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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
IDS filed 12/22/2025 is being considered by the examiner
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/16/2025 has been entered.
Response to Amendment
Applicant's amendment filed 10/16/2025 has been received and entered into the record. As a result, claims 1, 17, and 20 have been amended. Therefore, claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-5, 7-10, 14-18, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dayal et al. [U.S. Pub. 2024/0126130] ("Dayal").
With regard to claim 1, Dayal a method, comprising:
at a device with one or more processors and a memory ("Controller 605 comprises … processor and memory [par. 0089]"):
receiving, via a standalone sensing device ("The controller may acquire data from the one or more sensors [par. 0092]" and "Sensors … sensor module [par. 0082]") positioned in a monitored building environment ("The target environment may be the location at which the sensor is installed in an enclosure [par. 0095]" and "home, building, vehicle, or compartment thereof [par. 0080]"), real-time ("Processing of the sensor data may utilize historical sensor data, and/or current (e.g., real time) sensor data [par. 0120]" and "The control can comprise real time or off-line control [par. 0092]") environmental sensor data ("the sensor data is responsive to the environment in the enclosure and/or to any inducer(s) of a change (e.g., any environmental disruptor) in this environment [par. 0116]") of a plurality of different data types from a plurality of different sensing modalities ("Sensors may be configured to process, measure, analyze, detect and/or react to one or more of: data, temperature, humidity, sound, force, pressure, electromagnetic waves, position, distance, movement, flow, acceleration, speed, vibration, dust, light, glare, color, gas(es), and/or other aspects (e.g., characteristics) of an environment (e.g., of an enclosure) [par. 0094]"),
wherein the real-time environmental sensor data comprises contextual sensor data measured, generated, and provided by the standalone sensing device within the monitored building environment ("Sensors may be configured to process, measure, analyze, detect and/or react to one or more of: data, temperature, humidity, sound, force, pressure, electromagnetic waves, position, distance, movement, flow, acceleration, speed, vibration, dust, light, glare, color, gas(es), and/or other aspects (e.g., characteristics) of an environment (e.g., of an enclosure) [par. 0094];" where the sensor data is contextual sensor data, e.g., a radar sensor determines presence of a number of individuals in an enclosure [par. 0097] and an increase in infrared energy is correlated with an increase in measured temperature [par. 0089]), and
wherein the standalone sensing device comprises a single housing and is independent of existing building devices ("FIG. 5 shows an example of a diagram 500 of an ensemble of sensors organized into a sensor module. Sensors 510A, 510B, 510C, and 510D are shown as included in sensor ensemble 505. An ensemble of sensors organized into a sensor module may include at least 1, 2, 4, 5, 8, 10, 20, 50, or 500 sensors [par. 0083]" and "an arrangement of sensor ensembles distributed within an enclosure … Any number of additional sensors and/or sensor modules may be positioned at other locations of conference room 902. The sensor ensembles may be disposed anywhere in the enclosure [par. 0114];" where the sensor ensembles can be disposed anywhere since they are independent of various existing building devices such as an HVAC and do not necessarily need to be next to an HVAC or other various building devices);
generating, for the monitored building environment and based on the real-time environmental sensor data, an environmental building inference ("The data processing (e.g., utilizing the model) may be used to project an environmental change in the enclosure, and/or recommend actions to alleviate, adjust, or otherwise react to the change [par. 0120]") indicative of a state of at least one of
an occupancy of the monitored building environment ("an example of relatively steep and high concentration of carbon dioxide towards the location of occupant 1505, relative to low concentration 1510 in an unoccupied region of the enclosure [par. 0145]"),
an asset within the monitored building environment, or
an ambience of the monitored building environment,
wherein the environmental building inference is generated using a combination of the plurality of different sensor modalities ("The data processing (e.g., utilizing the model) may be used to project an environmental change in the enclosure, and/or recommend actions to alleviate, adjust, or otherwise react to the change [par. 0120]" and "The data in graph 2000 illustrate how sensor synergy can be used to increase the accuracy and reduce detection time for detecting the attribute that the room is occupied [par. 0159]") including at least one of vibration, acoustic, temperature, RPM, humidity, magnetic flux, air quality, lighting, has detection, occupancy, ingress, infrared, or radio frequency data ("Sensors may be configured to process, measure, analyze, detect and/or react to one or more of: data, temperature, humidity, sound, force, pressure, electromagnetic waves, position, distance, movement, flow, acceleration, speed, vibration, dust, light, glare, color, gas(es), and/or other aspects (e.g., characteristics) of an environment (e.g., of an enclosure) [par. 0094]"); and
generating, for the monitored building environment based on the environmental building inference, a prescriptive building insight ("Processing of the sensor data may utilize historical sensor data, and/or current (e.g., real time) sensor data. The data processing (e.g., utilizing the model) may be used to project an environmental change in the enclosure, and/or recommend actions to alleviate, adjust, or otherwise react to the change [par. 0120]"),
wherein the prescriptive building insight comprises actionable control instructions for optimizing building operations based on contextual and holistic real-time environmental sensor data ("The use of sensor synergy to increase the accuracy and/or speed of attribute detection can be useful in a variety of ways. In the example of FIG. 20, for instance, faster detection of the occupancy in a room can be used to trigger an HVAC system in the room to help ensure CO2 and temperature levels are comfortable for occupants. Increased accuracy of the occupancy detection can be used to determine patterns of occupancy, which can be used to accurately predict occupancy and, in turn, operate HVAC and/or other systems in a predictive manner [par. 0160]").
