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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/12/2024 and 6/10/2024 was in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 12 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite “transmitting metadata corresponding to an environment of a thermostatic device installed in a building; receiving the thermostatic machine learning model for storage of the thermostatic device, wherein the thermostatic machine learning model is selected based on the metadata, from among a plurality of pretrained thermostatic machine learning models stored; detecting a temperature of the environment of the thermostatic device to determine a temperature data; associating the temperature data with additional data comprising at least one of: an external temperature, external humidity, on-off status of a HVAC controlled by the thermostatic device, fan speed, and combination thereof; and waiting a predetermined number of days before adjusting at least one of the fan speed, the temperature of the environment, or combination thereof, based on an output data from the thermostatic machine learning model.”
The recited limitations above are a process that, under the broadest reasonable interpretation, covers performance of the limitation done by a human but for the recitation of generic computer components under mental steps (human using pen and paper). That is, other than reciting “processors” nothing in the claim element precludes the steps from practically being performed by a human using generic computer components. For example, “transmitting”, “receiving”, “detecting”, “associating” and “waiting” in the context of this claim encompasses the user to manually adjust temperature of a building.
This judicial exception is not integrated into a practical application. In particular, the claims only recite the following additional elements- a “processor”, “memory”, “sensor”, “computing device”, “server” and “interface” to perform the above recited steps. The computer elements recited at a high-level of generality (generic computer elements performing a generic computer function of receiving information, identifying solutions and determining what should be presented such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements recited do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the computer elements to perform the steps of claims 1, 12 and 17 amount to no more than mere instructions to apply the exception using a generic computer component cannot provide an inventive concept.
The limitations of the dependent claims 2-11, 13-16 and 18-20, further describe the identified abstract idea. In addition, the limitations of claims 4-8, 10, 13, 15 and 20 define how the temperature of a thermostat is determined which further describes the abstract idea. The generic computer component of claims 2-3, 9, 11, 14, 16 and 18-19 (server, sensor, gateway device and computing device) merely serve as the generic computer component and the functions performed by the generic computer components essentially amount to the abstract idea identified above. None of the dependent claims when taken separately in combination with each dependent claims parent claim overcome the above analysis and are therefore similarly rejected as being ineligible.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1 and 3-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Venkatesh et al. referred herein as Ven (U.S. Patent No. 11,598,542).
As to claim 1, Ven teaches a system comprising:
a thermostatic device including a temperature sensor, the thermostatic device configured to: detect a temperature of an environment of the thermostatic device to determine a temperature data; (col 7 lines 6-12 and col 23 lines 46-59)
associate the temperature data with additional data comprising two or more of: an external temperature, external humidity, on-off status of a HVAC controlled by the thermostatic device, fan speed, and combination thereof; (Col 23 lines 46-59, the system comprises data such as fan speed and external temperature)
transmit, over a communications network, the temperature data including the associated additional data; (col 23 lines 46-59 and col 7 lines 6-12)
adjust at least one of the fan speed, the temperature of the environment, or combination thereof, by controlling the thermostatic device, based on an output data from a machine learning model; (col 4 lines 25-54, col 9-10 lines 65-6 and col 13-14 lines 62-5)
a computing device including a processor and a memory storing a pretrained thermostatic machine learning model, wherein the computing device is communicatively coupled to the thermostatic device over the communications network, and wherein the computing device is configured to: receive the temperature data and store it in the memory of the computing device; (col 7 lines 6-12)
receive the additional data and store it in the memory of the computing device; (col 7 lines 6-12 and col 23 lines 46-59)
tune the pretrained thermostatic machine learning model based on the temperature data and the additional data to update the machine learning model; (col 4 lines 1-24 and col 11 lines 15-27)
infer the output data based on inputting the temperature data and additional data into the updated machine learning model; (col 4 lines 1-24 and col 11 lines 15-27 and fig. 7)
transmit the output data to the thermostatic device. (fig. 7, 14 and col 20 lines 19-24)
As to claim 3, Ven teaches all the limitations of claim 1 as discussed above.
Ven further teaches:
wherein the thermostatic device further includes a movement sensor, wherein the additional data further includes an occupancy data determined by whether the movement sensor detects one or more persons in the environment of the thermostatic device. (col 1-2 lines 63-4, col 4 lines 1-24 and col 7 lines 14-28)
As to claim 4, Ven teaches all the limitations of claim 3 as discussed above.
Ven further teaches:
wherein energy consumption by the thermostatic system is reduced by the machine learning model being configured to transmit, in the output data, a command to adjust the on-off status of the HVAC. (col 7 lines 6-12 and col 9-10 lines 65-6)
As to claim 5, Ven teaches all the limitations of claim 1 as discussed above.
Ven further teaches:
wherein the output data further includes a temperature set point data, thermostatic state data for adjusting the thermostatic device between on and off, and commands for controlling one or more fans in the environment of the thermostatic device. (col 9-10 lines 65-6)
As to claim 6, Ven teaches all the limitations of claim 1 as discussed above.
Ven further teaches:
wherein the environment of the thermostatic device is a room or an office space within a building. (col 21-22 lines 60-7)
As to claim 7, Ven teaches all the limitations of claim 1 as discussed above.
Ven further teaches:
wherein the tuning of the pretrained thermostatic machine learning model comprises weight freezing all but a particular layer of a neural network for the machine learning model. (col 10-11 lines 55-27)
As to claim 8, Ven teaches all the limitations of claim 1 as discussed above.
