DETAILED ACTION
The following is a Final Office Action in response to the Amendment/Remarks received on 20 November 2025. Claims 1, 3, and 6 have been amended. Claims 4 and 5 have been cancelled. Claim 2 was previously cancelled. Claims 1, 3 and 6 remain pending in this application.
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 Arguments
Applicant’s arguments, see Remarks, pg. 6, filed 20 November 2025, with respect to objected claim 3 have been fully considered and are persuasive in light of the claim amendment filed on 20 November 2025. The objection of claim 3 has been withdrawn.
Applicant’s arguments, see Remarks, pg. 7, filed 20 November 2025, with respect to rejected claims 1, 3, and 6 under 35 U.S.C. 112(b) have been fully considered and are persuasive in light of the claim amendment filed on 20 November 2025. The rejections of claims 1, 3, and 6 have been withdrawn.
Applicant's arguments, see Remarks, pgs. 7-12, filed 20 November 2025, with respect to rejected claims 1, 3, and 6 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
With respect to the Applicant’s argument,
It is evident that the Examiner appears to be mainly concerned with the use of the terminology "predict" in claim 3 with regard to these rejections. The Examiner explains her concerns in this regard in the paragraph spanning pages 21-22 and the first full paragraph of page 22 in the Response to Arguments section of the Office Action.
For example, claim 1 is not rejected under 35 U.S.C. § 101 apparently because it does not use the terminology "predict". Accordingly, Applicant has opted to replace the "prediction device" terminology in the preamble of claim 3 with "control device for controlling operation by an arbitrary wire-cut electrical discharge machine" in an effort to overcome the rejection of claim 3 under 35 U.S.C. § 101. Accordingly, Applicant respectfully submits that the device of claim 3, as now more precisely amended in this paper, will not merely make predictions but will also control something, namely operation by an arbitrary wire-cut electrical discharge machine, using the prediction results. (see Remarks, pg. 7, paragraph 6 - pg. 8, paragraph 1)
The Examiner respectfully disagrees.
MPEP 2111.02(II):
II. PREAMBLE STATEMENTS RECITING PURPOSE OR INTENDED USE
The claim preamble must be read in the context of the entire claim. The determination of whether preamble recitations are structural limitations or mere statements of purpose or use "can be resolved only on review of the entirety of the [record] to gain an understanding of what the inventors actually invented and intended to encompass by the claim" as drafted without importing "‘extraneous’ limitations from the specification." Corning Glass Works, 868 F.2d at 1257, 9 USPQ2d at 1966. If the body of a claim fully and intrinsically sets forth all of the limitations of the claimed invention, and the preamble merely states, for example, the purpose or intended use of the invention, rather than any distinct definition of any of the claimed invention’s limitations, then the preamble is not considered a limitation and is of no significance to claim construction. Shoes by Firebug LLC v. Stride Rite Children’s Grp., LLC, 962 F.3d 1362, 2020 USPQ2d 10701 (Fed. Cir. 2020) (The court found that the preamble in one patent’s claim is limiting but is not in a related patent); Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See also Rowe v. Dror, 112 F.3d 473, 478, 42 USPQ2d 1550, 1553 (Fed. Cir. 1997) ("where a patentee defines a structurally complete invention in the claim body and uses the preamble only to state a purpose or intended use for the invention, the preamble is not a claim limitation"); Kropa v. Robie, 187 F.2d at 152, 88 USPQ2d at 480-81 (preamble is not a limitation where claim is directed to a product and the preamble merely recites a property inherent in an old product defined by the remainder of the claim); STX LLC. v. Brine, 211 F.3d 588, 591, 54 USPQ2d 1347, 1350 (Fed. Cir. 2000) (holding that the preamble phrase "which provides improved playing and handling characteristics" in a claim drawn to a head for a lacrosse stick was not a claim limitation). Compare Jansen v. Rexall Sundown, Inc., 342 F.3d 1329, 1333-34, 68 USPQ2d 1154, 1158 (Fed. Cir. 2003) (In a claim directed to a method of treating or preventing pernicious anemia in humans by administering a certain vitamin preparation to "a human in need thereof," the court held that the preamble is not merely a statement of effect that may or may not be desired or appreciated, but rather is a statement of the intentional purpose for which the method must be performed. Thus the claim is properly interpreted to mean that the vitamin preparation must be administered to a human with a recognized need to treat or prevent pernicious anemia.); Nantkwest , Inc. v. Lee, 686 Fed. App'x 864, 867 (Fed. Cir. 2017) (nonprecedential) (The court found that the preamble phrase "treating a cancer" "’require[s] lysis of many cells, in order to accomplish the goal of treating cancer’ and not merely lysing one or a few cancer cells."); In re Cruciferous Sprout Litig., 301 F.3d 1343, 1346-48, 64 USPQ2d 1202, 1204-05 (Fed. Cir. 2002) (A claim at issue was directed to a method of preparing a food rich in glucosinolates wherein cruciferous sprouts are harvested prior to the 2-leaf stage. The court held that the preamble phrase "rich in glucosinolates" helps define the claimed invention, as evidenced by the specification and prosecution history, and thus is a limitation of the claim (although the claim was anticipated by prior art that produced sprouts inherently "rich in glucosinolates")).
During examination, statements in the preamble reciting the purpose or intended use of the claimed invention must be evaluated to determine whether or not the recited purpose or intended use results in a structural difference (or, in the case of process claims, manipulative difference) between the claimed invention and the prior art. If so, the recitation serves to limit the claim. See, e.g., In re Otto, 312 F.2d 937, 938, 136 USPQ 458, 459 (CCPA 1963) (The claims were directed to a core member for hair curlers and a process of making a core member for hair curlers. The court held that the intended use of hair curling was of no significance to the structure and process of making.); In re Sinex, 309 F.2d 488, 492, 135 USPQ 302, 305 (CCPA 1962) (statement of intended use in an apparatus claim did not distinguish over the prior art apparatus). To satisfy an intended use limitation which is limiting, a prior art structure which is capable of performing the intended use as recited in the preamble meets the claim. See, e.g., In re Schreiber, 128 F.3d 1473, 1477, 44 USPQ2d 1429, 1431 (Fed. Cir. 1997) (anticipation rejection affirmed based on Board’s factual finding that the reference dispenser (a spout disclosed as useful for purposes such as dispensing oil from an oil can) would be capable of dispensing popcorn in the manner set forth in appellant’s claim 1 (a dispensing top for dispensing popcorn in a specified manner)) and cases cited therein. See also MPEP § 2112 - MPEP § 2112.02.
However, a "preamble may provide context for claim construction, particularly, where … that preamble’s statement of intended use forms the basis for distinguishing the prior art in the patent’s prosecution history." Metabolite Labs., Inc. v. Corp. of Am. Holdings, 370 F.3d 1354, 1358-62, 71 USPQ2d 1081, 1084-87 (Fed. Cir. 2004). The patent claim at issue was directed to a two-step method for detecting a deficiency of vitamin B12 or folic acid, involving (i) assaying a body fluid for an "elevated level" of homocysteine, and (ii) "correlating" an "elevated" level with a vitamin deficiency. Id. at 1358-59, 71 USPQ2d at 1084. The court stated that the disputed claim term "correlating" can include comparing with either an unelevated level or elevated level, as opposed to only an elevated level because adding the "correlating" step in the claim during prosecution to overcome prior art tied the preamble directly to the "correlating" step. Id. at 1362, 71 USPQ2d at 1087. The recitation of the intended use of "detecting" a vitamin deficiency in the preamble rendered the claimed invention a method for "detecting," and, thus, was not limited to detecting "elevated" levels. Id.
