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 .
Examiner’s Notes
There is a significant amount of “OR” statements in the currently pending claims which, as would be detailed in the forthcoming action, are causing a broad interpretation of the claims as well as antecedent bases issues. For example, claim 1 introduces “UE hardware capability;” “real-time UE capability;” OR “UE requirement.” Then claim 2 discusses all three. This gives rise to an antecedent issue since claim 1 only requires one of the terms discussed herein in order to be anticipated by the prior art, but claim 2 discusses other terms. In other words, if the prior art teaches only “hardware capability,” and the claim is deemed to be anticipated by the prior art, the “OR” statement in claim 2 refers to “real-time UE capability,” and therefore indefinite based on lack of antecedent basis. This is only one example of the issue; however, this rationale applies to the many other possibilities. Further, an amendment to the claims correcting the antecedent basis will necessarily change the scope of the claims.
Generally, the excessive use of “OR” statements in the pending claims is causing a broad reading of the claims in addition to a significant issue with an antecedent basis type 112 (b) rejection. The forthcoming action includes a best effort attempt to cover all potential antecedent basis issues, however, some issues might not be specifically addressed due to large number of potential variations.
Claims 13 and 34 are rejected separately under 112(b) antecedent basis for including the language “configuring the PUCCH;” while previous language of the claims uses an “OR” statement and thus giving rise to the option of excluding (PUCCH) from the prior art. Therefore, there is no antecedent basis for this term.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 5, 7, 8, 11, 13, 16, 19, 22-23, 26, 28-29, 32, and 34 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 2, and 23, claims 1 and 22 introduce the language “UE hardware capability;” “real-time UE capability;” OR “UE requirement;” but claims 2 and 23 refer to all the terms. It is not clear to which term the claims are referring.
Claims 5, 7, 8, 26, and 28-29 are rejected under similar rational because it is not clear which to which term the claim language is referring, i.e., UE hardware capability;” “real-time UE capability;” OR “UE requirement.” Claims 11, 13, 32, and 34 are rejected under similar rationale for the dependency upon claims 8 and 29.
Claims 16 and 19 are rejected under 112(b) indefiniteness type for not including either an “AND” or an “OR” statement to complete the “at least one of” language as underlined in the forthcoming action. It is unclear which operator the Applicant intends. The claims have been treated as indicating an “OR” statement in reference to the context of the pending claims. However, an amendment or a convincing argument regarding the claim language is necessary. An amendment correcting the indefiniteness issues would necessarily change the scope of the pending claims.
Claim Rejections - 35 USC § 102
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.
Claims 1-2, 5, 7-8, 11, 13, 16, 19, 22-23, 26, 28-29, 32, 34, 37-40, and 45-46 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhu (Publication number: US 2022/0360973).
Consider Claim 1, Zhu et al shows a model processing method based on a user equipment (UE) capability, performed by a base station (see figure 6), comprising:
(a) Sending a request message to a UE (see figure 6; and paragraphs 76-78); (Read as UE capability request 608a).
(b) Wherein the request message is configured to be used for requesting at least one of UE hardware capability information, real-time UE capability information, or real-time UE requirement information for a model (see figures 5 and 6; and paragraphs 76-78); (The UE responds with a UE capability indication 610a. Further, figure 5 shows general hardware acceleration capability).
(c) Obtaining feedback information sent, based on the request message, by the UE, wherein the feedback information comprises information requested by the request message (see figures 5 and 6; and paragraphs 76-78); (The UE responds with a UE capability indication 610a).
(d) Determining at least one of a model training scheme or a model inference scheme based on the feedback information, to train the model based on the model training scheme, or perform an inference on the model based on the model inference scheme or train the model based on the model training scheme and perform an inference on the model based on the model inference scheme (see paragraphs 74-76); (A second capability parameter may correspond to a memory capability. The memory capability may include a maximum model size for training and/or a maximum model size for inference. A third capability parameter may correspond to a general hardware acceleration capability, which may be associated with a neural network processor. The general hardware acceleration capability may include determining (e.g., Yes/No) whether an AI processor is available for training).
