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 (IDSs) submitted on 11/05/2024, 01/27/2025, and 01/09/2026 comply with the provisions of 37 CFR 1.97. Accordingly, the IDSs are being considered by the examiner.
Claim Objections
Claims 2-3, 13 and 19 are objected to because of the following informalities:
Claim 2 (line7) and Claim 3 (line 1) “the representation” should read - - the at least one of the representation - -.
Claim 13 (line 2), “a conditional handover decision” should read - - the CHO decision - - or - - the conditional handover (CHO) decision - -.
Claim 19 (line 8), “the representation” should read - - the at least one of the representation - -.
Appropriate correction is required.
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.
Claim 22 is 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.
Claim 22 (line 1) recites the limitation "the RRM action". There is insufficient antecedent basis for this limitation in the claim.
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.
The factual inquiries 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.
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.
Claims 1-2, 4-12, 15 and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Isaksson et al. (US 20200413316 A1 disclosed in IDS) hereinafter Isaksson in view of REN et al. (US 20230209419 A1), hereinafter REN.
Regarding Claim 1, Isaksson teaches a computer-implemented method, in a wireless device (Isaksson FIG. 3, see also Claim 1) (see also REN Claim 1, FIG. 9), comprising:
receiving [a first set of sensor] data from [one or more sensors] of the wireless device (Isaksson, ¶0042 the model is trained at a radio network node, such as the first radio network node 12, or a standalone network node 15, with received data from one or more wireless communication devices. . . [that] may comprise. . . capability of supporting one or more models of the one or more wireless communication devices; and movement related data of the one or more wireless communication devices)
receiving a first set of radio measurements from a radio interface of the wireless device that is distinct from the one or more sensors (Isaksson, FIG. 3, at 307; ¶0059; FIG. 4, at 403, ¶0064, input to the model such as ) measured RSRP and/or RSRQ of neighbouring beams or cells on serving frequency and/or other frequencies than the serving frequency, timing Advance (TA) for the serving cell or beam . . . ¶0070 Measured RSRP and/or RSRQ is a wireless communication device based feature where the wireless communication device in the wireless communications network is assumed to send RSRP and/or RSRQ measurement reports, containing e.g. L3-measurements of the measured values of the serving cell and up to eight neighbouring cells on the serving carrier or frequency; see also ¶0071-0075);
processing [the first set of sensor] data and the first set of radio measurements at a radio resource management (RRM) neural network of the wireless device to generate a first output representative of a first RRM action (Isaksson, FIG. 3, at 307; ¶0059; [t]he wireless communication device 10 executes the selected model using the obtained one or more trained model parameters; see also FIG. 4, at 403, ¶0064, at 403; FIG. 7, ¶0101 the executing unit 704 (of the wireless communication device 10)); and
performing the first RRM action at the wireless device (Isaksson, FIG. 3, ¶0060, at 308, [t]he wireless communication device 10 then triggers a process based on the output of the executed model. E.g. the wireless communication device may: trigger sending measurement reports to the radio network node, which is responsible for the handover decision; trigger a handover directly, e.g. conditional handover; or trigger measurements on neighbouring nodes, possibly on other frequencies / “a first RRM action”; see also FIG. 4, ¶0065 at 404).
Isaksson does not explicitly teach [the data] is a first set of sensor data from one or more sensors of the wireless device.
However, in analogous art, REN explicitly discloses [the data] is a first set of sensor data from one or more sensors of the wireless device (REN, Abstract, FIG. 9, ¶0112, at 902 [a] method of wireless communication by a user equipment (UE), comprises inputting sensor data, captured at the UE, to a local machine learning model; ¶0098-0099,the sensors comprise at least one of a radar sensor, an accelerometer, a gyroscope, a camera, and a position location sensor; see also ¶0092-0094; and Claim 1-2).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Isaksson’s method performed by a wireless communication device for managing communication in a wireless communications network (utilizing machine learning ML) with REN’s techniques and apparatuses for 5G new radio (NR) machine learning-based handover prediction by a network based on sensor data captured at a wireless device. The motivation would be to provide a machine learning model that predicts handover actions or a future handover [REN, ¶0101-0102].
