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 .
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 2, 8-10, and 10-14 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.
Regarding claim 2, the claim recites “the incoming live stream of energy meter data”. However, there is no antecedent enrichment procedure or live stream, so the claim is indefinite as to what this references.
Regarding claims 8-10, and 10-14, claim limitations “a first cloud module configured to”, “a second cloud module configured to”, and “a third cloud module configured to” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Contrary to the false assertions in Applicants remarks (pg. 7) where Applicants claim the modules in the cloud platform are “Internet-connected remote servers that can store, manage, and process data”, the application as originally filed has no such disclosure. The words internet and server never appear in the application as originally filed. Nor for that matter is a single mention of a computer, processor, or memory anywhere in the specification. The closest it comes is the alarm database 35 that is outside of the cloud platform 12, but there is no disclosure of an internet connection between them. The “cloud platform” 12 shown in figure 1 of the application is a software diagram comprising software per se. You have streaming sources 26, 27, and 28, going into enrichment job 24, the output of which goes into table 31. That’s fed into training pipeline 26, weather API storage 32 and alarm generating job 1 that has symbol 25. Then the output of final table 34 and alarm generating job 1 25 is output from the cloud platform 12 of figure 1. Everything depicted in cloud platform 12 of figure 1 is software per se having no actual physical structures involved, and definitely no disclosure of any “internet-connected remote servers” within cloud platform 12 of figure 1.
Regarding the first module, the specification only discloses that a baseline is computed using machine learning methods, but no actual structure is disclosed as providing a generalized energy consumption baseline line of a physical asset. Similarly, there is no disclosure of a structure to measure energy consumption of the physical asset, nor is there a disclosed structure to compare the energy consumption measured from the physical asset with the generalized energy consumption baseline of the physical asset. The broadest reasonable interpretation of these elements is software per se. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 and 12-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of the “cloud platform” of claims 1-7 and “cloud platform energy consumption monitoring system” of claims 8-10 and 12-14 is that they are both software per se. MPEP §2106.03(I). Claims 1-7 recite a prediction model, specific model of a building, a framework, and a digital twin. These are not tangible structural elements so as to qualify as a machine, manufacture or composition of matter, and no steps are recited so as to qualify as a process. Claims 8-14 recite a first, second and third module configured to perform various things, but as best understood these modules similarly correspond to software pe se and mathematical calculations. As discussed in the §112 rejection above, contrary to Applicants assertions there is not a single mention or disclosure of any “Internet-connected remote servers”, and the cloud platform 12 depicted in figure 1 is a standard software flow diagram. There are no servers disclosed within cloud platform 12 in figure 1. Accordingly, claims 1-10 and 12-14 are not directed to a process, machine, manufacture, or composition of matter.
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 Kamel (US 2013/0134962) (hereinafter Kamel) in view of Park et al. (US 2021/0373753) (hereinafter Park).
Regarding claim 1, Kamel teaches a platform for establishing and maintaining baseline energy consumption for a building, comprising:
a generalized energy consumption baseline prediction model (ph. [0070], “an energy baseline can be calculated for any measured or calculated metric, and correlated with ambient weather conditions, facility usage, and facility schedule. The calculated baseline can be used to project the value of the metric given projections of ambient weather conditions, facility usage, facility schedule, or changes in energy systems.”);
a site-specific model of a building which learns site-specific relationships among energy consumption, weather patterns, occupant behavior, site structural characteristics and geo-location characteristics of the building (ph. [0040]-[0042], “the energy search engine 102 receives in a substantially continuous way dynamic data relating to energy usage from one or more of a Building Information Modeling (BIM) 106, a power grid 112, a utility company 114, building management 116, and an environmental service 118. For example, the BIM 106 can provide, but is not limited to specifications for the systems, subsystem, and components 104a installed in the facility 104… The building management 116 can provide, but is not limited to facility and zone level scheduling of the facility 104, occupancy information, system status information (e.g. open doors, open windows, open shutters, etc.), and the like. The environmental service, such as a weather service, can provide, but is not limited to dynamic weather data for the location of the facility 104, projected weather for the location of the facility 104, sever weather alerts, geographical factors, and the like. The energy search engine 102 analyzes the static data and the dynamic data received in the substantially continuous way and provides a substantially continuous energy assessment”);
a digital twin of the building which is a digital representation of the building that captures characteristics of the building, relationship and change among various assets in the building (ph. [0041], “a Building Information Modeling (BIM) 106, a power grid 112, a utility company 114, building management 116, and an environmental service 118. For example, the BIM 106 can provide, but is not limited to specifications for the systems, subsystem, and components 104a installed in the facility 104, specifications for the systems, subsystem, and components with a higher energy rating that could have been installed in the facility 104, and the like.”); and
wherein energy consumption is automatically compared with the generalized energy baseline prediction model baseline (ph. [0080]-[0081], “Target energy consumption can be a calculated energy consumption based on baseline performance… At block 516, the process 500 compares the measurement of the actual energy consumption with the target energy consumption for the facility 104, and at block 518, the process 500 calculates a substantially continuous energy performance assessment based at least in part on the comparison of the measurement of the actual energy consumption with the target energy consumption.”).
