DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 9, 2026 has been entered.
Response to Amendment
This correspondence is in response to amendments filed on June 9, 2026. Claim 1 is amended. Claims 3, 6-7, and 9 are filed as previously presented. Claims 2, 4-5, and 8 are cancelled. Examiner’s response to arguments regarding the 101 rejection and prior art are included below.
Response to Arguments
Applicant argues that the current claims are directed to eligible subject matter and that the associated 101 rejection should therefore be withdrawn. Such arguments are presented by Applicant in responses filed on June 9, 2026 and additionally December 29, 2025. Applicant has further requested reconsideration of the rejection and a detailed traversal of each argument presented by Applicant. Examiner asserts that in accordance with MPEP 707.07(f) and MPEP 706.07 as referenced on Page 7 of Remarks filed with the Request for Continued Examination on June 9, 2026, in previous correspondence mailed on February 9, 2026 and May 21, 2026 Examiner acknowledged Applicant’s arguments, answered the substance of said arguments, and provided such rebuttal of any argument which was raised in Applicant’s reply. Examiner “…elected to reiterate their position with respect to the claim limitations…” and additionally “…support[ed] their assertions with excerpts from the MPEP…” because Applicant did not direct their arguments to specific limitations of the claims which they felt were improperly rejected with regard to the 101 rejection. Applicant instead relied on excerpts from the specification in providing evidence for USPTO directives associated with 101 rejection. Applicant is reminded that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Thus, the 101 evaluation is considered with respect to the claim limitations themselves and future arguments should be directed to these specific limitations which are rejected.
Nevertheless, a detailed traversal of each concern raised by Applicant on both June 9, 2026 and December 29, 2025 are included as follows:
On Page 8 of Remarks filed on June 9, 2026, Applicant asserts that the claimed operations cannot practically be performed in the human mind without the aid of a special-processing computer programmed to apply the specialized algorithms disclosed in the specification. First, Examiner notes that the claimed computer is not one of “special-processing” but rather a generic CPU as designated in Paragraph [0015] of Applicant’s specification which performs mere algebra on data collected from the robot. Said algebra need not be a “mental process” as such algebra is directed to mathematical calculations, relationships, and concepts. Such math is recited directly and explicitly in the claim as indicated in the 101 rejection below.
On Page 9 of Remarks filed on June 9, 2026, Applicant asserts that there are apparent improvements described in the specification directed to the accurate prediction of robot failure without erroneously detecting the evaluation data as a failure even when the robot’s operation pattern has changed. Examiner notes that the alleged improvements do not alter the function of the computer itself nor do they alter the function of the robot. Even more, the same such “improvements” are additionally contemplated by the prior art of record, Fortuny, for reducing the rate of false failure identifications as identified in the rationale for combination disclosed in the 103 rejection included below. In either case, Applicant should note MPEP 2106.05(a)(II) regarding examples which the courts have indicated may not be sufficient to show an improvement to technology as including “Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48”.
Additionally on Page 9 of Remarks filed on June 9, 2026, Applicant asserts that the amendment more precisely recites improvements to “machine learning models and/or logical structures”. It is noted by Examiner that the claim recites no such machine learning, only algebraic formulae. The logical structure is directly linked to said algebra, not sufficiently more. Thus, the alleged features which “improve” the technology are mathematical concepts, not sufficiently more.
Similar remarks are included on Page 10 of the June 9, 2026 filing regarding the amended limitations of the claim. These limitations are now included in the rejection below corresponding to this action.
Page 7 of Remarks filed on December 29, 2025 argues that independent claim 1 does not recite any features that recite mental processes and that said features cannot be performed by the human mind. However, Applicant does not indicate which of the features which are rejected as being directed to a mental process are to be reconsidered, nor how they are not in fact mental processes. Instead, Applicant continues on Pages 7-8 to include excerpts from Paragraph [0015] describing the computer and Paragraph [0017] describing the process for collecting the data to be evaluated from the drive shaft. Regarding Paragraph [0015], the description does not support the use of a “special-processing computer” but rather a generic CPU which requires no special computer function beyond generic programmed instructions. Regarding Paragraph [0017], the passage is directed to the gathering of data from the robot drive shaft which are single values of torque with respect to time, not raw sensor data which would require sufficiently more (see MPEP 2106.05(g) regarding Insignificant Extra-Solution Activity pertaining to mere data gathering and data output).