See also Catalina Mktg. Int’l, 289 F.3d at 808-09, 62 USPQ2d at 1785 ("[C]lear reliance on the preamble during prosecution to distinguish the claimed invention from the prior art transforms the preamble into a claim limitation because such reliance indicates use of the preamble to define, in part, the claimed invention.…Without such reliance, however, a preamble generally is not limiting when the claim body describes a structurally complete invention such that deletion of the preamble phrase does not affect the structure or steps of the claimed invention." Consequently, "preamble language merely extolling benefits or features of the claimed invention does not limit the claim scope without clear reliance on those benefits or features as patentably significant."). In Poly-America LP v. GSE Lining Tech. Inc., 383 F.3d 1303, 1310, 72 USPQ2d 1685, 1689 (Fed. Cir. 2004), the court stated that "a ‘[r]eview of the entirety of the ’047 patent reveals that the preamble language relating to ‘blown-film’ does not state a purpose or an intended use of the invention, but rather discloses a fundamental characteristic of the claimed invention that is properly construed as a limitation of the claim.’" Compare Intirtool, Ltd. v. Texar Corp., 369 F.3d 1289, 1294-96, 70 USPQ2d 1780, 1783-84 (Fed. Cir. 2004) (holding that the preamble of a patent claim directed to a "hand-held punch pliers for simultaneously punching and connecting overlapping sheet metal" was not a limitation of the claim because (i) the body of the claim described a "structurally complete invention" without the preamble, and (ii) statements in prosecution history referring to "punching and connecting" function of invention did not constitute "clear reliance" on the preamble needed to make the preamble a limitation).
Applicant’s arguments rely on language solely recited in preamble recitations in claim 3. When reading the preamble in the context of the entire claim, the recitation “… control device for controlling operation by an arbitrary wire-cut electrical discharge machine …” is not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.
In regards to the Applicant’s argument,
A USPTO Memorandum dated August 4, 2025 entitled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" reiterated the directives indicated in the previous paragraph of these remarks and went on to more particularly indicate, in the last paragraph of page 2, that "[t]he mental process grouping is not without limits. Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind (emphasis added)."
It is evident that the subject features recited in both the previous form of claim 1, and especially as amended in this paper, could not practically be performed in the human mind without the associated hardware and the assistance of a special purpose computer programmed to apply the specialized algorithms disclosed in the specification of the present application and recited in the claims. (see Remarks, pg. 8, paragraph 5 - pg. 9, paragraph 1)
The Examiner respectfully disagrees.
MPEP 2106.04(a)(2)(III) recites:
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Mental processes performed by humans with the assistance of physical aids such as pens or paper are explained further below with respect to point B.
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). Mental processes recited in claims that require computers are explained further below with respect to point C.
MPEP 2106.04(a)(2)(III)(C) recites:
C. A Claim That Requires a Computer May Still Recite a Mental Process
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").
In evaluating whether a claim that requires a computer recites a mental process, Examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, Examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and Applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
1. Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01.
2. Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were "the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries." 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
3. Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of "anonymous loan shopping", which was a concept that could be "performed by humans without a computer." 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
MPEP 2106.05(f) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. 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 Examiner recognizes neither the claims nor the specification recites a special purpose computer programmed to apply the specialized algorithms …”. Hence, the Applicant’s argument directed to “… a special purpose computer programmed to apply the specialized algorithms …” is found unpersuasive. The Examiner respectfully notes, the Applicant’s argument is broad and conclusionary and lacks any support from the specification to assert the argument.
In addition, the Examiner notes the limitation of “determine an output of an alarm in a case where at least one degree of deterioration selected from degrees of deterioration that are for the ion exchange resin, the power supply die, and the electrode line guide roller exceeds a preset threshold …” is an abstract idea that falls within “Mental Processes” grouping of abstracts since the limitation as drafted, is a process, under its broadest reasonable interpretation, that can be performed in the mind. The Examiner recognizes the Applicant has not presented any evidence/rational(s) as why the limitation directed to “determine an output of an alarm in a case where at least one degree of deterioration selected from degrees of deterioration that are for the ion exchange resin, the power supply die, and the electrode line guide roller exceeds a preset threshold …” is so complex that they could not reasonably analyzed per a comparison of data by a human mind (see MPEP 2106.04(a)(2)(III)); i.e. the Applicant has not presented any evidence/rational(s) to why the claimed data is so complex that a human mind could not compare different pieces of data to ascertain information about the data).
Also, the Examiner emphasizes the Courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid. Thus, the use of a processor as recited in claim 3 is merely using a processor as a tool to perform the mental concept of “determine”; hence the Applicant’s argument is unpersuasive.
With respect to the Applicant’s argument,
The August 4, 2025 USPTO Memorandum entitled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" included similar directives in this regard indicating, in the second paragraph of page 2, that "[t]his limitation requires specific mathematical calculations by referring to the mathematical calculations by name, i.e., a backpropagation algorithm and a gradient descent algorithm, and therefore recites a judicial exception, namely an abstract idea (emphasis added)."
It is evident that none of the recitations of independent claim 1 of the present application require any specific mathematical calculations by referring to the mathematical calculations by name as indicated the above-quoted USPTO Memorandum dated August 4, 2025. (see Remarks, pg. 9, paragraphs 1-2)
Applicant believes that these directives from the August 4, 2025 USPTO Memorandum bolster Applicant's previously-filed remarks as set forth at pages 16-24 of the response filed on December 11, 2024 in this application explaining why any alleged recited judicial exception in claim 1 is integrated into a practical application and, under the USPTO's 2019 101 Guidelines, the claim should be deemed to be eligible because it is not directed to any recited judicial exception.
The Examiner agrees.
The Examiner maintains all the limitations of claim 1 were addressed in the Step 2A, prong two and Step 2B of the 35 U.S.C. 103 rejection of claim 3. The Examiner respectfully notes the Applicant has not addressed the rejection of claim 3, inclusive of the limitation of claim 1, as set forth in the Non-Final Office mailed on 20 August 2025; hence, the argument is not persuasive.
In regards to the Applicant’s argument
Applicant respectfully submits that the predictions made by the device of claim 3 do not only control something, namely operation by an arbitrary wire-cut electrical discharge machine, using the prediction results, as discussed above, but also the claimed prediction features result in particular improvements and advantages that directly result from these claimed prediction features. These include improvements and advantages as discussed in the portions of the specification quoted at pages 18-21 of the response filed on December 11, 2024 in this application. As a result, these logical structures and prediction processes, as recited in claim 3 of the present application, bring claim 3 into the realm of patent eligibility as discussed in the above-quoted Ex parte Desjardins Appeals Review Panel decision. (see Remarks, pg. 11, paragraph 3 – pg. 12, paragraph 4)
The Examiner respectfully disagrees.
As set forth above (pg. 3, paragraph 5), the Examiner maintains the Applicant’s argument to “control” relies on language solely recited in preamble recitations in claim 3. When reading the preamble in the context of the entire claim, the recitation “… control device for controlling operation by an arbitrary wire-cut electrical discharge machine …” is not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.
Further, the Examiner maintains the position set forth in the Non-Final Office Action mailed on 20 August 2025 directed to the claimed invention providing an improvement over the prior art, recited below in its entirety for brevity:
5. In regards to the Applicant’s argument,
If these rejections are maintained, the Examiner is requested to clarify in the next Office Communication why these above-quoted directives from MPEP § 2106.04(a)(2) do not apply in this case. Applicant respectfully submits that these directives clearly do apply in this case. For example, claim 1 explicitly recites “collecting the bundle of detection values for each of the plurality of sensor values as reference” and “estimate, for the at least one sensor value, an abnormality cause... .” (see Remarks, pg. 12, paragraph 2)
The Examiner respectfully disagrees.
MPEP 2106.04(a)(2): Abstract Idea Groupings:
I. MATHEMATICAL CONCEPTS
The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. The Supreme Court has identified a number of concepts falling within this grouping as abstract ideas including: a procedure for converting binary-coded decimal numerals into pure binary form, Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972); a mathematical formula for calculating an alarm limit, Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ2d 193, 195 (1978); the Arrhenius equation, Diamond v. Diehr, 450 U.S. 175, 191, 209 USPQ 1, 15 (1981); and a mathematical formula for hedging, Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ 2d 1001, 1004 (2010).
