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
`
`prerems
`
`OR
`PEA
`PEARNE & GORDON LLP
`1801 EAST 9TH STREET
`SUITE 1200
`CLEVELAND,OH 44114-3108
`
`ZAAB, SHARAH
`
`2863
`
`PAPER NUMBER
`
`NOTIFICATION DATE
`
`DELIVERY MODE
`
`01/27/2023
`
`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 09/27/2021.
`C} A declaration(s)/affidavit(s) under 37 CFR 1.130(b) was/werefiled on
`
`2a)() This action is FINAL. 2b)¥)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*
`1-6 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) 1-6 is/are rejected.
`S)
`) © Claim(s)___is/are objected to.
`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) The drawing(s) filed on 09/27/2021 is/are: a)[¥) 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) Acknowledgment is made of a claim for foreign priority under 35 U.S.C. § 119(a)-(d) or (f).
`Certified copies:
`_—_c)L) None ofthe:
`b)L) Some**
`a)¥) All
`1.4) Certified copies of the priority documents have been received.
`2.2 Certified copies of the priority documents have been received in Application No.
`3.4.) 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)
`
`Information Disclosure Statement(s) (PTO/SB/08a and/or PTO/SB/08b)
`2)
`Paper No(s)/Mail Date 09/27/2021.
`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 20230116A
`
`

`

`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 componentloading
`
`operation for loading the component on a board; a rule base with which at least one
`
`machine parameter for executing the componentloading operation performed by
`
`the at least one component loading device can be calculated; an operation
`
`information aggregator that aggregates and accumulates, for each component
`
`data, results of processing executed by the at least one component loading
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 3
`
`device, together with operation information; and an estimator that selects, as
`
`actual training data, component data that correspondsto 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
`
`actualtraining data, the rule base, and basic information of the new component”.
`
`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 such in 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 —
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 4
`
`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
`
`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 processthat, 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
`
`performedin 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 particularrule.
`
`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
`
`integrate the exception into a practical application of that exception.
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 5
`
`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;
`
`(obtaining) basic information about 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
`
`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.
`
`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
`
`

