DETAILED ACTION
This action is responsive to amended claims filed on 22 January 2026.
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 15 January 2026, with respect to the rejection(s) of claims 1-15 and 21-25 under 35 USC 103 (using the Froehlich and Gheorghiu references) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Aldana et al. (US 20200120458 A1).
Applicant argues that Froehlich and Gheorghiu, alone or in combination, fail to teach limitation (c), specifically “selecting, based on the particular service type being requested at least one of a knowledge base or a machine learning (ML) model, one of the frequency bands included in the set of two or more frequency bands.” Applicant further amended the claims to recite “a priority level of the particular service type.” The amendment has been considered but is not persuasive. The rejection of claims 1 and 21, and their dependent claims is therefore maintained.
In view of the amendment reciting “a priority level of the particular service type,” the rejection has been modified to additionally rely on Al, which explains that communication characteristics of a data connection may be determined based on requested service type and an associated quality of service (QoS) class (Al, see fig. 79, [0326]-[0341]). [0343]-[0370], [0496]-[0500], [0705]-[0721]). Al further discloses that the QoS class may indicate communication requirements associated with the service type, including a priority level used to control packet forwarding treatment and resource allocation (Al, see fig. 79, [0326]-[0341], [0343]-[0370]. For example, Al describes that QoS parameters such as priority level, packet delay budget, and packet error rate may be used to determine communication resources utilized for a particular connection (Al, [0343]-[0370], [0496]-[0500], [0705]-[0721]). Thus, Al teachers determining communication characteristics associated with a requested service type and identifying a priority level associated with that service.
Al further explains that communication resources, including carriers or radio communication technologies, may be selected based on those determined characteristics (Al, [0713]). Because the QoS class denies the priority level and associated communication requirements of the request service type, selection of communication resources based on those characteristics corresponds to selecting a frequency band based on a priority level of the particular service type as recited.
When applied to the system of Froehlich and Gheorghiu, the priority-based resource selection described by Al would have resulted in selecting one of the available frequency bands based on the requested service type and its associated priority level. Accordingly, the amendment does not overcome the rejection of claims 1 and 21.
Applicant states Froehlich and Gheorghiu fails to disclose claims 1-15 and 21-25. The examiner respectfully disagrees with applicant. As mentioned above for claim 1 and 21, while not identical to claims 1 and 21, the limitations of the dependent claims correspond to the functional steps of claim 1 and apparatus of claim 21.
Thus, the examiner maintains 35 U.S.C. 103 rejection of claims 1-15 and 21-25 based on Froehlich et al. (US 20200022006 A1) (hereinafter Froehlich) in view of Gheorghiu et al. (US 20200322954 A1) (hereinafter Gheorghiu) and further in view of Aldana et al. (US 20200120458 A1) (hereinafter Al).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-15 and 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Froehlich et al. (US 20200022006 A1) (hereinafter Froehlich) in view of Gheorghiu et al. (US 20200322954 A1) (hereinafter Gheorghiu) and further in view of Aldana et al. (US 20200120458 A1) (hereinafter Al).
Regarding Claim 1 and 21, Froehlich-Gheorghiu-Al teaches a method (Froehlich, fig. 2) / an apparatus (Froehlich, fig. 2):
for selecting a frequency band for a device, the method comprising (Froehlich, Fig. 1, [0018]- [0032]: [ 0021] The wireless communications system may further include a Wi-Fi access point (AP) 114 in communication with Wi-Fi stations (STAs) 116 via communication links 118 in a 5 GHz unlicensed frequency spectrum. When communicating in an unlicensed frequency spectrum, the STAs 116/AP 114 may perform a clear channel assessment (CCA) prior to communicating in order to determine whether the channel is available.):
the apparatus comprising a memory and processing circuitry coupled to the memory, the apparatus being configured to (Froehlich, Fig. 4, [0065]- [0069]: [0066] The servers 150 and 160 are shown in FIG. 4 in the form of a general-purpose computing device. The components of the servers 150 and 160 may include, but are not limited to, one or more processors or processing units 416, a system memory 428, and a bus 418 that couples various system components including the system memory 428 to the processor 416.):
(a) receiving(Froehlich, Fig. 2: Step 202-204, [0034]- [0035]: [0034] At step 202, the RAN analysis engine 166 receives RAN area of interest information and desired outcome information from the user 168 via GUI 162. In other words, at step 202, the user 168 indicates a geographical area of interest where RAN troubleshooting or RAN optimization is needed. For example, RAN area of interest may be as large as a particular geographical market or as small as a particular group of adjacent cells. RAN troubleshooting and/or RAN optimization may be related to network-based issues such as, but not limited to, subscriber accessibility (e.g., attaching to an access network), retainability (e.g., call drops), or Quality of Service (QoS) (e.g., capacity/speed of a serving cell of the access network, voice/video MOS (Mean Opinion Score)), and the like.;
Thus, Froehlich does not explicitly teach (b) determine a set of two or more frequency bands that are available for the device, said set of two or more frequency bands comprising a first frequency band and a second frequency band or (c) select, based on the particular service type being requested, a priority level of the particular service type and at least one of a knowledge base or a machine learning (ML) model, one of the frequency bands included in the set of two or more frequency bands.
Similar to the system of Froehlich and Al, Gheorghiu teaches determining the first band and second band and being implemented in various devices, systems, and methods, which can be seen as, (b) determine a set of two or more frequency bands that are available for the device, said set of two or more frequency bands comprising a first frequency band and a second frequency band (Gheorghiu, fig. 3, [0061]-[0073], [0058]: [0058] Various aspects of the present disclosure are directed to providing a mechanism for reporting to a network (e.g., network entities within a wireless communication system, such as base stations, relay nodes, access points, etc.), CA combinations (e.g., band combinations) including particular bands. In aspects, the reporting may include signaling only the CA combinations that include either one of a particular band or a superset of that band. For example, in the example illustrated in FIG. 5 UE 115 may signal only CA combinations that include either first band 510 or second band 511. In aspects, a network entity receiving the signaling may determine all CA combination supported by the UE including the first and second bands based on the signaled CA combinations. For example, base station 105 may determine all CA combination supported by U E 115 that include first band 510 and all CA combinations that include second band 511 based on the signaled CA combinations (for either first band 510 or second band 511.).