Ven further teaches:
wherein parameters of a tuning layer of the machine learning model are tuned by the temperature data and the additional data received from the thermostatic device. (col 9-10 lines 65-6)
As to claim 9, Ven teaches all the limitations of claim 1 as discussed above.
Ven further teaches:
a server computer storing a plurality of pretrained thermostatic machine learning models, wherein the pretrained thermostatic machine learning model stored in the memory of the computing device was received from the server and is one of the plurality of pretrained thermostatic machine learning models, the server computer configured to: classify each of the plurality of pretrained thermostatic machine learning models based on metadata corresponding to an environment where pretraining occurred; (col 5 lines 14-29 and col 11 lines 16-27)
provide from among the plurality of pretrained thermostatic machine learning models in response to a request from the thermostatic device to initialize, wherein the request includes a sub-environment of the thermostatic device. (col 5 lines 14-29 and col 11 lines 16-27)
As to claim 10, Ven teaches all the limitations of claim 1 as discussed above.
Ven further teaches:
collect the temperature data and the associated additional data for a predetermined number of days upon initiation of the thermostatic device before receiving the output data from the machine learning model, wherein the pretrained thermostatic machine learning model used by the computing device is based on metadata corresponding to the environment of the thermostatic device. (col 11-12 lines 52-7 and fig. 9)
As to claim 11, Ven teaches all the limitations of claim 1 as discussed above.
Ven further teaches:
wherein the computing device is a gateway device on a same premises as a building in which the thermostatic device is installed. (col 21 lines 47-58)
As to claims 12 and 17, Ven teaches a device and a method comprising:
transmitting, through an interface communicatively coupled to a server computer, metadata corresponding to an environment of a thermostatic device installed in a building; (col 7 lines 6-12 and col 23 lines 46-59)
receiving, through the interface from the server computer, the thermostatic machine learning model for storage in a memory of the thermostatic device, wherein the thermostatic machine learning model is selected, by the server computer, based on the metadata, from among a plurality of pretrained thermostatic machine learning models stored at the server computer; (col 7 lines 6-12)
detecting, using a temperature sensor of the thermostatic device, a temperature of the environment of the thermostatic device to determine a temperature data; (col 7 lines 6-12)
associating the temperature data with additional data comprising at least one of: an external temperature, external humidity, on-off status of a HVAC controlled by the thermostatic device, fan speed, and combination thereof; (Col 23 lines 46-59, the system comprises data such as fan speed and external temperature)
waiting a predetermined number of days before adjusting, by a processor of the thermostatic device, at least one of the fan speed, the temperature of the environment, or combination thereof, based on an output data from the thermostatic machine learning model. (col 11-12 lines 52-7 and fig. 9)
As to claims 13 and 18, Ven teaches all the limitations of claims 12 and 17 as discussed above.
Ven further teaches:
wherein the pretrained thermostatic machine learning model received from the server computer is updated by performing steps to: tune the pretrained thermostatic machine learning model based on the temperature data and the additional data to update the machine learning model; (col 4 lines 1-24 and col 11 lines 15-27)
infer the output data based on inputting the temperature data and additional data into the updated machine learning model. (col 4 lines 1-24 and col 11 lines 15-27 and fig. 7)
As to claim 14, Ven teaches all the limitations of claim 12 as discussed above.
Ven further teaches:
detect whether one or more persons are occupying the environment of the thermostatic device; (col 1-2 lines 63-4, col 4 lines 1-24 and col 7 lines 14-28)
cause the output data to adjust the thermostatic device to reduce energy consumption by the thermostatic system during non-occupancy in the environment. (col 4 lines 25-35 and col 6-7 lines 40-12)
As to claim 15, Ven teaches all the limitations of claim 12 as discussed above.
Ven further teaches:
wherein the environment of the thermostatic device is a room in a building. (col 21-22 lines 60-7)
As to claim 16, Ven teaches all the limitations of claim 12 as discussed above.
Ven further teaches:
wherein each of the plurality of pretrained thermostatic machine learning models corresponds to a unique sub-environment, and the server computer is configured to perform the selecting based on metadata corresponding to the environment of the thermostatic device. (col 11-12 lines 52-7 and fig. 9)
As to claim 20, Ven teaches all the limitations of claim 17 as discussed above.
Ven further teaches:
wherein parameters of a tuning layer of the machine learning model are tuned by the temperature data and the associated additional data. (col 4 lines 1-24 and col 11 lines 15-27)
Claim Rejections - 35 USC § 103
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 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries 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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Venkatesh et al. referred herein as Ven (U.S. Patent No. 11,598,542) in view of Call et al. referred herein as Call (U.S. Patent Application Publication No. 2016/0062332).
As to claim 2, Ven teaches all the limitations of claim 1 as discussed above.
Ven does not teach:
wherein a second and third thermostatic devices are communicatively coupled to the computing device.
However, Call teaches:
wherein a second and third thermostatic devices are communicatively coupled to the computing device. (para 24)
It would have been obvious to one having skill in the art at the effective filling date of the invention to communicate multiple thermostatic devices together in Ven as taught by Call. Motivation to do so comes from the knowledge taught by Call that doing so would allow airflow adjustment in each room.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZEINA ELCHANTI whose telephone number is (313)446-6561. The examiner can normally be reached M-F 8:00 AM-5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Zimmerman can be reached at 571-272-4602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ZEINA ELCHANTI/Primary Examiner, Art Unit 3628