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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 stand rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, claim 1 recites “…wherein a number of beams pertaining to the first beam set is associated with a number of beams corresponding to beam quality-related information inputted by an artificial intelligence model…” (emphasis added). However, the specification appears to describe that beam quality-related information is an input to an artificial intelligence model, rather than an output provide by an artificial intelligence model. See, for example, paragraphs [0059] and [0122]. Accordingly, claim 1 is unclear. Examiner suggests amending claim 1 to recite “…wherein a number of beams pertaining to the first beam set is associated with a number of beams corresponding to beam quality-related information inputted to an artificial intelligence model…”. Similar amendments were made by Applicant in European Patent Application 23773898.4 in an amendment filed December 4, 2025. For purposes of examination, Claim 1 is being interpreted as if the beam quality-related information is an input to an artificial intelligence model consistent with the disclosure of the specification.
Claims 2, 4, 14, and 18 are rejected for similar reasons. Claims 2-18 are also rejected for being dependent upon rejected claim 1.
Claims 19-20 are rejected for the same reasons as discussed with respect to claim 1.
Claim 17 recites the limitation "the first reference signal" in line 10. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 7, 10-11, 14, 17, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al. (US 2025/0030473 A1)(hereinafter “Li”).
Regarding claim 1, Li discloses a beam processing method, comprising:
determining, by a first device, a first beam set, wherein a number of beams pertaining to the first beam set is associated with a number of beams corresponding to beam quality-related information inputted by an artificial intelligence model (Fig. 7, [0093]: in accordance with a first model associated with the first base station 110-1 depicted in FIG. 7, the UE 120 (“a first device”) may measure a plurality of beams 1-8. For example, the UE 120 may perform RSRP measurements using one or more reference signal (e.g., CSI-RS/SSB) resources. The UE 120 may map the RSRP measurement associated with a beam, according to one or more mapping rules, to an LSTM input of the machine learning model. For example, the UE 120 may map an RSRP value (“beam quality-related information”) of the first beam to a first input, an RSRP value of the second beam to a second input, an RSRP value of the third beam to a third input, an RSRP value of the fourth beam to a fourth input, an RSRP value of the fifth beam to a fifth input, an RSRP value of the sixth beam to a sixth input, an RSRP value of the seventh beam to a seventh input, and an RSRP value of the eighth beam to an eighth input. [0069]: the UE 120 may perform beam prediction, in accordance with an artificial intelligence or machine learning model, based at least in part on one or more reference signal measurements. [0118]: in some aspects, the machine learning model may be an artificial intelligence machine learning model.); and
the artificial intelligence model is configured for a beam-related function ([0093]: the UE 120 may perform beam prediction (e.g., beam change prediction) (“a beam-related function”) for the beams associated with the first base station 110-1 based at least in part on an output of the first model.).
Regarding claim 7, Li discloses all features of claim 1 as outlined above.
after the determining, by the first device, the first beam set, further comprising:
transmitting, by the first device, the first beam set to a second device (Fig. 13, [0165]: process 1300 may include receiving an indication of a beam prediction based at least in part on the one or more mapping rules (block 1320). For example, the base station (“a second device”) (e.g., using communication manager 150 and/or reception component 1502, depicted in FIG. 15) may receive an indication of a beam prediction based at least in part on the one or more mapping rules. The beam prediction is received from the UE (“the first device”).).
Regarding claim 10, Li discloses all features of claim 1 as outlined above.
wherein the determining, by the first device, the first beam set comprises:
determining, by the first device, the first beam set through interaction with the second device, wherein a mode of the interaction comprises at least one of the following:
transmitting; reporting; indicating; configuring; requesting; or pre-agreeing (Fig. 13, [0164]: as shown in FIG. 13, in some aspects, process 1300 may include transmitting a configuration that includes one or more mapping rules, each of the one or more mapping rules indicating a mapping between one or more RSRP measurements, associated with one or more reference signal resources, and one or more indices, associated with one or more feature input vectors for a machine learning model (block 1310). For example, the base station (“a second device”) (e.g., using communication manager 150 and/or transmission component 1504, depicted in FIG. 15) may transmit a configuration that includes one or more mapping rules, each of the one or more mapping rules indicating a mapping between one or more RSRP measurements, associated with one or more reference signal resources, and one or more indices, associated with one or more feature input vectors for a machine learning model. The configuration is transmitted to the UE (“the first device”).).
Regarding claim 11, Li discloses all features of claim 1 as outlined above.
wherein the first beam set comprises at least one of the following:
beam-related information ([0103]: in some aspects, the output of the machine learning model may include a beam characteristic or a channel characteristic that is predicted over a time domain, a spatial domain, or a frequency domain.);
a beam starting position and a number of intervals (an alternative limitation not given mapping in the claims.); or
a pre-configured beam group identification (an alternative limitation not given mapping in the claims.).