On Page 9 of Remarks filed on December 29, 2025, Applicant further reiterates directives from “October 2019 Update: Subject Matter Eligibility” and “Reminders on Evaluating Subject Matter Eligibility of Claims Under 35 U.S.C. 101” without specifically arguing with respect to the limitations challenged by Examiner. Examiner has provided written rebuttal for maintaining rejections corresponding to the mental process grouping including additional MPEP excerpts in previous response mailed on February 9, 2026. Applicant should respond to Examiner’s assertions for the limitations rejected as mental processes to further identify why such limitations may not be performed by the human mind.
Page 9 of Remarks filed on December 29, 2025 further identify that the features of claim 1 cannot be performed by the human mind because the require the assistance of a special purpose computer. The computer as described in Paragraph [0015] is not a special purpose computer but rather a generic CPU which performs the designated steps of the installed program. Said program can be applicably performed by the human mind as described by Examiner in rejection and associated responses to arguments.
Page 9 of Remarks filed on December 29, 2025 additionally indicates that Paragraph [0023] and [0030] require that algorithms are connected to “a flow that is repeatedly executed while the robot R is operating based on the work program”. Examiner notes that this feature of repeated execution is not claimed by Applicant. Alternatively, Examiner notes MPEP 2106.05(d)(II) which includes computer functions recognized as well-understood, routine, and conventional functions when they are claimed in a generic manner or as insignificant extra-solution activity to include example (ii) indicating “Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")”. Thus, no special purpose computer need be required to perform such repeated calculation.
On Pages 10-11 of Remarks filed on December 29, 2025 provide excerpts and examples including Examples 38 and 41, directives from “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101”, and directives from MPEP 2016.04(a)(2) to provide evidence that mathematical concepts are merely alluded to but not explicitly claimed. However, Applicant has included no such argument as to why the specific limitations rejected as mathematical concepts should not be considered as such. Examiner provides a detailed account of why the limitations of claim 1 recite mathematical concepts explicitly and associated MPEP sections to back such assertions in the correspondence mailed out on February 9, 2026. Such rejections are maintained below and should be reviewed accordingly for specifics which Applicant does not believe qualifies as explicit mathematical recitation. Further, the claims and specification do not include any such sensors or “raw data” which is used to support the decision regarding the selected excerpt from MPEP 2106.04(a)(2) on Pages 11-12 of Remarks filed on December 29, 2025.
On Pages 13-14 of Remarks filed on December 29, 2025, Applicant includes various directives and court decisions which render a demonstration of improvements made to technology or technological field integrate a judicial exception into a practical application. Examiner notes that Applicant’s alleged “improvement” is already contemplated by the prior art as identified in the rationale for the corresponding rejection under 35 U.S.C. 103 below, therefore rendering their argument that the invention provides features which determine specific technological improvements over conventional failure prediction devices and methodologies moot. Additionally, the computer function and the robot themselves are not improved by the features of said invention and therefore need not be considered as an improvement to the technology. Regarding the select Paragraphs from the specification included on Pages 15-16 of Remarks filed December 29, 2025, Applicant merely provides examples of applicable prior art which highlights the alleged improvement as a general goal of the inventive concept rather than an improvement to an entire technical field, recites features of Paragraphs [0008] and [0041] which are not claimed such as operation patterns, and recites features of Paragraph [0031] which provide nothing beyond basic algebraic operations and judgements solution corresponding to the data. It is again noted that MPEP 2106.05(a)(II) includes “examples that the courts have indicated may not be sufficient to show an improvement to technology” which furthers “Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48”. Examiner maintains that the alleged improvements described on Page 17 of Remarks filed on December 29, 2025 are merely generic goals of the claimed invention, and additionally do not make clear how the claimed features specifically contribute to said alleged improvements beyond generic gathering and analyzing information using conventional techniques and displaying the result.
