Search-reportNotice 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 .
1. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
2. 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 an abstract idea without significantly more.
Claim 1, recite determining that a machine learning model is either capable or not capable of generating a predicted performance of the building equipment based on the sensor data;
generating the predicted performance of the building equipment using the machine learning model in response to determining that the machine learning model is capable of generating the predicted performance based on the sensor data, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (Mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)).
determining that the building equipment is in need of maintenance based on at least one of the predicted performances of the building equipment generated using the machine learning model or the additional data related to the predicted performance of the building equipment as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (Mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)); and automatically initiating a maintenance activity for the building equipment in response to determining that the building equipment is in need of maintenance. noticing that a machine needs repair and deciding to fix it—as a traditional, long-standing human activity or mental process (Mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III)). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites additional elements of a memory and processor. However, the computer elements (processor, operatively coupled to the memory, that executes the executable components) are recited at a high level of generality and given the broadest reasonable interpretation are simply generic computers performing generic computer functions. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea and mere instructions to implement an abstract idea on a computer. In addition, the claim recites addition element of sensor to collect data, however, See MPEP 2106.05(d) that states "Receiving or transmitting data over a network, e.g., using the Internet to gather data is conventional when claimed in a merely generic manner (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC V. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) (insignificant extra-solution activity, mere data gathering, sec MPEP 2106.05 I A and see 2106.04(a)(2) III A. As of the addition element of machine learning, i.e. that the abstract idea is implemented by a data processing apparatus and applying it with a trained machine learning and determine the performance of machine learning (merely applying the exception with a generic computer using a broadly recited known computing algorithm - see MPEP 2106.04(a)(2) III C), involves an industrial process (linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)) the limitation of automatically initiating a maintenance activity for the building equipment in response to determining that the building equipment is in need of maintenance ("automatically initiating a maintenance activity" as insignificant extra-solution activity under USPTO MPEP 2106.05(g).
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, that the abstract idea is implemented by a data processing apparatus and applying it with a trained a machine learning model (merely applying the exception with a generic computer using a broadly recited known computing algorithm - see MPEP 2106.04(a)(2) III C), involves an industrial process (linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), collecting data from the sensor (insignificant extra-solution activity, see 2106.04(a)(2) III A regarding collecting information from the sensor mere data gathering, see MPEP 2106.05 I A)), and automatically initiating a maintenance activity for the building equipment in response to determining that the building equipment is in need of maintenance ("automatically initiating a maintenance activity" (linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), are not considered significantly more. Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Thus, claim 1 is not patent eligible.
Claims 28-30 recite similar limitations to claims 2-4 and are rejected under the same respective
The analysis above applies to all statutory categories of invention. As such, the presentation of claim 1, otherwise styled as a method or computer program product with similar limitations, for example, would be subject to the same analysis. Therefore, claims 9 and 17 are rejected for the same reason that applied to claim 1 Thus, the independent of claims 1, 9 and 17 are not patentable eligible.
As the dependent claims 2-8, 10-16 and 18-20 further limit the abstract idea of an analysis that can be performed mentally or certain methods of human activity that were already rejected in claims 1, 9 and 17, but fail to remedy the deficiencies of the parent claim as they do not impose any limitations that amount to significantly more than the abstract idea itself.
Claims 2, 10 and 18, recite additional data related to the predicted performance of the building equipment comprise feedback from an expert (getting a recommendation from the expert is group of "mental processes", abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. See MPEP 2106.04(a)(2). Thus, the claim is an abstract idea.
Claims 3, 11 and 19, recite the sensor data that are generated during a first time period while operating the building equipment to affect the one or more environmental conditions of the building and obtained by the system during or after the first time period (collecting sensor data, which is insignificant extra solution activity (see MPEP 2106.05(g). Thus, the claim is an abstract idea.
As claims 4 and 12, recite computing device located in a same geographic location as the one or more sensors (determining the location of the components); wherein obtaining the additional data comprises sending a request from the computing device to a cloud service located in a different geographic location than the computing device (collecting addition data, which is insignificant extra solution activity (see MPEP 2106.05(g). Thus, the claim is an abstract idea.
As claims 5, 13 and 20, recites generating an updated machine learning model using the additional data; and using the updated machine learning model to generate the predicted performance of the equipment (training a model of a machine learning to generate the predication is a mathematical concept, (see MPEP § 2106.04(a)(2), subsection I); the process of training a model on more data is a well-known mathematical/statistical process and insignificant extra solution activity (see MPEP 2106.05(g). Thus, the claims are an abstract idea. Thus, the claims are an abstract idea.
