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 .
This action is in response to amendment filed 06/08/2026.
Response to Amendment/Remarks
Applicant's remark in an amendment filed June 08, 2026, have been fully considered but they are not persuasive. It is alleged that prior art applied in the rejection of the claim does not disclose, “transmit, to a second network entity, a request for an identifier (ID) to be used for indexing a set of datasets associated with at least one artificial intelligence (AI) or machine learning (ML) (AI/ML) model related to positioning,”. As recited in claim 1.
Examiner respectfully disagrees.
The prior art referenced as D1, discloses, gNB sends Position Reference Signal (PRS) to the UE that contains the AI/ML model (section 2.1, observation 2, page 2, last bullet line), at section 2.1, page 8 of D1, further discloses "For Case 1 (UE-based positioning with UE-side model, direct AI/ML or AI/ML assisted positioning). The standard impact depends on the information the UE chooses to request from or report to the network", wherein the UE of D1 is a first network entity (in addition see claim 13 of the present application), and the network of D1 is a second network entity, "[5, OPPO] proposed that for UE-side model for AI/ML based positioning, …. UE-initiated, requested to the LMF if UE decides the AI model, some type of signaling (e.g., some 'ID') is needed to indicate/identify the scenarios/configuration so that UE can choose a suitable Al model matching the target case(s)" also see section 2.1, page 38 of D1, "allowing source entities, UE, PRU, and TRP, request labelling assistance from LMF", "The following are identified as assistance information to be associated with data collected at UE/PRU side:", "Timestamping of measurements and labels: UE side needs to tie labels with their corresponding measurements (e.g., when labelling is provided from LMF). Examples of timestamping includes UTC timing and/or indices (i.e., SFN, slot, OFDM) of resources used to compute the label"; section 2.1, page 37, "UE/PRU can request, from LMF, configuring PRS resources for training data collection"; section 2.2, page 52, 57 "Assistance signaling and procedure to facilitate generating training data, request signaling/indication for data collection", "Reference signal (e.g., PRS/SRS) configuration(s) and configuration identifier", "Assistance information, e.g., between LMF and UE/PRU, for label calculation/generation, and label validity/quality condition, etc."; section 2.4, page 73, "Conditions and requirements, e.g., required assistance signaling and/or reference signals configurations, dataset information", "Study LCM procedure on the basis that an AI/ML model has a model ID with associated information and/or model functionality at least for some AI/ML operations"; section 2.4, page 74. D1, implicitly and explicitly discloses request for an ID for use with AI/ML model related to positioning, as analyzed above. Applicant’s attempt to overcome the cited art, deemed not persuasive, the rejection has been maintained.
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) 1-20, is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by 3GPP TSG RAN, R1-2302019, “FL summary #3 of other aspects on AI/ML for position accuracy enhancement”, submitted IDS to ISR, 20 pages) (hereinafter referred to as D1).
Regarding claim 1, 15, 20, D1 discloses an apparatus for wireless communication at a first network entity, comprising:
at least one memory; and at least one processor coupled to the at least one memory, the at least one processor, individually or in any combination, is configured to:
transmit, to a second network entity, a request for an identifier (ID) to be used for indexing a set of datasets associated with at least one artificial intelligence (AI) or machine learning (ML) (AI/ML) model related to positioning (section 2.1, page 8 of D1, "For Case 1 (UE-based positioning with UE-side model, direct AI/ML or AI/ML assisted positioning): The standard impact depends on the information the UE chooses to request from or report to the network", wherein the UE of D1 is a first network entity (in addition see claim 13 of the present application), and the network of D1 is a second network entity, also see section 2.1, page 38 of D1, "allowing source entities, UE, PRU, and TRP, request labelling assistance from LMF", "The following are identified as assistance information to be associated with data collected at UE/PRU side:", "Timestamping of measurements and labels: UE side needs to tie labels with their corresponding measurements (e.g., when labelling is provided from LMF). Examples of timestamping includes UTC timing and/or indices (i.e., SFN, slot, OFDM) of resources used to compute the label"; section 2.1, page 37, "UE/PRU can request, from LMF, configuring PRS resources for training data collection"; section 2.2, page 52, 57 "Assistance signaling and procedure to facilitate generating training data, request signaling/indication for data collection", "Reference signal (e.g., PRS/SRS) configuration(s) and configuration identifier", "Assistance information, e.g., between LMF and UE/PRU, for label calculation/generation, and label validity/quality condition, etc."; section 2.4, page 73, "Conditions and requirements, e.g., required assistance signaling and/or reference signals configurations, dataset information", "Study LCM procedure on the basis that an AI/ML model has a model ID with associated information and/or model functionality at least for some AI/ML operations"; section 2.4, page 74, "[5, OPPO] proposed that for UE-side model for AI/ML based positioning, …. UE-initiated, requested to the LMF if UE decides the AI model, some type of signaling (e.g., some 'ID') is needed to indicate/identify the scenarios/configuration so that UE can choose a suitable Al model matching the target case(s)"; that the apparatus comprises at least one memory and at least one processor coupled to the at least one memory is implicit to a person skilled in the art in what is explicitly mentioned in D1) (see section 2.1, page 8);
receive, from the second network entity based on the request, the ID to be used for indexing the set of datasets associated with the at least one AI/ML model related to positioning (see section 2.1, pages 8, 37 and 38; also section 2.2 page 52; section 2.4, pages 73-74, in particular as cited above);
store, based on the ID, at least one of a set of positioning configurations or a set of radio statistics associated with the first network entity; and index the set of datasets with the ID (wherein for example tying labels with corresponding measurements via time stamping is an indexing and wherein it is implicitly implied that the PRS configurations are sored, see section 2.1, page 8, 37, 38) and (wherein the use of a configuration identifier implies storing the position configurations, section 2.2, page 52); (If monitoring based on model input, Monitoring metric, e.g., statistics (e.g., RSRP and/or SINR) of measurement corresponding to model input, Assistance signaling and procedure, e.g., RS configuration(s) for measurement, input data statistics related to the training data, section 2.4, page 73, 74; section 2.3, page 65); (If monitoring based on model input, Monitoring metric, e.g., RSRP and/or SINR of measurement corresponding to model input, Assistance signaling and procedure, e.g., input data statistics related to the training data e.g. out of distribution detection, section 2.3, page 61).
