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 submitted on 9/25/2024 and 2/10/2026 have been considered by the Examiner and made of record in the application file.
Preliminary Amendment
The present Office Action is based upon the original patent application filed on 9/25/2024, as modified by the preliminary amendment also filed on the same day. Claims 1, 2, 4, 6, 8-11, 13-19, and 25-29 are now pending in the present application.
Drawings
The drawings are objected to because the font size of at least some of the text is too small in Figures 1-9. Numbers, letters, and reference characters must measure at least 1/8 inch in height.
The drawings are objected to because the top margin is too small for figures 2-7 and 9. Each sheet must include a top margin of at least 1 inch, a left side margin of at least 1 inch, a right side margin of at least 5/8 inch, and a bottom margin of at least 3/8 inch. One of the shorter sides of the sheet is regarded as its top.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office Action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the Examiner, the Applicant will be notified and informed of any required corrective action in the next Office Action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 13 objected to because of the following informalities: there are two periods at the end of the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claims 25 and 26 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.
Claims 25 and 26 are both dependent on canceled claim 20. Because they are dependent on a canceled claim, the scope of claims 25 and 26 cannot be determined and they are indefinite.
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 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, 4, 11, 13, 14, 19, and 27-29 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kaya et al. (U.S. Patent Application Publication No. 2022/0190883 A1) (hereinafter Kaya).
Regarding claim 1, Kaya discloses a data collection method for beam management based on machine learning, executable in at least one wireless communication device (Paragraphs 0051 and 0058 disclose a method or technique may include determining, by a BS, a past beam sequence for a user equipment (UE); determining, by the BS based on the past beam sequence for the UE and a beam sequence model, a predicted future beam sequence for the UE, wherein the predicted future beam sequence indicates a predicted sequence of beams over time for communication between the UE and BS; and performing, by the BS, a beam-related action based on the predicted future beam sequence for the UE. The beam sequence model may be provided or implemented as an artificial intelligence (AI) neural network (which may be referred to as a neural network, a neural network model, an AI neural network model, an AI model, a machine learning model or algorithm, or other term). According to an example embodiment, neural networks may be or may include computational models used in machine learning made up of nodes organized in layers), comprising:
collecting data units for beam management based on machine learning, wherein the data units associate a plurality of beams with a plurality of attributes, and each data unit in the data units associates a beam with at least one attribute (Figure 4 and paragraph 0072 disclose at 410, step 1 may include data collection. For example, after initial deployment, a BS may use CSI-RS feedback (e.g., measurement reports) during training phase to gather data. Thus, a source of this data may include measurement reports (and/or a list of beams actually used by a BS for a UE) for estimating the quality of different beams for a UE/user(s). When BSs are first deployed, they may be operated in a training mode where beam refinement procedures are implemented with extensive CSI-reporting configuration. During this time each UE may be requested to report a substantial number of measurement reports for estimating the quality e.g., based on a RSRP or other signal measurement) of different beams as the UE obtains service or is connected to the BS/gNB. This phase provides substantial amount of training data for the beam sequence model (e.g., which may be implemented as a neural network, such as a deep neural network)).
Regarding claim 4, as applied to claim 1 above, Kaya further discloses wherein the at least one attribute in the data unit comprises measurement on the beam associated by the data unit; and wherein the measurement on the beam associated by the data unit comprises a reference signal received power (RSRP), reference signal received quality (RSRQ), or hypothetical block error rate (BLER) (Figure 4 and paragraph 0072 disclose at 410, step 1 may include data collection. For example, after initial deployment, a BS may use CSI-RS feedback (e.g., measurement reports) during training phase to gather data. Thus, a source of this data may include measurement reports (and/or a list of beams actually used by a BS for a UE) for estimating the quality of different beams for a UE/user(s). When BSs are first deployed, they may be operated in a training mode where beam refinement procedures are implemented with extensive CSI-reporting configuration. During this time each UE may be requested to report a substantial number of measurement reports for estimating the quality e.g., based on a RSRP or other signal measurement) of different beams as the UE obtains service or is connected to the BS/gNB. This phase provides substantial amount of training data for the beam sequence model (e.g., which may be implemented as a neural network, such as a deep neural network)).
