DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. Claims 1 – 20 are currently pending in this application.
Claims 1-2, 5, 7, 11, 13, 15, and 19 are amended as filed on 03/12/2026.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-15 of U.S. Patent No. 12,095,628 B2, hereinafter 628. Although the claims at issue are not identical, they are not patentably distinct from each other because:
18/829627
628
Claims 1, 11, and 19:
A system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to: receive an indication of a performance issue affecting a user equipment communicating on a telecommunications network,
wherein the indication is reported from the user equipment at a point in time in response to a user-input to a mobile application executing on the user equipment;
in response to the indication of the performance issue, receive data from a user domain and data from a network domain related to the performance issue, wherein the data from user domain includes test data from a diagnostic test performed at the user equipment in response to reporting the indication of the performance issue, wherein the data from the network domain includes data related to a state of the telecommunications network collected by one or more first network nodes of the telecommunications network during a time period that includes the point in time;
correlate the data from the user domain and the data from the network domain with a call detail record (CDR) recorded by a second network node of the telecommunications network during the time period to generate an enhanced CDR; and cause a machine learning model to generate a prediction of a cause of the performance issue based on the enhanced CDR, wherein the machine learning model is trained to diagnose performance issues of the telecommunications network or the user equipment based on a set of unique datapoint signatures labeled with respective causes of previous performance issues,
wherein correlating the data from the user domain and the data from the network domain with the CDR includes: identifying, within the data from the network domain, network information that indicates one or more states of one or more network nodes during an action initiated by the user equipment on the telecommunications network, identifying, within the CDR, a CDR entry that indicates the action initiated by the user equipment on the telecommunications network, identifying, within the data from the user domain, a performance metric of the user equipment that is relevant to performance the action initiated by the user equipment on the telecommunications network, and generating the enhanced CDR to include the network information and the performance metric of the wireless device in correlation with the CDR entry;
wherein each of the unique datapoint signatures associated with a respective previous performance issue includes at least a previous enhanced CDR generated from previous user domain data related to the respective previous performance issue, previous network domain data related to the respective previous performance issue, and a previous CDR related to the respective previous performance issue
Claim 1:
A system for diagnosing issues in a telecommunications network comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to: receive an indication of a performance issue affecting a user equipment,
wherein the indication is reported from the user equipment at a point in time in response to a user-input to a mobile application executing on the user equipment;
in response to the indication of the performance issue, cause the user equipment to perform a diagnostic test; receive test result data of the diagnostic test performed at the user equipment;
&
correlate the test result data with a call detail record (CDR) recorded by a core network node of the telecommunications network that spans a time period including the point in time when the indication of the performance issue was reported;
correlate the test result data with a call detail record (CDR) recorded by a core network node of the telecommunications network that spans a time period including the point in time when the indication of the performance issue was reported; cause a machine learning model to generate a prediction of a cause of the performance issue based on the CDR correlated with the test result data, wherein the machine learning model is trained to diagnose performance issues of the telecommunications network or the user equipment based on a set of unique datapoint signatures labeled with respective causes of respective performance issues,
correlate the test result data with a call detail record (CDR) recorded by a core network node of the telecommunications network that spans a time period including the point in time when the indication of the performance issue was reported; cause a machine learning model to generate a prediction of a cause of the performance issue based on the CDR correlated with the test result data, wherein the machine learning model is trained to diagnose performance issues of the telecommunications network or the user equipment based on a set of unique datapoint signatures labeled with respective causes of respective performance issues,
wherein each of the unique datapoint signatures correspond to a respective user equipment experiencing the respective performance issue, wherein each of the unique datapoint signatures comprise a device log collected from a diagnostic test of the respective user equipment performed by a third-party service and correlated, based on a network identifier of the respective user equipment, with a respective CDR of the respective user equipment, wherein the network identifier is included within the device log, wherein the respective CDR of the respective user equipment is contemporaneous with a respective time period that includes a time in which the respective user equipment experienced the respective performance issue
As for claims 2-10, 12-18, and 20 they are rejected, at least, based on their respective dependencies on claims 1, 11, and 19.