Regarding claim 14, Li discloses all features of claim 1 as outlined above.
wherein after the determining, by the first device, the first beam set, further comprising:
determining, by the first device based on the first beam set, beam-related information inputted by the artificial intelligence model ([0103]: in some aspects, the output of the machine learning model may include a beam characteristic or a channel characteristic that is predicted over a time domain, a spatial domain, or a frequency domain.); or,
wherein the method further comprising:
determining, by the first device, beam-related information corresponding to a fifth beam set, wherein the fifth beam set comprises at least one of the following: a second beam set, a third beam set, or a fourth beam set (an alternative limitation not given mapping in the claims.).
Regarding claim 17, Li discloses all features of claim 14 as outlined above.
wherein the beam-related information is represented by quantized and coded information, wherein the quantized and coded information is obtained based on a quantization interval (an alternative limitation not given mapping in the claims.); or,
wherein in a case that the beam-related information is not associated with a synchronization signal block (SSB), quasi co-location (QCL) information of the SSB having a same identification and a same frequency domain location is kept unchanged (an alternative limitation not given mapping in the claims.); or,
wherein the beam-related information is determined based on configuration information corresponding to the first reference signal ([0048]: in some aspects, the UE 120 may include a communication manager 140. As described in more detail elsewhere herein, the communication manager 140 may receive a configuration that includes one or more mapping rules, each of the one or more mapping rules indicating a mapping between one or more reference signal received power (RSRP) measurements, associated with one or more reference signal resources, and one or more indices, associated with one or more feature input vectors for a machine learning model; and initiate a beam prediction based at least in part on the one or more mapping rules.); or
the beam-related information is determined based on configuration information of a second reference signal that is quasi co-located with the first reference signal (an alternative limitation not given mapping in the claims.);
wherein the second reference signal comprises at least one of the following signals on a quasi co-location relationship chain of the first reference signal (an alternative limitation not given mapping in the claims.):
a reference signal for which beam-related information is configured first (an alternative limitation not given mapping in the claims.);
a reference signal for which beam-related information is configured last (an alternative limitation not given mapping in the claims.);
an SSB(an alternative limitation not given mapping in the claims.); or
an SSB for which beam-related information is configured (an alternative limitation not given mapping in the claims.).
Regarding claim 19, Li discloses a communication device, comprising a processor and a memory, wherein the memory stores programs or indications executable on the processor, wherein the programs or the indications, when executed by the processor, cause the communication device to perform ([0007]: some aspects described herein relate to an apparatus for wireless communication performed by a UE. The apparatus may include a memory and one or more processors, coupled to the memory. The one or more processors may be configured to receive a configuration that includes one or more mapping rules, each of the one or more mapping rules indicating a mapping between one or more RSRP measurements, associated with one or more reference signal resources, and one or more indices, associated with one or more feature input vectors for a machine learning model. The one or more processors may be configured to initiate a beam prediction based at least in part on the one or more mapping rules.):
determining a first beam set, wherein a number of beams pertaining to the first beam set is associated with a number of beams corresponding to beam quality-related information inputted by an artificial intelligence model (Fig. 7, [0093]: in accordance with a first model associated with the first base station 110-1 depicted in FIG. 7, the UE 120 may measure a plurality of beams 1-8. For example, the UE 120 may perform RSRP measurements using one or more reference signal (e.g., CSI-RS/SSB) resources. The UE 120 may map the RSRP measurement associated with a beam, according to one or more mapping rules, to an LSTM input of the machine learning model. For example, the UE 120 may map an RSRP value (“beam quality-related information”) of the first beam to a first input, an RSRP value of the second beam to a second input, an RSRP value of the third beam to a third input, an RSRP value of the fourth beam to a fourth input, an RSRP value of the fifth beam to a fifth input, an RSRP value of the sixth beam to a sixth input, an RSRP value of the seventh beam to a seventh input, and an RSRP value of the eighth beam to an eighth input. [0069]: the UE 120 may perform beam prediction, in accordance with an artificial intelligence or machine learning model, based at least in part on one or more reference signal measurements. [0118]: in some aspects, the machine learning model may be an artificial intelligence machine learning model.); and
the artificial intelligence model is configured for a beam-related function ([0093]: the UE 120 may perform beam prediction (e.g., beam change prediction) (“a beam-related function”) for the beams associated with the first base station 110-1 based at least in part on an output of the first model.).