Regarding claim 11, as applied to claim 1 above, Kaya further discloses training a machine learning (ML) model by inputting the data units to the machine learning model (Figure 4 and paragraph 0073 disclose at 420, step 2 may include Training a Beam Sequence Model for Prediction. This step may include training the beam sequence model (e.g., neural network) at the BS/TRP with (or based on) data gathered from or with respect to multiple UEs, e.g., to allow the beam sequence model to provide or output a predicted future beam sequence for one or more UEs);
operating the trained machine learning model to output a beam operation advice, wherein the machine learning model operates in relation to a channel-estimation-based beam management function that outputs a channel estimation result (Figure 4 and paragraph 0074 disclose at 430, step 3 may include Inference Mode For Prediction. For example, in this step, a past beam sequence for a UE may be input to the beam sequence model, e.g., to determine a predicted future beam sequence for the UE (e.g., predict a best k refined beams, and/or predict the TRP/BS, to best serve the UE/user over a next several hundred ms)); and
performing a beam operation based on the beam operation advice and the channel estimation result (Figure 4 and paragraph 0075 disclose at 440, step 4 may include Performing a Beam-related action: e.g., such as: 1) selecting a beam based on predicted future beam sequence for the UE (e.g., pro-actively switch a beam used by the BS to a beam listed in the predicted future beam sequence); 2) Request a measurement report for one or more beams listed in predicted future beam sequence for UE; 3) pro-actively initiate cell switch or UE handover to a cell or TRP/BS if the next beam to be used (e.g., indicated in the future predicted beam sequence) is provided by a different cell or different TRP/BS; 4) Configure CSI-RS resources and Measurement Reports to reduce overhead; 5) adjust a frequency or number of measurement reports, or adjust a number of beams (or a specific set of beams) to be measured and/or reported).
Regarding claim 13, as applied to claim 11 above, Kaya further discloses wherein the relation comprises a parallel relation in which the machine learning model operates in parallel to the channel-estimation-based beam management function (Figure 4 and paragraph 0074 disclose at 430, step 3 may include Inference Mode For Prediction. For example, in this step, a past beam sequence for a UE may be input to the beam sequence model, e.g., to determine a predicted future beam sequence for the UE (e.g., predict a best k refined beams, and/or predict the TRP/BS, to best serve the UE/user over a next several hundred ms)).
Regarding claim 14, as applied to claim 11 above, Kaya further discloses wherein the relation comprises a substitution relation in which the machine learning model operates in substitution for the current channel-estimation-based beam management function (Figure 4 and paragraph 0074 disclose at 430, step 3 may include Inference Mode For Prediction. For example, in this step, a past beam sequence for a UE may be input to the beam sequence model, e.g., to determine a predicted future beam sequence for the UE (e.g., predict a best k refined beams, and/or predict the TRP/BS, to best serve the UE/user over a next several hundred ms)).
Regarding claim 19, as applied to claim 11 above, Kaya further discloses wherein the at least one wireless communication device comprises a base station, and the machine learning model is deployed in the base station (Paragraphs 0045 and 0046 disclose a BS (e.g., gNB, a TRP or a radio access network (RAN) node) or other network node may use or employ an artificial intelligence (AI) neural network (which may be referred to as a neural network, a neural network model, an AI neural network model, an AI model, a machine learning model or algorithm, or other term) to implement a beam sequence model. The beam sequence model, e.g., which may be implemented as a neural network, or other model, may be trained, e.g., based on a past beam sequence for one or more UEs. A BS may determine a predicted future sequence of beams for a UE based on the beam sequence model and a past beam sequence for the UE. For example, the beam sequence model (e.g., implemented as a neural network) may output a predicted future sequence of beams for the UE, based on an input that includes a past beam sequence for the UE. Paragraph 0068 discloses these predictions may be performed using artificial intelligence (AI) algorithms running on a BS or gNB, for example).
