www.uspto.gov
`
`UNITED STATES DEPARTMENT OF COMMERCE
`United States Patent and TrademarkOffice
`Address: COMMISSIONER FOR PATENTS
`P.O. Box 1450
`Alexandria, Virginia 22313-1450
`
`17/598,381
`
`09/27/2021
`
`Taichi SHIMIZU
`
`NIIP-65099
`
`9881
`
`mens
`
`OR
`PEA
`PEARNE & GORDON LLP
`1801 EAST 9TH STREET
`SUITE 1200
`CLEVELAND,OH 44114-3108
`
`ZAAB, SHARAH
`
`2863
`
`PAPER NUMBER
`
`NOTIFICATION DATE
`
`DELIVERY MODE
`
`06/08/2023
`
`ELECTRONIC
`
`Please find below and/or attached an Office communication concerning this application or proceeding.
`
`Thetime period for reply, if any, is set in the attached communication.
`
`Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
`following e-mail address(es):
`
`patdocket@ pearne.com
`
`PTOL-90A (Rev. 04/07)
`
`

`

`Office Action Summary
`
`Application No.
`17/598,381
`Examiner
`SHARAH ZAAB
`
`Applicant(s)
`SHIMIZU et al.
`Art Unit
`2863
`
`AIA (FITF) Status
`Yes
`
`-- The MAILING DATEof this communication appears on the cover sheet with the correspondence address --
`Period for Reply
`
`A SHORTENED STATUTORY PERIOD FOR REPLYIS SET TO EXPIRE 3 MONTHS FROM THE MAILING
`DATE OF THIS COMMUNICATION.
`Extensions of time may be available underthe provisions of 37 CFR 1.136(a). In no event, however, may a reply betimely filed after SIX (6) MONTHSfrom the mailing
`date of this communication.
`If NO period for reply is specified above, the maximum statutory period will apply and will expire SIX (6) MONTHSfrom the mailing date of this communication.
`-
`- Failure to reply within the set or extended period for reply will, by statute, cause the application to become ABANDONED (35 U.S.C. § 133).
`Any reply received by the Office later than three months after the mailing date of this communication, evenif timely filed, may reduce any earned patent term
`adjustment. See 37 CFR 1.704(b).
`
`Status
`
`
`
`1) Responsive to communication(s) filed on 04/12/2023.
`C} A declaration(s)/affidavit(s) under 37 CFR 1.130(b) was/werefiled on
`2a)[¥) This action is FINAL.
`2b) (J This action is non-final.
`3)02 An election was madeby the applicant in responseto a restriction requirement set forth during the interview
`on
`; the restriction requirement and election have been incorporated into this action.
`4)\0) Since this application is in condition for allowance except for formal matters, prosecution as to the merits is
`closed in accordance with the practice under Exparte Quayle, 1935 C.D. 11, 453 O.G. 213.
`
`Disposition of Claims*
`1-2 and 5-6 is/are pending in the application.
`)
`Claim(s)
`5a) Of the above claim(s) ___ is/are withdrawn from consideration.
`Cj} Claim(s)
`is/are allowed.
`Claim(s) 1-2 and 5-6 is/are rejected.
`1) Claim(s)__is/are objectedto.
`Cj) Claim(s
`are subjectto restriction and/or election requirement
`S)
`* If any claims have been determined allowable, you maybeeligible to benefit from the Patent Prosecution Highway program at a
`participating intellectual property office for the corresponding application. For more information, please see
`http://Awww.uspto.gov/patents/init_events/pph/index.jsp or send an inquiry to PPHfeedback@uspto.gov.
`
`) ) ) )
`
`Application Papers
`10) The specification is objected to by the Examiner.
`11)0) The drawing(s) filedon__ is/are: a)(J accepted or b)( objected to by the Examiner.
`Applicant may not request that any objection to the drawing(s) be held in abeyance. See 37 CFR 1.85(a).
`Replacement drawing sheet(s) including the correction is required if the drawing(s) is objected to. See 37 CFR 1.121 (d).
