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 .
Response to Amendment
The amendment filed on 06/11/2026 has been entered. Claims 1-13, 16-22 remain pending in the application. Claims 1, 3, 5, 7, 9-10, 12, 16-19 are withdrawn. Applicant’s amendments to the Specification, Drawings, and Claims have overcome each and every objection and 112(b), 101 rejections previously set forth in the Office Action mailed on 03/11/2026. The terminal disclaimer submitted by the applicant on 06/11/2026 against US application 18006230 is approved and hence double patenting rejection previously set forth in the Office Action mailed on 03/11/2026 is withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2, 4, 6, 8, 11, 13, 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nagahama et al., US 20200030880 (hereafter Nagahama), and further in view of Takagi et al., US 20220083700 (hereafter Takagi), Zhang et al., US 20220161344 (hereafter Zhang).
Regarding claim 2,
A building plan assistance method for assisting creation of a building plan(Title)
indicating each of a material of a built object, a welding condition of weld beads, and a welding track when the built object is manufactured by additive manufacturing, in a desired shape, (Fig. 3 in Nagahama)
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Fig. 3 in Nagahama
….the method comprising: respectively generating, by a processor, a first mathematical model and a second mathematical model, (65b and 65c in Fig. 2. Paragraph [44] teaches “The machine learning apparatus 65 (a) generates a first learning model to a seventh learning model for determining the manufacturing conditions, (b) generates an eighth learning model to a tenth learning model for estimating the shaped article statuses, (c) determines the manufacturing conditions by using the first learning model to the seventh learning model, and (d) estimates the statuses of the shaped article W by using the eighth learning model to the tenth learning model.” )
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Fig. 2 in Nagahama
the first mathematical model relating input information to intermediate output information, (6th learning model in Fig. 7)
the input information including items of the material of the built object, the welding condition, and the welding track, (Fig. 3)
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Fig. 7 in Nagahama
the intermediate output information including information regarding a temperature history of the built object when additive manufacturing is performed under conditions indicated by the items of the input information, (Paragraph [58] teaches “the sixth learning model is obtained through machine learning that uses, as the learning data, the laser power, the scanning speed, the scanning pitch, the radiation spot diameter, the layer thickness, and the material of the metal powder P serving as the first-stage manufacturing conditions, the irradiated point temperature, the sputter amount, the shaped surface image, and the weld pool size serving as the first-stage shaped article statuses, the quality serving as the second-stage shaped article status, and the heat treatment conditions serving as the second-stage manufacturing conditions.” Here irradiated point temperature corresponds to the intermediate output. )
the second mathematical model relating the intermediate output information to output information including a property value of the built object; (Paragraph [65] teaches “The tenth learning model is configured such that, when the first-stage manufacturing conditions, the second-stage manufacturing conditions, and the first-stage shaped article statuses described above are set as input data, the quality of the second-stage shaped article W2 can be set as output data.”)
creating, by the processor, a database indicating a correspondence between the input information and the output information by using the first mathematical model and the second mathematical model; (Fig. 2)
searching, by the processor, the database to obtain the temperature history, the material of the built object, the welding condition, and the welding track corresponding to a target property value of the built object to be manufactured; (Paragraph [9] teaches “A manufacturing condition determination apparatus for a shaped article to be produced by additive manufacturing according to another aspect of the present invention includes a condition determination unit configured to determine, by using the learning model of the additive manufacturing learning model generation apparatus described above, the manufacturing condition while the shaped article status is set as the input data. Thus, the manufacturing condition of the shaped article can be determined easily.” Here shaped article status corresponds to a target property value in the instant claim.
Fig. 3 teaches a database with material, welding condition, and welding track values. Fig. 4 teaches a database with irradiated point temperature values. Paragraph [40] teaches “The manufacturing condition database 64a and the shaped article status database 64b are stored in association with each other for each target shaped article W.”)
and presenting, by the processor, the obtained material of the built object, welding condition, and welding track corresponding to the target property value, (Paragraph [9] teaches “A manufacturing condition determination apparatus for a shaped article to be produced by additive manufacturing according to another aspect of the present invention includes a condition determination unit configured to determine, by using the learning model of the additive manufacturing learning model generation apparatus described above, the manufacturing condition while the shaped article status is set as the input data. Thus, the manufacturing condition of the shaped article can be determined easily.”
