DETAILED ACTION
This communication is in responsive to amendment for Application 17/975197 filed on 9/18/2025. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims:
Claims 1-5 are presented for examination.
Response to Arguments
3. Examiner statements in the mailed Non-Final with respect to obvious limitations including common knowledge or well-known in the art are taken to be admitted prior art because applicant failed to traverse the Examiner’s assertion, see MPEP 2144.03 C.
4. Applicant’s arguments in the amendment filed on 9/18/2025 regarding claim rejection under 35 USC § 102/103 with respect to Claims 1-5 have been considered and were found unpersuasive because the cited art still teaches claims 1 and 5.
Applicant argues that Brady does not include online sensors, offline senor or dedicated analyzers in a biopharmaceutical process, factory or food or steel manufacturing process (Remarks p. 9-10). Examine disagrees because Brady teaches sensors as mapped below. For example, see (¶0021 & ¶0066-¶0070; temperature sensors) and pressure sensor (¶0022 & ¶0081, Fig. 6; LPHW). Those sensers are mapped in details below to illustrate that each of them is equivalent to online or offline sensors as supported for instance specification. Applicant provides no arguments as to why the temp sensor or the LPHW of Brady is not the same as the claimed limitation. Instead, applicant argues that, as understood by the Examiner, since Brady does not teach “biopharmaceutical process, factory or food or steel manufacturing process” then it does not teach the sensors. This argument is rejected because Brady still teaches a process where different sensors are being used that is similar to the claimed language. The functions of Brady’s sensors are similar to the claimed sensors. the process of Brady is also similar to the broad processes claimed. For example, Brady expressly teaches that the output is provided to a computer to perform different mathematical operations and functions. See ¶0077, ¶0089 & Fig. 4. As to the new amendment “…in a biopharmaceutical process, a smart factory manufacturing process, a food manufacturing process, or a steel manufacturing process” Examiner cites a secondary art for support.
Additionally, in response to applicant's argument that Brady is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Brady is in the field of the inventor’s endeavor because Brady teaches a manufacturing process that includes different sensors. Also, merely amending the claims to stat biopharmaceutical process, smart or food or steel process does not change the fact that the claim uses the existing technology. Brady is on point and is applicable to different type of process.
Also, applicant also argues that Huang is not applied for biopharmaceutical process, food or steel process. The response above still applies here. Again, merely amending the claims to state biopharmaceutical process, smart or food or steel process does not change the fact that the claim uses the existing technology. Huang is on point and is applicable to different type of process.
Thus, Examiner maintains his interpretation and art rejection.
Claim Rejections - 35 USC § 112
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.
Claims 1 & 5 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The underline limitation is not clear:
Claim 5 limitation “…provides statistical data by analyzing accumulated big data, visualizes and outputs the statistical data…” The term “big data” is a relative term which renders the claim indefinite. The term “big data” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al. (hereinafter Brady) US 2019/0064004 A1 in view of Cooley et al. (hereinafter Cooley) US 2022/0147018 A1.
