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 .
Continued Examination Under 37 CFR 1.114
1. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/18/2026 has been entered.
Response to Arguments
2. Applicant’s arguments filed on 03/18/2026 regarding claims 1-20 in the remarks are fully considered but moot in view of new ground(s) of rejection.
Response to Amendments
Claim Rejections - 35 USC § 103
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.
4. Claim(s) 1, 3, 9, 10, 12, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Soldati (US PG Pub. No. 2024/0243984) in view of Kalkunte (US Pub. No. 2023/0039386).
As per claim 1:
Soldati teaches an apparatus for wireless communication at a user equipment (UE) (see paragraph [0024] teaches a system and method for monitoring the performance of an artificial intelligence (AI)/machine learning (ML) model or algorithm. The method performed by a first node in a radio communication network to monitor the performance of an AI/ML model or algorithm are disclosed. Figure 14 shows wireless communication device 1400), comprising:
one or more memories storing processor-executable code (see Figure 14, paragraph [0225], memory 1404 for storing software and executed by processor(s) 1402);
and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code (see figure 14, processor(s) 1402 coupled to memory 1404 for executing software stored in memory 1404) to cause the UE to:
transmit, by the UE (paragraph [0120] disclose said “network device” is not just limited to a RAN node but also a UE. Thus, it is evident that interactions (i.e., transmission and reception) between first network node and second network node could be UE-UE, UE-RAN node or even RAN-RAN interactions. Note: For examinations purposes, examiner will construe said first network node as recited continuously throughput this prior art as said UE and said second network node as a RAN-node (i.e., network side)) to a network entity within a wireless communications network, a request to subscribe to a network measurement configuration associated with one or more measurements (see paragraphs [0122]-[0123], first network node sends at least one first message to a second network node of the radio communication network, the first message comprising a subscription request to obtain from the second node one or more historical data associated with the AI/ML model or an algorithm to be monitored. Said subscription request contains information such as [0130], indications of the time or period of the collection of data, such as data collected for a certain time prior to the present time. Said historical data samples comprising [0132] historical inference input data associated with the AI/ML model to be monitored, such as measurements or estimate of the network state, [0134], historical measurements of information that the AI/ML model to be monitored is configured to estimate or predict), the one or more measurements associated with operation of the wireless communications network (see paragraphs [0132], [0133], [0134], the historical inference input and output data as well as the historical measurements are associated with AI/ML model to be monitored);
receive, by the UE (paragraph [0120] disclose said “network device” is not just limited to a RAN node but also a UE. Thus, it is evident that interactions (i.e., transmission and reception) between first network node and second network node could be UE-UE, UE-RAN node or even RAN-RAN interactions. Note: For examinations purposes, examiner will construe said first network node as recited continuously throughput this prior art as said UE and said second network node as a RAN-node (i.e., network side)) from the network entity and in response to the request to subscribe to the network measurement configuration (see paragraph [0058] [0151], the first network node sends to the second network node a subscription request to obtain historical data required to either train or execute an AI/ML model. [0151] disclose receiving at least one second message from the second network node comprising one or more historical data associated with the AI/ML model or algorithm to be monitored by the first network node. Therefore, the second message is sent in response to receiving the subscription request), information associated with the one or more measurements (paragraph [0151] disclose receiving at least one second message from the second network node comprising one or more historical data associated with the AI/ML model or algorithm to be monitored by the first network node: [0153] an identifier of the subscription request, [0155] historical data stored or collected by the second network node associated with the AI/ML model or algorithm to be monitored);
and perform a training or inference operation using a machine learning model at the UE, the training or inference operation based at least in part on the information associated with the one or more measurements (see paragraphs [0058], [0091] disclose in response to receiving the second message, the historical data samples are used to either train or execute an AI/ML model. Paragraph [0063] also disclose first network node is responsible for monitoring inference function for the AI/ML model).
Soldati does not teach wherein at least a portion of the one or more measurements comprises real time data collected by the network entity in response to the request and indicative of real time network measurements.
