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 .
Status of Claims
This action is in reply to the claims filed on 18 August 2025.
Claims 1, 3-4, 7, 13, 15, and 17-20 has been amended.
Claims 1-20 are currently pending and have been examined.
Claim Objections
Claims 4, 15 and 19 are objected to because of the following informalities:
Claim 4, line 2 recites “the first input”. This appears to be a typographical error of “the first input of the first plurality of inputs”.
Claim 15, line 3; and Claim 19, line 5-6 recites “a second plurality of inputs”. This appears to be a typographical error of “a third plurality of inputs”.
Claims 15, line 4; and Claim 19, line 6 recites “the second plurality of inputs”. This appears to be a typographical error of “the third plurality of inputs”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 3, 15, and 19 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 3, 15, and 19 were amended to add “one or more characteristics having the same identifiers as the set of one or more characteristics for the first input”. The instant specification does not provide an explanation to how identifiers can be the same. Paragraph [0100] of the instant specification recites:
A work center may be given an identifier such that it is useful to compare routings involving that particular work center, but it may be difficult to compare that work center with other work centers, such as those associated with different facilities.
Paragraph [0101] of the instant specification recites:
However, different work centers may have characteristics that overlap to varying degrees. Two work centers, at different facilities, may be used to perform the same or similar operations, or may have the same or similar machinery. Accordingly, classifying or grouping processing resources by characteristics can allow processing resources that have different identifiers to be compared, or pooled for use in training data.
Paragraph [0100] and [0101] recite a work center having its own identifier and the work center having similar characteristics to other work centers, but it doesn’t disclose how the identifiers are linked to the characteristics.
This is potentially a drafting oversight issue and therefore, examiner has also included a 112(b) rejection below. For the purpose of examination, claims 3, 15, 19 will be interpreted in the manner shown below.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 15 and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 3, 15, and 19 recites “a second input comprising a set of one or more characteristics having the same identifiers as the set of one or more characteristics for the first input”. Claims 3, 15, and 19 also recites “the third plurality of inputs do not comprise an input having the first identifier but comprise a second input”. It is unclear how an the second input can comprise the same identifiers as the first input while also not having the first identifier. Paragraph [0100] of the instant specification recites:
A work center may be given an identifier such that it is useful to compare routings involving that particular work center, but it may be difficult to compare that work center with other work centers, such as those associated with different facilities.
Paragraph [0101] of the instant specification recites:
However, different work centers may have characteristics that overlap to varying degrees. Two work centers, at different facilities, may be used to perform the same or similar operations, or may have the same or similar machinery. Accordingly, classifying or grouping processing resources by characteristics can allow processing resources that have different identifiers to be compared, or pooled for use in training data.
Paragraph [0100] and [0101] of the instant specification explains how work centers can have different identifiers but overlapping characteristics, but does not explain how the identifiers are linked to the characteristics. For examination purposes, the Examiner will interpret the claim to recite:
3. (Currently Amended) The computing system of claim 1, wherein a first input of the first plurality of inputs has a first identifier, the operations further comprising:
training the predictive model with at least a portion of characteristics of a third plurality of inputs, wherein the third plurality of inputs do not comprise an input having the first identifier but comprise a second input comprising a set of one or more characteristics that are the same as the set of one or more characteristics for the first input.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claims 1-12 is/are drawn to a system (i.e., a machine), claims 13-16 is/are drawn to a method (i.e., a process), and claims 17-20 is/are drawn to a non-transitory machine-readable storage medium (i.e., a manufacture). As such, claims 1-20 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Representative Claim 1:
receiving a first plurality of inputs, wherein respective inputs of the first plurality of inputs are associated with respective sets of one or more characteristics;
receiving values for the respective sets of one or more characteristics;
associating the sets of one or more characteristics with at least one set of labels for an element of a routing;
obtaining a first set of inference data, the first set of inference data comprising a second plurality of inputs and characteristic values for respective sets of one or more characteristics associated with respective inputs of the second plurality of inputs;
analyzing the first set of inference data to predict a first set of one or more values for the set of labels;
obtaining a first inference result identifying the first set of one or more values for the first set of labels;
executing at least a portion of a second routing operation that includes assignments of work centers, execution sequences, or resources based on the first set of one or more values for the set of labels;
obtaining a second set of inference data, the second set of inference data comprising a third plurality of inputs and characteristic values for respective sets of one or more characteristics associated with respective inputs of the third plurality of inputs;
analyzing the second set of inference data using to predict a second set of one or more values for the set of labels; and
obtaining a second inference result identifying the second set of one or more values for the set of labels; and
executing at least a portion of a third routing operation that includes assignments of work centers, execution sequences, or resources based on the second set of one or more values for the set of labels, wherein at least on assignment of the third routing operation differs from an assignment of the second routing operation based on a difference between the second set of one or more values for the set of labels and the first set of one or more values for the set of labels.
