Detailed Action
This communication is in response to the Request for Continued Examination filed on 6/8/2026.
Claims 1-20 are pending and have been examined.
Claims 1-20 are rejected.
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 Amendment
The Applicants have amended the independent claims to include “at a base station of” and “base station” “the mobile device is connected to the wireless telecommunication network via the base station, wherein the corpus of data further includes data generated bv the base station interfacing with the wireless telecommunication network to perform radio configuration and scheduling for communication with the mobile device,” “apply the selected grammar to a first triple of the multiple triples to obtain an indication of whether the first triple is correct; and upon obtaining the indication that the first triple is incorrect, determine whether the selected grammar is spam.”
Regarding the Examiner Interview Summary
Applicant notes during the telephonic interview conducted on May 19, 2026 ("Interview"). During the Interview, proposed amendments related to the types of data included in the corpus of data were discussed. Additionally, further amendments suggested by the examiner relating to including limitations of claim 2 were discussed. The examiner indicated that these amendments would overcome the cited references.
Examiner notes the amendments as discussed were noted to possibly require further search and/or consideration.
Regarding the 35 U.S.C. § 103 rejections, Applicant’s arguments with respect to claim(s) 1, 8 and 14 have been considered but are moot because the new ground of rejection does not rely on the primary reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Hence, new grounds for rejection have been made in view of Hence, new grounds for rejection have been made in view of He (US Publication Number US-20250168669-A1), in view of Chang (US Publication Number US-20130013549-A1).(examiner notes this later version of He includes more detail about the wireless telecommunication aspects including base station over the previously cited He reference.
Specification
The amendment filed 10/28/2025 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows: “wherein the corpus of data is received as network packets; convert the network packets of the corpus of data to a text format;” Examiner notes there is no mention of converting network packets to text format in the specification.
Applicant is required to cancel the new matter in the reply to this Office Action.
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.
Claims 1-4, 8-11, 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over HE (US Publication Number US 20250168669 A1), in view of Chang (US Publication Number US 20130013549 A1
Regarding claim 1, HE teaches 1. (Currently Amended) A non-transitory, computer-readable storage medium comprising instructions recorded thereon to create a grammar representing multiple concepts and multiple relationships based on a corpus of data associated with a wireless telecommunication network, (see He [0009] In a fourth aspect, the present disclosure further provides a non-transient computer readable storage medium, having a computer program stored thereon. The computer program implements, when being executed by a processor, steps of the method for intelligently analyzing and applying the wireless communication network knowledge graph described in the second aspect.”)(examiner notes grammar as ”knowledge graph“) wherein the instructions, when executed by at least one processor of a system of the wireless telecommunication network, cause the system to: [0169] The memory 620 is configured to store a computer program. The transceiver 610 is configured to receive and send data under the control of the processor 600.”) receive the corpus of data generated at a base station of the wireless telecommunication network, (see He [0077] Optionally, in the embodiment of the present disclosure, the different data types include one or more of: terminal-side wireless air interface data, base station-side wireless air interface data, core network data, and network management data.”) wherein the corpus of data includes data generated by interactions between a mobile device and the base station, wherein the mobile device is connected to the wireless telecommunication network via the base station, (see He [0078] The data collected may generally include four data types, but is not limited to the four data types of data. The first data type is the terminal-side wireless air interface data, which mainly includes L1, L2, and L3 layer data;( (see He [0077] Optionally, in the embodiment of the present disclosure, the different data types include one or more of: terminal-side wireless air interface data, base station-side wireless air interface data, core network data, and network management data.”) (examiner notes mobile device connected to as ”terminal-side“) the second data type is the base station-side wireless air interface data, which mainly includes L1, L2, and L3 layer data; the third data type is the core network data, which mainly includes two data types: user plane data and control plane data; and the fourth data type is the network management data, which mainly includes an alarm, configuration, and performance information. It is to be understood that the above listed types of the wireless communication network data are only exemplary descriptions, and the present disclosure is not limited thereto. All data transmitted between network elements in the wireless communication network as well as air interface data in the related art fall within the scope of protection of the present disclosure.”) wherein the corpus of data further includes data generated by the base station interfacing with the wireless telecommunication network to perform radio configuration (see He [0006] In a first aspect, the present disclosure provides a system for intelligently analyzing and applying a wireless communication network knowledge graph, including at least: a knowledge graph unit, an intelligent traceability unit, and a tuning strategy unit; (examiner notes configuration as”tuning“) wherein the knowledge graph unit is configured to construct the wireless communication network knowledge graph based on endogenous factors of a wireless communication network and a graph construction method, and perform deep analysis and graph cyclic tuning in combination with classified wireless communication network data; the intelligent traceability unit is configured to determine a diagnostic positioning result for a wireless communication network anomaly based on the tuned wireless communication network knowledge graph, an anomaly detection algorithm and an intelligent reasoning algorithm; and the tuning strategy unit is configured to determine a plurality of tuning strategies based on a strategy generation algorithm and the diagnostic positioning result, and determine, in combination with an intelligent decision algorithm, one optimal tuning strategy from the plurality of tuning strategies by using a best result when considering an execution efficiency and an execution effect of a tuning strategy as a target, so as to perform tuning on the wireless communication network.”) [0008] In a third aspect, the present disclosure further provides an electronic device, including a memory, a transceiver, and a processor; the memory is configured