The arguments presented on Pages 19-20 of Remarks filed on December 29, 2025 further the above-identified alleged “improvements” to a technological field but further such arguments by claiming that the invention is directed to a “a particular, limited application”, “a particular, practical application”, and a “technology based solution”. Examiner notes MPEP 2106.05(b) designating directives regarding “a particular machine”. In Section I, “It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions). If applicant amends a claim to add a generic computer or generic computer components and asserts that the claim recites significantly more because the generic computer is 'specially programmed' (as in Alappat, now considered superseded) or is a 'particular machine' (as in Bilski), the examiner should look at whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008).” Additionally, Section II recites “ For example, as described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. See, e.g., Versata Development Group v. SAP America, 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015) (explaining that in order for a machine to add significantly more, it must "play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly")”. Further, Section III indicates, “Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 197 (1978)), and CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690 (Fed. Cir. 2011)”. Regarding Field of Use and Technological Environment, MPEP 2106.05(h) includes “examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception” which determines “Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)”. Given that the claim is directed to a generic computer which collects information, analyzes the information, and displays certain results of the collection and analysis to data related to a robotic drive shaft, Examiner ascertains that the connection to the robotic drive shaft is merely generic and does not provide significantly more to the judicial exception. The failure prediction device may not be considered as a “particular machine” as it is merely a generic computer serving as an obvious mechanism for permitting a solution to be achieved more quickly.
In the excerpt pertaining to the PTAB decision included on Page 21 of Remarks filed on December 29, 2025, Applicant acknowledges statements to the improvement of computer functionality and improvement for how machine learning model operates not a mathematical calculation. Examiner notes that Applicant does not disclose any such machine learning model which would be applicable to this excerpt. Instead, Applicant includes generic algebraic processing of single value data and does not recite any such improvement to the function of the computer itself.
Regarding Pages 23-24 of Remarks filed on December 29, 2025, Applicant further identifies that the limitations are not well-understood, routine, and conventional (WURC) because the claims are directed to a “particular, limited application”, and that the failure prediction device requires “a flow that is repeatedly executed while the robot R is operating based on the work program”. The claim is directed to a generic computer, not any feature of the robot itself. As identified above with regard to MPEP 2106.05(b) and 2106.05(h), the claims are not directed to any “particular machine” and are generally linked to a technological field. Additionally, with respect to MPEP 2106.05(d)(II), the courts have identified “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")” as WURC. Thus, the generic computer which is claimed as a failure prediction device, performs WURC transmission and receiving of data over a network when it receives data from the robot controller and then transmits data to the monitor. Additionally, although not claimed, the flow which is repeatedly executed is the WURC process as it is the performance of a repetitive calculation.
Provided the above response to any such argument presented by Applicant, Examiner maintains that the claims do not include any additional elements which would amount to significantly more than the judicial exception. Examiner has amended the rejection below to include the amended limitations and attempts to further clarify any features rejected which seem to be misunderstood in the previous round of prosecution. In deciding to maintain this rejection regarding 35 U.S.C. 101, Examiner consulted multiple experts in the field. Examiner would be open to discuss the rejection should Applicant schedule an interview after the mailing of this rejection and prior to the filing of subsequent amendments. Additionally, should Applicant decide to contest Examiner’s rejection, Examiner requests that reference be made to specific limitations rejected by Examiner and the cause for rejection associated with said limitation. By linking arguments directly to rejected limitations of the claims (emphasis added), responses may be provided with additional clarity pertaining to specific limitations.
Applicant argues that the disclosure of Fortuny is a correlation analysis between two redundant pieces of equipment, and does not approximate a temporal change of data of a single piece of equipment (Remarks Page 12-13). Additionally, Applicant argues that the disclosure of Fortuny does not include an equation for approximating a temporal change of evaluation data (Remarks Page 13). As identified in the previous response to arguments mailed on February 9, 2026, Fortuny is a secondary reference which teaches the data processing specifics for failure analysis which are not contemplated by Hatanaka. Fortuny performs specific linear regression on the collected data over a temporal change (operating cycle(s)), and additionally determines a threshold of said operating parameters which vary as a constant over time (step function approximating temporal change of the data). The data of the two functions are compared to determine whether the outlying data is merely noise contained within a threshold range (due to a factor other than the failure of a robot) or if the outlying data is indicative of a malfunctioning device (due to a factor which causes the failure of a robot).