Regarding Claim 2, Isaksson and REN teach Claim 1.
Isaksson further teaches the wireless device is a user equipment and the method further comprises:
providing at least one of a representation of [sensor] capabilities of the wireless device [for the one or more sensors] for receipt by an infrastructure component of a network infrastructure that is wirelessly connected to the wireless device (Isaksson, ¶0038, in the wireless communications network 1, wireless communication devices e.g. a wireless communication device 10 such as [. . .]a user equipment (UE) may communicate via one or more Access Networks (AN), e.g. RAN, to one or more core networks (CN); [and] FIG. 5, ¶0088, at 501 may send a capability indication to the radio network node; see also ¶0042; FIG. 3,¶0055; Claim 5, at 304, the radio network node may select the model out of a number of models based on the capability, of the wireless communication device 10/UE);
receiving a first neural network architectural configuration from the infrastructure component in response to providing the representation of sensor capabilities (Isaksson, FIG. 3, ¶0055 at 303; the wireless communication device 10 signals the network which one or more models the wireless communication device 10 supports; [and] ¶0056, at 304, the radio network node may take the capability into consideration and thus negotiate a model to use; [and] ¶0057, [t]he radio network node provides to the wireless communication device 10, the indicator indicating the model and the one or more trained model parameters for the model; see also ¶0044 a trained model comprises a structure of the model also and associated one or more trained model parameters such as weights; [and] ¶0083 [t]he model may be a neural network and the one or more model parameters may represent connections between nodes in the neural network and the strength of those connections, e.g. weights; see also FIG. 6 at steps 603-605); and
implementing the first neural network architectural configuration at the RRM neural network (Isaksson, FIG 3. ¶0058, at 306 the wireless communication device 10 thus selects the model based on the indicator; ¶0059 at 307 executes selected model using one or more trained model parameters; [and] ¶0060, at 308 triggers a process based on the output of the executed model. E.g. the wireless communication device may: trigger sending measurement reports to the radio network node, which is responsible for the handover decision; trigger a handover directly, e.g. conditional handover; or trigger measurements on neighbouring nodes, possibly on other frequencies; examiner interprets as collectively, Implementing the first neural network architectural configuration).
Isaksson does not explicitly teach [the data] is a first set of sensor data from one or more sensors of the wireless device.
However, in analogous art, REN explicitly discloses [the data] is a first set of sensor data from one or more sensors of the wireless device (REN, Abstract, FIG. 9, ¶0112, at 902 [a] method of wireless communication by a user equipment (UE), comprises inputting sensor data, captured at the UE, to a local machine learning model; ¶0098-0099,the sensors comprise at least one of a radar sensor, an accelerometer, a gyroscope, a camera, and a position location sensor; see also ¶0092-0094; and Claim 1-2).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Isaksson’s method performed by a wireless communication device for managing communication in a wireless communications network (utilizing machine learning ML) with REN’s techniques and apparatuses for 5G new radio (NR) machine learning-based handover prediction by a network based on sensor data captured at a wireless device. The motivation would be to provide a machine learning model that predicts handover actions or a future handover [REN, ¶0101-0102].
Regarding Claim 4, Isaksson and REN teach Claim 2.
Isaksson further teaches providing data to the infrastructure component (Isaksson, ¶0038, a user equipment (UE) may communicate via one or more Access Networks (AN), e.g. RAN, to one or more core networks (CN); [and] FIG. 5, ¶0088, at 501 may send a capability indication to the radio network node; as cited in Claim 2 herein).
Isaksson does not explicitly teach providing at least one of the first set of sensor data or the first set of radio measurements to the infrastructure component; receiving a second neural network architectural configuration from the infrastructure component in response to providing the at least one of the first set of sensor data or the first set of radio measurements, the second neural network architectural configuration representing a modification of the first neural network architectural configuration based on the at least one of the first set of sensor data or the first set of radio measurements; and implementing the second neural network architectural configuration at the RRM neural network.