Kalkunte teaches wherein at least a portion of the one or more measurements comprises real time data collected by the network entity in response to the request and indicative of real time network measurements (see paragraph [0048], the inference server may be configured receive real-time requests from all the edge devices. The inference server may then process these requests within few milliseconds and return a response that includes the best initial information (such as a best donor beam index, a best service beam index, a specific ARFCN, etc.) and return back to the requesting edge device). Paragraph [0024] also disclose in response to receiving the real-time request(s) from edge device(s), the inference server may also communicate a response indicating of the wireless connectivity within less than a specified threshold to each of the edge device(s)/RSU devices).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the processing of real-time requests by the inference server (as disclosed in Kalkunte) into Soldati as a way of enabling the machine learning model to quickly adapt to the configuration changes of the base station (please see paragraph [0046] of Kalkunte). Therefore, through the training and inference processes, improved signal transmission paths to reach the respective edge devices may be identified (please see paragraph [0035] of Kalkunte).
As per claim 3:
Soldati in view of Kalkunte teaches the apparatus of claim 1, wherein the request to subscribe to the network measurement configuration comprises at least one of an indication of one or more parameters (Soldati, see paragraph [0130], indication(s) of the time or period of the collection of data, such as data collected for a certain time prior to the present time), an indication of a periodic notification process (Soldati, see paragraph [0135], timing related indications, indicating, e.g., a validity time associated with the subscription), an indication of an event-triggered notification process (Soldati, see paragraph [0127], indication indicating the reason or the cause for the request such as reason or cause value “model performance monitoring”), or any combination thereof (Soldati, see paragraph [0123], disclose said subscription request can comprise one or more of the following including paragraphs [0123], [0127], [0130], [0135]).
As per claim 9:
Soldati in view of Kalkunte teaches the apparatus of claim 1, wherein the one or more processors are individually or collectively further operable to execute the code to cause the apparatus to:
transmit, in association with the request to subscribe to the network measurement configuration, at least one of a UE identifier (Soldati, see paragraph [0124], an identifier of the first network node), a list of the one or more measurements (Soldati, see paragraph [0131], an indication of the type of data requested, which may include one or more of historical data samples comprising [0134], historical measurements of information that the AI/ML model or algorithm to be monitored is configured to estimate or predict), a data response configuration (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art), a measurement period (Soldati, see paragraph [0130], indication(s) of the time or period of collection of data such as data collected for a certain time prior to the present time), a measurement interval (Soldati, see paragraph [0130], indication(s) of the time or period of collection of data such as data collected for a certain time prior to the present time), or any combination thereof (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art).
As per claim 10:
Soldati teaches an apparatus for wireless communication at one or more network entities within a wireless communications network (see Figure 11, radio access node 1100), comprising:
one or more memories storing processor-executable code (see Figure 11, paragraph [0219], memory 1106 for storing software);
and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code (see Figure 11, processor(s) 1104 coupled to memory 1106 and for executing software stored in memory 1106) to cause the one or more network entities to:
receive, from a user equipment (UE) (paragraph [0120] disclose said “network device” is not just limited to a RAN node but also a UE. Thus, it is evident that interactions (i.e., transmission and reception) between first network node and second network node could be UE-UE, UE-RAN node or even RAN-RAN interactions. Note: For examinations purposes, examiner will construe said first network node as recited continuously throughput this prior art as said UE and said second network node as a RAN-node (i.e., network side)) within the wireless communications network, a request to subscribe to a network measurement configuration associated with one or more measurements (see paragraphs [0122]-[0123], first network node sends at least one first message to a second network node of the radio communication network, the first message comprising a subscription request to obtain from the second node one or more historical data associated with the AI/ML model or an algorithm to be monitored. Said subscription request contains information such as [0130], indications of the time or period of the collection of data, such as data collected for a certain time prior to the present time. Said historical data samples comprising [0132] historical inference input data associated with the AI/ML model to be monitored, such as measurements or estimate of the network state, [0134], historical measurements of information that the AI/ML model to be monitored is configured to estimate or predict), the one or more measurements associated with operation of the wireless communications network (see paragraphs [0132], [0133], [0134], the historical inference input and output data as well as the historical measurements are associated with AI/ML model to be monitored);