to store a computer program; the transceiver is configured to receive and send data under a control of the processor; and the processor is configured to read the computer program in the memory and implement steps of the method for intelligently analyzing and applying a wireless communication network knowledge graph described in the second aspect.”) (see He [0077] the third data type is the core network data, which mainly includes two data types: user plane data and control plane data; and the fourth data type is the network management data, which mainly includes an alarm, configuration, and performance information. “) and scheduling for communication with the mobile device, and (see He [0078] The data collected may generally include four data types, but is not limited to the four data types of data. The first data type is the terminal-side wireless air interface data, which mainly includes L1, L2, and L3 layer data; the second data type is the base station-side wireless air interface data, which mainly includes L1, L2, and L3 layer data; the third data type is the core network data, which mainly includes two data types: user plane data and control plane data; and the fourth data type is the network management data, (examiner notes scheduling as”management“) which mainly includes an alarm, configuration, and performance information. It is to be understood that the above listed types of the wireless communication network data are only exemplary descriptions, and the present disclosure is not limited thereto. All data transmitted between network elements in the wireless communication network as well as air interface data in the related art fall within the scope of protection of the present disclosure.”) (see He [0079] The alarm is mainly alarm for alarming anomalies in node data of the wireless communication network, for example, an alarm for alarming relatively large load on the node data of the wireless communication network, or anomalies in some nodes of the wireless communication network.”) wherein the corpus of data is received as network packets; convert the network packets of the corpus of data to a text format; (see He [0069] Optionally, in the embodiment of the present disclosure, the system for intelligently analyzing and applying the wireless communication network knowledge graph further includes a wireless data unit. The wireless data unit is configured to acquire raw data of the wireless communication network, perform pre-processing on the raw data, and then perform classification storage on the preprocessed data according to different data types, wherein the classification storage of different data types is implemented based on a distributed system architecture.”) (see He [0070] As at least one alternative embodiment, the system for intelligently analyzing and applying the wireless communication network knowledge graph further includes the wireless data unit.”) (see He [0071] (4) The wireless data unit mainly performs collection and pre-processing on the raw data of the wireless communication network, wherein the pre-processing includes data format conversion, data cleaning, etc.; and performs classification storage according to different types.”) (examiner interprets network packets as “raw data”, examiner interprets text format as “data format conversion”)
extract, from the corpus of data, multiple triples representing the corpus of data, wherein a first element in a triple among the multiple triples indicates a first record among the corpus of data, (see He [0027] “…The wireless communication network knowledge graph is constructed by triples, wherein the triples consist of elements in an entity library (examiner interprets corpus of data as “entity library”) initialized based on the endogenous factors of the wireless communication network and elements in an inter-entity relationship library.”(examiner interprets first record as “factors” and “elements”) “) wherein a second element in the triple among the multiple triples indicates a second record among the corpus of data, (see He [0032] “The deep analysis module uses the entity library and inter-entity relationship library for constituting initially the wireless communication network knowledge graph as inputs, and the deep analysis module mainly has two functions. The first function is to quantify and measure the association degree of the relationship between the two entities from the perspective of the graph model algorithm, so as to establish edge connection strength; and the second function is to perform, from the perspective of data, deep analysis on one or more of: the new entities, the new entity types, the new entity attributes, the new inter-entity relationships, and the association degrees in the wireless communication network knowledge graph. The entity library and the inter-entity relationship library for constituting initially the wireless communication network knowledge graph are determined based on the endogenous factors of the wireless communication network; and the endogenous factors of the wireless communication network comprise the indicators and the data fields specified by the wireless communication network protocols. Each element in the entity library includes an entity type corresponding to the element and all attributes owned by the element; and the inter-entity relationship library includes a specific relationship between every two elements, an association degree of the specific relationship between every two elements, etc. “) (examiner interprets second record as “elements”) wherein a third element in the triple among the multiple triples indicates a relationship between the first record and the second record, (see HE [0051] Given a social network graph augmented with a vertex representing the message z and edges connecting this vertex to the vertices in the target user set t.sub.z, the numerator of the above equation is equal to the number of closed triplets around the central vertex that represents the message z. A closed triplet simply represents an edge between two vertices in the target user set t.sub.z. This edge forms a triangle when connected with the vertex that represents the message z. In summary, in order to compute CC.sub.z, we need to count the number of triangles formed by the vertex that represents z and the two vertices from the target user set t.sub.z of the message z, where the latter two are neighbors in the social network graph. FIG. 5 shows a sample social network graph 500 to which the present principles may be applied, in accordance with an embodiment of the present principles. The graph 500 includes nodes A, B, C, D, E, F, and G. The relationships of these nodes constructs the social network graph 500. The graph 500 may serve as an input to step 310 of method 300 and/or as an input to step 410 of method 400, where in both steps 310 and 410 the vertex relationships in a graph are converted into rule patterns. Thus, an edge 510 between node E and node F means they have a connection or relationship. The nodes A, B, C, D, E, F, and G are triangle vertices collectively denoted by the reference numeral 550. Hence, in the example of FIG. 5, the clustering coefficient for message z is calculated as follows:”) wherein the first record and the second record are configured to indicate a data record of the mobile device associated with the wireless telecommunication network or a process running on the mobile device