The Court’s rationale for identifying these "mathematical concepts" as judicial exceptions is that a ‘‘mathematical formula as such is not accorded the protection of our patent laws,’’ Diehr, 450 U.S. at 191, 209 USPQ at 15 (citing Benson, 409 U.S. 63, 175 USPQ 673), and thus ‘‘the discovery of [a mathematical formula] cannot support a patent unless there is some other inventive concept in its application.’’ Flook, 437 U.S. at 594, 198 USPQ at 199. In the past, the Supreme Court sometimes described mathematical concepts as laws of nature, and at other times described these concepts as judicial exceptions without specifying a particular type of exception. See, e.g., Benson, 409 U.S. at 65, 175 USPQ2d at 674; Flook, 437 U.S. at 589, 198 USPQ2d at 197; Mackay Radio & Telegraph Co. v. Radio Corp. of Am., 306 U.S. 86, 94, 40 USPQ 199, 202 (1939) (‘‘[A] scientific truth, or the mathematical expression of it, is not patentable invention[.]’’). More recent opinions of the Supreme Court, however, have affirmatively characterized mathematical relationships and formulas as abstract ideas. See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 218, 110 USPQ2d 1976, 1981 (2014) (describing Flook as holding "that a mathematical formula for computing ‘alarm limits’ in a catalytic conversion process was also a patent-ineligible abstract idea."); Bilski v. Kappos, 561 U.S. 593, 611-12, 95 USPQ2d 1001, 1010 (2010) (noting that the claimed "concept of hedging, described in claim 1 and reduced to a mathematical formula in claim 4, is an unpatentable abstract idea,").
When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), Examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept. A claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept. See, e.g., Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902-03 (Fed. Cir. 2017) (determining that the claims to a particular configuration of inertial sensors and a particular method of using the raw data from the sensors in order to more accurately calculate the position and orientation of an object on a moving platform did not merely recite "the abstract idea of using ‘mathematical equations for determining the relative position of a moving object to a moving reference frame’."). For example, a limitation that is merely based on or involves a mathematical concept described in the specification may not be sufficient to fall into this grouping, provided the mathematical concept itself is not recited in the claim.
It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).
A. Mathematical Relationships
A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.
Examples of mathematical relationships recited in a claim include:
i. a relationship between reaction rate and temperature, which relationship can be expressed in the form of a formula called the Arrhenius equation, Diamond v. Diehr; 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981);
ii. a conversion between binary coded decimal and pure binary, Benson, 409 U.S. at 64, 175 USPQ at 674;
iii. a mathematical relationship between enhanced directional radio activity and antenna conductor arrangement (i.e., the length of the conductors with respect to the operating wave length and the angle between the conductors), Mackay Radio & Tel. Co. v. Radio Corp. of America, 306 U.S. 86, 91, 40 USPQ 199, 201 (1939) (while the litigated claims 15 and 16 of U.S. Patent No. 1,974,387 expressed this mathematical relationship using a formula that described the angle between the conductors, other claims in the patent (e.g., claim 1) expressed the mathematical relationship in words); and
iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721.
B. Mathematical Formulas or Equations
A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).
Examples of mathematical equations or formulas recited in a claim include:
i. a formula describing certain electromagnetic standing wave phenomena, Mackay Radio & Tel. Co. v. Radio Corp. of America, 306 U.S. 86, 91, 40 USPQ 199, 201 (1939) (50.9(l/lambda<-0.513>);
ii. the Arrhenius equation, Diamond v. Diehr; 450 U.S. 175, 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981) (ln v = CZ + x);
iii. a formula for computing an alarm limit, Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978) (B1=B0 (1.0–F) + PVL(F)); and
iv. a mathematical formula for hedging (claim 4), Bilski v. Kappos, 561 U.S. 593, 599, 95 USPQ2d 1001, 1004 (2010) (Fixed Bill Price = Fi + [(Ci + Ti + LDi) x (α + βE(Wi))]).
C. Mathematical Calculations
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Examples of mathematical calculations recited in a claim include:
i. performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. Investpic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. Investpic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018);
ii. calculating a number representing an alarm limit value using the mathematical formula ‘‘B1=B0 (1.0–F) + PVL(F)’’, Parker v. Flook, 437 U.S. 584, 585, 198 USPQ 193, 195 (1978);
iii. using a formula to convert geospatial coordinates into natural numbers, Burnett v. Panasonic Corp., 741 Fed. Appx. 777, 780 (Fed. Cir. 2018) (non-precedential);
iv. managing a stable value protected life insurance policy via performing calculations, Bancorp Servs., LLC v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012);
v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979); and
vi. calculating the difference between local and average data values, In re Abele, 684 F.2d 902, 903, 214 USPQ 682, 683-84 (CCPA 1982).
U.S. Patent Publication No. 2023/0212980 A1 (instant application):
The MD distance calculation unit 203 calculates a Mahalanobis distance indicating the state of the plant 1 based on the unit space stored by the unit space storage unit 202, with the bundles of detection values acquired by the sensor value acquisition unit 201 as the specifications. The Mahalanobis distance is a measure showing the size of a difference between a reference sample expressed as a unit space and a newly obtained sample. (pg. 2, par. [0039])
The high value abnormality/low value abnormality determination unit 207 identifies, for each of a plurality of sensor values, whether an abnormality that has occurred is a high value abnormality, which is an abnormality caused by a high detection value, which is a sensor value, or a low value abnormality, which is an abnormality caused by a low detection value. That is, the high value abnormality/low value abnormality determination unit 207 identifies whether an increase in the Mahalanobis distance is caused by an increase in the detection value or is caused by a decrease in the detection value. Specifically, the high value abnormality/low value abnormality determination unit 207 calculates a Mahalanobis distance when a value of a bundle of detection values acquired by the sensor value acquisition unit 201 is increased or decreased for each sensor value and identifies whether an abnormality is a high value abnormality or a low value abnormality based on an increase or a decrease in the Mahalanobis distance caused by a change in the value. In a case where an increase in the Mahalanobis distance has occurred due to an increase in the detection value, it is understood that the sensor value has a high value abnormality. In a case where an increase in the Mahalanobis distance has occurred due to a decrease in the detection value, it is understood that the sensor value has a low value abnormality. (Japanese Patent Application No. 2019-063575) (pg. 3, par. [0043])
The abnormality cause estimation unit 209 generates a matrix with M*2 rows and N columns from the failure part estimation database. The failure part estimation database (herein, a portion of M*2 is doubled to distinguish between high value/low value abnormalities) contains information amounts in association with M evaluation items and a high value abnormality and a low value abnormality. For this reason, the abnormality cause estimation unit 209 generates a matrix with M*2 rows and N columns by reading an information amount associated with the high value abnormality/low value abnormality determination unit 207 for each of the M evaluation items. The abnormality cause estimation unit 209 obtains a vector with N rows and 1 column, of which an element is certainty of an abnormality cause, by multiplying a vector with 1 row and M*2 rows, of which an element is a larger-the-better SN ratio of each evaluation item, by the generated matrix with M*2 rows and N columns. The abnormality cause estimation unit 209 estimates that an abnormality cause related to a row having a large element value in the obtained vector with N rows and 1 column is an abnormality cause generated in the plant 1. That is, the abnormality cause estimation unit 209 calculates, for each abnormality cause, a weighted sum of a larger-the-better SN ratio of each evaluation item and an information amount related to an abnormality of the item and estimates an abnormality cause based on the weighted sum. (pgs. 3-4, par. [0049])
The argument directed to “collecting the bundle of detection values for each of the plurality of sensor values as reference” in claim 1 is found unpersuasive since the limitation was addressed in Step 2A, prong two and Step 2B of the subject matter eligibility analysis.
