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 statements (IDS) submitted on 4/25/2024, 9/23/2025, and 12/3/2025 have been entered and considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Regarding claim 63, the claim recites “means for receiving,” “means for determining,” and “means for transmitting.” The structure for such limitations appears to be recited in at least Fig. 11 and its corresponding description.
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 1-28 and 62-63 are 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 claims 1, 24, and 63, the claims recite “a beam,” “a first beam,” and “a second beam.” Such separately recited claim elements are typically interpreted as different claim elements, but the claims also recite “determining a probability associated with an adjustment of the beam, the probability indicating whether the beam will change over a duration from a first beam associated with a first beam index to a second beam associated with a second beam index based at least in part on the reference signal.” Such claim language appears to potentially imply that “the beam” is the same as “the first beam” when the claimed steps place. However, dependent claim 17 recites “the beam index associated with the beam comprises the first beam index or the second beam index,” and such claim language appears to allow for “the beam” to actually be “the first beam” or “the second beam.” Such a possible interpretation makes the claim more confusing, because it is unclear how “the beam” could ever “change over a duration from a first beam associated with a first beam index to a second beam associated with a second beam index” if “the beam” is initially “the second beam.” It is therefore unclear if “the beam” is the same or different from “the first beam” or “the second beam,” and what effect “the beam” being “the second beam” initially is intended to have on the “determining” step. Claims 1, 24, and 63 are thus indefinite. For the purpose of this examination, the Examiner will interpret “the beam,” “the first beam,” and “the second beam” as potentially being different beams. Regarding claims 2-23, 25-28, and 62, the claims are rejected because they depend from rejected claims.
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.
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.
Claim(s) 1-28 and 62-63 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bai et al. (US 2020/0259575, provided by Applicant, Bai hereinafter) in view of Monogioudis et al. (US 2019/0182614, Monogioudis hereinafter). Regarding claims 1, 24, and 63, Bai teaches a method, a user equipment (UE) (User equipment (UE); Bai; Figs. 3 and 7; [0045]), and an apparatus for wireless communication at a user equipment (UE) (User equipment (UE); Bai; Figs. 3 and 7; [0045]), comprising: a processor (The UE may be comprised of a processor; Bai; Figs. 3 and 7; [0045], [0048]); and memory coupled with the processor (The UE may be comprised of memory coupled with the processor. At least the components depicted in Fig. 3 may also be interpreted as teaching “means for receiving,” “means for determining,” and “means for transmitting” recited in claim 63; Bai; Figs. 3 and 7; [0045], [0048]-[0049]) and storing instructions executable by the processor to cause the apparatus to: receive, from a network node, a reference signal associated with a beam of a set of beams used for wireless communications at the UE (As can be seen in at least step 706 of Fig. 7, a second device (e.g., a base station) may transmit a reference signal associated with a beam of a set of beams used for wireless communications at the UE; Bai; Fig. 7; [0078]-[0080]); determine a probability associated with an adjustment of the beam, the probability indicating whether the beam will change over a duration from a first beam to a second beam based at least in part on the reference signal (As can be seen in at least steps 708-712 of Fig. 7, the UE may predict one or more future channel conditions for the one or more channels based at least in part on channel conditions in a set of measurements. The future channel condition may include a predicted metric or an indication of whether a metric will fall below a threshold at some point or within a time period in the future. For instance, the future channel condition may indicate that it is predicted that beam failure is predicted to occur soon with a high likelihood. As can be seen for instance in at least step 718, such a prediction may cause assignment of a new beam (i.e., the beam changes over a duration from a first beam to a second beam based at least in part on the reference signal). The UE may thus be interpreted as determining a probability associated with an adjustment of the beam, the probability indicating whether the beam will change over a duration from a first beam to a second beam based at least in part on the reference signal; Bai; Fig. 7; [0081]-[0089]); and transmit, to the network node, an indication of the probability associated with the adjustment of the beam (As can be seen in at least steps 712-714, the UE