvector comprises n number of eigenvectors (Examiner notes that one or more eigenvectors is inherent from Claim 4), and the fire detection method further comprises:
However, McQuilkin, in a similar field of endeavor (SPECTRAL IMAGING SYSTEM FOR REMOTE AND NONINVASIVE DETECTION OF TARGET SUBSTANCES USING SPECTRAL FILTER ARRAYS AND IMAGE CAPTURE ARRAYS), discloses:
… a step of converting a ratio of the n number of eigenvectors into a plurality of angular values; and
a step of analyzing a relationship between the plurality of angular values to determine whether the smoke is caused by a fire or a non-fire (McQuilkin, [0170], “Examples of algorithm methods include, but are not limited to, eigenvector, basis function, least squares, principle component analysis, ratios, differences, matched filter, neural networks, cross-correlation, multivariate analysis, and numerous classification methods”).
It would have been obvious to PHOSITA before the effective filing date of the claimed invention to modify the combination of Deliwala and Ling with the analysis methods of McQuilkin. PHOSITA would have known about the uses of the analysis methods as disclosed by McQuilkin and how to use them to modify the combination of Deliwala and Ling. PHOSITA would have been motivated to do this as a use of known technique to improve similar devices in the same way (See MPEP § 2143 (I)(C)), specifically the use of known analysis methods in similar systems.
Regarding Claim 13, the combination of Deliwala, Kadwell, and Ling discloses Claim 11, and further discloses:
… wherein the eigenvector comprises n number of eigenvectors (Examiner notes that one or more eigenvectors is inherent from Claim 11), and …
However, McQuilkin, in a similar field of endeavor (SPECTRAL IMAGING SYSTEM FOR REMOTE AND NONINVASIVE DETECTION OF TARGET SUBSTANCES USING SPECTRAL FILTER ARRAYS AND IMAGE CAPTURE ARRAYS), discloses:
… the processor analyzes a ratio of the n number of eigenvectors to determine whether the smoke is caused by a fire or a non-fire (McQuilkin, [0170], “Examples of algorithm methods include, but are not limited to, eigenvector, basis function, least squares, principle component analysis, ratios, differences, matched filter, neural networks, cross-correlation, multivariate analysis, and numerous classification methods”).
It would have been obvious to PHOSITA before the effective filing date of the claimed invention to modify the combination of Deliwala and Ling with the analysis methods of McQuilkin. PHOSITA would have known about the uses of the analysis methods as disclosed by McQuilkin and how to use them to modify the combination of Deliwala and Ling. PHOSITA would have been motivated to do this as a use of known technique to improve similar devices in the same way (See MPEP § 2143 (I)(C)), specifically the use of known analysis methods in similar systems.
Regarding Claim 14, the combination of Deliwala, Kadwell, and Ling discloses Claim 11, and further discloses:
… wherein the eigenvector comprises n number of eigenvectors (Examiner notes that one or more eigenvectors is inherent from Claim 11), and …
However, McQuilkin, in a similar field of endeavor (SPECTRAL IMAGING SYSTEM FOR REMOTE AND NONINVASIVE DETECTION OF TARGET SUBSTANCES USING SPECTRAL FILTER ARRAYS AND IMAGE CAPTURE ARRAYS), discloses:
… the processor converts a ratio of the n number of eigenvectors into a plurality of angular values and analyzes a relationship between the plurality of angular values to determine whether the smoke is caused by a fire or a non-fire (McQuilkin, [0170], “Examples of algorithm methods include, but are not limited to, eigenvector, basis function, least squares, principle component analysis, ratios, differences, matched filter, neural networks, cross-correlation, multivariate analysis, and numerous classification methods”).
It would have been obvious to PHOSITA before the effective filing date of the claimed invention to modify the combination of Deliwala and Ling with the analysis methods of McQuilkin. PHOSITA would have known about the uses of the analysis methods as disclosed by McQuilkin and how to use them to modify the combination of Deliwala and Ling. PHOSITA would have been motivated to do this as a use of known technique to improve similar devices in the same way (See MPEP § 2143 (I)(C)), specifically the use of known analysis methods in similar systems.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD A REVERMAN whose telephone number is (571)270-0079. The examiner can normally be reached Mon-Fri 9-5 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kara Geisel can be reached at (571) 272-2416. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHAD ANDREW REVERMAN/Examiner, Art Unit 2877
/Kara E. Geisel/Supervisory Patent Examiner, Art Unit 2877
Prosecution Insights