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 .
Drawings
The drawings filed on 11/12/2024 are accepted.
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.
Claim 11 is 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.
Claim 11 recites “identify, for an ML operation of the plurality of ML operations, at least one of:
an input complexity value associated with input for the ML operation, or
an output complexity value associated with output of the ML operation;
and
identify the number of MPUs for the ML operation based at least in part on the input complexity value, the output complexity value, and a base complexity value for an AI/ML model used in the ML operation.”
The second “identify” step requires the input complexity value and the output complexity value, however the first “identify” step requires the input complexity value or the output complexity value. The varying requirements of the same elements within the same claim calls into question the metes and bounds of claim 11.
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)(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhu et al. (WO2024/264078-A2 hereinafter, Zhu).
Regarding claim 1, Zhu teaches a user equipment (Fig. 2 [202]) for wireless communication, comprising:
one or more memories; (Fig. 2 [206]) and
one or more processors (Fig. 2 [204]) coupled to the one or more memories (see additionally Fig. 15A), the one or more processors individually or collectively configured to:
receive configuration information indicating that the UE is to report a number of machine learning processing units (MPUs) for each of a plurality of machine learning (ML) operations; (Page 65 lines 3-6) and
transmit data indicating the number of MPUs for each of the plurality of ML operations (Page 4 [0029]) and a maximum number of MPUs supported by the UE. (Page12 [0119-0120] and Page 65 Claims 2 or 4 “wherein the first value comprises a number of machine-learning processing units (MPUs) associated with the first resource type available in the wireless device”)
Regarding claim 2, Zhu teaches wherein the plurality of ML operations comprises ML operations associated with one or more of:
interference prediction,
channel state information (CSI) prediction,
CSI compression,
beam prediction,
positioning,
sensing,
scheduling,
resource selection, or
reference signal design. (Page 8 [0090] “to apply AI/ML to solve PHY layer problems. In particular, this SI targets three use cases, e.g., CSI compression, beam management, and positioning”)
Regarding claim 3, Zhu teaches wherein at least one of the plurality of ML operations comprises a combination of two or more AI/ML model functionalities. (Page 2 [0012], Page 10 [0101] and Page 12 [0117] “The network estimates/controls the number of models to run in the UE to make sure the combined computational power will not exceed the maximum capability/capacity allowed or reserved”)
Regarding claim 4, Zhu teaches wherein at least two ML operations of the plurality of ML operations are associated with a same AI/ML model functionality (Page 2 [0012] “ the first AI/ML model represents a model group including a set of models to be inferenced together to support another AI/ML-enabled functionality or the first AI/ML-enabled functionality is associated with a model group including a set of models to be inferenced together to support the first AI/ML-enabled functionality”) and a different number of MPUs. (Pages 13-14 [0128])
Regarding claim 5, Zhu teaches wherein, for each of the plurality of ML operations, the number of MPUs is associated with at least one of an AI/ML model identifier (ID) or an AI/ML model functionality. (Page 41 [0352])
Regarding claim 6, Zhu teaches receive second configuration information (Fig. 7 [722]) indicating that the UE is to execute a subset of the plurality of ML operations without exceeding the maximum number of MPUs. (note: first configuration is seen in Fig. 7 [708], Page 28 [0236-0238] and Fig. 6 [602, 604, 610, Yes, 612])
Regarding claim 7, Zhu teaches execute a subset of the plurality of ML operations without exceeding the maximum number of MPUs. (Fig. 5 [518, Yes, 520] i.e. 518/Yes ensures the threshold won’t be exceeded)
Regarding claim 8, Zhu teaches transmit data indicating an MPU occupancy time associated with an ML operation of the plurality of ML operations, (Page 27 [0231])
wherein the MPU occupancy time is defined by a temporal separation between an earliest input and a latest output associated with the ML operation. (Page 3 [0024] “where each resource occupation time of the plurality of resource occupation times indicates an amount of time that a resource of the first resource type is utilized at corresponding value of the plurality of values of the first resource type”)
Regarding claim 9, Zhu teaches wherein the latest output associated with the ML operation is at least one of:
an ML output of the ML operation, or
transmission of data associated with the ML output of the ML operation. (Page 11 [0114])
Regarding claim 10, Zhu teaches wherein the number of MPUs for at least one of the plurality of ML operations is based at least in part on an operational mode associated with the at least one of the plurality of ML operations, the operational mode comprising at least one of:
an inference operational mode associated with ML execution, (Page 9 [0094])
a training operational mode associated with ML training, or
a monitoring operational associated with analyzing ML results.
