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 § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 7, 12, 17, 22, 23, 28, 29, and 32 is/are rejected under 35 U.S.C. 102 as being anticipated by Lundborg US 20210105057.
Regarding claim 1, 22, Lundborg teaches a method performed by a network node for a wireless network that utilizes transmit and/or receive beamforming (paragraph 9, " It is an object to provide beam assignment support that let slower control decisions influence the fast beam selection in a TRP beam assignment in a balanced way."), the method comprising:
dynamically selecting a subset of beams for a particular wireless communication device (paragraph 9, It is an object to provide beam assignment support that let slower control decisions influence the fast beam selection in a TRP beam assignment in a balanced way."; paragraph 111, FIG. 11 illustrates an application of individual subsets bias values on a beam application decision for more than one UE the bias values are selected to favour beams to the two UEs located far from each other. The bias values for UE 10A thus favour beam 26A relative to the beams 26B and 26C, whereas the bias values for UE 10B favour beam 26H relative to the beam 26G. "), the subset of beams being a subset of a set of available beams (paragraph 10, The set of beams are used by a first TRP. In the radio base station, a set of bias values is obtained, representing connection quality predictions for a respective beam of the set of beams for transmission to the user equipment. "; paragraph 12 "The set of bias values represents connection quality predictions for a set of beams for transmission to a user equipment."; Fig. 11),
wherein dynamically selecting the subset of beams for the particular wireless communication device comprises dynamically selecting which of the set of available beams are included in the subset of beams for the particular wireless communication device (paragraph 10, The set of beams are used by a first TRP. In the radio base station, a set of bias values is obtained, representing connection quality predictions for a respective beam of the set of beams for transmission to the user equipment. "); and comprises dynamically selecting the subset of beams for the particular wireless communication device (paragraph 10, The set of beams are used by a first TRP. In the radio base station, a set of bias values is obtained, representing connection quality predictions for a respective beam of the set of beams for transmission to the user equipment. ") based on information that models or represents probabilities that the particular wireless communication device will switch from a current serving beam to each other beam in the set of available beams (Fig. 11; paragraph 75, " To this end, a set of "bias" values, one subset for each possible beam direction and UE, and for a time span of near future time slots, is implemented in the base station."; paragraph 76, It will also be possible to punish beam directions that has lesser probability of achieving high ranks than other beams"; paragraph 78. "The use of bias value may also lead to better beam selection for fast moving traffic by increasing the probability of selecting a beam further along the predicted"; paragraph 79, "Bias values may be based on predictions that are calculated using measurements from either or both the radio node, UE and external sources." paragraph 90, The bias values 61 will influence the assignment, but will in most cases only decrease the probability for assigning certain beams, not removing it completely. paragraph 82, "The proposed invention enables statistics based coordination of beam assignment for multiple users, when individual UE resource usage is predicted."; paragraph 115, "In a preferred embodiment, the connection quality predictions are estimations utilizing statistical information about used beams and the transmission experience thereof." paragraph 116, "the step S15 of calculating the set of bias values or the step S4 of calculating connection quality predictions utilizes statistical information about used beams"; paragraph 126, Such beam assignment may be statistics based. For example, with reference to FIG. 3, an implementation example can be given, considering a recorded history of measurements being used for predicting estimates of system level performance impact of possible beam allocations) and
performing one or more actions based on the selected subset of beams (paragraph 111, Assigning of beams 26A and 26H for the respective UE is thereby favourable.").
Regarding claims 2, 23, performing the one or more actions comprises:
(a) sending, to the particular wireless communication device, information that indicates the subset of beams (A UE 10 measures the reference power of the beam reference symbol for each of the beams 26A-C, the BRSRP measurements are reported from the UE 10 to the RBS 20, [0061]);
(b) monitoring for transmissions from the particular wireless communication device on the subset of beams;
(c) performing one or more measurements on the subset of beams at the network node;
(d) receiving measurements about beams in the subset of beams from the particular wireless communication device; or
(e) a combination of any two or more of (a)-(d).
Regarding claim 7, 28, the information that models or
represents the probabilities that the particular wireless communication device will switch from the current serving beam to the each other beam in the set of available beams is a trained machine learning model that models the probabilities that the particular wireless communication device will switch from the current serving beam to the each other beam in the
set of available beams (Note that at a next beam assignment, the original channel gain estimations 60 may be changed. One particular scenery may be that the relative strength of beam 26B relative to beam 26A is increased so much that the bias values cannot compensate for the difference, [0090]).
Regarding claim 12, 29, performing the one or more actions comprises receiving, from the particular wireless communication device, one or more measurements for at least one beam in the set of available beams or one or more measurements for at least one beam in the subset of beams; and
the method further comprises evaluating the subset of beams based on the one or more measurements for the at least one beam in the set of available beams or the one or more
measurements for the at least one beam in the subset of beams (A UE 10 measures the reference power of the beam reference symbol for each of the beams 26A-C, the BRSRP measurements are reported from the UE 10 to the RBS 20, [0061]).
Regarding claim 17, 32, based on the evaluating, adjusting one or more parameters related to selection of a subset of beams; and
selecting a new subset of beams for the particular wireless communication device based on the one or more adjusted parameters (Note that at a next beam assignment, the original channel gain estimations 60 may be changed. One particular scenery may be that the relative strength of beam 26B relative to beam 26A is increased so much that the bias values cannot compensate for the difference, [0090]).
(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.
Claim(s) 1, 15, 16, 19, 22, 30, 31, and 34 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sheng CN 118872217.
