DETAILED ACTION
Claim Objections
Claim 10 is objected to because of the following informalities: The phrase “toward the edge surfaces” in lines 8-9 should be recited as --toward edge surfaces--. Appropriate correction is required.
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, 3, 6 and 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fujikura et al. (US Pub No. 2023/0303347 A1) in view of Gunji (US Pub No. 2020/0230981 A1).
Regarding Claim 1, Fujikura et al. discloses
a loading table (22) on which a plurality of sheets are loaded;
a flotation air supply unit (30, 50) that causes the sheets to float by supplying air toward edge surfaces of the plurality of sheets which are loaded on the loading table;
a suction conveying unit (40) that suctions and conveys a topmost sheet from among the plurality of sheets which are caused to float by the supply of air;
an image capturing unit (70/72, [0069]) that captures an image of a flotation state of the plurality of sheets which are caused to float by the supply of air;
a control parameter adjusting unit (61D, Fig. 8) that adjusts control parameters of the flotation air supply unit based on information on the flotation state obtained from the image captured by the image capturing unit ([0080]-[0084]); and
a control unit (61E, Fig. 8) that controls the flotation air supply unit based on the control parameters adjusted by the control parameter adjusting unit ([0085]), wherein
Fujikura et al. discloses adjusting control parameters based on flotation state information obtained from the captured image when the sheet feeding was performed as per above but does not disclose a learning model.
Gunji discloses a learning model obtained by prior reinforcement learning ([0064], [0069]) and trained by rewards ([0069]), for the purpose of precisely correcting a setting value.
It would have been obvious to one of ordinary skill in the art before the effective
filing date to modify the invention of Fujikura et al. by including the learning model as disclosed by Gunji, for the purpose of precisely correcting a setting value. It is noted that since Fujikura et al. already adjusts the control parameters using the claimed relationship, the learning model would merely be applied to the same relationship and that the rewards would be based on the flotation state/sheet feeding (i.e. the same input as used by Fujikura et al.) and that Fujikura et al. already performs adjustment based on whether flotation state information obtained from the captured image satisfies a predetermined condition (i.e. [0081]) and wherein the flotation state information includes a scattered state of sheets (i.e. defined in Applicant’s specification as sheet spacing in [0063], wherein Fujikura et al. discloses sheet spacing between P1 and P2 in [0081]).
Regarding Claim 3, Fujikura et al. discloses
the adjusting of the control parameters by the control parameter adjusting unit is further based on a result of the sheet feeding ([0080]).
Regarding Claim 6, Fujikura et al. does not disclose additional rewards.
Gunji discloses
the learning model is further trained by additional rewards which are determined based on a result of the sheet feeding (i.e. +1 and -1, [0077], in each trial t, [0091]), for the purpose of precisely correcting a setting value.
It would have been obvious to one of ordinary skill in the art before the effective
filing date to modify the invention of Fujikura et al. by including the additional rewards as disclosed by Gunji, for the purpose of precisely correcting a setting value.
Regarding Claim 8, Fujikura et al. discloses
causing a plurality of sheets of paper, loaded on a loading table (22), to float by supplying air (via 30, 50) toward edge surfaces of the plurality of sheets of paper;
suctioning and conveying (via 40) a topmost sheet of the plurality of floating sheets of paper;
capturing an image (via 70/72, [0069]) of a flotation state of the plurality of sheets of paper by supplying the air;
adjusting control parameters (via 61D, Fig. 8) of a flotation air supply unit that supplies the air based on information on the flotation state obtained from the captured image; and
controlling (61E, Fig. 8) the flotation air supply unit based on the adjusted control parameters, wherein
Fujikura et al. discloses adjusting control parameters based on flotation state information obtained from the captured image when the sheet feeding was performed as per above but does not disclose a learning model.
Gunji discloses a learning model obtained by prior reinforcement learning ([0064], [0069]) and trained by rewards ([0069]), for the purpose of precisely correcting a setting value.
It would have been obvious to one of ordinary skill in the art before the effective
filing date to modify the invention of Fujikura et al. by including the learning model as disclosed by Gunji, for the purpose of precisely correcting a setting value. It is noted that since Fujikura et al. already adjusts the control parameters using the claimed relationship, the learning model would merely be applied to the same relationship and that the rewards would be based on the flotation state/sheet feeding (i.e. the same input as used by Fujikura et al.) and that Fujikura et al. already performs adjustment based on whether flotation state information obtained from the captured image satisfies a predetermined condition (i.e. [0081]) and wherein the flotation state information includes a scattered state of sheets (i.e. defined in Applicant’s specification as sheet spacing in [0063], wherein Fujikura et al. discloses sheet spacing between P1 and P2 in [0081]).
Regarding Claim 9, Fujikura et al. discloses
A non-transitory computer-readable recording medium (63) containing a sheet supply program (i.e. 63A) that causes a computer to execute the steps of:
causing a plurality of sheets of paper, loaded on a loading table (22), to float by supplying air (via 30, 50) toward edge surfaces of the plurality of sheets of paper;
suctioning and conveying (via 40) a topmost sheet of the plurality of floating sheets of paper;
capturing an image (via 70/72, [0069]) of a flotation state of the plurality of sheets of paper by supplying the air;
adjusting control parameters (via 61D, Fig. 8) of a flotation air supply unit that supplies the air based on information on the flotation state obtained from the captured image; and
controlling (61E, Fig. 8) the flotation air supply unit based on the adjusted control parameters, wherein
Fujikura et al. discloses adjusting control parameters based on flotation state information obtained from the captured image when the sheet feeding was performed as per above but does not disclose a learning model.