Similar to the system of Froehlich and Gheorghiu, Al teaches determining characteristics of a data connection based on a service type and associated QoS class, wherein the QoS class may indicate a priority level and corresponding communication requirements, and selecting communication resource (e.g., carriers or radio technologies) based on those characteristics, which can be seen as, (c) select, based on the particular service type being requested, a priority level of the particular service type and at least one of a knowledge base or a machine learning (ML) model, one of the frequency bands included in the set of two or more frequency bands (Al, see fig. 79, [0326]-[0341], [0343]-[0370], [0496]-[0500], [0705]-[0721]: [0333] Radio communication technology selection criteria may include quality of service (QoS)-based parameters, such as those for maintaining a minimum QoS level to support a vertical application. QoS Class Identifiers (QCI), by way of example, may indicate QoS performance characteristics of each packet and control the packet forwarding treatment (e.g., scheduling weights, admission thresholds, queue management thresholds, link layer protocol configuration, etc.). For instance, a QCI may indicate whether or not a guaranteed bit rate (GBR) is set by the network. In this manner, a guaranteed bandwidth for traffic, such as uplink traffic (UL) or downlink traffic (DL), may be set. QCI may also be associated with a priority level, packet budget delay, packet error loss rate, and/or service type. [0366] Thus, the selected radio communication technology may be exclusive to the particular vehicular communication device to provide the minimum QoS level to support the vertical application, and/or to provide a frequency channel, time slot, and/or radio communication technology supported and/or requested by the particular vehicular communication device, etc. [0713] Communication processor 7210 may then evaluate the carrier characteristics based on target characteristics. As previously indicated, terminal device 6802 may use inter-operator carrier aggregation to transmit or receive data for a single data connection, where the data stream from the data connection can be separated into multiple separate substreams that are each transmitted on a different carrier. It may therefore be advantageous to select carriers that have suitable carrier characteristics for the data connection. For example, communication processor 7210 may have an active data connection (e.g., that terminal device 6802 is currently transmitting or receiving data on) or a potential data connection (e.g., that terminal device 6802 is planning to begin transmitting or receiving data on). Different data connections may have different service types, and thus may have differing requirements. These requirements may therefore be the target characteristics. For example, voice data connections may generally have stricter latency requirements, while best-effort data connections (e.g., browser or other Internet traffic) may have looser latency requirements. In another example, messaging data connections may have low data rate requirements, while audio or video streaming may have high data rate requirements. In some aspects, these target characteristics of the data connection may be indicated by a Quality of Service (QoS) class of the data connections, such as a QoS Class Indicator (QCI), which may specify the target characteristics as quantitative values for each QoS class. Communication processor 7210 may therefore determine the target characteristics based on the QoS class or a similar set of predefined requirements of the active or potential data connection).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Froehlich, Gheorghiu and Al in order to select communication resources based on service requirements associated QoS parameters that shared channel resources may be more efficiently utilized and interference reduced, thereby improving overall network efficiency (Gheorghiu, [0040]) and (Al, [0329]).
Regarding Claim 2, Froehlich-Gheorghiu-Al teaches the method of claim 1:
wherein said one of the frequency bands is selected based on at least one of the followings (Froehlich, [0038]):
a degree of interference that the service type can handle (Froehlich, [0038]: [0038] At least initial rule sets utilized by the rules engine 164 may include exemplary basic rules specifying typical RF power levels where drop calls start occurring and/or exemplary basic rules specifying RF interference levels where user's quality of experience starts dropping to unacceptable levels. As the automated classification model continually derives more information related to wireless network performance, the knowledge processing rules are selectively updated where relevant by the rules engine 164. The updated knowledge processing rules may be provided as real-time feedback by the rules engine 164 to the RAN analysis engine 166.),
a Quality of Service (QoS) requirement for the service type (Froehlich, [0034]: [0034] According to an embodiment of the present invention, at step 202, the RAN analysis engine 166 receives RAN area of interest information and desired outcome information from the user 168 via GUI 162. In other words, at step 202, the user 168 indicates a geographical area of interest where RAN troubleshooting or RAN optimization is needed. For example, RAN area of interest may be as large as a particular geographical market or as small as a particular group of adjacent cells. RAN troubleshooting and/or RAN optimization may be related to network-based issues such as, but not limited to, subscriber accessibility (e.g., attaching to an access network), retainability (e.g., call drops), or Quality of Service (QoS) (e.g., capacity/speed of a serving cell of the access network, voice/video MOS (Mean Opinion Score)), and the like. The desired outcome indicates the requested improvement. For example, the desired outcome may include, but is not limited to, improved cell coverage, improved call drop counts, improved data speed, and improved QoS metric or a combination thereof.),
a transmit power required for the service type (Froehlich, Fig. 1, [0031]- [0032], [0038]: [0038] At least initial rule sets utilized by the rules engine 164 may include exemplary basic rules specifying typical RF power levels where drop calls start occurring and/or exemplary basic rules specifying RF interference levels where user's quality of experience starts dropping to unacceptable levels. As the automated classification model continually derives more information related to wireless network performance, the knowledge processing rules are selectively updated where relevant by the rules engine 164. The updated knowledge processing rules may be provided as real-time feedback by the rules engine 164 to the RAN analysis engine 166.), and/or
a location of the device (Froehlich, [0047]: [0047] According to an embodiment of the present invention, the RAN analysis engine 166 may be configured to interact with the user 168 via the GUI 162. Continuing with the example of the outdoor radio coverage issue, the RAN analysis engine 166 may ask the user 168 to confirm that the location of interest is an outdoor cell.).
