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 .
Priority
The instant application claims foreign priority to 202210350469.2, filed 04/02/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted, IDS - 03/31/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 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.
Claims 1-3, 5-11, 13-15, and 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by JIANGLEI-MA et al. (US20230284139A1) hereinafter “JIANGLEI-MA”.
Regarding Claim 1,
JIANGLEI-MA discloses, ‘An information feedback method, comprising:
receiving, by a first device, requirement information sent by a second device, wherein the requirement information is used to indicate a requirement corresponding to an artificial intelligence (AI) service’ (AI-execution module in Fig. 9 to Fig. 11 implemented on a UE, one/more system node and a network node [0202]. In Fig. 22 illustrates measurement feedback. The BS transmits measurement request to the UE and in response, the UE performs the configured measurement [0355]. AI algorithm optimize service specific basis [0156]. );
And discloses, ‘and sending, by the first device, feedback information of the requirement information to the second device, wherein the feedback information is used to indicate whether to agree with a requirement corresponding to the requirement information.’ (The UE provides measurement feedback to the BS [0355]. The AI model optimization to meet the service requirement of the UEs and maximizes the efficiency of TRPs/NWs. Attributes of AI model: spectrum/carriers, data rate and/or coverage, traffic load, service type for each UE, KPI requirement of the services [0176]. The AI algorithm provide enhanced optimization [0177]. Optimization to meet the requirements of each service or each UE includes network performance, system capacity and power consumption [0157, 0176]. AI management module analyze the requirement select one/more AI models to achieve the requirements [0236].)
Regarding Claim 2,
‘The method according to claim 1’ (disclosed above),
JIANGLEI-MA discloses, ‘wherein the requirement information comprises a requirement item, or a requirement item and a requirement value corresponding to the requirement item.’ (AI operation includes control of power consumption [0267]. To monitor the KPIs and performance to particular threshold and doesn’t exceed a threshold for the UE and the NW [0269-0270].)
Regarding Claim 3,
‘The method according to claim 2’ (disclosed above),
JIANGLEI-MA discloses, ‘wherein in a case that the requirement information comprises a requirement item and a requirement value corresponding to the requirement item, the feedback information comprises any one of the following:
first indication information, wherein the first indication information is used to indicate to agree with the requirement item, and a device capability meets the requirement value’ (The UE capability of AI operation. The Indication of the type and/or level of complexity of the AI is capable of supporting. Which function/operation AI can support and/or what type of AI algorithm or model can be supported (e.g., AE, reinforcement learning, NN, DNN, how many layers supported etc.) [0260] and in Fig. 19 and 20. In Fig. 22 illustrates measurement feedback. Output from the AI algorithm; performance KPI as throughput, latency, spectrum, power consumption [0355]. Measurement request sent periodically predefined/preconfigured and dynamically [0356]. Optimized AI intelligent power saving/consumption for each UE, different type of services/UEs have different requirement for the power consumption meet requirements optimized by AI. Optimization based on quality requirement of the services, UE types, available power [0169]. Optimization of AI model to meet service requirement [0176].);
And discloses, ‘second indication information, wherein the second indication information is used to indicate to agree with the requirement item, the second indication information carries a device capability’ (In Fig. 23 illustrates the indication of the capability of perform/implement AI particular range [0387]. Mode of operation to implement AI receive configuration [0380]. In Fig. 8 illustrates the implementation of AI module [0147] identify the capability of UE AI/non-AI capable requirement to operate AI-mode supports services [0154-0155].])
And discloses, ‘or capability difference information corresponding to the requirement item, and the capability difference information is used to indicate a difference between the device capability and the requirement value’ ( power consumption within a particular range; network load falling within a particular range; a key performance indicator (KPI) falling within a particular range; channel quality within a particular range; or a change in service and/or traffic type for the apparatus [0395]. AI implementations, the AI inputs sensing, predict/track channels. Uses measurement, measures feedback channel AI/non-AI implementations to identify AI-capable results needs for measurement and/or feedback [0129]. AI model designed and trained to output an inference data that optimizes more than one parameter (e.g., to infer optimized parameters for multiple power control parameters) [0241]. );
And discloses, ‘or third indication information, wherein the third indication information is used to indicate to reject the requirement item.’ (the UE transmits the AI capability whether to capable to implement AI operation/function; kind of AI algorithm supported (autoencoder/DNN/reinforcement learning/NN; layers of NN ) [0260] and in Fig. 19. )
Regarding Claim 5,
‘The method according to claim 2’ (disclosed above),
And discloses, ‘wherein in a case that the requirement information comprises a requirement item, the feedback information comprises any one of the following:
fourth indication information, wherein the fourth indication information is used to indicate to reject the requirement item; or
fifth indication information, wherein the fifth indication information is used to indicate to agree with the requirement item, and the fifth indication information carries a device capability corresponding to the requirement item.’ (In Fig. 23 illustrates the capability of AI implementation and the indication to receive the response. And, capability report indication in Fig. 19 [0377]. Entering/leaving a training/re-training mode. The response of the capability includes power consumption/KPI/channel quality within the particular range [0387, 0395]. Configured to operate in a first mode or a second mode based on the capability [0391]. And, third mode of AI operation [0396].)
