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 .
DETAILED ACTION
This action is responsive to the application filed on 7/30/2024.
Claims 1-20 are pending in the case. Claims 1, 19, and 20 are independent claims.
This application claims benefit of foreign priority under 35 U.S.C. 119 (a)-(d) from India Intellectual Property Application No. IN202341052241, filed on 3/8/2023.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 6-13, and 15-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Alawieh et al., US Patent Application Publication No. 20260075453, effectively filed on 5/14/2023 (hereinafter Alawieh).
As for claim 1, Alawieh discloses an apparatus comprising:
at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:
(Alawieh paragraph [0396] discloses “The units or modules as well as the steps of the methods performed by these units may execute on one or more computer systems 600. The computer system 600 includes one or more processors 602, like a special purpose or a general-purpose digital signal processor. The processor 602 is connected to a communication infrastructure 604, like a bus or a network. The computer system 600 includes a main memory 606, e.g., a random-access memory, RAM, and a secondary memory 608, e.g., a hard disk drive and/or a removable storage drive. The secondary memory 608 may allow computer programs or other instructions to be loaded into the computer system 600. The computer system 600 may further include a communications interface 610 to allow software and data to be transferred between computer system 600 and external devices. The communication may be in the from electronic, electromagnetic, optical, or other signals capable of being handled by a communications interface. The communication may use a wire or a cable, fiber optics, a phone line, a cellular phone link, an RF link and other communications channels 612.”)
transmit a request (Request from data generation entity such as UE to LMF) to measure location coordinates to a device (determining the location of the UE);
(Alawieh paragraph [0134] discloses “Request from data generation entity (UE/PRU/TRP) to LMF and/or as LMF assistance signaling to UE/PRU/TRP”
Alawieh paragraph [0166] discloses “The LMF 903 (location management function) is responsible for determining the location of the UE 10a by interacting with one or more network functions and/or access network nodes and/or the UE and providing the location to the location service client, which may be another application function 911 (AF), an AMF 901, UE 10a, entity in the access network or in the external network.”)
receive (measurements on the received signal) from the device (UE), a model inference comprising at least one location coordinate (AI/ML model inference is UE location); and
(Alawieh paragraph [0184] discloses “The output of AI/ML model inference is UE location (e.g., fingerprinting based on channel observation as the input of AI/ML model). Accordingly, the “data collection” covers measurements on the received signal. Examples for the measurements are: [0185] Signal power [0186] Channel impulse response (CIR) [0187] Relevant parts of the CIR [0188] Estimated time-of-arrival (ToA) of the first path and/or additional paths [0189] Magnitude of each path [0190] Phase of each path [0191] Angle-of-arrival (AoA) estimates [0192] Angle-of-departure (AoD) estimates.”)
determine whether a difference (proximity detection) between the model inference (the location dependent information) and at least one ground truth (landmarks comprise a ground truth label) is within a threshold value (a certain threshold) for a defined number of samples (one or more TRP in response to a request by the UE), wherein the at least one ground truth (containing the ground truth information) comprises at least one known location coordinate of a test point (a ground truth label or as reference points).
(Alawieh paragraph [0081] discloses “According to embodiments, the one or more landmarks comprise a ground truth label, like a location dependent information and/or orientation dependent information; the UE may be configured to extract ground truth label from the one or more landmarks; the UE may use the one or more landmarks as a ground truth label or as reference points.”
Alawieh paragraph [0294] discloses “In a yet another example, the location dependent information may be encoded within a message transmitted by a network node (e.g. a TRP) or a second UE (e.g. using sidelink). The UE could be configured to report the ground truth based on proximity detection. For example, report when the RSRP is within a certain threshold. For example, the ground truth could be carried in a system information, such as positioning system information transmitted in one or more positioning system information blocks (posSibs). In an example, the system information containing the ground truth information transmitted by one or more TRP in response to a request by the UE.”)
As for claim 2 The apparatus of claim 1,
wherein the apparatus comprises a network entity (LMF), a base station, or a user equipment.
(Alawieh paragraph [0165] discloses “The core network contains one or more functions that can interact with each other using the so-called service-based architecture using interfaces. As an example, the AMF 901 can send message to LMF 903 via the Nlmf interface and the LMF 903 can send message to AMF 901 using the Namf interface.”
