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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 15, 2026 has been entered.
Claims 1, 7-8, and 14-15 have been amended. Claims 1-20 are subject to examination and have been examined.
Response to Arguments
Applicant's arguments with respect to the claims have been considered but are moot in view of the new grounds of rejection.
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 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.
Claims 1, 3-5, 7-8, 10-12, 14-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Xue et.al. (US Patent Application Publication, 20210376895, hereinafter, “Xue”) in view of Echigo et.al. (WIPO (PCT) Patent Application Publication, WO2024004218A1, hereinafter, “Echigo”).
Regarding claim 1, Xue teaches:
A method for a user equipment (UE) to report channel state information (CSI), the method comprising (Xue: [0084] FIG. 8 shows a call flow diagram of messages that may be exchanged between a UE 802 and a gNB 804 (or other network entity) … [0086] … gNB 804 may configure UE 802 with information about measurement occasions in which reference signals are transmitted to UE 802 and reporting occasions in which CSI feedback reports are to be transmitted to gNB 804... Fig. 8):
receiving, from a network, first information related to monitoring a performance of a machine learning (ML) model for predicting CSI (Xue: [0085] As illustrated, to configure UE 802 to predict CSI based on a CSI prediction model, gNB 804 transmits CSI prediction model and qualifying scheme 810 to the UE. The CSI prediction model, as discussed, may be a machine learning model configured to execute on the UE and predict CSI at a future point in time given a current CSI measurement and a time delay as input. The qualifying scheme generally identifies qualifying rules for determining whether a predicted CSI is a qualified prediction or an unqualified prediction, as discussed above … [0069] … ... a qualifying scheme that the UE can use to quantize the accuracy of the CSI predicted using the CSI prediction model [i.e., monitoring performance of the model] … Fig. 8);
receiving second information related to transmitting a performance monitoring report (Xue: [0086] … gNB 804 may configure UE 802 with information about measurement occasions in which reference signals are transmitted to UE 802 and reporting occasions in which CSI feedback reports are to be transmitted to gNB 804 ... Fig. 8);
receiving third information related to reception of CSI reference signals (CSI-RSs) for determining a ground-truth CSI on a cell (Xue: [0086] After UE 802 is configured with the CSI prediction model and qualifying scheme 810, UE 802 may be ready to perform CSI measurements and predict CSI based on the measured CSI ... As discussed, gNB 804 may configure UE 802 with information about measurement occasions in which reference signals are transmitted to UE 802 and reporting occasions in which CSI feedback reports [i.e., third information] are to be transmitted to gNB 804 … [0087] At 814, the UE measures CSI based on the reference signals [i.e., ground-truth CSI]. The UE also predicts CSI at a future time using the prediction model ... [0090] In some aspects, a gNB may arrange a session to qualify CSI predictions for a UE. During the session, the UE may transmit, to the gNB, a series of predicted CSI and measured CSI values (which may be referred to as “prediction and ground-truth pairs”) … Fig. 8;
receiving the CSI-RSs based on the third information (Xue: [0086] After UE 802 is configured with the CSI prediction model and qualifying scheme 810, UE 802 may be ready to perform CSI measurements and predict CSI based on the measured CSI. To do so, gNB 804 may transmit reference signals 812 to UE 802 ... Fig. 8);
determining the ground-truth CSI based on the reception of the CSI-RSs (Xue: [0088] Subsequently, gNB 804 transmits reference signals 816 to UE 802. At 818, the UE measures CSI based on the reference signals [i.e., ground-truth CSI] ... Fig. 8);
determining a performance monitoring report for the ML model based on the first information and the ground-truth CSI (Xue: [0088] ... The UE also calculates a difference between the predicted CSI generated at 814 and the measured CSI [i.e., ground-truth] generated by measuring reference signals 816 and quantizes the difference based on the qualifying scheme received from gNB 804 in message 810. As discussed, UE 802 may generate the quantized difference value into one of a plurality of values based on rules included in the qualifying scheme [i.e., part of first information] for classifying the difference value … [0069] … ... a qualifying scheme that the UE can use to quantize the accuracy of the CSI predicted using the CSI prediction model [i.e., monitoring performance of the model] … Fig. 8); and
transmitting a channel with the performance monitoring report based on the second information (Xue: [0089] After calculating the difference between the predicted CSI generated at 814 and the measured CSI generated by measuring reference signals 816 and quantizing the difference, UE 802 generates and transmits a message 820 including the measured CSI and the quantized difference 820. gNB 804 can use the measured CSI and the quantized difference 820 to determine whether the CSI prediction model is accurately generating predictions of channel state information ... Fig. 8).
