DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without “significantly more”. Claim(s) 1-30 is/are directed to Abstract Idea such as an idea standing alone such as an instantiated concept, pan or scheme, as well as a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper for example using measurement received from a mobile device, transmitting from the source relay node to a donor access node.
The apparatus and the method claim 1, 17, 23 and 26 recites limitation, specifically claim 1 and 23 recites “obtain a set of training measurement information associated with a user equipment (UE); obtain a training position value associated with the UE; and provide the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on measurement information” and claim 17 and 26 recites obtain training measurement information for a user equipment (UE), or information associated with the training measurement information, the training measurement information being associated with training or performance monitoring of a model using a machine learning (ML) technique; and output, for the UE, a training position value based at least in part on the training measurement information or the information associated with the training measurement information” with wording variation. Since the claim is directed to a process and a machine, which is one of the statutory categories of the invention (Step 1: YES).
The claim is then analyzed to determine whether it is directed to any judicial exception. The claim recites specifically for claim 1 and 23 recites “obtain a set of training measurement information associated with a user equipment (UE); obtain a training position value associated with the UE; and provide the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on measurement information” and claim 17 and 26 recites obtain training measurement information for a user equipment (UE), or information associated with the training measurement information, the training measurement information being associated with training or performance monitoring of a model using a machine learning (ML) technique; and output, for the UE, a training position value based at least in part on the training measurement information or the information associated with the training measurement information” with wording variation. The obtaining step and then using machine learning technique used to determine the location recited in the claim is no more than an abstract idea i.e., mental process of machine learning process where machine learning model take input data and provide output data based on model, i.e., "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) (Step 2A: Prong One Abstract Idea=Yes).
The claim is then analyzed if it requires an additional elements or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception – i.e., limitation that are indicative of integration into a practical application: improving to the functioning of a computer or to any other technology or technical field. In the current claims, there is no additional elements that would integrate the abstract idea into a practical application (Step 2A: Prong Two Abstract Idea=Yes).
Next the claim as a whole is analyzed to determine if there are additional limitation recited in the claim such that the claim amount to significantly more than an abstract idea. The claim requires the additional limitation of a computer with the central processing unit, memory, a printer, an input and output terminal and a program. These generic computer components are claimed to perform the basic functions of storing, retrieving and processing data through the program that enables. In the current scenario, there are no additional elements that would amount to significantly more than the abstract idea. Therefore, the claim does not amount to significantly more than the abstract idea itself (Step 2B: No). Accordingly, the claim is not patent eligible.
Further, dependent claims do not add any positive limitation or step that recite within the scope of the claim and does not carry patentable weight they are also rejected for the same reasons as independent claims.
Double Patenting Analysis
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s).
It is noted that when current application is compared with U.S. Patent Application No. 18/430, 391, 18/464, 021, 18/461, 970 for obviousness-type double patenting they are patentably distinct from each other and cannot be anticipated by, or would have been obvious over, the reference claim(s) and for that reasons, nonstatutory double patenting rejection with reference application is not feasible at this time. However, based on response filed, Examiner will re-consider reference application for obviousness-type double patenting.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
1. Patent No. US 11445465 B2
Claims 1-30 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-45 of Patent No. US 11445465 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in pending application are transparently found in Patent No. US 11445465 B2 with obvious wording variation. For example, compare Claim 23 of pending application with claim 12 of Patent No. US 11445465 B2, they both recite
A method of wireless communication performed by an apparatus, comprising (A method of determining a location of a user equipment, the method comprising):
obtaining a set of training measurement information associated with a user equipment (UE) (obtaining, at the user equipment, a position-determination model i.e., set of training information based on the coarse location of the user equipment, wherein the position-determination model comprises one or more feature vectors or a machine-learning model and wherein the one or more feature vectors comprise one or more signal measurements previously performed by one or more devices i.e., obtaining a set of training measurement information associated with a user equipment (UE));
obtaining a training position value associated with the UE (determining, at the user equipment, one or more first positioning measurements of one or more reference signals from one or more transmission/reception points (TRPs), wherein the one or more first positioning measurements comprises one or more reference signal time differences, one or more receive-transmit time difference, or a combination thereof; and determining, at the user equipment, the location of the user equipment based on the one or more first positioning measurements, one or more TRP identities associated with the one or more first positioning measurements, and the position-determination model; wherein the user equipment is a first user equipment, and wherein obtaining the position-determination model comprises training the position-determination model using a first feature vector including second positioning measurements from a second user equipment and a corresponding location i.e., obtaining a training position value associated with the UE); and
providing the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on measurement information (sending a request for the first feature vector wirelessly from the first user equipment in at least one of an uplink communication or a sidelink communication, the request for the first feature vector including a second feature vector including third positioning measurements of the same measurement types as the second positioning measurements i.e., providing the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on measurement information)
Further, analyzing and comparing dependent claims 24 and 25 of the pending application with claims 13-22 of Patent No. US 11445465 B2 it was found that they recite the same limitation with wording changes.