associated with the wireless telecommunication network; (see He [0075] FIG. 6 shows an architecture 600 of a spam filter application running on System S, in accordance with an embodiment of the present principles. The architecture 600 includes a message sender 601, a short message center 605 at the sender side, the spam filter application 620, a short message center 625 at the receiver side, and a plurality of recipients 640. The application 620 receives, as input, a stream of short message events. Each event represents a short message that was sent from a source number P.sub.0 601 to a set of target numbers {P.sub.1, . . . , P.sub.n} 640. For each event, the clustering coefficient is computed and compared against a threshold to detect if the short message involved is a spam message or not. If the clustering coefficient is smaller than or equal to a predefined threshold L, then the telecom operator will be informed that P.sub.0 is a spam message sender and should be blacklisted. If not, then the message is considered as clean, and is forwarded to the list of users in its target set 640. The source number P.sub.0 601 may be considered to correspond to (message) source node Z in FIG. 5, while target numbers {P.sub.1, . . . , P.sub.n} 640 may be considered to correspond to nodes A, B, C, D, E, F, and G (collectively denoted by the reference numeral 550) in FIG. 5”)
generate multiple grammars representing the multiple triples,
wherein the grammar among the multiple grammars includes the multiple concepts and the multiple relationships, (see He [0043] Optionally, the knowledge graph unit may further include the graph construction module. The graph construction module is mainly configured to construct one wireless communication network knowledge graph having a topological structure. A universal triple (head, relation, tail) of the wireless communication network is constructed according to the initialized entity library and the initialized inter-entity relationship library; the head is a head entity in the triple; the tail is a tail entity in the triple; the head entity and the tail entity in each triple both belong to one of the m entity types established in the entity library; and the relation is a relationship between the entities, and belongs to one of the n relationships established in the inter-entity relationship library. Then, the wireless communication network knowledge graph is constructed, and the head entity and the tail entity in the triple are respectively used as nodes of the wireless communication network knowledge graph. If there is a clear pointing relationship between the nodes, a directed line segment is used for connection, otherwise an undirected line segment is used for connection. All nodes having an edge connection relationship are connected, such that the wireless communication network knowledge graph having the topological structure may be obtained, the wireless communication network knowledge graph is generally a multi-relational graph.(examiner interprets multiple grammars as multi-relational graph”) ”) wherein the multiple concepts include a first concept representing the first record and a second concept representing the second record, (see He [0032] “The deep analysis module uses the entity library and inter-entity relationship library for constituting initially the wireless communication network knowledge graph as inputs, and the deep analysis module mainly has two functions. The first function is to quantify and measure the association degree of the relationship between the two entities from the perspective of the graph model algorithm, so as to establish edge connection strength; and the second function is to perform, from the perspective of data, deep analysis on one or more of: the new entities, the new entity types, the new entity attributes, the new inter-entity relationships, and the association degrees in the wireless communication network knowledge graph. The entity library and the inter-entity relationship library for constituting initially the wireless communication network knowledge graph are determined based on the endogenous factors of the wireless communication network; and the endogenous factors of the wireless communication network comprise the indicators and the data fields specified by the wireless communication network protocols. Each element in the entity library includes an entity type corresponding to the element and all attributes owned by the element; and the inter-entity relationship library includes a specific relationship between every two elements, an association degree of the specific relationship between every two elements, etc.”) wherein the multiple relationships represent the relationship between the first record and the second record; (see He [0036] As at least one alternative embodiment, the first of the main functions of the deep analysis module is that, the deep analysis module performs the data-driven deep analysis on wireless communication network knowledge graph, uses real-time data of the wireless communication network collected by a wireless data unit, deeply analyzes the association degree and dependency relationship between nodes (entities) in the wireless communication network knowledge graph from the perspective of data, and determines one or more of: the new entities, the new entity types, the new entity attributes, and the new inter-entity relationships. The second of the main functions of the deep analysis module is that, based on the wireless communication network knowledge graph constructed after shallow analysis, relies on a theoretical analysis method based on the graph model, the deep analysis module deeply excavates endogenous correlations between the nodes, such as a correlation between an indicator and a data field, a correlation between the indicator and the indicator, a correlation between the data field and the data field, etc., i.e., determines the association degrees and the dependency relationships between the entities. The association degree and the dependency relationship between two nodes (entities) are quantified and measured, so as to establish the edge connection strength.”) apply each grammar among the multiple grammars to the multiple triples to obtain an indication of whether each triple among the multiple triples is correct; (see HE [0047] The anomaly detection module is configured to perform anomaly detection on the wireless communication network based on the tuned wireless communication network knowledge graph in combination with the anomaly detection algorithm, so as to determine a fault type.”) based on the indication of whether each triple among the multiple triples is correct, determine an accuracy associated with the each grammar among the multiple grammars; (see HE [0051] The anomaly detection module is mainly configured to perform anomaly detection on the wireless communication network based on the anomaly detection algorithm and the tuned wireless communication network knowledge graph, and complete preliminary detection of the fault type when a network anomaly is found, i.e., analyze which type of entities or which part of a graph structure or which part of the inter-entity relationship is anomalous, including nodes (entities), edges (inter-entity relationships), sub-graph structures (entities need to be added or deleted) with anomalies, etc., so as to indicate a direction for subsequent root cause tracing.”)
