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 .
DETAILED ACTION
This Office Action is sent in response to Application’s Communication received on 09/01/2023 for application number 18/303968. The Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawing, Abstract, Oath/Declaration, and Claims.
Claims (1-9), (10-14) and (15-20) are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/01/2023 and 04/20/2023 were filed prior to current Office Action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 of this title, 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 1-11 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Jonathan Rothberg. Foreign Patent Application Publication CN 102301228 A (hereinafter Rothberg) in view of Lewis et al. US Patent Application Publication US 7122152 B2 (hereinafter Lewis).
Regarding claim 1, Rothberg teaches an integrated sensor array, comprising: a semiconductor substrate ([0003], [0050] wherein Rothberg teaches semiconductor substrate) a plurality of sensor sub-arrays formed on the substrate, wherein ([0028] wherein Rothberg describes the array can be one-dimensional or two-dimensional. a one-dimensional array is an array that has a first dimension of a row (or row) element and the second dimension of the plurality of row (or rows). a one-dimensional array of example is Ix 5 array. a two-dimensional array is an array of such a plurality of row (or rows) having a first dimension and a second dimension. the first dimension and line of second dimension (or row) number may be the same or different).
Rothberg does not teach each sensor sub-array includes a plurality of densely packed cross-reactive sensors, and each cross-reactive sensor of a same sensor sub-array is functionalized differently than each other cross-reactive sensor of the same sensor sub-array of the plurality of sensor sub-arrays.
However in analogous art of cross-reactive fluid sensor array, Lewis teaches each sensor sub-array includes a plurality of densely packed cross-reactive sensors (FIG. 7, [0022], [0032], [0039], [0126], [0129] wherein Lewis describes cross-reactive sensors and teaches spectral density) and each cross-reactive sensor of a same sensor sub-array is functionalized differently than each other cross-reactive sensor of the same sensor sub-array of the plurality of sensor sub-arrays ([0005], [0022] wherein Lewis describes spatial arrangement of cross-reactive sensors, wherein detectors are places in a such an array in in nominally spatially equivalent positions relative to the analyte flow path. In such a configuration, any spatiotemporal differences between detectors are minimized, and the array response pattern is determined by the differing physicochemical responses of the various detectors towards the analyte of interest. The variations in analyte sorption amongst various detectors thus determines the resolving power of the detector array and determines the other performance parameters of such systems).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Rothberg with Lewis by incorporating the method of each sensor sub-array includes a plurality of densely packed cross-reactive sensors; and each cross-reactive sensor of a same sensor sub-array is functionalized differently than each other cross-reactive sensor of the same sensor sub-array of the plurality of sensor sub-arrays of Lewis into the method a plurality of sensor sub-arrays formed on the substrate of Rothberg for the purpose of incorporating sensors that is configured to generate a response upon introduction of a fluid containing one or more analytes can be located on one or more surfaces relative to one or more fluid channels in an array. Fluid channels can take the form of pores or holes in a substrate material. (Lewis: Abstract).
Regarding claim 2, Rothberg as modified by Lewis teaches wherein two or more cross-reactive sensors in different sensor sub-arrays are functionalized to sense a single, predetermined analyte ([0004], [0036] wherein Rothberg describes a predetermined specific analyte).
Regarding claim 3, Rothberg as modified by Lewis teaches wherein one cross-reactive sensor of one or more sensor-subarrays is functionalized to sense more than one predetermined analyte ([0004], [0036] wherein Rothberg describes a predetermined specific analyte), ([0028] wherein Rothberg describes the array can be one-dimensional or two-dimensional. a one-dimensional array is an array that has a first dimension of a row (or row) element and the second dimension of the plurality of row (or rows). a one-dimensional array of example is Ix 5 array. a two-dimensional array is an array of such a plurality of row (or rows) having a first dimension and a second dimension. the first dimension and line of second dimension (or row) number may be the same or different).
Regarding claim 4, Rothberg as modified by Lewis teaches wherein each sub-array of the plurality of sensor sub-arrays is identical with each of the other sub-arrays ([0005], [0022] wherein Lewis describes spatial arrangement of cross-reactive sensors, wherein detectors are places in a such an array in in nominally spatially equivalent positions relative to the analyte flow path. In such a configuration, any spatiotemporal differences between detectors are minimized, and the array response pattern is determined by the differing physicochemical responses of the various detectors towards the analyte of interest. The variations in analyte sorption amongst various detectors thus determines the resolving power of the detector array and determines the other performance parameters of such systems).
Regarding claim 5, Rothberg as modified by Lewis teaches wherein the cross-reactive sensors of each of the plurality of sub-arrays is configured as a gas sensor ([0025] wherein Lewis teaches gas sensor).
Regarding claim 6, Rothberg as modified by Lewis teaches wherein a cross-reactive sensor of at least one sub-array of the plurality of sub-arrays is configured as a biosensor ([0004] wherein Rothberg incorporates biosensors).
Regarding claim 7, Rothberg as modified by Lewis teaches wherein a cross-reactive sensor of at least one sub-array of the plurality of sub-arrays is configured as an ion-sensitive transistor ([0004] wherein Rothberg incorporates an ion-sensitive transistor).
Regarding claim 8, Rothberg as modified by Lewis teaches wherein at least one of the cross-reactive sensors of each of the plurality of sub-arrays is configured as a bipolar junction transistor ([0033], [0044], [0079] wherein Rothberg incorporates transistors that provide 2 output signals).