Similarly, analyzing and comparing independent claims 1, 17 and 26 of the pending application including its dependent claims with claims 1, 23 and 34 including its dependent claims of Patent No. US 11445465 B2 it was found that they recite the same limitation with wording changes.
2. Patent No. US 12349100 B2
Claims 1-30 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-30 of Patent No. US 12349100 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in pending application are transparently found in Patent No. US 12349100 B2 with obvious wording variation. For example, compare Claim 1 of pending application with claim 1 of Patent No. US 12349100 B2, they both recite
an apparatus for wireless communication comprising (An apparatus for wireless communication at a network entity, comprising):
a memory (memory); and
one or more processors, coupled to the memory, configured to (at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to):
obtain a set of training measurement information associated with a user equipment (UE) (receive, from a user equipment (UE), a first set of measurements associated with at least one cell i.e., obtain a set of training measurement information associated with a user equipment (UE));
obtain a training position value associated with the UE (first set of measurements associated with the at least one cell, a second set of measurements for each of a set of reference UEs, or a location of each of the set of reference UEs i.e., obtain a training position value associated with the UE); and
provide the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique (perform a position estimation of the UE based on at least one of the first set of measurements associated with the at least one cell, a second set of measurements for each of a set of reference UEs, or a location of each of the set of reference UEs via at least one machine learning (ML) model i.e., provide the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique), the model being trained to output location information based at least in part on measurement information (wherein an output of the at least one ML model includes a similarity score between the UE and the set of reference UEs i.e., the model being trained to output location information based at least in part on measurement information)
Further, analyzing and comparing dependent claims 2-16 of the pending application with claims 2-16 of Patent No. US 12349100 B2 it was found that they recite the same limitation with wording changes.
Similarly, analyzing and comparing independent claims 17, 23 and 26 of the pending application including its dependent claims with claims 17, 29, 30 including its dependent claims of Patent No. US 12349100 B2 it was found that they recite the same limitation with wording changes.
Note the issued claims of Patent No. US 11445465 B2 and Patent No. US 12349100 B2 are narrower in scope such that the claimed limitations as recited in pending application are encompassed by Patent No. US 11445465 B2 and Patent No. US 12349100 B2 respectively.