Although Xue teaches, to configure the UE to predict CSI based on a CSI prediction model, the gNB transmits CSI prediction model and qualifying scheme to the UE; the CSI prediction model may be a machine learning model configured to execute on the UE and predict CSI at a future point in time, Xue does not explicitly teach:
wherein the first information indicates a performance index for measuring a performance of the ML model for predicting CSI including metrics based on squared generalized cosine similarity (SGCS).
However, in the same field of endeavor, Echigo teaches:
wherein the first information indicates a performance index for measuring a performance of the ML model for predicting CSI including metrics based on squared generalized cosine similarity (SGCS) (Echigo: [0095] The UE may be notified from the network of at least one of the following: which AI model's performance to monitor … [0098] Furthermore, the UE may be notified of CSI-RS resources/CSI-RS resource sets/CSI resource settings/CSI report settings for performance monitoring … [0099] In Embodiment 1.1, … the UE may monitor real-time performance (which may also be called actual performance). Actual performance may also refer to the performance of the CSI calculated based on the output of the AI model, compared to the target CSI … [0100] The performance monitored in Embodiment 1.1 may be at least one of the following: (1) Generalized Cosine Similarity (GCS)/Squared GCS (SGCS) between the CSI calculated based on the output of the AI model and the target CSI of the AI model (e.g., the CSI calculated based on channel measurements) (which may include extended GCS/SGCS for layer > 1) …).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Xue to include the features as taught by Echigo above in order to achieve suitable overhead reduction, channel estimation, and resource utilization, based on performance monitoring/reporting of Artificial Intelligence (AI)-based Channel State Information (CSI) feedback. (Echigo, ¶ [0010-0011]).
Regarding claim 8, Xue teaches:
A user equipment (UE) comprising (Xue: [0084] FIG. 8 shows a call flow diagram of messages that may be exchanged between a UE 802 and a gNB 804 (or other network entity) … Fig. 8):
a transceiver configured to (Xue: [0097] FIG. 10 illustrates a communications device 1000 (e.g., a user equipment) that may include … a processing system 1002 coupled to a transceiver 1008 (e.g., a transmitter and/or a receiver) ... Fig. 10):
receive, from a network, first information related to monitoring a performance of a machine learning (ML) model for predicting CSI (Xue: [0085] As illustrated, to configure UE 802 to predict CSI based on a CSI prediction model, gNB 804 transmits CSI prediction model and qualifying scheme 810 to the UE. The CSI prediction model, as discussed, may be a machine learning model configured to execute on the UE and predict CSI at a future point in time given a current CSI measurement and a time delay as input. The qualifying scheme generally identifies qualifying rules for determining whether a predicted CSI is a qualified prediction or an unqualified prediction, as discussed above … [0069] … ... a qualifying scheme that the UE can use to quantize the accuracy of the CSI predicted using the CSI prediction model [i.e., monitoring performance of the model] … Fig. 8),
receive second information related to transmitting a performance monitoring report (Xue: [0086] … gNB 804 may configure UE 802 with information about measurement occasions in which reference signals are transmitted to UE 802 and reporting occasions in which CSI feedback reports are to be transmitted to gNB 804 ... Fig. 8),
receive third information related to reception of CSI reference signals (CSI-RSs) for determining a ground-truth CSI on a cell (Xue: [0086] After UE 802 is configured with the CSI prediction model and qualifying scheme 810, UE 802 may be ready to perform CSI measurements and predict CSI based on the measured CSI ... As discussed, gNB 804 may configure UE 802 with information about measurement occasions in which reference signals are transmitted to UE 802 and reporting occasions in which CSI feedback reports [i.e., third information] are to be transmitted to gNB 804 … [0087] At 814, the UE measures CSI based on the reference signals [i.e., ground-truth CSI]. The UE also predicts CSI at a future time using the prediction model ... [0090] In some aspects, a gNB may arrange a session to qualify CSI predictions for a UE. During the session, the UE may transmit, to the gNB, a series of predicted CSI and measured CSI values (which may be referred to as “prediction and ground-truth pairs”) … Fig. 8), and