Thus, Froehlich does not explicitly teach a priority level of the service type.
Similar to the system of Froehlich and Gheorghiu, Al teaches determining characteristics of a data connection based on a service type and associated QoS class, wherein the QoS class may indicate a priority level and corresponding communication requirements, and selecting communication resources (e.g., carriers or radio technologies) based on those characteristics, which can be seen as, determining a priority level of the service type (Al, see fig. 79, [0326]-[0341], [0343]-[0370], [0496]-[0500], [0705]-[0721]: [0333] Radio communication technology selection criteria may include quality of service (QoS)-based parameters, such as those for maintaining a minimum QoS level to support a vertical application. QoS Class Identifiers (QCI), by way of example, may indicate QoS performance characteristics of each packet and control the packet forwarding treatment (e.g., scheduling weights, admission thresholds, queue management thresholds, link layer protocol configuration, etc.). For instance, a QCI may indicate whether or not a guaranteed bit rate (GBR) is set by the network. In this manner, a guaranteed bandwidth for traffic, such as uplink traffic (UL) or downlink traffic (DL), may be set. QCI may also be associated with a priority level, packet budget delay, packet error loss rate, and/or service type.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Froehlich, Gheorghiu and Al in order to select communication resources based on service requirements associated with QoS parameters such that shared channel resources may be more efficiently utilized and interference reduced, thereby improving overall network efficiency (Gheorghiu, [0040]) and (Al, [0329]).
Regarding Claim 3, Froehlich teaches the method of claim 1:
wherein the selecting is further based on information associated with the device (Froehlich, [0028]: [0028] Network equipment performance database 154 may store data collected by a performance management system. In access network 100, coverage areas 106 cover limited geographical areas. Performance database 154 may store performance counters and events for these cells among other performance related information. These counters provide information about the performance and traffic load in specific cells, such as cell capacity, the amount of signaling in specific cells, etc. The network equipment configuration database 156 stores data that defines a configuration for access network 100.).
Regarding Claim 4, Froehlich teaches the method of claim 3:
wherein the information associated with the device comprises subscription information identifying a type of subscription plan the device is registered on (Froehlich, [0035]: [0035] Examples of RAN variables may include NEM parameters and cell based counters and subscriber RRC data, such as radio bearer abnormal drop counters, radio frequency parameters such as RF frequency (channel), RF power (reference signal received power (RSRP)), RF interference (reference signal received quality (RSRQ)), signal-to-interference-plus-noise-ratio (SINR), RF RRC retransmissions and the like. Core network variables may include signaling related to subscriber sessions and communication flows associated with the subscribers, such as subscriber QoS attributes. Additionally, core network variables may include subscriber user plane signaling and QoS attributes such as uplink/downlink bandwidth, TCP retransmissions, voice/video MOS as a measure of user's quality of experience, application deep packet classification (e.g., Facebook application, Skype application, and the like).
Regarding Claim 5, Froehlich teaches the method of claim 1:
the method further comprising, prior to the selecting step (c):
identifying a primary service associated with said first frequency band (Froehlich, [0020]: [0020] The base stations 102 may wirelessly communicate with the UEs 104. Each of the base stations 102 may provide communication coverage for a respective geographic coverage area 106. There may be overlapping geographic coverage areas 106. For example, the small cell 102′ may have a coverage area 106′ that overlaps the coverage area 106 of one or more macro base stations 102. A network that includes both small cell and macro cells may be known as a heterogeneous network. A heterogeneous network may also include Home Evolved Node Bs (eNBs) (HeNBs), which may provide service to a restricted group known as a closed subscriber group (CSG). The communication links 108 between the base stations 102 and the UEs 104 may include uplink (UL) (also referred to as reverse link) transmissions from a UE 104 to a base station 102 and/or downlink (DL) (also referred to as forward link) transmissions from a base station 102 to a UE 104. The communication links 108 may use multiple-input and multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and/or transmit diversity. The communication links may be through one or more carriers. The base stations 102/UEs 104 may use spectrum up to Y MHz (e.g., 5, 10, 15, 20, 100 MHz) bandwidth per carrier allocated in a carrier aggregation of up to a total of Yx MHz (x component carriers) used for transmission in each direction. The carriers may or may not be adjacent to each other. Allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or less carriers may be allocated for DL than for UL). The component carriers may include a primary component carrier and one or more secondary component carriers. A primary component carrier may be referred to as a primary cell (PCell) and a secondary component carrier may be referred to as a secondary cell (SCell).; and
obtaining interference information indicating a degree of interference that said primary service can tolerate (Froehlich, [0038]: [0038] At least initial rule sets utilized by the rules engine 164 may include exemplary basic rules specifying typical RF power levels where drop calls start occurring and/or exemplary basic rules specifying RF interference levels where user's quality of experience starts dropping to unacceptable levels. As the automated classification model continually derives more information related to wireless network performance, the knowledge processing rules are selectively updated where relevant by the rules engine 164. The updated knowledge processing rules may be provided as real-time feedback by the rules engine 164 to the RAN analysis engine 166.).