Regarding Claim1, Brady teaches a softsensor analysis and measurement system for deriving a result by new process variables on a basis of data collected by various sensors (¶0067; FIG. 4 illustrates cognitive energy assessment and training of a machine-learning model [new process variables] in a computing environment, such as a computing environment 402 [softsensor analysis and measurement system] including a computing node that analysis and measure different sensor values using statistics and other variables then outputs the results. See Fig. 5 for processing variables. See Fig. 7 for outputting results), the system comprising:
an online sensor (¶0021 & ¶0066-¶0070; temperature sensors), an inline sensor (¶0073; machine learning module 406 may collect feedback information from the one or more IoT sensors associated with the IoT sensor component 416 to learn the behavior of the thermal energy fluid transfer system 430), or an offline sensor (data analytics processing 94);
a machine learning/deep learning module (¶0023 & ¶0073; ML);
and a softsensor server (¶0049-¶0058; cloud computing/servers 12 or computer system 12) for collecting and storing online sensor data collected by the online sensor through a sensor network and a gateway, Ethernet, or WLAN (Wi-Fi) (¶0072-¶0073 & ¶0086; the machine learning module 406 may include an estimation/prediction component 408 for cognitively predicting and/or cognitively estimating energy assessment using temperature data from a temperature signal collected by one or more IoT sensors associated with the IoT sensor component 416 located at one or more selected positions of a piping network (e.g., a return line in a pipe loop) in the thermal energy fluid transfer system 430. For example, the computer system 12, using the wasted energy usage component 410 and the cognitive energy assessment component 412, may cognitively determine the energy usage by one or more IoT sensors associated with the IoT sensor component 416. An energy usage profile of the thermal energy fluid transfer system 430 may be created, defined, stored, and maintained in the machine learning module 406, the features and/or parameters 404, or both. Also see Figs. 7, 11 & ¶0089; where output may be dynamically communicated to a cloud environment, saved, and/or provided as a report to a GUI of a computer),
collecting and storing inline sensor data measured by an IoT device equipped with the inline sensor (¶0073 & ¶0086; sampled data may be communicated and saved to a cloud computing environment and may continue to sample data for up to a selected time period (e.g., one month) in order to acquire sufficient training data. The training data may be used to generate tuning threshold levels for the thermal energy fluid transfer system, as described herein for learning behavior of the thermal energy fluid transfer system. Once a thermal energy fluid transfer system is tuned according to the trained data, a rule based data analytics operation may be applied on signal disambiguation detected events to report system anomalies during a selected time period (e.g., 24 hours). Also see Figs. 7, 11 & ¶0089; where output may be dynamically communicated to a cloud environment, saved, and/or provided as a report to a GUI of a computer) and/or
offline sensor data measured by the offline sensor through an analysis device (¶0065; workloads and functions 96 for cognitive energy assessments in a thermal energy fluid transfer system using an array of IoT sensors may include such operations as data analysis (including data collection and processing from various environmental sensors), and predictive data analytics functions. One of ordinary skill in the art will appreciate that the workloads and functions 96 for cognitive energy assessments in a thermal energy fluid transfer system using an array of IoT sensors may also work in conjunction with other portions of the various abstractions layers, such as those in hardware and software 60, virtualization 70, management 80, and other workloads 90 (such as data analytics processing 94, for example) to accomplish the various purposes of the illustrated embodiments of the present invention) or dedicated analyzer (¶0065; workloads and functions 96 for cognitive energy assessments in a thermal energy fluid transfer system using an array of IoT sensors may include such operations as data analysis (including data collection and processing from various environmental sensors), and predictive data analytics functions. One of ordinary skill in the art will appreciate that the workloads and functions 96 for cognitive energy assessments in a thermal energy fluid transfer system using an array of IoT sensors may also work in conjunction with other portions of the various abstractions layers, such as those in hardware and software 60, virtualization 70, management 80, and other workloads 90 (such as data analytics processing 94, for example) to accomplish the various purposes of the illustrated embodiments of the present invention),
and outputting a result by applying new process variables for target values for the online sensor data, the inline sensor data, or the offline sensor data of a manufacturing process by using the machine learning/deep learning module (Fig. 4 & ¶0077; the computing system 12/computing environment 402 may perform one or more calculations according to mathematical operations or functions that may involve one or more mathematical operations (e.g., solving differential equations or partial differential equations analytically or computationally, using addition, subtraction, division, multiplication, standard deviations, means, averages, percentages, statistical modeling using statistical distributions, by finding minimums, maximums or similar thresholds for combined variables, etc. Also see Figs. 7, 11 & ¶0089; where output may be dynamically communicated to a cloud environment, saved, and/or provided as a report to a GUI of a computer. Note to the “process variable (input)” that Brady still teaches that different combinations of parameters may be selected and applied to the input data for learning or training one or more machine learning models of the machine learning module 406. The features and/or parameters 404 may define one or more settings of one or more non-intrusive IoT sensors associated with the IoT sensor component 416 to enable the one or more non-intrusive IoT sensors to detect a temperature signal data via the IoT sensor component 416. The one or more non-intrusive IoT sensors associated with the IoT sensor component 416 may be coupled to the thermal energy fluid transfer system 430 at one or more defined distances from an alternative non-intrusive sensor),
