DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 01/22/2026 have been fully considered but they are not persuasive.
Regarding claim 1, Applicant alleges that the amended limitation “wherein: the inference device is a source access network device in the wireless communication network, and the measurement device is a target access network device in the wireless communication network, wherein the source access network device and the target access network device are devices in an access network (AN) configured to implement an access-related function and comprises a radio access network (RAN), or the inference device is an access network device in the wireless communication network, and the measurement device is a terminal device in the wireless communication network, wherein the access network device comprises a central unit (CU) and a distributed unit (DU), and the terminal device is a terminal accessing the wireless communication network and has wireless sending and receiving functions” is not taught by Faulhaber. After further reviewing Faulhaber, Examiner respectfully disagrees. Faulhaber discloses (¶0127) that the provider network system and the devices communicate over wireless network such as GSM/CDMA (radio access network), where (¶0036) the router, the analytics engine, and ground truth collector/judging system interact (access/provide content) with one another over the network; (claim 16) an analytics engine implemented by a one or more electronic devices (source device), and (¶0034, ¶0039) ground truth collector/judging system is associated with knowledge database/crowd-sourcing (target device) where the database is inherently associated with a physical device. See the updated rejection below.
Regarding newly added claims and all the amended claims, see the new rejection below.
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.
Claims 1-3, 6, 10-15, and 17-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US PG Pub 2019/0156247 to Faulhaber (“Faulhaber”).
Regarding claim 1, “A method for using machine learning technologies in a wireless communication network, comprising: determining, by an inference device, an inference output of a machine learning model based on an inference input of the machine learning model, wherein the machine learning model is determined based on first configuration information” reads on the analytics engine model that receives a plurality of inference results generated by a ML model where the results are provided from a dynamic router (¶0070) disclosed by Faulhaber and represented in Fig.1. Faulhaber further discloses (¶0033-¶0034) that the router provides inference results generated by the ML model to the analytics engine; (¶0092-¶0093) configuration information is provided to each ML model; (¶0031) analytics engine and the router are implemented using hardware/software; (¶0127) network includes any wireless network such as GSM/CDMA.
As to “sending, by the inference device, a first message to a measurement device, wherein the first message comprises a measurement object, wherein the measurement object is determined based on the inference output, and the first message is used to request a measurement result of the measurement object; receiving, by the inference device, the measurement result from the measurement device, wherein the measurement result corresponds to the inference output” Faulhaber discloses (¶0030, ¶0033) that the ML model identifies object depicted in the image such as the model predicts that dog exists in the image; (¶0036, ¶0071) the analytics model interacts with a ground truth collector by sending a set of requests to obtain ground truth data (measurement result) and compares this ground truth data with the inference results generated by the ML model to identify the true accuracy of the model.
As to “sending, by the inference device, an evaluation result to an update device, wherein the evaluation result is determined based on the inference output and the measurement result” Faulhaber discloses (¶0036-¶0037, ¶0039) that the analytics model, which compared the ground truth data with the inference results to identify differences between the results, and send the results (and/or differences, or unique different results) to logging system/client as represented in Figs. 1-2.
As to “wherein: the inference device is a source access network device in the wireless communication network, and the measurement device is a target access network device in the wireless communication network, wherein the source access network device and the target access network device are devices in an access network (AN) configured to implement an access-related function and comprises a radio access network (RAN), or the inference device is an access network device in the wireless communication network, and the measurement device is a terminal device in the wireless communication network, wherein the access network device comprises a central unit (CU) and a distributed unit (DU), and the terminal device is a terminal accessing the wireless communication network and has wireless sending and receiving functions” Faulhaber discloses (¶0127) that the provider network system and the devices communicate over wireless network such as GSM/CDMA (radio access network), where (¶0036) the router, the analytics engine, and ground truth collector/judging system interact (access/provide content) with one another over the network; (claim 16) an analytics engine implemented by a one or more electronic devices (source device), and (¶0034, ¶0039) ground truth collector/judging system is associated with knowledge database/crowd-sourcing (target device) where the database is inherently associated with a physical device; (¶0033-¶0037) the analytics engine (inference/source access device) obtains inference results from the router and requests ground truth data from the truth collector/judging system (measurement/target access device) to compare the ground truth data with the inference results to identify differences between the results, and send the results to a logging system/client (update/network device); (¶0128-¶0144) the devices are implemented in related network architecture as represented in Figs. 9-11.