Regarding claim 20, Li discloses a non-transitory readable storage medium, storing programs or indications, wherein the programs or indications, when executed by a processor of a communication device, cause the communication device to perform ([0009]: some aspects described herein relate to a non-transitory computer-readable medium that stores a set of instructions for wireless communication by a UE. The set of instructions, when executed by one or more processors of the UE, may cause the UE to receive a configuration that includes one or more mapping rules, each of the one or more mapping rules indicating a mapping between one or more RSRP measurements, associated with one or more reference signal resources, and one or more indices, associated with one or more feature input vectors for a machine learning model. The set of instructions, when executed by one or more processors of the UE, may cause the UE to initiate a beam prediction based at least in part on the one or more mapping rules.):
determining a first beam set, wherein a number of beams pertaining to the first beam set is associated with a number of beams corresponding to beam quality-related information inputted by an artificial intelligence model (Fig. 7, [0093]: in accordance with a first model associated with the first base station 110-1 depicted in FIG. 7, the UE 120 may measure a plurality of beams 1-8. For example, the UE 120 may perform RSRP measurements using one or more reference signal (e.g., CSI-RS/SSB) resources. The UE 120 may map the RSRP measurement associated with a beam, according to one or more mapping rules, to an LSTM input of the machine learning model. For example, the UE 120 may map an RSRP value (“beam quality-related information”) of the first beam to a first input, an RSRP value of the second beam to a second input, an RSRP value of the third beam to a third input, an RSRP value of the fourth beam to a fourth input, an RSRP value of the fifth beam to a fifth input, an RSRP value of the sixth beam to a sixth input, an RSRP value of the seventh beam to a seventh input, and an RSRP value of the eighth beam to an eighth input. [0069]: the UE 120 may perform beam prediction, in accordance with an artificial intelligence or machine learning model, based at least in part on one or more reference signal measurements. [0118]: in some aspects, the machine learning model may be an artificial intelligence machine learning model.); and
the artificial intelligence model is configured for a beam-related function ([0093]: the UE 120 may perform beam prediction (e.g., beam change prediction) (“a beam-related function”) for the beams associated with the first base station 110-1 based at least in part on an output of the first model.).
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.
Claim(s) 2-3, 8, 12, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Pezeshki et al. (US 2021/0336682 A1)(hereinafter “Pezeshki”)(cited in IDS dated 01/05/2026).
Regarding claim 2, Li discloses all features of claim 1 as outlined above.
Li does not disclose before the determining, by a first device, a first beam set, further comprising: determining, by the first device, a second beam set, wherein a number of beams pertaining to the second beam set is greater than or equal to the number of beams corresponding to the beam quality-related information inputted by the artificial intelligence model; and the determining, by a first device, a first beam set comprises: determining, by the first device, the first beam set based on the second beam set. However, Pezeshki discloses before the determining, by a first device, a first beam set, further comprising: determining, by the first device, a second beam set, wherein a number of beams pertaining to the second beam set is greater than or equal to the number of beams corresponding to the beam quality-related information inputted by the artificial intelligence model; and the determining, by a first device, a first beam set comprises: determining, by the first device, the first beam set based on the second beam set ([0044]: in some implementations, a network entity can use the position information, the quantized orientation information, and a machine learning model to predict a set of beams that may be suitable for communications to and from the UE and the network entity, which may be a subset of the beams that the network entity can generally use for communications to and from the UE and the network entity. Generally, the predicted set of beams that may be suitable for communications to and from the UE and the network entity may be beams that result in the received signal strength of a transmission exceeding a threshold signal strength correlated with successful reception of a transmission, beams that are most likely to be detected by a receiving device, or the like. Accordingly, Pezeshki discloses determining a subset of beams from a set of beams that is greater than or equal to the subset based on beam quality-related information (“received signal strength”) as input to a machine language model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the determining of the first beam set, wherein a number of beams pertaining to the first beam set is associated with a number of beams corresponding to beam quality-related information inputted to an artificial intelligence model, as taught by Li, by determining the first beam set as a subset of a set of beams, at taught by Pezeshki. Doing so allows for determining the beam set to include beams that are most likely to be detected by the receiving device. (See Pezeshki [0044].).
Regarding claim 3, Li in view of Pezeshki discloses all features of claim 2 as outlined above.
Li does not specifically disclose wherein the first beam set is a subset or a full set of the second beam set; or, at least N1 beams in the first beam set are comprised in the second beam set, wherein N1 is a positive integer. However, Pezeshki discloses wherein the first beam set is a subset or a full set of the second beam set; or, at least N1 beams in the first beam set are comprised in the second beam set, wherein N1 is a positive integer (([0044]: in some implementations, a network entity can use the position information, the quantized orientation information, and a machine learning model to predict a set of beams that may be suitable for communications to and from the UE and the network entity, which may be a subset of the beams that the network entity can generally use for communications to and from the UE and the network entity.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the determining of the first beam set, wherein a number of beams pertaining to the first beam set is associated with a number of beams corresponding to beam quality-related information inputted to an artificial intelligence model, as taught by Li, by determining the first beam set as a subset of a set of beams, at taught by Pezeshki. Doing so allows for determining the beam set to include beams that are most likely to be detected by the receiving device. (See Pezeshki [0044].).