and feeding back (¶0073; machine learning module 406 may collect feedback information from the one or more IoT sensors associated with the IoT sensor component 416 to learn the behavior of the thermal energy fluid transfer system 430, establish energy usage schedules, energy usage threshold standards and values, establish an energy usage profile of the thermal energy fluid transfer system 430, establish a health state of the thermal energy fluid transfer system 430, detect (in association with the wasted energy usage component 410) one or more anomalous thermal energy fluid transfer system events, or a combination thereof. The machine learning module 406 may use the feedback information to provide a cognitive estimate of an energy output of the thermal energy fluid transfer system 430 using the estimation/prediction component 408. That is, the estimation/prediction component 408 may cognitively assess the energy of the thermal energy fluid transfer system 430 by one or more IoT sensors associated with the IoT sensor component 416. In short, the machine learning module 406 may be initialized using feedback information to learn behavior of a thermal energy fluid transfer system 430) and applying the new process variables of the manufacturing process to a control computer of the manufacturing process (¶0089; where output may be dynamically communicated to a cloud environment, saved, and/or provided as a report to a GUI of a computer. Fig. 4 & ¶0077; the computing system 12/computing environment 402 may perform one or more calculations according to mathematical operations or functions that may involve one or more mathematical operations (e.g., solving differential equations or partial differential equations analytically or computationally, using addition, subtraction, division, multiplication, standard deviations, means, averages, percentages, statistical modeling using statistical distributions, by finding minimums, maximums or similar thresholds for combined variables, etc.))
Brady expressly teaches that the output is provided as a report to a computer to perform different mathematical operations and functions. See ¶0077, ¶0089 & Fig. 4. However, Brady does not expressly teach that this process is done “…in a biopharmaceutical process, a smart factory manufacturing process, a food manufacturing process, or a steel manufacturing process.”
Cooley on the other hand is analogous art because Cooley teaches using different data that is observed in a factory collected from a smart environment that includes different IoT devices or sensors for automation processing. See ¶0056 & Fig. 4.
Cooley also teaches that different sensors are in a smart factory manufacturing process. See ¶0111 & ¶0104-¶0108.
It would have been obvious to one of ordinary skill in the art before the effective filling to incorporate the teachings of Cooley into the system of Brady in order to monitor the automation environment for events or state changes in the data sources, detect one or more events or one or more state changes in one or more other data sources in a plurality of data sources (abstract). Utilizing such teachings enable the system to determine one or more relationships between the current data source and the one or more other data sources (abstract).
Regarding Claim 2, Brady and Cooley further teaches the system of claim 1, wherein the online sensor includes a temperature sensor (¶0021 & ¶0066-¶0070; temperature sensors), a humidity sensor, or a pressure sensor (¶0022 & ¶0081, Fig. 6; LPHW).
Regarding Claim 3, Brady and Cooley further teaches the system of claim 1, wherein the inline sensor includes a sensor attached to an IoT device used in a manufacturing process (¶0073; machine learning module 406 may collect feedback information from the one or more IoT sensors associated with the IoT sensor component 416 to learn the behavior of the thermal energy fluid transfer system 430, establish energy usage schedules, energy usage threshold standards and values, establish an energy usage profile of the thermal energy fluid transfer system 430, establish a health state of the thermal energy fluid transfer system 43).
Regarding Claim 4, Brady and Cooley further teaches the system of claim 1, the offline sensor includes an analysis device or a dedicated analyzer (¶0065; workloads and functions 96 for cognitive energy assessments in a thermal energy fluid transfer system using an array of IoT sensors may include such operations as data analysis (including data collection and processing from various environmental sensors), and predictive data analytics functions. One of ordinary skill in the art will appreciate that the workloads and functions 96 for cognitive energy assessments in a thermal energy fluid transfer system using an array of IoT sensors may also work in conjunction with other portions of the various abstractions layers, such as those in hardware and software 60, virtualization 70, management 80, and other workloads 90 (such as data analytics processing 94, for example) to accomplish the various purposes of the illustrated embodiments of the present invention).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Brady in view of Cooley in view of CN112394686 to Huang.