3. Patent Application No. US 18/458, 488
Claims 1-30 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-30 of Patent Application No. US 18/458, 488. Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in pending application are transparently found in Patent Application No. US 18/458, 488 with obvious wording variation. For example, compare Claim 23 of pending application with claim 1 of Patent Application No. US 18/458, 488, they both recite
A method of wireless communication performed by an apparatus, comprising (method of wireless communication performed by a user equipment (UE), the method comprising):
obtain a set of training measurement information associated with a user equipment (UE) (receiving, from a network entity, a configuration message associated with a first set of reference signals and a second set of reference signals; performing first measurements based on the first set of reference signals received from a first set of transmit/receive points (TRPs) to generate first measurement data);
obtain a training position value associated with the UE (providing the first measurement data as input data to a machine learning (ML) positioning model to generate first location information); and
provide the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on measurement information (claim 2, providing the first measurement data as input data to a machine learning (ML) positioning model to generate first location information, the ML positioning model configured to predict the first location information based on the first measurement data)
Further, analyzing and comparing dependent claims 24 and 25 of the pending application with claims 2-12 of Patent Application No. US 18/458, 488 it was found that they recite the same limitation with wording changes.
Similarly, analyzing and comparing independent claims 1, 17 and 26 of the pending application including its dependent claims with claims 13, 17, 26 including its dependent claims of Patent Application No. US 18/458, 488 it was found that they recite the same limitation with wording changes.
4. Patent Application No. US 18/582, 542
Claims 1-30 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-30 of Patent Application No. US 18/582, 542. Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in pending application are transparently found in Patent Application No. US 18/582, 542 with obvious wording variation. For example, compare Claim 1 of pending application with claim 1 of Patent Application No. US 18/582, 542, they both recite
an apparatus for wireless communication comprising (An apparatus for wireless communication at a wireless device):
a memory (at least one memory); and
one or more processors, coupled to the memory, configured to 9 at least one processor coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to):
obtain a set of training measurement information associated with a user equipment (UE) (measure the received set of positioning signals i.e., obtaining set of training measurement information);
obtain a training position value associated with the UE (calculate a subset of measurements of the measured set of positioning signals based on the selected subsampling configuration i.e., obtaining a training position value); and
provide the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique, the model being trained to output location information based at least in part on measurement information (claim 2, calculate a subset of measurements of the measured set of positioning signals based on the selected subsampling configuration)
Further, analyzing and comparing dependent claims 2-16 of the pending application with claims 2-16 of Patent Application No. US 18/582, 542 it was found that they recite the same limitation with wording changes.
Similarly, analyzing and comparing independent claims 17, 23 and 26 of the pending application including its dependent claims with claims 17, 29 and 30 including its dependent claims of Patent Application No. US 18/582, 542 it was found that they recite the same limitation with wording changes.
5. Patent Application No. US 18/324, 052
Claims 1-30 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-45 of Patent Application No. US 18/324, 052. Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in pending application are transparently found in Patent Application No. US 18/324, 052 with obvious wording variation. For example, compare Claim 1 of pending application with claim 1 of Patent Application No. US 18/324, 052, they both recite
an apparatus for wireless communication comprising (An apparatus for wireless communication at a wireless device, comprising):
a memory (at least one memory); and
one or more processors, coupled to the memory, configured to (at least one processor coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor):
obtain a set of training measurement information associated with a user equipment (UE) (measure the set of positioning signals based on a plurality of sparse pilot masks, wherein each sparse pilot mask of the plurality of sparse pilot masks is configured to mask at least some of a positioning signal of the received set of positioning signals i.e., obtain a set of training measurement information associated with a user equipment (UE));
obtain a training position value associated with the UE (receive a set of positioning signals); and
provide the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique (claim 3, train the positioning model at the wireless device based on the measured set of positioning signals i.e., provide the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique), the model being trained to output location information based at least in part on measurement information (output the measured set of positioning signals for training a positioning model)
Further, analyzing and comparing dependent claims 2-16 of the pending application with claims 2-10, 14-15 of Patent Application No. US 18/324, 052 it was found that they recite the same limitation with wording changes.
Similarly, analyzing and comparing independent claims 17 and 23 of the pending application including its dependent claims with claims 17 and 30 including its dependent claims of Patent Application No. US 18/324, 052 it was found that they recite the same limitation with wording changes.