Alawieh paragraph [0166] discloses “In the following, a brief description of the network entities/functions is provided to give a simplified view of the working principle of the core network. In the core network, the AMF (access and mobility function) is the network function through which control plane signaling from the core network are sent to the UE. A UE registers in the network with the AMF. It manages the mobility of user devices and handles access authentication and authorization in the 5G core network. The LMF 903 (location management function) is responsible for determining the location of the UE 10a by interacting with one or more network functions and/or access network nodes and/or the UE and providing the location to the location service client, which may be another application function 911 (AF), an AMF 901, UE 10a, entity in the access network or in the external network.”
Broadest reasonable interpretation selects the apparatus to be a core network entity as the Location Management Function (LMF).)
As for claim 3, Alawieh discloses the apparatus of claim 1,
wherein the device comprises a network entity, a base station, or a user equipment (determining the location of the UE).
(Alawieh paragraph [0166] discloses “In the following, a brief description of the network entities/functions is provided to give a simplified view of the working principle of the core network. In the core network, the AMF (access and mobility function) is the network function through which control plane signaling from the core network are sent to the UE. A UE registers in the network with the AMF. It manages the mobility of user devices and handles access authentication and authorization in the 5G core network. The LMF 903 (location management function) is responsible for determining the location of the UE 10a by interacting with one or more network functions and/or access network nodes and/or the UE and providing the location to the location service client, which may be another application function 911 (AF), an AMF 901, UE 10a, entity in the access network or in the external network.”
Broadest reasonable interpretation selects a device to be a user equipment.)
As for claim 4, Alawieh discloses the apparatus of claim 3,
wherein the user equipment comprises one (transceiver) or more receivers.
(Alawieh paragraph [0017] discloses “Embodiments provide a user equipment (UE) comprising a transceiver for exchanging data supporting life cycle management (LCM) of machine learning model (ML model) and being configured to identify one or more landmarks for/of the machine learning model to obtain one or more parameters on the one or more landmarks;”
Broadest reasonable interpretation selects the user equipment with one receiver.)
As for claim 6, Alawieh discloses the apparatus of claim 1,
wherein the at least one memory and the instructions, when executed by the at least one processor, further cause the apparatus at least to:
adjust at least one transmission parameter (transmitting a configuration to a UE) of the apparatus to simulate a change of position of the device (UE tracks its movement between landmarks using the associated beams).
(Alawieh paragraph [0084] discloses “Embodiments provide a network entity comprising a transceiver for exchanging data supporting life cycle management of a machine learning model and being configured to initiate identification of one or more landmarks of the machine learning model, such that a UE can obtain one or more parameters on the one or more landmarks; note exchanging data comprises transmitting a configuration to a UE, the configuration indicating information on the one or more landmarks or a criteria to identify the one or more landmarks and receiving one or more parameters or and information derived from the one or more parameters from the user equipment.”
Alawieh paragraph [0341] discloses “Embodiments use for example Tracking Between Landmarks: The UE receive information on beam profiles between multiple landmarks. This can be extremely helpful to avoid unnecessary handover and enhance user mobility. UE tracks its movement between landmarks using the associated beams. As the UE moves between landmarks, it dynamically adjusts the beam configuration based on the associated landmark information.”)
As for claim 7, Alawieh discloses the apparatus of claim 1,
wherein the request to measure location coordinates is based upon at least one of artificial intelligence based direct positioning or machine learning based direct positioning (AI/ML model used for direct or indirect positioning).
(Alawieh paragraph [0270] discloses “It goes without saying that a GT label for evaluating the accuracy of an AI/ML model used for direct or indirect positioning is valuable. When there is a request, either in frequent intervals either event-based from the monitoring entity (e.g., there is an alarm raised indicating there could be a potential performance drop in the model) to monitor and ensure the performance of an active AI/ML model, a GT label is needed to be generated/be available for several time-steps. In such ad-hoc requests, the flexibility and coverage of a landmark-based solution is significantly higher compared to a solution that needs PRUs being available at all times.”)
As for claim 8, Alawieh discloses the apparatus of claim 1,
wherein the at least one memory and the instructions, when executed by the at least one processor, further cause the apparatus at least to:
compare (proximity detection) the model inference (location dependent) to the at least one ground truth (ground truth based on proximity detection).