Applicant further argues that Hatanaka does not cure deficiencies of Fortuny (Remarks Page 13). Given that Hatanaka is considered as the primary reference, Hatanaka need not cure any deficiencies of Fortuny. Rather, the teachings of Fortuny suggest that the history of data collected by Hatanaka which is then superimposed to display trends between cycles may be evaluated using the methods of Fortuny to determine failure of the device over a temporal change in the stored data. Applicant is reminded that the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). Therefore, the arguments regarding the teachings of Fortuny which modify the teachings of Hatanaka have been considered but are NOT PERSUASIVE.
Applicant additionally argues that the motivation for combining the references requires hindsight (Remarks Page 13). In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Applicant further argues that Hatanaka nor Fortuny teach or suggest the derivation of a first threshold and a second threshold and comparing said first threshold to said second threshold (Remarks Page 14). Examiner clearly specifies what shall be considered as a first threshold and a second threshold when regarding Fortuny. Fortuny makes an obvious comparison between the two values which are indicated as the thresholds in Paragraph [0018]. The associated analysis of applicable failure entity is a clear example of a comparison made between the defined thresholds. As such, argument has been considered but is not PERSUASIVE.
It is noted that Examiner’s art rejection relies on the use of “step function processing”. Should Applicant amend the limitation to not include the “step function processing” and instead read only “binarization processing”, Examiner would reconsider the current art of record. Examiner notes that the prior art does not appear to teach “binarization processing”. Such assertions are provided merely as advice for applicant. Official reconsideration of the rejection and search shall be made upon filing of amendment.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
On January 7, 2019, the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1 and 9 are directed toward non-statutory subject matter, as shown below:
STEP 1: Do claims 1 and 9 fall within one of the statutory categories? Claims 1 and 9 are each directed to a device and as such fall within one of the statutory categories.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, claim 1 is directed to mathematical concepts and mental processes.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Regarding claim 1, the limitations which recite “…derive a first evaluation equation obtained by linear regression of the evaluation data and a second evaluation equation obtained by performing step function or binarization processing on the evaluation data, as evaluation equations that approximate a temporal change of the evaluation data using the collected evaluation data; derive a first threshold based on a difference between the evaluation data and a value of the first equation, and derive a second threshold based on a difference between the evaluation data and a value of the second evaluation equation…” are exemplary of mathematical concepts. Deriving a first and second equation using specifics of linear regression and step function/binarization processing and deriving a first and second threshold based on a difference are clear mathematical operations which determine formulae and values based on input data and values to construe mathematical relationships. Additionally, approximating temporal changes in data are clear mathematical concepts describing a mathematical relationship of a time-based function. Further, the equations are used to derive values which then indicate the threshold values which are determined by a difference. Thus, the equations referred to are textual replacements for what would otherwise be an alphanumeric expression (see MPEP 2106.04(a)(2) which describes that mathematical concepts need not be expressed as symbols and can be described instead by words). Additionally, associated written descriptions associated with the above limitations are described in Applicant’s specification as mathematical concepts (See Paragraphs [0016-0018] and [0020]). As such, these limitations are considered to be at their broadest reasonable interpretation mathematical concepts of deriving (i.e., calculating), not just mere allegations in reference to concepts.
Additionally, with regard to claim 1, the limitations which recite “…compare the first threshold with the second threshold, determine that the evaluation data is an evaluation data value due to a factor other than the failure of the robot when the first threshold is equal to or greater than the second threshold, and determine that the evaluation data is an evaluation data value due to a factor causing the failure of the robot when the first threshold is smaller than the second threshold; predict a failure of the drive shaft based on the evaluation data…” are mental processes. A human, through simple observation and judgement of the human mind, could compare two threshold values, described in Paragraphs [0020-0021] as single values. They could then first determine that a first threshold is greater than or equal to a second threshold value. Then, based on an opinion, i.e., a rule-based determination, a human could, practically with their mind, evaluate the results and the data according to the analysis and determine based on the judgement that a failure is either due to a factor other than the failure of the robot or due to a factor causing the failure of the robot, and from this opinion further predict that a failure is going to occur. Therefore, the steps of determining a failure based on observations of threshold values and further predicting a failure based on the observation of the data are mere opinionated judgements which can be practically performed by the human mind. The use of a computer to implement such steps is merely a tool used to mitigate human error, but are not necessary to making these determinations.