Regarding claim 2, “The method according to claim 1, wherein the evaluation result comprises accuracy of the machine learning model” Faulhaber discloses (¶0036-¶0039) that the analytics model compares the obtained ground truth with inference results to identify the true accuracy of the model and further sends updated message/analytics results to a judging system.
Regarding claim 3, “The method according to claim 1, wherein the evaluation result is carried in a third message, and the third message further comprises the inference input and the measurement result” Faulhaber discloses (¶0036-¶0039) that the analytics model compares the obtained ground truth with inference results to identify the true accuracy of the model and further sends updated message/analytics results to a judging system; the analytics engine identifies differences between the results, and sends the results (and/or differences, or unique different results) through a judging system, which can be an automated system.
Regarding claim 6, “A method, comprising: receiving, by a measurement device, a first message from an inference device, wherein the first message comprises a measurement object; and sending, by the measurement device, a measurement result to the inference device, wherein the measurement result is determined based on the measurement object” reads on the analytics engine model that receives a plurality of inference results generated by a ML model where the results are provided from a dynamic router (¶0070) disclosed by Faulhaber and represented in Fig.1. Faulhaber further discloses (¶0033-¶0034) that the router provides inference results generated by the ML model to the analytics engine; (¶0092-¶0093) configuration information is provided to each ML model; (¶0031) analytics engine and the router are implemented using hardware/software; (¶0127) network includes any wireless network such as GSM/CDMA. Faulhaber further discloses (¶0030, ¶0033) that the ML model identifies object depicted in the image such as the model predicts that dog exists in the image; (¶0036, ¶0071) the analytics model interacts with a ground truth collector by sending a set of requests to obtain ground truth data (measurement result) and compares this ground truth data with the inference results generated by the ML model to identify the true accuracy of the model; (¶0036-¶0037, ¶0039) the analytics model, which compared the ground truth data with the inference results to identify differences between the results, and send the results (and/or differences, or unique different results) to logging system/client as represented in Figs. 1-2.
As to “wherein: the inference device is a source access network device in the wireless communication network, and the measurement device is a target access network device in the wireless communication network, wherein the source access network device and the target access network device are devices in an access network (AN) configured to implement an access-related function and comprises a radio access network (RAN), or the inference device is an access network device in the wireless communication network, and the measurement device is a terminal device in the wireless communication network, wherein the access network device comprises a central unit (CU) and a distributed unit (DU), and the terminal device is a terminal accessing the wireless communication network and has wireless sending and receiving functions” Faulhaber discloses (¶0127) that the provider network system and the devices communicate over wireless network such as GSM/CDMA (radio access network), where (¶0036) the router, the analytics engine, and ground truth collector/judging system interact (access/provide content) with one another over the network; (claim 16) an analytics engine implemented by a one or more electronic devices (source device), and (¶0034, ¶0039) ground truth collector/judging system is associated with knowledge database/crowd-sourcing (target device) where the database is inherently associated with a physical device; (¶0033-¶0037) the analytics engine (inference/source access device) obtains inference results from the router and requests ground truth data from the truth collector/judging system (measurement/target access device) to compare the ground truth data with the inference results to identify differences between the results, and send the results to a logging system/client (update/network device); (¶0128-¶0144) the devices are implemented in related network architecture as represented in Figs. 9-11.
Regarding claim 10, see rejection similar to claim 1. Furthermore, Faulhaber discloses (¶0139-¶0151) that the processor of the device executes the instruction stored on the memory.
Regarding claim 11, see rejection similar to claim 2.
Regarding claim 12, see rejection similar to claim 3.
Regarding claim 13, “The system according to claim 10, wherein the inference device is the source access network device, the measurement device is the target access network device, and the update device is a network device, and wherein the first programming instructions are for execution by the at least one first processor to cause the inference device to: send the first message to the target access network device; receive the measurement result from the target access network device; and send the evaluation result to the network device” Faulhaber discloses (¶0033-¶0037) the analytics engine (inference/source access device) obtains inference results from the router and requests ground truth data from the truth collector/judging system (measurement/target access device) to compare the ground truth data with the inference results to identify differences between the results, and send the results to a logging system/client (update/network device); (¶0128-¶0144) the devices are implemented in related network architecture as represented in Figs. 9-11.
