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.
Response to Amendment
The amendments to the claims and drawings filed 07/01/2026 have been considered and entered.
Response to Arguments
Applicant's arguments filed 07/01/2026 have been fully considered.
Regarding the objection(s) to claim(s) 1 pertaining to an unclear use of a pronoun, Applicant argued that the amendment overcame said objection(s); the Examiner is in agreement, therefore said objection(s) has/have been withdrawn.
Regarding the objection(s) to the Drawings pertaining to labels, Applicant argued that the amendment overcame said objection(s); the Examiner is in agreement, therefore said objection(s) has/have been withdrawn.
Regarding the objection(s) to the Drawings pertaining to reproducibility, Applicant argued that the amendment overcame said objection(s). The Examiner is in partial agreement, noting the bona fide attempt and the improvement of numerous figures; however, figs. 2 & 4 still have difficult/impossible to read text, see present drawing objections.
Regarding the objection(s) to the Specification pertaining to acronym/abbreviation “COP21” without explanation what COP21 stood for, Applicant argued that COP21 is conventional and well-known in the field and therefore does not require correction. MPEP § 608.01 states that “The specification is a written description of the invention and of the manner and process of making and using the same. The specification must be in such full, clear, concise, and exact terms as to enable any person skilled in the art or science to which the invention pertains to make and use the same.” MPEP § 2141(II)(C) states: “Office personnel may rely on their own technical expertise to describe the knowledge and skills of a person of ordinary skill in the art. The Federal Circuit has stated that examiners and administrative patent judges on the Board are “persons of scientific competence in the fields in which they work” and that their findings are “informed by their scientific knowledge, as to the meaning of prior art references to persons of ordinary skill in the art.” In re Berg, 320 F.3d 1310, 1315, 65 USPQ2d 2003, 2007 (Fed. Cir. 2003). In addition, examiners “are assumed to have some expertise in interpreting the references and to be familiar from their work with the level of skill in the art.” PowerOasis, Inc. v. T-Mobile USA, Inc., 522 F.3d 1299, 86 USPQ2d 1385 (Fed. Cir. 2008) (quoting Am. Hoist & Derrick Co. v. Sowa & Sons, 725 F.2d 1350, 1360, 220 USPQ 763, 770 (Fed. Cir. 1984). In the present case, the Examiner notes that “COP21” is neither full nor clear, that the Examiner had to search for what the abbreviation/acronym “COP21” stood for and decide which was the intended reference by Applicant, and that Applicant’s possible future patent will remain available to the public well into the future where even obvious acronyms/abbreviations today might be forgotten/confusing in the future. For all of the aforementioned reasons, the Examiner therefore retains the objection and requests that Applicant include the corresponding name that “COP21” stands for.
Regarding the prior art rejections, Applicant argued that the amendment to include a model that is the product of two separately trained models overcomes said rejections. To the best understanding of the Examiner, see related 112(b)/2nd indefinite rejections, the Examiner is in agreement. However, see new prior art rejections in further view of newly cited US 20150254554 A1 “Kato”. The Examiner further notes that additional pertinent prior art can be found in the Conclusion, which were further considered when determining the level of ordinary skill in the art and the prevalency of applying the strategy of producing neural networks from the product of neural networks. It is the Examiner’s position that generically taking the product of two neural networks to form a third neural network therefrom is a known strategy to a known (generic) problem and requires only ordinary skill in the art; with respect to claim 8, the Examiner notes that the combination of limitations, including all of the limitations of the base claim (less the new confusing limitation) and the intervening claims, is still considered non-obvious when considered as a whole, and Applicant’s arguments about the advantages appear to be applicable thereto.
Information Disclosure Statement
The information disclosure statement(s)
(IDS) submitted on 03/25/2026 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. The Examiner notes this IDS had crossed in the mail, coming after the Examiner wrote and posted the last Office Action, but before the Office mailed said last Office Action.
Drawings
Unsatisfactorily Reproducible:
The drawings are objected to under 37 CFR 1.84(l) and corresponding PCT rule 11.13(a),
fig(s). 2 & 4
requiring correction.
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 (fig. 2) are further objected to under 37 CFR 1.84(p)(3) (and correspondingly PCT rule 11.13(h)). Numbers, letters, and reference characters must measure at least 1/8 inch (0.32 cm) in height. They should not be placed in the drawing so as to interfere with its comprehension. Therefore, they should not cross or mingle with the lines. They should not be placed upon hatched or shaded surfaces. When necessary, such as indicating a surface or cross section, a reference character may be underlined and a blank space may be left in the hatching or shading where the character occurs so that it appears distinct.
The Examiner is requesting in particular that in figs. 2 and 4, that at least all instances of the text be easily readable by a combination of darker and/or larger font as appropriate.
