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 .
Claim Rejections - 35 USC § 101
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.
2. Claims 19 and 20 are rejected under 35 U.S.C. 101 because 35 U.S.C. 101 reads as follows: the claimed invention is directed to non-statutory subject matter. Claim 19 does not fall within at least one of the four categories of patent eligible subject matter because it’s directed to a medium which could be interpreted as a “signal” which is not one of the four categories of invention and therefore non-statutory. It is suggested that the applicant amend claim 19 by inserting “non-transitory” prior to “computer-readable medium”. Claim 20 is rejected since its dependent upon claim 19.
Claim Rejections - 35 USC § 103
3. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
4. Claims 1, 2, 7, 9-11, 16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Srikrishnan et al US 2023/0417870 A1 in view of Halder et al US 2024/0275637 A1.
5. Consider claims 10 and 19. Srikrishnan et al teaches an apparatus and computer readable medium (624) for calibrating a radio frequency (RF) device (600 in fig 6), comprising: a memory (624); and at least one processor (602) coupled to the memory (624) and configured to: receive calibration data comprising RF measurements at a plurality of frequency points for a band and calibration index (CID) combination (fig 4, where the calibration index reads on look up table 624) ; select a subset (reads on representative selected in 624C) of the plurality of frequency points as input frequency points; and train an artificial intelligence (AI) model using the calibration data (0031). Srikrishnan et al fails to teach wherein to train the AI model, the at least one processor is further configured to: input RF measurements at the input frequency points to the AI model; generate predicted RF measurements for remaining frequency points of the plurality of frequency points using the AI model; and compare the predicted RF measurements to actual RF measurements in the calibration data to determine prediction errors. However, from the same field of endeavor, Halder et al teaches such (see 0036 for actual and predicted data, see 0037 for output error correction). It would have been obvious, before the effective date, to substitute Halder et al AI training for that taught by Srikrishnan et al in order to calibrate his radio more efficiently by saving memory space. Method claim 1 is rejected for the same reasons as apparatus claim 10, since the recited elements would perform the claimed steps.
6. Regarding claims 2, 11 and 20. Halder et al teaches (fig 2) wherein the input frequency points (measure F1) are fewer than the remaining frequency points (predicted F2).
7. Regarding claims 9 and 18. Halder et al teaches (0035) wherein: the RF measurements are path loss measurements for multiple gain modes (reads on different frequency bands).
8. Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Srikrishnan et al US 2023/0417870 A1 in view of Halder et al US 2024/0275637 A1 further in view of Jiang CN-116248202.
9. Regarding claims 7 and 16. The combination of Srikrishnan et al and Halder et al fail to explicitly state wherein the AI model comprises: one or more convolutional neural network layers; a flatten layer; and one or more fully connected layers. However, Jiang teaches that this type of AI structure is well known Convolutional Neural Network (CNN), where the convolution layer reads on the input layer, the flatten layer reads on the hidden layer and the connected layer reads on the output layer. It would have been obvious substitution over the AI models taught by the combination to allow faster processing of the calibration data.
Claim Objections
10. Claims 3-6, 8, 12-15 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 3 and 12. The prior art of record fails to teach or make obvious wherein the training further comprises: determining whether the prediction errors meet an error threshold; and when the prediction errors meet the error threshold, using the trained AI model to predict RF measurements for the remaining frequency points based on RF measurements at only the input frequency points. Claims 4 and 13 are objected to as allowable since they depend upon claims 3 and 12 respectively.
Regarding claims 5 and 14. The prior art of record fails to teach or make obvious wherein the training further comprises: converting floating point calibration data values to integer values by multiplying by a scaling factor; training the AI model using the integer values; and converting predicted values from the AI model back to floating point values by dividing by the scaling factor. Claims 6 and 15 are objected to as allowable since they depend upon claims 5 and 14 respectively.
Regarding claims 8 and 17. The prior art of record fails to teach or make obvious wherein the training comprises: dividing the calibration data into training data, validation data, and test data according to predetermined ratios; training the AI model using the training data; validating the AI model using the validation data; and testing accuracy of the AI model using the test data.
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cao CN-117377061-A teaches a method and a system for calibrating radio-frequency signal intensity, which are used for selecting corresponding calibration algorithm according to the type of the radio-frequency signal so as to meet the calibration requirement aiming at different radio-frequency signal intensities. The method comprises: collecting intensity values of multiple radio frequency signals, wherein each radio frequency signal comprises at least one intensity value; classifying and judging the collected multiple intensity values, determining the type of the radio frequency signal corresponding to each intensity value; using the calibration algorithm corresponding to the type of the radio frequency signal to calibrate the multiple intensity values to obtain the intensity calibration values of the multiple radio frequency signals; determining the positioning information according to the intensity calibration value.
Mitra et al teaches a core model to the second RF transceiver, the core model having been trained based on a training process comprising training an artificial intelligence model for a first RF transceiver, based on first data that is measured for the first RF transceiver. The method can further comprise applying, by the system, transfer learning on the core model at the second RF transceiver based on second data that is measured for the second RF transceiver, to produce a trained model. The method can further comprise calibrating, by the system, the second RF transceiver based on an output of the trained model to produce a calibrated second RF transceiver.
Albert et al US 2022/0053345 A1 teaches a core network entity (CNE) can predict received signal strength values for base station (BS). In response to a triggering event, the CNE fetches information on BS configuration parameters, including at least one of: a BS class, a BS location, a BS height, an orientation of the BS, a BS antenna pattern, and topographical details surrounding the BS. The BS obtains, processes and forwards to the CNE, measurement reports created by a user equipment (UE) including a signal strength value and a location of the UE. The CNE pools the measurement reports based on the BS class and, in response to another triggering event, recalibrates signal strength prediction tools, which can predict received signal strength values from the BS to a location in a vicinity of the BSs.
Conclusion
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CURTIS A KUNTZ whose telephone number is (571)272-7499. The examiner can normally be reached on M-Th from 530am to 330pm and Fri from 530am to 10am.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew D Anderson, can be reached at telephone number 5712724177. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CURTIS A KUNTZ/Primary examiner, Art Unit 2646
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