may transmit an indication of the probability associated with the adjustment of the beam to the network node (e.g., base station); Bai; Fig. 7; [0084]-[0089]). However, Bai does not specifically disclose each beam of the set of beams being associated with a respective beam index; a first beam associated with a first beam index; and a second beam associated with a second beam index. Monogioudis teaches each beam of the set of beams being associated with a respective beam index (Beams are described as being identified by beam indices, and thus each beam of the set of beams may be interpreted as being associated with a respective beam index; Monogioudis; Fig. 2; [0013], [0024]-[0026], [0029], [0037]); a first beam associated with a first beam index (Beams are described as being identified by beam indices, and thus a first beam may be interpreted as being associated with a first beam index; Monogioudis; Fig. 2; [0013], [0024]-[0026], [0029], [0037]); and a second beam associated with a second beam index (Beams are described as being identified by beam indices, and thus a second beam may be interpreted as being associated with a second beam index; Monogioudis; Fig. 2; [0013], [0024]-[0026], [0029], [0037]). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Monogioudis regarding beam measurement with the teachings as in Bai regarding beam measurement. The motivation for doing so would have been to increase performance at least by utilizing machine learning (e.g., a Hidden Markov Model) in order to obtain significant improvements in localization of mobile devices (Monogioudis; [0013]). Regarding claims 2 and 25, Bai and Monogioudis teach the limitations of claims 1 and 24 respectively. Bai further teaches the instructions to transmit an indication of the probability are executable by the processor to cause the apparatus to: transmit, to the network node, a status indication associated with whether the adjustment of the beam will occur, wherein transmitting the status indication is based at least in part on the probability (At least steps 712-714 in Fig. 7 may be interpreted as comprising transmitting, to the network node, a status indication associated with whether the adjustment of the beam will occur, wherein transmitting the status indication is based at least in part on the probability; Bai; Fig. 7; [0081]-[0089]). Regarding claims 3 and 26, Bai and Monogioudis teach the limitations of claims 2 and 25 respectively. Bai further teaches the instructions are further executable by the processor to cause the apparatus to: receive, from the network node, an indication of a threshold (The UE may receive the threshold from the network node; Bai; Fig. 7; [0079]-[0085]); and determine whether the probability satisfies the threshold, wherein transmitting the status indication is based at least in part on the probability satisfying the threshold (As can be seen in at least steps 708-712 of Fig. 7, the UE may predict one or more future channel conditions for the one or more channels based at least in part on channel conditions in a set of measurements. The UE may thus be interpreted as determining whether the probability satisfies the threshold, wherein transmitting the status indication is based at least in part on the probability satisfying the threshold; Bai; Fig. 7; [0081]-[0089]). Regarding claims 4 and 27, Bai and Monogioudis teach the limitations of claims 2 and 25 respectively. Bai further teaches the instructions are further executable by the processor to cause the apparatus to: transmit, to the network node, an indication of a threshold (The UE is described as potentially determining to report prediction(s) in steps 712-714 based on the future channel condition being below or above a threshold channel condition. Information regarding such prediction(s) that the future channel conditions will be below or above such a threshold may be interpreted as transmitting an indication of such a threshold; Bai; Fig. 7; [0081]-[0089]); and determine whether the probability satisfies the threshold, wherein transmitting the status indication is based at least in part on the probability satisfying the threshold (As can be seen in at least steps 708-712 of Fig. 7, the UE may predict one or more future channel conditions for the one or more channels based at least in part on channel conditions in a set of measurements. The UE may thus be interpreted as determining whether the probability satisfies the threshold, wherein transmitting the status indication is based at least in part on the probability satisfying the threshold; Bai; Fig. 7; [0081]-[0089]). Regarding claims 5 and 28, Bai and Monogioudis teach the limitations of claims 2 and 25 respectively. Bai further teaches the instructions are further executable by the processor to cause the apparatus to: determine whether the probability satisfies a threshold (As can be seen in at least steps 708-712 of Fig. 7, the UE may predict one or more future channel conditions for the one or more channels based at least in part on channel conditions in a set of measurements. The UE may thus be