Thus, for those reasons stated above, claim 1 recites limitations which are directed to a combination of mathematical concepts and mental processes.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claim 1 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. Also, as noted above, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application.
Applicant should also note that the failure prediction device may not be considered as a “particular machine” since the device is merely a generic computer which performs conventional computer functions as identified in MPEP 2106.05(b).
Claim 1 recites “…collect evaluation data of at least a drive shaft of a robot working based on a work program…”. In each of these limitations, collecting evaluation data of a robot and/or a drive shaft of a robot is mere data gathering for the purpose of providing inputs for the mathematical concepts described in rejections under Step 2A (Prong 1), which has been demonstrated to be insignificant extra-solution activity, specifically pre-solution activity (see MPEP 2106.05(g)).
In addition to mere data gathering, this claim limitation is generally linking the use of the judicial exception to a specific technological environment/field of use. The claimed invention limits the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to a robot drive shaft and monitoring of said drive shaft, which is similar the decision of Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) as identified in the Examples of MPEP 2106.05(h).
Claim 1 further recites “…when it is determined that the evaluation data is the evaluation data value due to the factor causing the failure of the robot and when the failure of the drive shaft is predicted, notify a user that failure is predicted…”. Similar to the collection of data, a notification of an output (the output which has been determined by a mental process of evaluation and judgement), mere data output for the purpose of providing results determined by a judicial exception is additionally demonstrated to be insignificant extra-solution activity, specifically post-solution activity (see MPEP 2106.05(g)).
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claim 1 does not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. Limitations identified as “apply it” in step 2A qualify as apply it in step 2B as well.
As described above, the collection of data from a robot and the outputting of the results of the evaluation are also determined as WURC activities, as they are examples of receiving and transmitting data over a network, i.e., passing of information between the control device, the failure prediction device, and the monitor (see MPEP 2106.05(d)).
CONCLUSION
Thus, since claim 1 is: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claims 1 are directed towards non-statutory subject matter.
DEPENDENT CLAIMS
Dependent claims 3, 6-7, and 9 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application as each of these claims further provide abstract ideas and/or data gathering processes. Therefore, dependent claims 3, 6-7, and 9 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1, 3, 6-7, and 9 are ineligible under 35 USC §101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 5-7, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Hatanaka (US 2020/0198128 A1) in view of Fortuny (US 2021/0232133 A1).
Regarding claim 1, Hatanaka teaches a failure prediction device (“In the embodiment described above, the robot control device 2, the learning data confirmation support device 3, the machine learning device 4, and the failure predicting device 5 are established as respectively independent devices; however, the present invention is not limited thereto. For example, the robot control device 2 may include the learning data confirmation support device 3, the machine learning device 4, and the failure predicting device 5” [0061]. Thus, any function of the control device, machine learning device, learning data confirmation support device, and failure predicting device will be considered as a single failure predicting device.) comprising:
a processor; a non-volatile storage device; program instructions stored in the non-volatile storage device that, when executed by the processor (“The components included in the robot control device 2, the learning data confirmation support device 3, the machine learning device 4, and the failure predicting device 5 can be realized by hardware, software, or combinations thereof… Here, the term “realized by software” indicates being realized by a computer reading and executing programs” [0056]. “The programs may be stored and supplied to the computer using various types of non-transitory computer readable medium” [0057]. Thus, there is a computer, i.e., processor, and a non-volatile storage device, wherein there are programs stored on the storage device that are executed by the computer to perform the functions of the disclosure.), cause the failure prediction device to:
