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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/02/2024 has been considered by the examiner.
Election/Restrictions
Applicant elects Group II, Claims 14-18, without traverse in the reply filed on 05/12/2026 is acknowledged.
Claim Objections
Claim 18 is objected to because of the following informalities:
Claim 18, please amend “a respective sensing element” to “the sensing element”.
Appropriate correction is required.
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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Markovsky (US 2021/0016271 A1).
Regarding Claim 14, Markovsky teaches a method for detecting analytes (in-line testing and product delivery assembly has a reader that generates a test result for detecting an analyte [para. 0009]), comprising:
receiving an input (system can include a user interface [para. 0063], which is designed for inputs) of a user-selected parameter (users may use the user interface to select recirculation loop, autosampler, and/or any of the readers [para. 0179]), wherein the input is received by a user input element (a user interface [para. 0063]) coupled to a device (a lateral flow assay system 1 [para. 0110]), the device comprising:
a sensor (reader 100, which includes a sensor; and an incubator which also may include a temperature sensor [paras. 0110, 0142]) comprising a plurality of sensors (image detector for reader 100 [para. 0110] and digital temperature sensors [para. 0142]), the plurality of sensors comprising:
a sensing element (imaging detector 102 [para. 0110], for example to measure optical reflectance [para. 0115]);
a heating element (incubator 102 a heater including an embedded temperature sensor [para. 0067]); and
a lighting element (sensor may be an optical detector aligned with a light source for detecting the transmission of light [para. 0069]); and
a controller (a microprocessor [para. 0130]), wherein the controller is communicatively coupled to the sensor, the heating element, and the lighting element (microprocessor may be in communication with the optical detector, particularly the sensor, and allows selection of temperature [paras. 0142, 0149]);
retrieving a set of values from a data set (set of values from a data set is a desired light-emission pattern [para. 0032] and a reference coding that may activate a corresponding temperature [para. 0030]) stored in computer readable media (data storage stores the desired light-emission pattern [para. 0032]), wherein the set of values is related to the user-selected parameter (selection of reader from user interface allows for user of predetermined light pattern associated with reader depending on analyte of choice [paras. 0068, 0179]);
adjusting at least one of the heating element (at least one temperature adjustment parameter is changed based on type of diagnostic test or assay being run [para. 0136]) and the lighting element (light processor may be used to trigger a desired pattern [para. 0032]) based on the retrieved set of values; and
generating, by the controller, a response (outputs indicative of the detected condition, a response, or no-response [para. 0155]), wherein the response is received by the user input element (user interface is an integrated circuit board and display board, user interface is used to view results or no-result response [para. 0063]).
Markovsky is silent on the sensor being a “sensor array” and the controller is coupled to the sensor array.
However, it would be obvious to one of ordinary skill in the art prior to duplicate the sensor of Markovsky so that the sensors comprise a sensor array, as each sensor can, for example, measure a reflectance value from an assay (Markovsky, [para. 0058]). The mere duplication of parts, without any new or unexpected results, is within the ambit of one of ordinary skill in the art. See In re Harza, 124 USPQ 378 (CCPA 1960) (see MPEP § 2144.04). In addition, as the sensors of Markovsky are coupled to the controller [paras. 0142, 0149], the sensor array would also be coupled to the controller.
Regarding Claim 16, teaches the method of claim 14, wherein the set of values comprises a temperature profile and an illumination profile (as outlined in the claim 14 rejection above, Markovsky teaches the set of values can be desired light pattern [para. 0032] and associated temperature for analyte of interest [para. 0030]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Markovsky, as applied to claim 14, and in view of Heikenfield (US 2018/0153451 A1).
Regarding Claim 15, Markovsky teaches the method of claim 14.
Markovsky is silent on wherein the user-selected parameter is a target analyte.
Heikenfield teaches a biosensor to measure various biomarkers (abstract), and teaches wherein the user-selected parameter is a target analyte (user selects an analyte to be detected [para. 0050]).