Kamel does not explicitly teach a framework that enables a machine learning model to retrain with updates, triggered events, and building changes. However, Park teaches a cloud platform (ph. [0284], “a remote (e.g., cloud-based) analytics system”) including a framework that enables a machine learning model to retrain with updates (ph. [0304], “In some embodiments, weather normalization module 5208 recalculates the energy consumption model on the first of each month with all available data up to but not exceeding three years.”, retrains based on updated new weather data), triggered events (ph. [0304], “In addition to automatically updating the energy consumption model periodically, a user-defined trigger can be used to force a recalculation of the baseline model.”), and building changes (ph. [0304], “The user-defined trigger can be a manual trigger (e.g., a user selecting an option to update the model) which allows the model to be updated in cases where a known change has occurred in the building (e.g., new zone added, hours of operation extended, etc.).”). One of ordinary skill in the art before the effective filing date would have been motivated to modify Kamel in the manner taught by Park in order to keep the energy consumption model up to date as weather and building conditions change.
Claim(s) 6 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over the Kamel/Park combination as applied to claim 1 above, and further in view of Ba et al. (US 2019/0205774) (hereinafter Ba).
Regarding claim 6, the Kamel/Park combination teaches the cloud platform of claim 1. The combination does not explicitly teach automated generation of real time alerts when the energy consumption is higher than a baseline. However, Ba teaches automated generation of real time alerts when the energy consumption is higher than a baseline (fig. 4, alert 422; ph. [0022], “The predicted power consumption may be compared with a standardized power consumption threshold of one or more buildings for detecting onset of abnormal power consumption. A recording system (e.g., reading systems such as “smart meters”) may be activated for capturing power consumption data so as to identify an exact location for anomalies and alerting a user.”). One of ordinary skill in the art before the effective filing date would have been motivated to modify the Kamel/Park combination in the manner taught by Ba to allow a person in the building to fix the issue, thereby saving energy.
Regarding claim 7, the Kamel/Park/Ba combination teaches the cloud platform of claim 6. Ba further teaches a recommendation module that highlights causes of an energy consumption of the building higher than the baseline (ph. [0075], “For example, the output to the device may be an alert that indicates or displays audibly and/or visually on the GUI 422 “ALERT! An energy anomaly is detected in sector “A” of building A.””). One of ordinary skill in the art before the effective filing date would have been motivated to modify the Kamel/Park combination in the manner taught by Ba to allow a person in the building to fix the issue, thereby saving energy.
Claim(s) 8-10, 13-17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kamel et al. (US 2013/0134962) (hereinafter Kamel) in view of Ba et al. (US 2019/0205774) (hereinafter Ba), and further in view of Park et al. (US 2021/0373753) (hereinafter Park).