He does not specifically teach based on the accuracy associated with the each grammar among the multiple grammars, select a grammar having a highest accuracy among the multiple grammars; However, Chang does teach this limitation. (see Chang [0049-0050] In essence, the application distinguishes spam from regular messages according to personal relationships between callers and callees as represented by a social network graph. In this graph, a vertex v.sub.i denotes a mobile phone user and an edge e.sub.i,j between v.sub.i and another vertex v.sub.j (representing another user) indicates that the two users i and j know each other, as they have called one another in the past. The basic assumption behind such a model is that spam messages are usually sent out to a large number of randomly selected targets. [0050] For a given message, the clustering coefficient is a measure of how connected a target user of the message is with the set of all target users of the message. If this measure is high, then it implies that the message is sent to a set of users that know each other for the most part and, thus, is not a spam message. To define this more formally, let z be a message and t.sub.z be the set of users that are targets of this message. To compute the clustering coefficient for message z, denoted by CC.sub.z, we first look at each user v.epsilon.t.sub.z in the target set of the message z and then compute the fraction of the users in the target set t.sub.z-{v} that are also in the set of v's neighbors, denoted by e.sub.v. This measure is given by |e.sub.v.andgate.t.sub.z|/(|t.sub.z|-1) for v.epsilon.t.sub.z. After averaging over all target users, we obtain the following:”) apply the selected grammar to a first triple of the multiple triples to obtain an indication of whether the first triple is correct; (see Chang [0049] In essence, the application distinguishes spam from regular messages according to personal relationships between callers and callees as represented by a social network graph. In this graph, a vertex v.sub.i denotes a mobile phone user and an edge e.sub.i,j between v.sub.i and another vertex v.sub.j (representing another user) indicates that the two users i and j know each other, as they have called one another in the past. The basic assumption behind such a model is that spam messages are usually sent out to a large number of randomly selected targets.”)(see Figure 6, Element 620) and upon obtaining the indication that the first triple is incorrect, determine whether the selected grammar is spam. (see Chang [0090] “Algorithm 1 gives the pseudo-code. The basic idea is to count the closed triplets around the vertex z that represents the source of the message. Lines 2 and 3 are used to get one edge u, w where u.epsilon.t.sub.z (u is in the target user set of z) and w.epsilon.e.sub.u (w is amongst the neighbors of u) in G; and line 4 checks whether z,u,w forms a closed triplet. This check can be implemented in different ways. A brute-force method simply iterates over every element in t.sub.z to determine if w.epsilon.t.sub.z, yielding a total running time complexity of O(d.n.sup.2), where n is the size of the target user set of the message, i.e., n=|t.sub.z|, and d is the average degree of a vertex in the graph. For messages that are likely to be spam, n is often much larger than d. Given this property, building a hash table out of t.sub.z can reduce the cost of the w.epsilon.t.sub.z check to O(1). This brings the overall algorithmic complexity to O(d.n).”)
He and Chang are in the same field of endeavor of signal processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the storage medium of He to incorporate the teachings of Chang to include based on the accuracy associated with the each grammar among the multiple grammars, select a grammar having a highest accuracy among the multiple grammars; apply the selected grammar to a first triple of the multiple triples to obtain an indication of whether the first triple is correct; and upon obtaining the indication that the first triple is incorrect, determine whether the selected grammar is spam. Doing so allows for improved synchronization and spam detection as recognized by Chang in [0045].
Regarding independent claim 8, claim 8 is a method claim with limitations similar to that of claim 1 and is rejected under the same rationale.
Regarding independent claim 14, Claim 14 is a system claim with limitations similar to that of claim 1 and is rejected under the same rationale.
As to Claim 2, He in view of Chang teaches 2. (Currently Amended) The non-transitory, computer-readable storage medium of claim 1,
Furthermore, Chang teaches, comprising instructions to: wherein the corpus of data further includes multiple messages passed between multiple mobile devices associated with the wireless telecommunication network, (See Chang [0043] The proliferation of mobile devices, coupled with continuous connectivity, has resulted in a world where massive amounts of data is being produced, on a daily basis, as a result of online interactions between people. These interactions are often captured as relationships in a social network graph, by service providers such as mobile carriers or social web applications. Social network analysis is becoming a common technique for extracting business intelligence from social network graphs in order to improve customer experience and provide better service. Some applications in this domain require processing massive data flows with high-throughput and low-latency, in order to deliver timely results. A spam filter application used for streaming social network analysis (hereinafter referred to as "spam filter" in short) fits this description. Such a spam filter can be used for real time filtering of short messages in mobile communications, with the goal of preventing spam. The ever increasing volume of mobile users and rates of messages make real-time detection of spam a challenging problem with respect to performance and scalability.”) wherein the first element in the triple among the multiple triples indicates a first mobile device, wherein the second element in the triple among the multiple triples indicates a second mobile device, wherein the third element in the triple among the multiple triples indicates a property of a message passed between the first mobile device and the second mobile device; (See Chang [0050] “For a given message, the clustering coefficient is a measure of how connected a target user of the message is with the set of all target users of the message. If this measure is high, then it implies that the message is sent to a set of users that know each other for the most part and, thus, is not a spam message. To define this more formally, let z be a message and t.sub.z be the set of users that are targets of this message. To compute the clustering coefficient for message z, denoted by CC.sub.z, we first look at each user v.epsilon.t.sub.z in the target set of the message z and then compute the fraction of the users in the target set t.sub.z-{v} that are also in the set of v's neighbors, denoted by e.sub.v. This measure is given by |e.sub.v.andgate.t.sub.z|/(|t.sub.z|-1) for v.epsilon.t.sub.z. After averaging over all target users, we obtain the following: [0051] Given a social network graph augmented with a vertex representing the message z and edges connecting this vertex to the vertices in the target user set t.sub.z, the numerator of the above equation is equal to the number of closed triplets around the central vertex that represents the message z. A closed triplet simply represents an edge between two vertices in the target user set t.sub.z. This edge forms a triangle when connected with the vertex that represents the message z. In summary, in order to compute CC.sub.z, we need to count the number of triangles formed by the vertex that represents z and the two vertices from the target user set t.sub.z of the message z, where the latter two are neighbors in the social network graph. FIG. 5 shows a sample social network graph 500 to which the present principles may be applied, in accordance with an embodiment of the present principles. The graph 500 includes nodes A, B, C, D, E, F, and G. The relationships of these nodes constructs the social network graph 500. The graph 500 may serve as an input to step 310 of method 300 and/or as an input to step 410 of method 400, where in both steps 310 and 410 the vertex relationships in a graph are converted into rule patterns. Thus, an edge 510 between node E and node F means they have a connection or relationship. The nodes A, B, C, D, E, F, and G are triangle vertices collectively denoted by the reference numeral 550. Hence, in the example of FIG. 5, the clustering coefficient for message z is calculated as follows:”) obtain a first message passed between the first mobile device and the second mobile device, wherein the message is not among the multiple messages; (see Chang [0075] FIG. 6 shows an architecture 600 of a spam filter application running on System S, in accordance with an embodiment of the present principles. The architecture 600 includes a message sender 601, a short message center 605 at the sender side, the spam filter application 620, a short message center 625 at the receiver side, and a plurality of recipients 640. The application 620 receives, as input, a stream of short message events. Each event represents a short message that was sent from a source number P.sub.0 601 to a set of target numbers {P.sub.1, . . . , P.sub.n} 640. For each event, the clustering coefficient is computed and compared against a threshold to detect if the short message involved is a spam message or not. If the clustering coefficient is smaller than or equal to a predefined threshold L, then the telecom operator will be informed that P.sub.0 is a spam message sender and should be blacklisted. If not, then the message is considered as clean, and is forwarded to the list of users in its target set 640. The source number P.sub.0 601 may be considered to correspond to (message) source node Z in FIG. 5, while target numbers {P.sub.1, . . . , P.sub.n} 640 may be considered to correspond to nodes A, B, C, D, E, F, and G (collectively denoted by the reference numeral 550) in FIG. 5”) create the first triple including the first mobile device, the second mobile device, and a property associated with the first message upon obtaining the indication that the first triple is incorrect, determine that the message is a spam message. (see Chang [0053] “In a typical configuration, the spam filter application manages a social network graph with several tens of millions of vertices. An important aspect of this dataset is that the social network evolves slowly. Thus, accurately scoring of incoming messages does not require updating the graph in a streaming fashion. Instead, the social network graph can be updated offline, using a batch process. In contrast, counting the number of closed triplets accurately is a key ingredient in our clustering coefficient computation that should be performed on a per-message basis. Interestingly, based on our profiling runs, we have established that the queries for assessing inter-user connectivity in social network graphs (used for the closed triplet counting) account for approximately 90% of the total execution time in determining whether a message is legitimate or not, when the edge-iterator algorithm is used.”)