Note the issued claims of Patent Application No. US 18/458, 488, Patent Application No. US 18/582, 542, Patent Application No. US 18/324, 052 are narrower in scope such that the claimed limitations as recited in pending application are encompassed by Patent Application No. US 18/458, 488, Patent Application No. US 18/582, 542, Patent Application No. US 18/324, 052 respectively.
This is a provisional nonstatutory double patenting rejection.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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) 1-30 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Manolakos et al. Pub. No. US 20210160812 A1.
Regarding Claim 1, Manolakos teaches an apparatus (Fig. 3 Unit 302, apparatus) for wireless communication (Para 70, The apparatus 302 include at least one wireless communication device (represented by communication devices 308) for communicating with other nodes via at least one designated RAT (e.g., LTE, 5G NR (New Radio) i.e., apparatus for wireless communication) comprising:
a memory (Fig. 3 Unit 338, memory component); and one or more processors (Fig. 3 Unit 332, processing system), coupled to the memory (Para 5, a processor communicatively coupled to the receiver and the memory), configured to:
obtain (Fig. 5 and Para 96, the UE 302 may be configured to send a request 520 to the base station 304 and/or to send a request 522 to a UE 510 for assistance data that the UE 302 may use for UE-based location determination to determine a location of the UE 302. The UE 510 may be configured similarly to the UE 302. A location server such as the LMF 306 may be configured to configure the UE 302, e.g., using positioning measurement configuration communications, to send a positioning feature vector to the LMF 306 i.e., obtain) a set of training measurement information (Fig. 8 Unit 800 and Para 98, form of a feature vector may have a format of a typical measurement report, e.g., in accordance with an existing communication protocol such as NR Release 16) associated with a user equipment (UE) (Fig. 5 Unit 302 and Para 96, the UE 302);
obtain (Para 102, the response 521, 523 may correspond to the requested assistance data from the request 520, 522. For example, the response 521, 523 may include one or more feature vectors with the same signal measurements as those in the request 520, 522. Also or alternatively, the response 521, 523 may include signal measurements corresponding to a position-determination technique indicated (directly or indirectly) by the corresponding request 520, 522. Thus, the response 521, 523 may include signal measurements not included in the request 521, 523. The feature vectors of the response 521, 523 may include CER information, RSTD information, RSRP information, etc., along with corresponding locations. Also or alternatively, the response 521, 523 may include a position-determination model and/or position-determination-model updates (e.g., model parameters or model parameter updates) corresponding to a position-determination technique indicated (directly or indirectly) by the request 520, 522 i.e., obtain) a training position value (Fig. 9 Unit 900 and Para 104, the assistance data may be a feature vector of signal measurements (i.e., values of parameters of measured signals such as RSSI, SNR, RSRP, RSRQ, RSTD, AoA, AoD, UE Rx-Tx, SPS signal measurements, RAT-independent signal measurements, etc.) and corresponding locations. A corresponding location may be the location at which one or more signals were measured by a UE to determine the signal measurements for a given feature vector (and this location may be referred to as the location of the signal measurements). A response 900 may be similar to (i.e., include some of the same types of information as) the request 800. The response 900 includes a location (e.g., a latitude value and a longitude value without a radius value) instead of the geographic region 817 in the request 800 i.e., training position value) associated with the UE (Fig. 5 Unit 302 and Para 96, the UE 302); and
provide the training position value and the set of training measurement information for training or performance monitoring of a model using a machine learning (ML) technique (Para 104 and Fig. 10, The feature vector may be used to train (e.g., establish or adapt) a position-determination model. The model may be a machine-learning model to provide a location based on a set of signal measurements. Also or alternatively, the assistance data may include model parameters (a position determination model) and/or model parameter updates for the position-determination model (i.e., a positioning model). For example, referring also to FIG. 10, a response 1010 includes a positioning technique field 1012, a model parameter(s) field 1014, a model parameter update(s) field 1016, and a location field 1018),
the model being trained to output location information based at least in part on measurement information (Para 110, The UE 302, e.g., the processor 332, may use one or more of the received feature vectors to train (e.g., establish or update/adapt) a position-determination model. The UE 302 may select a subset of received feature vectors to use to train the position-determination model, e.g., based on content of the feature vectors).
