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 .
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 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.
Priority
US National Stage of PCT
Acknowledgment is made that this application is the US national phase of international application PCT/EP2022/078306 filed 10/11/2022 which designated the U.S. and claims the benefit of FR2110983 filed 10/15/2021.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 03/18/2024 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the Examiner.
Drawings
The drawings are objected to under 37 CFR 1.84(l), and corresponding PCT rule 11.13(a), for being unsatisfactorily reproducible. All drawings must be made by a process which will give them satisfactory reproduction characteristics. Every line, number, and letter must be durable, clean, black (except for color drawings), sufficiently dense and dark, and uniformly thick and well-defined. The weight of all lines and letters must be heavy enough to permit adequate reproduction. This requirement applies to all lines however fine, to shading, and to lines representing cut surfaces in sectional views. Lines and strokes of different thicknesses may be used in the same drawing where different thicknesses have a different meaning.
The drawings are objected to because unlabeled non-descriptive representations are impermissible under 37 CFR 1.83(a) which states (bold for emphasis):
(a) The drawing in a nonprovisional application must show every feature of the invention specified in the claims. However, conventional features disclosed in the description and claims, where their detailed illustration is not essential for a proper understanding of the invention, should be illustrated in the drawing in the form of a graphical drawing symbol or a labeled representation (e.g., a labeled rectangular box). In addition, tables that are included in the specification and sequences that are included in sequence listings should not be duplicated in the drawings.
The drawings are correspondingly objected to for failing to comply with PCT Rule 11 as catchwords are indispensable to the understanding of the unlabeled non-descriptive representations, wherein PCT Rule 11.11 Words in Drawings states (bold for emphasis):
(a) The drawings shall not contain text matter, except a single word or words, when absolutely indispensable, such as "water," "steam," "open," "closed," "section on AB," and, in the case of electric circuits and block schematic or flow sheet diagrams, a few short catchwords indispensable for understanding.
(b) Any words used shall be so placed that, if translated, they may be pasted over without interfering with any lines of the drawings.
Non-descriptive representation(s) 1 in fig. 3 need (an) appropriate legend(s) in the form of descriptive text label(s) in addition to any reference character(s) already present. Empty or not labeled rectangular boxes and non-descriptive representations of features are not descriptive, and therefore incomplete. The Examiner emphasizes that the requested text matter is indispensable for proper understanding. The descriptive text labels should contain as few words as possible. See also 37 CFR 1.84(n) (conventional symbols), 1.84(o) (required descriptive legends), & 1.84(p) (standards for the text labels), MPEP 608.02(b)(II)(¶ 6.22) (“descriptive text label”), and MPEP Appendix T Rule 11.11. The Appropriate Correction is required.
The drawings are objected to under 37 CFR 1.84(o) for lacking suitable descriptive legends, and correspondingly objected to for failing to comply with PCT Rule 11 as catchwords are indispensable to the understanding of the plots. Suitable descriptive legends are required by the Examiner for understanding of the drawing. The descriptive legends should contain as few words as possible. In particular, the following legend(s) is/are required:
plot Title(s), Y-Axis label(s)/dimension/units (i.e., able to identify meaning of y-axis) for fig(s). 5; and
X-Axis & Y-Axis label(s)/dimension/units (i.e., able to identify meaning of both x-axis & y-axis) for fig(s).6.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action.
The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to
because of the following informalities:
The disclosure uses an abbreviation/acronym (page 1 State of the prior art “COP21”) before the appearance of the first instance of the definition of the abbreviation/acronym. Where the definitions are absent (especially where Applicant disagrees with the Examiner’s interpretation), the Examiner suggests that a brief explanation in Applicant’s Remarks of the jargon as conventional and in more esoteric cases reference to non-patent literature for support would likely be sufficient to avoid new matter upon introduction of the full term. The Examiner’s best guess based on context is that COP21 is in reference to the 21st Climate Change Conference in Paris.
Appropriate correction is required.
