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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/31/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed 08/13/2026 have been fully considered but they are not persuasive.
Double Patenting Rejection
Applicants have filed a TD. Therefore, the previous non-statutory double patenting rejection has been withdrawn
112b Rejection
The amendment to claim 16 has overcome the 112b rejection. Therefore, the previous 112b rejection has been withdrawn.
101 Rejection
The amendments and arguments, by the applicants, have overcome the 101 rejection. Therefore, the previous 101 rejection has been withdrawn.
103 Rejection
A) Argument, by applicants, towards 103 Rejections:
“Park does not disclose the specific claim sequence of claim 1 in which a terminal device receives configuration information of beams currently used and candidate beams, performs first and second measurements respectively on those two categories of beams, and then generates a beam switch decision for the currently used beams by applying a neural network to the results of both measurements” [Reply; page 8].
In response to argument:
The previous 103 rejection shows the Hu reference disclosing the claim limitations of claim 1, without “by applying a neural network”. The secondary reference, Park, discloses “applying a neural network” to select a beam, as in claim 1. The 103 rejection further describes Park with “the UE as the intelligent computing device for optimal beam selection” and citations figures 9 and 10 and paragraphs 0016, 0137, 0143-0144, 0235, 0239, and 0251-0254. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Therefore, the 103 rejection is maintained.
B) Argument, by applicants, towards 103 Rejections:
“Park does not disclose that the neural network outputs the claimed ‘beam switch decision’ in the sense required by claim 1” and “Selecting an optimal beam from clustered candidates is not the same as the specific claimed UE-initiated beam switch decision architecture” [Reply; page 8].
In response to argument:
The claim language does not specifically state a “UE-initiated”. Also, since the terminal device is “applying” a neural network, the UE probably would not initiate the decision. The primary reference, Hu, discloses a beam switch decision but does not specifically disclose “neural network”. The claim language does not show how the neural network is used to generate a beam switch decision. The claim language just discloses the “neural network” as a tool to select a beam. The combination of Hu-Park, as shown in the 103 rejection, does disclose the claim language within the independent claim 1. Therefore, the 103 rejection is maintained.
C) Argument, by applicants, towards 103 Rejections:
“The asserted motivation that Park offers "an algorithm that is more excellent than the existing beam management performance" is overly generalized” and “the proposed combination is based on hindsight” [Reply; 8].
In response to argument:
This motivation is from the Park reference, in order to overcome loss in quality with beams selection [Park; para 0006-0007]. One of ordinary skilled in the art would be motivated to add of a generic neural network, as claimed, as a tool to process a selection of a beam. The motivation would be as shown within the 103 rejection but, also, multiple reasonings from KSR (ie. combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, applying a known technique to a known device (method, or product) ready for improvement to yield predictable results, and "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success). Even with the addition of the newly amended claim limitation of “configuration information of the neural network”, does not separate the claimed generic neural network from the AI device, taught by Park. Therefore, the 103 rejection is maintained.
D) Argument, by applicants, towards 103 Rejections:
“Hu even fails to disclose the use of neural network, and cannot disclose transmitting to the terminal device information that configures the neural network itself for performing the beam switch decision” [Reply; page 9].
In response to argument
As shown in the 103 rejection, the Hu reference discloses most of the instant claim language but does not specifically “applying a neural network”. The Hu reference does use a computer to make the beam switch decision but does not specifically disclose the “neural network” as a tool for the decision. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Therefore, the 103 rejection is maintained.
E) Argument, by applicants, towards 103 Rejections:
“Park likewise does not disclose the claimed configuration information of the neural network for the beam switch decision” [Reply; page 9] and “Nor does Park disclose receiving configuration information of the neural network for beam switch decision. Further, Park fails to indicate applying the neural-network-based process in the beam failure recovery framework relied upon from Hu” [Reply; page 11].
In response to argument:
The newly added claim limitation of “receiving, by the terminal device, configuration information of the neural network for the beam switch decision”. The broad claim language only describes receiving configuration information for a neural network with the intended use of a decision. The new claim language of “configuration information” is not specific and, therefore, can be read upon the configuration of the artificial intelligence device, of the Park reference [Park; para 0137 and 0141-0143]. The Park reference discloses configuration information is sent and stored in memory [Park; para 0102, 0146, 0148, and 0172]. Therefore, the 103 rejection is maintained.
Claim Rejections - 35 USC § 103
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, 3-7, 9-12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. ("Hu") [USPAT 11,750,268] in view of Park [PGPUB 2021/0242928].