`
`Priority under 35 U.S.C. § 119
`12)1) Acknowledgment is made of a claim for foreign priority under 35 U.S.C. § 119(a)-(d) or (f).
`Certified copies:
`c)Z None ofthe:
`b)() Some**
`a)C All
`1... Certified copies of the priority documents have been received.
`2.1) Certified copies of the priority documents have beenreceived in Application No.
`3.1.) Copies of the certified copies of the priority documents have been receivedin this National Stage
`application from the International Bureau (PCT Rule 17.2(a)).
`* See the attached detailed Office action for a list of the certified copies not received.
`
`Attachment(s)
`
`1) ([] Notice of References Cited (PTO-892)
`
`2) (J Information Disclosure Statement(s) (PTO/SB/08a and/or PTO/SB/08b)
`Paper No(s)/Mail Date
`U.S. Patent and Trademark Office
`
`3)
`
`(LJ Interview Summary (PTO-413)
`Paper No(s)/Mail Date
`4) (J Other:
`
`PTOL-326 (Rev. 11-13)
`
`Office Action Summary
`
`Part of Paper No./Mail Date 20230523
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 2
`
`DETAILED ACTION
`
`Notice of Pre-AlA 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
`
`Claims 1-6 are rejected under 35 U.S.C. 101 becausethe claimed invention is
`
`directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an
`
`abstract idea) without significantly more.
`
`Specifically, representative Claim 1 recites:
`
`“A mounted board manufacturing system that manufactures a mounted board,
`
`which is a board mounted with a component, the mounted board manufacturing system
`
`comprising: at least one component loading device that executes a component loading
`
`operation for loading the component on a board, wherein eachof the at least one
`
`component loading device includes a component supplier for supplying the component,
`
`and a suction nozzle for taking out the component from the component supplier and
`
`placing the componentonto the board; at least one component mounting line, wherein
`
`the at least one component loading device is arranged in the at least one component
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 3
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`mounting line; a rule base with which at least one machine parameter for executing
`
`the component loading operation performedbythe at least one component loading
`
`device can be calculated, wherein the at least one machine parameter is a control
`
`parameter for use in controlling the component loading device when the component
`
`loading device performs the component loading operation; an operation information
`
`aggregator that aggregates and accumulates, for each component data, results of
`
`processing executed bythe at least one component loading device, together with
`
`operation information; and an estimator that selects, as actualtraining data,
`
`component data that corresponds to an operation result that exceeds a
`
`predetermined reference, from the operation information aggregator, and
`
`estimates at least one machine parameter of a new component, using the actual
`
`training data, the rule base, and basic information of the new component, wherein
`
`the estimator: performs an estimation on the basic information of the new
`
`component using the actual training data and a Bayesian statistical model to
`
`generate a predictive distribution that is a normal distribution of machine
`
`parameters applicable to the new component; generates an output of the rule base
`
`from the predictive distribution of machine parameters applicable to the new
`
`component; calculates a posterior distribution of the machine parameters
`
`applicable to the new componentbased on the outputof the rule base; and
`
`outputs a meanof the posterior distribution calculated, as a machine parameter to be
`
`applied to the new component among the machine parameters applicable to the new
`
`component.”
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 4
`
`The claim limitations in the abstract idea have been highlighted in bold
`
`above; the remaining limitations are “additional element”.
`
`Under the Step 1 of the eligibility analysis, we determine whether the
`
`claims are to a statutory category by considering whether the claimed subject
`
`matter falls within the four statutory categories of patentable subject matter
`
`identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of
`
`matter. The above claim is considered to be in a statutory category (process).
