DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments/remarks filed 2025-06-16 (“Remarks”) have been fully considered. Applicant amended claims 1, 3-5, 8, 10, 12-15, 17, and 18. Applicant canceled claims 2, 9, 11, and 16. Thus, claims 1, 3-8, 10, 12-15, and 17-20 are present for examination.
Applicant’s arguments with respect to claims 1-20 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.
Specification
The specification has been amended. The objection to the specification is withdrawn.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 and 3-7 are rejected under 35 U.S.C. 103 as being unpatentable over International Publication No. WO 2021/123285 A1 to Wei et al. (“Wei”) in view of U.S. Patent Publication No. 2023/0292198 A1 to Veijalainen et al. (“Veijalainen”) and U.S. Patent Publication No. 2023/0014613 A1 to Je et al. (“Je”).
As to claim 1, Wei discloses a mobile device (Figs. 3-7, UE 270) comprising a processor (Fig. 3, controller 290) configured to: determine a plurality of channel measurements of a serving channel and a candidate channel, the serving channel comprising a channel between the mobile device and a serving base station and the candidate channel comprising a channel between the mobile device and a candidate base station (p.9 lines 22-33); determine a probability of a handover (HO) condition (p.12 lines 7-13), wherein the plurality of channel measurements are an input (p.12 lines 7-13) and the probability of the HO condition is an output (p.12 lines 7-13), wherein the model is configured to output the probability of the HO condition (p.12 lines 7-13); and responsive to the probability of the HO condition exceeding a threshold value (Fig. 9; p.21 lines 19-20 and line 34), provide a HO request message to the serving base station to initiate the HO process for the mobile device to connect to the candidate base station (Fig. 6; p.13 lines 8-11).
Wei does not disclose: a reinforcement learning model; or based on a minimizing of a handover interruption time for a HO process for the mobile device to connect to the candidate base station.
However, Veijalainen discloses: a reinforcement learning model (Figs. 7-8; ¶0147-0148).
Additionally, Je discloses: based on a minimizing of a handover interruption time for a HO process for the mobile device to connect to the candidate base station (¶0193 and ¶0199).
Wei, Veijalainen, and Je are considered to be similar to the claimed invention because they are in one or more of the same fields of: computing arrangements based on specific computational models, e.g. reinforcement learning; hand-off measurements, transmission or use of hand-off measurement information, and/or transmission or use of information for re-establishing the radio link; and/or determination of triggering parameters for hand-off.
As such, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wei to incorporate the teachings of Veijalainen to include: a reinforcement learning model. Doing so would "[provide] the possibility of safely testing various conditions for triggering or performing the handover…[enable] the network node to experiment, through said exploration, the handover under conditions where it might not normally trigger the handover. Accordingly, the network node may determine to change the handover execution policy for detecting beneficial new conditions for triggering the handover, thus improving the system performance. The experimental handovers may be carried out on top of conventional (corresponding) handovers of connected terminal devices. As a consequence, the experimental handovers increase the statistics available to a machine learning algorithm managing the execution policy and making decisions of the handovers" (Veijalainen, ¶0111).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wei to incorporate the teachings of Je to include: based on a minimizing of a handover interruption time for a HO process for the mobile device to connect to the candidate base station. Doing so would help "effectively perform a handover in consideration of an individual situation of a UE or a BS by performing the handover using artificial intelligence (AI)" (Je, ¶0019).
As to claim 3, Wei in view of Veijalainen and Je discloses the mobile device of claim 1, wherein the processor is further configured to: identify at least one of a physical location of the mobile device and a cell type of a network (Wei, Fig. 5; p.8 lines 28-33; p.9 lines 22-39); and select the reinforcement learning model (Veijalainen, Figs. 7-8; ¶0147-0148) based on the identified physical location of the mobile device and the cell type of the network (Wei, p.12 lines 7-14).
As to claim 4, Wei in view of Veijalainen and Je discloses the mobile device of claim 1, wherein the processor is further configured to train the reinforcement learning model (Veijalainen, Figs. 7-8; ¶0147-0148) based on at least one of a HO interruption time setting, a system throughput setting, and a quality of service setting (Wei, p.9 lines 22-26).