Claim Objections
Claim(s) 1-10 is/are objected to because of the following informalities:
As to independent claim 1,
the Examiner objects to the use of the pronoun “it” in the claim, noting in particular that the use of a pronoun is ambiguous as to which element the pronoun is being substituted for. The Examiner suggests explicit recitations.
Dependent claim(s) of objected to claim(s) is/are likewise objected to.
Appropriate correction is required.
Claim Interpretation
The Examiner acknowledges the definition(s) in/on page 3, ll. 32-33 of the originally filed specification. MPEP § 2111 states that “the specification must provide a clear and intentional use of a special definition for the claim term to be treated as having a special definition”. Where Applicant’s definitions are optional or non-limiting the definitions are not considered special definitions and claim terms referencing such definitions will instead be considered under the broadest reasonable interpretation in view of the specification. In the present case, the Examiner notes that the special definition "enhancing a raw data item" means “an adjustment of the value of the raw data item after application of the trained neural network model”.
If Applicant wishes to provide further explanation or dispute the Examiner’s interpretation of the definitions or to identify missed definitions, Applicant should clearly identify the special definitions and corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action. Examples should be clearly delineated from required features.
Claim Rejections - 35 USC § 102/103
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.
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.
Claim(s) 1 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over newly cited Martin (US 20130174646 A1; hereafter “Martin”).
Regarding independent claim 1,
Martin teaches a method for enhancing measurements from a contaminant measurement sensor (fig. 1, air quality monitor 102) (Title “NETWORKED AIR QUALITY MONITORING”; Abstract “analysis regarding various air pollutant” and “wherein the air quality monitor is configured to measure the level of an air pollutant” and “a server that is communicatively coupled to the air quality monitor”; [0017 “detect an acute spike in specific hazardous gas concentrations, but also monitor developing trends in concentration”; [0119] “contaminated or polluted”), the measurement sensor (fig. 1, air quality monitor 102) being connected to a specialized remote server (fig. 1, server 108 with database 110) (at once envisaged that server is specialized server; additional obviousness analysis provided) comprising a neural network ([0046] “Various classification schemes and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, and data fusion engines) can be employed in connection with performing automatic and/or inferred action”; [0108] “functionalities and features utilized by server 108 can employ any suitable scheme (e.g., neural networks, expert systems, Bayesian belief networks, support vector machines (SVMs), Hidden Markov Models (HMMs), fuzzy logic, data fusion, etc.) in accordance with implementing various automated aspects”; [0041] “communicate via local and/or remote processes”; [0051] “server that is located remotely”), the method comprising the following steps:
creating a trained neural network model based on the neural network ([0044] “Artificial intelligence based systems”; [0045] “interference”; [0108] “analysis component 1102 can be aided through utilization of one or more artificial intelligence and/or machine learning techniques and/or technologies that can be included within artificial intelligence component 1106. For instance, artificial intelligence component 1106 can employ artificial intelligence and/or machine learning techniques and/or technologies that employ probabilistic-based or statistical-based approaches, for example, in connection with making determinations or inferences. Inferences can be based at least in part on explicit training of classifiers or implicit training based at least in part upon system feedback and/or a users' or a systems' previous actions, commands, instructions, and the like. The intelligence functionalities and features utilized by server 108 can employ any suitable scheme (e.g., neural networks”), and
for each measurement performed by the measurement sensor (fig. 1, air quality monitor 102):