As claims 6 and 14, recite the one or more sensors comprise at least one of an accelerometer, a magnetometer, a gyroscope, a thermometer, or a microphone (listing A type of sensor, which is insignificant extra solution activity (see MPEP 2106.05(g). Thus, the claims are an abstract idea Thus, the claims are an abstract idea.
As claims 7 and 15, recite the sensor data comprise vibration data; the operations further comprising transforming the vibration data from a time domain to a frequency domain and providing the vibration data in the frequency domain as input to the machine learning model (transfer the vibration data in the frequency domain, is group of mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
As claims 8 and 16, recite evaluating the sensor data to determine whether the building equipment is operating abnormally; and transmitting the sensor data from in response to determining that the building equipment is operating abnormally (evaluating the sensor data to determine abnormality) is group of "mental processes", abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. See MPEP 2106.04(a)(2). Thus, the claims are an abstract idea.
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.
3. 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.
3.1 Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Picardi (US 2021/0071889) in view of Zhang (US 2019/0033171).
Regarding claims 1, 9 and 17, Picardi discloses a system, method and non-transitory computer-readable storage media for predicting performance of building equipment ([0039]-[0041], detect and predict failures associated with the HVAC system 146), the system, method and non-transitory computer-readable storage media comprising one or more processors and memory storing instructions ([0123], a processor will receive instructions and data from a read-only memory and/or a random access memory. Storage devices suitable for tangibly embodying computer program instructions) that, when executed by the one or more processors ([0012], [0013], [0039], [0044], [0123], systems, apparatus, and computer programs, configured to perform the actions of the methods, a computer processor, and a computer program product tangibly embodied in a machine-readable storage device for execution by a programmable processor), cause the one or more processors to perform operations comprising:
obtaining sensor data generated by one or more sensors (Abstract, [0012], [0036], one or more sensors 114 that are located throughout the property and that are configured to generate sensor data that indicates activity of the HVAC system) while operating the building equipment to affect one or more environmental conditions of a building ([0041], The HVAC model 101 can receive thermostat information such as current temperature of the thermostat, an operating state of the thermostat, information based on changes of operating state of the thermostat such as when the thermostat is instructed to turn on and turn off, set points of the thermostat indicating target temperature, as well as a current outdoor temperature);
generating the predicted performance of the building equipment ([0039], detect and predict issues associated with the HVAC system 146) using the machine learning model ([0039], use a machine-learning algorithm such as a deep learning algorithm an anomaly detection algorithm, a linear regression algorithm, or a logistical regression algorithm), in response to determining that the machine learning model is capable of generating the predicted performance based on the sensor data (Abstract, [0006], [0014]-[0015], [0039], HVAC model 101 from the trained machine learning algorithm determine the error of the HVAC system and will be able to detect and predict failures associated with the HVAC system 146);
obtaining additional data ([0060], obtain addition data from thermostat information of the monitored property 102) related to the predicted performance of the building equipment([0061], an HVAC system issued based on an analysis of the thermostat information using a trained model) in response to determining that the machine learning model is not capable of generating the predicted performance based on the sensor data ([0054], [0058], [0060], [0081], [0082], if any of these four data points provided by the HVAC system 146 is contradictory, the filter will not provide the temperature information from the HVAC system 146 to the HVAC model 101 because the data is not reliable for a prediction.
determining that the building equipment is in need of maintenance based on at least one of the predicted performances of the building equipment generated using the machine learning model [0059], determining an HVAC system issue based on an analysis of the thermostat information using a trained model; sorting the HVAC issue into a category that specifies a type of the HVAC system issue; alerting the customer of the HVAC issue and taking additional actions based on the sorted category; alerting a dealer of the HVAC issue; and, obtaining thermostat information from the household indicating that the HVAC system issue has been fixed) or the additional data related to the predicted performance of the building equipment ([0055],The indicators provided by the HVAC technician 132 to the security system 128 can be stored in the HVAC database 126 and used to train the HVAC model 101 for further detections of these indicators); and
automatically initiating a maintenance activity for the building equipment in response to determining that the building equipment needs maintenance (Abstract, [0012], [0051], [0086], The monitor control unit determines an error of the HVAC system from the HVAC model output. The monitor control unit determines an action for correcting the error of the HVAC system. The monitor control unit provides, for output, data identifying the error of the HVAC system and the action for correcting the error of the HVAC system).
In addition, Picardi discloses HVAC system 146 is contradictory, the filter will not provide the temperature information from the HVAC system 146 to the HVAC model 101 because the data is not reliable for prediction. By providing this filter before the HVAC model 101, the efficacy of the HVAC model 101 is improved in Par. ([0006],[0054], [0057]-[0058]. but fails to disclose determining that a machine learning model is either capable or not capable of generating a predicted performance of the building equipment based on the sensor data.
