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 .
Detailed Action
This office action is in response to the amended listing of claims filed on April 9, 2025. Claim 6 is now cancelled and claims 1-5 and 7-8 are currently amended. Claims 1-5 and 7-8 are currently pending.
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 1-5 and 7-8 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.
The terms “estimation” and “estimate” in claims 1-5 and 7-8 are relative terms which renders the claim indefinite. The terms “estimation” and “estimate” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what amount of calculation or assessment qualifies under the claimed “estimation” and “estimate”. As such, the claims are deemed indefinite.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5 and 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) estimation of change in data. This judicial exception is not integrated into a practical application because the claimed invention can be interpreted to be nothing more than a mental process. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely input and estimate/calculate data.
A. Claim 1 is analyzed according to SME flowchart. Claim 1 is directed to an apparatus for estimating a range of changes. According to limitation 1, of claim 1 comprises input position information. This data is used in calculating the amount of change in position from a baseline. Claim 1 also comprises estimating statistical values related to communication quality, based on the position information, using a learned model. The apparatus of claim 1 fits one of the statutory categories and claim 1 qualifies as apparatus/machine.
B. When considering step 2A, prong 1, claim 1 recites an abstract idea. The type of abstract is categorized under a mental process. In essence, claim 1 takes input data in the form of a position information and determines the amount of change/drift to estimate a quality statistic. With respect to dependent claims 2-4, further details are provided into more processes performed on the data.
C. The additional features of claim 1 are considered in step 2A, prong 2. The additional features of claim 1 are considered for determination of whether the claims are integrated in a practical application. The first addition feature is the use of a learned model to perform the estimation. The learned model is used as a tool for implementing the functions claimed not to make an improvement to computer technology. The second additional limitation is the estimation/calculation yielded, which can in broadest reasonable interpretation be a mental process. These are not an improvement in computer technology. Thus, claim 1 fails to recite additional features to integrate into a practical application in a technological environment or field of use. Dependent claims 2-4 recite further ways to yield the estimation, such as using upper and lower limits to establish a desired range. However, these features fail to integrate claims 2-4 into a practical application.
D. The additional limitations of claim 1 are considered in Step 2B. Claim 1 does not recite additional limitations the amount to more than the judicial exception. One additional limitation is the estimation which is a well understood approach to using data and limits to assess acceptable deviations. The estimation does not integrate the invention in a technological field or a particular machine. Using a learned model to achieve the estimation is not an integration into a technical solution. Another additional limitation is the calculated statistical value which also does not amount to significant more than the abstract idea. Dependent claims 2-4, recite further assessments on the data and fail to provide an improvement to computer network technology.
Claims 5 and 7-8 represent a method and computer-readable medium forms of claim 1. They suffer the same issues highlighted above and as such, are similarly rejected.
Claims 1-5 and 7-8 are therefore rejected under 101.
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 1-5 and 7-8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Maughan et al (US PGPub No: 2017/0372232)
With regard to claims 1, 5, and 7, Maughan teaches an estimation apparatus that estimates a range of changes in communication quality at a certain position, the estimation apparatus comprising: a processor; and a memory storing program instructions that cause the processor to: input position information indicating the position (Maughan teaches the quality analysis module capturing data, such as initialization data to be used in detecting and/or predicting drift; see paragraph 89-90, Maughan.);
and estimate one or more statistical values related to the communication quality, from the position information, by using a learned model, and estimate the range of changes based on the one or more statistical values (Maughan teaches a quality analysis module determining/estimating a baseline variation of data, called a drift; see paragraph 95, Maughan. The quality analysis module provides a drift summary with a drift statistic (statistical value related to communication quality); see paragraph 99, Maughan. An estimate is determined of the impact of the drift; see paragraph 99, Maughan. Maughan also teaches setting a baseline variation, such as set a threshold for detecting drift higher than 3%; see paragraph 95, Maughan. The drift is detected by the quality analysis module by monitoring metrics from the machine learning model; see paragraphs 94 and 96, Maughan).
With regards to claim 2, Maughan teaches the estimation apparatus wherein the one or more statistical values are a mean value and a variance, and the program instructions cause the processor to estimate the range of changes by using a distribution having the mean value and the variance (Maughan supports the quality analysis module determining/calculating a mean and variance; see paragraph 51, Maughan).
With regards to claim 3, Maughan teaches the estimation apparatus wherein the program instructions cause the processor to estimate an upper limit value and a lower limit value corresponding to a desired range in the distribution, as the range of changes (Maughan teaches setting a baseline variation, such as set a threshold for detecting drift higher than 3%; see paragraph 95, Maughan).
With regard to claims 4 and 8, Maughan teaches a learning apparatus that learns a model used to estimate a range of changes in communication quality at a certain position, the learning apparatus comprising: a processor; and a memory storing program instructions that cause the processor to: calculate one or more statistical values for each of a plurality of positions, from measurement data of communication quality at each of the positions (Maughan teaches a quality analysis module determining/estimating a baseline variation of data, called a drift; see paragraph 95, Maughan. The quality analysis module provides a drift summary with a drift statistic (statistical value related to communication quality); see paragraph 99, Maughan. An estimate is determined of the impact of the drift; see paragraph 99, Maughan. Maughan also teaches setting a baseline variation, such as set a threshold for detecting drift higher than 3%; see paragraph 95, Maughan. The drift is detected by the quality analysis module by monitoring metrics from the machine learning model; see paragraphs 94 and 96, Maughan);
and learn the model by using position information of each of the positions and the one or more statistical values as learning data (Maughan teaches retraining the machine learning in response to the drift; see paragraph 100, Maughan. Again, the quality analysis module provides a drift summary with a drift statistic (statistical value related to communication quality); see paragraph 99, Maughan).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AZIZUL Q CHOUDHURY whose telephone number is (571)272-3909. The examiner can normally be reached M-F.
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/AZIZUL CHOUDHURY/Primary Examiner, Art Unit 2455