As noted by the claim limitations above, the independent claimed invention is directed to determining elements of a routing. This is considered to be an abstract idea because it is manager a personal interaction between people, which falls within the category of “certain methods of organizing human activity.”
See MPEP 2106.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s): at least one hardware processor;
at least one memory coupled to the at least one hardware processor; one or more computer-readable storage media comprising computer-executable instructions that, when executed, cause the computing system to perform operations; training a predictive model using at least a portion of characteristics of the sets of one or more characteristics and the at least one set of labels; and analyzing the set of inference data using the predictive model. This/these additional elements individually or in combination do not integrate the exception into a practical application because they merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, these additional element(s) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) merely use a computer as a tool to perform an abstract idea, which does not render a claim as being significantly more than the judicial exception. Accordingly, claim 1 is ineligible.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claim 1 is not eligible subject matter under 35 USC 101.
Dependent claim(s) 2 and 4-8 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 1. Therefor claim(s) 2 and 4-8 are ineligible.
Dependent claim(s) 3 and 9-12 further recite(s) the additional element(s): training the predictive model with at least a portion of characteristics of a second plurality of inputs (claim 3), a database (claim 9), training the predictive model comprises training the predictive model using a value specified for the first group (claim 10), the training does not use identifiers for at least a portion of inputs of the first plurality of inputs (claim 11), and the training does not use a semantic description for at least a portion of inputs of the first plurality of inputs (claim 12). This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 3 and 9-12 is/are ineligible.
Claim 13 is parallel in nature to claim 1. Claim 13 recites an abstract idea similar in nature to claim 1. Furthermore, claim 13 recites the following additional elements: one hardware processor, at least one memory coupled to the at least one hardware processor, training a predictive model using at least a portion of characteristics of the sets of one or more characteristics and the at least one set of labels, and analyzing the set of inference data using the predictive model. These additional elements do no more than use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim into a practical application nor does it render a claim as being significantly more than the abstract idea.
Dependent claim(s) 14 and 16 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 1. Therefor claim(s) 14 and 16 are ineligible.
Dependent claim(s) 15 further recite(s) the additional element(s): training the predictive model with at least a portion of characteristics of a second plurality of inputs (claim 3). This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 15 is/are ineligible.
Claim 17 is parallel in nature to claim 1. Claim 17 recites an abstract idea similar in nature to claim 1. Furthermore, claim 17 recites the following additional elements: one or more non-transitory computer-readable storage media; computer-executable instructions executed by a computing system, the computing system comprising at least one hardware processor and at least one memory coupled to the at least one hardware processor, training a predictive model using at least a portion of characteristics of the sets of one or more characteristics and the at least one set of labels, and analyzing the set of inference data using the predictive model. These additional elements do no more than use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim into a practical application nor does it render a claim as being significantly more than the abstract idea.
Dependent claim(s) 18 and 20 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 1. Therefore, claim(s) 18 and 20 are ineligible.
Dependent claim(s) 19 further recite(s) the additional element(s): training the predictive model with at least a portion of characteristics of a second plurality of inputs (claim 3). This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 15 is/are ineligible.
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) 1-9 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (US 20240165731 A1) in view of Stebbins (US 20210097454 A1).
