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 statement (IDS) submitted on 01/11/2024 has been considered by examiner and made of record in the application file.
Election/Restrictions
Applicant’s election without traverse of Species I, Sub-Species C corresponding to claims 1,7-10, and 29 in the reply filed on 03/23/2026 is acknowledged.
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.
Claim(s) 1, 7-10, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Vivo'639 (3GPP TSG RAN WG1 #110bis-e, R1-2208639) in view of Vivo'448 (3GPP TSG RAN WG1 #112, R1-2300448).
Regarding Claim 1, Vivo'639 discloses an apparatus for wireless Communication at a user equipment (UE) (Fig.3, UE), comprising: at least one memory;
and at least one processor coupled to the at least one memory (Fig.3, UE (i.e., the processor and memory are inherited of the UE.)) 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:
receive a positioning model registration message comprising a set of positioning model IDs and an indicator of a set of positioning model configurations (model pool) (page 10, 3.3.3. Model Selection, bullet 1, Fig.7, "Network side sends a model pool to UE side in advance, the models in which are used for positioning." and bullet 2, "Network side sends a model selection instruction to the target UE to perform model selection. This instruction may consist of an ID set of candidate models or other assistance information to support UE side perform model selection." and page 17, 3.6. A general model management procedure Fig.17:2 model transfer (i.e., UE is provided with a model pool to be used for positioning. The “a positioning model registration message” is reading as long as a message sent by the network device contains models to the UE with a set of ID of candidate then that is a “registration message” as the purpose of the message is to provide UE with models in order to have the UE to select models for positioning measurement as shown in page 10 and page 17.)),
wherein each of the set of positioning model IDs is associated with at least one of the set of positioning model configurations (page 10, 3.3.3. Model Selection, bullet 1, Fig.7, "Network side sends a model pool to UE side in advance, the models in which are used for positioning." and bullet 2, "Network side sends a model selection instruction to the target UE to perform model selection. This instruction may consist of an ID set of candidate models or other assistance information to support UE side perform model selection."(i.e., the models have an ID for the UE to pick to perform measurement using the model.)); select a positioning model ID from the set of positioning model IDs based on the set of positioning model configurations and an environmental attribute of the UE (page 9, 3.3.2. Model activation/deactivation, paragraph 1, Fig.6, "An AI/ML model may not always work well due to user mobility and environmental changes. For example, the user is out of the current model's service area or the surroundings have changed significantly. In such case, a new AI/ML model is required to continue the high-accuracy positioning service for target UEs." and page 10, 3.3.3. Model Selection, first paragraph, Fig.7, "Model selection is the process of selecting a suitable model from a pre-deployed model pool. In practice, considering the dynamics and complexity of the environment, a model pool may be deployed in advance at UE side to enable seamless model switching. When the current model does not work well, network side can indicate the target UE to conduct model selection immediately, so as to adapt to the new environment," and bullet 3,"UE side selects a suitable model from the model pool with reference to the model selection instruction." and page 16, Fig.17, "Model monitoring" (i.e., Although the citations are for re-selection, its clear the UE is performing an initial selection of AI/ML model to be used for positioning measurement, and when those AI/ML model are not satisfactory based on the environment, the UE would ask the network model recommendation to replace the current selection. See page 17 Fig.16 "Model monitoring"));
and calculate a set of positioning model outputs based on the measured set of positioning signals and a positioning model associated with the selected positioning model ID (page 11, 3.4.1. Model monitoring, bullet 3, Fig.8, "Network side should send a model monitoring instruction to inform the target UE to measure and report related performance metrics for model monitoring at network side. Moreover, the process of such model monitoring can also be triggered by UE side." and bullet 4, "UE side reports the assistance information for model monitoring to network side. This assistance information contains the required performance metrics of model monitoring."(i.e., UE reporting performance metrics of the models based on the selection.)).
However, Vivo’639 does not explicitly disclose receive a set of positioning signals; measure the set of positioning signals.