Regarding claim 27, as applied to claim 1 above, Kaya further discloses wireless communication device comprising: a processor, configured to call and run a computer program stored in a memory, to cause a device in which the processor is installed to execute the method of claim 1 (Figure 12 and paragraphs 0135 and 0136 disclose a wireless station (e.g., AP, BS or user device, or other network node) 1200. Processor 1204 may also make decisions or determinations, generate frames, packets, or messages for transmission, decode received frames or messages for further processing, and other tasks or functions described herein. Processor 1204, which may be a baseband processor, for example, may generate messages, packets, frames, or other signals for transmission via wireless transceiver 1202 (1202A or 1202B). Processor 1204 may control transmission of signals or messages over a wireless network, and may control the reception of signals or messages, etc., via a wireless network (e.g., after being down-converted by wireless transceiver 1202, for example). Processor 1204 may be programmable and capable of executing software or other instructions stored in memory or on other computer media to perform the various tasks and functions described above, such as one or more of the tasks or methods described above. Processor 1204 may be (or may include), for example, hardware, programmable logic, a programmable processor that executes software or firmware, and/or any combination of these).
Regarding claim 28, as applied to claim 1 above, Kaya further discloses chip, comprising: a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute the method of claim 1 (Figure 12 and paragraphs 0135 and 0136 disclose a wireless station (e.g., AP, BS or user device, or other network node) 1200. Processor 1204 may also make decisions or determinations, generate frames, packets, or messages for transmission, decode received frames or messages for further processing, and other tasks or functions described herein. Processor 1204, which may be a baseband processor, for example, may generate messages, packets, frames, or other signals for transmission via wireless transceiver 1202 (1202A or 1202B). Processor 1204 may control transmission of signals or messages over a wireless network, and may control the reception of signals or messages, etc., via a wireless network (e.g., after being down-converted by wireless transceiver 1202, for example). Processor 1204 may be programmable and capable of executing software or other instructions stored in memory or on other computer media to perform the various tasks and functions described above, such as one or more of the tasks or methods described above. Processor 1204 may be (or may include), for example, hardware, programmable logic, a programmable processor that executes software or firmware, and/or any combination of these).
Regarding claim 29, as applied to claim 1 above, Kaya further discloses computer-readable storage medium, in which a computer program is stored, wherein the computer program causes a computer to execute the method of claim 1 (Figure 12 and paragraphs 0135 and 0137 disclose a wireless station (e.g., AP, BS or user device, or other network node) 1200. The wireless station also includes a processor or control unit/entity (controller) 1204 to execute instructions or software and control transmission and receptions of signals, and a memory 1206 to store data and/or instructions. A storage medium may be provided that includes stored instructions, which when executed by a controller or processor may result in the processor 1204, or other controller or processor, performing one or more of the functions or tasks described above).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 8 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kaya in view of Butt et al. (U.S. Patent Application Publication No. 2023/0171836 A1) (hereinafter Butt).
Regarding claim 8, as applied to claim 1 above, Kaya discloses the claimed invention except explicitly disclosing wherein the at least one attribute in the data unit comprises location information of the at least one wireless communication device.
In analogous art, Butt discloses wherein the at least one attribute in the data unit comprises location information of the at least one wireless communication device (Paragraph 0014 discloses a method for training a machine learning (ML) model at a network device may be disclosed. The method may comprise receiving, from at least one user equipment, (UE) a plurality of UE locations and a threshold, beamFailureInstanceMaxCount, the threshold being indicative of a maximal number of consecutive beam failure instances, determining a training set by labeling pairs comprising a UE location and a beam failure instance counter, BFI counter, with a beam failure probability factor, wherein the BFI counter takes values between 1 and the threshold, beamFailureInstanceMaxCount, and training the ML model using the labeled training set).
