DETAILED ACTION
Claims 6-11 are pending. Claims 1-5 are cancelled.
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 .
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to Japanese Patent Application No. 2020-05037, filed on 5/29/2020.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/30/26 has been entered.
Response to Arguments
Applicant’s arguments, filed 7/30/26, have been fully considered but are not persuasive, except where noted below.
Applicant’s arguments with regard to the rejections under 35 U.S.C. § 112 (page 7) are persuasive and the claims are no longer rejected are no longer rejected for these specific reasons. However, new grounds of rejection under 35 U.S.C. § 112(b) are presented below.
Applicant’s arguments with regard to the rejection under 35 U.S.C. § 103 (pages 8-11) are moot in view of the newly cited reference, Shinkawa.
For at least these reasons, the rejection of the claims is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 6-9 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 6 recites ‘previously store causes of abnormal irregularity actions for handling the causes’ and it is unclear what the storing is previous to.
The respective dependent claims are also rejected under 35 U.S.C. § 112 as they inherit all of the characteristics of the claim from which they depend and none of the dependent claims provide a cure for the indefiniteness of the parent claims.
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.
Claim(s) 6-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception — the mental/mathematical process of diagnosing an abnormality based on a calculation, determining if the abnormality is accurate, and deciding how to handle the cause of the abnormality (abstract idea).
Claim 6 recites an abnormal irregularity cause display device, i.e. a machine, which is a statutory category of invention. The claim recites the following:
calculate an abnormality degree representing an extent of an irregularity of process data of the pieces of process data read by the process data acquisition unit;
determine, for each of the pieces of process data output by the corresponding one of the plurality of sensors, whether the calculated abnormality degree satisfies a predetermined criterion by using causal relation information defining a combination between a cause and the irregularity of the process data output by each of the plurality of sensors, the irregularity appearing as an influence resulting from the cause, and determine whether the abnormality is accurately determined based on processing data… … when the abnormality is determined to be accurate that be performed… wherein a candidate for an operating condition of the production facility for suppressing the abnormal irregularity is obtained based on the previously stored causes in the human mind, or by a human using a pen and paper. Thus the claim recites an abstract idea (mental/mathematical process), see MPEP 2106.04(a).
This judicial exception is not integrated into a practical application because the additional elements, i.e. a cause display device (insignificant extra-solution activity, see 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)), a processor and a storage device (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C) and a plurality of sensors included in a production facility (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)) and read, from a storage device storing pieces of process data… (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)) , performs an action by using the calculated abnormality degree (insignificant extra-solution activity, see 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d); see 0078-0079 of the specification/PGPub that indicates the action involves either ‘an alarm or an action to handle the abnormality is output’; alternatively this limitation may be considered mere instructions to apply the exception using a technique recited at a high level of generality, see MPEP 2106.05(f)), previously store causes of abnormal irregularity actions for handling the causes (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C) and the obtained candidate is proposed to a user of the production facility (insignificant extra-solution activity, see 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)) do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, a cause display device (insignificant extra-solution activity, see 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)), a processor and a storage device (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C) and a plurality of sensors included in a production facility (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)) and read, from a storage device storing pieces of process data… (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d)) , performs an action by using the calculated abnormality degree (insignificant extra-solution activity, see 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d) — see 0078-0079 of the specification/PGPub that indicates the action involves either ‘an alarm or an action to handle the abnormality is output’; alternatively this limitation may be considered insignificant extra-solution activity because it constitutes mere instructions to apply the exception using a technique recited at a high level of generality, see MPEP 2106.05(f)), previously store causes of abnormal irregularity actions for handling the causes (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C) and the obtained candidate is proposed to a user of the production facility (insignificant extra-solution activity, see 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)) is not considered significantly more. Considering the additionally elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea.
Note that production facilities with sensors are well-understood, routine and conventional, see for example Gribble et al. U.S. Patent No. 4862950 [col. 8] and Miyata et al. U.S. Patent Publication No. 20090292373 [0004].