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.
Claim(s) 1-5, 7-9, 11-13, 15-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Babu Balasubramani et al. (Patent No. US 11,252,052 B1), hereinafter Babu, in view of Tapia et al. (Pre-Grant Publication No. US 2020/0304364 A1), hereinafter Tapia, and in further view of O’Reilly et al. (Patent No. US 5,825,769), hereinafter O’Reilly.
2. With respect to claims 1, 11, and 19, Babu taught a system comprising: at least one hardware processor (4:3-18); and at least one non-transitory memory storing instructions (4:3-18), which, when executed by the at least one hardware processor, cause the system to: receive an indication of a performance issue affecting a user equipment communicating on a telecommunications network (6:32-40), in response to the indication of the performance issue, receive data from a user domain and data from a network domain related to the performance issue (2:19-38 & 6:32-40), wherein the data from user domain includes test data indicating operation of the user equipment captured from a diagnostic test performed at the user equipment in response to reporting the indication of the performance issue (29:18-33), wherein the data from the network domain includes data related to a state of the telecommunications network collected by one or more first network nodes of the telecommunications network during a time period that includes the point in time (4:47-61 & 4:62-5:12); correlate the data from the user domain and the data from the network domain with a record recorded by a second network node of the telecommunications network during the time period to generate an enhanced record (2:1-20, the analytical records); and cause a machine learning model to generate a prediction of a cause of the performance issue based on the enhanced record (2:1-20), wherein the machine learning model is trained to diagnose performance issues of the telecommunications network or the user equipment based on a set of unique datapoint signatures labeled with respective causes of previous performance issues (10:16-47, where the patterns, classifications, and types of failures combine to form unique datapoint signature labels for a particular type of issue under broadest reasonable interpretation), wherein each of the unique datapoint signatures associated with a respective previous performance issue includes at least a previous enhanced record generated from previous user domain data related to the respective previous performance issue, previous network domain data related to the respective previous performance issue, and a previous record related to the respective previous performance issue (10:16-47, where the NAR reflects the enhanced records, the data associated with the historical patterns reflects the previous performance issues, and the historical data is stored in the previous records).
However, Babu did not explicitly state that the data records were call data records and wherein the indication is reported from the user equipment at a point in time in response to a user-input to a mobile application executing on the user equipment. On the other hand, Tapia did teach that the data records were call data records (0055) and wherein the indication is reported from the user equipment at a point in time in response to a user-input to a mobile application executing on the user equipment (0063). Both of the systems of Babu and Tapia are directed towards telecommunication fault mitigation and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings of Babu, to utilize CDRs and customer feedback, as taught by Tapia, in order to better effectuate fault mitigation by receiving a higher volume of relevant data.
However, Babu did not explicitly state wherein correlating the data from the user domain and the data from the network domain with the CDR includes: identifying, within the data from the network domain, network information that indicates one or more states of one or more network nodes during an action initiated by the user equipment on the telecommunications network, identifying, within the CDR, a CDR entry that indicates the action initiated by the user equipment on the telecommunications network, identifying, within the data from the user domain, a performance metric of the user equipment that is relevant to performance the action initiated by the user equipment on the telecommunications network, and generating the enhanced CDR to include the network information and the performance metric of the wireless device in correlation with the CDR entry. On the other hand, O’Reilly did teach wherein correlating the data from the user domain and the data from the network domain with the CDR includes: identifying, within the data from the network domain, network information that indicates one or more states of one or more network nodes during an action initiated by the user equipment on the telecommunications network, identifying, within the CDR, a CDR entry that indicates the action initiated by the user equipment on the telecommunications network, identifying, within the data from the user domain, a performance metric of the user equipment that is relevant to performance the action initiated by the user equipment on the telecommunications network, and generating the enhanced CDR to include the network information and the performance metric of the wireless device in correlation with the CDR entry (2:13-25 & 15:17-35). Both of the systems of Babu and O’Reilly are directed towards managing telecommunications and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings of Babu, to utilize specific ECDRs, as taught by Babu, in order to for effectively handle telecommunication faults.