measuring at least one raw data item by means of the measurement sensor (fig. 1, air quality monitor 102) ([0065] “Air quality module 102 can also include sensor module 106 that can include sensors for detecting the presence of airborne particulate matter such as mold spores, animal hair and dander, and dust, volatile organic compounds typically released from building materials utilized in home construction, such as formaldehyde, and the like, nitrogen oxides, carbon monoxide, combustible gases, such as methane, ethane, etc., carbon dioxide, cigarette smoke, chemicals from cleaning products, gases seeping through house foundations, and the like. Additionally, sensor module 106 can also include temperature sensors and/or relative humidity sensors that can detect rises and falls in temperature and/or relative humidity”; [0086] “sensor components (e.g., particulate sensor 204, temperature sensor 206, relative humidity sensor 208, volatile organic compound sensor 210, nitrogen oxide sensor 212, carbon monoxide sensor 214, combustible gas sensor 216, carbon dioxide sensor 218, and/or formaldehyde sensor 220)”);
sending the at least one raw data item to the specialized remote server (fig. 1, server 108 with database 110) ([0018] “networked air quality monitor system, comprising a sensor component that continuously monitors residential air quality to establish data points with respect to disparate pollutants, and a radio module that broadcasts the data points to a server”); and
enhancing the at least one raw data item by means of the trained neural network model, making it possible to obtain the measurement of the concentration of the contaminant ([0091] “air quality monitor 102 can be subjected to calibration and/or re-calibration, wherein the sensors can be calibrated by individually placing the sensors, placing two or more sensors, or placing air quality monitor 102 in a calibration chamber wherein gases, such as, nitrogen oxide, carbon monoxide, carbon dioxide, hydrogen sulfide, volatile organic compounds, combustible gases, and the like can be introduced into the calibration chamber at identified levels. In response to the specified levels of introduced gases, the one or more sensors can react with an identifiable voltage level which can be noted and charted. Thus, a voltage level for a particular introduced gas can be associated with an identifiable concentration of gas, typically measured in parts per million (ppm) or parts per billion (ppb). The curves determined or ascertained from these calibration activities can be utilized by air quality monitor 102 and/or server 108 to provide indication of the air quality”; [0092] “Additionally and/or alternatively, because sensor accuracy drifts over time, a self calibration feature is provided wherein, once sensors have been deployed in the field, these sensors can be calibrated or recalibrated through communication with server 108, for example. Generally, where more up-to-date calibration curves have been obtained by server 108, for instance, through calibration activities as described above, these updated calibration curves can be supplied (through wireless or wired modalities) to the sensors associated with a remotely situated air quality monitor (e.g., air quality monitor 102 situated in a residential house)”; [0093] “In addition, in the context of calibration and re-calibration of sensors associated with deployed air quality monitors, measurements from various sensors deployed in one or more deployed air quality monitor located in a single residential house or multiple residential houses dispersed across various geographical areas can be employed for purposes of generating calibration curves that can be employed by server 108 for purposes of calibration and/or recalibration of sensors in deployed air quality monitors (e.g., air quality monitor 102). It should also be noted, that the calibration/recalibration of sensors in deployed air quality monitors can be automated”; [0046], [0108] “neural networks”).
With further regards to the server being a specialized server, either one of ordinary skill in the art at the time the invention was effectively filed would at once envisaged that Martin’s server is a specialized remote server (i.e., Martin’s server may be remote to the sensor(s) and its use is the specialization), or nevertheless, or in the alternative, the Examiner takes Official Notice that specializing a server to the purpose for which it serves is conventional and the Examiner takes Official Notice that placing a specialized server remotely is conventional, and therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to explicitly optimize Martin’s server to be a remote specialized server for the expected purpose of convenient location and sharing between sensors placed in different locations as well as for the benefit of optimizing the server for the purpose of said server.
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.
Claim(s) 2-4, 7, and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over newly cited Martin in view of newly cited Tian* et al (CN 105651939 A; hereafter “Tian”).