Regarding Claim 6,
‘The method according to claim 2’ (disclosed above),
And discloses, ‘wherein the requirement item comprises at least one of the following:
an AI computing power requirement,
an AI algorithm requirement, or
an AI data requirement.’ (AI-based communication include intelligent power
control, improve algorithm performance [0074] and in Fig. 7. AI implemented to one/more components to improve performance/efficiency. Reduce power consumption [0006]. The AI modules implement by processor executed one/more AI algorithm [0080].)
Regarding Claim 7,
‘The method according to claim 6’ (disclosed above),
And discloses, ‘wherein the AI computing power requirement comprises at least one of the following:
a computing power;
storage;
a total computing workload;
a processing time limitation; or
power consumption.’ (AI-based communication include intelligent power control,
improve algorithm performance [0074]. AI implemented to one/more components to improve performance/efficiency. Reduce power consumption [0006]. Intelligence to Air interface and uses computational power [0093]. AI input/output processing functions. AI management module use a trained global AI model to output an inferred control parameter (e.g., transmit power, beamforming parameters, data rates, etc.) for RRM [0212]. AI model includes UE power availability, traffic load [0176].)
Regarding Claim 8,
‘The method according to claim 6’ (disclosed above),
And discloses, ‘wherein the AI algorithm requirement comprises at least one of the following:
AI task classification;
an AI learning framework;
an AI network development environment; or
an AI basic model library.’ (Network task associated to AI model. The network task involve cooperation among multiple nodes to perform AI-related task to train a AI model managed by AI management module [0209].)
Regarding Claim 9,
‘The method according to claim 8’ (disclosed above),
And discloses, ‘wherein the AI task classification comprises at least one of the following:
model training;
model inference;
model validation;
model monitoring; or
model deployment.’ (task-driven approach to define AI model [0242]. AI management module generates inference data for a particular network task [0209]. AI modeling configuration functions includes configure multiple AI model splits AI task/operation into sub-tasks [0213.])
Regarding Claim 10,
‘ The method according to claim 8’ (disclosed above),
And discloses, ‘wherein the AI learning framework comprises at least one of the following:
supervised deep learning;
unsupervised deep learning;
meta-learning;
transfer learning;
reinforcement learning; or
federated learning.’ (AI-enabled and capability for reinforcement learning, deep NN [0079]. Federated learning [0209]. )
Regarding Claim 11,
‘he method according to claim 8’ (disclosed above),
And discloses, ‘wherein the AI data requirement comprises at least one of the following:
label data;
a number of label dimensions;
a label dimension;
an order of label dimensions;
a data amount of a label dimension;
a data interval of the label;
AI model input data;
a number of AI model input dimensions;
an AI model input dimension;
an order of AI model input dimensions;
a data amount of an AI model input dimension;
a collection interval of AI model input data; or
an AI model label delay.’ (AI model encompasses NN composed of a plurality of
computational units/neurons arranged in layers referred as forward propagation [0095]. In forward propagation, each layer receives an input (which may have any suitable data format, such as vector, matrix, or multidimensional array) and performs computations to generate an output [0095]. NN trained is trained to optimize the parameters (e.g., weights) of the NN includes an input data to generate output value. Adjust/update weight in the NN uses backpropagation optimized and converged in number of iterations [0096] and in Fig. 8. AI input processing function of the AI model provided by AI-management functions AIMF receive input data from the UEs and/or nodes. To enable the input data to be used to train/execution of AI model [0211]. AI model uses coding scheme includes a label AI model input [0225]. );
Regarding Claim 13,
JIANGLEI-MA discloses, ‘An information feedback method, comprising:
sending, by a second device, requirement information to a first device, wherein the requirement information is used to indicate a requirement corresponding to an artificial intelligence (AI) service; and
receiving, by the second device, feedback information of the requirement information and sent by the first device, wherein the feedback information is used to indicate whether to agree with a requirement corresponding to the requirement information.’ (Transmitting/receiving between UE-BS in Fig. 22 and [0355]. And, similar to Claim 1 disclosed above. )
Regarding Claim 14,
‘The method according to claim 13’ (disclosed above),
Similar to Claim 2 disclosed above, ‘wherein the requirement information comprises a requirement item, or a requirement item and a requirement value corresponding to the requirement item.’