Consider Claim 22, Zhu et al shows a model processing method based on a user equipment (UE) capability, performed by a UE (see figure 6), comprising:
(a) Obtaining a request message sent by the base station (see figure 6; and paragraphs 76-78); (Read as UE capability request 608a).
(b) Wherein the request message is configured to be used for requesting at least one of UE hardware capability information, real-time UE capability information, or real-time UE requirement information for a model (see figures 5 and 6; and paragraphs 76-78); (The UE responds with a UE capability indication 610a. Further, figure 5 shows general hardware acceleration capability).
(c) Sending feedback information to the base station based on the request message, wherein the feedback information is information requested by the request message (see figures 5 and 6; and paragraphs 76-78); (The UE responds with a UE capability indication 610a).
Consider Claim 45, Zhu et al shows a communication device, comprising a processor and a memory, wherein the memory has a computer program stored thereon, and when the computer program stored on the memory is executed by the processor (see figure 3), the processor is configured to:
(a) Send a request message to a UE (see figure 6; and paragraphs 76-78); (Read as UE capability request 608a).
(b) Wherein the request message is configured to be used for requesting at least one of UE hardware capability information, real-time UE capability information, or real-time UE requirement information for a model (see figures 5 and 6; and paragraphs 76-78); (The UE responds with a UE capability indication 610a. Further, figure 5 shows general hardware acceleration capability).
(c) Obtain feedback information sent, based on the request message, by the UE, wherein the feedback information comprises information requested by the request message (see figures 5 and 6; and paragraphs 76-78); (The UE responds with a UE capability indication 610a).
(d) Determine at least one of a model training scheme or a model inference scheme based on the feedback information, to train the model based on the model training scheme, or perform an inference on the model based on the model inference scheme or train the model based on the model training scheme and perform an inference on the model based on the model inference scheme (see paragraphs 74-76); (A second capability parameter may correspond to a memory capability. The memory capability may include a maximum model size for training and/or a maximum model size for inference. A third capability parameter may correspond to a general hardware acceleration capability, which may be associated with a neural network processor. The general hardware acceleration capability may include determining (e.g., Yes/No) whether an AI processor is available for training).
Consider Claim 2, Zhu et al shows that the UE hardware capability information comprises at least one of: a number of Central Processing Units (CPUs) of the UE; a number of Graphics Processing Units (GPUs) of the UE; a clock rate of a CPU of the UE; a clock rate of a GPU of the UE; a cache capacity of the CPU of the UE; a video memory capacity of the GPU of the UE; a Tera Operations Per Second (TOPS) of the UE; or a Floating-point Operations Per Second (FLOPS) of the UE; wherein the real-time UE capability information comprises at least one of: real-time computing power information of the UE; wherein the real-time computing power information of the UE comprises at least one of a real-time memory occupancy rate of the UE, a real-time Central Processing Unit (CPU) occupancy rate of the UE, a real-time Graphics Processing Unit (GPU) occupancy rate of the UE, or a real-time computing speed of the UE; or real-time energy consumption information of the UE; wherein the real-time energy consumption information of the UE comprises at least one of a remaining quantity of electricity of the UE or an activation status of a power saving mode of the UE; wherein the real-time UE requirement information for the model comprises at least one of: a requirement for a precision of the model; a requirement for a model inference latency; or a requirement for privacy of model data, wherein the requirement for the privacy of the model data comprises information on whether the UE is allowed to report the model data of a UE side, and the model data comprises at least one of model training data, model inference data, or model inference intermediate information (see figure 5; and paragraphs 70-75); (The claim is read as only referring back to the hardware capability in claim 1 in view of the pertinent discussion regarding the claim language within this office action).