However, in analogous art, REN explicitly discloses providing at least one of the first set of sensor data to the infrastructure component (REN, FIG. 9, at 902 inputting sensor data captured at the UE to a local ML machine learning model and at 906 transmitting to a base station / “infrastructure component”) or the first set of radio measurements to the infrastructure component;
receiving a second neural network architectural configuration from the infrastructure component in response to providing the at least one of the first set of sensor data (REN, ¶0105 network controlled updates, for example, may occur based on UE positioning information /”sensor data” observed at the base station), or the first set of radio measurements the second neural network architectural configuration representing a modification of the first neural network architectural configuration based on the at least one of the first set of sensor data (REN, ¶0103, the network delivers model-1 to the UE side. The UE side model includes a model structure and corresponding neural network weights. An update of the model may be based on a UE request, or network control, or the update may occur in accordance with a predefined pattern); and
implementing the second neural network architectural configuration at the RRM neural network (REN, FIG. 8; ¶0105 the network may provide an updated model (i.e. an updated model structure/second NN configuration [and] with the updated model, the UE may accurately execute a successful handover . . .; see also ¶0080). (see also REN, Claims including but not limited to 1, 5, 25, 73-75).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Isaksson’s method performed by a wireless communication device for managing communication in a wireless communications network (utilizing machine learning ML) with REN’s techniques and apparatuses for 5G new radio (NR) machine learning-based handover prediction by a network based on sensor data captured at a wireless device. The motivation would be to provide a machine learning model that predicts handover actions or a future handover [REN, ¶0101-0102].
Regarding Claim 5, Isaksson and REN teach Claim 2.
Isaksson teaches modifying the first neural network architectural configuration and measurement reporting to support different neural network configurations (Isaksson, ¶0083 NN model structure A trained model may be represented by a set of such weights and the model structure, i.e. how the nodes of the neural network are connected; [and] as cited in Claims 1 and 2 herein).
Isaksson does not explicitly teach modifying the first neural network architectural configuration based on at least one of the first set of sensor data or the first set of radio measurements to generate a second neural network architectural configuration.
However, in the analogous art, REN explicitly discloses modifying the first neural network architectural configuration based on at least one of the first set of sensor data [to generate a second neural network architectural configuration] (REN, FIG. 8; ¶0103 the network delivers model-1 to the UE side. [and] the UE side model includes a model structure and corresponding neural network weights [and] an update of the model may be based on a UE request, or network control, . . . ;¶0105 network controlled updates, for example, may occur based on UE positioning information observed at the base station; ) or the first set of radio measurements to generate a second neural network architectural configuration; and
implementing the second neural network architectural configuration at the RRM neural network (REN, ¶0105 the network may provide an updated model (i.e. an updated model structure/second NN configuration). (see also REN, Claims including but not limited to 1, 5, 25, 73-75).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Isaksson’s method performed by a wireless communication device for managing communication in a wireless communications network (utilizing machine learning ML) with REN’s techniques and apparatuses for 5G new radio (NR) machine learning-based handover prediction by a network based on sensor data captured at a wireless device. The motivation would be to provide a machine learning model that predicts handover actions or a future handover [REN, ¶0101-0102].
Regarding Claim 6, Isaksson and REN teach Claim 1.
Isaksson further teaches receiving a representation of an operational state of the wireless device (Isaksson, FIG. 2; ¶0051 the previous state/”an operational state” cited herein ); and wherein processing comprises processing [the first set of sensor] data, the first set of radio measurements, and the operational state at the RRM neural network to generate the first output (Isaksson, FIG. 2; ¶0051 each LSTM cell, c, has the possibility of memorizing and forgetting the previous state before coming up with the output h /”first output”; see also ¶0051, [t]he model used in embodiments herein may be based on a Long short-term memory (LSTM) model . . . which is a type of artificial neural network that is capable of learning order dependence in sequence prediction problems).
Isaksson does not explicitly teach processing the first set of sensor data.