collect evaluation data of at least a drive shaft of a robot working based on a work program (“As described above, when the robot control device 2 causes the robot 1 to perform a certain operation in accordance with the health check program according to a preset schedule or the like, the data acquisition unit 31 acquires measurement data including time-series data representing at least one of a predetermined state quantity and control quantity relating to the control when the operation is performed, and stores the acquired time-series data as learning data or failure diagnosis data in the measurement data storage unit 361 together with the acquisition time (time stamp)” [0036]. Thus, the data acquisition unit acquires measurement data of state quantities and control quantities, inclusive of those of the drive shaft, which relate to the control when the operation is being performed, i.e., collects evaluation data of at least the drive shaft of a robot working based on a work program.);
derive a first evaluation equation …as evaluation equations that approximate a temporal change of the evaluation data using the collected evaluation data (Fig. 2A-4B show the alignment of the acquired data in waveforms which are linear estimates of the groups of data which approximate a temporal change of the evaluation data. The alignment/linear estimation/waveform is the derived equation in this case.), …
predict a failure of the drive shaft based on the evaluation data (“…an anomaly diagnosis unit (for example, an anomaly diagnosis unit 51) that performs, in response to an input of the measurement data acquired by the data acquisition unit, anomaly diagnosis of the industrial machine on a basis of a learning model created by the learning unit” [0019]. Thus, the anomaly diagnosis unit, i.e., prediction determination unit, which is a part of the failure prediction device as indicated in rejection of claim 1, predicts a failure of the robot, inclusive of failures of the drive shaft (disclosed as a drive unit), based on the learned model acquired by training the evaluation data.); and
when it is determined that the evaluation data is the evaluation data value due to the factor causing the failure of the robot and when the failure of the drive shaft is predicted, notify a user that failure is predicted (“The anomaly notification unit 52 outputs the diagnosis information of the robot 1 to, for example, the display unit 57 on the basis of the anomaly diagnosis result by the anomaly diagnosis unit 51. With such a configuration, the failure predicting device 5 can output the anomaly diagnosis information relating to the presence or absence of an anomaly of, for example, the drive unit of the robot 1, that is, the anomaly diagnosis information as to whether there is a defect, a failure, or a sign of a defect or a failure, by inputting the failure diagnosis data on the basis of the learned model (normal model)” [0048]. Thus, there is a notification to a user via a display that failure of the drive unit or other part of the robot is predicted when it is determined that the evaluation data is due to a factor causing failure of the robot, i.e., defect/failure or sign of such defect failure, and that failure of the drive unit, i.e., drive shaft, is predicted.).
However, Hatanaka does not explicitly teach …derive a first evaluation equation obtained by linear regression of the evaluation data and a second evaluation equation obtained by performing step function or binarization processing on the evaluation data, as evaluation equations that approximate a temporal change of the evaluation data using the collected evaluation data,
derive a first threshold based on a difference between the evaluation data and a value of the first evaluation equation, and
derive a second threshold based on a difference between the evaluation data and a value of the second evaluation equation;
compare the first threshold with the second threshold, determine that the evaluation data is an evaluation data value due to a factor other than the failure of the robot when the first threshold is equal to or greater than the second threshold, and determine that the evaluation data is an evaluation data value due to a factor causing the failure of the robot when the first threshold is smaller than the second threshold; …
Fortuny, pertinent to the problem at hand, teaches … a first evaluation equation obtained by linear regression of the evaluation data (“the method further comprises the steps of determining the equation of the linear regression between the first operating parameter of the first equipment and the first operating parameter of the second equipment for one or several operating cycles, or one or several parts of the cycle(s)” [0021]. Thus, there is a first equation which is obtained by linear regression of the evaluation data, i.e., operating parameter.) and a second evaluation equation obtained by performing step function or binarization processing on the evaluation data (“This first determined threshold is a function of the redundant equipments that are tracked. It is preferably established based on one or several operating cycles in which neither of the two redundant equipments have experienced a malfunction or a failure. Preferably, this first determined threshold is updated continuously, as a function of the first coefficients of determination established for operating cycles without failures” [0058]. Thus, given that the threshold is a constant which is updated continuously as a function of the first coefficients of determination for operating cycles without failure, this threshold value is obtained over time through a step function processing, in which the value is updated between constant threshold values after cycles without failure.), as evaluation equations that approximate a temporal change of the evaluation data using the collected evaluation data (All derived equations are those due to a collection of data over one or multiple cycles and thus are evaluation equations that approximate a temporal change of the evaluation data using said collected evaluation data.),