He in view of Chang are in the same field of endeavor of signal processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of He to incorporate the teachings of Chang to include wherein the corpus of data further includes multiple messages passed between multiple mobile devices associated with the wireless telecommunication network, wherein the first element in the triple among the multiple triples indicates a first mobile device, wherein the second element in the triple among the multiple triples indicates a second mobile device, wherein the third element in the triple among the multiple triples indicates a property of a message passed between the first mobile device and the second mobile device; obtain a first message passed between the first mobile device and the second mobile device, wherein the message is not among the multiple messages; create the first triple including the first mobile device, the second mobile device, and a property associated with the first message upon obtaining the indication that the first triple is incorrect, determine that the message is a spam message. Doing so allows for improved synchronization and spam detection as recognized by Chang in [0045].
As to Claim 3, HE in view of Chang teaches 3. (Currently Amended) The non-transitory, computer-readable storage medium of claim 1,
Furthermore, He teaches comprising instructions to: wherein the corpus of data further includes multiple relationships between multiple components of the wireless telecommunication network, wherein the multiple components include a radio network, a core network, and an Internet protocol (IP) network, (see He [0004] One wireless communication network is constituted by a plurality of elements such as a user terminal, an access network, a core network, etc., and the network structure is generally relatively complex, including tens of thousands of performance indicators and data fields, and involving different network elements, protocol stacks, and so on. These indicators are generally used for characterizing the performance of the wireless communication network, and are important parameters to measure an operating state of one wireless communication network. In another aspect, these indicators are also closely related to Quality of Experience (QoE) of users. In view of the complexity of a wireless communication network structure and the diversity of service types, these endogenous factor correlations between wireless communication network data are often complex. Thus, there are numerous factors that affect the performance of the wireless communication networks and the QoE of the users, and the structure and operating mechanism of the entire wireless communication network can be better understood by effectively clarifying the correlations among the various indicators and the data fields, so as to accurately position causes of these changes when the performance of networks deteriorates and the QoE of the users degrades, thereby providing corresponding tuning solutions for specific causes.”) (see He [0042] “For example, the indicators includes a Radio Resource Control (RRC) connection establishment success rate,”) (see He [0111] According to the method for intelligently analyzing and applying the wireless communication network knowledge graph provided in the present disclosure, through the endogenous factors of the wireless communication network, the wireless communication network knowledge graph is preliminarily constructed in combination with the communication expert knowledge and the 3GPP communication protocols, deep analysis of the correlations between the endogenous factors is performed, the tuning and updating of the graph are completed by using analysis results, after a network communication anomaly is found, root cause tracing and diagnostic positioning are performed by using the intelligent reasoning module, the strategy generation module is configured to provide the plurality of feasible tuning strategies, and then the strategy decision module selects and issues one specific strategy to the wireless communication network for being execution, so as to cyclically tune the wireless communication network, such that the performance of the wireless communication network and the QoE of users are effectively improved.”)