Regarding claim 1, the Hu reference discloses a method, comprising: receiving, by the terminal device, configuration information of beams currently used by the terminal device [ie. configuration of camping cell set of SSB index; Hu; column 13, lines 4-16 and col 16, lines 18-21 and 46-55];
receiving, by the terminal device, configuration information of candidate beams [ie. configuration of candidate set of SSB indexes; Hu; col 9, lines 2-27 and col 10, lines 3-19];
performing, by the terminal device, a first measurement on the beams currently used by the terminal device and a second measurement on the candidate beams [Hu; col 2, lines 39-54, col 5, lines 24-35, col 13, line 64 – col 14, line 12]; and
generating, by the terminal device, a beam switch decision for the beams currently used by the terminal device by applying a decision on results of the first and second the measurements [ie. select a new beam (“beam switch decision”); Hu; col 5, lines 32-35, col 15, lines 5-35, and 18, lines 28-61].
The Hu reference discloses the UE selects a new beam from candidate beams [Hu; col 15, lines 5-35] but does not specifically disclose “applying a neural network on results” and “wherein the method further comprises: receiving, by the terminal device, configuration information of the neural network for the beam switch decision”.
However, in the same field of endeavor, the Park reference discloses applying a neural network on results to select a beam [ie. the UE as the intelligent computing device for optimal beam selection; Park; figures 9 and 10; paragraph 0016, 0137, 0143-0144, 0235, 0239, and 0251-0254] and wherein the method further comprises: receiving, by the terminal device, configuration information of the neural network for the beam switch decision [ie. beam management config; Park; fig 5; para 0146, 0150-0151, 0215-0218, and 0235]. The Hu and Park references are analogous art, since they have similar problem solving area in being able to manage beam selection of a UE. It would have been obvious to a person of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the teaching of the neural network, taught by Park, into the system, taught by Hu. The motivation for doing so would have been “an algorithm that is more excellent than the existing beam management performance” [Park; para 0007].
Regarding claim 3, the combination of Hu-Park further discloses performing, by the terminal device, the beam switch decision by switching from at least one of the beams currently used by the terminal device to at least one of the candidate beams [ie. select a new beam (“beam switch decision”); Hu; col 5, lines 32-35, col 15, lines 5-35, and 18, lines 28-61] [Park; para 0235, 0239, and 0251-0254].
Regarding claim 4, the combination of Hu-Park further discloses transmitting, by the terminal device, information corresponding to the beam switch decision to a base station, wherein the information corresponding to the beam switch decision includes identification of at least one RS resource in a set of one or more Channel State Information Reference Signal (CSI-RS) resources and Synchronization Signal / Physical Broadcast Channel Block (SSBs) [Hu; col 6, lines 1-13, col 11, lines 10-23, col 15, lines 37-43 and col 23, lines 55-64].
Regarding claim 5, the combination of Hu-Park further discloses receiving, by the terminal device, an acknowledgement from the base station [Hu; col 9, lines 25-43 and col 23, line 65 – col 24, line 12].
Regarding claim 6, the combination of Hu-Park further discloses the first measurement includes a Reference Signal Received Power (RSRP) measurement [Hu, col 5, lines 24-35 and col 9, lines 1-27].
Regarding claim 7, the combination of Hu-Park further discloses the first measurement includes a Reference Signal Received Quality (RSRQ) measurement [Hu, col 5, lines 24-35 and col 9, lines 1-27].
Regarding claim 9, the combination of Hu-Park further discloses the first measurement includes a Received Signal Strength Indication (RSSI) measurement [Hu, col 5, lines 24-35 and col 9, lines 1-27] [Park; para 0234-0235].
Regarding claim 10, the combination of Hu-Park further discloses the first measurement includes a hypothetical Block Error Rate (BLER) measurement [Hu, col 5, lines 24-35, col 9, lines 1-27, and col 11, lines 11-22].
Regarding claim 11, the combination of Hu-Park further discloses the second measurement includes an RSRP measurement [Hu, col 5, lines 24-35 and col 10, lines 3-19].
Regarding claim 12, the combination of Hu-Park further discloses the second measurement includes an RSRQ measurement [Hu, col 5, lines 24-35 and col 10, lines 3-19].
Regarding claim 14, the combination of Hu-Park further discloses the second measurement includes an RSSI measurement [Hu, col 5, lines 24-35 and col 10, lines 3-19] [Park; para 0234-0235].
Regarding claim 15, the combination of Hu-Park further discloses the second measurement includes a hypothetical BLER measurement [Hu, col 5, lines 24-35 and col 10, lines 3-19].
Regarding claim 16, the Hu reference discloses a method, comprising: receiving, by the terminal device, configuration information of a first set of one or more Channel State Information Reference Signal (CSI-RS) resources [Hu; column 13, lines 4-16 and col 16, lines 18-21 and 46-55-];
receiving, by the terminal device, configuration information of a second set of one or more CSI-RS resources and Synchronization Signal/ Physical Broadcast Channel Block (SSBs) [Hu; col 9, lines 2-27 and col 10, lines 3-19]
performing, by the terminal device, a first measurement on the first set of one or more CSI-RS resources so as to generate a first set of measurement result [Hu; col 2, lines 39-54, col 5, lines 24-35, col 13, line 64 – col 14, line 12];
performing, by the terminal device, a second measurement on the second set of one or more CSI-RS resources and SSBs so as to generate a second set of measurement result [Hu; col 2, lines 39-54, col 5, lines 24-35, col 13, line 64 – col 14, line 12]; and
inputting, by the terminal device, the first and second sets of measurement results to obtain an output [ie. select a new beam (“beam switch decision”); Hu; col 5, lines 32-35, col 15, lines 5-35, and 18, lines 28-61].