Regarding Claim 6, Froehlich teaches the method of claim 1:
comprising: identifying a primary service associated with said first frequency band (Froehlich, [0020]: See above for [0020].); and
obtaining frequency information indicating how often a device utilizing the primary service needs to communicate with a network node (Froehlich, [0024]: [0024] The EPC 130 may include a Mobility Management Entity (MME) 132, other MMEs 134, a Serving Gateway 136, a Multimedia Broadcast Multicast Service (MBMS) Gateway 138, a Broadcast Multicast Service Center (BM-SC) 140, and a Packet Data Network (PDN) Gateway 142. The MME 132 may be in communication with a Home Subscriber Server (HSS) 144. The MME 132 is the control node that processes the signaling between the UEs 104 and the EPC 130. Generally, the MME 132 provides bearer and connection management. All user Internet protocol (IP) packets are transferred through the Serving Gateway 136, which itself is connected to the PDN Gateway 142. The PDN Gateway 142 provides UE IP address allocation as well as other functions. The PDN Gateway 142 and the BM-SC 140 are connected to the IP Services 146. The IP Services 146 may include the Internet, an intranet, an IP Multimedia Subsystem (IMS), a PS Streaming Service (PSS), and/or other IP services. The BM-SC 140 may provide functions for MBMS user service provisioning and delivery. The BM-SC 140 may serve as an entry point for content provider MBMS transmission, may be used to authorize and initiate MBMS Bearer Services within a public land mobile network (PLMN), and may be used to schedule MBMS transmissions. The MBMS Gateway 138 may be used to distribute MBMS traffic to the base stations 102 belonging to a Multicast Broadcast Single Frequency Network (MBSFN) area broadcasting a particular service, and may be responsible for session management (start/stop) and for collecting eMBMS related charging information.).
Regarding Claim 7, Froehlich-Gheorghiu-Al teaches the method of claim 1:
further comprising: identifying a primary service associated with said first frequency band (Froehlich, [0020]: See above for [0020].), wherein
Thus, Froehlich does not explicitly teach the primary service is of a first service type; and obtaining priority information indicating a priority level of the first service type.
Similar to the system of Froehlich, Gheorghiu and Al teach elements that correspond to the claimed features, wherein the primary service is of a first service type (Gheorghiu, [0032]- [0033], [0036], [0039]: [0039] In operation at 5G network 100, base stations 105a-105c serve UEs 115a and 115b using 3D beamforming and coordinated spatial techniques, such as coordinated multipoint (CoMP) or multi-connectivity. Macro base station 105d performs backhaul communications with base stations 105a-105c, as well as small cell, base station 105f. Macro base station 105d also transmits multicast services which are subscribed to and received by UEs 115c and 115d. Such multicast services may include mobile television or stream video, or may include other services for providing community information, such as weather emergencies or alerts, such as Amber alerts or gray alerts.) and (Al, see fig. 79, [0326]-[0341], [0343]-[0370], [0496]-[0500], [0705]-[0721]: See paragraph [0333].); and
obtaining priority information indicating a priority level of the first service type (Gheorghiu, [0046]: [0046] For example, a network operating entity may be allocated certain time resources reserved for exclusive communication by the network operating entity using the entirety of the shared spectrum. The network operating entity may also be allocated other time resources where the entity is given priority over other network operating entities to communicate using the shared spectrum. These time resources, prioritized for use by the network operating entity, may be utilized by other network operating entities on an opportunistic basis if the prioritized network operating entity does not utilize the resources. Additional time resources may be allocated for any network operator to use on an opportunistic basis.) and (Al, see fig. 79, [0326]-[0341], [0343]-[0370], [0496]-[0500], [0705]-[0721]: See paragraph [0333].).
Similar to the system of Froehlich and Gheorghiu, Al teaches determining characteristics of a data connection based on a service type and associated QoS class, wherein the QoS class may indicate a priority level and corresponding communication requirements, and selecting communication resources (e.g., carriers or radio technologies) based on those characteristics, which can be seen as, a service type (Al, see fig. 79, [0326]-[0341], [0343]-[0370], [0496]-[0500], [0705]-[0721]: [0333] Radio communication technology selection criteria may include quality of service (QoS)-based parameters, such as those for maintaining a minimum QoS level to support a vertical application. QoS Class Identifiers (QCI), by way of example, may indicate QoS performance characteristics of each packet and control the packet forwarding treatment (e.g., scheduling weights, admission thresholds, queue management thresholds, link layer protocol configuration, etc.). For instance, a QCI may indicate whether or not a guaranteed bit rate (GBR) is set by the network. In this manner, a guaranteed bandwidth for traffic, such as uplink traffic (UL) or downlink traffic (DL), may be set. QCI may also be associated with a priority level, packet budget delay, packet error loss rate, and/or service type.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Froehlich, Gheorghiu and Al in order to select communication resources based on service requirements associated with QoS parameters such that shared channel resources may be more efficiently utilized and interference reduced, thereby improving overall network efficiency (Gheorghiu, [0040]) and (Al, [0329]).
Regarding Claim 8, Froehlich teaches the method of claim 1:
wherein the steps (b) and (c) are performed periodically (Froehlich, [0026]: [0026] Advantageously, various embodiments of the present invention contemplate a monitoring and troubleshooting system that efficiently and accurately evaluates signaling, performance and configuration data of both EPC 130 and RAN to identify at least one cause of reported and/or anticipated network related failures and applies procedural and iterative root cause analysis to provide automatic recommendations for resolving the reported network failures. According to an embodiment of the present invention, a troubleshooting system 150 may include, but not limited to, a computer server operatively coupled to each segment of access network 100 and to an operations support systems (OSS) platform (interchangeably referred to herein as the “network management platform”). At the highest level, the network management platform provides the computer resources required to perform various network management functions, such as billing, customer care, network management, inventory control, maintenance, trouble ticket reporting, surveillance and service provisioning. In some embodiments, the network management platform comprises a plurality of applications, such as performance, fault, configuration and security management applications. As shown in FIG. 1, troubleshooting system 150 may communicate with various data repositories maintained by the network management platform, such as, but not limited to network inventory database 152, network equipment performance database 154, network equipment configuration database 156, network signaling database 158, fault management database 159, and the like.).