The argument directed to “estimate” in claim 1 is moot in light of the current rejection of claim 1 under 35 U.S.C. 101. The newly amended limitation of “estimate” recites an abstract idea falling in the abstract grouping of mathematical concepts as set forth below since the claim clearly recites the limitation of “estimate” comprises a mathematical operation of multiplication (see MPEP 2106.04(a)(2)(C)). Hence, the Applicant’s argument is found not persuasive.
Hence, the Applicant’s argument is found unpersuasive.
Applicant’s arguments, see Remarks, pgs. 12-14, filed 20 November 2025, with respect to the rejections of claims 1, 3, and 6 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made as follows:
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2018/0246494 A1 (hereinafter Nakahama) in view of U.S. Patent Publication No. 2017/0031329 A1 (hereinafter Inagaki) in further view of U.S. Patent Publication No. 2004/0238417 A1 (hereinafter Arakawa), U.S. Patent Publication No. 2017/0060105 A1 (hereinafter Onodera) and U.S. Patent Publication No. 2019/0163172 A1 (hereinafter Daniel).
Claims 3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Nakahama in view of Inagaki in further view of Arakawa, Onodera, Daniel, U.S. Patent Publication No. 2016/0282400 A1 (hereinafter Yumbe), and U.S. Patent Publication No. 2017/0028593 A1 (hereinafter Maruyama).
Claims 3 and 6 stand rejected under 35 U.S.C. 101 and claims 1, 3, and 6 stand rejected under 35 U.S.C. 103 as set forth below.
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 3 and 6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 3:
At step 1, the claim recites “(a) control device”, therefore is a machine, which is a statutory category of invention.
At step 2A, prong one, the claim recites “… predict a degree of deterioration after wire-cut electrical discharge machining in accordance with the machining condition and the consumables information using the trained model for the at least one selected from the ion exchange resin, the power supply die, and the electrode line guide roller” and “determine an output of an alarm in a case where at least one degree of deterioration selected from degrees of deterioration that are for the ion exchange resin, the power supply die, and the electrode line guide roller exceeds a preset threshold …”.
The limitation of “… predict a degree of deterioration after wire-cut electrical discharge machining in accordance with the machining condition and the consumables information using the trained model generated by the machine learning device for the at least one selected from the power supply die, and the electrode line guide roller” (see pgs. 28-31, paragraphs [0041]-[0047] of the specification as filed on 19 August 2022 (cited by the applicant in the Remarks, pg. 13, paragraph 2, filed on 11 December 2024: “Paragraph [0041] through to paragraph [0047] of the specification of the present application describe the processes and algorithms associated with examples of operation for prediction processing by the prediction device in an operation phase in connection with the flowchart of Fig. 5.”), as drafted, is a process, under its broadest reasonable interpretation covers performing the limitation by use of a mathematical calculation(s).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations per use of mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The limitation of “determine an output of an alarm in a case where at least one degree of deterioration selected from degrees of deterioration that are for the ion exchange resin, the power supply die, and the electrode line guide roller exceeds a preset threshold …”, as drafted, is a process, under its broadest reasonable interpretation covers performing the limitation in the mind. Where, nothing in the claim precludes the step from being practically performed in the mind. For example, “determine” in the context of the claim encompasses evaluating data to identify data is larger than a second piece of data (MPEP 2106.04(a)(2): The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., deriving new data) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another.)
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In the alternative, the limitation of “determine an output of an alarm in a case where at least one degree of deterioration selected from degrees of deterioration that are for the ion exchange resin, the power supply die, and the electrode line guide roller exceeds a preset threshold …” (see pgs. 28-31, paragraphs [0041]-[0047] of the specification as filed on 19 August 2022 (cited by the applicant in the Remarks, pg. 13, paragraph 2, filed on 11 December 2024: “Paragraph [0041] through to paragraph [0047] of the specification of the present application describe the processes and algorithms associated with examples of operation for prediction processing by the prediction device in an operation phase in connection with the flowchart of Fig. 5.”), as drafted, is a process, under its broadest reasonable interpretation covers performing the limitation by use of a mathematical calculation(s).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations per use of mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites “a trained model generated by the machine learning device … of claim 1” –
Claim 1 recites:
“a first memory configured to store a first program”; “a first processor configured to execute the first program and control the machining learning device and control the machine learning device …”; “obtain input data that includes a machining condition for an arbitrary wire-cut electrical discharge machining with respect to an arbitrary workpiece by arbitrary wire-cut electrical discharge machine and consumables information including a degree of deterioration before wire-cut electrical discharge machining in accordance with the machining condition for at least one selected from an ion exchange resin, a power supply die, and an electrode line guide roller, wherein the machining condition includes at least one selected from a type of machining fluid, a fluid pressure for the machining fluid, a feeding speed for an electrode line, and a workpiece plate thickness”; “obtain label data indicating a degree of deterioration after wire-cut electrical discharge machining in accordance with the machining condition included in the input data for the at least one selected from the ion exchange resin, the power supply die, and the electrode line guide roller”; and “use the input data and the label data to execute supervised learning and generate a trained model”;
Claim 3 – “the machine learning device according to claim 1”, “the trained model generated by the machine learning device”; “a second memory configured to store a second program”; “a second processor configured to execute the second program and control the prediction device …”; and “input, … , before wire-cut electrical discharge machining to be performed by a wire-cut electrical discharge machine, a machining condition for the wire-cut electrical discharge machining and the consumables information including a degree of deterioration before the wire-cut electrical discharge machining for at least one selected from an ion exchange resin, a power supply die, and an electrode line guide roller”; and “… the alarm makes an instruction to exchange the ion exchange resin, the power supply die, or the electrode line guide roller for which the predicted degree of deterioration exceeded the threshold, or makes an instruction to adjust the machining condition”.
The limitations of “a machine learning device” (claim 1), “a first memory configured to store a first program” (claim 1); “a first processor configured to execute the first program and control the machining learning device …” (claim 1); “the machine learning device according to claim 1” (claim 3), “the trained model generated by the machine learning device” (claim 3); “a second memory configured to store a second program” (claim 3); and “a second processor configured to execute the second program and control the prediction device …” (claim 3) are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer component (see MPEP 2106.05(f)).
The limitations of “obtain input data that includes a machining condition for an arbitrary wire-cut electrical discharge machining with respect to an arbitrary workpiece by arbitrary wire-cut electrical discharge machine and consumables information including a degree of deterioration before wire-cut electrical discharge machining in accordance with the machining condition for at least one selected from an ion exchange resin, a power supply die, and an electrode line guide roller, wherein the machining condition includes at least one selected from a type of machining fluid, a fluid pressure for the machining fluid, a feeding speed for an electrode line, and a workpiece plate thickness” (claim 1); “obtain label data indicating a degree of deterioration after wire-cut electrical discharge machining in accordance with the machining condition included in the input data for the at least one selected from the ion exchange resin, the power supply die, and the electrode line guide roller” (claim 1); “use the input data and the label data to execute supervised learning and generate a trained model” (claim 1); “input, …, before wire-cut electrical discharge machining to be performed by a wire-cut electrical discharge machine, a machining condition for the wire-cut electrical discharge machining and the consumables information including a degree of deterioration before the wire-cut electrical discharge machining for at least one selected from an ion exchange resin, a power supply die, and an electrode line guide roller” (claim 3); and “… the alarm makes an instruction to exchange the ion exchange resin, the power supply die, or the electrode line guide roller for which the predicted degree of deterioration exceeded the threshold, or makes an instruction to adjust the machining condition” (claim 3) represent mere data gathering to obtain data for performing the abstract idea of “predict”. The limitations are recited at a high level of generality and so generically it represents an insignificant extra-solution activity of gathering data (see MPEP 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As previously discussed with respect to the integration of the abstract idea into a practical application, the additional elements of “a machine learning device” (claim 1), “a first memory configured to store a first program” (claim 1); “a first processor configured to execute the first program and control the machining learning device …” (claim 1); “the machine learning device according to claim 1” (claim 3), “the trained model generated by the machine learning device” (claim 3); “a second memory configured to store a second program” (claim 3); and “a second processor configured to execute the second program and control the prediction device …” (claim 3) amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(d)(II), “Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).”