receive the CSI-RSs based on the third information (Xue: [0086] After UE 802 is configured with the CSI prediction model and qualifying scheme 810, UE 802 may be ready to perform CSI measurements and predict CSI based on the measured CSI. To do so, gNB 804 may transmit reference signals 812 to UE 802 ... Fig. 8); and
a processor operably coupled to the transceiver, the processor configured to (Xue: [0097] FIG. 10 illustrates a communications device 1000 (e.g., a user equipment) that may include … a processing system 1002 coupled to a transceiver 1008 (e.g., a transmitter and/or a receiver) ... Fig. 10):
determine the ground-truth CSI based on the reception of the CSI-RSs (Xue: [0088] Subsequently, gNB 804 transmits reference signals 816 to UE 802. At 818, the UE measures CSI based on the reference signals [i.e., ground-truth CSI] ... Fig. 8), and
determine a performance monitoring report for the ML model based on the first information and the ground-truth CSI (Xue: [0088] ... The UE also calculates a difference between the predicted CSI generated at 814 and the measured CSI [i.e., ground-truth] generated by measuring reference signals 816 and quantizes the difference based on the qualifying scheme received from gNB 804 in message 810. As discussed, UE 802 may generate the quantized difference value into one of a plurality of values based on rules included in the qualifying scheme [i.e., part of first information] for classifying the difference value … [0069] … ... a qualifying scheme that the UE can use to quantize the accuracy of the CSI predicted using the CSI prediction model [i.e., monitoring performance of the model] … Fig. 8),
wherein the transceiver is further configured to transmit a channel with the performance monitoring report based on the second information (Xue: [0089] After calculating the difference between the predicted CSI generated at 814 and the measured CSI generated by measuring reference signals 816 and quantizing the difference, UE 802 generates and transmits a message 820 including the measured CSI and the quantized difference 820. gNB 804 can use the measured CSI and the quantized difference 820 to determine whether the CSI prediction model is accurately generating predictions of channel state information ... Fig. 8).
Although Xue teaches, to configure the UE to predict CSI based on a CSI prediction model, the gNB transmits CSI prediction model and qualifying scheme to the UE; the CSI prediction model may be a machine learning model configured to execute on the UE and predict CSI at a future point in time, Xue does not explicitly teach:
wherein the first information indicates a performance index for measuring a performance of the ML model for predicting CSI including metrics based on squared generalized cosine similarity (SGCS).
However, in the same field of endeavor, Echigo teaches:
wherein the first information indicates a performance index for measuring a performance of the ML model for predicting CSI including metrics based on squared generalized cosine similarity (SGCS) (Echigo: [0095] The UE may be notified from the network of at least one of the following: which AI model's performance to monitor … [0098] Furthermore, the UE may be notified of CSI-RS resources/CSI-RS resource sets/CSI resource settings/CSI report settings for performance monitoring … [0099] In Embodiment 1.1, … the UE may monitor real-time performance (which may also be called actual performance). Actual performance may also refer to the performance of the CSI calculated based on the output of the AI model, compared to the target CSI … [0100] The performance monitored in Embodiment 1.1 may be at least one of the following: (1) Generalized Cosine Similarity (GCS)/Squared GCS (SGCS) between the CSI calculated based on the output of the AI model and the target CSI of the AI model (e.g., the CSI calculated based on channel measurements) (which may include extended GCS/SGCS for layer > 1) …).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Xue to include the features as taught by Echigo above in order to achieve suitable overhead reduction, channel estimation, and resource utilization, based on performance monitoring/reporting of Artificial Intelligence (AI)-based Channel State Information (CSI) feedback. (Echigo, ¶ [0010-0011]).