derive a first threshold based on a difference between the evaluation data and a value of the first evaluation equation (The coefficient of determination, i.e., first threshold, is derived from the square of the coefficient of correlation, which is the difference between the evaluation data and the value of the linear regression, i.e., first equation. See [0053].), and
derive a second threshold based on a difference between the evaluation data and a value of the second evaluation equation (The threshold, i.e., second threshold, is derived based on a comparison, i.e., difference, between the evaluation data and the continuous updating of the coefficients of determination from previous cycles which did not experience failure, i.e., the second evaluation equation. See [0058].);
compare the first threshold with the second threshold (See below descriptions of [0018] wherein the defined first threshold (first coefficient of determination) is compared to the second threshold (threshold) to determine a failure.), determine that the evaluation data is an evaluation data value due to a factor other than the failure of the robot when the first threshold is equal to or greater than the second threshold (“…if the first coefficient of determination is greater than or equal to the first threshold, emitting a notification indicating an absence of malfunction of the first and/or second equipment item(s)…” [0018]. Thus, in the event that the resulting coefficient, i.e., first threshold, is greater than or equal to the threshold, i.e., second threshold, then the value is due to an absence of a malfunction, i.e., a factor other than the failure of the robot.), and determine that the evaluation data is an evaluation data value due to a factor causing the failure of the robot when the first threshold is smaller than the second threshold (“…if, for one or several operating cycles, or one or several parts of the cycle(s), the first coefficient of determination is below a first determined threshold, emitting a notification indicating the malfunction of the first and second equipment(s)…” [0018]. Thus, in the event that the resulting coefficient, i.e., first threshold, is smaller than the determined threshold, i.e., second threshold, then the value is due to a malfunction of the equipment, i.e., a factor causing the failure of the robot.)…
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the false detection determination unit as taught by Hatanaka and instead implement the data analysis methods for false detection determination as taught by Fortuny with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because by updating data for analysis through every cycle of operation and making a direct comparison to the continuous distribution of the data, such a method for predicting failures may improve reliability and performance of the system/assembly, as well as reduce the rate of false failure notifications (Fortuny, [0016-0017]). Further motivation for using the methods of determination as described by Fortuny would be through a simple substitution of known determination methods to obtain predictable results (see MPEP 2143.I(B)).
Regarding claim 3, Hatanaka as modified by Fortuny teaches the failure prediction device according to claim 1, wherein the program instructions further cause the failure prediction device to:
with Hatanaka further teaching predict a failure of the drive shaft based on the evaluation data when it is determined that the evaluation data is the evaluation data value due to the factor causing the failure of the robot (“…an anomaly diagnosis unit (for example, an anomaly diagnosis unit 51) that performs, in response to an input of the measurement data acquired by the data acquisition unit, anomaly diagnosis of the industrial machine on a basis of a learning model created by the learning unit” [0019]. The anomaly diagnosis unit predicts a failure of the drive shaft based on the evaluation data when it is provided a model derived from the data which is deemed to be appropriate, i.e., when it is determined that the evaluation data is an evaluation data value due to the factor causing the failure of the robot.).
Regarding claim 6, Hatanaka as modified by Fortuny teaches the failure prediction device according to claim 3,
with Hatanaka further teaching wherein the program instructions further cause the failure prediction device to:
skip the prediction of the failure of the drive shaft when it is determined that the evaluation data is the evaluation data value due to the factor other than the failure of the robot (“ The data selection unit 33 excludes, from the measurement data storage unit 361, the time-series data which is determined to be inappropriate data as the learning data from the plurality of time-series data displayed by the display control unit 32” [0042]. Thus, the inappropriate data, i.e., the evaluation data in which the value of the data is due to a factor other than the failure of the robot, is skipped in the prediction determination which determines a failure of the drive shaft.).