The limitations of “obtain input data that includes a machining condition for an arbitrary wire-cut electrical discharge machining with respect to an arbitrary workpiece by arbitrary wire-cut electrical discharge machine and consumables information including a degree of deterioration before wire-cut electrical discharge machining in accordance with the machining condition for at least one selected from an ion exchange resin, a power supply die, and an electrode line guide roller, wherein the machining condition includes at least one selected from an amount of machining time, a type of machining fluid, a fluid pressure for the machining fluid, a feeding speed for an electrode line, and a workpiece plate thickness” (claim 1); “obtain label data indicating a degree of deterioration after wire-cut electrical discharge machining in accordance with the machining condition included in the input data for the at least one selected from the ion exchange resin, the power supply die, and the electrode line guide roller” (claim 1); “use the input data and the label data to execute supervised learning and generate a trained model” (claim 1); and “input, before wire-cut electrical discharge machining to be performed by a wire-cut electrical discharge machine, a machining condition for the wire-cut electrical discharge machining and the consumables information including a degree of deterioration before the wire-cut electrical discharge machining for at least one selected from an ion exchange resin, a power supply die, and an electrode line guide roller” (claim 3), as discussed above, represent an insignificant extra-solution activity of data gathering. Further, the limitations are well-understood, routine and conventional; wherein the courts have found limitations directed to obtaining data, recited at high level of generality, to be well-routine, and conventional. See MPEP 2106.05(d)(II), “storing and retrieving information in memory”.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
Claim 6:
At step 2A, prong two, the claim recites “… the trained model is provided in a server connected so as to be accessible from the prediction device via a network”.
The limitations of “… a server …”, “… the prediction device …”; and “… a network … ” are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer component (see MPEP 2106.05(f)).
The limitation of “… the trained model is provided in a server” is recited at a high level of generally and recited so generically it represents no more than an insignificant extra-solution activity of gathering data (see MPEP 2106.05(g)).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As previously discussed with respect to the integration of the abstract idea into a practical application, the additional elements of “… a server …”; “… the prediction device …”; and “… a network … ”, amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(d)(II), “Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).”
The limitation of “… the trained model is provided in a server” amounts to no more than mere data gathering. In addition, the limitation is well-understood, routine and conventional; wherein the courts have found limitations directed to obtaining data, recited at high level of generality, to be well-understood, routine and conventional. See MPEP 2106.05(d)(II), “storing and retrieving information in memory”.
Considering the additional elements individually and in combination and the claim as a whole, the additional element does not provide significantly more than the abstract idea. The claim is not patent eligible.
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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2018/0246494 A1 (hereinafter Nakahama) in view of U.S. Patent Publication No. 2017/0031329 A1 (hereinafter Inagaki) in further view of U.S. Patent Publication No. 2004/0238417 A1 (hereinafter Arakawa), U.S. Patent Publication No. 2017/0060105 A1 (hereinafter Onodera) and U.S. Patent Publication No. 2019/0163172 A1 (hereinafter Daniel).
As per claim 1, Nakahama teaches the limitations of a machine learning device (pg. 3, par. [0039], [0046]-[0048], pg. 4, par. [0061] and Fig. 2, element 111 of Fig. 2, element 100; i.e. a tool state estimation apparatus comprising of a learning section (i.e. a neural network) that performs supervised learning), comprising:
a first memory (pg. 3, par. [0048]; i.e. “The neural network is constituted by a calculation unit, a memory, and the like that realize a neural network following a neuron model as shown in, for example, FIG. 4.”); and
a first processor configured control the machine learning device (pg. 3, par. [0039] and [0048]; i.e. [0039]: “The tool state estimation apparatus 100 of the embodiment may be realized by a controller that controls a machine tool at the production facility of a manufacturing industry, a personal computer connected to the controller, a host computer that comprehensively manages the respective controllers, or the like.” and [0048]: “The neural network is constituted by a calculation unit, a memory, and the like that realize a neural network following a neuron model as shown in, for example, FIG. 4.”) to:
obtain input data that includes a machining condition for machining with respect to a workpiece by a machine (pg. 2, par. [0033], pg. 3, par. [0041]-[0043] and pg. 4, par. [0062]; i.e. [0041]: “… dynamic information on the vicinity of a tool may include image information (a still image and a moving image) around a tool collected by an image pickup unit such as a camera, information on sound picked up by a sound pickup unit such as a microphone and generated between a tool and a workpiece in machining, a type of a tool for machining, a material of a workpiece, a type of coolant, a federate of a tool, a spindle speed, a blade tip temperature, a cumulative cutting time/cumulative cutting distance for each tool, cutting resistance (amplifier current values of a feed axis and a spindle), or the like.”, [0042]: “The log data storage section 200 may store dynamic information on the vicinity of a tool collected from a plurality of machine tools as log data.”, and [0062]: “The state observation section 112 generates input data from log data stored in the log data storage section 200 and outputs the generated input data to the learning section 111.”);
obtain label data indicating a degree of deterioration in accordance with the machining condition included in the input data (pgs. 4-5, par. [0066]: “The label acquisition section 113 generates teacher data (fine/deteriorated tool state) corresponding to input data simultaneously with the generation of the input data by the state observation section 112 based on log data stored in the log data storage section 200, and then outputs the generated teacher data to the learning section 111.”); and
use the input data and the label data to execute supervised learning and generate a trained model (pg. 4, par. [0061]; i.e. “The learning section 111 performs supervised learning based on input data acquired by the state observation section 112 and teacher data (also called a label) acquired by the label acquisition section 113 to construct a learning model and stores the constructed learning model in the learning model storage section 114.”).
Not explicitly taught are a first memory configured to store a first program; and
a first processor configured to execute the first program to:
input data that includes a machining condition for arbitrary wire-cut electrical discharge machining with respect to an arbitrary workpiece by an arbitrary wire-cut electrical discharge machine and consumables information including a degree of deterioration before wire-cut electrical discharge machining in accordance with the machining condition for at least one selected from an ion exchange resin, a power supply die, and an electrode line guide roller, wherein the machining condition includes at least one selected from a type of machining fluid, a fluid pressure for the machining fluid, a feeding speed for an electrode line, and a workpiece plate thickness; and
label data indicating a degree of deterioration after wire-cut electrical discharge machining in accordance with the machining condition included in the input data for the at least one selected from the ion exchange resin, the power supply die, and the electrode line guide roller.
However Inagaki, in an analogous art of machine learning (pg. 1, par. [0003]), teaches the missing limitation of input data that includes consumables information including a degree of deterioration before machining (pg. 2, par. [0035], pg. 3, par. [0046], [0047], and [0049]-[0051]; i.e. [0047]: “The internal data may include at least one of the torque, the position, the velocity, the acceleration, the jerk, the current, the voltage, and the estimated disturbance value. The estimated disturbance value is, for example, a disturbance value estimated by an observer based on a torque command and velocity feedback.”, [0050]: “When learning is started, the state observation unit 52 obtains a state variable including, e.g., output data, internal data, or computational data in step S201. In step S202, the determination data obtaining unit 51 obtains determination data on the basis of the determination result obtained by the fault determination unit 31.”, and [0051]: “In step S203, the learning unit 53 learns fault conditions in accordance with a training data set generated based on a combination of the state variable obtained in step S201 and the determination data obtained in step S202.”); and
label data indicating a degree of deterioration after machining in accordance with a machining condition (pgs. 2-3, par. [0035], [0039], and [0045]; i.e. [0039]: “Alternatively, the fault determination unit 31 may determine that a fault has occurred in the robot 2, based on internal data of control software stored in the robot controller 3. In this manner, the fault determination unit 31 determines faults based on various factors. The determination result obtained by the fault determination unit 31 is input to a determination data obtaining unit 51 of the machine learning device 5 (to be described later).” and [0045]: “The determination data is defined as data used to determine whether a fault has occurred or the degree of fault.”) for the purpose of performing machine learning (pg. 1, par. [0009]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Nakahama to include the addition of the limitations of input data that includes consumables information including a degree of deterioration before machining; and label data indicating a degree of deterioration after machining in accordance with a machining condition to advantageously perform accurate fault prediction even when factors which may lead to faults are complicated and make it difficult to preset fault conditions (Inagaki: pg. 4, par. [0060]).