Regarding Claim 9, Froehlich teaches the method of claim 1:
wherein the method is performed by a radio access network (RAN) (Frohlich, [0019]: [0019] The base stations 102 (collectively referred to as Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (E-UTRAN)) interface with the EPC 130 through backhaul links 110 (e.g., S1 interface). In addition to other functions, the base stations 102 may perform one or more of the following functions: transfer of user data, radio channel ciphering and deciphering, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection setup and release, load balancing, distribution for non-access stratum (NAS) messages, NAS node selection, synchronization, radio access network (RAN) sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment trace, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations 102 may communicate directly or indirectly (e.g., through the EPC 130) with each other over backhaul links 112 (e.g., X2 interface). The backhaul links 112 may be wired or wireless.).
Regarding Claim 10, Froehlich teaches the method of claim 9:
wherein the step (c) is performed by a machine learning (ML) agent implemented in the RAN (Froehlich, Fig. 2, [0033]- [0044], [0038]- [0040]: [0038] According to embodiment of the present invention, at step 210, the RAN analysis engine 166 performs root cause analysis of identified and/or stored network monitoring parameters using automated classification model by applying one or more knowledge processing rules. Broadly, knowledge representation is the activity of making abstract knowledge explicit, as concrete data structures, to support machine-based storage, management (e.g., information location and extraction), and reasoning systems. Knowledge processing rules may be applied using the rules engine software component 164, e.g., implemented by programming instructions encoded in one or more tangible, non-transitory computer-readable storage media executed by one or more processors of the KBS 160 to provide the rules engine 164. According to embodiments of the present invention, these knowledge processing rules managed by the rules engine 164 may feed the machine learning based automated classification model. At least initial rule sets utilized by the rules engine 164 may include exemplary basic rules specifying typical RF power levels where drop calls start occurring and/or exemplary basic rules specifying RF interference levels where user's quality of experience starts dropping to unacceptable levels. As the automated classification model continually derives more information related to wireless network performance, the knowledge processing rules are selectively updated where relevant by the rules engine 164. The updated knowledge processing rules may be provided as real-time feedback by the rules engine 164 to the RAN analysis engine 166.).
Regarding Claim 11, Froehlich teaches the method of claim 1:
wherein the knowledge base is in the form of a directed-graph or a database (Froehlich, Fig.1, [0029], [0031]- [0033]: [0029] A session, call, or data record is created for each UE 104 using messages, signals, and/or data collected or intercepted by monitoring probes from various network interfaces. A monitoring system, such as monitoring probes and monitoring server (not shown in FIG. 1), may be coupled to interfaces and links in the network to passively monitor and collect signaling data from one or more interfaces in the network. The monitoring system may collect user plane and control plane data from the interfaces. The monitoring probes, may comprise, for example, one or more processors running one or more software applications that collect, correlate and analyze Protocol Data Units (PDU) and data packets from both RAN and EPC 130 network interfaces and links. All collected data may be stored in network signaling database 158. Another example of signaling data collected from access network 100 would be to obtain switching and routing table information or subscribers IP trace route information using well-known real time processing programs such as rtTrace.).
Regarding Claim 12, Froehlich- Gheorghiu teaches the method of claim 11:
the directed-graph or the database relates the service type with a subset of one or more frequency bands (Froehlich, [0020], [0031], [0071]: [0031] RAN analysis engine 166 may be, for example, a computer program or program component utilized as the inference engine of knowledge based system 160 that matches the current inputs to relevant elements in knowledge base 160. In some embodiments, RAN analysis engine 166 may provide the means to re-assess the state of a situation during each cycle of a reasoning mechanism. As a result, RAN analysis engine 166 may be capable of reacting to a dynamic situation more readily than conventional programs., and
Thus, Froehlich does not explicitly teach said subset of one or more frequency bands is a subset of said set of two or more frequency bands.
Similar to the system of Froehlich, Gheorghiu teaches the base station, determines CA combinations that include the first band and CA combinations that include the second band based on the at least one CA combination, which can be seen as, said subset of one or more frequency bands is a subset of said set of two or more frequency bands (Gheorghiu, Fig. 4, Fig. 6A: step 402-404, [0063]- [0065]: [0065] At block 404, the base station, determines CA combinations that include the first band and CA combinations that include the second band based on the at least one CA combination. For example, gNB 105 may execute, under control of controller/processor 240, CA combination estimator 702, stored in memory 242. The execution environment of CA combination estimator 702 provides the procedural steps for determining, by gNB 105, CA combinations that include the first band and CA combinations that include the second band based on the at least one CA combination.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Froehlich with Gheorghiu. Froehlich teaches the RAN analysis engine that receives user provided area-of-interest and desired outcome information to guide RAN troubleshooting and optimization. Gheorghiu teaches a base station that determines carrier aggregation (CA) combinations including a first band and combinations including a second band based on the at least one CA combination. The combination would have predictably reduced operator guesswork, simplified configuration, and improved troubleshooting efficiency, thereby achieving better RAN performance with reduce repair time (Froehlich, [0054]).
Regarding Claim 22, Froehlich-Al teaches the apparatus of claim 21:
Wherein prior to the selecting step (c), the apparatus is configured to:
(i) identify a primary service associated with said first frequency band, and obtain interference information indicating a degree of interference that said primary service can tolerate (Froehlich, [0031]- [0032], [0046]),
(ii) identify a primary service associated with said first frequency band, and obtain frequency information indicating how often a device utilizing the primary service needs to communicate with a network node (Froehlich, [0031]- [0032], [0046]), and or
(iii) identify a primary service associated with said first frequency band, wherein the primary service is of a first service type, and obtain priority information indicating a priority level of the first service type (Froehlich, [0034]) and (Al, see fig. 79, [0326]-[0341], [0343]-[0370], [0496]-[0500], [0705]-[0721]: See paragraph [0333].).