interpreted as determining whether the probability satisfies the threshold; Bai; Fig. 7; [0081]-[0089]), wherein transmitting the status indication comprises: transmit, to the network node, a report comprising the status indication and an indication of the threshold (The UE is described as potentially determining to report prediction(s) in steps 712-714 based on the future channel condition being below or above a threshold channel condition. Information regarding such prediction(s) that the future channel conditions will be below or above such a threshold may be interpreted as transmitting a report comprising a status indication and an indication of such a threshold; Bai; Fig. 7; [0081]-[0089]). Regarding claims 6 and 62, Bai and Monogioudis teach the limitations of claims 2 and 25 respectively. Bai further teaches the instructions are further executable by the processor to cause the apparatus to: receive, from the network node, an indication of a set of thresholds (The UE may receive the threshold from the network node, which may be interpreted as a set of thresholds (i.e., a set may be comprised of a single item); Bai; Fig. 7; [0079]-[0085]); select a threshold of the set of thresholds based at least in part on a performance metric associated with performing a measurement on the beam (Configuration of the UE with a configuration for prediction that includes the threshold may be interpreted as comprising selecting a threshold of the set of thresholds based at least in part on a performance metric associated with performing a measurement on the beam; Bai; Fig. 7; [0079]-[0085]); and determine whether the probability satisfies the threshold (As can be seen in at least steps 708-712 of Fig. 7, the UE may predict one or more future channel conditions for the one or more channels based at least in part on channel conditions in a set of measurements. The UE may thus be interpreted as determining whether the probability satisfies the threshold; Bai; Fig. 7; [0081]-[0089]), wherein transmitting the status indication is based at least in part on the probability satisfying the threshold (The UE is described as potentially determining to report prediction(s) in steps 712-714 based on the future channel condition being below or above a threshold channel condition. Information regarding such prediction(s) that the future channel conditions will be below or above such a threshold may be interpreted as transmitting a report comprising a status indication and an indication of such a threshold; Bai; Fig. 7; [0081]-[0089]). Regarding claim 7, Bai and Monogioudis teach the limitations of claim 1. Bai further teaches transmitting, to the network node, an indication of a capability of the UE to determine the probability associated with the adjustment of the beam (The UE is described as potentially determining to report prediction(s) in steps 712-714 based on the future channel condition being below or above a threshold channel condition. Such a transmission may be interpreted as an indication of a capability of the UE to determine the probability associated with the adjustment of the beam; Bai; Fig. 7; [0081]-[0089]). Regarding claim 8, Bai and Monogioudis teach the limitations of claim 1. Bai further teaches determining the probability is based at least in part on a machine learning model (The prediction algorithm may utilize prediction methods or algorithms such as a deep learning neural network with long and/or short-term memory, a Kalman filter, or various other algorithms or methods. Determining the probability may thus be interpreted as being based at least in part on a machine learning model; Bai; Fig. 7; [0061], [0078]-[0085]). Regarding claim 9, Bai and Monogioudis teach the limitations of claim 8. Bai further teaches the machine learning model comprises one or more of a long short-term memory neural network, a gated recurrent unit neural network, a convolution neural network, a recurrent neural network, or a deep neural network (The prediction algorithm may utilize prediction methods or algorithms such as a deep learning neural network with long and/or short-term memory, a Kalman filter, or various other algorithms or methods; Bai; Fig. 7; [0061], [0078]-[0085]). Regarding claim 10, Bai and Monogioudis teach the limitations of claim 9. Bai further teaches receiving, from the network node, an indication of the machine learning model, wherein determining the probability is based at least in part on receiving the indication (The prediction algorithm may utilize prediction methods or algorithms such as a deep learning neural network with long and/or short-term memory, a Kalman filter, or various other algorithms or methods. The prediction algorithm is also described as potentially being received from the network node, and the probability determination is also described as using the prediction algorithm; Bai; Fig. 7; [0061], [0078]-[0085]). Regarding claim 11, Bai and Monogioudis teach the limitations of claim 9. Bai further teaches determining the machine learning model at the UE, wherein determining the probability is based at least in