Regarding claim 15, Xue teaches:
A base station comprising (Xue: [0084] FIG. 8 shows a call flow diagram of messages that may be exchanged between a UE 802 and a gNB 804 (or other network entity) … Fig. 8):
a transceiver configured to Xue: [0099] FIG. 11 illustrates a communications device 1100 (e.g., a network entity, such as a gNodeB) that may include … a processing system 1102 coupled to a transceiver 1108 (e.g., a transmitter and/or a receiver) .... Fig. 11):
transmit, to a user equipment (UE), first information related to monitoring a performance of a machine learning (ML) model for predicting CSI (Xue: [0085] As illustrated, to configure UE 802 to predict CSI based on a CSI prediction model, gNB 804 transmits CSI prediction model and qualifying scheme 810 to the UE. The CSI prediction model, as discussed, may be a machine learning model configured to execute on the UE and predict CSI at a future point in time given a current CSI measurement and a time delay as input. The qualifying scheme generally identifies qualifying rules for determining whether a predicted CSI is a qualified prediction or an unqualified prediction, as discussed above … [0069] … ... a qualifying scheme that the UE can use to quantize the accuracy of the CSI predicted using the CSI prediction model [i.e., monitoring performance of the model] … Fig. 8),
transmit second information related to transmitting a performance monitoring report (Xue: [0086] … gNB 804 may configure UE 802 with information about measurement occasions in which reference signals are transmitted to UE 802 and reporting occasions in which CSI feedback reports are to be transmitted to gNB 804 ... Fig. 8),
transmit third information related to transmission of CSI reference signals (CSI-RSs) for determining a ground-truth CSI on a cell (Xue: [0086] After UE 802 is configured with the CSI prediction model and qualifying scheme 810, UE 802 may be ready to perform CSI measurements and predict CSI based on the measured CSI ... As discussed, gNB 804 may configure UE 802 with information about measurement occasions in which reference signals are transmitted to UE 802 and reporting occasions in which CSI feedback reports [i.e., third information] are to be transmitted to gNB 804 … [0087] At 814, the UE measures CSI based on the reference signals [i.e., ground-truth CSI]. The UE also predicts CSI at a future time using the prediction model ... [0090] In some aspects, a gNB may arrange a session to qualify CSI predictions for a UE. During the session, the UE may transmit, to the gNB, a series of predicted CSI and measured CSI values (which may be referred to as “prediction and ground-truth pairs”) … Fig. 8),
transmit the CSI-RSs based on the third information (Xue: [0086] After UE 802 is configured with the CSI prediction model and qualifying scheme 810, UE 802 may be ready to perform CSI measurements and predict CSI based on the measured CSI. To do so, gNB 804 may transmit reference signals 812 to UE 802 ... Fig. 8), and
receive, based on the second information (Xue: [0089] After calculating the difference between the predicted CSI generated at 814 and the measured CSI generated by measuring reference signals 816 and quantizing the difference, UE 802 generates and transmits a message 820 including the measured CSI and the quantized difference 820. gNB 804 can use the measured CSI and the quantized difference 820 to determine whether the CSI prediction model is accurately generating predictions of channel state information ... Fig. 8), a channel with the performance monitoring report for the ML model based on the first information and the ground-truth CSI (Xue: [0088] ... The UE also calculates a difference between the predicted CSI generated at 814 and the measured CSI [i.e., ground-truth] generated by measuring reference signals 816 and quantizes the difference based on the qualifying scheme received from gNB 804 in message 810. As discussed, UE 802 may generate the quantized difference value into one of a plurality of values based on rules included in the qualifying scheme [i.e., part of first information] for classifying the difference value … [0069] … ... a qualifying scheme that the UE can use to quantize the accuracy of the CSI predicted using the CSI prediction model [i.e., monitoring performance of the model] … Fig. 8).