However, in the analogous art, REN explicitly discloses processing the first set of sensor data ( REN, FIG. 9, at 900 extracting features/”processing” from the sensor data, with the local machine learning model . . . the user equipment (UE) (e.g., controller/processor 280, and/or memory 282) can extract features from the sensor data ; see also ¶0033, local model at the UE/”wireless device” side extracts features from the sensor data; FIG. 3, ¶0059).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Isaksson’s method performed by a wireless communication device for managing communication in a wireless communications network (utilizing machine learning ML) with REN’s techniques and apparatuses for 5G new radio (NR) machine learning-based handover prediction by a network based on sensor data captured at a wireless device. The motivation would be to provide a machine learning model that predicts handover actions or a future handover [REN, ¶0101-0102].
Regarding Claim 7, Isaksson and REN teach Claim 6.
Isaksson further teaches the operational state comprises a radio resource control (RRC) state of the wireless device (Isaksson, [t]he indicator may be transmitted in a radio resource control (RRC) message or similar, e.g. at initial context setup of the wireless communication device 10). see also REN, ¶0047 the base station 110 may configure a UE 120 via downlink control information (DCI), radio resource control (RRC) signaling).
Regarding Claim 8, Isaksson and REN teach Claim 1.
Isaksson does not explicitly teach the one or more sensors comprise at least one of: a positional sensor; a pose sensor; an accelerometer, a pressure sensor; or a proximity sensor.
However, in the analogous art REN explicitly discloses the one or more sensors comprise at least one of: a positional sensor; a pose sensor; an accelerometer, a pressure sensor; or a proximity sensor (REN, ¶0098-0099,the sensors comprise at least one of a radar sensor, an accelerometer, a gyroscope, a camera, and a position location sensor).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Isaksson’s method performed by a wireless communication device for managing communication in a wireless communications network (utilizing machine learning ML) with REN’s techniques and apparatuses for 5G new radio (NR) machine learning-based handover prediction by a network based on sensor data captured at a wireless device. The motivation would be to provide a machine learning model that predicts handover actions or a future handover [REN, ¶0101-0102].
Regarding Claim 9, Isaksson and REN teach Claim 1.
Isaksson further teaches the first set of radio measurements comprises at least one signal power measurement of a serving cell or a neighboring cell (Isaksson, FIG. 3, ¶0059 wireless communication device 10 executes selected model using the one or more trained model parameters. Further input to the model may be provided from the wireless communication device 10 such as a current serving beam or cell, measured RSRP and/or RSRQ of neighbouring beams or cells).
Regarding Claim 10, Isaksson and REN teach Claim 1.
Isaksson further teaches the RRM action comprises at least one of: performing an RRM-related measurement by the wireless device; configuring a characteristic of an RRM-related measurement to be performed by the wireless device; or performing an RRM reporting process at the wireless device (Isaksson, FIG. 3, ¶0059, at 307 The wireless communication device 10 executes selected model using the one or more trained model parameters. Further input to the model may be provided from the wireless communication device 10 such as a current serving beam or cell, measured RSRP and/or RSRQ of neighbouring beams or cells on serving frequency and/or other frequencies than the serving frequency, timing Advance (TA) for the serving cell or beam).
Regarding Claim 11, Isaksson and REN teach Claim 1.
Isaksson further teaches the characteristic of the RRM-related measurement comprises at least one of: a frequency or timing of the RRM-related measurement or a frequency band or channel of the RRM-related measurement (Isaksson, FIG. 3, ¶0059, at 307, the wireless communication device 10 executes selected model using the one or more trained model parameters. Further input to the model may be provided from the wireless communication device 10 such as a current serving beam or cell, measured RSRP and/or RSRQ of neighbouring beams or cells on serving frequency and/or other frequencies than the serving frequency, timing Advance (TA) for the serving cell or beam on serving frequency and/or other frequencies than the serving frequency, timing Advance (TA) for the serving cell or beam, . . . ).
Regarding Claim 12, Isaksson and REN teach Claim 1.
Isaksson further teaches the RRM action comprises execution of a conditional handover (CHO) decision or a Conditional PSCell change (CPC) decision (Isaksson, ¶0045, the wireless communication device 10 then uses this model to decide whether to trigger one or more of the following processes: . . ¶0048 c) initiate “a conditional handover” / CHO; FIG. 3, ¶0060 at 308; see also FIG. 4, ¶0065, at 404, FIG. 5, ¶0092, at 505, ¶0102, [t]he wireless communication device 10 then trigger [s] a handover directly, e.g. conditional handover; Claim 2).