Regarding claim 1, Zhou teaches a computing system comprising:
at least one hardware processor; (Paragraph [0060] “a processor”)
at least one memory coupled to the at least one hardware processor; (Paragraph [0060] “a memory”) and
one or more computer-readable storage media comprising computer-executable instructions that,(Paragraph [0061] “The memory unit 21 stores therein a welding data processing program 210”) when executed, cause the computing system to perform operations comprising:
receiving a first plurality of inputs, wherein respective inputs of the first plurality of inputs are associated with respective sets of one or more characteristics; (Paragraph [0205] “The machine learning module 2021 […] inputs a plurality of learning data pairs to a learning model 25 […] Each learning data pair is configured by […] the weld point table 211A as the input data and […] the weld point table 212A as the training data”; “learning phase” of Fig. 22; Fig. 6; Fig. 7)
receiving values for the respective sets of one or more characteristics; (Paragraph [0205] “The machine learning module 2021 […] inputs a plurality of learning data pairs to a learning model 25 […] Each learning data pair is configured by […] the weld point table 211A as the input data and […] the weld point table 212A as the training data”; “learning phase” of Fig. 22; Fig. 6; Fig. 7)
associating the sets of one or more characteristics with at least one set of labels (“weld point” of Fig. 6 and Fig. 7)
training a predictive model using at least a portion of the characteristics of the sets of one or more characteristics and the at least one set of labels; (Paragraph [0205] “The machine learning module 2021 performs machine learning processing that inputs a plurality of learning data pairs to a learning model 25 to cause the learning model 25 to learn a correlation between input data and training data by machine learning. Each learning data pair is configured by […] the weld point table 211A as the input data and […] the weld point table 212A as the training data”; “learning phase” of Fig. 22; Fig. 6; Fig. 7)
obtaining a first set of inference data, the first set of inference data comprising a second plurality of inputs and characteristic values for respective sets of one or more characteristics associated with respective inputs of the second plurality of inputs; (Paragraph [0208] “inputting a welding condition and a welding execution record at the weld point 11a to the learning model 25 (with its weight parameters adjusted) as the input data.”; Fig. 6; Fig. 22)
analyzing the first set of inference data using the predictive model to predict a first set of one or more values for the set of labels; (Paragraph [0208] “The defect inference module 202J performs defect inference processing […] by inputting a welding condition and a welding execution record at the weld point 11a to the learning model 25 (with its weight parameters adjusted) as the input data.”; Fig. 22)
obtaining a first inference result identifying the first set of one or more values for the first set of labels; (Paragraph [0208] “The defect inference module 202J performs defect inference processing of inferring whether there is a weld defect at a weld point 11a as an inference target and generating advice information including the inference result by inputting a welding condition and a welding execution record at the weld point 11a to the learning model 25 (with its weight parameters adjusted) as the input data.”; Fig. 22)
obtaining a second set of inference data, the second set of inference data comprising a third plurality of inputs and characteristic values for respective sets of one or more characteristics associated with respective inputs of the third plurality of inputs; (Paragraph [0063] “welding condition, required qualification, and welding execution record are registered for each weld point 11”; Paragraph [0205] “The machine learning module 2021 performs machine learning processing that inputs a plurality of learning data pairs to a learning model 25 […] Each learning data pair is configured by associating, for a common weld point 11, a welding condition and a welding execution record”; Paragraph [0208] “inputting a welding condition and a welding execution record at the weld point 11a to the learning model 25 (with its weight parameters adjusted) as the input data.”; Fig. 6; Fig. 22; Examiner notes that paragraph [0063] and [0205] demonstrates that the each weld point has its own dataset that is input into the model.)
analyzing the second set of inference data using the predictive model to predict a second set of one or more values for the set of labels; (Paragraph [0208] “The defect inference module 202J performs defect inference processing […] by inputting a welding condition and a welding execution record at the weld point 11a to the learning model 25 (with its weight parameters adjusted) as the input data.”; Fig. 22) and
obtaining a second inference result identifying the second set of one or more values for the set of labels; (Paragraph [0208] “The defect inference module 202J performs defect inference processing of inferring whether there is a weld defect at a weld point 11a as an inference target and generating advice information including the inference result by inputting a welding condition and a welding execution record at the weld point 11a to the learning model 25 (with its weight parameters adjusted) as the input data.”; Fig. 22) and
Zhou does not teach:
associating the sets of one or more characteristics with at least one set of labels that define operational routing attributes for respective elements of a first routing operation for an element of a routing;
executing at least a portion of a second routing operation that includes assignments of work centers, execution sequences, or resources based on the first set of one or more values for the set of labels; and
executing at least a portion of a third routing operation that includes assignments of work centers, execution sequences, or resources based on the second set of one or more values for the set of labels, wherein at least one assignment of the third routing operation differs from an assignment of the second routing operation based on a difference between the second set of one or more values for the set of labels and the first set of one or more values for the set of labels.