It 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 to incorporate receiving UE location information for training an ML model, as described in Butt, with data collection for ML training, as described in Kaya, because doing so is combining prior art elements according to known methods to yield predictable results. Combining receiving UE location information for training an ML model of Butt with data collection for ML training of Kaya was within the ordinary ability of one of ordinary skill in the art based on the teachings of Butt.
Therefore, it 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 to combine the teachings of Kaya and Butt to obtain the invention as specified in claim 8.
Regarding claim 15, as applied to claim 11 above, Kaya discloses the claimed invention except explicitly disclosing wherein the beam operation advice output from the machine learning model comprises a probability of beam failure associated with the beam at a time unit.
In analogous art, Butt discloses wherein the beam operation advice output from the machine learning model comprises a probability of beam failure associated with the beam at a time unit (Figure 2 and paragraph 0065 disclose a beam failure probability factor may be determined based at least on the location of the UE 104 and the BFI counter, at step 206. In one example embodiment, the beam failure probability factor may be determined by a trainable machine learning, ML, model. Further, the beam failure probability factor may be indicative of a probability of occurrence of a beam failure at the location of the UE 104, after a plurality of further time instances).
It 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 to incorporate determining a beam failure probability after a plurality of time instances, as described in Butt, with determining a sequence of beams for future use, as described in Kaya, because doing so is combining prior art elements according to known methods to yield predictable results. Combining determining a beam failure probability after a plurality of time instances of Butt with determining a sequence of beams for future use of Kaya was within the ordinary ability of one of ordinary skill in the art based on the teachings of Butt.
Therefore, it 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 to combine the teachings of Kaya and Butt to obtain the invention as specified in claim 15.
Regarding claim 16, as applied to claim 11 above, Kaya discloses the claimed invention except explicitly disclosing wherein the beam operation advice output from the machine learning model comprises a quantized probability of beam failure associated with the beam at a time unit.
In analogous art, Butt discloses wherein the beam operation advice output from the machine learning model comprises a quantized probability of beam failure associated with the beam at a time unit (Figure 2 and paragraph 0065 disclose a beam failure probability factor may be determined based at least on the location of the UE 104 and the BFI counter, at step 206. In one example embodiment, the beam failure probability factor may be determined by a trainable machine learning, ML, model. Further, the beam failure probability factor may be indicative of a probability of occurrence of a beam failure at the location of the UE 104, after a plurality of further time instances).
It 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 to incorporate determining a beam failure probability after a plurality of time instances, as described in Butt, with determining a sequence of beams for future use, as described in Kaya, because doing so is combining prior art elements according to known methods to yield predictable results. Combining determining a beam failure probability after a plurality of time instances of Butt with determining a sequence of beams for future use of Kaya was within the ordinary ability of one of ordinary skill in the art based on the teachings of Butt.
Therefore, it 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 to combine the teachings of Kaya and Butt to obtain the invention as specified in claim 16.
Regarding claim 17, as applied to claim 11 above, Kaya discloses the claimed invention except explicitly disclosing wherein each data element in an output of the machine learning model comprises a quantized probability of beam failure associated with a beam in a candidate beam set at a time unit or a probability of beam failure associated with a new candidate beam at a time unit.
In analogous art, Butt discloses wherein each data element in an output of the machine learning model comprises a quantized probability of beam failure associated with a beam in a candidate beam set at a time unit or a probability of beam failure associated with a new candidate beam at a time unit (Figure 2 and paragraph 0065 disclose a beam failure probability factor may be determined based at least on the location of the UE 104 and the BFI counter, at step 206. In one example embodiment, the beam failure probability factor may be determined by a trainable machine learning, ML, model. Further, the beam failure probability factor may be indicative of a probability of occurrence of a beam failure at the location of the UE 104, after a plurality of further time instances).