The Hu reference discloses the UE selects a new beam from candidate beams [Hu; col 15, lines 5-35] but does not specifically disclose “measurement results to a neural network so as to obtain an output wherein the output is used for initialing beam switch by the terminal device, wherein the method further comprises: receiving, by the terminal device, configuration information of the neural network for beam switch decision”.
However, in the same field of endeavor, the Park reference discloses measurement results to a neural network to obtain an output, wherein the output is used for initialing beam switch by the terminal device [ie. the UE as the intelligent computing device for optimal beam selection; Park; figures 9 and 10; paragraph 0016, 0137, 0143-0144, 0235, 0239, and 0251-0254] and wherein the method further comprises: receiving, by the terminal device, configuration information of the neural network for beam switch decision [ie. beam management config; Park; fig 5; para 0146, 0150-0151, 0215-0218, and 0235]. The Hu and Park references are analogous art, since they have similar problem solving area in being able to manage beam selection of a UE. It would have been obvious to a person of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the teaching of the neural network, taught by Park, into the system, taught by Hu. The motivation for doing so would have been “an algorithm that is more excellent than the existing beam management performance” [Park; para 0007].
Regarding claim 17, the combination of Hu-Park further discloses transmitting, by the terminal device, information corresponding to the output to a base station, wherein the information corresponding to the output includes identification of at least one RS resource in the second set; and receiving, by the terminal device, an acknowledgement from the base station [Hu; col 6, lines 1-13, col 9, lines 25-43, col 15, lines 37-43 and col 23, line 55 - col 24, line 12].
Regarding claim 18, the combination of Hu-Park further discloses the first measurement includes at least one of the following: an RSRP measurement, an RSRQ measurement, an SINR measurement, an RSSI measurement, and a hypothetical BLER measurement [Hu, col 5, lines 24-35 and col 9, lines 1-27].
Regarding claim 19, the combination of Hu-Park further discloses the second measurement includes at least one of the following: an RSRP measurement, an RSRQ measurement, an SINR measurement, an RSSI measurement, and a hypothetical BLER measurement [Hu, col 5, lines 24-35 and col 10, lines 3-19].
Regarding claim 20, the apparatus of claim 20 perform the similar steps as the method of claim 1. The combination of Hu-Park teaches the method of claim 1, as referenced above. The additional limitations of a “processor” and a “memory” are rejected with the citation of figure 8 and col 24, lines 16-34 of Hu. Therefore, claim 20 are rejected using the same art and rationale set forth above in the rejection of claim 1, by the teachings of combination of Hu-Park.
Claims 8 and 13 are rejected under 35 U.S. C. 103 as being unpatentable over Hu-Park as applied to claim 1 above, and further in view of Li et al. ("Li") [USPAT 12,323,210].
Regarding claim 8, the combination of Hu-Park further discloses measurements but does not specifically disclose the first measurement includes a Signal to Interference Noise Ratio (SINR) measurement.
However, in the same field of endeavor, the Li reference discloses a first measurement includes a Signal to Interference Noise Ratio (SINR) measurement [Li; col 17, line 41 – col 18, line 15]. The Hu-Park and Li references are analogous art, since they have similar problem solving area in being able to manage wireless communication. It would have been obvious to a person of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the teaching of the SINR measurement, taught by Li, into the system, taught by Hu-Park. The motivation for doing so would have been have another measurement option.
Regarding claim 13, the combination of Hu-Park further discloses measurements but does not specifically disclose the second measurement includes a Signal to Interference Noise Ratio (SINR) measurement.
However, in the same field of endeavor, the Li reference discloses a second measurement includes a Signal to Interference Noise Ratio (SINR) measurement [Li; col 17, line 41 – col 18, line 15]. The Hu-Park and Li references are analogous art, since they have similar problem solving area in being able to manage wireless communication. It would have been obvious to a person of ordinary skill in the art, before the effective filling date of the claimed invention, to combine the teaching of the SINR measurement, taught by Li, into the system, taught by Hu-Park. The motivation for doing so would have been have another measurement option.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang et al. [PGPUB 2020/0366340] describes machine learning model selecting a beam.
Awoniyi-Oteri et al. [PGPUB 2023/0057661] describes monitoring configuration for updating a beam.
Karapantelakis et al. [PGPUB 2024/0291548] describes beam management based on machine learning agent.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JASON D CARDONE/Primary Examiner, Art Unit 2458