*machine translation provided by Examiner with foreign document and utilized for English citations
Regarding claim 2, which depends on claim 1,
Martin teaches characterized in that the step of creating a trained neural network model is broken down into three substeps ([0091] “Prior to deployment and/or periodically over the life expectancy of air quality monitor 102, air quality monitor 102 and/or the sensors included within air quality monitor 102 can be subjected to calibration and/or re-calibration”; [0093] “should also be noted, that the calibration/recalibration of sensors in deployed air quality monitors can be automated”):
generating a scenario (specified levels of introduced gases) for calibrating the measurement sensor (fig. 1, air quality monitor 102), the calibration scenario being generated from a parameter measurement instruction as a function of time (e.g., periodically);
creating a neural network model by supplying the neural network with the calibration scenario of said measurement sensor (fig. 1, air quality monitor 102); and
training the neural network model ([0046], [0108] “neural networks”; [0091] “wherein gases, such as, nitrogen oxide, carbon monoxide, carbon dioxide, hydrogen sulfide, volatile organic compounds, combustible gases, and the like can be introduced into the calibration chamber at identified levels. In response to the specified levels of introduced gases, the one or more sensors can react with an identifiable voltage level which can be noted and charted. Thus, a voltage level for a particular introduced gas can be associated with an identifiable concentration of gas, typically measured in parts per million (ppm) or parts per billion (ppb). The curves determined or ascertained from these calibration activities can be utilized by air quality monitor 102 and/or server 108 to provide indication of the air quality”).
While Martin teaches measuring environmental parameters (e.g., temperature, relative humidity), Martin does not teach varying the at least one environmental parameter as part of a scenario for training the neural network model and calibrating the measurement sensor.
Tian teaches a method for enhancing measurements from a contaminant (at once envisaged that measurement sensor could be utilized for contaminant measurements, Examiner noting that species measured include those known in the art to be considered contaminants) measurement sensor (electronic nose) (Title “Based On The Concentration Detection Accuracy Correction Method Of The Convex Set Projection Electronic Nose System”; Abstract “electronic nose system based on the concentration detection accuracy correction method” and “method to adjust each response signal of the gas sensor to be corrected. computing network input value correction gas concentration, the gas sensor to be corrected measured standard gas concentration value close to high precision standard gas sensor measured by standard gas concentration value to determine sensor correction coefficient, the corrected concentration detection result of the other gas. Its effect is that improves the electronic nose detection precision of the measured gas concentration, effectively solves the problem that the electronic nose sensor differences and long-term drift problem”; second paragraph from bottom of page 4 “detection of gas species containing formaldehyde, benzene, toluene, carbon monoxide, nitrogen dioxide, and ammonia gas, setting is standard gas of formaldehyde, benzene, toluene, carbon monoxide, nitrogen dioxide, and ammonia as the gas to be detected”), the measurement sensor (electronic nose) being connected to a specialized remote server (sensor array detecting server) comprising a neural network (third paragraph from bottom of page 3 & about middle of page 5 “artificial neural network”; see fig. 4), the method comprising the following steps: creating a trained neural network model based on the neural network (about middle of page 5 “optimized artificial neural network using intelligent optimization algorithm, so as to obtain higher prediction precision”), and for each measurement performed by the measurement sensor (electronic nose): measuring at least one raw data item by means of the measurement sensor (electronic nose) (bottom of page 2 “each electronic nose terminal obtains the sensor signal comprises a standard gas sensor signals, one temperature signal, a humidity signal and n paths of gas sensor signal to be corrected, and the electronic nose terminal the various signals transmitted to said server”);sending the at least one raw data item to the specialized remote server (bottom of page 2 “signals transmitted to said server”; about middle of page 3 “communication module is used for realizing information transmission between electronic nose terminal and server”); and enhancing the at least one raw data item by means of the trained neural network model, making it possible to obtain the measurement of the concentration of the contaminant (about middle of page 5 “gas concentration detection networks of different gas uses an intelligent algorithm to optimize the artificial neural network, the artificial neural network algorithm is easy to become part optimized; the corresponding optimization algorithm needs to be designed. Therefore, we optimized artificial neural network using intelligent optimization algorithm, so as to obtain higher prediction precision, and the input value of the network is one of temperature, humidity and four normalized after correction sensor (TGS2602, TGS2620; TGS2201A/B) the response value”; Abstract “improves the electronic nose detection precision of the measured gas concentration, effectively solves the problem that the electronic nose sensor differences and long-term drift problem”; Technology Field “electronic nose system based on the concentration detection accuracy correction”; about middle of page 6 “correct the sensor response signal in real time and accurately online correction”), characterized in that the step of creating a trained neural network model is broken down into three substeps: generating a scenario for calibrating the measurement sensor (electronic nose), the calibration scenario being generated from a parameter measurement instruction as a function of time (training measurement process functionally occurs over time while changing parameters and taking measurements), said parameters comprising at least one environmental parameter (e.g., temperature, relative humidity) (page 2 second paragraph “short-term drift of the sensor response signal along with the change of temperature, humidity, air pressure and other environment factors and fluctuates. short change period of the wave, generally for several hours to several days, so called short term drift”); creating a neural network model by supplying the neural network with the calibration scenario of said measurement sensor (electronic nose) (third paragraph from bottom of page 3 & about middle of page 5 “artificial neural network”; see fig. 4); and training the neural network model by varying the at least one environmental parameter (e.g., temperature, relative humidity) (page 6 second paragraph to about middle of page “in the training process provided by this embodiment temperature storage way is set by every 2 ℃ from 0 ℃ to 40 ℃ is provided with a temperature storage point, namely the temperature there are 21 storage point, humidity storage mode setting is a humidity memory point is set every 5% from 40% relative humidity to 90%, namely humidity there are 11 storage point. so that temperature and humidity combined memory point number is 231, sensor correction coefficient storage area there are 231 correction coefficient vector ATH. then verifying the technical effect of the invention by test, in the test process, every 8 ℃ from 0 ℃ to 40 ℃ is provided with a temperature traversal point, namely the temperature there are 6 traversing points; humidity traversing way is set every 25% from 40% relative humidity to 90% provided a humidity traversal points, namely the humidity there are 3 traversal points, obtaining temperature and humidity combined traversal point number is 18. the specific implementation process is as follows: adjusting temperature and humidity constant-temperature constant-humidity test box, continuously 4 times into the formaldehyde gas in the box body by a sensor array collecting each inflating the formaldehyde gas sensor response signal, and obtaining the detection result of electrochemical formaldehyde sensor with high precision. the temperature is adjusted to be 0 ℃, 8 ℃, 16 ℃, 24 ℃, 32 ℃ and 40 ℃, the relative humidity is adjusted to 40%, 65% and 90%, respectively, to test in each group of temperature and humidity combinations”), characterized in that the measurement sensor (electronic nose) is calibrated a first time based on the calibration scenario, the first calibration corresponding to a multipoint calibration bottom of page 2 through top of page 3 and third paragraph of page 5 “combined n-dimensional correction”; fourth “combined with the artificial neural network”; fifth paragraphs of page 5 “optimize the artificial neural network”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tian’s scenario involving varying the at least one environmental parameter with Martin’s scenario, thereby providing the expected advantages of correcting sensor response signals due to fluctuations of environmental factors including for changes in temperature, humidity, and/or pressure and therefore increasing the accuracy of the sensor measurements. Complimentarily, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s explicit contamination measurements as well as Martin’s periodic recalibrations with Tian’s method for the expected benefits of increased utility & versatility and associated health assessments as well as for automating recalibration based on other sensor measurements.
Regarding claim 3, which depends on claim 2,
Martin teaches characterized in that the measurement sensor (fig. 1, air quality monitor 102) is calibrated a first time based on the calibration scenario, the first calibration corresponding to a multipoint calibration ([0091] “sensors can be calibrated by individually placing the sensors, placing two or more sensors, or placing air quality monitor 102 in a calibration chamber wherein gases, such as, nitrogen oxide, carbon monoxide, carbon dioxide, hydrogen sulfide, volatile organic compounds, combustible gases, and the like can be introduced into the calibration chamber at identified levels. In response to the specified levels of introduced gases, the one or more sensors can react with an identifiable voltage level which can be noted and charted. Thus, a voltage level for a particular introduced gas can be associated with an identifiable concentration of gas, typically measured in parts per million (ppm) or parts per billion (ppb). The curves determined or ascertained from these calibration activities can be utilized by air quality monitor 102 and/or server 108 to provide indication of the air quality”). See also assessment in claim 1 that Tian likewise teaches this limitation.