Thus, Froehlich does not explicitly teach the term service type.
Similar to the system of Froehlich and Gheorghiu, Al teaches determining characteristics of a data connection based on a service type and associated QoS class, wherein the QoS class may indicate a priority level and corresponding communication requirements, and selecting communication resources (e.g., carriers or radio technologies) based on those characteristics, which can be seen as, a service type (Al, see fig. 79, [0326]-[0341], [0343]-[0370], [0496]-[0500], [0705]-[0721]: [0333] Radio communication technology selection criteria may include quality of service (QoS)-based parameters, such as those for maintaining a minimum QoS level to support a vertical application. QoS Class Identifiers (QCI), by way of example, may indicate QoS performance characteristics of each packet and control the packet forwarding treatment (e.g., scheduling weights, admission thresholds, queue management thresholds, link layer protocol configuration, etc.). For instance, a QCI may indicate whether or not a guaranteed bit rate (GBR) is set by the network. In this manner, a guaranteed bandwidth for traffic, such as uplink traffic (UL) or downlink traffic (DL), may be set. QCI may also be associated with a priority level, packet budget delay, packet error loss rate, and/or service type.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Froehlich, Gheorghiu and Al in order to select communication resources based on service requirements associated with QoS parameters such that shared channel resources may be more efficiently utilized and interference reduced, thereby improving overall network efficiency (Gheorghiu, [0040]) and (Al, [0329]).
Regarding Claim 13 and 23, Froehlich-Gheorghiu teaches a method (Froehlich, fig. 2) / an apparatus (Froehlich, fig. 2):
the selecting of said one of the frequency bands is performed based on the knowledge base (Froehlich, [0031]- [0032]: See above for [0031].),
the knowledge base is a knowledge graph (Froehlich, Fig. 4, [0031]- [0032], [0063]- [0066]: See above for [0031].),
the second group of nodes is connected to the first group of nodes via a first group of links and further wherein each of the first group of links indicates a relationship between one of the first group of nodes and one of the second group of nodes (Froehlich, [0031]- [0032]: See above for [0031].), and
a third group of nodes each of which identifies a categorized frequency band, wherein the third group of nodes is connected to the first and/or second group of nodes via a second group of links and further wherein each of the second group of links indicates whether a device type and/or a service type identified by one of the first or second group of nodes can occupy the categorized frequency band of one of the third group of nodes (Froehlich, Fig. 3A-3C, [0018]- [0021], [0046]-[0053]: [0049] It should be noted that each RAN parameter added to a particular decision tree doubles the number of decision points (nodes). According to an embodiment of the present invention, the RAN analysis engine 166 may utilize a plurality of decision trees, wherein each decision tree represents a rule set associated with a particular cause. FIG. 3B depicts an example of the plurality of decision trees utilized by the automated classification model, in accordance with an embodiment of the present invention. Each of the two additional RAN parameters RSRQ 316 and distance from the serving cell 320 double the number of decision points in the decision tree 300a, as compared to the decision tree 300 in FIG. 3A. Furthermore, FIG. 3B illustrates a plurality of decision trees 300a-300d where each decision tree represents a rule set associated with a particular cause.).
the knowledge graph comprises (Froehlich, Fig. 3A, [0046]: [0046] FIG. 3A depicts an example of a decision tree utilized by the automated classification model, in accordance with an embodiment of the present invention. More specifically, the decision tree 300 depicted in FIG. 3A represents the rule set (both pre-conditions and corresponding confidence factors) stored in the Table 1. In FIG. 3A, nodes 304 and 306 represent true and false probabilities, respectively, for a particular cause, nodes 308 and 310 represent true and false probabilities, respectively, for serving cell RSRP values being between −120 dBm and −100 dBm, nodes 312 and 314 represent true and false probabilities, respectively, for a serving cell being an outdoor cell. According to an embodiment of the present invention, the RAN analysis engine 166 may determine a confidence factor that a root cause is an outdoor radio coverage issue by combining corresponding individual probabilities of preconditions 304, 308 and 312 using the decision tree 300.):
a first group of nodes each of which identifies a device type and/or a service type (Froehlich, Fig. 3A, [0046]- [0047]),
a second group of nodes each of which identifies a device type and/or a service type (Froehlich, Fig. 3A-Fig. 3C [0046], [0052]: [0052] In FIG. 3C, nodes 332 and 334 represent true and false probabilities, respectively, for serving cell's RSRQ value being less than −16 dBm, nodes 336 and 338 represent true and false probabilities, respectively, for neighbor cell RSRP values being less than −16 dBm, nodes 340 and 342 represent true and false probabilities, respectively, for distance from serving cell being greater than 800 meters, and nodes 344 and 346 represent true and false probabilities, respectively, for traffic hotspot detected at location with distance from serving cell greater than 800 m.).
Thus, Froehlich does not explicitly teach a second group of nodes each of which identifies a device type and/or a service type.
Similar to the system of Froehlich, Gheorghiu teaches UEs in a closed subscriber group, which can be seen as, a second group of nodes each of which identifies a device type and/or a service type (Gheorghiu, Fig. 1, [0032]- [0036], [0039]: See above for [0036]), wherein.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Froehlich with Gheorghiu. Froehlich teaches using a knowledge graph and decision nodes to represent device/service types for RAN optimization, while Gheorghiu teaches base stations/UEs determine CA combinations across multiple frequency bands. The combination would reduce operator guesswork and improve network efficiency.