Nakahama in view of Inagaki does not expressly teach a first memory configured to store a first program; and
a first processor configured to execute the first program to:
a machining condition for arbitrary wire-cut electrical discharge machining with respect to an arbitrary workpiece by an arbitrary wire-cut electrical discharge machine and consumables information including a degree of deterioration before wire-cut electrical discharge machining in accordance with the machining condition for at least one selected from an ion exchange resin, a power supply die, and an electrode line guide roller, wherein the machining condition includes at least one selected from a type of machining fluid, a fluid pressure for the machining fluid, a feeding speed for an electrode line, and a workpiece plate thickness; and
label data indicating a degree of deterioration after wire-cut electrical discharge machining in accordance with the machining condition included in the input data for the at least one selected from the ion exchange resin, the power supply die, and the electrode line guide roller.
However Arakawa, in an analogous art of a wire-cut electric discharge machine (pg. 1, par. [0002]), teaches the missing limitation of wire-cut electrical discharge machining in accordance with a machining condition for an ion exchange resin (pg. 2, par. [0015]-[0017]; i.e. [0015]: “… in a machining fluid treating device for a wire-cut electric discharge machine arranged to circulate machining fluid to pass it through ion exchange resin, a monitoring means for monitoring the ion-exchange capacity of the ion exchange resin is provided so that display of the ion-exchange capacity of the ion exchange resin and calculation and display of the life of the ion exchange resin can be performed on the basis of the result of monitoring by the monitoring means.”, [0016]: “Thus, the operator can easily predict the time left until the ion-exchange capacity reaches a capacity lowering limit predetermined as an indicator of need to replace resin.”, and [0017]: “When the prediction curve is displayed, the operator can know the time to replace the resin, more easily and definitely. Further, the time at which the ion-exchange capacity reaches the capacity lowering limit can be obtained as an intersection of the prediction curve and a line representing the capacity lowering limit, and the rest of the life (time left until replacement of the resin becomes necessary) can be displayed directly.”) for the purpose of predicting the time left until an ion exchange resin need to be replaced (pg. 2, par. [0016]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Nakahama in view of Inagaki to include the addition of the limitation of wire-cut electrical discharge machining in accordance with a machining condition for an ion exchange resin to advantageously prevent production of defective goods and an increase in running cost in wire-cut electric discharge machining (Arakawa: pgs. 1-2, par. [0014]).
Nakahama in view of Inagaki in further view of Arakawa does not expressly teach a first memory configured to store a first program;
a first processor configured to execute the first program; and
wherein the machining condition includes at least one selected from a type of machining fluid, a fluid pressure for the machining fluid, a feeding speed for an electrode line, and a workpiece plate thickness.
However Onodera, in an analogous art of a wire electric discharge machine (pg. 1, par. [0002]), teaches the missing limitation of a machining condition includes at least one selected from a fluid pressure for the machining fluid, a feeding speed for an electrode line, and a workpiece plate thickness (pg. 4, par. [0043] and [0047]; i.e. [0043]: “… information for which the machine learning device 20 specifies an environment (the state s.sub.t described in <1. Machine learning>): environment information such as a plate thickness of a workpiece which is a machining object, a material of a workpiece, a wire diameter of a wire used for machining, and a nozzle gap representing a distance between upper and lower nozzles, which stretch a wire, and a workpiece; position information which is a coordinate value of each axis of the wire electric discharge machine; and machining information such as an actual voltage value, an actual current value, a machining speed, the number of times of electric discharge, a fluid pressure of a machining fluid, and an occurrence of short circuit/disconnection which are measured in machining of a workpiece.” and [0047]: “… the machine learning device 20 performs machine learning based on the above-mentioned input data, output data, and reward.”) for the purpose of performing machine learning (pg. 4, par. [0043] and [0047]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Nakahama in view of Inagaki in further view of Arakawa to include the addition of the limitations of a machining condition includes at least one selected from a fluid pressure for the machining fluid, a feeding speed for an electrode line, and a workpiece plate thickness to efficiently perform machine learning (Onodera: pgs. 6-7, par. [0083]).
Nakahama in view of Inagaki in further view of Arakawa does not expressly teach a first memory configured to store a first program; and
a first processor configured to execute the first program.
However Daniel, in an analogous art of predictive event determination (pg. 5, par. [0042]), teaches the missing limitations of a first memory configured to store a first program (pg. 5, par. [0041]; i.e. “For each component type (e.g., a gas nozzle) for which cell data is collected, a set of aggregated cell data for that component type is generated. The central controller 220 (or 230) analyzes (e.g., trains on) the set of aggregated cell data and generates a predictive model (PM) related to future maintenance of the component type. A predictive model generated by a central controller is in the form of a set of computer-executable instructions and/or data stored in a memory and capable of being executed by a processor, in accordance with one embodiment.”); and
a first processor configured to execute the first program (pg. 5, par. [0041]; i.e. “For each component type (e.g., a gas nozzle) for which cell data is collected, a set of aggregated cell data for that component type is generated. The central controller 220 (or 230) analyzes (e.g., trains on) the set of aggregated cell data and generates a predictive model (PM) related to future maintenance of the component type. A predictive model generated by a central controller is in the form of a set of computer-executable instructions and/or data stored in a memory and capable of being executed by a processor, in accordance with one embodiment.”) to determine when a component should be serviced or replaced (pg. 1, par. [0005]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Nakahama in view of Inagaki in further view of Arakawa and Onodera to include the addition of the limitations of a first memory configured to store a first program; and a first processor configured to execute the first program to advantageously minimize waste and maximize productivity (Daniel: pgs. 1, par. [0003] and pg. 6, par. [0051]).
Claims 3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Nakahama in view of Inagaki in further view of Arakawa, Onodera, Daniel, U.S. Patent Publication No. 2016/0282400 A1 (hereinafter Yumbe), and U.S. Patent Publication No. 2017/0028593 A1 (hereinafter Maruyama).
As per claim 3, the limitations of “the machine learning device according to claim 1” and “the trained model generated by the machine learning device” stand rejected for the same rationale as set forth in claim 1 by virtue of their incorporation of the machine leaning device and the trained model of claim 1.