and transmit, to the UE (paragraph [0120] disclose said “network device” is not just limited to a RAN node but also a UE. Thus, it is evident that interactions (i.e., transmission and reception) between first network node and second network node could be UE-UE, UE-RAN node or even RAN-RAN interactions. Note: For examinations purposes, examiner will construe said first network node as recited continuously throughput this prior art as said UE and said second network node as a RAN-node (i.e., network side)) and in response to the request to subscribe to the network measurement configuration (see paragraph [0058], the first network node sends to the second network node a subscription request to obtain historical data required to either train or execute an AI/ML model. Therefore, the second message is send in response to receiving the subscription request), information associated with the one or more measurements (paragraph [0151] disclose receiving at least one second message from the second network node comprising one or more historical data associated with the AI/ML model or algorithm to be monitored by the first network node: [0153] an identifier of the subscription request, [0155] historical data stored or collected by the second network node associated with the AI/ML model or algorithm to be monitored), wherein the information is for a training or inference operation using a machine learning model at the UE (see paragraphs [0058], [0091] disclose in response to receiving the second message, the historical data samples are used to either train or execute an AI/ML model. Paragraph [0063] also disclose first network node is responsible for monitoring inference function for the AI/ML model).
Soldati does not teach wherein at least a portion of the one or more measurements comprises real time data collected by the network entity in response to the request and indicative of real time network measurements.
Kalkunte teaches wherein at least a portion of the one or more measurements comprises real time data collected by the network entity in response to the request and indicative of real time network measurements (see paragraph [0048], the inference server may be configured receive real-time requests from all the edge devices. The inference server may then process these requests within few milliseconds and return a response that includes the best initial information (such as a best donor beam index, a best service beam index, a specific ARFCN, etc.) and return back to the requesting edge device). Paragraph [0024] also disclose in response to receiving the real-time request(s) from edge device(s), the inference server may also communicate a response indicating of the wireless connectivity within less than a specified threshold to each of the edge device(s)/RSU devices).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the processing of real-time requests by the inference server (as disclosed in Kalkunte) into Soldati as a way of enabling the machine learning model to quickly adapt to the configuration changes of the base station (please see paragraph [0046] of Kalkunte). Therefore, through the training and inference processes, improved signal transmission paths to reach the respective edge devices may be identified (please see paragraph [0035] of Kalkunte).
Claim 12 is rejected in the same scope as claim 3.
Claim 18 is rejected in the same scope as claim 9.
As per claim 19:
Soldati teaches a method for wireless communication at a user equipment (UE) (see abstract), comprising:
transmitting, by the UE (paragraph [0120] disclose said “network device” is not just limited to a RAN node but also a UE. Thus, it is evident that interactions (i.e., transmission and reception) between first network node and second network node could be UE-UE, UE-RAN node or even RAN-RAN interactions. Note: For examinations purposes, examiner will construe said first network node as recited continuously throughput this prior art as said UE and said second network node as a RAN-node (i.e., network side)) to a network entity within a wireless communications network, a request to subscribe to a network measurement configuration associated with one or more measurements (see paragraphs [0122]-[0123], first network node sends at least one first message to a second network node of the radio communication network, the first message comprising a subscription request to obtain from the second node one or more historical data associated with the AI/ML model or an algorithm to be monitored. Said subscription request contains information such as [0130], indications of the time or period of the collection of data, such as data collected for a certain time prior to the present time. Said historical data samples comprising [0132] historical inference input data associated with the AI/ML model to be monitored, such as measurements or estimate of the network state, [0134], historical measurements of information that the AI/ML model to be monitored is configured to estimate or predict), the one or more measurements associated with operation of the wireless communications network (see paragraphs [0132], [0133], [0134], the historical inference input and output data as well as the historical measurements are associated with AI/ML model to be monitored);
receiving, by the UE (paragraph [0120] disclose said “network device” is not just limited to a RAN node but also a UE. Thus, it is evident that interactions (i.e., transmission and reception) between first network node and second network node could be UE-UE, UE-RAN node or even RAN-RAN interactions. Note: For examinations purposes, examiner will construe said first network node as recited continuously throughput this prior art as said UE and said second network node as a RAN-node (i.e., network side)) from the network entity and in response to the request to subscribe to the network measurement configuration (see paragraph [0058], the first network node sends to the second network node a subscription request to obtain historical data required to either train or execute an AI/ML model. Therefore, the second message is send in response to receiving the subscription request), information associated with the one or more measurements (paragraph [0151] disclose receiving at least one second message from the second network node comprising one or more historical data associated with the AI/ML model or algorithm to be monitored by the first network node: [0153] an identifier of the subscription request, [0155] historical data stored or collected by the second network node associated with the AI/ML model or algorithm to be monitored);
and performing a training or inference operation using a machine learning model at the UE, the training or inference operation based at least in part on the information associated with the one or more measurements (see paragraphs [0058], [0091] disclose in response to receiving the second message, the historical data samples are used to either train or execute an AI/ML model. Paragraph [0063] also disclose first network node is responsible for monitoring inference function for the AI/ML model).