Note: claim is presented in the alternative.
With regard to claim 3, Dayal teaches the method of claim 1, wherein the method further comprises:
providing, to a monitored building device separate from the standalone sensing device, one or more instructions for implementing the prescriptive building insight ("trigger an HVAC system in the room to help ensure CO2 and temperature levels are comfortable for occupants [par. 0160]").
With regard to claim 4, Dayal teaches the method of claim 1, wherein the environmental building inference comprises occupancy data indicative of a number of one or more active entities within the monitored building environment ("The number of occupants in the room may be obtained based on information such as the degree to which outlier values 1810 depart from the model 1805 and/or other related information [par. 0154]") and one or more entity attributes for at least one active entity of the one or more active entities ("The sensor data may be responsive to an activity taking place in the room. The activity may include human activity, and/or non-human activity. The activity may include electronic activity, gaseous activity, and/or chemical activity. The activity may include a sensual activity (e.g., visual, tactile, olfactory, auditory, and/or gustatory). The activity may include an electronic and/or magnetic activity [par. 0116]" and "The activity may comprise (i) cleaning of an enclosure, (ii) movement of one or more personnel in the enclosure, (iii) a change in an environmental condition, (iv) one or more personnel entering into the enclosure, (v) one or more personnel exiting the enclosure, (vi) activity in the enclosure, (vii) exceeding of a maximum occupancy of the enclosure [par. 0164]").
With regard to claim 5, Dayal teaches the method of claim 4, further comprising:
determining the occupancy data ("The number of occupants in the room may be obtained based on information such as the degree to which outlier values 1810 depart from the model 1805 and/or other related information [par. 0154]") based on at least one of
occupancy sensor data ("CO2 and SPL sensors can be used to detect an occupancy status (e.g., whether a room or spaces occupied) or occupancy number [par. 0151]"), air quality sensor data, ingress sensor data, radio frequency data, or infrared sensor data.
Note: claim is presented in the alternative.
With regard to claim 7, Dayal teaches the method of claim 4, wherein the prescriptive building insight comprises an occupancy-based energy insight, based on the occupancy data, that is indicative of one or more energy saving measures for the monitored building environment ("to maintain a comfortable environment for the occupants of the building 104 while minimizing heating and cooling energy losses and costs … optimize the synergy between various systems, for example, to conserve energy and lower building operation costs [par. 0063]").
With regard to claim 8, Dayal teaches the method of claim 7, wherein the occupancy-based energy insight includes a climate insight based on the occupancy data, humidity sensor data, and temperature sensor data ("CO2 and SPL sensors can be used to detect an occupancy status (e.g., whether a room or spaces occupied) or occupancy number [par. 0151]" and "The number of occupants in the room may be obtained based on information such as the degree to which outlier values 1810 depart from the model 1805 and/or other related information (e.g., time information indicative of a rate of relative humidity and/or temperature increase) [par. 0154]").
With regard to claim 9, Dayal teaches the method of claim 7, wherein the occupancy-based energy insight includes a lighting insight ("The control may include reducing energy consumption of a heating, ventilation, air conditioning and/or lighting systems [par. 0069]") based on the occupancy data and lighting sensor data ("occupancy of the room may be difficult to accurately detect or predict using data from CO2 and SPL sensors alone. However, when coupled with a lux sensor, occupancy can be detected from values 2020 (e.g., using outlier detection in view of natural state data values 2010). In particular, data from the lux sensor can be used to detect when a person turns on a light when entering a room, and the sound and CO2 sensors can be used to detect whether the person stays in the room. In the example shown in the graph 2000, the person stays in the room, which elevates the CO2 values until they peak at 2030, after which the person turns the light off when exiting the room, and the peak CO.sub.2 values then begin to drop [par. 0158]").