Further, Nakahama teaches a control device for controlling operation by a machine tool (pg. 3, par. [0039], Fig. 2, element 100, Fig. 9, element 100; i.e. tool state estimation apparatus and [0039]: “A tool state estimation apparatus 100 of the embodiment may be realized by a controller that controls a machine tool at the production facility of a manufacturing industry, a personal computer connected to the controller, a host computer that comprehensively manages the respective controllers, or the like.”), comprising:
a prediction device (pg. 5, par. [0071] and Fig. 9, element 100 comprising of Fig. 9, element 115; i.e. a tool state estimation apparatus comprising an estimation section), comprising:
input, from the control device, before machining a machining condition for the machining (pg. 2, par. [0033], pg. 3, par. [0041]-[0043], pg. 4, par. [0062], and pg. 5, par. [0071] and [0072]; i.e. [0041]: “… dynamic information on the vicinity of a tool may include image information (a still image and a moving image) around a tool collected by an image pickup unit such as a camera, information on sound picked up by a sound pickup unit such as a microphone and generated between a tool and a workpiece in machining, a type of a tool for machining, a material of a workpiece, a type of coolant, a federate of a tool, a spindle speed, a blade tip temperature, a cumulative cutting time/cumulative cutting distance for each tool, cutting resistance (amplifier current values of a feed axis and a spindle), or the like.”, [0042]: “The log data storage section 200 may store dynamic information on the vicinity of a tool collected from a plurality of machine tools as log data.”, [0062]: “The state observation section 112 generates input data from log data stored in the log data storage section 200 and outputs the generated input data to the learning section 111.”, [0071]: “The tool state estimation apparatus 100 in FIG. 9 has the state observation section 112, the learning model storage section 114, and an estimation section 115.”, and [0072]: “The state observation section 112 acquires dynamic information on the vicinity of a tool used as input data in the learning described above via the input/output section 17 during the operation of the machine tool 1, generates input data based on the acquired information …”);
predict a state in accordance with the machining condition after the machining using the trained model (pg. 5, par. [0072] and [0073]; i.e. “The estimation section 115 estimates a state of a tool based on input data (dynamic information on the vicinity of the tool) input from the state observation section 112 using a learning model stored in the learning model storage section 114, and outputs a result of the estimation to the input/output section 17.”); and
determine an output of an alarm (pg. 5, par. [0073]; i.e. “Then, when the result of the estimation of the state of the tool input from the estimation section 115 indicates that the state of the tool has been deteriorated, the input/output section 17 instructs the alert section 23 to issue an alert.”).
Nakahama does not expressly teach a second memory configured to store a second program;
a second processor configured to execute the second program and control the prediction device;
input, before wire-cut electrical discharge machining to be performed by a wire-cut electrical discharge machine, a machining condition for the wire-cut electrical discharge machining and the consumables information including a degree of deterioration before the wire-cut electrical discharge machining for at least one selected from an ion exchange resin, a power supply die, and an electrode line guide roller;
predict a degree of deterioration after wire-cut electrical discharge machining in accordance with the consumables information using the trained model for the at least one selected from the ion exchange resin, the power supply die, and the electrode line guide roller; and
determine an output of an alarm in a case where at least one degree of deterioration selected from degrees of deterioration that are for the ion exchange resin, the power supply die, and the electrode line guide roller exceeds a preset threshold, the alarm makes an instruction to exchange the ion exchange resin, the power supply die, or the electrode line guide roller for which the predicted degree of deterioration exceeded the threshold, or makes an instruction to adjust the machining condition.
However Inagaki, in an analogous art of machine learning (pg. 1, par. [0003]), teaches the missing limitation of the consumables information including a degree of deterioration before machining (pg. 2, par. [0035], pg. 3, par. [0046], [0047], and [0049]-[0051]; i.e. [0047]: “The internal data may include at least one of the torque, the position, the velocity, the acceleration, the jerk, the current, the voltage, and the estimated disturbance value. The estimated disturbance value is, for example, a disturbance value estimated by an observer based on a torque command and velocity feedback.”, [0050]: “When learning is started, the state observation unit 52 obtains a state variable including, e.g., output data, internal data, or computational data in step S201. In step S202, the determination data obtaining unit 51 obtains determination data on the basis of the determination result obtained by the fault determination unit 31.”, and [0051]: “In step S203, the learning unit 53 learns fault conditions in accordance with a training data set generated based on a combination of the state variable obtained in step S201 and the determination data obtained in step S202.”);
predict a degree of deterioration after the machining in accordance with the consumables information using a trained model (pg. 2, par. [0035], pg. 4, par. [0064] and [0065]; i.e. [0064]: “A fault prediction system 1 includes a fault prediction device 4 which generates fault information for a robot 2, using the learning result obtained by a machine learning device 5.” and [0065]: “The state observation unit 41 functions similarly to the state observation unit 52 described with reference to FIG. 1 and obtains a state variable reflecting the state of the robot 2 or the surrounding environment. The fault information output unit 42 outputs the fault information of the robot 2 in response to input of the state variable via the state observation unit 41, based on the result of learning by a learning unit 53 of the above-mentioned machine learning device 5 in accordance with a training data set.”) for the purpose of performing machine learning (pg. 1, par. [0009]); and
determine an output of at least one degree of deterioration exceeds a preset threshold (pgs. 4-5, par. [0065]-[0067]; i.e. [0065]: “The fault information output unit 42 outputs the fault information of the robot 2 in response to input of the state variable via the state observation unit 41, based on the result of learning by a learning unit 53 of the above-mentioned machine learning device 5 in accordance with a training data set.”, [0066]: “… a notifying unit (fault information notifying unit) 32, as depicted as FIG. 6. The notifying unit 32 notifies the operator of the fault information output from the fault information output unit 42. The mode in which the operator is notified of the fault information is not particularly limited as long as the fault information is identifiable to the operator. For example, information indicating whether a predicted fault has occurred or the degree of fault may be displayed on a display (not illustrated) or an alarm sound may be produced in accordance with the details of the fault information”, and [0067]: “… the first example, an index value representing the “degree of fault” may be set higher for a state closer to a fault, and the fault information output unit 42 can output the index value obtained by learning, directly as fault information, as illustrated as (a) of FIG. 7. In, e.g., the second example, a threshold may be set for the above-mentioned index value, and the fault information output unit 42 can output as fault information, information indicating whether a fault has occurred by defining a value equal to or larger than the threshold as being abnormal, and a value smaller than the threshold as being normal, as illustrated as (b) of FIG. 7. In, e.g., the third example, a plurality of thresholds (thresholds 1 to 3) may be set for the above-mentioned index value, and the fault information output unit 42 can output as fault information, threshold-specific levels (fault levels 1 to 4), as illustrated as (c) of FIG. 7.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Nakahama to include the addition of the limitations of the consumables information including a degree of deterioration before machining; predict a degree of deterioration after the machining in accordance with the consumables information using a trained model; and determine an output of at least one degree of deterioration exceeds a preset threshold to advantageously perform accurate fault prediction even when factors which may lead to faults are complicated and make it difficult to preset fault conditions (Inagaki: pg. 4, par. [0060]).
Nakahama does not expressly teach a second memory configured to store a second program;
a second processor configured to execute the second program and control the prediction device;
wire-cut electrical discharge machining to be performed by a wire-cut electrical discharge machine, a machining condition for the wire-cut electrical discharge machining and the consumables information including a degree of deterioration before the wire-cut electrical discharge machining for at least one selected from an ion exchange resin, a power supply die, and an electrode line guide roller;
predict a degree of deterioration after wire-cut electrical discharge machining for the at least one selected from the ion exchange resin, the power supply die, and the electrode line guide roller; and
determine an output in a case where at least one degree of deterioration selected from degrees of deterioration that are for the ion exchange resin, the power supply die, and the electrode line guide roller exceeds a preset threshold, the alarm makes an instruction to exchange the ion exchange resin, the power supply die, or the electrode line guide roller for which the predicted degree of deterioration exceeded the threshold, or makes an instruction to adjust the machining condition.