Regarding Claim 14 and 24, Froehlich-Al teaches a method (Froehlich, fig. 2) / an apparatus (Froehlich, fig. 2):
wherein the selecting of said one of the frequency bands is performed using the ML model, the method further comprises, prior to performing the step (a) (Froehlich, [0038]- [0040]: [0038] According to embodiment of the present invention, at step 210, the RAN analysis engine 166 performs root cause analysis of identified and/or stored network monitoring parameters using automated classification model by applying one or more knowledge processing rules. Broadly, knowledge representation is the activity of making abstract knowledge explicit, as concrete data structures, to support machine-based storage, management (e.g., information location and extraction), and reasoning systems. Knowledge processing rules may be applied using the rules engine software component 164, e.g., implemented by programming instructions encoded in one or more tangible, non-transitory computer-readable storage media executed by one or more processors of the KBS 160 to provide the rules engine 164. According to embodiments of the present invention, these knowledge processing rules managed by the rules engine 164 may feed the machine learning based automated classification model. At least initial rule sets utilized by the rules engine 164 may include exemplary basic rules specifying typical RF power levels where drop calls start occurring and/or exemplary basic rules specifying RF interference levels where user's quality of experience starts dropping to unacceptable levels. As the automated classification model continually derives more information related to wireless network performance, the knowledge processing rules are selectively updated where relevant by the rules engine 164. The updated knowledge processing rules may be provided as real-time feedback by the rules engine 164 to the RAN analysis engine 166.):
providing to the ML model ML input data and ML desired output data (Froehlich, Fig. 1, [0031]- [0032]: [0031] As shown in FIG. 1, troubleshooting system 150 may be communicatively connected to a knowledge base system (KBS) 160. In some embodiments, the KBS 160 may comprise a cloud-based system suited for complex tasks. According to an embodiment of the present invention, KBS 160 may be capable of providing resolution recommendations for a plurality of detected network failures by communicating with the troubleshooting system 150. In one embodiment, KBS 160 may be designed to apply formal representations of domain knowledge or expertise to solve network related problems. Symbolic descriptions (e.g., in the form of rules) of this expertise characterize the definitional and empirical relationships in a domain. This approach of knowledge models has been found advantageous in automating troubleshooting tasks that may become too complex to be accomplished by human experts. In an embodiment of the present invention, KBS 130 may comprise, or otherwise may cooperate with a rules engine 164 and RAN analysis engine software program 166. RAN analysis engine 166 may comprise program instructions stored on one or more computer-readable storage devices, which may include internal storage on the knowledge base system 160. RAN analysis engine 166 may be, for example, a computer program or program component utilized as the inference engine of knowledge based system 160 that matches the current inputs to relevant elements in knowledge base 160. In some embodiments, RAN analysis engine 166 may provide the means to re-assess the state of a situation during each cycle of a reasoning mechanism. As a result, RAN analysis engine 166 may be capable of reacting to a dynamic situation more readily than conventional programs.); and
training the ML model using the ML input data and the ML desired output data, and the ML input data comprises any one or a combination of the followings (Froehlich, [0044]: [0044] According to embodiments of the present invention, the automated classification model is a Bayesian statistical model that utilizes sets of knowledge processing rules. Expert knowledge is used to seed training of the model by a machine. This knowledge-based seeding of the model may more effectively create a predictive model. In some embodiments, the seed may represent relationships between the plurality of network monitoring parameters. At least some of the rules may include one or more preconditions associated with certain confidence factors based on the knowledge-based seeding. The reliability of the derived recommendation depends on each confidence factor utilized during derivation process. These confidence factors are indicative of the probability of occurrence of a corresponding cause. According to an embodiment of the present invention, the automated classification model utilized by the RAN analysis engine 166 is capable of improving credibility results for each derived recommendation as confidence factors can be automatically updated after each successful/unsuccessful prediction.):
a degree of interference that a service type can handle (Froehlich, [0038]: See above for [0038].),
a Quality of Service (QoS) requirement for a service type (Froehlich, [0034]: See above for [0034].),
a transmit power required for a service type (Froehlich, Fig. 1, [0031]- [0032], [0038]: See above for [0038].), and/or
a location of the device (Froehlich, [0047]: See above for [0047].).
Thus, Froehlich does not explicitly teach a priority level of a service type.
Similar to the system of Froehlich and Gheorghiu, Al teaches determining characteristics of a data connection based on a service type and associated QoS class, wherein the QoS class may indicate a priority level and corresponding communication requirements, and selecting communication resources (e.g., carriers or radio technologies) based on those characteristics, which can be seen as, determining a priority level of a service type (Al, see fig. 79, [0326]-[0341], [0343]-[0370], [0496]-[0500], [0705]-[0721]: [0333] Radio communication technology selection criteria may include quality of service (QoS)-based parameters, such as those for maintaining a minimum QoS level to support a vertical application. QoS Class Identifiers (QCI), by way of example, may indicate QoS performance characteristics of each packet and control the packet forwarding treatment (e.g., scheduling weights, admission thresholds, queue management thresholds, link layer protocol configuration, etc.). For instance, a QCI may indicate whether or not a guaranteed bit rate (GBR) is set by the network. In this manner, a guaranteed bandwidth for traffic, such as uplink traffic (UL) or downlink traffic (DL), may be set. QCI may also be associated with a priority level, packet budget delay, packet error loss rate, and/or service type.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Froehlich, Gheorghiu and Al in order to select communication resources based on service requirements associated with QoS parameters such that shared channel resources may be more efficiently utilized and interference reduced, thereby improving overall network efficiency (Gheorghiu, [0040]) and (Al, [0329]).