`
`Under the Step 2A, Prong One, we consider whether the claim recites a
`
`judicial exception (abstract idea). In the above claim, the highlighted portion
`
`constitutes an abstract idea because, under a broadest reasonable interpretation,
`
`it recites limitations thatfall into/recite an abstract idea exceptions. Specifically,
`
`under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the
`
`groupings of subject matter when recited as suchin a claim limitation thatfalls
`
`into the grouping of subject matter when recited as such in a claim limitation, that
`
`covers mathematical concepts - mathematical relationships, mathematical
`
`formulas or equations, mathematical calculations and mental processes —
`
`concepts performed in the human mind including an observation, evaluation,
`
`judgement, and/or opinion.
`
`For example, steps of “a rule base with whichat least one machine
`
`parameter for executing the component loading operation can be
`
`calculated; an operation information aggregator that aggregates and
`
`accumulates, for each component data, results of processing executed by
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 5
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`the at least one component loading device, together with operation
`
`information; and an estimator that selects, as actual training data,
`
`component data that corresponds to an operation result that exceeds a
`
`predetermined reference, from the operation information aggregator, and
`
`estimates at least one machine parameter of a new component, using the
`
`actual training data, and the rule base.”are treated as belonging to the mental
`
`process grouping. This mental step represents a process that, under its broadest
`
`reasonable interpretation, covers performance ofthe limitation in the mind. That
`
`is, nothing in the claim element precludes the step from practically being
`
`performed in the mind. In the context of this claim, it encompasses the user
`
`making mental decisions (evaluation/judgement) with regards to mounting a
`
`component on a board following a particular rule.
`
`The steps of “the estimator: performs an estimation on the basic
`
`information of the new component using the actual training data and a
`
`Bayesian statistical model to generate a predictive distribution that is a
`
`normal distribution of machine parameters applicable to the new
`
`component; calculates a posterior distribution of the machine parameters
`
`applicable to the new component based onthe output of the rule base” are
`
`treated as belonging to the mathematical calculations grouping.
`
`Next, under the Step 2A, Prong Two, we consider whether the claim that
`
`recites a judicial exception is integrated into a practical application.
`
`In this step, we evaluate whether the claim recites additional elements that
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 6
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`integrate the exception into a practical application of that exception.
`
`The above claims comprise the following additional elements:
`
`e Claim 1: A mounted board manufacturing system that manufactures a
`
`mounted board, which is a board mounted with a component, the mounted
`
`board manufacturing system comprising: at least one component loading
`
`device that executes a component loading operation for loading the
`
`component on a board, wherein each of the at least one component
`
`loading device includes a component supplier for supplying the
`
`component, and a suction nozzle for taking out the component from the
`
`component supplier and placing the component onto the board; at least
`
`one component mounting line, wherein the at least one component
`
`loading device is arrangedin the at least one component mounting line;
`
`generates an output of the rule base from the predictive distribution of
`
`machine parameters applicable to the new component; and outputs a
`
`mean of the posterior distribution calculated, as a machine parameter to
`
`be applied to the new component among the machine parameters
`
`applicable to the new component.
`
`The above additional elements of at least one component loading device
`
`that executes a componentloading operation for loading the component on a
`
`board are generically recited, not meaningful, do not represent a particular
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
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`Page 7
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`machine and/or eligible transformation, they do notindicate a practical
`
`application. In addition, with regards to a step of obtaining “basic information
`
`about the new component’, the step corresponds to mere data gathering thatis
`
`recited in generality and is not meaningful- represents insignificant extra-solution
`
`activity.
`
`The newly recited limitations “wherein each of the at least one component
`
`loading device includes a component supplier for supplying the component, and
`
`a suction nozzle for taking out the component from the component supplier and
`
`placing the componentonto the board; at least one component mounting line,
`
`wherein the at least one component loading device is arrangedin the at least one
`
`component mounting line” are also recited in generality and are not meaningful.
`
`They represent insignificant extra-solution activity. According to MPEP 2106, “As
`
`explained by the Supreme Court, the addition of insignificant extra-solution
`
`activity does not amount to an inventive concept, particularly when the activity is
`
`well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198
`
`USPQ 193, 196 (1978). In Flook, the Court reasoned that "[t]he notion that post-
`
`solution activity, no matter how conventional or obvious in itself, can transform an
`
`unpatentable principle into a patentable process exalts form over substance.” In
`
`addition, these limitations are only “nominally or tangentially related to the
`
`invention’.