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 specification fails to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). While the Examiner acknowledges MPEP § 2173.05(e)(lI) wherein there is no requirement that the words in the claim must match those used in the specification disclosure, “the trained neural model” (at least claim 1), “two trained neural models” (at least claim 1) do not provide a reasonable degree of clarity and precision to define Applicant's invention, the Examiner emphasizing that Applicant appears to be both equating “trained neural model” and “trained neural network model” (see antecedence shown by “the” in claim 1, and therefore appearing to merely be a small objection by itself for inconsistent nomenclature; however see second issue) but also that for the “two trained neural models” (no antecedence in claim 1) that neither of these two have antecedence and therefore are not the same as an already introduced “trained neural network model”, but which appears to be contradictory to both the specification and dependent claim 8, wherein for claim 8, the “trained neural network model” is the product of “the two trained neural network models” and which includes the (first) “trained neural network model” and a “second trained neural network model”. Applicant clarification of the intended meaning of the terms and the removal of contradictions between the disclosure and the claims is respectfully requested. See related 112(b)/2nd indefinite rejections.
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). Applicant is further reminded that the specification must include a written description of the invention or discovery and of the manner and process of making and using the same, and is required to be in such full, clear, concise, and exact terms as to enable any person skilled in the art or science to which the invention or discovery appertains, or with which it is most nearly connected, to make and use the same, whereas the use of the abbreviation/acronym without the explanation is neither clear nor full. The Examiner notes that to determine Applicant’s intended meaning, the Examiner had to search “COP21” and then consider the results thereof before ascertaining Applicant’s meaning. The Examiner and Applicant appear to be in agreement that COP21 is in reference to the 21st Climate Change Conference in Paris, and Applicant is respectfully requested to make such explicit in the Specification
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 trained neural model corresponding to a product of two trained neural models” is not itself a step (note for example the passive & narrative language), and to the best understanding of the Examiner appears to be an incomplete statement that simply lacks a conjunction, the Examiner suggesting starting with “wherein” and rephrasing the limitation.
Dependent claim(s) of objected to claim(s) is/are likewise objected to.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
MPEP § 2173.02(I) states in part: “if the language of a claim, given its broadest reasonable interpretation, is such that a person of ordinary skill in the relevant art would read it with more than one reasonable interpretation, then a rejection under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph is appropriate”.
MPEP § 2173.03 states in part each of:
“A claim, although clear on its face, may also be indefinite when a conflict or inconsistency between the claimed subject matter and the specification disclosure renders the scope of the claim uncertain as inconsistency with the specification disclosure or prior art teachings may make an otherwise definite claim take on an unreasonable degree of uncertainty. In re Moore, 439 F.2d 1232, 1235-36, 169 USPQ 236, 239 (CCPA 1971); In re Cohn, 438 F.2d 989, 169 USPQ 95 (CCPA 1971); In re Hammack, 427 F.2d 1378, 166 USPQ 204 (CCPA 1970).”
"When faced with this unknown and undefined phrase, a skilled artisan would look for clarification not only in the specification but also in '[o]ther claims of the patent in question,' which 'can also be valuable sources of enlightenment as to the meaning of a claim term.'" Id. at 460 (quoting Philips v. AWH Corp., 415 F.3d 1303, 1314, 75 USPQ2d 1321, 1327 (Fed. Cir. 2005)).
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding independent claim 1,
there is insufficient antecedent basis for the limitation "the trained neural model". The Examiner has looked to the disclosure for guidance, however there are no instances of “trained neural model” or even just “neural model”, instead the Examiner finds instances of “neural network”, “trained neural network model”, and “second trained neural network model”, and “final trained neural network model”. As best understood by the Examiner, when Applicant now writes “neural model”, Applicant is referencing the “neural network model” discussed in the specification and appearing earlier in the claim. Applicant then goes on to claim “corresponding to a product of two trained neural models”, however, as noted above, this might be understood instead as referencing “two trained neural network models”. A valuable source of enlightenment can also be found in dependent claim 8 which states “that a second trained neural network model is created based on the update of the trained neural network model, the final trained neural network model of the measurement sensor corresponding to the product of the two trained neural network models”, and which appears to be the source of inspiration for the inclusion of the new limitation into independent claim 1, Applicant having made the comment that “The modification made to claim 1 is directly related to the technical features of claim 8”. The Examiner notes however that the trained neural network model in claim 1 is contradictory to both the specification and dependent claim 8, wherein (using exemplary language from claim 8) the “trained neural network model” is the product of “the two trained neural network models” and which includes the (first) “trained neural network model” and a “second trained neural network model”. Finally, the Examiner notes that the added limitation in independent claim 1 is narrative, and instead of being active steps pertaining to the method, could instead be reasonably interpreted as merely “corresponding” to a decomposition property. To the best understanding of the Examiner, and for the purpose of examination, the Examiner interprets this narrative limitation as describing the property of the trained neural network model as having a corresponding multiplicative (product) neural model.
Dependent claim(s) of rejected claim(s) is/are likewise rejected.