However Arakawa, in an analogous art of a wire-cut electric discharge machine (pg. 1, par. [0002]), teaches the missing limitations of wire-cut electrical discharge machining performed by a wire-cut electrical discharge machine (pg. 2, par. [0015]; i.e.: “… in a machining fluid treating device for a wire-cut electric discharge machine arranged to circulate machining fluid to pass it through ion exchange resin, a monitoring means for monitoring the ion-exchange capacity of the ion exchange resin is provided so that display of the ion-exchange capacity of the ion exchange resin and calculation and display of the life of the ion exchange resin can be performed on the basis of the result of monitoring by the monitoring means.”);
information for wire-cut electrical discharge machining of the ion exchange resin (pg. 2, par. [0015]; i.e. [0015]: “… in a machining fluid treating device for a wire-cut electric discharge machine arranged to circulate machining fluid to pass it through ion exchange resin, a monitoring means for monitoring the ion-exchange capacity of the ion exchange resin is provided so that display of the ion-exchange capacity of the ion exchange resin and calculation and display of the life of the ion exchange resin can be performed on the basis of the result of monitoring by the monitoring means.”);
predict after wire-cut electrical discharge machining for the ion exchange resin pg. 2, par. [0015]-[0017]; i.e. [0015]: “… in a machining fluid treating device for a wire-cut electric discharge machine arranged to circulate machining fluid to pass it through ion exchange resin, a monitoring means for monitoring the ion-exchange capacity of the ion exchange resin is provided so that display of the ion-exchange capacity of the ion exchange resin and calculation and display of the life of the ion exchange resin can be performed on the basis of the result of monitoring by the monitoring means.”, [0016]: “Thus, the operator can easily predict the time left until the ion-exchange capacity reaches a capacity lowering limit predetermined as an indicator of need to replace resin.”, and [0017]: “When the prediction curve is displayed, the operator can know the time to replace the resin, more easily and definitely. Further, the time at which the ion-exchange capacity reaches the capacity lowering limit can be obtained as an intersection of the prediction curve and a line representing the capacity lowering limit, and the rest of the life (time left until replacement of the resin becomes necessary) can be displayed directly.”); and
an alarm indicates a need to exchange the ion exchange resin (pg. 6, par. [0074]; i.e. “… An alarm 2 indicating that the resin life will expire in a short time is outputted, and the procedure returns to Step S1 and repeats the processing. For example, a yellow lamp of the alarm 15 is made to blink and/or the buzzer is sounded (at a low level) to warn the operator about the situation. Normally, at this stage, the operator stops the operation of the machine (and also terminates the present process forcibly) and replaces the ion exchange resin with new resin.”) for the purpose of predicting the time left until an ion exchange resin need to be replaced (pg. 2, par. [0016]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Nakahama in view of Inagaki to include the addition of the limitations of wire-cut electrical discharge machining performed by a wire-cut electrical discharge machine; Information for wire-cut electrical discharge machining of the ion exchange resin; predict after wire-cut electrical discharge machining for the ion exchange resin; and an alarm indicates a need to exchange the ion exchange resin to advantageously prevent production of defective goods and an increase in running cost in wire-cut electric discharge machining (Arakawa: pgs. 1-2, par. [0014]).
Nakahama in view of Inagaki in further view of Arakawa does not expressly teach a second memory configured to store a second program;
a second processor configured to execute the second program and control the prediction device; and
the alarm makes an instruction to exchange the ion exchange resin, the power supply die, or the electrode line guide roller for which the predicted degree of deterioration exceeded the threshold, or makes an instruction to adjust the machining condition.
Nakahama in view of Inagaki in further view of Arakawa and Onodera does not expressly teach a second memory configured to store a second program;
a second processor configured to execute the second program and control the prediction device; and
the alarm makes an instruction to exchange the ion exchange resin, the power supply die, or the electrode line guide roller for which the predicted degree of deterioration exceeded the threshold, or makes an instruction to adjust the machining condition.
Nakahama in view of Inagaki in further view of Arakawa, Onodera, and Daniel does not expressly teach a second memory configured to store a second program;
a second processor configured to execute the second program and control the prediction device; and
the alarm makes an instruction to exchange the ion exchange resin, the power supply die, or the electrode line guide roller for which the predicted degree of deterioration exceeded the threshold, or makes an instruction to adjust the machining condition.
However Yumbe, in analogous art of a prediction system (pg. 1, par. [0002]), teaches the missing limitations of a memory (Fig. 1, element 116) configured to store a program (pg. 4, par. [0040] and [0041]; i.e. [0040]: “The equipment failure prediction device 3 configured to control the equipment failure prediction system 1 according to the present embodiment includes a central processing unit (CPU) 115, a memory 116, a network interface 118, a display unit 119, a device I/O 120, a database 121, and an operation unit 122.” and [0041]: “The memory 116 is a non-volatile storage unit storing therein a failure prediction program 117.”); and
a processor (Fig. 1, element 115; a central processing unit (CPU)) configured to execute the program and control a prediction device (pg. 4, par. [0040] and [0041] and Fig 1, element 3; i.e. an equipment failure prediction device, [0040]: “The equipment failure prediction device 3 configured to control the equipment failure prediction system 1 according to the present embodiment includes a central processing unit (CPU) 115, a memory 116, a network interface 118, a display unit 119, a device I/O 120, a database 121, and an operation unit 122.” and [0041]: “The CPU 115 is a control unit that executes the failure prediction program 117 to control the equipment failure prediction device 3.”) for the purpose of predicting an occurrence of failure in equipment (pg. 3, par. [0044]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Nakahama in view of Inagaki in further view of Arakawa, Onodera, and Daniel to include the addition of the limitations of a memory configured to store a program; and a processor configured to execute the program and control a prediction device to advantageously predict occurrence of failure in an inspection target (Yumbe: pg. 1, par. [0006]).
Nakahama in view of Inagaki in further view of Arakawa, Onodera, Daniel, and Yumbe does not expressly teach the alarm makes an instruction to exchange the ion exchange resin, the power supply die, or the electrode line guide roller for which the predicted degree of deterioration exceeded the threshold, or makes an instruction to adjust the machining condition.
However Maruyama, in an analogous art of fault determination (pg. 1, par. [0002], teaches an alarm makes an instruction to adjust a machining condition (pg. 3, par. [0047]-[0050]; i.e. [0047]: “… the state prediction unit 15 adjusts the value of the state variable supposed to be the cause of the occurrence of alarming or failure to the value for canceling the occurrence of the alarming or the failure if the occurrence of the alarming or the failure is predicted or if the alarming or the failure is actually caused.” and [0049]: “… the state prediction unit 15 outputs information on the injection molding machine predicted to be subject to the occurrence of alarming or failure, the state variable causative of the predicted occurrence of alarming or failure, and the adjusted value of the causative state variable to a prediction result output unit 16.”) for the purpose of adjusting a causative state variable of an alarm (pg. 3, par. [0047] and [0048]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Nakahama in view of Inagaki in further view of Arakawa, Onodera, Daniel, and Yumbe to include the addition of the limitation of an alarm makes an instruction to adjust a machining condition to advantageously determine a cause of failure in high reliability diagnosis regardless of a knowledge and experience of an analysts (Maruyama: pg. 1, par. [0002]).
As per claim 6, Nakahama teaches the trained model is provided in a server connected so as to be accessible from the prediction device via a network (pg. 6, par. [0091]: “… a learning model may be stored in advance on a server installed by a manufacturer and shared between tool state estimation apparatuses 100 of clients.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following references are cited to further show the state of the art with respect to machine learning and manufacturing/production systems.
U.S. Patent Publication No. 2016/0175956 A1 discloses a controller for a wire electric discharge machine having a consumable exchange function.
U.S. Patent Publication No. 2019/0155245 A1 discloses a controller and a machine learning device.
U.S. Patent Publication No. 2019/0061031 A1 discloses a method to control a wire electrical discharge machining process based on information of space and time of occurring discharges.
U.S. Patent Publication No. 2020/0150632 A1 discloses a wire disconnection prediction device.
U.S. Patent Publication No. 2023/0241698 A1 discloses a wire spark machining apparatus that performs spark machining to cut out a plurality of plate-like members collectively from a workpiece using a wire electrode, and to a method for producing a semiconductor wafer.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER L NORTON whose telephone number is (571)272-3694. The examiner can normally be reached Monday - Friday 9:00 am - 5:30 p.m..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JENNIFER L NORTON/Primary Examiner, Art Unit 2117