Regarding claim 4, which depends on claim 3,
Martin teaches characterized in that the measurement sensor (fig. 1, air quality monitor 102) is associated to a reference apparatus (other air quality monitor), the measurement sensor (fig. 1, air quality monitor 102) being calibrated a second time after a use period T with respect to the reference apparatus (other air quality monitor) ([0092] “Additionally and/or alternatively, because sensor accuracy drifts over time, a self calibration feature is provided wherein, once sensors have been deployed in the field, these sensors can be calibrated or recalibrated through communication with server 108, for example. Generally, where more up-to-date calibration curves have been obtained by server 108, for instance, through calibration activities as described above, these updated calibration curves can be supplied (through wireless or wired modalities) to the sensors associated with a remotely situated air quality monitor (e.g., air quality monitor 102 situated in a residential house)”; [0093] “In addition, in the context of calibration and re-calibration of sensors associated with deployed air quality monitors, measurements from various sensors deployed in one or more deployed air quality monitor located in a single residential house or multiple residential houses dispersed across various geographical areas can be employed for purposes of generating calibration curves that can be employed by server 108 for purposes of calibration and/or recalibration of sensors in deployed air quality monitors (e.g., air quality monitor 102). It should also be noted, that the calibration/recalibration of sensors in deployed air quality monitors can be automated”).
Regarding claim 7, which depends on claim 4,
Martin teaches characterized in that the second calibration (recalibration; more up-to-date calibration) of the measurement sensor (fig. 1, air quality monitor 102) generates new parameter measurements, the new parameter measurements being supplied to the neural network to update the trained neural network model ([0092] “these sensors can be calibrated or recalibrated”; [0093] “calibration and/or recalibration of sensors in deployed air quality monitors (e.g., air quality monitor 102)”; [0044]-[0045]; [0046], [0108] “neural networks”).
Regarding claim 9, which depends on claim 4,
Martin teaches characterized in that the second calibration of the measurement sensor (fig. 1, air quality monitor 102) is carried out remotely ([0092] “sensors can be calibrated or recalibrated through communication with server 108, for example. Generally, where more up-to-date calibration curves have been obtained by server 108, for instance, through calibration activities as described above, these updated calibration curves can be supplied (through wireless or wired modalities) to the sensors associated with a remotely situated air quality monitor (e.g., air quality monitor 102 situated in a residential house)”; [0093] “In addition, in the context of calibration and re-calibration of sensors associated with deployed air quality monitors, measurements from various sensors deployed in one or more deployed air quality monitor located in a single residential house or multiple residential houses dispersed across various geographical areas can be employed for purposes of generating calibration curves that can be employed by server 108 for purposes of calibration and/or recalibration of sensors in deployed air quality monitors (e.g., air quality monitor 102). It should also be noted, that the calibration/recalibration of sensors in deployed air quality monitors can be automated”).
Regarding claim 10, which depends on claim 1,
Martin reasonably suggests characterized in that the measurement of the concentration of the contaminant is obtained in real time (Abstract “continuously monitoring residential air quality and providing a trend based analysis regarding various air pollutants”; [0017] “send real-time updates or alerts to a homeowner or healthcare provider via e-mail or text message”. The Examiner notes that if the updates/alerts for the measured concentration are in real-time, then obtaining the measurements of concentration are likewise reasonably obtained in real-time.
Furthermore, Tian teaches that the measurement of the concentration of the contaminant is obtained in real time (about middle of page 6 “It can be seen from the results, the sensor response corrected value curve and the main board sensor response value (standard sensor response value) curve fitting is good, the invention can correct the sensor response signal in real time and accurately online correction”).
Therefore, either one of ordinary skill in the art at the time the invention was effectively filed would at once envisaged that Martin reasonably teaches real-time obtainment of the concentration, or nevertheless, or in the alternative, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tian’s explicit real-time measurements for the expected and conventional reason of providing the user data more quickly and therefore enabling the aforementioned updates/alerts to be in real-time and thus decreasing prolonged health risks.