obtain an indication of a failure associated with the wireless telecommunication network; obtain an interaction between the first component and the second component; (see He [0165] Step IV, detection and reasoning are performed. When fluctuations or anomalies occur in the KPIs in the performance of the wireless communication network, such as the problems that delay or the error rate suddenly increases, reductive inference is performed according to the constructed wireless communication network knowledge graph and the correlation between the nodes (inter-entity relationship) based on algorithms such as a graph neural network algorithm, an approximate reasoning algorithm, a causal inference algorithm, etc., so as to position the indicators or the data fields at which layer there is a problem. In this embodiment, the reductive inference is performed if the error rate is higher than 10.sup.−5 and the air interface delay is higher than 1 ms.”) create the first triple including the first component, the second component, and the interaction; and (see He [0163] Step II, a knowledge graph is constructed. Through wireless communication network protocol (such as 3GPP communication protocols) specifications or in combination with an expert knowledge in the field of communications, the endogenous factors of the wireless communication network constituted by the indicators and the data fields specified therein is determined, the indicators and the data fields specified in the wireless communication network are sorted, and attributes such as communication layers, types, tunability, parameter values, etc. are clarified as entity nodes of the knowledge graph. The relationship between the entities is clarified; a triple (i.e., a head entity, a relation, and a tail entity) is constructed; and the wireless communication network knowledge graph having a topological structure is constructed. FIG. 5 shows a partial schematic diagram of an air interface delay knowledge graph. For example, an entity of which entity type is determined as a general non-tunable data parameter includes: a Physical Uplink Share Channel (PUSCH) block error rate, a total number of Uplink (UL) responses per second, the number of UL negative responses per second, and the number of New Radio (NR) Control Channel Elements (CCEs), and the entity type corresponding to Inner Loop Link Adaptation (ILLA). An entity of which entity type is determined as a tunable data parameter includes: an Outer Loop Link Adaptation offset (OLLA offset), uplink averaging Modulation and Coding Scheme (MCS) for non-retransmission, uplink Binary Phase Shift Keying (BPSK) modulation proportion per second, uplink Quadrature Phase Shift Keying (QPSK) modulation proportion per second, uplink 16 Quadrature Amplitude Modulation (16QAM) proportion per second, uplink 64 Quadrature Amplitude Modulation (64QAM) proportion per second, and uplink 256 Quadrature Amplitude Modulation (256QAM) proportion per second. In combination with requirements of a scenario, in this embodiment, key indicators of the wireless communication network include a network delay of an air interface and a core network at various stages, and an error rate. The data field relates to the air interface and the core network data.”) upon determining that the grammar is spam, determine that the interaction between the first component and the second component is associated with the failure associated with the wireless telecommunication network. (see He [0057] As at least one alternative embodiment, the intelligent reasoning module uses the fault types obtained by the anomaly detection module as inputs; and according to the constructed wireless communication network knowledge graph and the endogenous factor correlations of the wireless communication network knowledge, root cause tracing of problems affecting the performance of the wireless communication network is completed by means of artificial intelligence methods such as an approximate reasoning method, a causal inference method, etc., i.e., which indicators or data fields at which layer cause unusual fluctuations in the corresponding indicators are deeply excavated, thereby a specific entity type of the entity is determined. There may be one or more fault types, and there may also be one or more corresponding entities. Which data fields are tunable parameters are screened according to the entity attributes to which the entity belongs; and specific causes (e.g., wireless channel fading, network congestion, etc.) of changes in these indicators or data fields are reasoned; and other entities associated with the entity are determined according to the inter-entity relationships, and which data fields of the associated entity are tunable parameters, as well as the specific causes of changes in these indicators or data fields, are reasoned by using the same method..”)
Furthermore Chang teaches wherein the first element in the triple among the multiple triples indicates a first component of the wireless telecommunication network, wherein the second element in the triple among the multiple triples indicates a second component of the wireless telecommunication network, wherein the third element in the triple among the multiple triples indicates a relationship between the first component and the second component; (See Chang [0050] “For a given message, the clustering coefficient is a measure of how connected a target user of the message is with the set of all target users of the message. If this measure is high, then it implies that the message is sent to a set of users that know each other for the most part and, thus, is not a spam message. To define this more formally, let z be a message and t.sub.z be the set of users that are targets of this message. To compute the clustering coefficient for message z, denoted by CC.sub.z, we first look at each user v.epsilon.t.sub.z in the target set of the message z and then compute the fraction of the users in the target set t.sub.z-{v} that are also in the set of v's neighbors, denoted by e.sub.v. This measure is given by |e.sub.v.andgate.t.sub.z|/(|t.sub.z|-1) for v.epsilon.t.sub.z. After averaging over all target users, we obtain the following: [0051] Given a social network graph augmented with a vertex representing the message z and edges connecting this vertex to the vertices in the target user set t.sub.z, the numerator of the above equation is equal to the number of closed triplets around the central vertex that represents the message z. A closed triplet simply represents an edge between two vertices in the target user set t.sub.z. This edge forms a triangle when connected with the vertex that represents the message z. In summary, in order to compute CC.sub.z, we need to count the number of triangles formed by the vertex that represents z and the two vertices from the target user set t.sub.z of the message z, where the latter two are neighbors in the social network graph. FIG. 5 shows a sample social network graph 500 to which the present principles may be applied, in accordance with an embodiment of the present principles. The graph 500 includes nodes A, B, C, D, E, F, and G. The relationships of these nodes constructs the social network graph 500. The graph 500 may serve as an input to step 310 of method 300 and/or as an input to step 410 of method 400, where in both steps 310 and 410 the vertex relationships in a graph are converted into rule patterns. Thus, an edge 510 between node E and node F means they have a connection or relationship. The nodes A, B, C, D, E, F, and G are triangle vertices collectively denoted by the reference numeral 550. Hence, in the example of FIG. 5, the clustering coefficient for message z is calculated as follows:”)
He in view of Chang are in the same field of endeavor of signal processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of He and Chang to incorporate the teachings of Chang to include the first element in the triple among the multiple triples indicates a first component of the wireless telecommunication network, wherein the second element in the triple among the multiple triples indicates a second component of the wireless telecommunication network, wherein the third element in the triple among the multiple triples indicates a relationship between the first component and the second component. Doing so allows for improved synchronization and spam detection as recognized by Chang in [0045].
