Prosecution Insights
Last updated: April 19, 2026
Application No. 18/225,691

COMPUTING SYSTEM AND COMPUTING METHOD FOR CARBON EMISSION OF ENERGY-CONSUMING DEVICES

Non-Final OA §101§102
Filed
Jul 25, 2023
Examiner
LAU, TUNG S
Art Unit
2857
Tech Center
2800 — Semiconductors & Electrical Systems
Assignee
Chicony Power Technology Co. Ltd.
OA Round
1 (Non-Final)
83%
Grant Probability
Favorable
1-2
OA Rounds
3y 0m
To Grant
97%
With Interview

Examiner Intelligence

Grants 83% — above average
83%
Career Allow Rate
921 granted / 1112 resolved
+14.8% vs TC avg
Moderate +14% lift
Without
With
+14.0%
Interview Lift
resolved cases with interview
Typical timeline
3y 0m
Avg Prosecution
38 currently pending
Career history
1150
Total Applications
across all art units

Statute-Specific Performance

§101
20.9%
-19.1% vs TC avg
§103
23.1%
-16.9% vs TC avg
§102
27.9%
-12.1% vs TC avg
§112
14.3%
-25.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1112 resolved cases

Office Action

§101 §102
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 . 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 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. Claim status Claims 1-16 filed on 07/25/2023 noted by the examiner, claims 1-16 are pending. 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 1-16 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. Claim 1, Step 1 the claim is a process (or machine) (Yes), Step 2A Prong One, does the claim recite an abstract idea? current claim related to an edge data process system, configured to collect real-time data of each of the energy-consuming devices while the multiple energy-consuming devices operate and compute an operation data of each of the energy-consuming devices based on specification information and the real-time data of each of the energy-consuming devices, wherein the operation data at least comprises a device energy value (DEV), a platform data process system, configured to perform a measurement splitting procedure, wherein the measurement splitting procedure comprises: computing an estimated device carbon emission (EDCE) of each of the energy- consuming devices based on the DEV of each of the energy-consuming devices, a device performance parameter (DPP) of each of the energy-consuming devices, and a carbon- emission factor; accumulating the EDCE of all of the energy-consuming devices connected with the meter to generate an estimated device carbon emission sum (EDCES); computing a percentage of the EDCE in the EDCES for each of the energy- consuming devices to rank the carbon-emission of the multiple energy-consuming devices and find at least one key carbon-emission source from the multiple energy- consuming devices which is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes. Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of a computing system for carbon-emission of energy-consuming devices, multiple energy-consuming devices connected with same meter, wherein the meter generates a meter energy value (MEV); a carbon-emission monitor kit connected with the multiple energy-consuming devices and the meter are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application, Step 2A Prong Two: NO. Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional element of issuing an alarm for the at least one key carbon-emission source appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 1 not eligible. Claim 11, Step 1 the claim is a process (or machine) (Yes), Step 2A Prong One, does the claim recite an abstract idea? current claim related to a) controlling the multiple energy-consuming devices to operate, wherein the multiple energy-consuming devices are connected with same meter and the meter generates a meter energy value (MEV), while the multiple energy-consuming devices operate, collecting real-time data of each of the energy-consuming devices and computing an operation data of each of the energy-consuming devices based on a specification information and the real-time data of each of the energy-consuming devices by a carbon-emission monitor kit of the carbon- emission computing system, wherein the operation data at least comprises a device energy value (DEV); c) computing an estimated device carbon emission (EDCE) of each of the energy- consuming devices based on the DEV of each of the energy-consuming devices, a device performance parameter (DPP) of each of the energy-consuming devices, and a carbon- emission factor by a carbon-emission analysis subsystem of the carbon-emission computing system; d) accumulating the EDCE of all of the energy-consuming devices connected with the meter by the carbon-emission analysis subsystem to generate an estimated device carbon emission sum (EDCES); e) computing a percentage of the EDCE in the EDCES for each of the energy- consuming devices to rank the carbon-emission of the multiple energy-consuming devices by the carbon-emission analysis subsystem and finding at least one key carbon- emission source from the multiple energy-consuming devices which is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes. Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of a computing method for carbon-emission of energy-consuming devices, incorporated with a carbon-emission computing system are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application, Step 2A Prong Two: NO. Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional element of ) issuing an alarm for the at least one key carbon-emission source by the carbon- emission analysis subsystem appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 11 not eligible. Claim 2 related to computing a reciprocal of the DPP of each of the energy-consuming devices to generate a device performance index (DPI) of each of the energy-consuming devices; continuously monitoring the DPI of the multiple energy-consuming devices and determining a performance trend of each of the energy-consuming devices based on the DPI; and determining that the performance trend of one of the multiple energy-consuming devices is declining and a declining degree is greater than a threshold and issuing the alarm for the energy-consuming device having the performance trend declining. Claim 3 related to generate a reference model in an establishment phase and generate a dynamic model in an adjustment phase and select one of the reference model and the dynamic model to compute the DPP of each of the energy-consuming devices. Claim 4 related to a device information database, storing the specification information of the multiple energy-consuming devices; a device operation data management subsystem connected with the device information database, configured to compute the operation data of each of the energy- consuming devices; and a device operation database connected with the device operation data management subsystem, storing the real-time data of the multiple energy-consuming devices, the operation data of the multiple energy-consuming devices, and the MEV. Claim 5 related to one of a device name, an energy type, a rated power, an inverter frequency, a frequency conversion loss, and an energy-consumption computing reference of each of the energy-consuming devices. Claim 6 related to a carbon-emission factor database, storing the carbon-emission factor; a platform carbon-emission analysis subsystem connected with the carbon- emission factor database and the device operation database, configured to perform the measurement splitting procedure; and a device carbon-emission performance database connected with the platform carbon-emission analysis subsystem. Claim 7 related to executing a data collection mode to control each of the energy-consuming devices to respectively operate according to a data collection strategy; computing a first device energy value (FDEV) of each of the energy-consuming devices based on the specification information and the real-time data while each of the energy-consuming devices operate, and reading the MEV from the meter; computing a device performance reference parameter (DPRP) of each of the energy-consuming devices based on the MEV and the FDEV of each of the energy- consuming devices; computing a first device energy value sum (FDEVS) of the multiple energy- consuming devices and computing a device energy reference deviation (DERD) between the MEV and the FDEVS; computing a first meter energy value adjustment (FMEVA) based on the DPRP of each of the energy-consuming devices, wherein the FMEVA indicates an adjusted value based on a deviation between an estimated energy consumption sum of the multiple energy-consuming devices in the establishment phase and the MEV; and establishing the reference model based on the MEV, the FDEVS, the FMEVA, and the DPRP of each of the energy-consuming devices. Claim 8 related to generate the dynamic model in the adjustment phase, wherein the adjustment phase is performed after the establishment phase: controlling the multiple energy-consuming devices to normally operate, computing a second device energy value (SDEV) of each of the energy-consuming devices based on the specification information and the real-time data while each of the energy- consuming devices operate, and reading the MEV from the meter; computing a device performance operation parameter (DPOP) of each of the energy-consuming devices based on the MEV and the SDEV of each of the energy- consuming devices; computing a second device energy value sum (SDEVS) of the multiple energy- consuming devices and computing a device energy operation deviation (DEOD) between the MEV and the SDEVS; computing a second meter energy value adjustment (SMEVA) based on the DPOP of each of the energy-consuming devices, wherein the SMEVA indicates an adjusted value based on a deviation between an estimated energy consumption sum of the multiple energy-consuming devices in the adjustment phase and the MEV; and establishing the dynamic model based on the MEV, the SDEVS, the SMEVA, and the DPOP of each of the energy-consuming devices Claim 9 related to configured to execute actions below to select one of the reference model and the dynamic model in the measurement splitting procedure to compute the DPP of each of the energy- consuming devices: determining that the performance of the reference model is better than the performance of the dynamic model and computing the DPP of each of the energy- consuming devices based on the DPRP of each of the energy-consuming devices recorded in the reference model; and determining that the performance of the dynamic model is better than the performance of the reference model, computing the DPP of each of the energy- consuming devices based on the DPOP of each of the energy-consuming devices recorded in the dynamic model, and replacing the reference model with the dynamic model to be a new reference model. Claim 10 related to execute actions below to determine the performance of the reference model and the performance of the dynamic model: obtaining the DPRP of each of the energy-consuming devices from the reference model and computing a device energy reference deviation adjustment (DERDA) according to the DPRP, the FDEV in real-time, and the MEV in real-time, wherein the DERDA indicates a difference between the MEV and the FMEVA; obtaining the DPOP of each of