(Alawieh paragraph [0294] discloses “In a yet another example, the location dependent information may be encoded within a message transmitted by a network node (e.g. a TRP) or a second UE (e.g. using sidelink). The UE could be configured to report the ground truth based on proximity detection. For example, report when the RSRP is within a certain threshold. For example, the ground truth could be carried in a system information, such as positioning system information transmitted in one or more positioning system information blocks (posSibs). In an example, the system information containing the ground truth information transmitted by one or more TRP in response to a request by the UE.”)
As for claim 9, Alawieh discloses the apparatus of claim 1,
wherein the known location coordinate (by comparing the data to a known location) of the test point comprises at least one actual location coordinate of the test point (ground truth label).
(Alawieh paragraph [0267] discloses “Using the ground truth label to assess the accuracy of the ML model's predictions by evaluating the model performance. Or using the landmark as a reference point to track the performance of the ML model over time. Performance evaluation can for example be performed by comparing the captured data or data distribution with the expected data or by comparing the data to a known location or by using techniques estimate the landmark's position in case of positioning application.”)
As for claim 10, Alawieh discloses the apparatus of claim 1,
wherein the at least one memory and the instructions, when executed by the at least one processor, further cause the apparatus at least to:
transmit a command for the device (UE receive information on beam profiles) to move from the test point to another test point (multiple landmarks).
(Alawieh paragraph [0341] discloses “Embodiments use for example Tracking Between Landmarks: The UE receive information on beam profiles between multiple landmarks. This can be extremely helpful to avoid unnecessary handover and enhance user mobility. UE tracks its movement between landmarks using the associated beams. As the UE moves between landmarks, it dynamically adjusts the beam configuration based on the associated landmark information.”
Generating ground truth labels with known landmarks established known test points. UE receives beam profile for each landmark from the network element. UE move to new landmark and reports the beam profile as it is moving to another test points.)
As for claim 11, Alawieh discloses the apparatus of claim 1,
wherein at least one of a period of time (Time stamp), a starting time, or an ending time indicates when the device is positioned at the test point (time stamp for measurement and ground truth label).
(Alawieh paragraph [0136]- [0138] discloses “Time stamp.
Alawieh paragraph [0137] discloses “At least for and/or associated with training data for model training.”
Alawieh paragraph [0138] discloses “Separate time stamp for measurement and ground truth label, when measurement and ground truth label are generated by different entities.”)
As for claim 12, Alawieh discloses the apparatus of claim 1,
wherein a trajectory of the device is preconfigured (the known trajectory can be the path between two or more landmarks) or defined by synchronization signaling.
(Alawieh paragraph [0277] discloses “In a different aspect, the UE can use the landmarks as a ground truth labels or reference points. The UE generates additional labels relative to this reference point. The UE can for example move in a known trajectory or report the trajectory using the UE's on-board sensors (such as motion sensors). In a more robust approach, the known trajectory can be the path between two or more landmarks. Hence the UE uses coarse Ground truth labels and creates relative to these landmarks finer labels.”)
As for claim 13, Alawieh discloses the apparatus of claim 1,
wherein the at least one memory and the instructions, when executed by the at least one processor, further cause the apparatus at least to:
transmit (output) at least one of an artificial intelligence inference comprising at least one location coordinate, or a machine learning inference (the ML inference entity) comprising at least one location coordinate (ground truth labels).
(Alawieh paragraph [0288] discloses “Below an embodiment for determining whether a deployed model performs well in an applicable condition before it can be activated will be discussed. In order to verify the correct operation of a UE-side or two-sided ML model after identification but prior to its first active use, it may be needed to validate the model based on its performance and accuracy in detecting rare situations or events that do not frequently occur in the training data. To do this, the ML inference entity is given a pattern to look for in the input/output/side-information data (e.g., specific distribution of RSRP measurements in Set B for beam management or specific patterns in the CSI/PRS for positioning). Once it detects the pattern, data can be collected for either training or monitoring purposes, including the use of ground truth labels obtained through landmark detection. It is important to ensure that sufficient data coverage is obtained during validation to reliably validate the model.”