Claim 7 recites the processor is configured to multiply each of a plurality of types of pieces of the process data is multiplied by a coefficient in accordance with a type of the process data or a coefficient based on a magnitude of the abnormality degree to obtain accuracy of the cause of the irregularity (mental/mathematical process performed by a generic computer) and the processor is configured to make an output device output, for the irregularity, a plurality of candidates of a possible cause of the irregularity and accuracy of the possible cause (insignificant extra-solution activity, see 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 8 recites the processor is configured to make the output device output a logic tree where, by using the irregularity as a root and the cause of the irregularity as a leaf, events appearing in a course from the cause to the irregularity are connected in a hierarchical manner based on the causal relation information, and makes an output device output the information indicating the handling to be taken for the cause in association with the cause (insignificant extra-solution activity, see 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 9 recites the processor is configured to make the output device output a logic tree where, by using the irregularity as a root and the cause of the irregularity as a leaf, events appearing in a course from the cause to the irregularity are connected in a hierarchical manner based on the causal relation information, and to output the information indicating the handling to be taken for the cause in association with the cause (insignificant extra-solution activity, see 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 10 recites an abnormal irregularity cause display method executed by a computer, i.e. a process, which is a statutory category of invention. However, the recited method is similar to that recited in claim 6 and is rejected under the same rationale. Note that applying the exception with generic computer technology is not considered significantly more, see MPEP 2106.04(a)(2) III C, MPEP 2106.05(d) II and MPEP 2106.05(g).
Claim 11 recites a non-transitory computer readable medium, i.e. an article of manufacture, which is a statutory category of invention. However, the recited method performed by the computer-readable medium is similar to that recited in claim 6 and is rejected under the same rationale. Note that a non-transitory computer readable medium is considered applying the exception with generic computer technology is not considered significantly more, see MPEP 2106.04(a)(2) III C, MPEP 2106.05(d) II and MPEP 2106.05(g).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim(s) 6, 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayashi et al. U.S. Patent Publication No. 20190226943 (hereinafter Hayashi) in view of Shinkawa et al. U.S. Patent Publication No. 20200263669 (hereinafter Shinkawa).
Regarding claim 6, Hayashi teaches an abnormal irregularity cause display device [0059-0060, Fig. 2 — facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6. The facility condition monitoring device 1 comprises a computer, for instance, including a CPU (processor, not shown), a storage device m such as a memory, including ROM and RAM, and an external storage device, and a communication interface; 0072-0073 — a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the user] comprising:
a processor configured to:
read, from a storage device storing pieces of process data of each equipment in a production facility continuously output by a plurality of sensors included in the production facility, the pieces of process data in a predetermined period of time [0059-0060, Fig. 2 — facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6. The facility condition monitoring device 1 comprises a computer, for instance, including a CPU (processor, not shown), a storage device m such as a memory, including ROM and RAM, and an external storage device, and a communication interface; 0053, Fig. 1 — state-quantity fluctuation data D representative of a time-dependent change of a state quantity measured on the monitoring target facility 9. The monitoring target facility 9 is a facility (apparatus) such as, for instance, a wind turbine power generating apparatus (wind turbine) (see FIG. 1) (power production facility) or an engine (not shown) having a piston reciprocably disposed within a cylinder. The state-quantity fluctuation data D contains a set of sensor values (measurement data) obtained by multiple measurement in a certain period (period of time) with sensors 8; 0075 — the feature extraction part 12 may be performed via the storage device m. Specifically, the state-quantity-fluctuation-data acquisition part 11 may be configured to store the acquired state-quantity fluctuation data D in the storage device m, while the feature extraction part 12 may be configured to acquire the state-quantity fluctuation data D from the storage device m];
calculate an abnormality degree representing an extent of an irregularity of process data of the read pieces of process data [0060, Fig. 2 — As shown in FIG. 2, the facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6.; 0085, Fig. 7 — a feature acquisition step (S1), an abnormality-degree-calculation-model construction step (S2), an abnormality degree calculation step (S3), an abnormality determination step (S4), an abnormality-contribution-rate calculation step (S5), and an abnormality cause identification step (S6)];
determine, for each of the pieces of process data output by the corresponding one of the plurality of sensors, whether the calculated abnormality degree satisfies a predetermined criterion by using causal relation information defining a combination between a cause and the irregularity of the process data output by each of the plurality of sensors, the irregularity appearing as an influence resulting from the cause [0060, Fig. 2 — As shown in FIG. 2, the facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6.; 0085, Fig. 7 — a feature acquisition step (S1), an abnormality-degree-calculation-model construction step (S2), an abnormality degree calculation step (S3), an abnormality determination step (S4), an abnormality-contribution-rate calculation step (S5), and an abnormality cause identification step (S6); 0067 — abnormality determination part 4 determines whether an abnormality is present in the monitoring target facility 9, based on the abnormality degree Q calculated by the abnormality degree calculation part 3. Specifically, the presence of abnormality may be determined based on a comparison between the abnormality degree Q and an abnormality determination threshold L, for instance, it may be determined as normal (no abnormality is present) if the abnormality degree Q is equal to or less than the abnormality determination threshold L, whereas it may be determined as abnormal (an abnormality is present) if the abnormality degree Q is more than the abnormality determination threshold L.]; and
previously store causes of abnormal irregularity actions for handling the causes, wherein a candidate for an operating condition of the production facility for suppressing the abnormal irregularity is obtained based on the previously stored causes [0072-0073, Fig. 2 — the abnormality-degree-calculation-model construction part 2 is configured to store the abnormality degree calculation model M in the storage device m. The abnormality degree calculation part 3 is connected to the abnormality determination part 4 and input the abnormality degree Q calculated using the abnormality degree calculation model M stored in the storage device m into the abnormality determination part 4… a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the user] and
wherein the obtained candidate is proposed to a user of the production facility [0071-0072, Fig. 6 — a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the user. Thus, the operator can confirm the identification result R of the abnormality cause by checking the display device 16].