Regarding Claim 15 and 25, Froehlich-Gheorghiu-Al teaches a method (Froehlich, fig. 2) / an apparatus (Froehlich, fig. 2):
the selecting step (c) further comprises:
identifying, among a first group of nodes included in a knowledge graph, a node which identifies the particular service type (Froehlich, Fig. 3A, [0031]- [0032], [0038]: [0046] FIG. 3A depicts an example of a decision tree utilized by the automated classification model, in accordance with an embodiment of the present invention. More specifically, the decision tree 300 depicted in FIG. 3A represents the rule set (both pre-conditions and corresponding confidence factors) stored in the Table 1. In FIG. 3A, nodes 304 and 306 represent true and false probabilities, respectively, for a particular cause, nodes 308 and 310 represent true and false probabilities, respectively, for serving cell RSRP values being between −120 dBm and −100 dBm, nodes 312 and 314 represent true and false probabilities, respectively, for a serving cell being an outdoor cell. According to an embodiment of the present invention, the RAN analysis engine 166 may determine a confidence factor that a root cause is an outdoor radio coverage issue by combining corresponding individual probabilities of preconditions 304, 308 and 312 using the decision tree 300. Using the example in FIG. 3A, the RAN analysis engine 166 determines confidence factor of root cause being an outdoor coverage issue=P (cause)*P (RSRP value being between −120 dBm and −100 dBm)*P (outdoor serving cell)=0.95*0.7*0.6=0.399=39.9%. Similarly, the RAN analysis engine 166 determines that probability of root cause being an indoor coverage issue=P (cause)*P (RSRP value being between −120 dBm and −100 dBm)*P (indoor serving cell)=0.95*0.7*0.4=0.266=26.6%. As yet another example, the RAN analysis engine 166 determines that probability of root cause being an outdoor radio coverage issue, while RSRP measurement is outside of the range between −120 dBm and −100 dBm=P(cause)*P (RSRP value being outside of the −120 dBm and −100 dBm range)*P (outdoor serving cell)=0.95*0.3*0.6=0.171=17.1%. In a similar fashion, the RAN analysis engine 166 may determine confidence factors for each possible combination of nodes 304-314 in order to determine the most likely cause of the reported problem. In the illustrated case, the RAN analysis engine 166 may derive that the outdoor radio coverage issue is the most likely cause by comparing all computed confidence factors.) and (Al, see fig. 79, [0326]-[0341], [0343]-[0370], [0496]-[0500], [0705]-[0721]: See above for paragraph [0333], [0366], and [0713]).
Thus, Froehlich does not explicitly teach based on the determined set of two or more frequency bands, identifying, among a second group of nodes included in the knowledge graph, one or more nodes each of which identifies a service type, wherein the second group of nodes is connected to the first group of nodes via a group of links each of which indicates a relationship between one of the first group of nodes and one of the second group of nodes; and based on the relationship indicated by at least one of the links, selecting one of the frequency bands included in the set of two or more frequency bands and the term particular service type.
Similar to the system of Froehlich, Gheorghiu teaches first band and second band that are a part of the bases of providing various communication services, which can be seen as, based on the determined set of two or more frequency bands, identifying, among a second group of nodes included in the knowledge graph, one or more nodes each of which identifies a service type, wherein the second group of nodes is connected to the first group of nodes via a group of links each of which indicates a relationship between one of the first group of nodes and one of the second group of nodes (Gheorghiu, Fig. 4, Fig, 5, [0054]-[0058]: [0054] FIG. 5 shows a diagram illustrating wireless communication system 500, configured in accordance with aspects of the present disclosure. In particular, wireless communication system 500 may include UE 115, configured to support CA. As noted above, UE 115 may be configured to support inter-band and intra-band CA combinations. The CA combinations supported by UE 115 may include CA combinations that include first band 510, and may also include CA combinations that include second band 511. In aspects, first band 510 may be a superset of second band 511. In this case, as noted above, first band 510 may include a wider frequency range than second band 511, and second band 511 may fall within the bandwidth of first band 510. Additionally, or alternatively, first band 510 may have tighter RF requirements than second band 511. Furthermore, first band 510 may support features and/or may have additional requirements that are not supported and/or required by second band 511. For example, a combination using second band 511 may require that 2×2 MIMO be used, whereas a combination using first band 510 may require that 4×4 MIMO be used. In this case, the 4×4 MIMO requirement may also cover the 2×2 MIMO requirement and thus, first band 510 may support additional features than second band 511.); and
based on the relationship indicated by at least one of the links, selecting one of the frequency bands included in the set of two or more frequency bands (Gheorghiu, [0058]: [0058] Various aspects of the present disclosure are directed to providing a mechanism for reporting to a network (e.g., network entities within a wireless communication system, such as base stations, relay nodes, access points, etc.), CA combinations (e.g., band combinations) including particular bands. In aspects, the reporting may include signaling only the CA combinations that include either one of a particular band or a superset of that band. For example, in the example illustrated in FIG. 5 UE 115 may signal only CA combinations that include either first band 510 or second band 511. In aspects, a network entity receiving the signaling may determine all CA combination supported by the UE including the first and second bands based on the signaled CA combinations. For example, base station 105 may determine all CA combination supported by UE 115 that include first band 510 and all CA combinations that include second band 511 based on the signaled CA combinations (for either first band 510 or second band 511.).
Similar to the system of Froehlich and Gheorghiu, Al teaches determining characteristics of a data connection based on a service type and associated QoS class, wherein the QoS class may indicate a priority level and corresponding communication requirements, and selecting communication resource (e.g., carriers or radio technologies) based on those characteristics, which can be seen as, particular service type (Al, see fig. 79, [0326]-[0341], [0343]-[0370], [0496]-[0500], [0705]-[0721]: See above for paragraph [0333], [0366], and [0713]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Froehlich, Gheorghiu and Al in order to select communication resources based on service requirements associated QoS parameters that shared channel resources may be more efficiently utilized and interference reduced, thereby improving overall network efficiency (Gheorghiu, [0040]) and (Al, [0329]).
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Francesca Lima Santos whose telephone number is (571)272-6521. The examiner can normally be reached Monday thru Friday 7:30am-5pm, ET.
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/FRANCESCA LIMA SANTOS/Examiner, Art Unit 2468
/MARCUS SMITH/Supervisory Patent Examiner, Art Unit 2468