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over newly cited Martin in view of newly cited Tian and in further view of newly cited U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division (NPL Quality Assurance Handbook for Air Pollution Measurement Systems Volume II Ambient Air Quality Monitoring Program; hereafter “EPA Handbook”) with newly cited Ning et al (US 20150253297 A1; hereafter “Ning”).
Regarding claim 5, which depends on claim 2,
Martin teaches characterized in that the measurement instruction comprises at least two levels between zero concentration to a concentration corresponding to the maximum of the measurement sensor (fig. 1, air quality monitor 102) ([0091] “calibration chamber wherein gases, such as, nitrogen oxide, carbon monoxide, carbon dioxide, hydrogen sulfide, volatile organic compounds, combustible gases, and the like can be introduced into the calibration chamber at identified levels”; Examiner additionally notes that calibration curves suggests non-linearity and likely therefore more than two levels).
Martin is silent to characterized in that the measurement instruction comprises at least ten levels starting from a zero concentration to a concentration corresponding to the maximum of the measurement sensor.
However:
The Examiner takes Official Notice that measuring the zero and maximum/span levels for calibration is conventional in the art (e.g., two point measurements), and that taking a plurality of measurements therebetween for nonlinear (i.e., curve) calibration requirements is likewise conventional (e.g., five point measurements).
Furthermore, and factually supporting the aforementioned assertion, EPA Handbook teaches the measurement instruction comprises a plurality of levels (calibration points) starting from a zero concentration (zero test concentration) to a concentration corresponding to the maximum (highest test concentration) of the measurement sensor (12.2 “If a non-linear analyzer is being calibrated, additional calibration points should be included to adequately define the calibration relationship, which should be a smooth curve. Calibration points should be plotted or evaluated statistically as they are obtained so that any deviant points can be investigated or repeated immediately. Most analyzers have zero and span adjustment controls, which should be adjusted based on the zero and highest test concentrations, respectively, to provide the desired scale range within the analyzer's specifications (see section 12.5)”).
It has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art, see MPEP § 2144.05 and In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955). In the present case, it is the Examiner’s position that only ordinary skill in the art is required to include additional measurements.
MPEP § 2145(III)(X)(B) states “An “obvious to try” rationale may support a conclusion that a claim would have been obvious where one skilled in the art is choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success. “[A] person of ordinary skill has good reason to pursue the known options within his or her technical grasp. If this leads to the anticipated success, it is likely that product [was] not of innovation but of ordinary skill and common sense. In that instance the fact that a combination was obvious to try might show that it was obvious under § 103.” KSR Int'l Co. v. Teleflex Inc., 550 U.S. 538, 421,82 USPQ2d 1385, 1397 (2007).” It is the Examiner’s position that choosing to include additional measurements for a calibration that is nonlinear/curve merely requires common sense with the known tradeoffs of extra time/costs for increased precision/accuracy.
Furthermore, and factually supporting the assertion that ten levels of concentration measurement is within ordinary skill in the art, Ning teaches at least ten levels of concentration measurement for a gas sensor (Title “METHOD AND A DEVICE FOR DETECTING A SUBSTANCE”; Abstract; [0052] “gas sensors”; [0067] “employ a look-up calibration table to correct for the non-linear effect”; [0070] “The measurement results from the dispersive infrared instrument were validated by using a series of known concentration standard CO.sub.2 gases (21% oxygen (O.sub.2), 79% nitrogen (N.sub.2), CO.sub.2.ltoreq.2 ppmv). Ten concentration points were used spanning from 100 to 1000 ppmv supplied by a gas distribution system and compared to the retrieved CO.sub.2 concentration using our retrieval algorithm”).
In view of the above, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine conventional zero & max span calibration measurements—as factually supported by EPA Handbook—for the expected purpose of providing important measurement points that define the boundary conditions of Martin’s calibration curve and therefore ensuring correlation of field measurements over the full range to known test amounts. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to optimize the number of calibration measurement points to adequately define the calibration relationship for a non-linear analyzer as supported by EPA Handbook, further including at least ten measurements—as factually supported by Ning—thereby ensuring that Martin’s calibration curve is a smooth curve and thus increasing accuracy of Martin’s measurements.