the energy-consuming devices from the dynamic model and computing a device energy operation deviation adjustment (DEODA) according to the DPOP, the SDEV in real-time, and the MEV in real-time, wherein the DEODA indicates a difference between the MEV and the SMEVA; computing an average value of the DERDA and another average value of the DEODA; and determining that the performance of the reference model is better than the 36 performance of the dynamic model when the average value of the DERDE is smaller than the average value of the DEODA and determining that the performance of the dynamic model is better than the performance of the reference model when the average value of the DERDA is greater than or equal to the average value of the DEODA. Claim 12 related to computing a reciprocal of the DPP of each of the energy-consuming devices to generate a device performance index (DPI) of each of the energy-consuming devices; continuously monitoring the DPI of the multiple energy-consuming devices and determining a performance trend of each of the energy-consuming devices based on the DPI; and determining that the performance trend of one of the multiple energy-consuming devices is declining and a declining degree is greater than a threshold and issuing the alarm for the energy-consuming device having the performance trend declining. Claim 13 related to generating a reference model in an establishment phase; a02) generating a dynamic model in an adjustment phase, wherein the adjustment phase is performed after the establishment phase; and a03) selecting one of the reference model and the dynamic model to compute the DPP of each of the energy-consuming devices. Claim 14 related to executing a data collection mode to control each of the energy-consuming devices to respectively operate according to a data collection strategy; a012) computing a first device energy value (FDEV) of each of the energy- consuming devices based on the specification information and the real-time data while each of the energy-consuming devices operate and reading the MEV from the meter; aO13) computing a device performance reference parameter (DPRP) of each of the energy-consuming devices based on the MEV and the FDEV of each of the energy- 38 consuming devices; a014) computing a first device energy value sum (FDEVS) of the multiple energy- consuming devices and computing a device energy reference deviation (DERD) between the MEV and the FDEVS; a015) computing a first meter energy value adjustment (FMEVA) based on the DPRP of each of the energy-consuming devices, wherein the FMEVA indicates an adjusted value based on a deviation between an estimated energy consumption sum of the multiple energy-consuming devices in the establishment phase and the MEV; and a016) establishing the reference model based on the MEV, the FDEVS, the FMEVA, and the DPRP of each of the energy-consuming devices. Claim 15 related to controlling the multiple energy-consuming devices to normally operate, computing a second device energy value (SDEV) of each of the energy-consuming devices based on the specification information and the real-time data while each of the energy-consuming devices operate, and reading the MEV from the meter; a022) computing a device performance operation parameter (DPOP) of each of the energy-consuming devices based on the MEV and the SDEV of each of the energy- consuming devices; a023) computing a second device energy value sum (SDEVS) of the multiple energy-consuming devices and computing a device energy operation deviation (DEOD) between the MEV and the SDEVS; a024) computing a second meter energy value adjustment (SMEVA) according to the DPOP of each of the energy-consuming devices, wherein the SMEVA indicates an adjusted value based on a deviation between an estimated energy consumption sum of the multiple energy-consuming devices in the adjustment phase and the MEV; and a025) establishing the dynamic model based on the MEV, the SDEVS, the SMEVA, 39 and the DPOP of the each of the energy-consuming devices. Claim 16 related to obtaining the DPRP of each of the energy-consuming devices from the reference model and computing a device energy reference deviation adjustment (DERDA) based on the DPRP, the FDEV in real-time, and the MEV in real-time, wherein the DERDA indicates a difference between the MEV and the FMEVA; a032) obtaining the DPOP of each of the energy-consuming devices from the dynamic model and computing a device energy operation deviation adjustment (DEODA) based on the DPOP, the SDEV in real-time, and the MEV in real-time, wherein the DEODA indicates a difference between the MEV and the SMEVA; a033) computing an average value of the DERDA and another average value of the DEODA; a034) determining that the average value of the DERDA is smaller than the average value of the DEODA and computing the DPP of each of the energy-consuming devices based on the DPRP of each of the energy-consuming devices recorded in the reference model; and a035) determining that the average value of the DERDA is greater than or equal to the average value of the DEODA, computing the DPP of each of the energy-consuming devices based on the DPOP of each of the energy-consuming devices recorded in the dynamic model, and replacing the reference model with the dynamic model to be a new reference model. Claims 2-10 and 12-16 appears recite further data characterization and mathematical concepts that are part of the abstract idea, claims 2-10 and 12-16 not eligible as well. Claim Rejections - 35 USC § 102 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. Claim(s) 1, 11, 2, 3, 4, 5, 6, 12 and 13 