As to Claim 4, HE in view of Chang teaches 4. The non-transitory, computer-readable storage medium of claim 1,
Furthermore He teaches wherein the second element in the triple among the multiple triples indicates the first component associated with the wireless telecommunication network, wherein the third element in the triple among the multiple triples indicates an interaction between the mobile device and the first component associated with the wireless telecommunication network; (see He (see He [0163] Step II, a knowledge graph is constructed. Through wireless communication network protocol (such as 3GPP communication protocols) specifications or in combination with an expert knowledge in the field of communications, the endogenous factors of the wireless communication network constituted by the indicators and the data fields specified therein is determined, the indicators and the data fields specified in the wireless communication network are sorted, and attributes such as communication layers, types, tunability, parameter values, etc. are clarified as entity nodes of the knowledge graph. The relationship between the entities is clarified; a triple (i.e., a head entity, a relation, and a tail entity) is constructed; and the wireless communication network knowledge graph having a topological structure is constructed. FIG. 5 shows a partial schematic diagram of an air interface delay knowledge graph. For example, an entity of which entity type is determined as a general non-tunable data parameter includes: a Physical Uplink Share Channel (PUSCH) block error rate, a total number of Uplink (UL) responses per second, the number of UL negative responses per second, and the number of New Radio (NR) Control Channel Elements (CCEs), and the entity type corresponding to Inner Loop Link Adaptation (ILLA). An entity of which entity type is determined as a tunable data parameter includes: an Outer Loop Link Adaptation offset (OLLA offset), uplink averaging Modulation and Coding Scheme (MCS) for non-retransmission, uplink Binary Phase Shift Keying (BPSK) modulation proportion per second, uplink Quadrature Phase Shift Keying (QPSK) modulation proportion per second, uplink 16 Quadrature Amplitude Modulation (16QAM) proportion per second, uplink 64 Quadrature Amplitude Modulation (64QAM) proportion per second, and uplink 256 Quadrature Amplitude Modulation (256QAM) proportion per second. In combination with requirements of a scenario, in this embodiment, key indicators of the wireless communication network include a network delay of an air interface and a core network at various stages, and an error rate. The data field relates to the air interface and the core network data.”)
Furthermore Chang teaches, comprising instructions to: wherein the corpus of data further includes multiple interactions between a user of a mobile device and a first component associated with the wireless telecommunication network, wherein the first element in the triple among the multiple triples indicates the mobile device, (see Chang [0051] Given a social network graph augmented with a vertex representing the message z and edges connecting this vertex to the vertices in the target user set t.sub.z, the numerator of the above equation is equal to the number of closed triplets around the central vertex that represents the message z. A closed triplet simply represents an edge between two vertices in the target user set t.sub.z. This edge forms a triangle when connected with the vertex that represents the message z. In summary, in order to compute CC.sub.z, we need to count the number of triangles formed by the vertex that represents z and the two vertices from the target user set t.sub.z of the message z, where the latter two are neighbors in the social network graph. FIG. 5 shows a sample social network graph 500 to which the present principles may be applied, in accordance with an embodiment of the present principles. The graph 500 includes nodes A, B, C, D, E, F, and G. The relationships of these nodes constructs the social network graph 500. The graph 500 may serve as an input to step 310 of method 300 and/or as an input to step 410 of method 400, where in both steps 310 and 410 the vertex relationships in a graph are converted into rule patterns. Thus, an edge 510 between node E and node F means they have a connection or relationship. The nodes A, B, C, D, E, F, and G are triangle vertices collectively denoted by the reference numeral 550. Hence, in the example of FIG. 5, the clustering coefficient for message z is calculated as follows:”)
obtain an information campaign associated with the wireless telecommunication network, and a second component associated with the wireless telecommunication network and associated with the information campaign; determine whether the first component and the second component correspond to each other; and upon determining that the first component and the second component correspond to each other, present the information campaign to the user of the mobile device. (see Chang [0075] “FIG. 6 shows an architecture 600 of a spam filter application running on System S, in accordance with an embodiment of the present principles. The architecture 600 includes a message sender 601, a short message center 605 at the sender side, the spam filter application 620, a short message center 625 at the receiver side, and a plurality of recipients 640. The application 620 receives, as input, a stream of short message events. Each event represents a short message that was sent from a source number P.sub.0 601 to a set of target numbers {P.sub.1, . . . , P.sub.n} 640. For each event, the clustering coefficient is computed and compared against a threshold to detect if the short message involved is a spam message or not. If the clustering coefficient is smaller than or equal to a predefined threshold L, then the telecom operator will be informed that P.sub.0 is a spam message sender and should be blacklisted. If not, then the message is considered as clean, and is forwarded to the list of users in its target set 640. The source number P.sub.0 601 may be considered to correspond to (message) source node Z in FIG. 5, while target numbers {P.sub.1, . . . , P.sub.n} 640 may be considered to correspond to nodes A, B, C, D, E, F, and G (collectively denoted by the reference numeral 550) in FIG. 5.”)
He and Chang are in the same field of endeavor of signal processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the storage medium of He to incorporate the teachings of Chang to include comprising instructions to: wherein the corpus of data further includes multiple interactions between a user of a mobile device and a first component associated with the wireless telecommunication network, wherein the first element in the triple among the multiple triples indicates the mobile device, obtain an information campaign associated with the wireless telecommunication network, and a second component associated with the wireless telecommunication network and associated with the information campaign; determine whether the first component and the second component correspond to each other; and upon determining that the first component and the second component correspond to each other, present the information campaign to the user of the mobile device. Doing so allows for improved synchronization and spam detection as recognized by Chang in [0045].
As to Claim 9, claim 9 is a device claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to Claim 10, claim 10 is a device claim with limitations similar to that of claim 3 and is rejected under the same rationale.
As to Claim 11, claim 11 is a device claim with limitations similar to that of claim 4 and is rejected under the same rationale.
Regarding Claim 15, claim 15 is a device claim with limitations similar to that of claim 2 and is rejected under the same rationale.
Regarding Claim 16, claim 16 is a device claim with limitations similar to that of claim 3 and is rejected under the same rationale.
Regarding claim 17, claim 17 is a device claim with limitations similar to that of claim 4 and is rejected under the same rationale.