With regard to claim 10, Dayal teaches the method of claim 7, wherein the one or more entity attributes for the at least one active entity is indicative of an internal body temperature for the at least one active entity ("The number of occupants in the room may be obtained based on information such as the degree to which outlier values 1810 depart from the model 1805 and/or other related information (e.g., time information indicative of a rate of relative humidity and/or temperature increase) [par. 0154]") and
wherein the prescriptive building insight comprise an optimized environment temperature based on the internal body temperature for the at least one active entity ("to maintain a comfortable environment for the occupants of the building 104 while minimizing heating and cooling energy losses and costs … optimize the synergy between various systems, for example, to conserve energy and lower building operation costs [par. 0063]").
With regard to claim 14, Dayal teaches the method of claim 1, wherein the environmental building inference comprises ambient data indicative of one or more ambient attributes for the monitored building environment ("The controller may acquire data from the one or more sensors. Acquire may comprise receive or extract. The data may comprise measurement, estimation, determination, generation, or any combination thereof [par. 0092]"), the one or more ambient attributes indicative of at least one of an air quality, magnetic flux, or presence of gas within the monitored building environment ("trigger an HVAC system in the room to help ensure CO2 and temperature levels are comfortable for occupants [par. 0160]" and "CO2 and SPL sensors can be used to detect an occupancy status (e.g., whether a room or spaces occupied) or occupancy number [par. 0151]").
Note: claim is presented in the alternative.
With regard to claim 15, Dayal teaches the method of claim 14, further comprises:
determining the ambient data based on gas detection sensor data ("CO2 and SPL sensors can be used to detect an occupancy status (e.g., whether a room or spaces occupied) or occupancy number [par. 0151]"), air quality sensor data ("CO2 and SPL sensors can be used to detect an occupancy status (e.g., whether a room or spaces occupied) or occupancy number [par. 0151]"), and magnetic flux sensor data ("magnetic waves [par. 0094]" and "magnetic sensor [par. 0177]").
With regard to claim 16, Dayal teaches the method of claim 14, wherein the prescriptive building insight comprises an ambient insight, based on the ambient data, that includes an environmental quality insight and a quality measure for optimizing the environmental quality insight ("trigger an HVAC system in the room to help ensure CO2 and temperature levels are comfortable for occupants [par. 0160]" and "CO2 and SPL sensors can be used to detect an occupancy status (e.g., whether a room or spaces occupied) or occupancy number [par. 0151]" and "The number of occupants in the room may be obtained based on information such as the degree to which outlier values 1810 depart from the model 1805 and/or other related information (e.g., time information indicative of a rate of relative humidity and/or temperature increase) [par. 0154]").
With regard to claim 17, Dayal teaches claim 1 above. Claim 17 recites limitations having the same scope as those pertaining to claim 1; therefore, claim 17 is rejected along the same grounds as claim 1.
Claim 17 different from claim 1 where claim 17 recites (and Dayal teaches) the additional limitations of a memory including computer program code stored thereon (" Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system [par. 0173]").
With regard to claim 18, Dayal teaches the system of claim 17, wherein the standalone sensing device comprises a housing and a plurality of sensors disposed within the housing ("Sensors of a sensor ensemble may be organized into a sensor module [par. 0082]" and "The first sensor may be disposed in a housing [par. 0164]"), wherein the plurality of sensors comprise one or more components of at least one of a:
(i) vibration sensor; (ii) acoustic emission sensor; (iii) temperature sensor; (iv) revolutions-per-minute (RPM) sensor; (v) humidity sensor; (vi) magnetic flux sensor; (vii) indoor air quality (IAQ) sensor; (viii) lighting sensor; (ix) gas detection sensor ("gases [par. 0094]"); and (x) occupancy sensor.
Note: claim is presented in the alternative.
With regard to claim 20, Dayal teaches claim 1 above. Claim 20 recites limitations having the same scope as those pertaining to claim 1; therefore, claim 20 is rejected along the same grounds as claim 1.
Claim 20 different from claim 1 where claim 20 recites (and Dayal teaches) the additional limitations of a non-transitory computer-readable storage medium comprising computer program code for execution by one or more processors ("the present disclosure provides systems, apparatuses (e.g., controllers), and/or non-transitory computer-readable medium (e.g., software) that implement any of the methods disclosed herein [par. 0010]").