However Stebbins teaches:
associating the sets of one or more characteristics with at least one set of labels that define operational routing attributes for respective elements of a first routing operation for an element of a routing; (Paragraph [0044] “Referring to FIG. 5, a diagram of an example data storage structure 540 according to some embodiments is shown. In some embodiments, the data storage structure 540 may comprise a plurality of data tables, such as a resource table 544a, a schedule table 544b, and/or a claims table 544c.”; Fig. 5 of Stebbins; Examiner notes Fig. 5 shows 3 data tables. The tables have several labels defining operational routing attributes such as “Resource ID”, “Location”, “Schedule ID”, etc.)
executing at least a portion of a second routing operation that includes assignments of work centers, execution sequences, or resources based on the first set of one or more values for the set of labels; (Paragraph [0048] “AI-based damage triage and/or resource allocation, routing, and/or scheduling decisions may be defined and/or provided by relationships established between two or more of the data tables 544a-c.”; Paragraph [0058 “the method 600 may comprise AI resource allocation 620.”; Paragraph [0058] “the method 600 (e.g., the AI resource allocation 620) may also or alternatively comprise assigning (e.g., by the electronic processing device executing the AI resource allocation algorithm) locations to resources, at 620-2.”; el. 620 of Fig. 6 of Stebbins) and
executing at least a portion of a third routing operation that includes assignments of work centers, execution sequences, or resources based on the second set of one or more values for the set of labels, wherein at least one assignment of the third routing operation differs from an assignment of the second routing operation based on a difference between the second set of one or more values for the set of labels and the first set of one or more values for the set of labels. (Paragraph [0048] “AI-based damage triage and/or resource allocation, routing, and/or scheduling decisions may be defined and/or provided by relationships established between two or more of the data tables 544a-c.”; Paragraph [0058 “the method 600 may comprise AI resource allocation 620.”; Paragraph [0058] “the method 600 (e.g., the AI resource allocation 620) may also or alternatively comprise assigning (e.g., by the electronic processing device executing the AI resource allocation algorithm) locations to resources, at 620-2.”; Paragraph [0062] “route updates, etc., may be utilized, for example, to determine if a schedule for a resource needs to be checked for updates. According to some embodiments, the checking may comprise a software listener module that is triggered by the occurrence of certain predefined events, such as a resource staying longer than estimated/scheduled at an appointment, a resource skipping an appointment or visiting appointments out of order, and/or a resource visiting a location assigned to a different resource. In some embodiments, it may be determined whether changes are needed, at 640-4. In the case that changes are determined to be needed, the method 600 may loop back to reassigning or reallocating resources at 620-2 (and/or recomputing route plans at 630-3 and/or recomputing schedules at 640-1).”; el. 620 of Fig. 6 of Stebbins; Examiner notes when a schedule parameter is changes, the resources are reallocated based on the schedule change.)
This operation of Stebbins is applicable to the system of Zhou as they both share characteristics and capabilities, namely, they are directed to managing data for a resource using a table. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the system of Zhou to incorporate labels that define operational routing attributes for respective elements of a first routing operation for an element of a routing; executing at least a portion of a second routing operation; and executing at least a portion of a third routing operation as taught by Stebbins. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Zhou in order to create event response plans and strategies that may be dynamically (e.g., in real-time or near real-time) modified to account for changing circumstances with speed, uniformity, and efficiency (see paragraph [0013] of Stebbins).
Regarding claim 2, Zhou in view of Stebbins teaches the computing system of claim 1. Zhou further teaches:
wherein at least a portion of characteristics of the respective sets of one or more characteristics reflect physical properties of respective inputs. (Paragraph [0063] “In the weld point table 211A, […] The welding condition includes, as its fields, diameter, thickness, material (base material),” Paragraph [0067] “The image data 14 includes one or a plurality of images for one weld point 11.”; el. 211A of Fig. 6; el. 212A of Fig. 7)
Regarding claim 3, Zhou in view of Stebbins teaches the computing system of claim 1. Zhou further teaches:
wherein a first input of the first plurality of inputs has a first identifier, (Paragraph [0091] “the inspection registration module 201 registers welding site ID, welding area ID, drawing number, and weld point number”) the operations further comprising:
training the predictive model with at least a portion of characteristics of a third plurality of inputs, wherein the third plurality of inputs do not comprise an input having the first identifier but comprise a second input comprising a set of one or more characteristics that are the same as the set of one or more characteristics for the first input. (Paragraph [0091] “That is, the inspection registration module 201 registers welding site ID, welding area ID, drawing number, and weld point number for each weld point 11.”; Paragraph [0205] “The machine learning module 2021 performs machine learning processing that inputs a plurality of learning data pairs to a learning model 25 to cause the learning model 25 to learn a correlation between input data and training data by machine learning. Each learning data pair is configured by […] the weld point table 211A as the input data and […] the weld point table 212A as the training data”; Paragraph [0207] “The machine learning module 2021 repeats the above-mentioned operation through use of a plurality of learning data pairs”; “learning phase” of Fig. 22; Fig. 6; Fig. 7; Examiner notes that each learning data pair would use the data structures 211A and 212A. Therefore, each input would have equal characteristics. Furthermore, two inputs with different weld points would have different IDs.)