As to claim 5, Wei in view of Veijalainen and Je discloses the mobile device of claim 4, wherein: the serving base station and the candidate base station are within a network (Wei, Fig. 1, base stations 101 and core network 102; p.3 line 23); and the processor is further configured to receive a training channel measurement dataset from the serving base station (Wei, Figs. 4 and 5; p.11 lines 23-31), wherein the reinforcement learning model (Veijalainen, Figs. 7-8; ¶0147-0148) is trained using the training channel measurement dataset (Wei, Fig. 5; p.11 lines 32-39).
As to claim 6, Wei in view of Veijalainen and Je discloses the mobile device of claim 5, wherein the training channel measurement dataset comprises: at least one of a received signal strength indicator, a reference signal received power, a reference signal receive quality, and a channel quality indicator of an additional channel within the network (Wei, p.9 lines 22-26 and p.11 lines 29-31); and kinematic information corresponding to an additional mobile device within the network (Wei, p.10 lines 5-7 and p.12 lines 28-30).
As to claim 7, Wei in view of Veijalainen and Je discloses the mobile device of claim 1, wherein: the processor is further configured to determine a confidence level of the probability of the HO condition (Wei, p.12 lines 7-13); and the probability of the HO condition is further based on the confidence level of the probability of the HO condition (Wei, p.12 lines 14-17).
Claims 8, 10, 12-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wei in view of Veijalainen and U.S. Patent Publication No. 2023/0344119 A1 to Mohan et al. (“Mohan”).
As to claim 8, Wei discloses a serving base station within a network (Fig. 1, base stations 101 and core network 102; Figs. 3-7, infrastructure equipment 272 and 372), the serving base station comprising a processor (Fig. 3, controller 280 and 380) configured to: receive a channel measurement dataset comprising a plurality of channel measurements of a serving channel and a candidate channel, the serving channel comprising a channel between a mobile device and the serving base station and the candidate channel comprising a channel between the mobile device and a candidate base station (p.9 lines 22-33); determine a probability of a handover (HO) condition (p.12 lines 7-13), wherein the plurality of channel measurements are an input (p.12 lines 7-13) and the probability of the HO condition is an output (p.12 lines 7-13), wherein the model is configured to output the probability of the HO condition (p.12 lines 7-13); and responsive to the probability of the HO condition exceeding a threshold value (Fig. 9; p.21 lines 19-20 and line 34), provide a HO preparation message to the candidate base station to prepare the candidate base station for a HO process for the mobile device to connect to the candidate base station (Fig. 6; p.13 lines 8-11).
Wei does not disclose: a reinforcement learning model; or based on a maximizing of system throughput for the network.
However, Veijalainen discloses: a reinforcement learning model (Figs. 7-8; ¶0147-0148).
Additionally, Mohan discloses: based on a maximizing of system throughput for the network (¶0089-92, reinforcement learning algorithm is rewarded when it achieves better average throughput).
Wei, Veijalainen, and Mohan are considered to be similar to the claimed invention because they are in one or more of the same fields of: computing arrangements based on specific computational models, e.g. reinforcement learning; hand-off measurements, transmission or use of hand-off measurement information, and/or transmission or use of information for re-establishing the radio link; and/or determination of triggering parameters for hand-off.
As such, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wei to incorporate the teachings of Veijalainen to include: a reinforcement learning model. Doing so would "[provide] the possibility of safely testing various conditions for triggering or performing the handover…[enable] the network node to experiment, through said exploration, the handover under conditions where it might not normally trigger the handover. Accordingly, the network node may determine to change the handover execution policy for detecting beneficial new conditions for triggering the handover, thus improving the system performance. The experimental handovers may be carried out on top of conventional (corresponding) handovers of connected terminal devices. As a consequence, the experimental handovers increase the statistics available to a machine learning algorithm managing the execution policy and making decisions of the handovers" (Veijalainen, ¶0111).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wei to incorporate the teachings of Mohan to include: based on a maximizing of system throughput for the network. Doing so would allow the system to increase user experience by increasing quality of service, increasing throughput, reducing interruption time, and/or reducing the frequency of dropped/unsuccessful handovers as well as allow the system to "provide real time fast recommendations" (Mohan, ¶0096) and "offer continued improvement of user recommendation" (Mohan, ¶0099).