Consider Claims 5 and 26, Zhu et al shows that the request message is configured to be used for requesting the UE hardware capability information; wherein sending the request message to the UE comprises: sending a UE Capability Enquiry message to the UE; and wherein obtaining the feedback information sent, based on the request message, by the UE comprises: obtaining UE capability information sent, based on the UE Capability Enquiry message, by the UE, wherein the UE Capability Information comprises the UE hardware capability information (see figure 6; and paragraphs 77-80); (The UE 602 may transmit, at 614a, to the base station 604, the UE ML capability included in the radio capability, which may be further transmitted, at 614c, to the core network 606 via the base station 604. In examples, the UE radio capability may be indicated based on a radio capability identifier (ID). The UE 602 may transmit, at 616a, to the base station 604, the UE ML capability included in the core network (CN) capability, which may be further transmitted, at 616c, to the core network 606 via the base station 604. The UE 602 may transmit, at 618a, to the base station 604, a separate information element (IE) for the UE ML capability, which may be further transmitted, at 618c, to the core network 606 via the base station 604. In examples, the UE ML capability may be indicated based on an AI/ML capability ID).
Consider Claims 7 and 28, Zhu et al shows that a request message is configured to be used for requesting at least one of the real-time UE capability information or the real-time UE requirement information for the model; and wherein sending the request message to the UE comprises: sending an information request message to the UE, wherein the information request message is configured to be used for requesting the UE to report at least one of the real-time UE capability information or the real-time UE requirement information for the model, and the information request message comprises a reporting mode for the UE (see figure 6; and paragraphs 77-80); (The UE 602 may transmit, at 614a, to the base station 604, the UE ML capability included in the radio capability, which may be further transmitted, at 614c, to the core network 606 via the base station 604. In examples, the UE radio capability may be indicated based on a radio capability identifier (ID). The UE 602 may transmit, at 616a, to the base station 604, the UE ML capability included in the core network (CN) capability, which may be further transmitted, at 616c, to the core network 606 via the base station 604. The UE 602 may transmit, at 618a, to the base station 604, a separate information element (IE) for the UE ML capability, which may be further transmitted, at 618c, to the core network 606 via the base station 604. In examples, the UE ML capability may be indicated based on an AI/ML capability ID).
Consider Claims 8 and 29, Zhu et al shows obtaining the feedback information sent, based on the request message, by the UE comprises: obtaining at least one of the real-time UE capability information or real-time UE requirement information for the model reported, based on the reporting mode, by the UE; wherein the reporting mode comprises at least one of: a periodic reporting; a semi-persistent reporting; or a trigger-based reporting; and wherein the method further comprises at least one of: sending a reporting period corresponding to the periodic reporting to the UE; sending a reporting condition corresponding to the semi-persistent reporting to the UE; or sending a trigger condition corresponding to the trigger-based reporting to the UE (see paragraphs 82 and 83).
Consider Claims 11 and 32, Zhu et al shows obtaining at least one of the real-time UE capability information or the real-time UE requirement information for the model reported, based on the reporting mode, by the UE comprises at least one of: obtaining at least one of the real-time UE capability information or the real-time UE requirement information for the model incrementally reported, based on the reporting mode, by the UE; or obtaining at least one of: information whose privacy level is equal to or lower than a predetermined privacy level among the real-time UE capability information reported, based on the reporting mode, by the UE, or information whose privacy level is equal to or lower than the predetermined privacy level among the real-time UE requirement information for the model reported, based on the reporting mode, by the UE; wherein the predetermined privacy level is determined by the UE; wherein the method comprises at least one of: different information comprised in the UE hardware capability information corresponds to different privacy levels, different information comprised in the real-time UE capability information corresponds to different privacy levels, or different information comprised in the real-time UE requirement information for the model corresponds to different privacy levels (see figure 6; and paragraphs 77-80); (The UE 602 may transmit, at 614a, to the base station 604, the UE ML capability included in the radio capability, which may be further transmitted, at 614c, to the core network 606 via the base station 604. In examples, the UE radio capability may be indicated based on a radio capability identifier (ID). The UE 602 may transmit, at 616a, to the base station 604, the UE ML capability included in the core network (CN) capability, which may be further transmitted, at 616c, to the core network 606 via the base station 604. The UE 602 may transmit, at 618a, to the base station 604, a separate information element (IE) for the UE ML capability, which may be further transmitted, at 618c, to the core network 606 via the base station 604. In examples, the UE ML capability may be indicated based on an AI/ML capability ID).