Regarding claims 1, 22, Sheng teaches a method performed by a network node for a wireless network that utilizes transmit and/or receive beamforming, the method comprising:
dynamically selecting a subset of beams for a particular wireless communication device, the subset of beams being a subset of a set of available beams, wherein dynamically selecting the subset of beams for the particular wireless communication device comprises dynamically selecting which of the set of available beams are included in the subset of beams for the particular wireless communication device and comprises dynamically selecting the subset of beams for the particular wireless communication device based on information that models or represents probabilities that the particular wireless communication device will switch from a current serving beam to each other beam in the set of available beams; and
performing one or more actions based on the selected subset of beams (In an embodiment where the machine learning model is
deployed in a base station, the base station may indicate a beam switch (e.g., a predicted beam is used as a new beam in a beam switch operation). The base station may send an indication (e.g., downlink control information (DCI)) to trigger the non-competitive RACH of the UE 10 when the probability
prediction output by the machine learning model will occur
beam failure. The indication may also indicate a predicted beam).
Regarding claim 15, evaluating the subset of beams comprise evaluating the subset of beams based on the one or more measurements for the at least one beam in the subset of beams, and a machine learning model (The data collection unit 101 is a function of providing input data to the model training unit 102 and the model reasoning unit 104. The AI/ML algorithm-specific data preparation (e.g., data pre-processing and cleaning, formatting, and conversion) is not performed in the data collection unit 101. Examples of input data may include measurements from the UE or different network entities, feedback from the actuator 103, and output from the AI/ML model, e.g., a predicted beam is used as a new beam in a beam switch operation). The base station may send an indication (e.g., downlink control information (DCI)) to trigger the non-competitive RACH of the UE 10 when the probability
prediction output by the machine learning model will occur
beam failure. The indication may also indicate a predicted beam).
Regarding claim 16, the machine learning model
predicts a best beam for the particular wireless communication device from among the set of available beams based on the one or more measurements for the at least one beam in the subset
of beams (The data collection unit 101 is a function of providing input data to the model training unit 102 and the model reasoning unit 104. The AI/ML algorithm-specific data preparation (e.g., data pre-processing and cleaning, formatting, and conversion) is not performed in the data collection unit 101. Examples of input data may include measurements from the UE or different network entities, feedback from the actuator 103, and output from the AI/ML model, e.g., a predicted beam is used as a new beam in a beam switch operation). The base station may send an indication (e.g., downlink control information (DCI)) to trigger the non-competitive RACH of the UE 10 when the probability prediction output by the machine learning model
will occur beam failure. The indication may also indicate a predicted beam).
Regarding claim 19, 34, providing a result of the evaluating (604) to another node for updating of a machine learning
model used for beam subset selection (The model deployment / update between units 102 and 104 involves deploying or
updating the AI/ML model (e.g., trained machine learning model 105a or 105b) to the model inference unit 104).
Regarding claim 30, in order to evaluate the subset of beams, the processing circuitry is further configured to cause the network node evaluate the subset of beams based on the one
or more measurements for the at least one beam in the subset of beams, and a machine learning model (The data collection unit 101 is a function of providing input data to the model training unit 102 and the model reasoning unit 104. The AI/ML algorithm-specific data preparation (e.g., data pre-processing and cleaning, formatting, and conversion) is not performed in the data collection unit 101. Examples of input data may include measurements from the UE or different network entities, feedback from the actuator 103, and output from the AI/ML model, e.g., a predicted beam is used as a new beam in a beam switch operation). The base station may send an indication (e.g., downlink control information (DCI)) to trigger the non-competitive RACH of the UE 10 when the probability prediction output by the machine learning model
will occur beam failure. The indication may also indicate a predicted beam).
Regarding claim 31, the machine learning model predicts a best beam for the particular wireless communication device from among the set of available beams based on the one or more measurements for the at least one beam in the subset of beam (The data collection unit 101 is a function of providing input data to the model training unit 102 and the model reasoning unit 104. The AI/ML algorithm-specific data preparation (e.g., data pre-processing and cleaning, formatting, and conversion) is not performed in the data collection unit 101. Examples of input data may include measurements from the UE or different network entities, feedback from the actuator 103, and output from the AI/ML model, e.g., a predicted beam is used as a new beam in a beam switch operation). The base station may send an indication (e.g., downlink control information (DCI)) to trigger the non-competitive RACH of the UE 10 when the probability prediction output by the machine learning model
will occur beam failure. The indication may also indicate a predicted beam).
Claim(s) 24, 26 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sheng CN 118872217.
Regarding claims 24, 26, Sheng teaches a computer-implemented method comprising:
receiving beam switch related information for a plurality of wireless communication devices;
training a beam switch probability model that models a probability of a beam switch from any first beam in a set of available beams for a wireless network to any second beam in the set of available beams for the wireless network; and
providing the beam switch probability model to a network node in the wireless network (In an embodiment where the
machine learning model is deployed in a base station, the base station may indicate a beam switch (e.g., a predicted beam is used as a new beam in a beam switch operation). The base station may send an indication (e.g., downlink control information (DCI)) to trigger the non-competitive RACH of the UE 10 when the probability prediction output by the machine learning model
will occur beam failure. The indication may also indicate a predicted beam).
Allowable Subject Matter
Claims 18 and 33 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RONALD B ABELSON whose telephone number is (571)272-3165. The examiner can normally be reached M-F 8:00-4:30.
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, Ayaz Sheikh can be reached at 571-272-3795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RONALD B ABELSON/Primary Examiner, Art Unit 2476
Prosecution Insights