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
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
1. Applicant's arguments been fully considered as follows:
(a) Applicant argues that the amendment “L is a positive integer greater than 1” is supported in the wording of the original Claim 1 itself.
Examiner respectfully disagrees.
The original Claim 1 recites: “performing, by a terminal, a target task by using L models respectively and obtaining a first result output by the L models, wherein L is a positive integer;”
Presumably, Applicant believes that “models” is sufficient to show that the original claim limited L to being a positive integer greater than 1. However, this is not the case.
First, it should be understood that “L” is an undefined positive integer, and under standard grammar when a variable is undefined it is treated within the plural tense. For example, if you say “the computer received X bits of data,” you are not saying that the computer received at least more than 1 bit of data, rather you are saying the computer received some undefined bits of data. This is a well-understood grammatical practice. As such, in order for the original claim to limit the variable “L” as a positive integer greater than 1, the claim would require more support for this limitation, and none exist explicitly.
Second, the original claim even fails to implicitly support “L” as being greater than 1.
First, when reading through the claims, there is no performative or functional issue when L = 1. The claim can be plainly understood as performing a task using 1 model, and obtaining a result output using 1 model, where 1 is a positive integer, and performing… sending the first result to a network-side device.
Second, as the claims define “L models” passively, i.e. the claims do not determine what value L is. As such, reading the claims broadly (i.e. L models = 1 model) is more appropriate as doing so would read a functionality to the claim that is not explicitly stated.
Third, the original claim only explicitly requires a first result output by the L models. This further supports that only a single model is necessary as the claim does not explicitly account for any corresponding plurality of output respective to the models, but rather only concerns itself with a singular result output which would support the reading that claim covers a single model. (Note: a second result is only recited via a Markush group and can be read out of the claims)
(b) Applicant argues that the prior art does not teach Feature B.
Examiner respectfully disagrees.
Applicant concedes that Nagalapatti teaches the “discriminator 120 receives real data samples 102 from the real data 101 and generated data samples 112 created by generator 110 as inputs.” As such, Examiner notes that the generated data samples 112 comprise a first result of performing a first task. (Nagalapatti, FIG. 2, step 202, wherein additional data samples are generated by a generator 110)
Examiner therefore respectfully disagrees.
(c) Applicant argues that the prior art does not teach Feature C.
Applicant’s argument has been 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, Specifically Zhou et al. (US 20240160196 A1).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
2. Claims 1, 3-16, and 18-22 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “wherein L is a positive integer greater than L.”
However, Examiner has not found explicit support in the specification, not does Examiner find Applicant’s argument that the original claims support the amendment compelling. As noted, it is standard grammatical convention for an undefined variable to be treated in the plural tense, even if the variable could be singular. Further, given that the claims materially do not require or meaningfully suggest the necessity of a plurality of models, Examiner finds the original claim as not supporting the claim amendment.
Claims 3-16, and 18-22 are rejected for the same reasons as Claim 1.
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 Nagalapatti et al. (US 20230177110 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)
determining, by the terminal, the L models, wherein 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; and determining, by the terminal, the L models based on the configuration information 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;
configuring, by the terminal, the L models based on at least one way of autonomously determining and informing the network-side device, protocol pre-definition, or higher-layer pre-configuration; and
selecting, by the terminal, the L models from a model pool based on target information, (¶0069, wherein the selection is based on prediction accuracy information) wherein the model pool comprises K models, K being greater than or equal to L, and K being a positive integer. (¶0066-¶0069, wherein the two or more models are a subset of a model pool, wherein the model pool K is greater than the selection of L models)
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; 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, Nagalapatti teaches
performing, by the terminal, (Nagalapatti, FIG. 1, ¶0051, a generative adversarial network, GAN, comprising generator 110) a target task by using the L models respectively, wherein L is a positive integer (Nagalapatti, FIG. 2, step 202, ¶0028, wherein a task comprises training a machine learning model, using the one model) and obtaining a first result output by the L models; (Nagalapatti, ¶0028, obtaining additional data samples) and
performing, by the terminal, any one of the following operations: (Examiner notes that only one of the following alternative recitations needs to be taught)
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; (Nagalapatti, FIG. 1, ¶0015, sending additional data sample, i.e. a first result, to a network-side device, i.e. discriminator 120) 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 the teachings of Nagalapatti in order to more efficiently manage data between network-side devices.
Claim 3 Zhou in view of Nagalapatti 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 Nagalapatti 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 Nagalapatti 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 Nagalapatti 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 Nagalapatti 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 Nagalapatti 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 Nagalapatti 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 Nagalapatti 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 Nagalapatti teaches Claim 1, and further teaches wherein the method further comprises:
receiving, by the terminal, eleventh information sent by the network-side device, (Nagalapatti, ¶0015, receiving classification information from the discriminator, i.e. 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 Nagalapatti as described for Claim 1.
Claim 18 Zhou in view of Nagalapatti teaches Claim 16, and further teaches wherein the method further comprises:
determining, by the network-side device, the M models; (Nagalapatti, ¶0012, training the machine learning model, i.e. determining the model)
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, (Nagalapatti, FIG. 1, ¶0016, determining the model to the discriminator 120) 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 Nagalapatti as described for Claim 1.
Claim 20 is taught by Zhou in view of Nagalapatti as described for Claim 16.
Claim 21 Zhou in view of Nagalapatti 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 Nagalapatti 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 Nagalapatti et al. (US 20230177110 A1) and Khare et al. (US 20230404038 A1).
Claim 6 Zhou in view of Nagalapatti 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 Nagalapatti to incorporate the data utilized in Khare in order to provide better data analysis models and predictions.
Claim 8 Zhou in view of Nagalapatti 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 Nagalapatti to incorporate the data utilized in Khare in order to provide better data analysis models and predictions.
Claim 9 Zhou in view of Nagalapatti 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 Nagalapatti to incorporate the data utilized in Khare in order to provide better data analysis models and predictions.
Claim 10 Zhou in view of Nagalapatti 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 Nagalapatti to incorporate the data utilized in Khare in order to provide better data analysis models and predictions.
Conclusion
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Follansbee can be reached at (571) 272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHRISTOPHER P CADORNA/Examiner, Art Unit 2444
/JOHN A FOLLANSBEE/Supervisory Patent Examiner, Art Unit 2444
Prosecution Insights