Consider Claims 13, and 34, Zhu et al shows obtaining at least one of the real-time UE capability information or the real-time UE requirement information for the model reported, based on the reporting mode, by the UE comprises at least one of: obtaining at least one of the real-time UE capability information or the real-time UE requirement information for the model reported, through Radio Resource Control (RRC) signaling, by the UE; obtaining at least one of the real-time UE capability information or the real-time UE requirement information for the model reported, through Medium Access Control-Control Element (MAC CE) signaling, by the UE; or obtaining at least one of the real-time UE capability information or the real-time UE requirement information for the model reported, through a Physical Uplink Control Channel (PUCCH), by the UE; wherein obtaining at least one of the real-time UE capability information or the real-time UE requirement information for the model reported, through the PUCCH, by the UE comprises: obtaining a report information list sent by the UE, wherein the report information list is configured to indicate all information that the UE is able to report to the base station among at least one of the real-time UE capability information or the real-time UE requirement information for the model requested by the base station; configuring the PUCCH for the UE; and obtaining, through the PUCCH, at least one of the real-time UE capability information or the real-time UE requirement information UE for the model sent by the UE; and wherein configuring the PUCCH for the UE comprises at least one of: configuring the PUCCH for the UE in a semi-static resource allocation manner; or configuring the PUCCH for the UE in a dynamic resource allocation manner (see figure 1; and paragraphs 80-85); (The UE 602 may report an ML capability container via RRC to the base station 604 in association with signaling the UE radio capability 612(1). The base station 604 may request, at 608a, the ULE radio capability 612(1) from the UE 602 based on a UECapabilityEnquiry message, and the UE 602 may report, at 610a, the UE radio capability 612(1) based on a UECapabilityInformation message).
Consider Claim 16, Zhu et al shows determining the model training scheme based on the feedback information comprises at least one of: (i) when the base station determines, based on the feedback information, that a model training capability of the UE is less than a first threshold and a requirement for privacy of model data indicates allowing to report model training data, determining that the model training scheme is training a first model by the base station; wherein training the first model by the base station comprises: sending a message for requesting the model training data to the UE; obtaining the model training data sent by the UE; and training the first model based on the model training data; (ii) when the base station determines, based on the feedback information, that a model training capability of the UE is greater than or equal to a first threshold and less than a second threshold, determining that the model training scheme is training a first model by the base station and the UE respectively; wherein training the first model by the base station and the UE respectively comprises: obtaining a pre-trained model by pre-training the first model based on local training data of the base station; sending the pre-trained model to the UE to allow the UE to retrain the pre-trained model; and obtaining a retrained model and model performance information sent by the UE; [the Applicant does not insert either an “AND” or and “OR”] (iii) when the base station determines, based on the feedback information, that a model training capability of the UE is greater than or equal to a second threshold, determining that the model training scheme is training a first model by the UE; wherein training the first model by the UE comprises: configuring the first model for the UE, to allow the UE to train the first model; and obtaining a trained model and model performance information sent by the UE (see figure 6; and paragraphs 77-80); (The UE 602 may transmit, at 614a, to the base station 604, the UE ML capability included in the radio capability, which may be further transmitted, at 614c, to the core network 606 via the base station 604. In examples, the UE radio capability may be indicated based on a radio capability identifier (ID). The UE 602 may transmit, at 616a, to the base station 604, the UE ML capability included in the core network (CN) capability, which may be further transmitted, at 616c, to the core network 606 via the base station 604. The UE 602 may transmit, at 618a, to the base station 604, a separate information element (IE) for the UE ML capability, which may be further transmitted, at 618c, to the core network 606 via the base station 604. In examples, the UE ML capability may be indicated based on an AI/ML capability ID).