Although Xue teaches, to configure the UE to predict CSI based on a CSI prediction model, the gNB transmits CSI prediction model and qualifying scheme to the UE; the CSI prediction model may be a machine learning model configured to execute on the UE and predict CSI at a future point in time, Xue does not explicitly teach:
wherein the first information indicates a performance index for measuring a performance of the ML model for predicting CSI including metrics based on squared generalized cosine similarity (SGCS).
However, in the same field of endeavor, Echigo teaches:
wherein the first information indicates a performance index for measuring a performance of the ML model for predicting CSI including metrics based on squared generalized cosine similarity (SGCS) (Echigo: [0095] The UE may be notified from the network of at least one of the following: which AI model's performance to monitor … [0098] Furthermore, the UE may be notified of CSI-RS resources/CSI-RS resource sets/CSI resource settings/CSI report settings for performance monitoring … [0099] In Embodiment 1.1, … the UE may monitor real-time performance (which may also be called actual performance). Actual performance may also refer to the performance of the CSI calculated based on the output of the AI model, compared to the target CSI … [0100] The performance monitored in Embodiment 1.1 may be at least one of the following: (1) Generalized Cosine Similarity (GCS)/Squared GCS (SGCS) between the CSI calculated based on the output of the AI model and the target CSI of the AI model (e.g., the CSI calculated based on channel measurements) (which may include extended GCS/SGCS for layer > 1) …).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Xue to include the features as taught by Echigo above in order to achieve suitable overhead reduction, channel estimation, and resource utilization, based on performance monitoring/reporting of Artificial Intelligence (AI)-based Channel State Information (CSI) feedback. (Echigo, ¶ [0010-0011]).
Regarding claims 3, 10, and 17, Xue-Echigo discloses on the features with respect to claims 1, 8, and 15 as outlined above.
Xue further teaches:
wherein the second information indicates at least one of:
an uplink channel for the transmission of the performance monitoring report (Xue: [0086] … gNB 804 may configure UE 802 with information about measurement occasions in which reference signals are transmitted to UE 802 and reporting occasions in which CSI feedback reports are to be transmitted to gNB 804 ... Fig. 8).
Regarding claims 4, 11, and 18, Xue-Echigo-Demir discloses on the features with respect to claims 1, 8, and 15 as outlined above.
Xue further teaches:
wherein the performance monitoring report indicates at least one of:
the ground-truth CSI (Xue: [0089] After calculating the difference between the predicted CSI generated at 814 and the measured CSI generated by measuring reference signals 816 and quantizing the difference, UE 802 generates and transmits a message 820 including the measured CSI [i.e., ground-truth CSI] and the quantized difference 820. gNB 804 can use the measured CSI and the quantized difference 820 to determine whether the CSI prediction model is accurately generating predictions of channel state information ... Fig. 8).
Regarding claims 5, 12, and 19, Xue-Echigo discloses on the features with respect to claims 1, 8, and 15 as outlined above.
Xue further teaches:
wherein the determination of the performance monitoring report is based on one or more predicted CSIs for one or more instances in time (Xue: [0087] At 814, the UE measures CSI based on the reference signals. The UE also predicts CSI at a future time using the prediction model. As discussed, the prediction model uses the measured CSI and a time gap between the measurement based on reference signals 812 and when another transmission is expected to be received from gNB 804 (e.g., in this example, when gNB 804 transmits reference signals 816 to UE 802) … [0088] ... At 818, the UE measures CSI based on the reference signals. The UE also calculates a difference between the predicted CSI generated at 814 and the measured CSI generated by measuring reference signals 816 and quantizes the difference based on the qualifying scheme received from gNB 804 in message 810. As discussed, UE 802 may generate the quantized difference value into one of a plurality of values based on rules included in the qualifying scheme for classifying the difference value … [0089] After calculating the difference between the predicted CSI generated at 814 and the measured CSI generated by measuring reference signals 816 and quantizing the difference, UE 802 generates and transmits a message 820 including the measured CSI and the quantized difference 820 …).