However, Zhang discloses determining that a machine learning model is either capable or not capable of generating a predicted performance of the building equipment based on the sensor data ([0031], [0037], [0055], the machine learning or pattern recognition outputs may be applied to real-time model updates to increase the accuracy of the models generated by the modeling software 44 for future calculations or predictions. The real-time simulated data and/or vibration sensor data may be processed, such as by using machine learning or pattern recognition, to generate data that may be used to update, or retrain, the models).
Picardi and Zhang are analogous art. They relate to monitoring a fault diagnosis. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify the detecting wear or failure of Zhang and alerting individuals of HVAC system issues of Picardi in order to compare time domain information of the vibration sensor data to time domain information of the simulated data, or frequency domain information of the vibration sensor data to frequency domain information of the simulated data, to identify the wear or failure of the coupling.
Regarding claims 2, 10 and 18, Picardi discloses the additional data related to the predicted performance of the building equipment comprises feedback from an expert ([0052], event rules are triggered in response to the detection. the HVAC technician 132 may be notified via the network 130 at mobile device 134 after the security system 128 and the control unit server 104 log the detection in memory to fix the issue).
Regarding claims 3, 11 and 19, Picardi discloses the sensor data generated during a first time-period while operating the building equipment to affect the one or more environmental conditions of the building (Abstract, a monitoring system monitors a property that includes sensors located throughout the property and generates sensor data. A monitor control unit receives thermostat data from a thermostat that indicates activity of the HVAC system; and determines an error of the HVAC system from the HVAC model output) and obtained by the system during or after the first time period ([0018], [0054], determining the error of the HVAC system based on applying the thermostat data and the door or window sensor data to the HVAC model in real time or the security system 128 may execute another filter that determines whether the operating state of the HVAC system 146, the set point temperature of the HVAC system 146, the aggregate runtime, and the HVAC operating mode remain in sync during operation of the HVAC system 146).
Regarding claims 4 and 12, Picardi discloses the one or more processors and the memory are components of a computing device ([Fig. 1, [0123], the control unit server 104 has a memory and a processor) located in a same geographic location (Fig. 1, located inside the monitored property 102) as the one or more sensors (sensors 114); wherein obtaining the additional data comprises sending a request from the computing device to a cloud service located in a different geographic location than the computing device (Fig. 1, [0005], [0037], the server 104 is commination using a cloud network 130 to HVAC database and security system, which is outside the monitored property 102, as shown in Fig, 1).
Regarding claims 5, 13 and 20, Zhang discloses generating an updated machine learning model using the additional data ([0037],[0055], the machine learning or pattern recognition outputs may be applied to real-time model updates to increase accuracy of the normal performance model 118 and the abnormal performance model 120 for future calculations or predictions); and using the updated machine learning model to generate the predicted performance of the equipment ([0055], to identify wear or failure of the coupling 18).
Regarding claim 6 and 14, Zhang discloses one or more sensors (The system 28 may include various sensors 30) comprise at least one of an accelerometer, a magnetometer, a gyroscope, a thermometer, or a microphone ([0019], one vibration sensor 32, such as an accelerometer, configured to measure vibrations of the genset power system 10).
Regarding claim 7 and 15, Zhang discloses the sensor data comprises vibration data (Abstract, [0028], vibration sensor 32 for measuring vibrations); and the operations comprise transforming the vibration data from a time domain to a frequency domain and providing the vibration data in the frequency domain as input to the machine learning model (Abstract, Fig. 4, [0037], [0055], the controller applies time domain information of at least one of the simulated data and vibration sensor data to the modeling software using a machine learning algorithm, comparison, the time domain information of the vibration sensor data to time domain information of the simulated data or frequency domain information of the vibration sensor data to frequency domain information of the simulated data, to identify wear or failure of the coupling).
Regarding claims 8 and 16, Picardi discloses evaluating the sensor data to determine whether the building equipment is operating abnormally (Abstract, [0005], [0012], [0014], sensor data identifying the error of the HVAC system; determine the error of the HVAC system by determining the error of the HVAC system based on applying the thermostat data and the motion data to the HVAC model); and transmitting the sensor data from in response to determining that the building equipment is operating abnormally ([0045]-[0052], [0080], [0103], [0109], continuously transmit sensed values to the controller 612, periodically transmit sensed values to the controller 612, or transmit sensed values to the controller 612 in response to a change in a sensed value; and HVAC data indicating that a sensor 620 detected a flow rate of air in the air handling unit 152. The central alarm station server 670 may receive the HVAC data and route the HVAC data to terminal 672 for processing by an operator associated with terminal 672).