Claims 5-7, 12, 13, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over HE (US Patent Number US 20250168669 A1), in view of Chang (US Patent Number US 20130013549 A1) and further in view of AKIMA (US Patent Number US-20230297782-A1),
As to Claim 5, HE in view of Chang teaches 5. The non-transitory, computer-readable storage medium of claim 1,
HE in view of Chang do not specifically teach comprising instructions to: apply the grammar to the multiple triples representing the corpus of data to identify a subset of the multiple triples that are incorrect according to the grammar; and tag the subset of the multiple triples as invalid data. (However, AKIMA does teach this limitation (see AKIMA [0039-0041] “Then, the triple generation unit 11 creates a verb list (VL) by referring to the part-of-speech tags in the AMR syntax analysis result 10a (S3). Then, the triple generation unit 11 determines whether or not the verb list VL is empty (S4) and, when the verb list VL is not empty (S4: No), performs the processes in S5 to S7. [0040] For example, the triple generation unit 11 takes out a verb V from the verb list VL (S5). Then, the triple generation unit 11 extracts the arguments (ARG_m (head) and ARG_n (tail)) of the verb V with reference to the AMR syntax analysis result 10a (S6). For example, in S6, the triple generation unit 11 extracts the subject and the object with respect to the verb V. [0041] Then, the triple generation unit 11 adds the triple (head, V, tail) to a triple list (TL) (S7) and returns the process to S4.”)
HE in view of Chang and Akima are in the same field of endeavor of signal processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of HE and Chang to incorporate the teachings of Akima to include comprising instructions to: apply the grammar to the multiple triples representing the corpus of data to identify a subset of the multiple triples that are incorrect according to the grammar; and tag the subset of the multiple triples as invalid data. Doing so allows for analysis of the text corpus as recognized by Akima in [0038].
Regarding claim 6, HE in view of Chang teaches 6. The non-transitory, computer-readable storage medium of claim 1,
HE in view of Chang do not specifically teach wherein the instructions to extract the multiple triples comprise instructions to: obtain a second corpus of data; extract, from the second corpus of data, a second multiplicity of triples representing the second corpus of data; apply the grammar to the second corpus of data to obtain a second accuracy associated with the grammar and the second corpus of data; and adjust the grammar based on the second accuracy. (However, AKIMA does teach this limitation (see AKIMA [0039-0041] Then, the triple generation unit 11 creates a verb list (VL) by referring to the part-of-speech tags in the AMR syntax analysis result 10a (S3). Then, the triple generation unit 11 determines whether or not the verb list VL is empty (S4) and, when the verb list VL is not empty (S4: No), performs the processes in S5 to S7. [0040] For example, the triple generation unit 11 takes out a verb V from the verb list VL (S5). Then, the triple generation unit 11 extracts the arguments (ARG_m (head) and ARG_n (tail)) of the verb V with reference to the AMR syntax analysis result 10a (S6). For example, in S6, the triple generation unit 11 extracts the subject and the object with respect to the verb V.[0041] Then, the triple generation unit 11 adds the triple (head, V, tail) to a triple list (TL) (S7) and returns the process to S4.”)
HE in view of Chang and Akima are in the same field of endeavor of signal processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of HE and Chang to incorporate the teachings of Akima to include wherein the instructions to extract the multiple triples comprise instructions to: obtain a second corpus of data; extract, from the second corpus of data, a second multiplicity of triples representing the second corpus of data; apply the grammar to the second corpus of data to obtain a second accuracy associated with the grammar and the second corpus of data; and adjust the grammar based on the second accuracy. Doing so allows for analysis of the text corpus as recognized by Akima in [0038].
Regarding claim 7, HE in view of Chang teaches 7. The non-transitory, computer-readable storage medium of claim 1,
HE in view of Chang do not specifically teach, wherein the instructions to extract the multiple triples comprise instructions to: use artificial intelligence (AI) to extract, from the corpus of data, the multiple triples. (However, AKIMA does teach this limitation (see AKIMA [0033] As an example, the text 30a acquired from the text corpus DB 30 is assumed as “Because Taro opened the window, the wind came into the room”. The AMR syntax analysis unit 10 obtains the AMR syntax analysis result 10a by analyzing the text 30a using a known technique such as StoG. (examiner interprets AI as “SToG”)
HE in view of Chang and Akima are in the same field of endeavor of signal processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of HE and Chang to incorporate the teachings of Akima to include wherein the instructions to extract the multiple triples comprise instructions to: use artificial intelligence (AI) to extract, from the corpus of data, the multiple triples. Doing so allows for analysis of the text corpus as recognized by Akima in [0038].
As to Claim 12, claim 12 is a device claim with limitations similar to that of claim 5 and is rejected under the same rationale.
As to Claim 13, claim 13 is a device claim with limitations similar to that of claim 6 and is rejected under the same rationale.
Regarding claim 18, claim 18 is a device claim with limitations similar to that of claim 5 and is rejected under the same rationale.
Regarding claim 19, claim 19 is a device claim with limitations similar to that of claim 6 and is rejected under the same rationale.
Regarding claim 20, claim 20 is a device claim with limitations similar to that of claim 7 and is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee (US Publication No. US 20260268173 A1) METHOD AND DEVICE FOR TRANSMITTING AND RECEIVING BACKGROUND KNOWLEDGE
Stock (US Patent No. US-20170302793-A1) BLOCKING UNWANTED COMMUNICATIONS FOR A SUBSCRIBER POOL
Brosnan (US Publication No. US 11133999 B1) Network Sensor Deployment For Deep Packet Inspection
Singaraju (US Publication No. US 20200057946 A1) TECHNIQUES FOR BUILDING A KNOWLEDGE GRAPH IN LIMITED KNOWLEDGE DOMAINS
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/KRISTEN MICHELLE MASTERS/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
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