Regarding claim 4, Zhou in view of Stebbins teaches the computing system of claim 3. Zhou further teaches:
wherein a first value of a first characteristic of the set of one or more characteristics for the first input of the first plurality of inputs is different than a second value of the same characteristic for the second input. (Paragraph [0091] “That is, the inspection registration module 201 registers welding site ID, welding area ID, drawing number, and weld point number for each weld point 11.”; Paragraph [0205] “The machine learning module 2021 performs machine learning processing that inputs a plurality of learning data pairs to a learning model 25 to cause the learning model 25 to learn a correlation between input data and training data by machine learning. Each learning data pair is configured by […] the weld point table 211A as the input data and […] the weld point table 212A as the training data”; Paragraph [0207] “The machine learning module 2021 repeats the above-mentioned operation through use of a plurality of learning data pairs”; “learning phase” of Fig. 22; Fig. 6; Fig. 7; Examiner notes that two inputs with different weld points would have different IDs. Therefore the “welding site ID” characteristic, “welding area ID” characteristic, etc. would have different values.)
Regarding claim 5, Zhou in view of Stebbins teaches the computing system of claim 1. Zhou further teaches:
wherein the at least one set of labels identifies processing resources used in processing the first plurality of inputs. (Paragraph [0064] “In the welding machine table 211B, […] registered for each welding machine 6 […] the welder table 211C […] registered for each welder 50A identified by a welder ID.”; Paragraph [0068] “In the inspection machine table 212B, […] registered for each inspection machine 7 […] the imaging worker table 212C, […] registered for each imaging worker 50C” el. 211B and 211C of Fig. 6; el. 212B, and 212C of Fig. 7)
Regarding claim 6, Zhou in view of Stebbins teaches the computing system of claim 1. Zhou further teaches:
wherein the at least one set of labels identifies operations performed on, or using, inputs of the first plurality of inputs. (Paragraph [0063] “In the weld point table 211A, […] welding execution record are registered for each weld point 11”; Fig. 6)
Regarding claim 7, Zhou in view of Stebbins teaches the computing system of claim 1. Zhou further teaches:
wherein the at least one set of labels identifies at least one pre-defined standard value for at least one operation performed on, or using, inputs of the first plurality of inputs. (Paragraph [0108] “in Step S141, the deviation judgment module 202B refers to the welding process database 211 and judges whether the preheating data 13 received from the temperature measurement device 61 deviates beyond a preheating deviation judgment criterion with respect to the welding condition (preheating) registered in the welding process target record of the weld point table 211A […] the deviation judgment module 202B compares, for example, a preheating temperature or a preheating time indicated by the preheating data 13 with the preheating temperature or the preheating time in the welding condition and, when the difference between the indicated preheating temperature or preheating time and the preheating temperature or the preheating time in the welding condition exceeds the preheating deviation judgment criterion (a predetermined threshold value), judges that the preheating data 13 deviates.”)
Regarding claim 8, Zhou in view of Stebbins teaches the computing system of claim 1. Zhou further teaches the operations further comprising:
obtaining an identifier of a first input of the first plurality of inputs; (Paragraph [0091] “the inspection registration module 201 registers welding site ID, welding area ID, drawing number, and weld point number”) and
retrieving values for the respective set of one or more characteristics for the first input using the identifier. (Paragraph [0212] “in Step S910, the defect inference module 202J refers to the welding process database 211 and identifies a record corresponding to the weld point 11 identified by the welding process target record”; Paragraph [0066] “each weld point 11 identified by a welding site ID, a welding area ID, a drawing number, and a weld point number”; “step S910” of Fig. 23)
Regarding claim 9, Zhou in view of Stebbins teaches the computing system of claim 8. Zhou further teaches:
wherein the retrieving the values comprises querying a database. (Paragraph [0212] “Paragraph [0212] “in Step S910, the defect inference module 202J refers to the welding process database 211”)
Regarding claim 12, Zhou in view of Stebbins teaches the computing system of claim 1. Zhou further teaches:
wherein the training does not use a semantic description for at least a portion of inputs of the first plurality of inputs. (Fig. 6 and Fig. 7 does not include a semantic description as described in Paragraph [0127] of the instant specification).