Regarding Claim 15,
‘The method according to claim 14’ (disclosed above),
Similar to Claim 3 disclosed above, ‘wherein in a case that the requirement information comprises a requirement item and a requirement value corresponding to the requirement item, the feedback information comprises any one of the following:
first indication information, wherein the first indication information is used to indicate to agree with the requirement item, and a device capability meets the requirement value;
second indication information, wherein the second indication information is used to indicate to agree with the requirement item, the second indication information carries a device capability or capability difference information corresponding to the requirement item, and the capability difference information is used to indicate a difference between the device capability and the requirement value; or
third indication information, wherein the third indication information is used to indicate to reject the requirement item.’
Regarding Claim 17,
‘The method according to claim 14’ (disclosed above),
Similar to Claim 5 disclosed above, ‘wherein in a case that the requirement information comprises a requirement item, the feedback information comprises any one of the following:
fourth indication information, wherein the fourth indication information is used to indicate to reject the requirement item; or
fifth indication information, wherein the fifth indication information is used to indicate to agree with the requirement item, and the fifth indication information carries a device capability corresponding to the requirement item;
or,
wherein the requirement item comprises at least one of the following:
an AI computing power requirement, an AI algorithm requirement, or an AI data requirement.’
Regarding Claim 18,
Similar to Claim 1 method claim disclosed above, ‘A first device, comprising a processor and a memory, wherein the memory stores a program or an instruction that can be run on the processor, wherein the program or the instruction, when executed by the processor, causes the first device to perform:
receiving requirement information sent by a second device, wherein the requirement information is used to indicate a requirement corresponding to an artificial intelligence (AI) service;
and sending feedback information of the requirement information to the second device, wherein the feedback information is used to indicate whether to agree with a requirement corresponding to the requirement information.’
Regarding Claim 19,
‘The first device according to claim 18’ (disclosed above),
Similar to Claim 2 method claim disclosed above, ‘wherein the requirement information comprises a requirement item, or a requirement item and a requirement value corresponding to the requirement item.’
Regarding Claim 20,
Similar to Claim 13 method claim disclosed above, ‘A second device, comprising a processor and a memory, wherein the memory stores a program or an instruction that can be run on the processor, and the program or the instruction is executed by the processor to implement the steps of the information feedback method according to claim 13.’
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
he 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:
• Determining the scope and contents of the prior art.
• Ascertaining the differences between the prior art and the claims at issue.
• Resolving the level of ordinary skill in the pertinent art.
• 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.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable
over JIANGLEI-MA et al. in view of Jain et al. (US-20240007414-A1) hereinafter “Jain”.
Regarding Claim 4,
‘The method according to claim 3’ (disclosed above),
And discloses, ‘wherein the requirement item, the second indication information carries a first requirement item and a device capability or capability difference information corresponding to the first requirement item, and the first requirement item is a requirement item that does not match the device capability requirement item.’ (In Fig. 23 illustrates receive the indication regarding the capability of to implement AI; identifies the requirement item power consumption within the particular range. The network load and KPI within the particular range [0387]. And, determination of the power consumption exceeds a threshold; performance of the AI to a particular threshold [0270]. )
And though discloses at least one requirement item of the first requirement item, didn’t disclose, ‘at least two requirement items’ of the first requirement item.
Jain in the relevant art discloses, two requirement items of the first requirement item. To optimize requirement AI computing/computation power that is a workload applied to processor/circuitry to identify the device and generate optimization metrics and meet the requirement of the services [0243, 0249]. Fig. 5 ID5_A illustrates example of framework to optimize workload include workload platform resources are available at a given moment. Include any number and/or type of devices capable of performing workload tasks includes CPU/GPU, accelerators etc. [0219]. Weights/values represented data structures includes floating-point [0219]. The BS augmented with compute and acceleration resources directly process service workload for connected UE without communicating to BH-NWs [0092].