As to claim 10, Wei in view of Veijalainen and Mohan discloses the serving base station of claim 8, wherein the reinforcement learning model comprises at least one of a Q learning algorithm, a deep Q learning algorithm, a recurrent neural network algorithm, a reinforcement learning algorithm, and a Markov decision process algorithm (Veijalainen, Figs. 7-8; ¶0147-0148).
As to claim 12, Wei in view of Veijalainen and Mohan discloses the serving base station of claim 8, wherein the processor is further configured to: identify at least one of a physical location of the mobile device and a cell type of the network (Wei, Fig. 5; p.8 lines 28-33; p.9 lines 22-39); and select the reinforcement learning mode (Veijalainen, Figs. 7-8; ¶0147-0148) based on the identified physical location of the mobile device and the cell type of the network (Wei, p.12 lines 7-14).
As to claim 13, Wei in view of Veijalainen and Mohan discloses the serving base station of claim 8, wherein the processor is further configured to train the reinforcement learning model (Veijalainen, Figs. 7-8; ¶0147-0148) based on at least one of a HO interruption time setting and a system throughput setting or quality of service setting (Wei, p.9 lines 22-26).
As to claim 14, Wei in view of Veijalainen and Mohan discloses the serving base station of claim 8, wherein the processor is configured to: continuously receive the channel measurement dataset; and continuously train the reinforcement learning model using the channel measurement dataset (Wei, Fig. 9, p.19 lines 22-24 and p.20 lines 28-41”; the process described in the cited sections and illustrated in Fig. 9 depicts a processor which is configured to continuously receive channel measurement data (“input parameters”) and continuously train the algorithm (“estimate/measure loss function value” and “update model”)).
As to claim 15, Wei discloses a non-transitory computer-readable medium having a memory having computer-readable instructions stored thereon and a processor operatively coupled to the memory and configured to read and execute the computer-readable instructions to perform or control performance of operations (Figs. 1, 3-7, UE 270, p.6 lines 3-8, and p.12 lines 31-36) including: determining a plurality of channel measurements of a serving channel and a candidate channel, the serving channel comprising a channel between a mobile device and a serving base station and the candidate channel comprising a channel between the mobile device and a candidate base station (p.9 lines 22-33); determining a probability of a handover (HO) condition (p.12 lines 7-13), wherein the plurality of channel measurements are an input (p.12 lines 7-13) and the probability of the HO condition is an output (p.12 lines 7-13), wherein the model is configured to output the probability of the HO condition (p.12 lines 7-13); and responsive to the probability of the HO condition exceeding a threshold value (Fig. 9; p.21 lines 19-20 and line 34), providing a HO request message to the serving base station to initiate a HO process for the mobile device to connect to the candidate base station (Fig. 6; p.13 lines 8-11).
Wei does not disclose: a reinforcement learning model; or based on a maximizing a quality of service for the mobile device over the serving channel or the candidate channel.
However, Veijalainen discloses: a reinforcement learning model (Figs. 7-8; ¶0147-0148).
Additionally, Mohan discloses: based on a maximizing a quality of service for the mobile device over the serving channel or the candidate channel (¶0089-92, reinforcement learning algorithm is rewarded when it achieves better quality of service for the user).
Wei, Veijalainen, and Mohan are considered to be similar to the claimed invention because they are in one or more of the same fields of: computing arrangements based on specific computational models, e.g. reinforcement learning; hand-off measurements, transmission or use of hand-off measurement information, and/or transmission or use of information for re-establishing the radio link; and/or determination of triggering parameters for hand-off.
As such, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wei to incorporate the teachings of Veijalainen to include: a reinforcement learning model. Doing so would "[provide] the possibility of safely testing various conditions for triggering or performing the handover…[enable] the network node to experiment, through said exploration, the handover under conditions where it might not normally trigger the handover. Accordingly, the network node may determine to change the handover execution policy for detecting beneficial new conditions for triggering the handover, thus improving the system performance. The experimental handovers may be carried out on top of conventional (corresponding) handovers of connected terminal devices. As a consequence, the experimental handovers increase the statistics available to a machine learning algorithm managing the execution policy and making decisions of the handovers" (Veijalainen, ¶0111).