Regarding Claims 13-15, Claims 13-15 are directed to a method. Claims 13-15 recite limitations parallel in nature as those addressed above for claims 1-3 which are directed towards a system. Claims 13-15 are therefore rejected for the same reasons as set forth above for claims 1-3 respectively.
Regarding claim 16, Zhou in view of Stebbins teaches the method of claim 13. Zhou further teaches:
obtaining an identifier of a first input of the first plurality of inputs; (Paragraph [0091] “the inspection registration module 201 registers welding site ID, welding area ID, drawing number, and weld point number”) and
retrieving values for the respective set of one or more characteristics for the first input. (Paragraph [0212] “in Step S910, the defect inference module 202J refers to the welding process database 211 and identifies a record corresponding to the weld point 11 identified by the welding process target record”; Paragraph [0066] “each weld point 11 identified by a welding site ID, a welding area ID, a drawing number, and a weld point number”; “step S910” of Fig. 23)
Regarding Claims 17-19, Claims 17-19 are directed to a non-transitory computer-readable storage media. Claims 17-19 recite limitations parallel in nature as those addressed above for claims 1-3 which are directed towards a system. Claims 17-19 are therefore rejected for the same reasons as set forth above for claims 1-3 respectively. Claims 17-19 further recite a non-transitory computer-readable storage media (see Paragraph [0081] of Zhou).
Regarding claim 20, Zhou in view of Stebbins teaches the one or more computer-readable storage media of claim 17. Zhou further teaches:
computer-executable instructions that, when executed by the computing system, cause the computing system to obtain an identifier of a first input of the first plurality of inputs; (Paragraph [0091] “the inspection registration module 201 registers welding site ID, welding area ID, drawing number, and weld point number”) and
computer-executable instructions that, when executed by the computing system, cause the computing system to retrieve values for the respective set of one or more characteristics for the first input. (Paragraph [0212] “in Step S910, the defect inference module 202J refers to the welding process database 211 and identifies a record corresponding to the weld point 11 identified by the welding process target record”; Paragraph [0066] “each weld point 11 identified by a welding site ID, a welding area ID, a drawing number, and a weld point number”; “step S910” of Fig. 23)
Claim(s) 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (US 20240165731 A1) in view of Stebbins (US 20210097454 A1) in further view of Song (US 20130315475 A1).
Regarding claim 10, Zhou in view of Stebbins teaches the computing system of claim 1. Zhou in view of Stebbins does not teach:
for a first input of the first plurality of inputs, classifying the first input into a first group based at least in part on a value of a characteristic of the respective set of one or more characteristics of the first input; and
wherein the training the predictive model comprises training the predictive model using a value specified for the first group.
However, Song teaches:
for a first input of the first plurality of inputs, classifying the first input into a first group based at least in part on a value of a characteristic of the respective set of one or more characteristics of the first input; (Paragraph [0089] “Cluster analysis can be conducted using the principal component scores as independent variables to identify different body shapes and create body shape categories.”) and
wherein the training the predictive model comprises training the predictive model using a value specified for the first group. (Paragraph [0145] “Data inputs can comprise data from a number of subjects with known good-fit patterns and can be used to train the database initially.”; Paragraph [0006] “body type, sometimes referred to as "body shape," has increasingly been recognized as a fundamental factor to a good fit.”)
This operation of Song is applicable to the system of Zhou as they both share characteristics and capabilities, namely, they are directed to training a machine learning model to manage data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the system of Zhou to incorporate classifying the first input int a first group and training the predictive model using a value from the first group as taught by Song. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Zhou in order to adjust calculations to match the calculated average key measurements for each group (see paragraph [0048] of Zhou).
Regarding claim 11, Zhou in view of Stebbins teaches the computing system of claim 1. Zhou in view of Stebbins does not teach:
wherein the training does not use identifiers for at least a portion of inputs of the first plurality of inputs.