part on determining the machine learning model (The prediction algorithm may utilize prediction methods or algorithms such as a deep learning neural network with long and/or short-term memory, a Kalman filter, or various other algorithms or methods. Such an algorithm is also described as potentially being up to the UE to implement rather than being previously configured by the base station. The UE may thus be interpreted as determining the machine learning model at the UE, wherein determining the probability is based at least in part on determining the machine learning model; Bai; Fig. 7; [0061], [0078]-[0085]). Regarding claim 12, Bai and Monogioudis teach the limitations of claim 8. Monogioudis further teaches the machine learning model comprises a hidden Markov Model (A Hidden Markov Model (HMM) may be used; Monogioudis; Fig. 2; Abstract; [0013], [0032]-[0033]). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Monogioudis regarding beam measurement with the teachings as in Bai regarding beam measurement. The motivation for doing so would have been to increase performance at least by utilizing machine learning (e.g., a Hidden Markov Model) in order to obtain significant improvements in localization of mobile devices (Monogioudis; [0013]). Regarding claim 13, Bai and Monogioudis teach the limitations of claim 12. Bai further teaches receiving, from the network node, an indication of a machine learning model (The prediction algorithm may utilize prediction methods or algorithms such as a deep learning neural network with long and/or short-term memory, a Kalman filter, or various other algorithms or methods. The prediction algorithm is also described as potentially being received from the network node, and the probability determination is also described as using the prediction algorithm; Bai; Fig. 7; [0061], [0078]-[0085]); wherein determining the probability is based at least in part on receiving the indication of the machine learning model (The prediction algorithm is described as being used in steps 710-714, and thus determining the probability may be interpreted as being based at least in part on receiving the indication of the machine learning model; Bai; Fig. 7; [0061], [0078]-[0085]). Bai further teaches the machine learning model is a hidden Markov model configuration (A Hidden Markov Model (HMM) may be used; Monogioudis; Fig. 2; Abstract; [0013], [0032]-[0033]), the hidden Markov model configuration comprising a set of transition probabilities associated with the set of beams and a set of emission probabilities associated with the set of beams, each transition probability of the set of transition probabilities corresponding to a transition of at least one beam of the set of beams from a first status to a second status and each emission probability of the set of emission probabilities corresponding to an observed status of at least one beam of the set of beams (The HMM is described as being configured to support autonomous localization of mobile devices in a wireless communication network and may be constructed based on use of localization information (e.g., location, velocity, or the like, as well as various combinations thereof) to define the hidden states of the HMM and based on use of signal strength measurement reporting information (e.g., localization identification information, cell identification information, beam identification information, and signal strength information) to define possible observations of the HMM. The HMM 200 includes a set of states 210 and a set of possible observations 220. The measurement states of the mobile devices may be defined based on various types of measurement information which may be reported by mobile devices (e.g., cell index, beam index, and received power, or the like, as well as various combinations thereof). For example, as depicted in FIG. 2, measurement states of mobile devices may be defined based on a combination of cell index, beam index, and received power. The state transitions are potential transitions between states 210. It is noted that, for each state 210, there may be state transitions from that state 210 to one or more other states 210. The HMM 200 may be defined and refined based on various organizations of the data (e.g., based on various organizations of data which may include geographic locations as the states 210 and combinations of observations data (e.g., cell ID, beam index, and signal strength) as the possible observations 220. The HMM configuration may thus be interpreted as comprising a set of transition probabilities associated with the set of beams and a set of emission probabilities associated with the set of beams, each transition probability of the set of transition probabilities corresponding to a transition of at least one beam of the set of beams from a first status to a second status and each emission probability of the set of emission probabilities corresponding to an observed status of at least one beam of the set of beams; Monogioudis; Fig. 2; Abstract; [0013], [0024], [0029], [0033]-[0043]). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Monogioudis regarding beam measurement with the teachings as in Bai regarding beam measurement. The motivation for doing so would have been to increase performance at least by utilizing machine learning (e.g., a Hidden Markov Model) in order to obtain significant improvements in localization of mobile devices (Monogioudis; [0013]). Regarding claim 14, Bai and Monogioudis teach the limitations of claim 1. Bai further teaches transmitting the indication of the probability comprises transmitting, to the network node, a report comprising the indication of the probability (At least the indication of future channel condition in step 714 may be interpreted as comprising transmitting the indication of the probability comprises transmitting, to the network node, a report comprising the indication of the probability; Bai; Fig. 7; [0081]-[0089]). Regarding claim 15, Bai and Monogioudis teach the limitations of claim 14. Bai further teaches transmitting the report comprising the indication of the probability comprises: transmit a first channel state information report that indicates one or more measured channel characteristics of a channel used for the wireless communications between the UE and the network node (At least the indication of future channel condition in step 714 may be interpreted as comprising transmitting a first channel state information report that indicates one or more measured channel characteristics of a channel used for the wireless communications between the UE and the network node; Bai; Fig. 7; [0081]-[0089]); and transmit a second channel state information report that indicates the probability (The reporting configuration (e.g., at steps 712-714) is described as potentially being periodic, and thus at least steps 712-714 may be interpreted as being performed multiple times. At least the indication of future channel condition in step 714 may thus be interpreted as comprising transmitting a second channel state information report that indicates the probability; Bai; Fig. 7; [0081]-[0089]). Regarding claim 16, Bai and Monogioudis teach the limitations of claim 1. Bai further teaches performing a measurement on the beam over a beam measurement cycle (At least step 710 may be interpreted as comprising performing a measurement on the beam over a beam measurement cycle; Bai; Fig. 7; [0081]-[0089]); and determining a beam metric value based at least in part on performing the measurement on the beam, wherein the probability is based at least in part on the beam metric value and the beam (At least steps 710-714 may be interpreted as comprising determining a beam metric value based at least in part on performing the measurement on the beam, wherein the probability is based at least in part on the beam metric value and the beam; Bai; Fig. 7; [0081]-[0089]). Monogioudis further teaches a beam index associated with the beam (Beams are described as being identified by beam indices, and thus a beam index may be interpreted as being associated with a beam; Monogioudis; Fig. 2; [0013], [0024]-[0026], [0029], [0037]). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Monogioudis regarding beam measurement with the teachings as in Bai regarding beam measurement. The motivation for doing so would have been to increase performance at least by utilizing machine learning (e.g., a Hidden Markov Model) in order to obtain significant improvements in localization of mobile devices (Monogioudis; [0013]). Regarding claim 17, Bai and Monogioudis teach the limitations of claim 16. Bai further teaches the beam is the first beam or the second beam (A beam that has a probability to change from a first beam to a second beam may be interpreted as being the first beam or the second beam; Bai; Fig. 7; [0081]-[0089]). Monogioudis further teaches the beam index associated with the beam comprises the first beam index or the second beam index (Beams are described as being identified by beam indices, and thus a beam index associated with the beam (i.e., the beam index) may be interpreted as being the first beam index or the second beam index; Monogioudis; Fig. 2; [0013], [0024]-[0026], [0029], [0037]). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Monogioudis regarding beam measurement with the teachings as in Bai regarding beam measurement. The motivation for doing so would have been to increase performance at least by utilizing machine learning (e.g., a Hidden Markov Model) in order to obtain significant improvements in localization of mobile devices (Monogioudis; [0013]). Regarding claim 18, Bai and Monogioudis teach the limitations of claim 16. Bai further teaches determining one or both of whether the beam metric value changed relative to a second beam metric value associated with a second beam measurement cycle or whether the beam index changed relative to the second beam index associated with the second beam measurement cycle (The channel measurement, prediction of future channel conditions, and indication of such future channel conditions (e.g., steps 708-714) are described as potentially being performed