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
4.1 Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No.12,093,844. Although the claims at issue are not identical, they are not patentably distinct from each other because both the Instant US application 18/819,974 and Parent US Patent 12,093,844 have a similar limitation and the same subject matter as shown below:
Instant US application 18/819,974
Parent US Patent 12,093,844
Claim 1. A system for predicting performance of building equipment, the system comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: obtaining sensor data generated by one or more sensors while operating the building equipment to affect one or more environmental conditions of a building;
determining that a machine learning model is either capable or not capable of generating a predicted performance of the building equipment based on the sensor data;
generating the predicted performance of the building equipment using the machine learning model in response to determining that the machine learning model is capable of generating the predicted performance based on the sensor data;
Claim 2. The system of Claim 1, wherein the additional data related to the predicted performance of the building equipment comprise feedback from an expert.
obtaining additional data related to the predicted performance of the building equipment in response to determining that the machine learning model is not capable of generating the predicted performance based on the sensor data;
determining that the building equipment is in need of maintenance based on at least one of the predicted performance of the building equipment generated using the machine learning model or the additional data related to the predicted performance of the building equipment; and
automatically initiating a maintenance activity for the building equipment in response to determining that the building equipment is in need of maintenance.
Claims 9 and 17 have similar limitations.
Claim 1. A system for predicting performance of building equipment, the system comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: obtaining sensor data generated by one or more sensors while operating the building equipment to affect one or more environmental conditions of a building;
determining that a machine learning model is either capable or not capable of generating a predicted performance of the building equipment based on the sensor data and prior data available in the model;
generating the predicted performance of the building equipment using the machine learning model in response to determining that the machine learning model is capable of generating the predicted performance based on the sensor data and prior data available in the model;
requesting feedback from an expert related to the predicted performance of the building equipment in response to determining that the machine learning model is not capable of generating the predicted performance based on the sensor data and the prior data available in the model;
determining that the building equipment is in need of maintenance based on at least one of the predicted performance of the building equipment generated using the machine learning model or the feedback from the expert related to the predicted performance of the building equipment; and
automatically initiating a maintenance activity for the building equipment in response to determining that the building equipment is in need of maintenance.
Claims 8 and 15 have similar limitation.
Dependent claims 3-8, 11-16 and 19-20
Dependent claims 3, 5-7, 10-14 and 16-17
4.2 Claims 1-2, 4, 9-12 and 17-18 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 8-11 and 18-19 of U.S. Patent No. 11,605,011. Although the claims at issue are not identical, they are not patentably distinct from each other because both the Instant US application 18/819,974 and Parent US Patent 11,605,011 have a similar limitation and the same subject matter as shows below:
Instant US application 18/819,974
Parent US Patent 11,605,011
1. A system for predicting performance of building equipment, the system comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
obtaining sensor data generated by one or more sensors while operating the building equipment to affect one or more environmental conditions of a building;
determining that a machine learning model is either capable or not capable of generating a predicted performance of the building equipment based on sensor data;
4. The system of Claim 1, wherein the one or more processors and the memory are components of a computing device located in a same geographic location as the one or more sensors; wherein obtaining the additional data comprises sending a request from the computing device to a cloud service located in a different geographic location than the computing device;
2. The system of Claim 1, wherein the additional data related to the predicted performance of the building equipment comprise feedback from an expert.
Claims 9, 10, 12, 17 and 18 are similar limitations with
1. A system for predicting performance of building equipment, the system comprising:
the computing device comprising one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to:
one or more sensors in communication with the building equipment, the sensors operable to detect characteristics from the building equipment; receive data from the sensors, the data based on the detected characteristics;
generate, based on a machine learning model and the data, a predicted performance of the building equipment when the machine learning model comprises a prior data similar to the data; and
a computing device in communication with the sensors and in the same geographic location as the sensors,
request feedback from an expert if the data is different from the prior data, the feedback related to the predicted performance of the building equipment based on the data.
Claims 9, 10, 11,18 and 19
Independent claims 14 and 16
Independent claims 5 and 8
Citation Pertinent prior art
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ahmed (US 20180046149 A1) discloses degraded or other performance may be predicted with a machine-learnt classifier. Based on operation of many building automation systems, machine learning is applied and creates a predictor.
KOBAYASHI (US 20170061329 A1) discloses A machine learning management device executes each of a plurality of machine learning algorithms by using training data. The machine learning management device calculates, based on execution results of the plurality of machine learning algorithm.
Zara et al. (US20210174192A1) discloses systems and methods for training machine learning. models
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kidest Worku, whose telephone number is 571-272-3737. The examiner can normally be reached on Mon-Fri 9am to 5pm, ET.
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/KIDEST WORKU/ Primary Examiner, Art Unit 2119
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