Regarding Claim 2, Manolakos teaches wherein the training position value is based at least in part on at least one of an uplink reference signal or a measurement report of the UE (Para 119).
Regarding Claim 3, Manolakos teaches wherein the training position value comprises an estimate of a location of the UE or an estimate of an intermediate value derived from the uplink reference signal or the measurement report (Para 119).
Regarding Claim 4, Manolakos teaches wherein the training position value comprises an intermediate value comprising at least one of: a reference signal received power value, a reference signal time difference value, a channel multipath value, line of sight information, an uplink channel frequency response value, an uplink channel impulse response value, quasi co-location information, or a combination thereof (Fig. 9 and Para 104).
Regarding Claim 5, Manolakos teaches wherein the one or more processors, to obtain the training position value, are configured to receive the training position value from a node (Fig. 5 and Para 102).
Regarding Claim 6, Manolakos teaches wherein the node comprises a second UE (Fig. 5 Unit 510 and Para 102).
Regarding Claim 7, Manolakos teaches wherein the one or more processors, to receive the training position value from the node, are configured to receive the training position value from the node via a network node or another UE (Para 102).
Regarding Claim 8, Manolakos teaches wherein the one or more processors are further configured to transmit information indicating a measurement value to the node, the training position value being based at least in part on the measurement value.
Regarding Claim 9, Manolakos teaches wherein the training position value indicates at least one of: a corrected value of the set of training measurement information based at least in part on the measurement value, or a corrected measurement value based at least in part on the measurement value (Fig. 5 and 8 and Para 96).
Regarding Claim 10, Manolakos teaches wherein the measurement value indicates measurements of a set of cells, and wherein the training position value relates to the set of cells (Para 98).
Regarding Claim 11, Manolakos teaches wherein the training position value comprises at least one of: an explicit value of the training position value, a probability function, a range or a range delimiter of the training position value, an uncertainty value associated with the training position value, or a combination thereof (Para 104).
Regarding Claim 12, Manolakos teaches wherein the one or more processors, to receive the training position value, are configured to receive the training position value within a time window after transmitting a measurement report (Para 105).
Regarding Claim 13, Manolakos teaches wherein the one or more processors, to receive the training position value, are configured to receive the training position value in a batch of training position values (Para 102).
Regarding Claim 14, Manolakos teaches wherein the UE comprises the apparatus (Fig. 3 Unit 302).
Regarding Claim 15, Manolakos teaches wherein the one or more processors, to provide the set of training measurement information and the training position value for training or performance monitoring of the model, are configured to train the model using the set of training measurement information and the training position value (Para 110).
Regarding Claim 16, Manolakos teaches wherein the one or more processors, to provide the set of training measurement information and the training position value for training or performance monitoring of the model, are configured to provide the set of training measurement information and the training position value to a server associated with the model (Fig. 11 and Para 115-117).