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.
Claims 2 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Dayal in view of Hoelgaard et al. [U.S. Pub. 2022/0373995] ("Hoelgaard").
With regard to claim 2, Dayal teaches the method of claim 1, wherein the real-time environmental sensor data comprises:
(i) vibration sensor data ("vibration [par. 0094]"), (ii) acoustic sensor data ("sound [par. 0094]"), (iii) temperature sensor data ("temperature [par. 0094]"), ("humidity [par. 0094]"), (vi) magnetic flux sensor data ("magnetic waves [par. 0094]" and "magnetic sensor [par. 0177]"), (vii) air quality sensor data ("gases [par. 0094]"), (viii) lighting sensor data ("light [par. 0094]"), (ix) gas detection sensor data ("gases [par. 0094]"), (x) occupancy sensor data ("motion [par. 0131]"), (xi) ingress sensor data ("occupant entering a room [par. 0005]"), (xii) infrared sensor data ("infrared [par. 0115]"), and (xiii) radio frequency data ("radio [par. 0115]").
Although Dayal teaches using various sensors, Dayal does not explicitly teach using revolutions-per-minute (RPM) sensor data.
In the same field of endeavor (monitoring building assets), Hoelgaard teaches revolutions-per-minute (RPM) sensor data ("the sensor of the pump system may be selected from one or more of the following: … RPM sensor [par. 0027]").
Hoelgaard further teaches, "Controlling and monitoring is an important aspect in the operational running of a pump system [par. 0003]" and "A pump system is preferable meant a system comprising at least one pump and at least one or more sub-systems connected to the pump, such as a HVAC system [par. 0059]" and "Such inconsistencies could be abnormal running condition, alarms detected by the pump 20 operating system, sensor values over a threshold predefined from the factory etc. [par. 0100]"
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have included Hoelgaard's teachings of monitoring a component of an HVAC system with an RPM sensor, with the teachings Dayal, for the benefit of determining an abnormal running condition of the HVAC component.
With regard to claim 11, Dayal teaches the method of claim 1. Although Dayal teaches the environmental building inference within the monitored building environment (as presented in claim 1 above), Dayal does not explicitly teach asset data indicative of one or more asset attributes for at least one building asset within the monitored building environment, wherein the one or more asset attributes are indicative of an operability of the at least one building asset.
In an analogous art (monitoring building assets), Hoelgaard teaches asset data indicative of one or more asset attributes for at least one building asset within a monitored building environment, wherein the one or more asset attributes are indicative of an operability of the at least one building asset ("the sensor of the pump system may be selected from one or more of the following: a differential pressure sensor (pressure head), a temperature sensor, a vibration sensor, a flow sensor, such as a volume flow sensor, an RPM sensor, a power sensor, a sound sensor, a water level sensor, a pH sensor, an ultrasonic sensor, an efficiency sensor, a vibration sensor, a turbidity sensor, a viscosity sensor, valve sensors [par. 0027]").
Hoelgaard further teaches, "Controlling and monitoring is an important aspect in the operational running of a pump system [par. 0003]" and "A pump system is preferable meant a system comprising at least one pump and at least one or more sub-systems connected to the pump, such as a HVAC system [par. 0059]" and "Such inconsistencies could be abnormal running condition, alarms detected by the pump 20 operating system, sensor values over a threshold predefined from the factory etc. [par. 0100]"
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have included Hoelgaard's teachings of monitoring asset attributes, with the teachings Dayal, for the benefit of determining an abnormal running condition of the HVAC component.
With regard to claim 12, the combination above teaches the method of claim 11. Hoelgaard in the combination teaches the method further comprises:
determining the asset data based on vibration sensor data, revolutions-per-minute (RPM) sensor data, and acoustic sensor data ("the sensor of the pump system may be selected from one or more of the following: a differential pressure sensor (pressure head), a temperature sensor, a vibration sensor, a flow sensor, such as a volume flow sensor, an RPM sensor, a power sensor, a sound sensor, a water level sensor, a pH sensor, an ultrasonic sensor, an efficiency sensor, a vibration sensor, a turbidity sensor, a viscosity sensor, valve sensors [par. 0027]").