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Howe at al. (US Patent Application Publication 20120185108 A1, Date Published: 2012-07-19. Regarding claim 1: Howe described a computing system for carbon-emission of energy-consuming devices (abstract, 0001, on consuming devices), comprising: multiple energy-consuming devices connected with same meter, wherein the meter generates a meter energy value (MEV) (0007, consuming devices, 0022, the amount actually consumed); a carbon-emission monitor kit connected with the multiple energy-consuming devices and the meter (0055, carbon dioxide emission saving of a population of power-consuming devices by monitoring), comprising: an edge data process system, configured to collect real-time data of each of the energy-consuming devices while the multiple energy-consuming devices operate and compute an operation data of each of the energy-consuming devices based on specification information and the real-time data of each of the energy-consuming devices, wherein the operation data at least comprises a device energy value (DEV) (0148, real-time sampled data from two refrigerators, calculating least-squares errors, or similar error indication, between the modelled and measured results for the plurality of refrigerators to ensure a satisfactory goodness of fit); and a carbon-emission management platform connected with the carbon-emission monitor kit, comprising: a platform data process system, configured to perform a measurement splitting procedure, wherein the measurement splitting procedure comprises: computing an estimated device carbon emission (EDCE) of each of the energy- consuming devices based on the DEV of each of the energy-consuming devices, a device performance parameter (DPP) of each of the energy-consuming devices, and a carbon- emission factor (0148, sampled data from two refrigerators, calculating least-squares errors, or similar error indication, between the modelled and measured results for the plurality of refrigerators to ensure a satisfactory goodness of fit); accumulating the EDCE of all of the energy-consuming devices connected with the meter to generate an estimated device carbon emission sum (EDCES) (0015, carbon dioxide generation associated with rapid-response gas fired electricity generating plant, 0127, dynamic calculate carbon dioxide); computing a percentage of the EDCE in the EDCES for each of the energy- consuming devices to rank the carbon-emission of the multiple energy-consuming devices and find at least one key carbon-emission source from the multiple energy- consuming devices (0055, a percentage of time available in which the one or more power-consuming devices are able to provide responsive-load service, 0148, real-time sampled data from two refrigerators, calculating least-squares errors, or similar error indication, between the modelled and measured results for the plurality of refrigerators to ensure a satisfactory goodness of fit); and issuing an alarm for the at least one key carbon-emission source (0061-0062, 0103, BMS alarms). Regarding claim 11: Howe described a computing method for carbon-emission of energy-consuming devices, incorporated with a carbon-emission computing system (abstract, 0001, on consuming devices), comprising: a) controlling the multiple energy-consuming devices to operate, wherein the multiple energy-consuming devices are connected with same meter and the meter generates a meter energy value (MEV) (0007, consuming devices, 0022, the amount actually consumed); b) while the multiple energy-consuming devices operate, collecting real-time data of each of the energy-consuming devices and computing an operation data of each of the energy-consuming devices based on a specification information and the real-time data of each of the energy-consuming devices by a carbon-emission monitor kit of the carbon- emission computing system, wherein the operation data at least comprises a device energy value (DEV) (0015, carbon dioxide generation associated with rapid-response gas fired electricity generating plant, 0127, dynamic calculate carbon dioxide , 0148, real-time sampled data from two refrigerators, calculating least-squares errors, or similar error indication, between the modelled and measured results for the plurality of refrigerators to ensure a satisfactory goodness of fit); c) computing an estimated device carbon emission (EDCE) of each of the energy- consuming devices based on the DEV of each of the energy-consuming devices, a device performance parameter (DPP) of each of the energy-consuming devices, and a carbon- emission factor by a carbon-emission analysis subsystem of the carbon-emission computing system (0015, carbon dioxide generation associated with rapid-response gas fired electricity generating plant, 0127, dynamic calculate carbon dioxide, 0148, sampled data from two refrigerators, calculating least-squares errors, or similar error indication, between the modelled and measured results for the plurality of refrigerators to ensure a satisfactory goodness of fit); d) accumulating the EDCE of all of the energy-consuming devices connected with the meter by the carbon-emission analysis subsystem to generate an estimated device carbon emission sum (EDCES (0055, a percentage of time available in which the one or more power-consuming devices are able to provide responsive-load service, 0148, real-time sampled data from two refrigerators, calculating least-squares errors, or similar error indication, between the modelled and measured results for the plurality of refrigerators to ensure a satisfactory goodness of fit)); e) computing a percentage of the EDCE in the