Regarding claim 8, Li in view of Pezeshki discloses all features of claim 2 as outlined above.
Li further discloses after the determining, by the first device, the second beam set, further comprising: transmitting, by the first device, the second beam set to a second device (Fig. 13, [0165]: in some aspects, process 1300 may include receiving an indication of a beam prediction based at least in part on the one or more mapping rules (block 1320). For example, the base station (e.g., using communication manager 150 and/or reception component 1502, depicted in FIG. 15) may receive an indication of a beam prediction based at least in part on the one or more mapping rules.).
Regarding claim 12, Li in view of Pezeshki discloses all features of claim 2 as outlined above.
Li does not explicitly disclose wherein transmitting beams in the second beam set are all different. However, Pezeshki discloses wherein transmitting beams in the second beam set are all different ([0068]: typical beam management procedures may entail a beam sweeping procedure in which a network entity, such as a base station, transmits beams consecutively in each of multiple directions. The beam directions may, for example, collectively cover 360 degrees around the network entity over a plurality of directions (for example, 64 different beam directions).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the determining of the first beam set, wherein a number of beams pertaining to the first beam set is associated with a number of beams corresponding to beam quality-related information inputted to an artificial intelligence model, as taught by Li, in which transmitting beams in second beam set are all different, at taught by Pezeshki. Doing so allows for preventing of duplicate beams in the beam set to avoid unnecessary duplication of resource usage. (See Pezeshki [0068].).
Regarding claim 15, Li in view of Pezeshki discloses all features of claim 8 as outlined above.
Li further discloses wherein the method further comprises: transmitting, by the first device, the beam-related information corresponding to the second beam set to the second device (Fig. 13, [0165]: in some aspects, process 1300 may include receiving an indication of a beam prediction based at least in part on the one or more mapping rules (block 1320). For example, the base station (e.g., using communication manager 150 and/or reception component 1502, depicted in FIG. 15) may receive an indication of a beam prediction based at least in part on the one or more mapping rules.).
Regarding claim 18, Li discloses all features of claim 1 as outlined above.
Li does not explicitly disclose wherein a number of beams pertaining to the first beam set is less than or equal to a number of beams corresponding to the beam quality-related information inputted by the artificial intelligence model. However, ___ discloses wherein a number of beams pertaining to the first beam set is less than or equal to a number of beams corresponding to the beam quality-related information inputted by the artificial intelligence model ([0044]: in some implementations, a network entity can use the position information, the quantized orientation information, and a machine learning model to predict a set of beams that may be suitable for communications to and from the UE and the network entity, which may be a subset of the beams that the network entity can generally use for communications to and from the UE and the network entity. Generally, the predicted set of beams that may be suitable for communications to and from the UE and the network entity may be beams that result in the received signal strength of a transmission exceeding a threshold signal strength correlated with successful reception of a transmission, beams that are most likely to be detected by a receiving device, or the like. Accordingly, Pezeshki discloses determining a subset of beams from a set of beams in which the subset is less than or equal to the number of beams of the set based on beam quality-related information (“received signal strength”) as input to a machine language model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the determining of the first beam set, wherein a number of beams pertaining to the first beam set is associated with a number of beams corresponding to beam quality-related information inputted to an artificial intelligence model, as taught by Li, by determining the first beam set as a subset of a set of beams, at taught by Pezeshki. Doing so allows for determining the beam set to include beams that are most likely to be detected by the receiving device. (See Pezeshki [0044].).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kazmi et. al. (US 2024/0298191 A1)- Methods and Devices For Beam Selection – discloses selecting a subset of beams based on signal strength.
Shen et al. (US 2022/0385342 A1) – Beam Determination Method, Apparatus, Electronic Devices and Computer Readable Storage Medium – discloses predicting beams using an artificial intelligence (AI) prediction model based on RSRP values.
Kaya et al. (US 2022/0190883 A1) – Beam Prediction For Wireless Networks – discloses predicting a future beam sequence for communication between user equipment and a base station, and performing, by the base station, a beam-related action based on the predicted future beam sequence for the user equipment.
Zhang et al. (US 2020/0366340 A1) – Beam Management Method, Apparatus, Electronic Device and Computer Readable Medium – disclose obtaining RSRP measurements as input to a machine learning model, outputting a predicted beam state of the receiving beam, and making a corresponding beam management decision according to the prediction result.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W MADDOX whose telephone number is (571)272-5834. The examiner can normally be reached M-Th 7:30am-5:00pm, 1st F 7:30am-4:00pm, 2nd F off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Asad M Nawaz can be reached at 571-272-3988. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL WAYNE MADDOX/Examiner, Art Unit 2463
/CHI TANG P CHENG/Primary Examiner, Art Unit 2463
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