Vivo'448 discloses receive a set of positioning signals (page 3, 3.1 Direct AI/ML positioning, paragraph 1, Fig.2, "For direct AI/ML positioning, UE position can be directly estimated according to multiple TRPs’ Channel Impulse Response (CIR) vectors, as shown in Figure 2. Note that, AI/ML model can be deployed at the UE side or network side." (i.e., explicitly disclose of receiving positioning signals.));
measure the set of positioning signals (page 3, 3.1 Direct AI/ML positioning, paragraph 1, Fig.2, "For direct AI/ML positioning, UE position can be directly estimated according to multiple TRPs’ Channel Impulse Response (CIR) vectors, as shown in Figure 2. Note that, AI/ML model can be deployed at the UE side or network side." (i.e., Fig.2 shows the signals are being measured.)).
Vivo’639 and Vivo’448 are considered to be analogous to the claimed invention because they are in the same field wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Vivo’639 to implement the apparatus of Vivo’448 as it provides advantages in positioning performance, deployment flexibility, and compatible with existing positioning protocol framework of measuring the reference signals (Vivo’448, page 23, observation 15, “AI/ML based TOA estimation has great advantages in positioning performance, deployment flexibility, compatibility with existing positioning protocol framework, and generalization capability.”).
Regarding Claim 7, Vivo’639 in view of Vivo’448 discloses all the limitation of claim 1.
Vivo’639 further discloses wherein the at least one processor, individually or in any combination, is further configured to: transmit a report message comprising a second indicator of the selected positioning model ID (page 11, 3.4.1. Model monitoring, paragraph 1, Fig.8, “When the AI/ML model is deployed at UE side, model monitoring can be performed at UE side. In such case, network side should send a model monitoring instruction to the target UE, and then, the results of model monitoring should be fed back to network side.” and bullet 1, “Network side should send a model monitoring instruction to inform the target UE to perform model monitoring. This instruction may also include an assistance information request containing the specific model monitoring performance metrics which UE side should measure.” and bullet 2, "UE side performs model monitoring, and then feeds back a model validity identification to network side. Optionally, the reason for model invalidation can be also attached when UE side confirms the current model does not work well." (i.e., feeds back model validity identification. “A second indicator” is on bullet 2 wherein the UE is reporting the selected model of condition being good or does not work well.))
and a third indicator of at least one of the calculated set of positioning model outputs (page 11, 3.4.1. Model monitoring, bullet 3, Fig.8, "Network side should send a model monitoring instruction to inform the target UE to measure and report related performance metrics for model monitoring at network side. Moreover, the process of such model monitoring can also be triggered by UE side." and bullet 4, "UE side reports the assistance information for model monitoring to network side. This assistance information contains the required performance metrics of model monitoring."(i.e., reporting the outputs of the selected model.)).
Vivo’448 further discloses and a third indicator of at least one of the calculated set of positioning model outputs (page 2, 3.1 Direct AI/ML Learning, "" and page 54, 6.2. Semi-supervised learning with limited labeled data, paragraph 1, "Fortunately, the unlabeled data containing CIR only is relatively easy to obtain. For example, one way to collect unlabeled data at network side is that UEs report CIRs estimated from PRS measurement."(i.e., Vivo’448 also discloses of reporting PRS measurement using model output as shown in page 2, Fig.2, and page 54, 6.2, paragraph 1.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 8, Vivo’639 in view of Vivo’448 discloses all the limitation of claim 7.