Consider Claim 37, Zhu et al shows at least one of: (i) receiving a message for requesting model training data sent by the base station; and sending the model training data to the base station; (ii) receiving a pre-trained model sent by the base station; retraining the pre-trained model; and sending a retrained model and model performance information to the base station; or (iii) obtaining a first model configured by the base station; training the first model; and sending a trained model and model performance information to the base station (see figure 6; and paragraphs 77-80); (The UE 602 may transmit, at 614a, to the base station 604, the UE ML capability included in the radio capability, which may be further transmitted, at 614c, to the core network 606 via the base station 604. In examples, the UE radio capability may be indicated based on a radio capability identifier (ID). The UE 602 may transmit, at 616a, to the base station 604, the UE ML capability included in the core network (CN) capability, which may be further transmitted, at 616c, to the core network 606 via the base station 604. The UE 602 may transmit, at 618a, to the base station 604, a separate information element (IE) for the UE ML capability, which may be further transmitted, at 618c, to the core network 606 via the base station 604. In examples, the UE ML capability may be indicated based on an AI/ML capability ID).
Consider Claims 19, Zhu et al shows determining the model inference scheme based on the feedback information comprises at least one of: (a) when the base station determines, based on the feedback information, that a model inference capability of the UE is less than a third threshold and a requirement for privacy of model data indicates allowing to report model inference data, determining that the model inference scheme is performing an inference on a second model by the base station; wherein performing the inference on the second model by the base station comprises: determining the second model based on the feedback information; sending a message for requesting the model inference data to the UE; obtaining the model inference data sent by the UE; performing the inference on the second model based on the model inference data; and sending an inference result to the UE; (b) when the base station determines, based on the feedback information, that a model inference capability of the UE is greater than or equal to a third threshold and less than a fourth threshold and a requirement for privacy of model data indicates allowing to report model inference intermediate information, determining that the model inference scheme is jointly performing a model inference by the base station and the UE; wherein jointly performing the model inference by the base station and the UE comprises: determining a second model based on the feedback information, determining a model split point for the second model, and splitting the second model based on the model split point into two sub-model portions; sending a former sub-model portion of the second model to the UE or sending model information of the second model and the model split point to the UE to allow the UE to perform the inference on the former sub-model portion to obtain the model inference intermediate information; obtaining the model inference intermediate information sent by the UE; performing the model inference based on the model inference intermediate information and a latter sub-model portion of the second model; and sending an inference result to the UE; [the Applicant does not insert either an “AND” or an “OR] (c) when the base station determines, based on the feedback information, that a model inference capability of the UE is greater than or equal to a fourth threshold, determining that the model inference scheme is performing an inference on a second model by the UE; wherein performing the inference on the second model by the UE comprises: determining the second model based on the feedback information; and sending the second model to the UE to allow the UE to perform the inference of the second model (see paragraphs 74-76); (A second capability parameter may correspond to a memory capability. The memory capability may include a maximum model size for training and/or a maximum model size for inference. A third capability parameter may correspond to a general hardware acceleration capability, which may be associated with a neural network processor. The general hardware acceleration capability may include determining (e.g., Yes/No) whether an AI processor is available for training).
Consider Claim 40, Zhu et al shows at least one of: (a) obtaining a message for requesting model inference data sent by the base station; sending model inference data to the base station; and obtaining an inference result sent by the base station; (b) obtaining a former sub-model portion of a second model sent by the base station, or obtaining model information of the second model and a model split point of the second model sent by the base station, and splitting the second model based on the model split point into two sub-model portions; performing an inference on the former sub-model portion to obtain model inference intermediate information; sending the model inference intermediate information to the base station; and obtaining an inference result sent by the base station; or (c) obtaining a second model sent by the base station; and performing an inference on the second model (see paragraphs 74-76); (A second capability parameter may correspond to a memory capability. The memory capability may include a maximum model size for training and/or a maximum model size for inference. A third capability parameter may correspond to a general hardware acceleration capability, which may be associated with a neural network processor. The general hardware acceleration capability may include determining (e.g., Yes/No) whether an AI processor is available for training).
Consider Claim 46, Zhu et al shows a processor and a memory, wherein the memory has a computer program stored thereon, and when the computer program stored on the memory is executed by the processor, the processor is configured to perform the method of claim 22 (see figures 1 and 3).
Conclusion
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/MICHAEL A FARAGALLA/Primary Examiner, Art Unit 2624 06/22/2026
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