Alawieh paragraph [0246] discloses “To generate ground truth labels a known environment with known landmarks is needed. The landmarks can represent markers or objects with known positions in the environment, such as QR codes, markers or known obstacles.”)
As for claim 15, Alawieh discloses the apparatus of claim 1,
wherein the at least one ground truth comprises at least one global navigation satellite system-based location coordinate (GNSS).
(Alawieh paragraph [0154] discloses “For ML/AI based positioning typically a second positioning system is needed generating the “labels” (“ground truth data/labels”) for the training data. Examples for the second positioning system are:”
Alawieh paragraph [0157] discloses “Other positioning system (in case of outdoor application GNSS, for example)”)
As for claim 16, Alawieh discloses the apparatus of claim 1,
wherein the at least one memory and the instructions, when executed by the at least one processor, further cause the apparatus at least to:
transmit monitoring data comprising at least one model monitoring input (ML model comprising one of monitoring).
(Alawieh paragraph [0070] discloses “performing life cycle management of the ML model comprising one of monitoring, updating, verifying, testing, maintaining.”)
As for claim 17, Alawieh discloses the apparatus of claim 1,
wherein the at least one memory and the instructions, when executed by the at least one processor, further cause the apparatus at least to:
test a generalization (Model testing) of the model inference (the inference performance of the AI/ML model).
(Alawieh paragraph [0172] discloses “Model testing is defined subprocess of training, to evaluate the performance of a final AI/ML model using a dataset different from one used for model training and validation. Differently from AI/ML model validation, testing does not assume subsequent tuning of the model. Model monitoring is defined as a procedure that monitors the inference performance of the AI/ML model. Once the model is deployed, it needs to be continuously monitored to detect any performance degradation or errors. This stage involves tracking model performance metrics, detecting data drift, and retraining the model if needed.”)
As for claim 18, Alawieh discloses the apparatus of claim 17,
wherein the testing (validation) is performed using at least one configuration parameter (parameters) matching at least one parameter in a data set (ground truth label) used to train an artificial intelligence or machine learning model (ML model).
(Alawieh paragraph [0065] discloses “performing validation based on the one or more parameters and a ground truth label;”
Alawieh paragraph [0066] discloses “performing training based on the one or more parameters and a ground truth label;”
Alawieh paragraph [0067] discloses “transmitting a reference signal or receiving a reference signal;”
Alawieh paragraph [0068] discloses “reporting on measurements performed during identifying; and/or [0069] generating a user equipment position; and/or”
Alawieh paragraph [0070] discloses “performing life cycle management of the ML model comprising one of monitoring, updating, verifying, testing, maintaining.”)
As for claim 19, Alawieh discloses the apparatus comprising:
at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:
receive a request to measure location coordinates (RequestLocationInformation) from testing equipment (location server);
(Alawieh paragraph [0383] discloses “The RequestLocationInformation message (step 593) is according to embodiments used by the location server 19 to request the UE 10 to report the location measurements (step 595) indicated by the UE. The message indicates what information the server expects from a UE for a given positioning method perform one or more direct positioning measurements;”)
and transmit a model inference comprising (ground truth) at least one location coordinate (reporting the landmark) to the testing equipment (the network).
(Alawieh paragraph [0379] discloses “The example above shows one possible mechanism of indicating the landmark information to the UE to enable the UE to search for a landmark in a given area and report the landmark. In general, the ProvideAssistanceData may provide different type of information that are useful for the UE to find the ground truth, and report them to the network in next step”)
As for claim 20, claim 20 reflects the method comprising computer executable instructions for implementing the article of manufacture as claimed in claim 1, and is rejected along the same rationale.
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Alawieh in view of Bao et al., US Patent Application Publication No. 20240172168, filed on 3/2/2022 (hereinafter Bao).
As for claim 5, Alawieh discloses the apparatus of claim 4,
wherein the at least one memory and the instructions, when executed by the at least one processor, further cause the apparatus at least to:
(Alawieh paragraph [0396] discloses “FIG. 12 illustrates an example of a computer system 600. The units or modules as well as the steps of the methods performed by these units may execute on one or more computer systems 600. The computer system 600 includes one or more processors 602, like a special purpose or a general-purpose digital signal processor. The processor 602 is connected to a communication infrastructure 604, like a bus or a network. The computer system 600 includes a main memory 606, e.g., a random-access memory, RAM, and a secondary memory 608, e.g., a hard disk drive and/or a removable storage drive. The secondary memory 608 may allow computer programs or other instructions to be loaded into the computer system 600. The computer system 600 may further include a communications interface 610 to allow software and data to be transferred between computer system 600 and external devices.”)