Vivo’639 further discloses wherein the at least one processor, individually or in any combination, is further configured to: receive a positioning model registration failure message comprising a fourth indicator that the selected positioning model ID is invalid for registration (page 9, 3.3.2. Model activation/deactivation, bullet 2, "Network side should send a model deactivation signaling to invalidate the current model." and page 17, 3.6. A general model management procedure, Fig.17, 5. "Model deactivation/activation" (i.e., Fig.17 indicating a model failure by sending a model deactivation/activation.));
and receive a second positioning model registration message comprising a second set of positioning model IDs (page 10, 3.3.3. Model selection, paragraph 1, Fig.7, "When the current model does not work well, network side can indicate the target UE to conduct model selection immediately, so as to adapt to the new environment," and bullet 2, “Network side sends a model selection instruction to the target UE to perform model selection. This instruction may consist of an ID set of candidate models or other assistance information to support UE side perform model selection.” and proposal 8, “Network side could send a model selection instruction to instruct the target UE to select a suitable model from the model pool, when the current model does not work well.” and page 17, 3.6 A general model management procedure, Fig.17, 6-a. "Model selection" (i.e., network sending a re-selection to change the model in order to the use a model that is better suited for the environment. The “a second positioning model registration message” is reading as a message sent by the network device to comprises model comprises id for the UE to select a different model when the current model is not working.)),
wherein each of the second set of positioning model IDs is associated with at least one of a second set of positioning model configurations (page 10, 3.3.3. Model selection, paragraph 2, Fig.7, "In particular, to reduce the overhead of model transfer, the deployed model pool can consist of multiple AI/ML models with same structure but different parameters to adapt to varying environments. Meanwhile, each of AI/ML models within the model pool could be associated with a meta-information to assist model selection. For example, each of AI/ML models may be associated with one PRS configuration." and page 17, 3.6 A general model management procedure, Fig.17, 6-a. "Model selection" (i.e., the second provided from the network of the model is different from the initial model.)),
wherein the second set of positioning model IDs does not include the selected positioning model ID (page 10, 3.3.3. Model selection, paragraph 1, Fig.7, "When the current model does not work well, network side can indicate the target UE to conduct model selection immediately," and proposal 8, “Network side could send a model selection instruction to instruct the target UE to select a suitable model from the model pool, when the current model does not work well.” and page 17, 3.6 A general model management procedure, Fig.17, 6-a. "Model selection" (i.e., as explained above, selecting a different model indicated by the network.)).
Regarding Claim 9, Vivo’639 in view of Vivo’448 discloses all the limitation of claim 8.
Vivo’639 further discloses wherein the at least one processor, individually or in any combination, is further configured to: transmit a request message comprising a request for the second positioning model registration message before the reception of the second positioning model registration message (page 9, 3.3.2. Model activation/deactivation, paragraph 2, Fig.6, "When AI/ML model is deployed at UE side and the current model does not work well, network side should send a model deactivation signaling to invalidate the current model. Then, network side may transfer a new model to UE side or instruct UE side to fine-tune the current model. Optionally, falling back to non-AI methods should be also supported. Finally, network side should activate the new model to provide AI/ML based positioning service for UEs." and bullet 1, "UE side sends model deactivation request to network side when model deactivation is triggered by UE side." and page 17, 3.6 A general model management procedure, Fig.17, 4. "Performance feedback" (i.e., bullet 1 on page 9 shows the UE indicating of requesting of switching model. Although section 3.3.2. and 3.3.3. are different Fig.17 shows multiple proposals in the Vivo’639 can be combined to come to the claimed invention such as the UE reporting the model is not working as described in page 9, section 3.3.2. and have the UE perform selection as indicated by the network in page 10, section 3.3.3 and as shown page 17, Fig.17, steps 4-6.)).
Regarding Claim 10, Vivo’639 in view of Vivo’448 discloses all the limitation of claim 1.
Vivo’639 further discloses wherein the environmental attribute of the UE comprises at least one of: an area associated with a calculated location of the UE (page 9, 3.3.2. Model activation/deactivation, paragraph 1, "An AI/ML model may not always work well due to user mobility and environmental changes. For example, the user is out of the current model's service area or the surroundings have changed significantly." (i.e., environmental attribute is the area that is associated with the UE. Other options in claim 10 were given no patentable weight as claim recites “at least one of”)).
Regarding Claim 29, which is similar in scope to claim 1, thus rejected under the same rationale.
Conclusion
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ERKIN S. ABDULLAEV
Examiner
Art Unit 2648
/ERKIN ABDULLAEV/Examiner, Art Unit 2648
/WESLEY L KIM/Supervisory Patent Examiner, Art Unit 2648