Soldati does not teach wherein at least a portion of the one or more measurements comprises real time data collected by the network entity in response to the request and indicative of real time network measurements.
Kalkunte teaches wherein at least a portion of the one or more measurements comprises real time data collected by the network entity in response to the request and indicative of real time network measurements (see paragraph [0048], the inference server may be configured receive real-time requests from all the edge devices. The inference server may then process these requests within few milliseconds and return a response that includes the best initial information (such as a best donor beam index, a best service beam index, a specific ARFCN, etc.) and return back to the requesting edge device). Paragraph [0024] also disclose in response to receiving the real-time request(s) from edge device(s), the inference server may also communicate a response indicating of the wireless connectivity within less than a specified threshold to each of the edge device(s)/RSU devices).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the processing of real-time requests by the inference server (as disclosed in Kalkunte) into Soldati as a way of enabling the machine learning model to quickly adapt to the configuration changes of the base station (please see paragraph [0046] of Kalkunte). Therefore, through the training and inference processes, improved signal transmission paths to reach the respective edge devices may be identified (please see paragraph [0035] of Kalkunte).
5. Claim(s) 2, 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Soldati in view of Kalkunte and further in view of Fujishiro (US PG Pub. No. 2021/0076311).
As per claim 2:
Soldati in view of Kalkunte teaches the apparatus of claim 1 with the exception of:
wherein the one or more processors are individually or collectively further operable to execute the code to cause the apparatus to:
transmit, to the network entity, a request to unsubscribe from at least a subset of the one or more measurements associated with the network measurement configuration.
Fujishiro teaches wherein the one or more processors are individually or collectively further operable to execute the code to cause the apparatus to:
transmit, to the network entity, a request to unsubscribe from at least a subset of the one or more measurements associated with the network measurement configuration (see paragraph [0071], discloses the UE notifies the eNB of the request to unregister an RN for measurement reporting including an identifier of the RN).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the transmission of request to unregister a RN for report measurement (as disclosed in Fujishiro’311) into both Soldati and Kalkunte as a way of enabling the UE to take periodic measurements according to the measurement condition (please see paragraph [0071] of Fujishiro’311).
Claims 11 and 20 are rejected in the same scope as claim 2.
6. Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Soldati in view of Kalkunte and further in view of Ly (US PG Pub. No. 2022/0240213).
As per claim 4:
Soldati in view of Kalkunte teaches the apparatus of claim 1 with the exception of:
wherein, to transmit the request to subscribe to the network measurement configuration, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to:
transmit the request to subscribe to the network measurement configuration using a user plane connection via an Internet Protocol (IP) address of the network entity.