`
`The outputting steps of “generating an output of the rule base from the
`
`predictive distribution of machine parameters applicable to the new component
`
`and outputs a mean of the posterior distribution calculated” also represent
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 8
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`insignificant extra-solution activity. According to MPEP 2106.05(g), “Whether the
`
`limitation amounts to necessary data gathering and outputting, (i.¢e., all uses of
`
`the recited judicial exception require such data gathering or data output). See
`
`Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com,
`
`Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015)
`
`(presenting offers and gathering statistics amounted to mere data gathering).
`
`This is considered in Step 2A Prong Two and Step 2B.”.
`
`Therefore, the claims are directed to a judicial exception and require
`
`further analysis under the Step 2B.
`
`However, the above claims do not include additional elements that are
`
`sufficient to amountto significantly more than the judicial exception (Step 2B
`
`analysis) because these additional elements/steps are well-understood and
`
`conventional in the relevant art based on the prior art of record including
`
`references in the submitted IDS (9/27/2021) by the Applicant (Tan and Asakura).
`
`The independent claims, therefore, are not patent eligible.
`
`With regards to the dependent claims, claims 2-6 provide additional
`
`features/steps which are either part of an expandedabstract idea of the
`
`independent claims (additionally comprising mathematical/mental/organizing
`
`human activity process steps (Claims 2-6) or adding additional elements/steps
`
`that are not meaningful as they are recited in generality and/or not qualified as
`
`particular machine/ and/or eligible transformation and, therefore, do not reflect a
`
`practical application as well as not qualified for “significantly more” based on prior
`
`art of record (Claims 1-4).
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 9
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`Claim Rejections - 35 USC § 112
`
`The following is a quotation of 35 U.S.C. 112(b):
`
`(bo) CONCLUSION.—The specification shall conclude with one or more
`
`claims particularly pointing out and distinctly claiming the subject matter
`
`whichthe inventor or a joint inventor regards as the invention.
`
`The following is a quotation of 35 U.S.C. 112 (pre-AlA), second paragraph:
`
`The specification shall conclude with one or moreclaims particularly
`
`pointing out and distinctly claiming the subject matter which the applicant
`
`regards ashis invention.
`
`Claims 1-4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AlA),
`
`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 pre-AlA the
`
`applicant regards as the invention.
`
`With regards to Claim 1, the feature “...
`
`to be applied to the new component
`
`among the machine parameters applicable to the new component’is indefinite because
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 10
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`it is unclear what the patentable boundaries of this limitation are. This limitation
`
`describes intended use result without clear boundaries.
`
`Claim Rejections - 35 USC § 103
`
`The following is a quotation of 35 U.S.C. 103 which forms the basis for all
`
`obviousnessrejections 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 effectivefiling date of the claimed invention to a person having
`
`ordinaryskill in the art to which the claimed invention pertains.
`
`Patentability shall not be negated by the manner in which the invention
`
`was made.
`
`Claims 1-2, 4, and 5 are rejected under 35 U.S.C. 103 as being unpatentable
`
`Tan et al. (US 20180364687), hereinafter referred to as ‘Tan’ and in further view of
`
`Ishimoto et al. (US20160299499), hereinafter referred to as ‘Ishimoto’ and further in
`
`view of Heumann etal. (US7099435) hereinafter referred to as ‘Heumann’.