Regarding Claims 18, 19, 20 and 21 the claims disclose similar features of Claims 1, 2, 4 and 5 respectively, and are rejected based on the same rationales of Claims 1, 2, 4 and 5, in apparatus form (the wireless device is a user equipment (Isaksson ¶0038 wireless communication device/UE 10; FIG. 3, ¶0052; FIG. 7 UE 10); and the executable instructions are further configured to manipulate the at least one processor (Isaksson, FIG. 3, ¶0060 process of triggering and sending measuring reports); FIG. 7, ¶¶0098-0099, the wireless communication device 10/”UE” comprises a processing circuitry 701 and a memory 707, said memory 707 containing instructions executable by said processing circuitry 701 whereby said wireless communication device 10 is configured to perform the method herein).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Isaksson in view of REN and further in view of Da Silva et al. (US 20230025432 A1), hereinafter Da Silva.
Regarding Claim 3, Isaksson and REN teach Claim 2.
Isaksson and REN do not explicitly teach the representation of sensor capabilities comprises one or more fields of a UECapabilitiesInformation Radio Resource Control (RRC) message.
However, in the analogous art, REF 3 explicitly discloses the representation of sensor capabilities comprises one or more fields of a UECapabilitiesInformation Radio Resource Control (RRC) message (0209, the UE 101 may be configured, e.g. by the first network node 403a, via an RRC message, to utilize at least one of the above parameters as input to mobility predictions models . .. [and] . . . [t]he availability of these parameters, e.g. in case of sensors, the availability at the UE of a . . . depends on a capability information indicated to the network; see also ¶0206 parameters from sensors may also be used, such as UE positioning information, e.g. GPS coordinates, barometric sensor information or other indicators of height, rotation sensors, proximity sensors).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine Isaksson’s method performed by a wireless communication device for managing communication in a wireless communications network (utilizing machine learning ML), REN’s techniques and apparatuses for 5G new radio (NR) machine learning-based handover prediction by a network based on sensor data captured at a wireless device with Da Silva’s method performed by a User Equipment (UE) for handling mobility information in a communications network. The motivation would be to improve the robustness at handover and to decrease the interruption time at handover [Da Silva, ¶0066].
Allowable Subject Matter
Claims 13, 14 and 22 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.
Regarding Claims 13 and 22, the closest prior art, Isaksson, as cited herein, teaches an RRM action
comprising a conditional handover decision (CHO) and REN, as cited herein, teaches extracting features from the sensor data/processing sensor data, with the local machine learning model.
However, Isaksson and REN do not explicitly teach all the limitations cited in Claims 13 and 22, including in response to a CHO decision, receiving a first neural network architectural configuration from the infrastructure component of the cell in response to providing the representation of sensor capabilities; and replacing a second neural network architectural configuration of the RRM neural network with the first neural network architectural configuration.
Claim 14 includes the above allowable subject matter for being dependent on Claim 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hedley et al. (US 20190104493 A1): Abstract;¶0015; ¶0043;¶0052; ¶0060 receiving sensor data indicative of the movement of the first mobile device, wherein the sensor data is of a type different to the radio measurement; ¶0156, the mapping between a location and the corresponding environmental disturbance can be implemented in various approaches, including . . . artificial neural networks; see also ¶¶0162-0164.
Yi et al. (US 20200329523 A1): ¶0199, an RRC sublayer may support an RRC_Idle state, an RRC_Inactive state and/or an RRC_Connected state for a wireless device [and]In an RRC_Inactive state, a wireless device may perform at least one of: cell selection/re-selection; FIG. 15, 0295, FIG. 15 is an example diagram showing RRC state transitions of a wireless device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRACY L WILLIAMS whose telephone number is 571-270-7694. The examiner can normally be reached Mon - Fri 8:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ayman Abaza can be reached at 571-270-0422. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TRACY L WILLIAMS/Examiner, Art Unit 2465
/CHRISTOPHER T WYLLIE/Examiner, Art Unit 2465
Prosecution Insights