With regard to claim 13, the combination above teaches the method of claim 11. Hoelgaard in the combination further teaches wherein the prescriptive building insight comprises an asset value insight, based on the asset data, that is indicative of a predictive maintenance for the at least one building asset within the monitored building environment ("the sensor of the pump system may be selected from one or more of the following: a differential pressure sensor (pressure head), a temperature sensor, a vibration sensor, a flow sensor, such as a volume flow sensor, an RPM sensor, a power sensor, a sound sensor, a water level sensor, a pH sensor, an ultrasonic sensor, an efficiency sensor, a vibration sensor, a turbidity sensor, a viscosity sensor, valve sensors [par. 0027]" and "The generic data 17 can further be divided into situation based data and pump based data. The situation based data could be data regarding the operating difficulties, procedures or solutions based on a specific situation, such as a valve overheating [par. 0111]").
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Dayal in view of Yan et al. [U.S. Pub. 2021/0080915] ("Yan").
With regard to claim 6, Dayal teaches the method of claim 5, further comprising:
determining the number of the one or more active entities within the monitored building environment based on an aggregation ("sensor synergy can be used to increase the accuracy and reduce detection time for detecting the attribute that the room is occupied [par. 0159]") of:
(i) a first occupancy prediction based on proximity data associated with the occupancy sensor data ("CO2 and SPL sensors can be used to detect an occupancy status (e.g., whether a room or spaces occupied) or occupancy number. Lux and SPL sensors can be used to detect noises or lights that are loud (e.g., having a volume or brightness exceeding a threshold) or troublesome; CO2, VOC, and SPL sensors can be used to determine whether a cleaning is in progress [par. 0151]"),
(iii) a third occupancy prediction based on a CO2 level associated with the air quality sensor data ("when synergistically coupled with sound data (e.g., a door opening or closing) and CO2 data, detection of these anomalous values 2120 can be indicative of people entering and exiting the room with a high degree of certainty [par. 0161]").
Although Dayal teaches combining sensor data for more accurate results ("The use of sensor synergy to increase the accuracy and/or speed of attribute detection can be useful in a variety of ways [par. 0160]"), Dayal does not explicitly teach (ii) a second occupancy prediction based on one or more cellular networking signals of the radio frequency data.
In an analogous art (occupancy detection), Yan teaches a second occupancy prediction based on one or more cellular networking signals of the radio frequency data ("the real-time detected occupancy data 230 including Wi-Fi/cellular data 231 or data from other occupancy detectors 232 to predict the zone level occupancy variation [par. 0081]" and "a cellular network for a wireless device [par. 0041").
Yan further teaches, "these approaches could not predict the thermal load demand variation in a multiple facilities-based building, such as shopping or commercial malls, due to highly dynamic human flow across different facilities and different human activities in different facilities [par. 0006]."
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have included Yan's teachings of using cellular data to determine occupancy, with the teachings of Dayal, for the benefit of more accurately detecting occupancy.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Dayal in view of Newman [U.S. Pub. 2016/0187066].
With regard to claim 19, Dayan teaches the system of claim 18, wherein the standalone sensing device is
(i) a building asset position relative to a building asset within the monitored building environment,
(ii) an ingress position relative to an ingress point to the monitored building environment,
(iii) a boundary position relative to an exterior surface of the monitored building environment, or
(iv) an interior position relative to an interior surface of the monitored building environment ("within an enclosure … a first sensor ensemble 905A is disposed (e.g., installed) near point 915A, which may correspond to a location in a ceiling, wall, or other location to a side of a table [par. 0114]").
Dayan does not explicitly teach magnetically affixed.
In an analogous art (attaching sensors), Newman teaches where a sensor is magnetically affixed (" the operational sensor can be easily mounted to an external surface of the cooling system's casing 15. In the example shown in FIG. 2, the operational sensor 50 is simply and easily magnetically attached to the top of the A-C unit [par. 0029]").
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have include Newman's teachings of magnetically affixing a sensor, with the teachings of Dayal, for the benefit of providing a simple solution to placing a sensor.
Response to Arguments
Applicant's arguments filed 10/16/2025 have been fully considered but they are not persuasive. Specifically, newly cited portions of Dayal are relied upon to teach Applicant's amendment.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kwa [U.S. Pub. 2016/0377305] teaches Systems and methods for controlling the temperature and humidity in a plurality of spaces. The systems may receive inputs from a temperature sensor, a humidity sensor, an occupancy sensor, a door sensor, and a window sensor.
Eizenberg [U.S. Pub. 2022/0196785] teaches a method of determining the location of one or more wireless tags and determining an occupancy of a room based on the occupancy data of the room.
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 VINCENT W CHANG whose telephone number is (571)270-1214. The examiner can normally be reached (M-F) 10:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VINCENT WEN-LIANG CHANG/
Examiner
Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119