It 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 to incorporate determining a beam failure probability after a plurality of time instances, as described in Butt, with determining a sequence of beams for future use, as described in Kaya, because doing so is combining prior art elements according to known methods to yield predictable results. Combining determining a beam failure probability after a plurality of time instances of Butt with determining a sequence of beams for future use of Kaya was within the ordinary ability of one of ordinary skill in the art based on the teachings of Butt.
Therefore, it 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 to combine the teachings of Kaya and Butt to obtain the invention as specified in claim 17.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kaya in view of Cho et al. (U.S. Patent Application Publication No. 2022/0029286 A1) (hereinafter Cho).
Regarding claim 18, as applied to claim 11 above, Kaya discloses the claimed invention except explicitly disclosing wherein the at least one wireless communication device comprises a user equipment (UE), and the machine learning model is downloaded to the UE.
In analogous art, Cho discloses wherein the at least one wireless communication device comprises a user equipment (UE), and the machine learning model is downloaded to the UE (Figure 7 and paragraph 0097 disclose the electronic device 101 may download the artificial neural network module 740 from the server 108, and may use the same. Paragraph 0029 discloses the electronic devices may include, for example, a portable communication device (e.g., a smart phone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance).
It 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 to incorporate a UE downloading and artificial neural network module, as described in Cho, with an ML for use with UE data, as described in Kaya, because doing so is combining prior art elements according to known methods to yield predictable results. Combining a UE downloading and artificial neural network module of Cho with an ML for use with UE data of Kaya was within the ordinary ability of one of ordinary skill in the art based on the teachings of Cho.
Therefore, it 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 to combine the teachings of Kaya and Cho to obtain the invention as specified in claim 18.
Allowable Subject Matter
Claims 2, 6, 9, and 10 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.
The following is an Examiner’s statement of reasons for allowance:
Considering claim 2, the best prior art found during the prosecution of the present application, Kaya, fails to disclose, teach, or suggest the limitations of wherein the data unit comprises a quasi-co-location (QCL) relationship or a transmission configuration indicator (TCI) state denoting the beam in combination with and in the context of all of the other limitations in claim 2.
Considering claim 6, the best prior art found during the prosecution of the present application, Kaya, fails to disclose, teach, or suggest the limitations of wherein the measurement on the beam associated by the data unit comprises a quantized measurement value; wherein the quantized measurement value is one when the beam in a time unit has a measurement value greater than a threshold; and the quantized measurement value is zero when the beam in a time unit has a measurement value no greater than the threshold in combination with and in the context of all of the other limitations in claim 6.
Considering claim 9, the best prior art found during the prosecution of the present application, Kaya, fails to disclose, teach, or suggest the limitations of wherein the at least one attribute in the data unit comprises a connection duration on the beam associated by the data unit in combination with and in the context of all of the other limitations in claim 9.
Considering claim 10, the best prior art found during the prosecution of the present application, Kaya, fails to disclose, teach, or suggest the limitations of wherein a capability indicating whether the data units are sharable is configurable in combination with and in the context of all of the other limitations in claim 10.
Conclusion
Singh et al. (U.S. Patent Application Publication No. 2019/0097712 A1) discloses selection of beamforming directions based on learned performance;
Kalkunte (U.S. Patent Application Publication No. 2020/0162980 A1) discloses systems and methods for millimeter wave v2x wireless communications;
Kusashima et al. (U.S. Patent Application Publication No. 2020/0383110 A1) discloses a terminal device, base station device, and method; and
Cirkic et al. (U.S. Patent Application Publication No. 2021/0076267 A1) discloses reporting an indication of one or more estimated signal parameters.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to MARK G. PANNELL whose telephone number is (303) 297-4245. The Examiner can normally be reached Monday through Friday 8:00 am to 3:00 pm (Mountain Time).
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, Rafael Perez-Gutierrez can be reached on (571) 272-7915. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/Mark G. Pannell/Primary Examiner, Art Unit 2642