Fig. 3 teaches a database with material, welding condition, and welding track values.)
and applying the build plan …. to melt and solidify …. into the desired shape of the built object based on the correspondence between the input information and the output information in the database, (Paragraph [27] teaches applying light beam to metal powder to melt and solidify the powder into a shaped article. Abstract teaches that the light beam is controlled by the program.)
wherein each item of the input information, including the items of the material of the built object, the welding condition, and the welding track, ( Fig. 3)
Primary combination of references is silent about the weld beads formed by melting and solidifying a filler metal fed from a welding head, and applying the build plan to the filler metal to melt and solidify the filler metal into the desired shape of the built object….., wherein each item of the input information….. includes a plurality of input subitems that are mutually different, the intermediate output information includes individual intermediate values corresponding to the input subitems, the output information includes a plurality of individual property values corresponding to the individual intermediate values, …..and in the generating of the first mathematical model and the second mathematical model, the input subitems are respectively related to the individual intermediate values by the first mathematical model, and the individual intermediate values are respectively related to the individual property values by the second mathematical model.
Takagi teaches wherein each item of the input information, ….. includes a plurality of input subitems that are mutually different, the intermediate output information includes individual intermediate values corresponding to the input subitems, the output information includes a plurality of individual property values corresponding to the individual intermediate values, (Fig. 10 in Takagi.)
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Fig. 10 in Takagi
and in the generating of the first mathematical model and the second mathematical model, the input subitems are respectively related to the individual intermediate values by the first mathematical model, and the individual intermediate values are respectively related to the individual property values by the second mathematical model. (Fig. 10 in Takagi.)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify the model in Nagahama to include plurality of input subitems for each input item and corresponding output as taught in Takagi. One of ordinary skill in the art would have been motivated to do so because “A design aid method of aiding in metallic material design by a computer, the design aid method comprising: inputting a desired property value to a database and searching the database for an index indicating metallic microstructure state corresponding to the desired property value and a chemical composition of elements in metal and a production condition corresponding to the index indicating metallic microstructure state, the database being generated using at least one first mathematical model in which input information including a chemical composition of elements in metal and a production condition and intermediate output information including an index indicating metallic microstructure state are associated with each other and at least one second mathematical model in which the intermediate output information and output information including a property value of a metallic material are associated with each other, and storing, in association with input data of each mesh obtained by partitioning an input range corresponding to the input information into a plurality of intervals, intermediate output data of the first mathematical model and output data of the second mathematical model corresponding to the input data; and presenting the chemical composition of elements in metal and the production condition corresponding to the desired property value” as taught in claim 4 in Takagi.
Primary combination of references is silent about the weld beads formed by melting and solidifying a filler metal fed from a welding head, and applying the build plan to the filler metal to melt and solidify the filler metal into the desired shape of the built object.
Zhang teaches the weld beads formed by melting and solidifying a filler metal fed from a welding head,…. , and applying the build plan to the filler metal to melt and solidify the filler metal into the desired shape of the built object. (Paragraph [3] in Zhang teaches “Wire-Arc Additive Manufacture (WAAM) uses the arc generated by welding machines such as metal inert-gas welding (MIG), Tungsten inert-gas welding (TIG) and plasma welding power supply (PA) as the heat source. Through the addition of metal wires, under the control of program, layers are stacked on the substrate according to the set forming path until the metal parts are nearly net formed.” It is implied that the metal wire is melted and solidified during WAAM.)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify the model in Nagahama to apply it for melting and solidifying filler metal fed from a welding head as taught in Zhang. One of ordinary skill in the art would have been motivated to do so in order to obtain a method “using different welding process parameters in the same welding bead in the a wire-arc additive manufacturing process to obtain a welding bead with synchronous and dynamic changes in profile along with the dynamic changes of the welding process parameters” as taught in abstract in Zhang.
Regarding claim 4,
The building plan assistance method according to claim 2, wherein information regarding the material in the input information includes information regarding a type of the filler metal. (Fig. 3 in Nagahama teaches material of metal powder as input information.
However, Nagahama is silent about filler metal.
Paragraph [3] in Zhang teaches “Wire-Arc Additive Manufacture (WAAM) uses the arc generated by welding machines such as metal inert-gas welding (MIG), Tungsten inert-gas welding (TIG) and plasma welding power supply (PA) as the heat source. Through the addition of metal wires, under the control of program, layers are stacked on the substrate according to the set forming path until the metal parts are nearly net formed.” It is implied that the metal wire is melted and solidified during WAAM.)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify the model in Nagahama to set the material for filler metal as taught in Zhang. One of ordinary skill in the art would have been motivated to do so in order to obtain a method “using different welding process parameters in the same welding bead in the a wire-arc additive manufacturing process to obtain a welding bead with synchronous and dynamic changes in profile along with the dynamic changes of the welding process parameters” as taught in abstract in Zhang.
Regarding claim 6,
The building plan assistance method according to claim 2, wherein information regarding the welding condition in the input information includes information regarding at least one of a welding current, a welding voltage, a travel speed, a width of a pitch between adjacent welding tracks, an interpass time of moving from a specific welding track to another welding track among a plurality of welding tracks, a target position of the welding head, a welding position of the welding head, and a speed of feeding the filler metal when each weld bead is formed, or a combination thereof. (Fig. 3 in Nagahama teaches scanning speed.)