Regarding claim 2, D1 discloses initiate dataset indexing for the set of datasets associated with the at least one AI/ML model associated with positioning, wherein the transmission of the request is based on the initiation of the dataset indexing (see section 2.1, pages 8, 36, 37, section 2.2, page 52, and section 2.4, pages 73 and 74).
Regarding claim 3, D1 discloses train the at least one AI/ML model or a set of positioning functionalities associated with the at least one AI/ML model using the set of datasets indexed with the ID (see section 2.1, page 37, 38; section 2.2 page 52; section 2.4 pages 73, 74).
Regarding claims 4, 18, D1 discloses wherein the at least one AI/ML model is a UE-side AI/ML positioning model or a base station-side AI/ML positioning model (see section 2.1, page 4 and 8; section 2.4, page 74).
Regarding claims 5, 19, D1 discloses wherein the at least one processor, individually or in any combination, is further configured to: transmit, to the second network entity, the ID during an AI/ML inference or operation session; and receive, from the second network entity based on the ID, an indication of an AI/ML model to apply for the AI/ML inference or operation session (see section 2.1, pages 8, 37 and 38; section 2.2, page 52; section 2.4, pages 73-79).
Regarding claims 6, 7, D1 discloses transmit, to the second network entity, an indication of supporting first network entity-side AI/ML positioning that is valid for at least one of positioning configurations or radio statistics; and receive, from the second network entity based on the indication, a second request to apply a life cycle management (LCM) for the at least one AI/ML model or for a set of functionalities associated with the at least one AI/ML model (see section 2.1, pages 8, 37 and 38; section 2.2, pages 44 and 52; section 2.4, pages 73-79).
Regarding claim 8, D1 discloses receive, from the second network entity, an indication of running an AI/ML positioning enabled feature that is valid for at least one of positioning configurations or radio statistics; and modify or adjust, based on the indication, at least one of the set of positioning configurations or the set of radio statistics associated with the first network entity ((section 2.2, page 52; section 2.3, pages 61 and 65; section 2.4, pages 73-79).
Regarding claim 9, D1 discloses communicate, with the second network entity via at least one of the transceiver or the antenna, an indication to abort using the ID, modifying the ID, or replacing the ID (section 2.1, pages 8, 21, 37 and 38; section 2.2, page 52; section 2.4, pages 73-79).
Regarding claim 10, D1 discloses wherein the ID includes at least one of: a unique ID, a cell ID, a radio access network (RAN) area ID, a tracking area ID, a coordinated universal time (UTC) timing plus date, a start or stop timing and date, a landmark fix information, or an expiry time for the ID (section 2.1, pages 8, 31 - 38; section 2.2, page 52; section 2.4, pages 73-79).
Regarding claims 11, 12, D1 disclose a set of downlink (DL) positioning reference signal (PRS) beam shapes, a DL PRS antenna pattern, configuration, or down-tilting, a DL PRS transmission (TX) power, a set of radio unit locations or transmission reception point (TRP) locations, a mapping of PRS resources to a set of TRP physical locations, an uplink (UL) sounding reference signal (SRS) TX power, a set of UE SRS beam shapes, or a UE antenna pattern or a set of configurations used for sensing SRS (section 2.1, pages 8, 17, 37 and 38; section 2.2, page 52; section 2.3, pages 61 - 65; section 2.4, pages 73 and 74).
Regarding claim 13, D1 discloses wherein the first network entity is a user equipment (UE) or a base station, and wherein the second network entity is a location server or a location management function (LMF) (section 2.1, pages 8 and 37-39; section 2.2, pages 46 and 52; section 2.4, pages 73 and 74).
Regarding claim 14, Di discloses index the set of datasets with the ID during an active dataset collection session associated with the at least one AI/ML model (section 2.1, pages 8 and 37-39; section 2.2, page 52; section 2.4, pages 73 and 74).
Regarding claim 16, D1 discloses determine or configure the ID to be used for the set of datasets (section 2.1, pages 8, 37 and 38; section 2.2, page 52; section 2.4, pages 73 and 74).
Regarding claim 17, D1 discloses transmit, to a third network entity, the ID and an indication to log at least one of a second set of positioning configurations or a second set of radio statistics associated with the third network entity based on the ID (section 2.1, pages 8, 37-39; section 2.2, page 52; section 2.4, pages 73 and 74).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUTBUDDIN GHULAMALI whose telephone number is (571) 272-3014. The examiner can normally be reached 7:30am to 4:00pm.
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/QUTBUDDIN GHULAMALI/
Primary Examiner,
Art Unit 2632.
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