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 Note
1. Examiner notes that the claims have been interpreted as a series “Markush” claims, i.e. interpreted as a list of alternatives (for example, performing, by the terminal, any one of the following operations). When interpreting a Markush claim, only one alternative needs to be considered for the whole of the claim to be rejected.
Response to Arguments
2. Applicant's arguments been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument, Obeidi et al. (US 20230385275 A1).
Claim Rejections - 35 USC § 103
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.
3. Claims 1, 3-5, 7, 11-16, and 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US 20240160196 A1) in view of Obeidi et al. (US 20230385275 A1).
Claim 1 Zhou teaches a communication network prediction method, comprising:
determining, by a terminal, L models, wherein L is a positive integer greater than L; (FIG. 4, S113, ¶0069, determining two or more models) the determining, by the terminal, the L models, comprises any one of the following:
receiving, by the terminal, first information sent by the network-side device, wherein the first information comprises configuration information of the L models; (FIG. 4, S112, ¶0067, receiving first information comprising prediction accuracy information, wherein prediction accuracy information comprises configuration information) and determining, by the terminal, the L models based on the configuration information of the L models; (FIG. 4, S113, ¶0069, determining two or more models based on configuration information, for example, prediction accuracy, of the L models)
sending, by the terminal, request information to the network-side device, wherein the request information is used for requesting the network-side device to configure the models; receiving, by the terminal, the first information sent by the network-side device, wherein the first information comprises configuration information of the L models; and determining, by the terminal, the L models based on the configuration information of the L models; (Does not need to be taught, See note about Markush claims) and
autonomously configuring, by the terminal, the L models and informing the network-side device, configuring, by the terminal based on protocol pre-definition, the L models, and/or configuring, by the terminal based on higher-layer pre-configuration, the L models. (Does not need to be taught, See note about Markush claim)
However, Zhou does not explicitly teach performing, by the terminal, a target task by using the L models respectively, and obtaining a first result output by the L models, wherein L is a positive integer greater than 1; and the first result comprises a fusion result of L output results; and
performing, by the terminal, any one of the following operations:
determining, by the terminal, a prediction result of the target task based on the first result;
sending, by the terminal, the first result to a network-side device; and
receiving, by the terminal, a second result sent by the network-side device; and
determining, by the terminal, a prediction result of the target task based on the first result and the second result, wherein the second result is obtained by the network-side device by performing the target task using M models respectively, M being a positive integer.
From a related technology, Obeidi teaches performing, by a terminal, a target task by using the L models respectively, (FIG. 2, step 202, ¶0032, performing a natural language query 202; ¶0037, using subqueries) and obtaining a first result output by the L models, (FIG. 2, step 208, generating a result for each of the sub-queries) wherein L is a positive integer greater than 1; (FIG. 2, step 208, ¶0037, generating sub-queries, wherein a sub-query comprises a model, and wherein this would be a positive plural integer) and the first result comprises a fusion result of L output results; (FIG. 2, step 210, ¶0039, combining the results of each sub-query) and
performing, by the terminal, any one of the following operations:
determining, by the terminal, a prediction result of the target task based on the first result; (FIG. 2, ¶0039, wherein the prediction result comprises the fusion result, i.e. the combined result of each of the sub-queries)
sending, by the terminal, the first result to a network-side device; and
receiving, by the terminal, a second result sent by the network-side device; and
determining, by the terminal, a prediction result of the target task based on the first result and the second result, wherein the second result is obtained by the network-side device by performing the target task using M models respectively, M being a positive integer.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhou to incorporate managing multiple models when processing tasks as described by Obeidi in order to more effectively utilize network resources.
Claim 3 Zhou in view of Obeidi teaches Claim 1, and further teaches wherein the first information (Examiner notes that “the first information” is recited within an alternative embodiment of Claim 1, not being considered, and therefore does not have patentable weight) comprises at least one of the following:
model quantity information; model type information; model identification (ID) information; model priority information; model attribute information; model precision information; model error information; model computing capability requirement information; model storage capacity requirement information; model feature information; adaptive environment information; processing delay information; fusion manner information for output results of models; model life cycle information; measurement quantity information input by various types of models; or output information of various types of models.