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`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 6
`
`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 expandedabstractidea 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).
`
`Claim Rejections - 35 USC § 103
`
`The following is a quotation of 35 U.S.C. 103 which forms the basis for all
`
`obviousnessrejections setforth 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.
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 7
`
`Patentability shall not be negated by the manner in which the invention
`
`was made.
`
`Claims 1-2 and 5 are rejected under 35 U.S.C. 103 as being unpatentable Tan et
`
`al. (US 20180364687), hereinafter referred to as ‘Tan’ andin further view of Ishimoto et
`
`al. (US20160299499), hereinafter referred to as ‘Ishimoto’.
`
`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]); an operation information
`
`aggregator that aggregates and accumulates, for each component data (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 used for learning of learning unit 31 [0045]), results of processing executed
`
`by the 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
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 8
`
`the degree ofinfluence 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 scoreof 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]), 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)).
`
`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.
`
`Nevertheless, Ishimoto discloses a rule base with which at least one machine
`
`parameter for executing the componentloading operation performedbythe at
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 9
`
`least one component loading device can be calculated (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
`
`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 for stipulating in advance a correspondence relationship 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.
`
`Regarding Claim 2, Tan and Ishimoto disclose the claimed invention discussed in
`
`claim 1.
`
`Tan discloses one machine parameter (as discussed above).
`
`However, Tan does notdisclose the rule base includes two or more rules that do
`
`not match and that producedifferent outputs, for calculating the at least one machine
`
`parameter of the new component.
`
`Nevertheless, Ishimoto discloses the rule base includes two or more rules
`
`that do not match andthat producedifferent outputs, for calculating the at least
`
`one machine parameter of the new component (Rule table 33b is table data for
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 10
`
`stipulating in advance a correspondencerelationship to the setting value of operational
`
`parameter 55 that corresponds to the setting value of component parameter 52. In an
`
`example thatis illustrated in FIG. 6B, a setting value of each of “the number of times
`
`that a suction operation is re-performed” 57a, “feeding operation setting of a joint portion
`
`that joins carrier tapes” 57e, and “the number of times that a component recognition
`
`operation is re-performed” 57b, as operational parameter 55, is caused to belinked to
`
`two setting values (low price and high price) of price information 53a that is component
`
`parameter 52, and is set to be rule table 33b ([0069]).
`
`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 the
`
`rule base includes two or more rules that do not match and that producedifferent
`
`outputs, for calculating the at least one machine parameter of 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.
`
`Regarding Claim 5, Tan and Ishimoto disclose the claimed invention discussed in
`
`claim 2.
`
`Tan discloses wherein features of the component data that corresponds to
`
`the operation result that exceeds the predetermined reference (as discussed
`
`above) and machine learning (Learning result 50 which is created as described above
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 11
`
`is referred to when machine parameter 14 is corrected by component data corrector 30.
`
`That is, for the component data specified by “component n” 51, in a case whereit is
`
`determined that any of the score items configuring “score” 42 is defective, an item of
`
`which the degree ofinfluence is high with respect to the defect can be estimated by
`
`referring to learning result 50 based on the order of the degree of influence. When
`
`machine parameter 14 of component data 12 is correction component data corrector 30
`
`selects as a target to be corrected component data in the order from the item having the
`
`highest degree of influence of machine parameter 14. Moreover, as the data contents of
`
`learning result 50, various types of data other than the order of the degree of influence
`
`illustrated here can be used aslong asthey include contents serving as guidelines for
`
`the component data correction [0053)).
`
`However, Tan does not disclose the operation result that exceeds the
`
`predetermined reference difference between the rule base and machine learning.
`
`Nevertheless, Ishimoto discloses rule base (as discussed above).
`
`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 the
`
`rule basefor stipulating in advance a correspondencerelationship to the setting value of
`
`operational parameter 55 that correspondsto the setting value of component parameter
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 12
`
`(Ishimoto [0069]) and to manipulate operation information to interpret component
`
`loading data in a productive way.
`
`However, the combination does not disclose the operation result that exceeds
`
`the predetermined reference difference between the rule base and machine learning.
`
`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 the
`
`operation result that exceeds the predetermined reference difference between the rule
`
`base and machine learning 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.
`
`Claims 3-4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over
`
`Tan and Ishimoto, and further in view of Heumann et al. (US7099435) hereinafter
`
`referred to as ‘Heumann’.
`
`Regarding Claim 3, Tan and Ishimoto disclose the claimed invention discussed in
`
`claim 1.
`
`Tan discloses machine parameters applicable to the new component (as
`
`discussed above).
`
`However, does not disclose the estimator: performs an estimation on the basic
`
`information of the new component using a Bayesianstatistical model to generate a
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 13
`
`predictive distribution of machine parameters applicable to the new component;
`
`calculates a posterior distribution of the machine parameters applicable to the new
`
`component basedona fact that an output of the rule base is generated from a
`
`distribution having, as parameters, the 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.
`
`Nevertheless, Ishimoto discloses the rule base (as discussed above).
`
`However, the combination does not disclose the estimator: performs an
`
`estimation on the basic information of the new component using a Bayesianstatistical
`
`model to generate a predictive distribution of machine parameters applicable to the new
`
`component; calculates a posterior distribution of the machine parameters applicable to
`
`the new component based onafact that an output of the rule base is generated from a
`
`distribution having, as parameters, the 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.
`
`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 the
`
`rule basefor stipulating in advance a correspondencerelationship to the setting value of
`
`operational parameter 55 that correspondsto the setting value of component parameter
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 14
`
`(Ishimoto [0069]) and to manipulate operation information to interpret component
`
`loading data in a productive way.
`
`Nevertheless, Heumann discloses the estimator: performs an estimation on
`
`the basic information of the new componentusing a Bayesian statistical model to
`
`generate a predictive distribution of machine parameters applicable to the new
`
`component); calculates a posterior distribution (The reconstruction engine 120 may
`
`be configured to generate one or more of three different outputs. These outputs include
`
`an estimated model M.sub.EST, the posterior probability P(M|D), and/or expectation
`
`values &lt:f.sub.i(M)> of parameters or functions of interest using Bayesian
`
`reconstruction 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); and outputs a mean of the posterior
`
`distribution calculated (The posterior probability for any particular model can be
`
`calculated using Bayes'rule, Col. 8, Lines 56-57).
`
`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 and Ishimoto, in view of Heumann to
`
`incorporate the estimator which performs an estimation on the basic information of the
`
`new component using a Bayesianstatistical model to generate a 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
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 15
`
`manipulate operation information to interpret component loading data in a productive
`
`way.
`
`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 and Ishimoto, in view of Heumann to
`
`calculate a posterior distribution of the machine parameters applicable to the new
`
`component basedona fact that an output of the rule base is generated from a
`
`distribution having, as parameters, the machine parameters applicable to the new
`
`component for classification purposes, but can be helpful in initial investigation,
`
`particularly in selecting quantities whose expectations are to be estimated (Heumann,
`
`Col. 9, Lines 24-26) and to manipulate machine parameters to determine new
`
`components.
`
`Regarding Claim 4, Tan and Ishimoto disclose the claimed invention discussed in
`
`claim 1.
`
`Tan discloses machine parameters applicable to the new component(as
`
`discussed above) and basic information of a component and a corresponding
`
`machine parameter value that are included in the componentdata that
`
`correspondsto the operation result that exceeds the predetermined reference, to
`
`generate a predictive distribution of machine parameters applicable to the new
`
`component (as discussed above).
`
`However, the combination doesnot disclose the estimator: performs an
`
`estimation on the basic information of the new component using a Bayesianstatistical
`
`