(Alawieh paragraph [0138] discloses “Separate time stamp for measurement and ground truth label, when measurement and ground truth label are generated by different entities”
Current invention discloses a device can have one or more receivers; Broadest reasonable interpretation chose the device to have one transceiver. Thus, the device can compare measurements captured at different time stamps or two separate devices.)
Alawieh does not appear to explicitly disclose “compare a location coordinate of a first receiver of the user equipment, and a location coordinate of a second receiver of the user equipment”. However, Bao discloses “compare a location coordinate (compare) of a first receiver of the user equipment (first PRS resource measurement), and a location coordinate of a second receiver of the user equipment (second PRS resource measurement).”
(Bao paragraph [0441] discloses “compare the first PRS resource measurement and the second PRS resource measurement;”
Broadest reasonable interpretation, the UE in claim 4 is selected with one receiver. Thus, the positioning measurement consists of correlation amongst different sets of the measurements. Positioning Reference Signal (PRS) provides high-accuracy positioning beyond LTE capabilities.)
Accordingly, it would have been obvious to person of ordinary skill in the art before the
effective filing date of the claimed invention to combine Bao with Alawieh for the benefit
of having a device capable to perform positioning measurements and correlation among from positioning measurements as data to train the model. Further benefit to have more dataset to improve the accuracy of the trained positioning model.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Alawieh in view of Khirallah et al., US Patent Application Publication No. 20240283710, effectively filed on 2/17/2023 (hereinafter Khirallah).
As for claim 14, Alawieh discloses the apparatus of claim 1,
wherein the at least one memory and the instructions, when executed by the at least one processor, further cause the apparatus at least to:
(activate another AI/ML model).
(Alawieh paragraph [0269] discloses “In a related aspect, the UE uses the ground truth label extracted from the landmark to evaluate if a performance degradation is detected. A UE can decide to maintain current active model is to activate another AI/ML model or a non-AI/ML fallback operation. Alternatively, the UE or NW can update the model's training data or adjusting model parameters, or even changing its architecture to better fit the data being collected from the landmark.”)
Alawieh does not appear to explicitly disclose “transmit a command to acknowledge activation”. However, Khirallah discloses “transmit a command to acknowledge activation (in response to the AI/ML DATA COLLECTION ACTIVATION REQUEST message, the Network Entity 2 sends an AI/ML DATA COLLECTION ACTIVATION RESPONSE/ACKNOWLEDGE/FAILURE message acknowledging the request)”
(Khirallah paragraph [0200] discloses “Referring to FIG. 9, at operation S902, the Network Entity 1 sends an AI/ML DATA COLLECTION ACTIVATION REQUEST message to a Network Entity 2, where the message may include one or more of model information, functionality information, data collection information, a session ID and any other relevant information. At operation S904, in response to the AI/ML DATA COLLECTION ACTIVATION REQUEST message, the Network Entity 2 sends an AI/ML DATA COLLECTION ACTIVATION RESPONSE/ACKNOWLEDGE/FAILURE message acknowledging the request, approving the data collection activation request or indicating failure of the request, where the message may include one or more of model information, data collection information, functionality information, a failure cause value, a session ID and/or any other relevant information.”)
Accordingly, it would have been obvious to person of ordinary skill in the art before the
effective filing date of the claimed invention to combine Khirallah with Alawieh for the benefit
of having an apparatus capable of acknowledging upon receiving an activation of an AI/ML model. Further benefit is to avoid retransmission of a model and with proper timestamp for monitoring and update of the AI/ML model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH K NGUYEN whose telephone number is (571)467-6390. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeanette J Parker can be reached at 571-270-3647. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEPH KHANH NGUYEN/Examiner, Art Unit 2646
/JEANETTE J PARKER/Supervisory Patent Examiner, Art Unit 2646
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