Claim 4 Zhou in view of Obeidi teaches Claim 3, and further teaches wherein the measurement quantity information input (Examiner notes that “the first information” is recited within Claim 3, which is dependent upon an alternative embodiment of Claim 1 that is not being considered, and therefore does not have patentable weight) by various types of models comprises at least one of the following:
channel state information; received signal information; historical state information; or sensor information; and/or the output information of various types of models comprises at least one of the following: direct target parameter; intermediate quantity; or soft information of the direct target parameter or the intermediate quantity.
Claim 5 Zhou in view of Obeidi teaches Claim 1, and further teaches wherein the request information (Examiner notes that “the request information” is recited within an alternate embodiment of Claim 1, not being considered, and therefore does not have patentable weight) comprises second information, wherein the second information comprises at least one of the following:
mobility information of the terminal; environment information of the terminal; precision requirement information; or task information.
Claim 7 Zhou in view of Obeidi teaches Claim 1, and further teaches wherein after the receiving, by the terminal, first information sent by the network-side device, (Examiner notes that “the first information” is recited within an alternate embodiment of Claim 1, not being considered, and therefore does not have patentable weight) the method further comprises:
sending, by the terminal, feedback information to the network-side device, wherein the feedback information is used to indicate whether the terminal supports a model corresponding to the model configuration information.
Claim 11 Zhou in view of Obeidi teaches Claim 1, and further teaches wherein the determining, by the terminal, a prediction result of the target task based on the first result comprises: (Examiner notes that “a prediction result” is recited within an alternate embodiment of Claim 1, not being considered, and therefore does not have patentable weight)
determining, by the terminal, the prediction result of the target task based on a first fusion manner and the first result; or the determining, by the terminal, a prediction result of the target task based on the first result and the second result comprises: determining, by the terminal, the prediction result of the target task based on a first fusion manner, the first result, and the second result.
Claim 12 Zhou in view of Obeidi teaches Claim 11, and further teaches wherein the first fusion manner comprises: (Examiner notes that “the first fusion manner” is recited as part of Claim 11, and is based on alternate embodiment of Claim 1 that is not being considered, and therefore does not have patentable weight)
performing filtering on an output result of each model to obtain a prediction result; and/or determining a prediction result based on a weight and an output result of each model; and/or the method further comprises: determining, by the terminal, the first fusion manner based on target information.
Claim 13 Zhou in view of Obeidi teaches Claim 1, and further teaches wherein the target information comprises at least one of the following:
statistical information of an output result of each model; (Zhou, ¶0069, model prediction accuracy) statistical information of output results of a plurality of models; (Zhou, ¶0069, model prediction accuracy) model error information of each model; (Zhou, ¶0069, model prediction accuracy)
mobility information of the terminal;
environment information of the terminal;
precision requirement information;
task information;
measurement quantity information input by various types of models;
model priority information;
measurement information of a reference signal of a current terminal;
model configuration information of a reference terminal; or
measurement information of a reference signal of a reference terminal.
Claim 14 Zhou in view of Obeidi teaches Claim 1, and further teaches wherein the method further comprises:
making, by the terminal, a decision associated with the target task based on the prediction result of the target task; (Examiner notes that “the prediction result” is recited within an alternate embodiment of Claim 1, not being considered, and therefore does not have patentable weight)
and/or the first result comprises: L output results respectively output by the L models; or a fusion result of the L output results.
Claim 15 Zhou in view of Obeidi teaches Claim 1, and further teaches wherein the method further comprises:
receiving, by the terminal, eleventh information sent by the network-side device, wherein the eleventh information is used to indicate a prediction mode based on the first result and the second result; (Examiner notes that this is an intended use statement that does not have patentable weight)
wherein the prediction mode comprises any one of the following: determining, by the network-side device, the prediction result of the target task based on the first result and the second result; and determining, by the terminal, the prediction result of the target task based on the first result and the second result. (Examiner notes that the prediction mode is an element of the intended use statement, and therefore like the intended use, the mode itself would not have any patentable weight)
Claim 16 is taught by Zhou in view of Obeidi as described for Claim 1.
Claim 18 Zhou in view of Obeidi teaches Claim 16, and further teaches wherein the method further comprises:
determining, by the network-side device, the M models; (Obeidi, FIG. 2, step 208, ¶0037, generating a result for each of the sub-queries)
wherein the determining, by the network-side device, the M models comprises:
configuring, by the network-side device, the M models based on at least one way of autonomously determining, (Obeidi, FIG. 2, step 208, ¶0037, generating a result for each of the sub-queries) protocol pre-definition, or pre-configuration;
or
selecting, by the network-side device, the M models from a model pool based on target information, wherein the model pool comprises P models, P being greater than or equal to M, and P being a positive integer.