Markovsky and Heikenfield are considered analogous art to the claimed invention because they are in the same field of methods of detecting an analyte. It would be obvious to one of ordinary skill in the art to modify the user-selected parameter of Markovsky to be a target analyte, as taught by Heikenfield, as selection of an analyte of interest allows for various analytes of interests to be detected using the same instrument (Heikenfield, [para. 0050]).
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Markovsky, as applied to claim 16, and in view of Sankhala (A machine learning-based on-demand sweat glucose reporting platform. Nature Scientific Reports 2022; 12(2442), 1-11) and Schackart (Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors. Sensors 2021; 21(5519), 1-27).
Regarding Claim 17, teaches the method of claim 16.
Markovsky is silent on wherein: the temperature profile and the illumination profile are determined by a machine learning model trained to identify a temperature value and an illumination value that correspond to a sensitivity value for the sensing element.
Sankhala teaches a machine learning-based glucose biosensor (title and abstract), and teaches wherein: the temperature profile is determined by a machine learning model (a machine learning algorithm that uses a temperature one of the parameters to identify the analytical performance of the electrochemical biosensing system [Section of Dataset exploration, page 2]) trained to identify a temperature value (skin temperature value [last para. page 7]) that correspond to a sensitivity value for the sensing element (sensitivity value is impedance of the sensor [Section of machine learning, page 9]).
Markovsky and Sankhala are considered analogous art to the claimed invention because they are in the same field of methods of biosensor operation. It would be obvious to one of ordinary skill in the art to modify the method of Markovsky so that the temperature profile is determined by a machine learning model trained to identify a temperature value that corresponds to a sensitivity value for the sensing element, as taught by Sankhala, as using machine learning allows the sensor to accurately detect an analyte of interest (Sankhala, [abstract]).
Modified Markovsky is silent on wherein: the illumination profile is determined by a machine learning model trained to identify an illumination value that correspond to a sensitivity value for the sensing element.
Schackart teaches machine learning utilization for performance of biosensors (abstract), and teaches the illumination profile is determined by a machine learning model (machine learning can be used for optical biosensors [Section 5. Optical Bioreceptor-Free Biosensors, page 15]) trained to identify an illumination value that correspond to a sensitivity value for the sensing element (pattern of biosensor pattern response, such as luminescence or fluorescence [Section 5. Optical Bioreceptor-Free Biosensors, page 15]).
Modified Markovsky and Schackart are considered analogous art to the claimed invention because they are in the same field of methods of detecting analyte. It would be obvious to one of ordinary skill in the art to modify the method of modified Markovsky so that the illumination profile is determined by a machine learning model trained to identify an illumination value that correspond to a sensitivity value for the sensing element, as taught by Schackart, as using machine learning for enhanced performance of biosensors (Schackart, [third para. page 2]). Furthermore, since modified Markovsky teaches wherein the temperature profile is determined by a machine learning model trained to identify a temperature value that corresponds to a sensitivity value for the sensing element, one of ordinary skill in the art would have the illumination profile also determined by a machine learning model trained to identify an illumination value that corresponds to a sensitivity value for the sensing element.
Regarding Claim 18, modified Markovsky teaches the method of claim 17.
Markovsky is silent on wherein the sensitivity value for a respective sensing element is based on at least one of a quantity, a timing, or a length of a detected change in an electrical property of the sensing element.
Sankhala teaches wherein the sensitivity value for a respective sensing element is based on at least one of a quantity of a detected change in an electrical property of the sensing element (for example, parameters including temperature are related to the electrical property of the biosensor, which in the case of Sankhala is amount of impedance [Section of Dataset Exploration, page 2]).
It would be obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the sensitivity value of the respective sensing element of modified Markovsky to be based on at least one of a quantity of a detected change in an electrical property of the sensing element, as taught by Sankhala, as correlating temperature with the electrical parameter allows for accurate measuring of the analyte of interest (Sankhala, [Fig. 2A, page 4]).
Conclusion
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/R.L.G./Examiner, Art Unit 1795
/SHIZHI QIAN/Primary Examiner, Art Unit 1795
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