Regarding claim 8,
The building plan assistance method according to claim 4, wherein information regarding the welding condition in the input information includes information regarding at least one of a welding current, a welding voltage, a travel speed, a width of a pitch between adjacent welding tracks, an interpass time of moving from a specific welding track to another welding track among a plurality of welding tracks, a target position of the welding head, a welding position of the welding head, and a speed of feeding the filler metal when each weld bead is formed, or a combination thereof. (Fig. 3 in Nagahama teaches scanning speed.)
Regarding claim 11,
The building plan assistance method according to claim 2, wherein the welding track is a partial welding track corresponding to an element shape obtained by cutting out a part of an entire shape of the built object. (Paragraph [33] in Nagahama teaches “The additive manufacturing apparatus 1 acquires the 3D shape model generated by the 3D shape model generation apparatus 61, and manufactures the first-stage shaped article W1 based on the 3D shape model.” It is implied that in an additive method, each welding track is a partial welding track to an element shape obtained by cutting a part of an entire shape.)
Regarding claim 13,
The building plan assistance method according claim 2, wherein the output information includes information regarding at least one of an index indicating a state of a metal structure, a hardness, and a mechanical strength of the built object. (Paragraph [36] in Nagahama teaches “The inspection apparatus 63 inspects whether the second-stage shaped article W2 satisfies the product quality that is the requirement specification. For example, the inspection apparatus 63 inspects the accuracy of the product shape, the product strength, and the product durability.”)
Regarding claim 20,
The building plan assistance method according to claim 2, wherein information regarding the welding track in the input information includes information regarding at least one of passes forming each weld bead, the number of the passes, an order of forming each weld bead, and a cross-sectional shape of each weld bead. (Fig. 3 in Nagahama teaches scanning speed which corresponds to one of passes forming each weld bead in the instant claim.)
Regarding claim 21,
The building plan assistance method according to claim 2, wherein at least one of the first mathematical model or the second mathematical model is a learned model obtained by machine-learning of a relation between the input information and the output information. (Abstract in Nagahama teaches “The additive manufacturing learning model generation apparatus generates a learning model for determining a manufacturing condition or for estimating a shaped article status through machine learning that uses the manufacturing condition and the shaped article status as learning data.”)
Regarding claim 22,
The building plan assistance method according to claim 2, wherein an input range of the input information is restricted to a range limited based on a predetermined condition. (Nagahama is silent about this.
Takagi teaches in paragraph [49] “As the input data range in which the input data meshes are defined, chemical compositions of elements in steel and production conditions that are expected as steel material are taken to be the whole input range. That is, the input data range is limited to a predetermined range based on a predetermined condition such as metallurgical knowledge or evaluation function.”)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify the model in Nagahama to restrict the input range based on a predetermined condition as taught in Takagi. One of ordinary skill in the art would have been motivated to do so because it enables the processor to run the models efficiently.
Response to Arguments
Applicant’s arguments filed on 06/11/2026 with respect to claim(s) 2, 4, 6, 8, 11, 13, 20-22 have been considered but are not persuasive.
The applicant amended claim 2 to recite that “applying the build plan to the filler metal to melt and solidify the filler metal into the desired shape of the built object based on the correspondence between the input information and the output information in the database, wherein each item of the input information, including the items of the material of the built object, the welding condition, and the welding track, includes a plurality of input subitems that are mutually different,” and argued that this makes the claimed invention distinguishable from prior art. However, upon further consideration, a new ground(s) of rejection is made in view of prior art as discussed above.
The applicant argued on page 12 that Nagahama does not teach temperature history as intermediate output. However, 6th learning model in Fig. 7 teaches irradiated point temperature as first stage shaped article status. Here the measured irradiated point temperature corresponds to temperature history during additive manufacturing. This temperature history is used to generate second stage output data. The 6th model teaches that Nagahama optimizes the learning model to generate heat treatment conditions for a desired quality of the shaped article. In response to applicant's argument that output data includes heat treatment conditions, the fact that the inventor has recognized another advantage which would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See Ex parte Obiaya, 227 USPQ 58, 60 (Bd. Pat. App. & Inter. 1985).
In response to applicant's arguments on page 13 against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). In this case, Nagahama teaches input items material of the built object, the welding condition, and the welding track in Fig. 3. However, Nagahama is silent about a plurality of input subitems that are mutually different. Takagi teaches in Fig. 10 that an input item may comprise multiple different values. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to design each input item in Nagahama to have multiple different values as taught in Takagi. This would allow design options to manufacture articles with different properties for different uses.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/FAHMIDA FERDOUSI/ Examiner, Art Unit 3761
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