`

`Application/Control Number: 17/598,381
`Art Unit: 2863
`
`Page 16
`
`model that has been learned using, as learning data; calculates a posterior distribution
`
`of the machine parameters applicable to the new component based onafact that
`
`outputs of the two or morerules that do not match are generated from a distribution
`
`having, aS parameters, the 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.
`
`Nevertheless, Heumann discloses the estimator: performs an estimation on
`
`the basic information using a Bayesian statistical model that has been learned
`
`using, as learning data (as discussed above), to generate a predictive distribution
`
`(as discussed above) of machine parameters applicable to the new component;
`
`calculates a posterior distribution of the based on a fact that outputs of the two
`
`or morerules that do not match are generated from a distribution having, as
`
`parameters; and outputs a meanof the posterior distribution calculated (as
`
`discussed above).
`
`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 and Ishimoto, in view of Heumann to
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`perform an estimation on the basic information of the new component using a Bayesian
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`statistical model that has been learned using, as learning data, basic information of a
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`component and a corresponding machine parameter value that are included in the
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`component data that corresponds to the operation result that exceeds the
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`predetermined reference for classification purposes, but can be helpful in initial
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`investigation, particularly in selecting quantities whose expectations are to be estimated
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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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`(Heumann, Col. 9, Lines 24-26) and to manipulate machine parameters to determine
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`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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`generate a predictive distribution of machine parameters applicable to the new
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`component for classification purposes, but can be helpful 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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`calculate a posterior distribution of the machine parameters applicable to the new
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`component basedonafact that outputs of the two or morerules that do not match are
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`generated from a distribution having, as parameters, the machine parameters
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`applicable to the new component; and outputs a meanof the posterior distribution
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`calculated, as a machine parameter to be applied to the new component among the
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`machine parameters applicable to the new component for classification purposes, but
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`can be helpful in initial investigation, particularly in selecting quantities whose
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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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`expectations are to be estimated (Heumann, Col. 9, Lines 24-26) and to manipulate
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`machine parameters to determine new components.
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`Regarding Claim 6, Tan and Ishimoto disclose the claimed invention discussedin
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`claim 2.
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`Tan discloses a machine parameter that is actually used for executing the
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`component loading operation performed bythe at least one componentloading
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`device (as discussed above).
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`However, Tan does notdisclose an interface section that displays: a machine
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`parameter that is output by the estimator and is to be applied to the new component.
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`Nevertheless, Ishimoto discloses an interface section that displays: a
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`machine parameter that is output and is to be applied to the new component
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`(Input unit 36 performs operation instruction or data input. These data include a setting
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`value of component parameter 52 and a setting value of operational parameter 55. That
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`is, the setting value of component parameter 52 that includes at least one of component
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`information 53 relating to electronic component P and tape information 54 relating to
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`carrier tape 23 that is supplied by tape feeder 15 is input into input unit 36. A screen that
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`provides a guide at the time of the input by input unit 36, a screen for performing
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`processing by rule table creation processor 30a and operational parameter setting
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`

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`Application/Control Number: 17/598,381
`Art Unit: 2863
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`Page 19
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`processor 30b, or a screen on whicha result of performing the processing is displayed
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`is displayed on display unit 37 [0066]).
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`However, the combination does not disclose the estimator.
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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, in view of Ishimoto to incorporate an
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`interface section that displays: a machine parameter that is output and is to be applied
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`to the new component for a screen on which a result of performing the processing is
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`displayed (Ishimoto [0066]) and provide user with an immediate output.
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`Nevertheless, Heumann discloses the estimator (as discussed above).
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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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`the estimator for estimating an object from a set of projections D of an object under
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`

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`Application/Control Number: 17/598,381
`Art Unit: 2863
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`Page 20
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`inspection on a device under test (Heumann, Col. 7, Lines 2-4) and to manipulate
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`machine parameters to determine new components.
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`Conclusion
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`The prior art made of record and not relied upon is considered pertinent to
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`applicant's disclosure.
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`Frederick Discenzo (US20040267395) discloses mounting system with
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`component mounting.
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`Angel Sustaeta (US20090204245) discloses componentloading with shared data
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`diagnostics.
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`JJ Baier (CN101241358) discloses training data using statistical models.
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`Any inquiry concerning this communication or earlier co