periodically, and thus at least steps 708-714 may be interpreted as being performed multiple times (i.e., at least a second beam measurement cycle may be interpreted as existing). The UE may thus be interpreted as determining at least whether the beam metric value changed relative to a second beam metric value associated with a second beam measurement cycle; Bai; Fig. 7; [0081]-[0089]), wherein transmitting the indication of the probability is based at least in part on determining one or both of whether the beam metric value changed relative to the second beam metric value associated with the second beam measurement cycle or whether the beam index changed relative to the second beam index associated with the second beam measurement cycle (As can be seen in at least steps 708-712 of Fig. 7, the UE may predict one or more future channel conditions for the one or more channels based at least in part on channel conditions in a set of measurements. The future channel condition may include a predicted metric or an indication of whether a metric will fall below a threshold at some point or within a time period in the future. For instance, the future channel condition may indicate that it is predicted that beam failure is predicted to occur soon with a high likelihood. Transmission of the indication of the probability may be interpreted as being based at least in part on determining whether the beam metric value changed relative to the second beam metric value associated with the second beam measurement cycle; Bai; Fig. 7; [0081]-[0089]). Regarding claim 19, Bai and Monogioudis teach the limitations of claim 16. Bai further teaches receiving, from the network node, an indication comprising one or both of an indication to change the beam measurement cycle or an indication of a set of reference signals to be measured (The UE may receive the configuration for prediction (e.g., the set of reference signals to be measured) from the network node. The UE may also receive an indication of communication parameters in at least step 718; Bai; Fig. 7; [0079]-[0085], [0088]-[0089]). Regarding claim 20, Bai and Monogioudis teach the limitations of claim 19. Bai further teaches the indication is received via a radio resource control message, a downlink control information, or a medium access control control element (The UE may receive the configuration for prediction (i.e., control information) from the network node. Such control information received from the network node may also be interpreted as a downlink transmission, and thus such control information may be interpreted at least as downlink control information; Bai; Fig. 7; [0079]-[0085], [0088]-[0089]). Regarding claim 21, Bai and Monogioudis teach the limitations of claim 19. Monogioudis further teaches the indication is based at least in part on one or both of a position of the UE or a trajectory of the UE (As can be seen in at least Fig. 2 and its corresponding description, the HMM (i.e., the configuration) may be based on state information including at least location and velocity. The indication may thus be interpreted as being based at least in part on one or both of a position of the UE or a trajectory of the UE; Monogioudis; Fig. 2; Abstract; [0013], [0024], [0029], [0033]-[0043]). Therefore it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings as in Monogioudis regarding beam measurement with the teachings as in Bai regarding beam measurement. The motivation for doing so would have been to increase performance at least by utilizing machine learning (e.g., a Hidden Markov Model) in order to obtain significant improvements in localization of mobile devices (Monogioudis; [0013]). Regarding claim 22, Bai and Monogioudis teach the limitations of claim 1. Bai further teaches transmitting, to the network node, a request based at least in part on the probability, the request comprising one or more of a request to decrease a report periodicity, a request to trigger one or more reports, a request to change a number of resources associated with a report, a predicted change in the beam, or a predicted change of the beam from the first beam to the second beam different from the beam (As can be seen in at least steps 712-714, the UE may transmit an indication of the probability associated with the adjustment of the beam (e.g., at least a predicted change in the beam) to the network node (e.g., base station); Bai; Fig. 7; [0081]-[0089]). Regarding claim 23, Bai and Monogioudis teach the limitations of claim 1. Bai further teaches the beam of the set of beams used for the wireless communications at the UE is a top beam (At least a beam currently in use or currently being measured (e.g., the beam of the set of beams) may be interpreted as a top beam; Bai; Fig. 7; [0078]-[0089]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC A MYERS whose telephone number is (571)272-0997. The examiner can normally be reached Monday - Friday 10:30am to 7:00pm.
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/ERIC MYERS/Primary Examiner, Art Unit 2474
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