Ly teaches wherein, to transmit the request to subscribe to the network measurement configuration, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to:
transmit the request to subscribe to the network measurement configuration using a user plane connection via an Internet Protocol (IP) address of the network entity (see paragraph [0364], step s3908, the UE sends an indirect registration request to the N3IWF of PLMN2 over the user plane of PLMN1 by using the public IP address of the external network).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the teachings of Ly into both Soldati and Kalkunte. The motivation for doing so would be to support multi-SIM operations (please see paragraph [0005] of Ly).
Claim 13 is rejected in the same scope as claim 4.
7. Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Soldati in view of Kalkunte and further in view of Cao (US PG Pub. No. 2021/0084007).
As per claim 5:
Soldati in view of Kalkunte teaches the apparatus of claim 1 with the exception of:
wherein, to transmit the request to subscribe to the network measurement configuration, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to:
transmit the request to subscribe to the network measurement configuration using a control plane connection via a coordination entity associated with the network entity.
Cao teaches wherein, to transmit the request to subscribe to the network measurement configuration, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to:
transmit the request to subscribe to the network measurement configuration using a control plane connection via a coordination entity associated with the network entity (see paragraphs [0094]-[0096], the control plane network device receives a session establishment request and based service information requested by the terminal device whether the first user plane network device (construed as said coordination entity) is capable of allocating an IP address for the terminal).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the teachings of Cao into both Soldati and Kalkunte. The motivation for doing so would be to establish a session connection for the requested terminal by allocating an IP address (please see paragraph [0006] of Cao).
Claim 14 is rejected in the same scope as claim 5.
8. Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Soldati in view of Kalkunte and further in view of Rangasamy (US Patent No. 11,228,573).
As per claim 6:
Soldati in view of Kalkunte teaches the apparatus of claim 1 with the exception of:
wherein, to transmit the request to subscribe to the network measurement configuration, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to:
transmit the request to subscribe to the network measurement configuration using a service-based connection that is associated with an application programming interface associated with the network entity.
Rangasamy teaches wherein, to transmit the request to subscribe to the network measurement configuration, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to:
transmit the request to subscribe to the network measurement configuration using a service-based connection that is associated with an application programming interface associated with the network entity (see Col 1, lines 42-57, a customer of the API exchange may request access to a particular group of API bundle. The API exchange may responsively send, via a private connection to the service provider network that provides service corresponding to the requested API).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the exchange of API information requesting access to a particular API bundle (as disclosed in Rangasamy) into Soldati and Kalkunte as a way of allowing multiple applications to consume API feed data without requiring any dedicated, direct network-layer connectivity between each of the networks that expose the APIs with one another (please see Col 2, lines 16-20 of Rangasamy).
Claim 15 is rejected in the same scope as claim 6.
9. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Soldati in view of Kalkunte and further in view of Vasseur (US PG Pub. No. 2015/0319077).
As per claim 7:
Soldati in view of Kalkunte teaches the apparatus of claim 1 with the exception of:
wherein the one or more processors are individually or collectively further operable to execute the code to cause the apparatus to:
predict a throughput value, a network load value, a downlink queue length, a downlink delay value, or any combination thereof based at least in part on performing the training or inference operation.
Vasseur teaches wherein the one or more processors are individually or collectively further operable to execute the code to cause the apparatus to:
predict a throughput value (Note: Limitation(s) is recited in alternate form and thus not addressed by the prior art), a network load value (Note: Limitation(s) is recited in alternate form and thus not addressed by the prior art), a downlink queue length(Note: Limitation(s) is recited in alternate form and thus not addressed by the prior art), a downlink delay value (see paragraph [0046], discloses the device may begin a training period in which it collects delay information for its machine learning process. The received delay information that results from the delay probes may then be used to generate the delay predictions via machine learning process), or any combination thereof based at least in part on performing the training or inference operation (see paragraph [0046], discloses the device may begin a training period in which it collects delay information for its machine learning process. The received delay information that results from the delay probes may then be used to generate the delay predictions via machine learning process).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the collecting of delay information for machine learning process (as disclosed in Vasseur) into both Soldati and Kalkunte as a way of predicting delay measurements along the segment using the received delay information (please see paragraphs [0029]-[0030] of Vasseur). Therefore, by predicting delay measurements along a path, results in the increase in predictability of packet deliveries along the path (please see paragraph [0033] of Vasseur).
10. Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Soldati in view of Kalkunte and further in view of Wang (US PG Pub. No. 2022/0353803).
As per claim 8:
Sodalti in view of Kalkunte teaches the apparatus of claim 1 with the exception of:
wherein the network entity comprises at least one of an analytics and data repository function, a centralized unit data repository function, a centralized unit of a base station, a distributed unit of a base station, an access and mobility management function, a session management function, a network data analytics function, or any combination thereof.
Wang teaches wherein the network entity comprises at least one of an analytics and data repository function (Note: Limitation(s) is recited in alternate form and thus not addressed by the prior art(s)), a centralized unit data repository function (Note: Limitation(s) is recited in alternate form and thus not addressed by the prior art(s)), a centralized unit of a base station (Note: Limitation(s) is recited in alternate form and thus not addressed by the prior art(s)), a distributed unit of a base station (Note: Limitation(s) is recited in alternate form and thus not addressed by the prior art(s)), an access and mobility management function (see paragraph [0044], 5GC 150 includes an Access and Mobility Management Function) AMF 152), a session management function (Note: Limitation(s) is recited in alternate form and thus not addressed by the prior art(s)), a network data analytics function (Note: Limitation(s) is recited in alternate form and thus not addressed by the prior art(s)), or any combination thereof (Note: Limitation(s) is recited in alternate form and thus not addressed by the prior art(s)).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the transmission and reception of the requested machine-learning architecture (as disclosed in Wang) into both Soldati and Kalkunte as a way of satisfying the QoS associated with the network slice (please see paragraph [0066] of Wang). Therefore, by granting or denying access to the selected machine-learning architecture (as disclosed in Wang) enables the network to satisfy a diverse set of QoS levels (please see paragraphs [0001] and [0038] of Wang).
Claim 17 is rejected in the same scope as claim 8.
11. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Soldati in view of Kalkunte and further in view of Rosa (US PG Pub. No. 2016/0037402).
As per claim 16:
Soldati in view of Kalkunte teaches the apparatus of claim 10 with the exception of:
wherein the one or more processors are individually or collectively further operable to execute the code to cause the one or more network entities to:
transmit, to the UE, an indication to perform a handover operation from a first base station to a second base station;
and transmit, to the second base station and in response to the handover operation, an indication of the request to subscribe to the network measurement configuration associated with the one or more measurements associated with the operation of the wireless communications network.
Rosa teaches wherein the one or more processors are individually or collectively further operable to execute the code to cause the one or more network entities to:
transmit, to the UE, an indication to perform a handover operation from a first base station to a second base station (see paragraph [0092], discloses the source eNB conducts in connection with the processing in s20 and s30 for deciding on a handover and for assigning a set of communication resources to a UE deciding to send measurement report (i.e., requesting the handover) provided with one of the sets of communications resources for fast handover);
and transmit, to the second base station and in response to the handover operation, an indication of the request to subscribe to the network measurement configuration associated with the one or more measurements associated with the operation of the wireless communications network (see paragraphs [0093]-[0094], in response to receiving information indicative of allocated resources for accessing the target eNB, the UE attempts to access the target eNB by first sending an access request. In response to receiving the access request, the eNB sends radio channel parameters (construed as said one or more measurements) such as time advance value, transmit power level, etc. back to the UE).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the allocation of resource sets (as disclosed in Rosa) into both Soldati and Kalkunte as a way of achieving a fast handover procedure (please see paragraph [0030] of Rosa).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PRINCE AKWASI MENSAH whose telephone number is (571)270-7183. The examiner can normally be reached Mon-Fri 8:00am-4:00pm.
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, MICHAEL THIER can be reached at 571-272-2832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
PRINCE AKWASI. MENSAH
Examiner
Art Unit 2474
/PRINCE A MENSAH/Examiner, Art Unit 2474
/BENJAMIN H ELLIOTT IV/Primary Examiner, Art Unit 2474
Prosecution Insights