Regarding Claim 5, Brady in view of Cooley teaches the system of claim 1, wherein the softsensor server collects and stores the online sensor data, the inline sensor data, and the offline sensor data,
outputs the result by re-applying the new process variables (input) of the manufacturing process by using the machine learning/deep learning module (¶0071; one or more ML models are used to determine wasted energy usage),
feeds back the new process variables of the manufacturing process to the control computer of the manufacturing process (¶0073; machine learning module 406 may collect feedback information from the one or more IoT sensors associated with the IoT sensor component 416 to learn the behavior of the thermal energy fluid transfer system 430, establish energy usage schedules, energy usage threshold standards and values, establish an energy usage profile of the thermal energy fluid transfer system 430, establish a health state of the thermal energy fluid transfer system 430, detect (in association with the wasted energy usage component 410) one or more anomalous thermal energy fluid transfer system events, or a combination thereof. The machine learning module 406 may use the feedback information to provide a cognitive estimate of an energy output of the thermal energy fluid transfer system 430 using the estimation/prediction component 408. That is, the estimation/prediction component 408 may cognitively assess the energy of the thermal energy fluid transfer system 430 by one or more IoT sensors associated with the IoT sensor component 416. In short, the machine learning module 406 may be initialized using feedback information to learn behavior of a thermal energy fluid transfer system 430),
provides statistical data by analyzing accumulated big data of the online sensor, the inline sensor or the offline sensor in the manufacturing process, visualizes and outputs the statistical data (Figs. 5, 7 & ¶0077; the computing system 12/computing environment 402 may perform one or more calculations according to mathematical operations or functions that may involve one or more mathematical operations (e.g., solving differential equations or partial differential equations analytically or computationally, using addition, subtraction, division, multiplication, standard deviations, means, averages, percentages, statistical modeling using statistical distributions, by finding minimums, maximums or similar thresholds for combined variables, etc.)),
Brady in view of Cooley does not expressly teach and is connected to a laboratory information management system (LIMS)/Enterprise Resource Planning (ERP)/Legacy system.
Huang is analogues art because it r elates to a method, a system, a device and a medium for automatically calculating the total pollution discharge amount of an industrial enterprise, which comprises the following steps: step 1, obtaining an online data processing result by calculation according to an online data statistical rule; step 2, obtaining an artificial data processing result by calculation according to the artificial data statistical rule; step 3, acquiring a coefficient method data processing result; and 4, processing the on-line data processing result, the manual data processing result and the coefficient method data processing result to obtain the environment-friendly tax data. Compared with the prior art, the method, the system, the device and the medium for automatically calculating the total pollution discharge amount of the industrial enterprise have the following advantages: the method comprises the steps of establishing a data processing and analyzing functional module combination, presetting various rule conditions for logic adaptation, combining effective data and condition input items through a modularized functional combination, carrying out statistical calculation on emission and environmental protection tax, improving calculation efficiency and accuracy, and effectively helping environmental protection management departments to carry out statistics and reporting work. Abstract.
Huang teaches and is connected to a laboratory information management system (LIMS)/Enterprise Resource Planning (ERP)/Legacy system (p. 4, paragraph right after number 5; The invention integrates and utilizes multi-source data, including real-time data acquired from an online data acquisition instrument, monitoring data acquired from a LIMS manual monitoring system, yield data acquired from an ERP system, environmental protection equipment operation time and other data acquired from each production unit, performs data analysis on each pollution discharge factor of each discharge outlet,).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed limitation to incorporate the teachings of Huang in the system of Brady-Cooley in order to enables an analysis model to effectively operate and perform data statistics, calculation, screening and sorting through condition constraints such as detection degree, detection limit, detection mode, data screening rule and the like, and performs environmental protection tax calculation according to a preset environmental protection tax calculation rule to finally generate a total pollution discharge calculation result and an environmental protection tax calculation result. Compared with the traditional manual statistical method, the method has the defects of time consumption, complexity, low efficiency, high labor cost and the like, is easy to make mistakes and is difficult to find wrong places, and has obvious advantages. The invention also provides an automatic computing system for the total pollution discharge amount of the industrial enterprise (p. 4 last paragraph and top of p. 5).
Conclusion
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 MAHRAN ABU ROUMI whose telephone number is (469)295-9170. The examiner can normally be reached Monday-Thursday 6AM-5PM.
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MAHRAN ABU ROUMI
Primary Examiner
Art Unit 2455
/MAHRAN Y ABU ROUMI/ Primary Examiner, Art Unit 2455