Regarding Claim 17, Manolakos teaches a network node (Fig. 5 Unit 510) for wireless communication (Fig. 5), comprising: a memory (Fig. 3 Unit 338); and one or more processors (Fig. 3 Unit 332), coupled to the memory, configured to:
obtain (Fig. 5 and Para 96, the UE 302 may be configured to send a request 520 to the base station 304 and/or to send a request 522 to a UE 510 for assistance data that the UE 302 may use for UE-based location determination to determine a location of the UE 302. The UE 510 may be configured similarly to the UE 302. A location server such as the LMF 306 may be configured to configure the UE 302, e.g., using positioning measurement configuration communications, to send a positioning feature vector to the LMF 306 i.e., obtain) training measurement information (Fig. 8 Unit 800) for a user equipment (UE) (Fig. 5 Unit 302), or information associated with the training measurement information,
the training measurement information being associated with training or performance monitoring of a model using a machine learning (ML) technique(Para 104 and Fig. 10, The feature vector may be used to train (e.g., establish or adapt) a position-determination model. The model may be a machine-learning model to provide a location based on a set of signal measurements. Also, or alternatively, the assistance data may include model parameters (a position determination model) and/or model parameter updates for the position-determination model (i.e., a positioning model). For example, referring also to FIG. 10, a response 1010 includes a positioning technique field 1012, a model parameter(s) field 1014, a model parameter update(s) field 1016, and a location field 1018); and
output, for the UE(Para 102, the response 521, 523 may correspond to the requested assistance data from the request 520, 522. For example, the response 521, 523 may include one or more feature vectors with the same signal measurements as those in the request 520, 522. Also or alternatively, the response 521, 523 may include signal measurements corresponding to a position-determination technique indicated (directly or indirectly) by the corresponding request 520, 522. Thus, the response 521, 523 may include signal measurements not included in the request 521, 523. The feature vectors of the response 521, 523 may include CER information, RSTD information, RSRP information, etc., along with corresponding locations. Also or alternatively, the response 521, 523 may include a position-determination model and/or position-determination-model updates (e.g., model parameters or model parameter updates) corresponding to a position-determination technique indicated (directly or indirectly) by the request 520, 522 i.e.output for the UE), a training position value (Fig. 9 Unit 900) based at least in part on the training measurement information or the information associated with the training measurement information(Fig. 9 Unit 900 and Para 104, the assistance data may be a feature vector of signal measurements (i.e., values of parameters of measured signals such as RSSI, SNR, RSRP, RSRQ, RSTD, AoA, AoD, UE Rx-Tx, SPS signal measurements, RAT-independent signal measurements, etc.) and corresponding locations. A corresponding location may be the location at which one or more signals were measured by a UE to determine the signal measurements for a given feature vector (and this location may be referred to as the location of the signal measurements). A response 900 may be similar to (i.e., include some of the same types of information as) the request 800. The response 900 includes a location (e.g., a latitude value and a longitude value without a radius value) instead of the geographic region 817 in the request 800 i.e., training position value based at least in part of training measurement information).
Regarding Claim 18, it has been rejected for the same reasons as claim 2.
Regarding Claim 19, it has been rejected for the same reasons as claim 4.
Regarding Claim 20, Manolakos teaches wherein the information associated with the training measurement information comprises information indicating a downlink measurement value, wherein the one or more processors, to output the training position value, are configured to output the training position value based at least in part on the downlink measurement value (Para 96).
Regarding Claim 21, Manolakos teaches wherein the downlink measurement value relates to a set of cells, and wherein the training position value relates to the set of cells (Para 98).
Regarding Claim 22, Manolakos teaches wherein the training position value is one of: per transmission reception point, per positioning reference signal (PRS) resource set, or per PRS resource (Para 94).
Regarding Claim 23. It has been rejected for the same reasons as claim 1.
Regarding Claim 24. It has been rejected for the same reasons as claim 5.
Regarding Claim 25. It has been rejected for the same reasons as claim 2.
Regarding Claim 26. It has been rejected for the same reasons as claim 17.
Regarding Claim 27. It has been rejected for the same reasons as claim 18.
Regarding Claim 28, Manolakos teaches wherein the training position value comprises an estimate of a location of the UE or an estimate of an intermediate value derived from the uplink reference signal or the measurement report(Para 119).
Regarding Claim 29. It has been rejected for the same reasons as claim 19.
Regarding Claim 30. It has been rejected for the same reasons as claim 22.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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NIZAR N. SIVJI
Primary Examiner
Art Unit 2647
/NIZAR N SIVJI/Primary Examiner, Art Unit 2647
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