3. As for claim 2, it is rejected on the same basis as claim 1. In addition, Tapia taught to receive, from the user equipment, feedback from the mobile application relating to the generated prediction; provide the feedback to the machine learning model; and update a configuration of the machine learning model based on the feedback and the prediction (0056).
4. As for claim 3, it is rejected on the same basis as claim 1. In addition, Babu taught to correct the performance issue or generate a support ticket based on the prediction of the cause of the performance issue (19:63-67, where this, at least, teaches the support ticket limitation).
5. As for claim 4, 12, and 20, they are rejected on the same basis as claims 1, 11, and 19 (respectively). In addition, Babu taught wherein the indication of the performance issue comprises a report of a slow network speed, a report of a lack of network coverage, a report of a dropped voice call, a report of low audio quality during a completed voice call, or a report of video playback errors (12:43-61, where this, at least, teaches the network speed limitation).
6. As for claim 5 and 13, they are rejected on the same basis as claims 1 and 11 (respectively). In addition, Babu taught wherein the data from the user domain comprises voice quality data relating to a voice call at the user equipment (12:43-61, the voice connectivity performance).
7. As for claim 7 and 15, they are rejected on the same basis as claims 1 and 11 (respectively). In addition, Babu taught wherein the data from the user domain comprises communication speed data from a communication speed test between the user equipment and the telecommunications network (19:3-27, where the KPIs including speed can be seen in 12:43-61).
8. As for claim 8 and 16, they are rejected on the same basis as claims 1 and 11 (respectively). In addition, Babu taught wherein the data from the network domain comprises a Key Performance Indicator collected by the one or more first network nodes of the telecommunications network (22:41-60, where the KPIs can be seen in 8:4-22).
9. As for claim 9 and 17, they are rejected on the same basis as claims 1 and 11 (respectively). In addition, Tapia taught wherein the data from the network domain comprises a network trace collected by the one or more first network nodes of the telecommunications network (0026).
Claim(s) 6, 10, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Babu, in view of Tapia, in view of O’Reilly, and in further view of SCOBBIE (Pre-Grant Publication No. US 2011/0170433 A1), hereinafter Scobbie.
10. As for claim 6 and 14, they are rejected on the same basis as claims 1 and 11 (respectively). In addition, However, Babu did not explicitly state wherein the data from the user domain comprises short-message service connectivity data from an SMS connectivity test. On the other hand, Scobbie did teach wherein the data from the user domain comprises short-message service connectivity data from an SMS connectivity test (0005 & 0054). Both of the systems of Babu and Scobbie are directed towards managing network performance and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings of Babu, to utilize performing an SMS connectivity test, as taught by Scobbie, as an SMS system was a standard service that was widely utilized at the time of the invention.
11. As for claim 10 and 18, they are rejected on the same basis as claims 1 and 11 (respectively). However, Babu did not explicitly state wherein the data from the network domain comprises a packet capture collected by the one or more first network nodes of the telecommunications network. On the other hand, Scobbie did teach wherein the data from the network domain comprises a packet capture collected by the one or more first network nodes of the telecommunications network (0005). Both of the systems of Babu and Scobbie are directed towards managing network performance and therefore, it would have been obvious to a person having ordinary skill in the art, at the time of the effective filing of the invention, to modify the teachings of Babu, to utilize performing a packet capture, as taught by Scobbie, as packet captures was a commonly used method of diagnosing an issue that was contemporary to the time of the invention.
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JOSEPH L GREENE/Primary Examiner, Art Unit 2443
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