Regarding claim 14, “The system according to claim 10, wherein the inference device is the access network device, the measurement device is the terminal device, and the update device is a network device, and wherein the first programming instructions are for execution by the at least one first processor to cause the inference device to: send the first message to the terminal device; receive the measurement result from the terminal device; and send the evaluation result to the network device” Faulhaber discloses (¶0033-¶0037) the analytics engine (inference/source access device) obtains inference results from the router and requests ground truth data from the truth collector/judging system (measurement/target access device) to compare the ground truth data with the inference results to identify differences between the results, and send the results to a logging system/client (update/network device); (¶0128-¶0144) the devices are implemented in related network architecture as represented in Figs. 9-11.
Regarding claim 15, see rejection similar to claim 6.
Regarding claim 17, “The system according to claim 15, wherein the measurement device is the target access network device, and the inference device is the source access network device, and wherein the second programming instructions are for execution by the at least one second processor to cause the measurement device to: receive the first message from the source access network device; and send the measurement result to the source access network device” Faulhaber discloses (¶0033-¶0037) the analytics engine (inference/source access device) obtains inference results from the router and requests ground truth data from the truth collector/judging system (measurement/target access device) to compare the ground truth data with the inference results to identify differences between the results, and send the results to a logging system/client (update/network device); (¶0128-¶0144) the devices are implemented in related network architecture as represented in Figs. 9-11.
Regarding claim 18, “The system according to claim 15, wherein the measurement device is the terminal device, and the inference device is the access network device, and wherein the second programming instructions are for execution by the at least one second processor to cause the measurement device to: receive the first message from the access network device; and send the measurement result to the access network device” Faulhaber discloses (¶0033-¶0037) the analytics engine (inference/source access device) obtains inference results from the router and requests ground truth data from the truth collector/judging system (measurement/target access device) to compare the ground truth data with the inference results to identify differences between the results, and send the results to a logging system/client (update/network device); (¶0128-¶0144) the devices are implemented in related network architecture as represented in Figs. 9-11.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4-5, 8, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Faulhaber in view of NPL Article O-RAN Working Group 2 AI/ML workflow description and requirements, cited in IDS filed on 10/30/2023 (“O-Ran”).
Regarding claim 4, Faulhaber meets all the limitation of the claim except “The method according to claim 1, wherein: the inference input comprises load information of a neighboring cell of a terminal device, load information of the terminal device, and neighboring cell coverage information measured by the terminal device, and the inference output comprises predicted load information of a target cell to which the terminal device is successfully handed over, or the inference input comprises coverage configuration information and cell information of a cell, and coverage information of the terminal device accessing the cell, and the inference output comprises a cell coverage configuration and at least one of a predicted value of an uplink/downlink packet loss rate or a predicted value of a packet delay of the terminal device in the cell; or the inference input comprises service traffic of the terminal device, and the inference output comprises a discontinuous reception (DRX) configuration and a predicted value of a packet delay of the terminal device in the DRX configuration.” However, O-Ran discloses (section 4.2, bullet points top of pg.21; section 7.1) that the system predicts when a UE’s service cell QoE will deteriorate, and also predicts when neighbor’s cell’s QoE reaches an acceptable level to determine when to trigger a handover by doing following: A. RF signal strength predictor - Predicts RF signal KPIs a UE would experience with a neighbor cell at the current time "x", as well as predict the signal strength that same UE would experience with both its current serving cell and its neighbor cells at time "x+A". B: Cell utilization predictor - Predicts the cell utilization KPIs for both the serving and neighbor cells above at time "x-A". C: QoE predictor - Predicts the QoE KPIs that a UE would experience at time "x+A" for both its current serving and neighbor cells. Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the invention to modify Faulhaber’s system by using load and coverage information of current/neighboring cells to predict load information of target cell as taught by O-Ran in order to use signal strength and cell utilization prediction to predict device’s future QoE and to allow each prediction models to evolve separately (O-Ran – pg.21, first paragraph after bullet points).