Regarding claim 9, Rothberg as modified by Lewis teaches wherein at least one of the cross-reactive sensors of each of the plurality of sub-arrays is configured as a field effect transistor (Abstract, [0033] wherein Rothberg incorporates field effect transistor).
Regarding claim 10, Rothberg as modified by Lewis teaches wherein the processing circuit is configured to generate an indication of detected fluids based on signals generated by the integrated sensor array ([0019, [0075], [0129] wherein Rothberg teaches detecting difference or deviation of the arrival time of fluid in different holes, so as to reduce the total acquisition speed of all the signals from the array is maximized), ([0048], [0060-0061] wherein Lewis incorporates sensors that indicate fault detection, leak detection and hazardous leak and detection). The remaining claim limitations are similar to claim 1, therefore the claim is rejected under similar rationale.
Regarding claim 11, Rothberg as modified by Lewis teaches wherein the processing circuit implements a machine learning model trained to classify analytes based on output of the integrated sensor array
Claims 12-20 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over Jonathan Rothberg. Foreign Patent Application Publication CN 102301228 A (hereinafter Rothberg) in view of Lewis et al. US Patent Application Publication US 7122152 B2 (hereinafter Lewis) and further in view of Thomas Cleland et al. US Patent Application Publication US 20220198245 A1 (hereinafter Cleland).
Regarding claim 12, Rothberg and Lewis do not teach communication circuitry configured to convey output of the integrated sensor array to a machine learning classifier implemented in external circuitry.
However in analogous art of cross-reactive fluid sensor array, Cleland teaches communication circuitry configured to convey output of the integrated sensor array to a machine learning classifier implemented in external circuitry ([0005], [0033], [0089], [0094], [0096], [0187], [0195] wherein Cleland describes neural circuitry and sensors such as gas sensors using machine learning for detecting an activity pattern for chemical analytes).
It would have been obvious to a person in the ordinary skill in the art before the effective filing date of the claimed invention to combine Cleland with Rothberg and Lewis by incorporating the method of communication circuitry configured to convey output of the integrated sensor array to a machine learning classifier implemented in external circuitry of Cleland into the method a plurality of sensor sub-arrays formed on the substrate of Rothberg and Lewis for the purpose of incorporating machine learning systems for rapid and reliable pattern recognition (Cleland: [0004]).
Regarding claim 13, Rothberg as modified by Lewis and Cleland teaches wherein the communication circuitry is configured to convey the output of the integrated sensor array wirelessly ([0033] wherein Cleland provides a wireless network implemented using a wireless protocol).
Regarding claim 14, Rothberg as modified by Lewis and Cleland teaches wherein the processing circuit implements a machine learning model trained to classify analytes based on output of the integrated sensor array; and wherein the system further comprises communication circuitry configured to convey output of the integrated sensor array to a machine learning classifier implemented in external circuitry ([0327] wherein Cleland describes applying the machine leaning and processing the output statistics change).
Regarding claim 15, Rothberg as modified by Lewis and Cleland teaches processing the sensor measurements through a machine learning model to determine a prediction based on the plurality of sensor measurements; and outputting the prediction ([0010], [0233], [0310], [0333] wherein Cleland processes sensors data and generates and provides prediction). The remaining claim limitations are similar to claim 1, therefore the claim is rejected under similar rationale.
Regarding claim 16, Rothberg as modified by Lewis and Cleland teaches wherein the prediction is an initial prediction, and wherein the method further comprises: in response to the initial prediction meeting a predetermined condition, communicating the plurality of sensor measurements to an external system for generating a final prediction based on the plurality of sensor measurements (FIG. 11, [0010], [0233], [0310], [0333] wherein Cleland describes a neural network further comprises an inference network arranged between the principal neurons and the interneurons of the core network. The inference network is illustratively configured to deliver input to the interneurons that influences how the interneurons affect the principal neurons, such that the principal neurons thereby exert different effects on the interneurons and the inference network. For example, the inference network may be configured to selectively activate certain interneurons. By weakly or partially predicting a solution in this manner, the inference network substantially increases the likelihood of successful signal identification by the core network under extremely high impulse noise or other highly suboptimal conditions. As illustrated in FIG. 11, Cleland communicates the sensor measurements).
Regarding claim 17, Rothberg as modified by Lewis and Cleland teaches responding to the initial prediction’s meeting a predetermined condition by generating a warning ([0036] wherein Cleland generates alert).
Regarding claim 18, Rothberg as modified by Lewis and Cleland teaches wherein the machine learning model is a neural network trained to generate the prediction based on the plurality of sensor measurements ([0010], [0233], [0310], [0333] wherein Cleland processes sensors data and generates and provides prediction).
Regarding claim 19, Rothberg as modified by Lewis and Cleland teaches wherein the prediction indicates whether the fluid is a toxic gas or a harmful biofluid ([0019, [0075], [0129] wherein Rothberg teaches detecting difference or deviation of the arrival time of fluid in different holes, so as to reduce the total acquisition speed of all the signals from the array is maximized), ([0048], [0060-0061] wherein Lewis incorporates sensors that indicate fault detection, leak detection and hazardous leak and detection).
Regarding claim 20, Rothberg as modified by Lewis and Cleland teaches digitizing the sensor measurements for input to the machine learning model ([0268], [0302] wherein Cleland digitizes the data sample).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASSAN MRABI whose telephone number is (571)272-8875. The examiner can normally be reached on Monday-Friday, 7:30am-5pm. Alt, Friday, EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached on 571-270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HASSAN MRABI/Examiner, Art Unit 2144