Regarding claim 6, which depends on claim 5,
Martin as previously modified (see preceding analysis) suggests characterized in that, for each level of the measurement instruction (at once so envisaged that the parameters recorded are for each level of measurement; additional obviousness analysis provided), at least one of the measurements of the following parameters is recorded automatically by the measurement sensor (fig. 1, air quality monitor 102) ([0124] “can be located in both local and remote memory storage devices”; [0040] “localized on one computer and/or distributed between two or more computers”; [0041] “components can communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network, e.g., the Internet, a local area network, a wide area network, etc. with other systems via the signal)”; [0058] “allows local storage to guarantee that no sensor data is lost due to disruptions to broadband connectivity, enables sensor inputs to be stored locally”; [0066] “permit local storage of sensor data thereby ensuring that no sensor data or processed data is lost due to disruptions to broadband connectivity. Further advantages can also include the ability to compress or shape the locally stored sensor data and/or processed data to reduce the amount of data the needs to be transmitted to server 108 and/or to enable data transmissions of aggregated sensor inputs and/or processed data when the costs associated with data transmission are reduced”; [0047]):
concentration of the instruction ([0091] “calibration chamber wherein gases, such as, nitrogen oxide, carbon monoxide, carbon dioxide, hydrogen sulfide, volatile organic compounds, combustible gases, and the like can be introduced into the calibration chamber at identified levels”),
concentration of a reference apparatus (other air quality monitor) ([0092] “more up-to-date calibration curves have been obtained by server 108, for instance, through calibration activities as described above, these updated calibration curves can be supplied (through wireless or wired modalities) to the sensors associated with a remotely situated air quality monitor”; [0093] “measurements from various sensors deployed in one or more deployed air quality monitor located in a single residential house or multiple residential houses dispersed across various geographical areas can be employed for purposes of generating calibration curves that can be employed by server 108 for purposes of calibration and/or recalibration of sensors in deployed air quality monitors”),
measured raw concentration of the contaminant ([0068] “Server 108 on receipt of the detected pollution levels from air quality monitor 102 can persist the received information to database or data store 110 and thereafter can analyze the received information”),
measured raw concentration of at least one other contaminant which can act in terms of cross sensitivity (measures the at least one other contaminant, but silent to the possible cross-sensitivity),
a temperature ([0048] “sensor component further comprises a temperature module and a relative humidity module”; [0048], [0052], [0114] and “a temperature sensor, a relative humidity sensor”; [0115] “temperature sensor, relative humidity sensor”; see also further combination & analysis provided for claim 2 over Tian),
a relative humidity ([0048] “sensor component further comprises a temperature module and a relative humidity module”; [0048], [0052], [0114] and “a temperature sensor, a relative humidity sensor”; [0115] “temperature sensor, relative humidity sensor”; see also further combination & analysis provided for claim 2 over Tian),
a pressure (Martin silent to pressure; however, see combination & analysis provided for claim 2 over Tian).
With further regards to the for each level of measurement qualifier, the Examiner notes that either one of ordinary skill in the art at the time the invention was effectively filed would at once envisaged that Martin—or as noted above Martin in combination with Tian—teaches/suggests the aforementioned measurements for each calibration level, or nevertheless, or in the alternative, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to so collect the additional data for the expected purpose of providing said measurement data to the neural network for analysis thereof and thus commonsensically increasing the accuracy/precision of the neural network as opposed to having to rely on sparse/incomplete data.
Allowable Subject Matter
Claim(s) 8 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
When this application is finally acted upon and allowed (i.e., the Notice of Allowance), the Examiner will determine, at the same time, whether the reasons why the application is being allowed are sufficiently evident from the record; see MPEP § 1302.14(I).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is invited to review PTO form 892 accompanying this Office Action listing Prior Art relevant to the instant invention cited by the Examiner.
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/DAVID L SINGER/Primary Examiner, Art Unit 2855 21MAR2026
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