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 11
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`Regarding Claim 1, Tan discloses a mounted board manufacturing system
`
`that manufactures a mounted board, which is a board mounted with a
`
`component, the mounted board manufacturing system comprising (a mounting
`
`board manufacturing system including a component placing device; a library; an
`
`operation information counter; and a corrector [0003]): at least one component
`
`loading device that executes a component loading operation for loading the
`
`component on a board (The component placing device executes component placing
`
`workfor placing a component on a board [0004]); wherein each of the at least one
`
`component loading device includes a component supplier for supplying the
`
`component, and a suction nozzle for taking out the componentfrom the
`
`component supplier and placing the component ontothe board; at least one
`
`component mounting line, wherein the at least one component loading device is
`
`arrangedin the at least one component mounting line (Machine parameter 14 is a
`
`control parameter used for controlling component placing device 11 when the
`
`component placing work is executed by component placing device 11 disposed in
`
`component mounting line 10 with respect to the component defined in component data
`
`12 as a target. In machine parameter 14 as the major classification item, “nozzle
`
`setting” 14a, “speed parameter” 14b, “recognition” 14c, “suction” 14d, and “installation”
`
`14e are defined as the medium classification items [0031]; Nozzle setting” 14a is data
`
`regarding the suction nozzle used in a case where the component is sucked and held,
`
`and a “nozzle” for specifying the type of the suction nozzle capable of being selected as
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 12
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`the minorclassification item is defined. “Speed parameter” 14b is a control parameter
`
`regarding a moving speed of the suction nozzle in the work operation in which the
`
`component is picked up by the suction nozzle and is installed in the board. [0032]); ,
`
`wherein the at least one machine parameteris a control parameter for usein
`
`controlling the component loading device when the component loading device
`
`performs the componentloading operation (“Suction” 14d is a control parameter
`
`regarding the suction operation when the component is picked up by the suction nozzle
`
`from the componentsupplier. In the control parameters, “suction position X” and
`
`“suction position Y” indicating suction positions when the suction nozzle is landed on the
`
`component, and the like are defined as the minor classification items [0034]), an
`
`operation information aggregator that aggregates and accumulates, for each
`
`componentdata (Learning result storage 32 stores learning result 50 (FIG. 5B) learned
`
`by learning unit 31. Learning data set storage 33 stores learning data set 40 (FIG. 5A)
`
`for each component data 12 usedfor learning of learning unit 31 [0045]), results of
`
`processing executed bythe at least one component loading device, together with
`
`operation information (Learning data set 40 in which new pattern P is accumulated is
`
`stored in learning data set storage 33. Learning result 50 indicates a result obtained by
`
`learning the degree of influence by learning unit 31 for each component data of machine
`
`parameter 14 with respect to the score of the component placing work based on
`
`learning data set 40 created as described above [0051]); and an estimator that
`
`selects, as actual training data, componentdata that correspondsto an operation
`
`result that exceeds a predetermined reference (Componentdata corrector 30 selects
`
`the component data that is the target to be corrected based on the score of the
`
`

`

`Application/Control Number: 17/598,381
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`Page 13
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`component placing work and corrects machine parameter 14 included in the component
`
`data [0078]; The learning result is stored in learning result storage 32. Component data
`
`corrector 30 automatically corrects component data 12 in which the score of the
`
`component placing work does not reach a predetermined level based on the learning
`
`result stored in learning result storage [0058]), from the operation information
`
`aggregator, and estimates at least one machine parameter of a new component,
`
`using the actual training data and basic information of the new component
`
`(Learning data set 40 in which new pattern P is accumulated is stored in learning data
`
`set storage 33. Learning result 50 indicates a result obtained by learning the degree of
`
`influence by learning unit 31 for each component data of machine parameter 14 with
`
`respect to the score of the component placing work based on learning data set 40
`
`created as described above [0051]), wherein the estimator: performs an estimation
`
`on the basic information of the new component using the actual training data
`
`(Componentdata corrector 30 selects the componentdata that is the target to be
`
`corrected based on the score of the component placing work and corrects machine
`
`parameter 14 included in the component data [0078]; The learning result is stored in
`
`learning result storage 32. Component data corrector 30 automatically corrects
`
`component data 12 in which the score of the component placing work does not reach a
`
`predetermined level based on the learning result stored in learning result storage
`
`[0058]) and machine parameters (Learning data set 40 in which new pattern P is
`
`accumulated is stored in learning data set storage 33. Learning result 50 indicates a
`
`result obtained by learning the degreeof influence by learning unit 31 for each
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 14
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`component data of machine parameter 14 with respect to the score of the component
`
`placing work based on learning data set 40 created as described above [0051)).