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wei to incorporate the teachings of Mohan to include: based on a maximizing a quality of service for the mobile device over the serving channel or the candidate channel. Doing so would allow the system to increase user experience by increasing quality of service, increasing throughput, reducing interruption time, and/or reducing the frequency of dropped/unsuccessful handovers as well as allow the system to "provide real time fast recommendations" (Mohan, ¶0096) and "offer continued improvement of user recommendation" (Mohan, ¶0099).
As to claim 17, Wei in view of Veijalainen and Mohan discloses the non-transitory computer-readable medium of claim 15 the operations further comprising: identifying at least one of a physical location of the mobile device and a cell type of a network (Wei, Fig. 5; p.8 lines 28-33; p.9 lines 22-39); and selecting the reinforcement learning model (Veijalainen, Figs. 7-8; ¶0147-0148) based on the identified physical location of the mobile device and the cell type of the network (Wei, p.12 lines 7-14).
As to claim 18, Wei in view of Veijalainen and Mohan discloses the non-transitory computer-readable medium of claim 15 operations further comprising training the reinforcement learning model (Veijalainen, Figs. 7-8; ¶0147-0148) based on at least one of a HO interruption time setting and a system throughput setting or quality of service setting (Wei, p.9 lines 22-26).
As to claim 19, Wei in view of Veijalainen and Mohan discloses the non-transitory computer-readable medium of claim 15, wherein: the operations further comprise determining a confidence level of the probability of the HO condition (Wei, p.12 lines 7-13); and the probability of the HO condition is further based on the confidence level of the probability of the HO condition (Wei, p.12 lines 14-17).
As to claim 20, Wei in view of Veijalainen and Mohan discloses the non-transitory computer-readable medium of claim 15 the operations further comprising: receiving a HO acknowledgement message from the serving base station based on the HO request message (Wei, Fig. 6; p.13 lines 38-43); and providing subsequent data packets to the candidate base station via the candidate channel (Wei, Fig. 6; p.14 liens 1-4).
References Cited
Je, Donghyun et al. (2023). Device and method for performing handover in wireless communication system (US 2023/0014613 A1). Filed 2020-11-25.
Mohan, Saravanan et al. (2023). Positioning a device (US 2023/0344119 A1). Filed 2020-08-27.
Veijalainen, Teemu et al. (2023). Machine learning in radio connection management (US 2023/0292198 A1). Filed 2020-09-16.
Wei, YUXIN et al. (2021). Communications device, infrastructure equipment and methods for performing handover using a model based on machine learning (WO 2021/123285 A1). Filed 2020-12-18.
Other Pertinent References
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Fresia, Maria et al. (2019). First radio node and methods therein for adjusting a set of beams for communication in a wireless communications network (US 2019/0386726 A1). Filed 2017-06-29.
Huang, Chao-Hua et al. (2015). Cell selection or handover in wireless networks (US 2015/0126193 A1). Filed 2014-08-01.
Kumar, Akash (2022). Performing a handover based at least in part on a predicted user equipment mobility (US 2022/0272597 A1). Filed 2021-02-19.
Li, Ziyi et al. (2024). User equipment trajectory-assisted handover (US 2024/0205781 A1). Filed 2022-08-04.
Mishra, Vikash et al. (2021). Method and base station for handover management in wireless network (US 2021/0368405 A1). Filed 2021-05-21.
Madadi, Pranav et al. (2022). Method and apparatus for support of machine learning or artificial intelligence techniques for handover management in communication systems (US 2022/0286927 A1). Filed 2022-02-17.
Mohan, Santhosh et al. (2024). Method and apparatus of supervised learning approach for reducing latency during context switchover in 5g mec (US 12,114,202 B2). Filed 2021-08-27.
Ren, Yuwei et al. (2023). Machine learning handover prediction based on sensor data from wireless device (US 2023/0209419 A1). Filed 2020-07-09.
Sarkar, Debasish et al. (2023). Handover management in a communications network configured to support multi-rat dual connectivity (US 2023/0345317 A1). Filed 2020-12-31.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL H LEONARD whose telephone number is (571)272-5720. The examiner can normally be reached Monday – Friday, 7am – 4pm (PT). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yuwen (Kevin) Pan can be reached at (571)272-7855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAMUEL H. LEONARD/Examiner, Art Unit 2649 /YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649