Regarding claims 8 and 15, Kamel teaches an energy consumption monitoring system, and method comprising:
a first module configured to provide a generalized energy consumption baseline of a physical asset (ph. [0070], “an energy baseline can be calculated for any measured or calculated metric, and correlated with ambient weather conditions, facility usage, and facility schedule. The calculated baseline can be used to project the value of the metric given projections of ambient weather conditions, facility usage, facility schedule, or changes in energy systems.”);
a second module configured to measure energy consumption of the physical asset building (ph. [0040]-[0042], “the energy search engine 102 receives in a substantially continuous way dynamic data relating to energy usage from one or more of a Building Information Modeling (BIM) 106, a power grid 112, a utility company 114, building management 116, and an environmental service 118. For example, the BIM 106 can provide, but is not limited to specifications for the systems, subsystem, and components 104a installed in the facility 104… The building management 116 can provide, but is not limited to facility and zone level scheduling of the facility 104, occupancy information, system status information (e.g. open doors, open windows, open shutters, etc.), and the like. The environmental service, such as a weather service, can provide, but is not limited to dynamic weather data for the location of the facility 104, projected weather for the location of the facility 104, sever weather alerts, geographical factors, and the like. The energy search engine 102 analyzes the static data and the dynamic data received in the substantially continuous way and provides a substantially continuous energy assessment”); and
a third module configured to compare the energy consumption measured from the physical asset with the generalized energy consumption baseline of the physical asset (ph. [0080]-[0081], “Target energy consumption can be a calculated energy consumption based on baseline performance… At block 516, the process 500 compares the measurement of the actual energy consumption with the target energy consumption for the facility 104, and at block 518, the process 500 calculates a substantially continuous energy performance assessment based at least in part on the comparison of the measurement of the actual energy consumption with the target energy consumption.”).
While Kamel teaches alerts can be initiated when a metric exceeds or drops below a certain value, Kamel does not explicitly teach that if the energy consumption measured exceeds the generalized energy consumption baseline, then an alert is emanated. However, Ba teaches that if the energy consumption measured exceeds the generalized energy consumption baseline, then an alert is emanated (fig. 4, alert 422; ph. [0022], “The predicted power consumption may be compared with a standardized power consumption threshold of one or more buildings for detecting onset of abnormal power consumption. A recording system (e.g., reading systems such as “smart meters”) may be activated for capturing power consumption data so as to identify an exact location for anomalies and alerting a user.”; ph. [0075], “For example, the output to the device may be an alert that indicates or displays audibly and/or visually on the GUI 422 “ALERT! An energy anomaly is detected in sector “A” of building A.””)). One of ordinary skill in the art before the effective filing date would have been motivated to modify Kamel in the manner taught by Ba to allow a person in the building to fix the issue, thereby saving energy.
This Kamel/Ba combination does not explicitly teach a framework that enables a machine learning model to retrain with updates, triggered events, and building changes. However, Park teaches a cloud platform (ph. [0284], “a remote (e.g., cloud-based) analytics system”) including a framework that enables a machine learning model to retrain with updates (ph. [0304], “In some embodiments, weather normalization module 5208 recalculates the energy consumption model on the first of each month with all available data up to but not exceeding three years.”, retrains based on updated new weather data), triggered events (ph. [0304], “In addition to automatically updating the energy consumption model periodically, a user-defined trigger can be used to force a recalculation of the baseline model.”), and building changes (ph. [0304], “The user-defined trigger can be a manual trigger (e.g., a user selecting an option to update the model) which allows the model to be updated in cases where a known change has occurred in the building (e.g., new zone added, hours of operation extended, etc.).”). One of ordinary skill in the art before the effective filing date would have been motivated to modify the Kamel/Ba combination in the manner taught by Park in order to keep the energy consumption model up to date as weather and building conditions change.
Regarding claim 9, the Kamel/Ba/Park combination teaches the system of claim 8. Park further teaches to provide the generalized energy consumption baseline of the physical asset is automated using a machine learning model. (ph. [0305]. “In some embodiments, historical data collected before the user-defined trigger is excluded when retraining the energy consumption model in response to the user-defined trigger. Alternatively, the user-defined trigger can require the user to specify a date, which is used as a threshold before which all historical data is excluded when retraining the model. If a user does not specify a date, weather normalization module 5208 may use all available data by default. If the user specifies the current date, weather normalization module 5208 may wait for a predetermined amount of time (e.g., six months) before retraining the energy consumption model to ensure that sufficient data is collected.”). One of ordinary skill in the art before the effective filing date would have been motivated to modify the Kamel/Be combination in the manner taught by Park in order to keep the energy consumption model up to date as weather and building conditions change.