Regarding claims 7 and 14, Xue-Echigo discloses on the features with respect to claims 1 and 8 as outlined above.
Echigo further teaches:
receiving, from the network, an indication to switch the ML model for predicting CSI to a non-ML-based CSI reporting method (Echigo: [0048] … Model switching may also mean deactivating the currently active AI model for a specific function and activating a different AI model … [0085] In the model activation/deactivation steps, the UE may be instructed which scheme (model) will be activated. The UE may activate a … fallback scheme … [0096] In this disclosure, non-AI-based CSI feedback may also be called a fallback scheme and may correspond to a scheme in which the UE provides feedback such as CQI and PMI that are provided in existing standards.);
determining a CSI report using the non-ML-based CSI reporting method (Echigo: [0126] The UE may evaluate … the performance with non-AI-based CSI feedback) and decide at least one of the following: which performance to report …); and
transmitting a channel with the CSI report (Echigo: [0148] [Reporting Timing] The UE may submit performance reports based on information notified by the NW. For example, a UE may send performance reports on uplink resources that are scheduled periodically, semi-persistently, or aperiodically based on RRC/MAC CE/DCI …).
The rationale and motivation for adding this teaching of Echigo is the same as the rationale and motivation for claims 1 and 8.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Xue-Echigo in view of Li et.al. (US Patent Application Publication, 20250227497, hereinafter, “Li”).
Regarding claims 2, 9, and 16, Xue-Echigo discloses on the features with respect to claims 1, 8, and 15 as outlined above.
Xue-Echigo does not explicitly teach:
wherein the first information further indicates at least one of:
a normalized mean squared error (NMSE),
a throughput,
a block error rate (BLER),
an acknowledgement (ACK)/negative acknowledgement (NACK), or
information related to a monitoring periodicity.
However, in the same field of endeavor, Li teaches:
wherein the first information further indicates at least one of:
information related to a monitoring periodicity (Li: [0108] In some embodiments the UE performs the monitoring of the ML model performance periodically, according to an assessment period (or monitoring period or periodicity).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Xue-Echigo to include the features as taught by Li above in order to detect a performance problem in a machine learning model. (Li, ¶ [0028]).
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Xue-Echigo in view of Manolakos et.al. (US Patent Application Publication, 20240022288, hereinafter, “Manolakos”).
Regarding claims 6, 13, and 20, Xue-Echigo discloses on the features with respect to claims 1, 8, and 15 as outlined above.
Xue-Echigo does not explicitly teach:
receiving a physical downlink control channel (PDCCH) providing a downlink control information (DCI) format indicating to transmit the performance monitoring report,
wherein transmitting the channel with the performance monitoring report comprises transmitting the channel with the performance monitoring report based on the reception of the PDCCH providing the DCI format.
However, in the same field of endeavor, Manolakos teaches:
receiving a physical downlink control channel (PDCCH) providing a downlink control information (DCI) format indicating to transmit the performance monitoring report; and
wherein transmitting the channel with the performance monitoring report comprises transmitting the channel with the performance monitoring report based on the reception of the PDCCH providing the DCI format (Manolakos: [0124] ... The latent vector feedback request may be carried in at least one of a downlink control information (DCI) transmission, a MAC CE, or a combination thereof … [0126] In some aspects, the client 505 may determine an occurrence of an update reporting trigger event and may transmit, based at last on determining the occurrence of the update reporting trigger event, at least one of the update corresponding to the at least one observed environmental vector or the update corresponding to the at least one latent vector …).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Xue-Echigo to include the features as taught by Manolakos above in order to mitigate consumption of resources. (Manolakos, ¶ [0025]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIEM H NGUYEN whose telephone number is (408) 918-7636. The examiner can normally be reached on Monday-Friday, 8:30AM-5:00PM PT.
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, Noel Beharry can be reached on (571) 270-5630. 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 http://pair-direct.uspto.gov. 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.
/LIEM H. NGUYEN/Primary Examiner, Art Unit 2416
Prosecution Insights