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 .
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.
Claim(s) 1, 2 and 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dhar et al. (Dhar, WO 2023/240296) in view of Suzuki (EP3509009).
Referring to Claim 1, Dhar teaches an antenna (Fig. 2 #200; [0033]) configured to receive a radio wave transmitted from a wireless tag (Fig. 1 #100; [0029]); a detection unit configured to determine a received signal strength and a phase of the radio wave received by the antenna ([0030], [0033], [0038] and [0056-0058]); and a processor ([0049]) configured to: receive position data indicating a position of the antenna in conjunction with the received signal strength and the phase of the radio signal received by the antenna at the position, calculate in-phase data and quadrature data for the wireless tag based on the received signal strength and the phase of the radio signal at a plurality of positions of the antenna ([0030], [0033], [0038] and [0056-0058]), input the in-phase data and the quadrature data for the wireless tag to a learned model correlating in-phase data and quadrature data to positions of wireless tags, and estimate a position of the wireless tag using output of the learned model ([0049] teaches the system may use machine learning models on the appliance and processor coupled to the readers; and [0058] teaches correlation processes being performed by the reader or processor which would involve the machine learning described in [0049]), but does not explicitly disclose nor limit a drive unit configured to move the antenna through different positions along a fixed path.
However, Suzuki teaches a drive unit configured to move the antenna through different positions along a fixed path; [0035].
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Dhar with a drive unit as taught by Suzuki so as to create a mobile tag reading apparatus and help improve the accuracy of the readings.
Referring to Claim 2, Dhar as modified by Suzuki teaches wherein the learned model is a model generated by machine learning; [0017] of Dhar.
Referring to Claims 7, Dhar as modified by Suzuki teaches wherein the processor is configured to identify whether the wireless tag is within a first region or outside the first region when estimating the position of the wireless tag using the learned model; [0012] of Suzuki.
Referring to Claims 8 and 9, Dhar as modified by Suzuki teaches wherein the processor is configured to generate the learned model from learning data including in-phase data and quadrature data for a plurality of wireless tags at known positions; [0016-0017] and [0050] of Dhar.
Allowable Subject Matter
Claims 3-6 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.
Response to Arguments
Applicant's arguments filed 23 December 2025 have been fully considered but they are not persuasive. Applicant’s arguments with respect to Claim 1, are not found to be persuasive, and the Examiner maintains the rejection as previously applied. The citations have been added as they were originally in the rejected claim 10 and clarifying statements have been added, there has been no change of grounds or rationale in the current rejection. The Applicant argues that neither reference concerns nor suggest “a mobile tag reading apparatus.” While the tag readers of Dhar are not moved in the drawings the specification does not limit them to being stationary, as movement is discussed with respect to the antennas and the specification discloses the use of handheld tag readers which would implicitly be mobile in the disclosed environment and the description of how they are used in the specification; see [0036] and [0080]. The concept of mobility is disclosed in the Dhar reference. Suzuki teaches moving the antennas of the wireless tag reading apparatus using a driving device. The limitation that Suzuki was used to teach states: “…a drive unit configured to move the antenna through different positions along a fixed path…” The Applicant states that in Suzuki an antenna moves along a fixed path adjacent to a fixed reading zone. The Examiner agrees with the statement and interprets this action to be the same moving the antenna through different positions along a fixed path as claimed. The even if the reading zone is fixed there is defined length of the zone along that fixed path. The starting position and the ending position along that fixed path are different positions so it is not clear what the Applicant is attempting to say is different. The Examiner maintains this portion of the rejection as it clearly taught that combination will result in a mobile tag reading apparatus.
Applicant further argues that Dhar does not actually teach or suggest either the input to a learned model nor that any machine learning model might the one that is correlating. The Examiner disagrees with this assessment of the prior art and points to areas of the specification that were previously cited in the previous rejection. [0049] teaches that the tag-locating system may use machine learning models executing on the appliance or another processor coupled to the readers. [0058] teaches that correlation processes can be performed by the reader or processor for each IQ pair. The disclosure of both cited paragraphs are interpreted as the machine learning model that is operating on the processor as taught in [0049] would be involved in the correlation process being performed by the processor as taught in [0058]. [0050-0055] also disclose how the machine learning model is incorporated with the processor operations. The Examiner maintains this portion of the rejection as well and maintains that the claim is properly rejection based on the combined references above.
Conclusion
THIS ACTION IS MADE FINAL. 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 WHITNEY T MOORE whose telephone number is (571)270-3338. The examiner can normally be reached Monday-Friday from 7am-4pm.
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/WHITNEY MOORE/Primary Examiner, Art Unit 3646