Therefore, a person in the ordinary skill in the art before the effective filing date of
the claim invention would have recognized that the disclosure of JIANGLEI-MA and to include with that of Jain to come up with the claim invention,
JIANGLEI-MA motive to determine the capability to implement AI in Fig. 19 and Fig. 20. And, determination of the power consumption exceeds a threshold; performance of the AI to a particular threshold and/or network load [0270]. Jain complement the motive to enhance and optimize the AI capability Include any number and/or type of devices capable of performing workload tasks includes CPU/GPU, accelerators etc. [0219]. This would to improve KPI of the service, error rate, throughput, QoS and the requirement of service JANGLEI-MA [0149-0150]. And, workload execution for the KPI and service requirement Jain [0223].
Regarding Claim 16,
‘The method according to claim 15’ (disclosed above),
Similar to Claim 4 disclosed above, ‘wherein there are at least two requirement items, the second indication information carries a first requirement item and a device capability or capability difference information corresponding to the first requirement item, and the first requirement item is a requirement item that does not match the device capability in the at least two requirement items.’
Claims 12 is rejected under 35 U.S.C. 103 as being unpatentable over JIANGLEI-MA et al. in view of Chavva et al. (US20190277957A1) hereinafter “Chavva”.
Regarding Claim 12,
‘The method according to claim 11’ (disclosed above),
And didn’t disclose, ‘wherein the label data is used to indicate at least one of the following:
a reference signal received power (RSRP) of a beam channel;
reference signal received quality (RSRQ) of a beam channel;
a signal-to-noise and interference ratio (SINR) of a beam channel;
an RSRP of a cell channel;
RSRQ of a cell channel;
an SINR of a cell channel;
a received signal strength indicator (RSSI) of a cell channel;
an impulse response of a cell channel;
a precoding matrix indicator (PMI);
a rank indicator (RI); or
a channel quality indicator (CQI);
or,
wherein the data interval of the label is used to indicate at least one of the following:
a time interval;
a frequency interval;
a delay interval;
a phase interval;
a Doppler interval; or
a beam interval;
or,
wherein the AI model label delay is used to indicate at least one of the following:
a delay between ending of inference and obtaining of label data; or
a delay between receiving of AI model input data and obtaining of label data’,
Chavva in the relevant art discloses, label data uses NN architectures fetch previously measured channel metrics, beam pattern, sensors and doppler effect. Input processed by data-preprocessing layer data normalization to estimate/predict feedback parameters CQI, RSRP, rank/layer-indicator and PMI [0163-0164]. In Fig. 10 uses hierarchical NN to reduce computational complexity of computation/prediction [0165]. In Fig. 11 illustrates DNN model for computing and predicting feedback parameters. DNN includes NN channel predictor and a NN classifier. And, dimensional NN/CNN to predict PMI/RI [0168]. In Fig. 12 illustrates cascade NN [0172]. In Fig. 13 includes measure/report channel conditions in time instances that is a time interval. To deduce a probable pattern of channel conditions. Uses doppler spread in the channel conditions [0175-0176, 0178]. In Fig. 16 illustrates beam selection and beam scheduling based on RSRP prediction of the beams uses NN model at time instance t. NN can predict optimal beam at future time instances. At time instance t+T NN prediction highest RSRP. At time instances t+2T and ‘t−T’, ‘t−2T’, and ‘t−4T’ [0187-0188].
Therefore, a person in the ordinary skill in the art before the effective filing date of
the claim invention would have recognized that the disclosure of JIANGLEI-MA and to include with that of CHAVA to come up with the claim invention,
JIANGLEI-MA motive to optimize the AI operation include label input include AI input processing function by AIMF and AI execution module to train the AI model further include preprocessing received data for normalization [0211]. Measurement/monitor data (measurement of traffic, network performance, KPIs, etc.) [0216]. Uses AI compress-CSI for the CSI feedback [0348]. Includes sensing/non-sensing capability. Doppler beam directions. CSI includes PMI, CQI, RSRP [0349]. CHAVA complements the motive specify the label input data for the NN model and input processed by data-preprocessing layer data normalization to estimate/predict feedback parameters CQI, RSRP, rank/layer-indicator and PMI [0163-0164]. The NN preprocessing, obtain inputs from the database, channel metric, beam pattern and sensor measurement. Perform operation on the input for scaling and combining [0123, 0167, 0208, 0215]. And, to increase the throughput [0141].
Conclusion
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure:
Luca et al. (US20230403606A1) “Managing resources in a radio access network”. AI/ML QoS prediction. QoS includes service performance, reliability, throughput and SLA. Enable SLA at OAM uses AI/ML [0465-0467].
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/S.A./Examiner, Art Unit 2466
/CHRISTOPHER M CRUTCHFIELD/Primary Examiner, Art Unit 2466
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