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 .
Election/Restrictions
Applicant’s election without traverse of Group I (claims 1-12) in the reply filed on 05/04/2026 is acknowledged.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/07/2024 and 07/15/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
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 1-5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Begg (US Pat. 6,249,128) in view of KANNAMPALLI (US PGPUB 2023/0111796).
Regarding claim 1, Begg teaches a system comprising: a tester (100) configured to test a device under test (DUT) (106) (as shown in fig. 1-3); a connection setup (200) that is connectable to, and disconnectable from, the DUT (as shown in fig. 2-3); wherein the tester (100) is configured to transmit radio frequency (RF) signals (using RF source 112) over the connection setup (200) and to capture reflected signals (250) from the connection setup (200), the reflected signals being based on the RF signals (as disclosed in col. 3, lines 17-60); and one or more processing devices (108) (as disclosed in col. 3, lines 8-16) configured to determine a quality of a connection between the test system (100) and the DUT (106) based on the reflected signals (as shown in fig. 3, where adjustment is made to compensate for the return loss of the reflected signals).
Begg fails to specifically teach one or more processing devices configured to use a trained machine learning model. However, KANNAMPALLI teaches one or more processing devices configured to use a trained machine learning model (as shown in fig. 2-3 and disclosed for example in para. 0041).
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to combine and have one or more processing devices configured to use a trained machine learning model as taught by KANNAMPALLI with the invention of Beff in order to improve predictive accuracy (KANNAMPALLI para. 0042).
Regarding claim 2, the combination of Begg and KANNAMPALLI teaches the limitations of claim 1, in addition, Begg teaches wherein the tester (100) is configured to capture first data when the connection setup (200) is disconnected from the DUT (106), the first data being based on first ones of the reflected signals (steps 312-320, as shown in fig. 2-3); and wherein the tester (100) is configured to capture second data when the connection setup (200) is connected to the DUT (106), the second data being based on second ones of the reflected signals (steps 324-334, as shown in fig. 2-3).
Regarding claim 3, the combination of Begg and KANNAMPALLI teaches the limitations of claim 2, in addition, Begg teaches wherein the second ones of the reflected signals are each associated with a respective return loss, the respective return loss including a return loss contribution from the DUT (106) (step 326, as shown in fig. 3 and disclosed in col. 6, line 66 through col. 7, line 5).
Regarding claim 4, the combination of Begg and KANNAMPALLI teaches the limitations of claim 3, in addition, Begg teaches wherein determining the quality of the connection comprises determining a return loss of the connection setup (200) when the connection setup (200) is connected to the DUT (106) minus the return loss contribution from the DUT (106) (steps 328 and 330, as shown in fig. 3).
Regarding claim 5, the combination of Begg and KANNAMPALLI teaches the limitations of claim 3, in addition, Begg wherein the one or more processing devices (108) are configured to process data based on the reflected signals in the first data and the second data to remove a return loss contribution from the tester (100) (as shown in fig. 1-3 and disclosed in col. 3, lines 3-60).
Regarding claim 19, the combination of Begg and KANNAMPALLI teaches the limitations of claim 1.
Begg fails to specifically teach wherein the machine learning model comprises one or more of a neural network model or a large language model. However, KANNAMPALLI teaches wherein the machine learning model comprises one or more of a neural network model or a large language model (as disclosed in para. 0006).
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to combine and have the machine learning model comprise one or more of a neural network model or a large language model as taught by KANNAMPALLI with the invention of Beff in order to improve predictive accuracy (KANNAMPALLI para. 0042).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Begg (US Pat. 6,249,128) and KANNAMPALLI (US PGPUB 2023/0111796) as applied to claim 1 above, and further in view of Absher et al. (US PGPUB 2018/0074096).
Regarding claim 10, the combination of Begg and KANNAMPALLI teaches the limitations of claim 1.
The combination of Begg and KANNAMPALLI fails to specifically teach wherein the one or more processing devices are configured to use a classifier machine learning model to identify a type of the DUT; and wherein the classifier machine learning model selects the trained machine learning model based in the type of the DUT. However, Absher et al. teaches wherein the one or more processing devices (212) are configured to use a classifier machine learning model (333 and 335) to identify a type of the DUT (waveforms from device); and wherein the classifier machine learning model selects the trained machine learning model based in the type of the DUT (the one corresponding to the measured waveform, as disclosed in para. 0025 for example).
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to combine and have the one or more processing devices configured to use a classifier machine learning model to identify a type of the DUT; and wherein the classifier machine learning model selects the trained machine learning model based in the type of the DUT as taught by Absher et al. with the invention of the combination of Begg and KANNAMPALLI in order to save testing time and improve measurement accuracy.
Allowable Subject Matter
Claims 6-9, 11-12 and 18 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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 6-9, the prior art fails to specifically teach a system comprising: wherein, following processing, the one or more processing devices are configured to resample data based on the reflected signals in the first data and the second data, in combination with all the limitations of the claims.
Regarding claims 11-12, the prior art fails to specifically teach a system comprising: wherein the one or more processing devices are configured to use a classifier machine learning model to identify a type of the DUT; wherein the classifier machine learning model selects a second trained machine learning model based on the type of the DUT for determining electrical characteristics of a connection including the connection setup to the DUT; and wherein the one or more processing devices are configured to execute the second trained machine learning model to determine the electrical characteristics of the connection, in combination with all the limitations of the claims.
Regarding claim 18, the prior art fails to specifically teach a system comprising: wherein the machine learning model comprises one or more machine learning models and is configured also to classify the DUT and to characterize an electrical connection including the connection setup to the DUT, in combination with all the limitations of the claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERTO VELEZ whose telephone number is (571)272-8597. The examiner can normally be reached Mon-Fri 5:30am-3:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Huy Phan can be reached at (571)272-7924. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERTO VELEZ/Primary Examiner, Art Unit 2858
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