Claim 19 is taught by Zhou in view of Obeidi as described for Claim 1.
Claim 20 is taught by Zhou in view of Obeidi as described for Claim 16.
Claim 21 Zhou in view of Obeidi teaches Claim 19, and further teaches wherein the first information (Examiner notes that “the first information” is recited within an alternative embodiment of Claim 2, not being considered, and therefore does not have patentable weight) comprises at least one of the following:
model quantity information; model type information; model identification (ID) information; model priority information; model attribute information; model precision information; model error information; model computing capability requirement information; model storage capacity requirement information; model feature information; adaptive environment information; processing delay information; fusion manner information for output results of models; model life cycle information; measurement quantity information input by various types of models; or output information of various types of models.
Claim 22 Zhou in view of Obeidi teaches Claim 21, and further teaches wherein the measurement quantity information input (Examiner notes that “the first information” is recited within an alternative embodiment of Claim 1, not being considered, and therefore does not have patentable weight) by various types of models comprises at least one of the following:
channel state information; received signal information; historical state information; or sensor information; and/or the output information of various types of models comprises at least one of the following: direct target parameter; intermediate quantity; or soft information of the direct target parameter or the intermediate quantity.
4. Claims 6 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US 20240160196 A1) in view of Obeidi et al. (US 20230385275 A1) and Khare et al. (US 20230404038 A1).
Claim 6 Zhou in view of Obeidi teaches Claim 2, but does not explicitly teach sending, by the terminal, third information to the network-side device, wherein the third information is used to indicate capability information of the terminal; (Examiner notes that this is an intended use statement and does not have patentable weight)
wherein the third information comprises at least one of the following: sensor configuration information of the terminal; a data type available to the terminal; or hardware capability information of the terminal.
From a related technology, Khare teaches information comprising at least one of the following:
sensor configuration information of the terminal; (Khare, ¶0047, sensor model information)
a data type available to the terminal; or
hardware capability information of the terminal.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhou in view of Obeidi to incorporate the data utilized in Khare in order to provide better data analysis models and predictions.
Claim 8 Zhou in view of Obeidi teaches Claim 1, but does not explicitly teach sending, by the terminal, fifth information to the network-side device, wherein the fifth information comprises at least one of the following: mobility information of the terminal; environment information of the terminal; precision requirement information; or task information.
From a related technology, Khare teaches information comprising at least one of the following:
mobility information of the terminal;
environment information of the terminal; (Khare, ¶0047, sensor positioning, i.e. environment information of the device)
precision requirement information; or
task information.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhou in view of Obeidi to incorporate the data utilized in Khare in order to provide better data analysis models and predictions.
Claim 9 Zhou in view of Obeidi teaches Claim 1, but does not explicitly teach sending, by the terminal, sixth information to the network-side device, wherein the sixth information is used to indicate capability information of the terminal; (Examiner notes that this is an intended use statement that does not have patentable weight)
wherein the sixth information comprises at least one of the following: sensor configuration information of the terminal; a data type available to the terminal; or hardware capability information of the terminal.
From a related technology, Khare teaches information comprises at least one of the following:
sensor configuration information of the terminal; (Khare, ¶0047, sensor model information)
a data type available to the terminal; or
hardware capability information of the terminal.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhou in view of Obeidi to incorporate the data utilized in Khare in order to provide better data analysis models and predictions.
Claim 10 Zhou in view of Obeidi teaches Claim 1, andbut does not explicitrly teach seventh information comprises at least one of the following:
input requirements for models; model precision information; processing delay information; or model life cycle information.
From a related technology, Khare teaches information comprises at least one of the following:
input requirements for models; (Khare, ¶0047, sampling rates, wherein the sample are input requirements for the models)
model precision information;
processing delay information; or
model life cycle information.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhou in view of Obeidi to incorporate the data utilized in Khare in order to provide better data analysis models and predictions.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER PALACA CADORNA whose telephone number is (571)270-0584. The examiner can normally be reached M-F 10:00-7:00.
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/CHRISTOPHER P CADORNA/Examiner, Art Unit 2444
/JOHN A FOLLANSBEE/Supervisory Patent Examiner, Art Unit 2444
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