Regarding claim 7, Hatanaka as modified by Fortuny (references made directly in citation) teaches the failure prediction device according to claim 1, wherein the program instructions further cause the failure prediction device to:
predict a failure of the drive shaft based on the evaluation data (“…an anomaly diagnosis unit (for example, an anomaly diagnosis unit 51) that performs, in response to an input of the measurement data acquired by the data acquisition unit, anomaly diagnosis of the industrial machine on a basis of a learning model created by the learning unit” (Hatanaka, [0019]). Thus, the anomaly diagnosis unit predicts a failure of the robot, inclusive of failures of the drive shaft (disclosed as a drive unit), based on the learned model acquired by training the evaluation data.), and
determine whether the evaluation data is the evaluation data value due to the factor other than the failure of the robot or the evaluation data value due to the factor causing the failure of the robot after the failure of the drive shaft is predicted (“In this case, a second and third coefficients of determination are calculated and/or evaluated for one of the equipments, preferably both redundant equipments, in order to identify which of the two equipment items is not operating normally” (Fortuny, [0062]). “If the second coefficient of determination is greater than or equal to a second threshold, which is a function of the first operating parameter of the equipment and the second considered parameter, and the third coefficient of determination is greater than or equal to a third threshold, which is a function of the first operating parameter of the equipment and the third considered parameter, this means that the evaluated equipment is operating normally. Otherwise, the evaluated equipment is suffering from a malfunction and a notification is emitted to report it and so that an inspection, maintenance or a replacement is done” (Fortuny, [0067]). Thus, the system which determines whether or not failure is predicted for at least one of the equipment by use of a first coefficient of determination and a first threshold, thereafter determines a second and third coefficient of determination in order to evaluate whether the result is due to a malfunction present in the equipment, i.e., value due to a factor causing a failure of the robot, or if the result indicates normal operation, i.e., value due to a factor other than the failure of the robot.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date to have modified the prediction analysis processes of Hatanaka to include the prediction of failure before determining whether or not the evaluation values are due to a factor causing the failure of the robot, or something else as taught by Fortuny with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because by determining the precise source/cause of the failure after a failure has been predicted, false alarms may be reduced or eliminated (Fortuny, [0060]), therefore increasing system efficiency and reducing downtime for repairs and inspection.
Regarding claim 9, Hatanaka as modified by Fortuny teaches the failure prediction device according to claim 1,
with Hatanaka further teaching wherein the program instructions further cause the failure prediction device to:
display, on a display device, at least one of a prediction result of a failure of the robot predicted based on the evaluation data (“The anomaly notification unit 52 outputs the diagnosis information of the robot 1 to, for example, the display unit 57 on the basis of the anomaly diagnosis result by the anomaly diagnosis unit 51” [0048]. Thus, the anomaly notification unit outputs the resulting diagnosis information derived from the learned model based on measurement data, i.e., prediction result of a failure of the robot predicted based on the evaluation data.) or information indicating the determination result (“The display control unit 32 aligns a plurality of pieces of time-series data acquired by the data acquisition unit 31 in the direction of the time axis and, in such a state, superimposes the same type of data thereof on the display unit 37 for display in a graph” [0038]. Thus, this superimposed data is information indicating a determination result of the false detection determination unit, and is caused to be displayed on the display unit by the display control unit such that an evaluation of the data can be made by the operator. Such information will indicate if the data is appropriate (see Fig. 2A and 3A) or inappropriate (see Fig. 2B and 3B).),
wherein, when the evaluation data value due to the factor other than the failure of the robot is determined, preferentially display information indicating the evaluation data value due to the factor other than the failure of the robot (“More specifically, when time-series data (waveforms) relating to motor velocities, which are control quantities, stored in the measurement data storage unit 361 as learning data are superimposed and displayed on the display unit 37, and when different waveforms are displayed without all waveforms overlapping on about one line, all of the measurement data corresponding to the measurement times of the waveforms selected by the operator from among these waveforms are excluded from the measurement data storage unit 361 as inappropriate data as learning data” [0042]. Thus, when there exists inappropriate data which presents as multiple waveforms, the resulting waveforms are displayed on the display unit to indicate such data values which are inappropriate, i.e., due to factors other than the failure of the robot.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIDNEY L MOLNAR whose telephone number is (571)272-2276. The examiner can normally be reached 9 A.M. to 4 P.M. EST Monday-Friday.
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/S.L.M./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656
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