`
`However, Tan does notdisclose a rule base with which at least one machine
`
`parameter for executing the component loading operation performed by the at least one
`
`component loading device can be calculated and wherein the estimator: performs an
`
`estimation on the basic information of the new component using the actual training data
`
`and a Bayesianstatistical model to generate a predictive distribution that is a normal
`
`distribution of machine parameters applicable to the new component; generates an
`
`output of the rule base from the predictive distribution of machine parameters applicable
`
`to the new component; calculates a posterior distribution of the machine parameters
`
`applicable to the new component based on the output of the rule base; and outputs a
`
`meanof the posterior distribution calculated, as a machine parameter to be applied to
`
`the new component among the machine parameters applicable to the new component.
`
`Nevertheless, Ishimoto discloses a rule base with which at least one machine
`
`parameter for executing the componentloading operation performedby theat
`
`least one component loading device can be calculated and generates an output of
`
`the rule base from the predictive distribution of machine parameters applicable to
`
`the new component(the operator who checks the physical electronic component in
`
`component mounting line 4 by using rule table 33b that is created in this manner and
`
`pattern data 33c that are defined in rule table 33b can set setting values suitable for
`
`multiple operational parameters 55 [0074)).
`
`It would have been obvious to one of ordinary skill in the art before the effective
`
`filing date of the claimed invention to modify Tan in view of Ishimoto to incorporate a
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 15
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`rule base with which at least one machine parameter for executing the component
`
`loading operation performed by the at least one component loading device can be
`
`calculated and generating an output of the rule base from the predictive distribution of
`
`machine parameters applicable to the new component for stipulating in advance a
`
`correspondencerelationship to the setting value of operational parameter 55 that
`
`corresponds to the setting value of component parameter (Ishimoto [0069]) and to
`
`manipulate operation information to interpret component loading data in a productive
`
`way.
`
`However, the combination does not disclose the estimator: performs an
`
`estimation on the basic information of the new component using the actualtraining data
`
`and a Bayesianstatistical model to generate a predictive distribution that is a normal
`
`distribution of machine parameters applicable to the new component; calculates a
`
`posterior distribution of the machine parameters applicable to the new component
`
`based on the output of the rule base; and outputs a meanof the posterior distribution
`
`calculated, as a machine parameter to be applied to the new component among the
`
`machine parameters applicable to the new component.
`
`Nevertheless, Heumann discloses the estimator: performs an estimation on
`
`the basic information of the new componentusing the actual training data and a
`
`Bayesian statistical model to generate a predictive distribution that is a normal
`
`distribution of machine parameters (The reconstruction engine 120 may be
`
`configured to generate one or moreof three different outputs. These outputs include an
`
`estimated model M.sub.EST, the posterior probability P(M|D), and/or expectation values
`
`<f.sub.i(M)> of parameters or functions of interest using Bayesian reconstruction
`
`

`

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`Art Unit: 2863
`
`Page 16
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`analysis. A classification engine 130 classifies the reconstructed or estimated object into
`
`one or more classes based on the output(s) from the reconstruction engine 120, Col. 7,
`
`Lines 13-20; Bayesian estimation combines prior information, P(M), and measured
`
`results in an optimal fashion to arrive at these estimates, Col. 8, Lines 54-56);
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`calculates a posterior distribution of the machine parameters applicable to the
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`new componentbased on the output of the rule base and the posterior
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`distribution calculated (The posterior probability for any particular model can be
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`calculated using Bayes' rule, Col. 8, Lines 56-57).