Regarding claim 11, Zhu teaches wherein the one or more processors are further individually or collectively configured to:
identify, for an ML operation of the plurality of ML operations, at least one of:
an input complexity value associated with input for the ML operation, or
an output complexity value associated with output of the ML operation; (Page 40 [0349] “The granularity of MPU (for example, which is related to the computational complexity (and memory) of the basic AI/ML model/task) can be decided based on different strategical preference. For example, a finer and tighter granularity provides more precise measurement but may lead to less tolerance of measurement errors. Alternatively, a coarser granularity provides more cushion for measurement errors, including measurements under uncommon situations such as overloaded and underloaded scenarios, but may lead to over-provision of the UE’s processing power (e.g., a waste of computational power and memory)” note: the examiner views the granularity of MPU to be equivalent to the complexity of input and output requirements)
and
identify the number of MPUs for the ML operation based at least in part on the input complexity value, the output complexity value, and a base complexity value for an AI/ML model used in the ML operation. (Page 11 [0348] “he MPU is used to measure the UE’s capability for performing AI/ML model inference, as well as the demand of computational power (and memory) of each model, measured at the number of MPUs NMPU)”)
Regarding claim 12, Zhu teaches a network node (Fig. 7 [702]) for wireless communication, comprising:
one or more memories; (Fig. 15B [1558] see [0383] for description of being a base station) and
one or more processors (Fig. 15B [1550]) coupled to the one or more memories, the one or more processors individually or collectively configured to:
receive data (see visually in Fig. 12 [1212]) indicating a number of machine learning processing units (MPUs) for each of a plurality of machine learning (ML) operations and a maximum number of MPUs supported by a user equipment (UE); (Page12 [0119-0120] and Page 65 Claims 2 or 4 “wherein the first value comprises a number of machine-learning processing units (MPUs) associated with the first resource type available in the wireless device”) and
transmit configuration information indicating that the UE is to execute a subset of the plurality of ML operations, (Fig. 12 [1222])
wherein the number of MPUs associated with the subset does not exceed the maximum number of MPUs. (Pages 67 & 68 Claims 16 and 20 note: first value definition in claim 20 is used to determine if the UE can perform the AI/ML model functionality or not)
Regarding claims 13, 14 and 17, the limitations of claims 13, 14 and 17 are rejected as being the same reasons set forth above in claim 2, 5 and 10.
Regarding claim 15, Zhu teaches receive data indicating an MPU occupancy time associated with an ML operation of the plurality of ML operations, (Page 27 [0231])
wherein the MPU occupancy time is defined by a temporal separation between an earliest input and a latest output associated with the ML operation, (Page 3 [0024] “where each resource occupation time of the plurality of resource occupation times indicates an amount of time that a resource of the first resource type is utilized at corresponding value of the plurality of values of the first resource type”) and
wherein the subset is based at least in part on the MPU occupancy time. (Page 2 [0014-0016] and similarly see in claims 26 and 27 on Page 69)
Regarding claim 16, Zhu teaches wherein the latest output associated with the ML operation is at least one of:
an ML output of the ML operation, or
transmission of data associated with the ML output of the ML operation. (Page 11 [0114])
Regarding claim 18, Zhu teaches transmit second configuration information indicating that the UE is to report the number of MPUs based at least in part on the operation mode. (Page 40 [0348-0349], Fig. 7 [716] second configuration information, Fig. 7 [708] first configuration information)
Regarding claim 19, Zhu teaches wherein the subset is based at least in part on an input complexity value, an output complexity value and a base complexity value for an AI/ML model used in an ML operation associated with the subset, (Page 11 [0348] “he MPU is used to measure the UE’s capability for performing AI/ML model inference, as well as the demand of computational power (and memory) of each model, measured at the number of MPUs NMPU)”)
wherein the input complexity value is associated with input for the ML operation, and the output complexity value is associated with output of the ML operation. (Page 40 [0349] “The granularity of MPU (for example, which is related to the computational complexity (and memory) of the basic AI/ML model/task) can be decided based on different strategical preference. For example, a finer and tighter granularity provides more precise measurement but may lead to less tolerance of measurement errors. Alternatively, a coarser granularity provides more cushion for measurement errors, including measurements under uncommon situations such as overloaded and underloaded scenarios, but may lead to over-provision of the UE’s processing power (e.g., a waste of computational power and memory)” note: the examiner views the granularity of MPU to be equivalent to the complexity of input and output requirements)
Regarding claim 20, the limitations of claim 20 are rejected as being the same reasons set forth above in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO2024/238046-A1 to Yang et al. which discloses processing unit occupational rules and prioritizing algorithms for processing in AI use cases.
WO2023/081187-A1 to Narayanan et al. which discloses determine the AI model to utilize with measurement values at a UE.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW C SAMS whose telephone number is (571)272-8099. The examiner can normally be reached M-F 8:30-5 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Anderson can be reached at (571)272-4177. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Matthew C Sams/Primary Examiner, Art Unit 2646
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