The Examiner notes in particular that dependent claims fail to correct the confusion of independent claim 1, including that:
Claim 2 references the steps of creating a trained neural network model, but it is unclear how this would fit with the trained neural model corresponding to a product of two trained neural models; does this occur to each/one of the two neural models, or only after the product thereof?
Claim 7 references updating the trained neural model, but it is unclear how this would fit with the trained neural model corresponding to a product of two trained neural models; does this occur to each/one of the two neural models, or only after the product thereof?
Claim 8 references a second trained neural network model is created based on the update of the trained neural network model, the final trained neural network model of the measurement sensor corresponding to the product of the two trained neural network model, but it is unclear how this would fit with the trained neural model corresponding to a product of two trained neural models; is the two trained neural models of claim 1 the trained neural network model & the second neural network model of claim 8, and the product of the two trained neural models in claim 1 the same as the product of the two trained neural network models of claim 7?
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) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Martin (US 20130174646 A1; hereafter “Martin”) in view of newly cited Kato (US 20150254554 A1; hereafter “Kato”).
Regarding independent claim 1, as best understood,
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”; [0055] “predicting the future”), 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, permitting 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] “Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. 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 in connection with the disclosed subject matter”, [0108] “neural networks”).
Martin does not explicitly state item 1) wherein the server is a specialized remote server. Martin does not teach item 2): wherein the trained neural model corresponds to a product of two trained neural models.
Regarding item 1), the Examiner previously took Official Notice that specializing a server to the purpose for which it serves is conventional and the Examiner previously took Official Notice that placing a specialized server remotely is conventional. As the Applicant had not adequately traversed these assertions, said assertions are considered admitted prior art in accordance with MPEP § 2144.03(C).
Therefore, 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, 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.
Regarding item 2), solving a similar problem of learning and predicting, Kato teaches creating a trained neural model corresponding to a product of two trained neural models (Title “INFORMATION PROCESSING DEVICE AND LEARNING METHOD”; Abstract “An information processing device which generates a prediction model on time-series data by using neural networks in a short time is provided. A prediction model learning unit 121 learns a prediction model including a first neural network, a second neural network, and a third neural network. To the first neural network and a second neural network, subsets obtained by dividing a set that includes the time-series data values as elements are inputted respectively. To the third neural network, an inner product of outputs from the first neural network and the second neural network is inputted. The third neural network outputs a predicted data value for the prediction target type as of a prediction target time”; [0003] “For example, predictions are made with time-series data (time-series prediction) in a wide range of applications, such as commodity or electricity demand forecasting and weather forecasting”; [0006] “Time-series data predictions with neural networks require appropriate selection of input parameters for every prediction target. As input parameters, not only actual measured data values for a prediction target but also their processed values may be used. For example, a difference value, average, or standard deviation calculated from actual measured values”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kato’s specific prediction model utilizing the product of first & second neural networks with Martin’s generic neural network for prediction, thereby providing a specific neural network based learning & prediction method that is advantageous for being fast to train each neural network while still supporting complex data sets, and further enables parallel training of the first and second neural networks.
With regards to claim interpretation, the Examiner additionally notes that as presently written, the new limitation of item 2) is a narration about a property correspondence rather than a part of a method step, and the corresponding property is further not even properly referencing the elements of the other steps.
Claim(s) 2-4, 7, and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Martin in view of newly cited Kato and in further view of previously cited Tian* et al (CN 105651939 A; hereafter “Tian”).
*machine translation previously provided by Examiner with foreign document and utilized for English citations
Regarding claim 2, which depends on claim 1, as best understood,
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, as best understood,
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 previously cited Martin in view of newly cited Kato, previously cited Tian, and in further view of previously 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 previously 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 previously took 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). As the Applicant had not adequately traversed this assertion, this is considered admitted prior art in accordance with MPEP § 2144.03(C).
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 (as best understood) would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all* (exception; see note below) of the limitations of the base claim and any intervening claims.
*The Examiner suggests removing the limitation “the trained neural model corresponding to a product of two trained neural models” of independent claim 1 to overcome the rejection thereof, said confusing and contradictory limitation being unnecessary to the conditionally indicated allowability.
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. The Examiner notes the following as particularly pertinent to the fact that neural networks being the product of other neural networks is a well-known scheme:
US 20220391671 A1 [0032] “The output of the combination neural network is a dot product of the outputs of the two individual neural networks”.
US 20210387584 A1 [0017] “the output of the first neural network model and the second neural network model are concatenated, interleaved, multiplied, XOR'd, etc. to form the third model input”.
US 5377305 A Title “Outer Product Neural Network”; Summary of the Invention “a neural network system, including a plurality of cascade coupled neural network”.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to DAVID L SINGER whose telephone number is (303) 297-4317. The Examiner can normally be reached on Monday - Friday 8:00 am - 6:00pm CT, EXCEPT alternating Friday.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, John Breene can be reached on 571-272-4107. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID L SINGER/Primary Examiner, Art Unit 2855 12AUG2026
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