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`It would have been obvious to one of ordinary skill in the art before the effective
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`filing date of the claimed invention to modify Tan and Ishimoto, in view of Heumann to
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`incorporate the estimator that performs an estimation on the basic information of the
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`new component using the actual training data and a Bayesianstatistical model to
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`generate a predictive distribution that is a normal distribution of machine parameters
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`applicable to the new component to the operation result that exceeds the predetermined
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`reference for classification purposes, but can behelpful in initial investigation,
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`particularly in selecting quantities whose expectations are to be estimated (Heumann,
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`Col. 9, Lines 24-26) and to manipulate machine parameters to determine new
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`components.
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`It would have been obvious to one of ordinary skill in the art before the effective
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`filing date of the claimed invention to modify Tan and Ishimoto, in view of Heumann to
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`generates an output of the rule base from the predictive distribution of machine
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`parameters applicable to the new component for classification purposes, but can be
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`helpful in initial investigation, particularly in selecting quantities whose expectations are
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`

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`Application/Control Number: 17/598,381
`Art Unit: 2863
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`Page 17
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`to be estimated (Heumann, Col. 9, Lines 24-26) and to manipulate machine parameters
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`to determine new components.
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`It would have been obvious to one of ordinary skill in the art before the effective
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`filing date of the claimed invention to modify Tan and Ishimoto, in view of Heumann to
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`calculates a posterior distribution of the machine parameters applicable to the new
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`component basedon the output of the rule base; and outputs a meanof the posterior
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`distribution calculated, as a machine parameter to be applied to the new component
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`among the machine parameters applicable to the new component for classification
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`purposes, but can be helpful in initial investigation, particularly in selecting quantities
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`whose expectations are to be estimated (Heumann, Col. 9, Lines 24-26) and to
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`manipulate machine parameters to determine new components.
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`Regarding Claim 2, Tan, Ishimoto, and Heumann disclose the claimed invention
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`discussed in claim 1.
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`Tan discloses one machine parameter (as discussed above).
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`However, Tan does notdisclose the rule base includes two or more rules that do
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`not match and that producedifferent outputs, for calculating the at least one machine
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`parameter of the new component.
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`Nevertheless, Ishimoto discloses the rule base includes two or more rules
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`that do not match andthat producedifferent outputs, for calculating the at least
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`one machine parameter of the new component (Rule table 33b is table data for
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`stipulating in advance a correspondencerelationship to the setting value of operational
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`

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`Application/Control Number: 17/598,381
`Art Unit: 2863
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`Page 18
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`parameter 55 that corresponds to the setting value of component parameter 52. In an
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`example thatis illustrated in FIG. 6B, a setting value of each of “the number of times
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`that a suction operation is re-performed” 57a, “feeding operation setting of a joint portion
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`that joins carrier tapes” 57e, and “the number of times that a component recognition
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`operation is re-performed” 57b, as operational parameter 55, is caused to belinked to
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`two setting values (low price and high price) of price information 53a that is component
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`parameter 52, and is set to be rule table 33b ([0069)).
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`It would have been obvious to one of ordinary skill in the art before the effective
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`filing date of the claimed invention to modify Tan and Ishimoto, in view of Heumann to
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`incorporate the rule base includes two or more rules that do not match and that produce
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`different outputs, for calculating the at least one machine parameter of the new
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`component for stipulating in advance a correspondencerelationship to the setting value
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`of operational parameter 55 that corresponds to the setting value of component
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`parameter (Ishimoto [0069]) and to manipulate operation information to interpret
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`component loading data in a productive way.
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`Regarding Claim 5, Tan, Ishimoto, and Heumann disclose the claimed invention
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`discussed in claim 2.
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`Tan discloses wherein features of the component data that correspondsto
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`the operation result that exceeds the predetermined reference (as discussed
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`above) and machine learning (Learning result 50 which is created as described above
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`is referred to when machine parameter 14 is corrected by component data corrector 30.
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`Application/Control Number: 17/598,381
`Art Unit: 2863
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`Page 19
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`That is, for the component data specified by “component n” 51, in a case whereit is
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`determined that any of th