Gunji discloses a learning model obtained by prior reinforcement learning ([0064], [0069]) and trained by rewards ([0069]), for the purpose of precisely correcting a setting value.
It would have been obvious to one of ordinary skill in the art before the effective
filing date to modify the invention of Fujikura et al. by including the learning model as disclosed by Gunji, for the purpose of precisely correcting a setting value. It is noted that since Fujikura et al. already adjusts the control parameters using the claimed relationship, the learning model would merely be applied to the same relationship and that the rewards would be based on the flotation state/sheet feeding (i.e. the same input as used by Fujikura et al.) and that Fujikura et al. already performs adjustment based on whether flotation state information obtained from the captured image satisfies a predetermined condition (i.e. [0081]) and wherein the flotation state information includes a scattered state of sheets (i.e. defined in Applicant’s specification as sheet spacing in [0063], wherein Fujikura et al. discloses sheet spacing between P1 and P2 in [0081]).
Regarding Claim 10, Fujikura et al. discloses
a loading table (22) on which a plurality of sheets are loaded;
a flotation air supply unit (30, 50) that causes the sheets to float by supplying air toward the edge surfaces of the plurality of sheets which are loaded on the loading table;
a suction conveying unit (40) that suctions and conveys a topmost sheet from among the plurality of sheets which are caused to float by the supply of air;
an image capturing unit (70/72, [0069]) that captures an image of a flotation state of the plurality of sheets which are caused to float by the supply of air;
a control parameter adjusting unit (61D, Fig. 8) that adjusts control parameters of the flotation air supply unit based on information on the flotation state obtained from the image captured by the image capturing unit ([0080]-[0084]); and
a control unit (61E, Fig. 8) that controls the flotation air supply unit based on the control parameters adjusted by the control parameter adjusting unit ([0085]);
Fujikura et al. discloses adjusting control parameters based on flotation state information obtained from the captured image when the sheet feeding was performed as per above but does not disclose a learning model.
Gunji discloses a learning model obtained by prior reinforcement learning ([0064], [0069]) and trained by rewards ([0069]), for the purpose of precisely correcting a setting value.
It would have been obvious to one of ordinary skill in the art before the effective
filing date to modify the invention of Fujikura et al. by including the learning model as disclosed by Gunji, for the purpose of precisely correcting a setting value. It is noted that since Fujikura et al. already adjusts the control parameters using the claimed relationship, the learning model would merely be applied to the same relationship and that the rewards would be based on the flotation state/sheet feeding (i.e. the same input as used by Fujikura et al.) and that Fujikura et al. already performs adjustment based on whether flotation state information obtained from the captured image satisfies a predetermined condition (i.e. [0081]) and wherein the flotation state information includes a scattered state of sheets (i.e. defined in Applicant’s specification as sheet spacing in [0063], wherein Fujikura et al. discloses sheet spacing between P1 and P2 in [0081]).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fujikura et al. (US Pub No. 2023/0303347 A1) in view of Gunji (US Pub No. 2020/0230981 A1) in view of Takahashi et al. (US Patent No. 12,515,904 B2).
Regarding Claim 4, Fujikura et al. discloses adjusting the control parameters based on a result of the sheet feeding ([0080]) but not explicitly based on sheet quality information.
Takahashi et al. discloses doing so based on sheet quality information (lines 19- 43 of Column 4), for the purpose of reducing co-feeding. It is noted that as per [0065] of Applicant's specification, "sheet quality" refers to sheet size and type.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the invention of Fujikura et al. by including the sheet quality information basis as disclosed by Takahashi et al., for the purpose of reducing co- feeding.
Allowable Subject Matter
Claim 7 is 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: None of the prior art of record shows terminating adjustment of a control parameter when positive rewards are obtained consecutively by the same control parameters (Claim 7).
Response to Arguments
In response to Applicant’s argument that “In particular, Fujikura fails to disclose any learning model or reinforcement learning feature. Instead, Fujikura merely discloses supplying air to a sheet, detecting the position of the sheet, and modifying the feeding operation of sheets in response to the detected position. Accordingly, the Fujikura sheet feeding apparatus does not make adjustments based on a model that was developed through prior performances of the apparatus, in which the apparatus operated under predetermined control parameters and was provided a reward when predetermined conditions were satisfied. Additionally, Fujikura fails to disclose adjusting or modifying the sheet feeding operation based on a number of floating sheets, a shifted state of sheets, or a scattered state of sheets. Moreover, the Office Action concedes that Fujikura does not disclose a learning model.”, it is noted that Fujikura discloses making adjustments based on a scattered state of sheets ([0081]) and that Gunji is relied upon for teaching the learning model.
In response to Applicant’s argument that “In contrast, Gunji merely discloses a machine learning model that outputs a setting value of a transportation mechanism based on a print length, as well as the temperature and humidity around the printer. See e.g., abstract and paragraph [0066] of Gunji. Moreover, the Gunji printer does not supply air to float paper for loading. Thus, the Gunji machine learning model does not consider flotation state information, let alone a number of floating sheets, a shifted state of sheets, or a scattered state of sheets, for performing adjustments.”, it is noted that Gunji is relied upon merely for the concept of prior reinforcement learning in a learning model and that Fujikura already discloses the required flotation and feeding device as well as the claimed relationship, particularly a scattered state of sheets ([0081]).
Applicant's arguments filed 4/27/26 have been fully considered but they are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Araki et al. (US Pub No. 2025/0100823) discloses a sheet feeder having air separation, that uses a learning model to determine abnormalities in the camera itself.
Maruyama (US Pub No. 2022/0179348) lacks a camera or sheet floating but discloses generic parameter learning.
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.
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/PRASAD V GOKHALE/Primary Examiner, Art Unit 3653 May 19, 2026
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