Regarding claim 5, Faulhaber meets all the limitation of the claim except “The method according to claim 1, wherein: the inference output comprises predicted load information of a neighboring cell, and the measurement object comprises load information of the neighboring cell after a terminal device is handed over to the neighboring cell, or the inference output comprises at least one of a predicted value of an uplink/downlink packet loss rate or a predicted value of a packet delay of a terminal device in a cell, and the measurement object comprises at least one of the uplink/downlink packet loss rate or the packet delay of the terminal device in the cell after a cell coverage configuration is used, or the inference output comprises a DRX configuration and a predicted value of a packet delay of a terminal device in the DRX configuration, and the measurement object comprises a packet delay of the terminal device after the DRX configuration is used.” However, O-Ran discloses (section 4.2, bullet points top of pg.21; section 7.1) that the system predicts when a UE’s service cell QoE will deteriorate, and also predicts when neighbor’s cell’s QoE reaches an acceptable level to determine when to trigger a handover by doing following: A. RF signal strength predictor - Predicts RF signal KPIs a UE would experience with a neighbor cell at the current time "x", as well as predict the signal strength that same UE would experience with both its current serving cell and its neighbor cells at time "x+A". B: Cell utilization predictor - Predicts the cell utilization KPIs for both the serving and neighbor cells above at time "x-A". C: QoE predictor - Predicts the QoE KPIs that a UE would experience at time "x+A" for both its current serving and neighbor cells. Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the invention to modify Faulhaber’s system by using load and coverage information of current/neighboring cells to predict load information of target cell as taught by O-Ran in order to use signal strength and cell utilization prediction to predict device’s future QoE and to allow each prediction models to evolve separately (O-Ran – pg.21, first paragraph after bullet points).
Regarding claim 8, see rejection similar to claim 4.
Regarding claim 19, see rejection similar to claim 4.
Regarding claim 20, see rejection similar to claim 5.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Faulhaber in view of US PG Pub 2021/0304070 to Takashige (“Takashige”).
Regarding claim 7, Faulhaber meets all the limitations of the claim except “The method according to claim 6, wherein the measurement result is carried in a second message, the first message further comprises a transaction identifier, the second message further comprises the transaction identifier, and the transaction identifier corresponds to the measurement result.” However, Takashige discloses (¶0046-¶0047) that the inference log includes a transaction ID along with input value and output value; the transaction ID is an identifier that uniquely specifies a log line and the model ID is an identifier for uniquely specifying a model. This can be used to check whether distributions of data at the time of learning and data at the time of inference are consistent with each other by comparing an identifier at the time of learning with an identifier at the time of inference. Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the invention to modify Faulhaber’s system by using a transaction identifier in the message as taught by Takashige in order to check whether distributions of data at the time of learning and data at the time of inference are consistent with each other by comparing an identifier at the time of learning with an identifier at the time of inference (Takashige - ¶0047).
Regarding claim 16, see rejection similar to claim 7.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Faulhaber in view of US PG Pub 2023/0012910 to Kimba Dit Adamou (“Kimba”).
Regarding claim 9, Faulhaber meets all the limitations of the claim except “The method according to claim 6, wherein: the measurement object comprises at least one of an uplink/downlink packet loss rate or a packet delay of the terminal device in a cell after a cell coverage configuration is used, or the measurement object comprises a packet delay of the terminal device after a DRX configuration is used.” However, Kimba discloses (¶0046, claim 16) that the measurement object of a packet includes packet delay or a packet loss rate; (¶0049) when the measurement object is the Layer 2 packet loss rate, measurement information required for measuring the Layer 2 packet loss rate may include at least one of the following: the number of lost data packets within a specified time, the total number of data packets within the specified time, or the like; (¶0053-¶0057) the packet loss rate includes downlink packet loss rate and uplink packet loss rate. Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the invention to modify Faulhaber’s system by using uplink/downlink packet loss rate for the measurement object as taught by Kimba in order to detect network performance (Kimba - ¶0005).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PINKAL R CHOKSHI/Primary Examiner, Art Unit 2425