EDCES for each of the energy- consuming devices to rank the carbon-emission of the multiple energy-consuming devices by the carbon-emission analysis subsystem and finding at least one key carbon- emission source from the multiple energy-consuming devices (0055, a percentage of time available in which the one or more power-consuming devices are able to provide responsive-load service, 0148, real-time sampled data from two refrigerators, calculating least-squares errors, or similar error indication, between the modelled and measured results for the plurality of refrigerators to ensure a satisfactory goodness of fit); and f) issuing an alarm for the at least one key carbon-emission source by the carbon- emission analysis subsystem (0061-0062, 0103, BMS alarms). Regarding claim 2, Howe further described computing a reciprocal of the DPP of each of the energy-consuming devices to generate a device performance index (DPI) of each of the energy-consuming devices (0068, a frequency inverter, a pattern generating device); continuously monitoring the DPI of the multiple energy-consuming devices and determining a performance trend of each of the energy-consuming devices based on the DPI (0148, real-time sampled data from two refrigerators;); and determining that the performance trend of one of the multiple energy-consuming devices is declining and a declining degree is greater than a threshold and issuing the alarm for the energy-consuming device having the performance trend declining (0167, load performance that can be provided). Regarding claim 3, Howe further described wherein the platform data process system is configured to generate a reference model in an establishment phase and generate a dynamic model in an adjustment phase and select one of the reference model and the dynamic model to compute the DPP of each of the energy-consuming devices (0082, dynamic demand frequency response) Regarding claim 4, Howe further described a device information database, storing the specification information of the multiple energy-consuming devices (fig. 1, 60); a device operation data management subsystem connected with the device information database, configured to compute the operation data of each of the energy- consuming devices; and a device operation database connected with the device operation data management subsystem, storing the real-time data of the multiple energy-consuming devices, the operation data of the multiple energy-consuming devices, and the MEV (0015, carbon dioxide generation associated with rapid-response gas fired electricity generating plant, 0127, dynamic calculate carbon dioxide, 0148, real-time sampled data from two refrigerators, calculating least-squares errors, or similar error indication, between the modelled and measured results for the plurality of refrigerators to ensure a satisfactory goodness of fit). Regarding claim 5, Howe further described one of a device name (0149, sampled data from two refrigerators), an energy type, a rated power, an inverter frequency, a frequency conversion loss, and an energy-consumption computing reference of each of the energy-consuming devices (0149, sampled data from two refrigerators, calculating least-squares errors, or similar error indication, between the modelled and measured results for the plurality of refrigerators to ensure a satisfactory goodness of fit). Regarding claim 6, Howe further described a carbon-emission factor database, storing the carbon-emission factor; a platform carbon-emission analysis subsystem connected with the carbon- emission factor database and the device operation database, configured to perform the measurement splitting procedure; and a device carbon-emission performance database connected with the platform carbon-emission analysis subsystem, storing the DPP and the EDCE of the multiple energy-consuming devices (0055, carbon dioxide emission data, 0097, database to aggregate data). Regarding claim 12, Howe further described g) computing a reciprocal of the DPP of each of the energy-consuming devices to generate a device performance index (DPI) of each of the energy-consuming devices; h) continuously monitoring the DPI of the multiple energy-consuming devices and determining a performance trend of each of the energy-consuming devices based on the DPI (0082, dynamic demand frequency response); and i) determining that the performance trend of one of the multiple energy-consuming devices is declining and a declining degree is greater than a threshold and issuing the alarm for the energy-consuming device having the performance trend declining (fig. 2, 0061-0062, 0103, BMS alarms). Regarding claim 13, Howe further described ) generating a reference model in an establishment phase; generating a dynamic model in an adjustment (0064, phase dynamic-demand/frequency-response), wherein the adjustment phase is performed after the establishment phase; and selecting one of the reference model and the dynamic model to compute the DPP of each of the energy-consuming devices (0005, adjust what is needed, 0084, dynamic response). Contact information 4. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 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, TURNER SHELBY, can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll- free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272- 1000. /TUNG S LAU/Primary Examiner, Art Unit 2857 Technology Center 2800 December 16, 2025
Read full office action

Prosecution Timeline

Jul 25, 2023
Application Filed
Dec 16, 2025
Non-Final Rejection — §101, §102
Apr 10, 2026
Interview Requested

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