Regarding claim 10, the Kamel/Ba/Park combination teaches the system of claim 8. Kamel further teaches the generalized energy consumption baseline can be estimated in the absence of historical energy consumption data from energy meters of the physical asset, data of weather, occupancy, and building layout of the physical asset (ph. [0040]-[0042], “the energy search engine 102 receives in a substantially continuous way dynamic data relating to energy usage from one or more of a Building Information Modeling (BIM) 106, a power grid 112, a utility company 114, building management 116, and an environmental service 118. For example, the BIM 106 can provide, but is not limited to specifications for the systems, subsystem, and components 104a installed in the facility 104… The building management 116 can provide, but is not limited to facility and zone level scheduling of the facility 104, occupancy information, system status information (e.g. open doors, open windows, open shutters, etc.), and the like. The environmental service, such as a weather service, can provide, but is not limited to dynamic weather data for the location of the facility 104, projected weather for the location of the facility 104, sever weather alerts, geographical factors, and the like. The energy search engine 102 analyzes the static data and the dynamic data received in the substantially continuous way and provides a substantially continuous energy assessment”; ph. [0070], “an energy baseline can be calculated for any measured or calculated metric, and correlated with ambient weather conditions, facility usage, and facility schedule. The calculated baseline can be used to project the value of the metric given projections of ambient weather conditions, facility usage, facility schedule, or changes in energy systems.”)
Regarding claim 13, the Kamel/Ba/Park combination teaches the system of claim 10. Kamael further teaches a digital twin of the physical asset; and wherein the digital twin is a digital representation of the physical asset. (ph. [0041], “a Building Information Modeling (BIM) 106, a power grid 112, a utility company 114, building management 116, and an environmental service 118. For example, the BIM 106 can provide, but is not limited to specifications for the systems, subsystem, and components 104a installed in the facility 104, specifications for the systems, subsystem, and components with a higher energy rating that could have been installed in the facility 104, and the like.”).
Regarding claim 14, the Kamel/Ba/Park combination teaches the system of claim 13. Kamel further teaches the digital twin captures characteristics of the physical asset, relationships among various assets in the building like an HVAC and its properties, and operations in the physical asset (ph. [0040]-[0042], “the energy search engine 102 receives in a substantially continuous way dynamic data relating to energy usage from one or more of a Building Information Modeling (BIM) 106, a power grid 112, a utility company 114, building management 116, and an environmental service 118. For example, the BIM 106 can provide, but is not limited to specifications for the systems, subsystem, and components 104a installed in the facility 104… The building management 116 can provide, but is not limited to facility and zone level scheduling of the facility 104, occupancy information, system status information (e.g. open doors, open windows, open shutters, etc.), and the like. The environmental service, such as a weather service, can provide, but is not limited to dynamic weather data for the location of the facility 104, projected weather for the location of the facility 104, sever weather alerts, geographical factors, and the like. The energy search engine 102 analyzes the static data and the dynamic data received in the substantially continuous way and provides a substantially continuous energy assessment”).
Regarding claim 16, the Kamel/Ba/Park combination teaches the method of claim 15. Ba further teaches an issued alert is automatic (fig. 4, alert 422; ph. [0022], “The predicted power consumption may be compared with a standardized power consumption threshold of one or more buildings for detecting onset of abnormal power consumption. A recording system (e.g., reading systems such as “smart meters”) may be activated for capturing power consumption data so as to identify an exact location for anomalies and alerting a user.”; ph. [0075], “For example, the output to the device may be an alert that indicates or displays audibly and/or visually on the GUI 422 “ALERT! An energy anomaly is detected in sector “A” of building A.””)). One of ordinary skill in the art before the effective filing date would have been motivated to modify Kamel in the manner taught by Ba to allow a person in the building to fix the issue, thereby saving energy.
Regarding claim 17, the Kamel/Ba/Park combination teaches the method of claim 16. Ba further teaches upon detecting the automatic issued alert on an indicator, highlighting on the indicator occurs to show actionable insights that help a building management team fix faulty equipment that causes the measurement of the energy consumption to exceed the energy consumption baseline (fig. 4, alert 422; ph. [0022], “The predicted power consumption may be compared with a standardized power consumption threshold of one or more buildings for detecting onset of abnormal power consumption. A recording system (e.g., reading systems such as “smart meters”) may be activated for capturing power consumption data so as to identify an exact location for anomalies and alerting a user.”; ph. [0075], “For example, the output to the device may be an alert that indicates or displays audibly and/or visually on the GUI 422 “ALERT! An energy anomaly is detected in sector “A” of building A.””)). One of ordinary skill in the art before the effective filing date would have been motivated to modify Kamel in the manner taught by Ba to allow a person in the building to fix the issue, thereby saving energy.
Regarding claim 19, the Kamel/Be/Park combination teaches the method of claim 18. Park further teaches use of real-time or live occupancy information is an external factor in the machine learning model (ph. [0110], “The inputs received from other layers can include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, and the like.”), and real-time or live occupancy is correlated with the energy consumption of the machine learning model (ph. [0285], “By normalizing the energy consumption data in this way, changes in the normalized energy consumption data can be attributed factors other than weather (e.g., occupancy load, equipment efficiency, etc.).”).