However, Song teaches:
wherein the training does not use identifiers for at least a portion of inputs of the first plurality of inputs. (Tables 16 and 17 of Fig. 19 and table 18 of Fig. 20 do not include identifiers.)
This operation of Song is applicable to the system of Zhou as they both share characteristics and capabilities, namely, they are directed to training a machine learning model to manage data. The input data of Zhou is being used to train a machine learning model (see Paragraph [0205] of Shou), which is similar to how the input data of Song is being used to train a machine learning model (see Paragraph [0145] and [0148] of Song). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have substituted the input data of Zhou with the input data of Song which does not use identifiers. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself - that is in the substitution of the input data of Zhou with the input data of Song. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Response to Arguments
Applicant’s arguments, see Page 10, filed 18 August 2025, with respect to the claim objection of claim 4 have been fully considered, but are not persuasive because there have been no changes to “the first input” on line 2 of claim 4. The claim objection of claim 4 is maintained.
Applicant’s arguments, see Page 10, filed 18 August 2025, with respect to the claim objections of claim 17-20 have been fully considered and are persuasive due to the amendments of claims 17-20. The claim objections of claims 17-20 have been withdrawn.
Applicant’s arguments, see Page 11, filed 18 August 2025, with respect to the 112(b) rejections have been fully considered and are persuasive due to the amendments of claims 3, 4, 7, 15, and 19. The previous 112(b) rejections of claims 3, 4, 7, 15, and 19 have been withdrawn. However, due to the amendments of claim 3, 15, and 19, new issues have been identified. Therefore, a new grounds of rejection is made in view of the amendments of claims 3, 15, and 19.
Applicant's arguments, see Page(s) 12-13, filed 18 August 2025, with respect to the 35 USC § 101 rejection(s) of claim(s) 1-20 have been fully considered but they are not persuasive. Applicant argues 1) the claims as amended are directed to a statutory category of invention, 2) the claims are directed to a specific improvement in computer functionality, and 3) the claims provide technical advantages. The Examiner respectfully disagrees.
Regarding argument 1, the Examiner agrees the amendments to claims 17-20 make them now directed to a statutory category, however, the claims are still rejected for the reason described in the above 101 rejection and in the response to arguments 2 and 3 below.
Regarding argument 2, the Applicant argues the claims are directed to a specific improvement in computer functionality. Therefore they are eligible for reasons similar to McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016). The Examiner respectfully disagrees. The invention in McRO was eligible because it applied specific rules to automate a animation process that went beyond how a human would perform animation. The Applicant’s claimed invention, however, uses a model to perform the steps in the same way that a human would. More specifically, the claimed invention describes the additional elements of: at least one memory coupled to the at least one hardware processor; one or more computer-readable storage media comprising computer-executable instructions that, when executed, cause the computing system to perform operations; training a predictive model using at least a portion of characteristics of the sets of one or more characteristics and the at least one set of labels; and analyzing the set of inference data using the predictive model. The training and the steps being performed by the model are described at a high level in the claims. For example the claims recite “training a predictive model using at least a portion of the characteristics of the sets of one or more characteristics and the at least on labels”. A human can also be trained using characteristics of the sets of one or more characteristics and the at least one labels in order to perform the steps of obtaining a set of inference data, analyzing the set of inference data, and obtaining a first inference result. Furthermore, humans can execute a portion of a second routing operation as described by the claim. A human can perform the steps that the additional elements perform, therefore, the claims are not applying specific rules to automate the process. The claims are simply performing the steps in the same way as they have been previously done by humans, which does not make a claim eligible.
Regarding argument 3, the Applicant argues the claims provide technical advantages, for example, avoiding the need for manual authoring, storage, or evaluation of complex routing rules, reducing computational and memory burdens associated with large rule sets, and enabling timely adjustments to routing logic in response to changing conditions. Examiner respectfully disagrees. MPEP 2106.05(f) recites:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The improvements of avoiding the need for manual authoring, storage, or evaluation of complex routing rules, reducing computational and memory burdens associated with large rule sets, and enabling timely adjustments to routing logic in response to changing conditions simply claiming the improved speed or efficiency inherent with applying the abstract idea on a computer because these improvements would inherently happen when a generic computer is applied to the steps in the claim language. Claiming the inherent improvement of speed or efficiency does not integrate a judicial except