Allowable Subject Matter
Claim 20 is 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.
Response to Arguments
Claim Objection Argument
The amendment submitted on 12/05/2025 overcomes the objection to claim 1 in the office action mailed 09/17/2025. The objection has therefore been withdrawn.
§112 Rejection Argument
Applicants’ amendment to claim 1 overcomes the rejection in regards to the issue of whether the updates, triggered events, and building changes limitation is “and” or “or”. That part of the §112 rejection has therefore been withdrawn.
Applicants’ remarks (pg. 6) states that the claim has been amended to fix the antecedent basis issues in claim 2, but “the incoming live stream” still lacks antecedent basis. The rejection of claim 2 has therefore been maintained.
In regards to claims 8-14, Applicants argue that adding “cloud” to the claims means they “do not fail to disclose the corresponding structure, material, or acts for performing the entire claimed function and clearly link the structure, material, or acts to the function.” (Remarks, pg. 7). However, as explained in the §112 rejection above, there is no basis for Applicants assertion that the modules in the cloud platform are Internet-connected remote servers. The words internet and server never appear in the application as originally filed. Nor for that matter is a single mention of a computer, processor, or memory anywhere in the specification. The closest it comes is the alarm database 35 that is outside of the cloud platform 12, but there is no disclosure of an internet connection between them. The “cloud platform” 12 shown in figure 1 of the application is a software diagram comprising software per se. You have streaming sources 26, 27, and 28, going into enrichment job 24, the output of which goes into table 31. That’s fed into training pipeline 26, weather API storage 32 and alarm generating job 1 that has symbol 25. Then the output of final table 34 and alarm generating job 1 25 is output from the cloud platform 12 of figure 1. Everything depicted in cloud platform 12 of figure 1 is software per se having no actual physical structures involved, and definitely no disclosure of any “internet-connected remote servers” within cloud platform 12 of figure 1. Accordingly, the §112 rejection has maintained.
§101 Rejection Argument
Applicants argue that “the cloud platform provides the structural elements in order to execute the prediction model, specific model of the building, the framework, and the digital twin,” and further that “Applicant additionally has amended claim 8 to recite a first cloud module, a second cloud module, and a third cloud module, and similarly submits that cloud modules in the context of a cloud platform are not software per se but rather the cloud platform provides the structural elements in order to execute the cloud modules.” (Remarks, pg. 7). However, as explained above in regards to the §112 rejection, the “cloud platform” 12 shown in figure 1 of the application is a software diagram comprising software per se. Contrary to Applicants arguments, there is no mention of any server anywhere in the application. Nor for that matter is a single mention of a computer, processor, or memory anywhere in the specification. Accordingly, the §101 rejection of claims 1-10, and 12-14 as having a broadest reasonable interpretation of being software per se has been maintained.
In light of the recent precedential Squires decision in Ex parte Desjardins, the §101 abstract idea rejection has been withdrawn.
§103 Rejection Argument
Applicants argue that Park does not teach a framework that enables a machine learning model to retrain with updates, triggered events, and building changes. (Remarks, pg. 11). However, as explained in the §103 rejection above, Park teaches a cloud platform (ph. [0284], “a remote (e.g., cloud-based) analytics system”) including a framework that enables a machine learning model to retrain with updates (ph. [0304], “In some embodiments, weather normalization module 5208 recalculates the energy consumption model on the first of each month with all available data up to but not exceeding three years.”, retrains based on updated new weather data), triggered events (ph. [0304], “In addition to automatically updating the energy consumption model periodically, a user-defined trigger can be used to force a recalculation of the baseline model.”), and building changes (ph. [0304], “The user-defined trigger can be a manual trigger (e.g., a user selecting an option to update the model) which allows the model to be updated in cases where a known change has occurred in the building (e.g., new zone added, hours of operation extended, etc.).”).
Accordingly, the amended claims continue to be rejected under §103.
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 BRIAN W WATHEN whose telephone number is (571)270-5570. The examiner can normally be reached M-F 9-5:30pm.
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BRIAN W. WATHEN
Primary Examiner
Art Unit 2151
/BRIAN W WATHEN/Primary Examiner, Art Unit 2151