But Hayashi fails to clearly specify determine whether the abnormality is accurately determined based on processing data, and performs an action by using the calculated abnormality degree when the abnormality is determined to be accurate.
However, Shinkawa teaches determine whether the abnormality is accurately determined based on processing data, and performs an action by using the calculated abnormality degree when the abnormality is determined to be accurate [0027, 0034 — if at least one of the plurality of wind turbines is determined not to have an abnormality based on the abnormality degree calculated based on the operating condition (multiple parameter values), the validity (accuracy) of that abnormality negative determination is verified based on the statistic calculated from the plurality of abnormality degrees at the same timing. Thus, even if the abnormality degree is not more than the threshold, it is possible to early detect an abnormality, and it is possible to prevent a reduction in operating rate due to failure of the wind turbine and an increase in cost; 0110-0118, Figs. 8-9 — determination part 5 determines whether an abnormality is present in each of the plurality of wind turbines 2, based on the abnormality degree E of each of the wind turbines 2 calculated by the abnormality degree calculation part 4… first validity determination part 62 determines whether the abnormality positive determination Ja being verified is valid (first validity determination), based on the number of wind turbines 2 that is determined to have an abnormality as a result of determination based on the abnormality degree E regarding the one or more other wind turbines 2… the control unit 14 further includes a first notification part 64 that notifies that an abnormality is detected (performs an action) if the first validity determination part 62 determines that the abnormality positive determination Ja of the verification target is valid].
Hayashi and Shinkawa are analogous art. They relate to fault detection/monitoring systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above device, as taught by Hayashi, by incorporating the above limitations, as taught by Shinkawa.
One of ordinary skill in the art would have been motivated to do this modification in order to prevent a reduction in operating rate due to false detection and an increase in cost, as taught by Shinkawa [0027, 0034, 0127].
Regarding claim 10, Hayashi teaches an abnormal irregularity cause display method executed by a computer [0026 — a facility condition monitoring method; 0059-0060, Fig. 2 — facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6. The facility condition monitoring device 1 comprises a computer, for instance, including a CPU (processor, not shown), a storage device m such as a memory, including ROM and RAM, and an external storage device, and a communication interface; 0072-0073 — a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the use; 0084-0086, Fig. 7 — a facility condition monitoring method will be described in order of steps of FIG. 7], the method comprising:
reading, from a storage device storing pieces of process data of each equipment in a production facility continuously output by a plurality of sensors included in the production facility, the pieces of process data in a predetermined period of time [0059-0060, Fig. 2 — facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6. The facility condition monitoring device 1 comprises a computer, for instance, including a CPU (processor, not shown), a storage device m such as a memory, including ROM and RAM, and an external storage device, and a communication interface; 0053, Fig. 1 — state-quantity fluctuation data D representative of a time-dependent change of a state quantity measured on the monitoring target facility 9. The monitoring target facility 9 is a facility (apparatus) such as, for instance, a wind turbine power generating apparatus (wind turbine) (see FIG. 1) (power production facility) or an engine (not shown) having a piston reciprocably disposed within a cylinder. The state-quantity fluctuation data D contains a set of sensor values (measurement data) obtained by multiple measurement in a certain period (period of time) with sensors 8; 0075 — the feature extraction part 12 may be performed via the storage device m. Specifically, the state-quantity-fluctuation-data acquisition part 11 may be configured to store the acquired state-quantity fluctuation data D in the storage device m, while the feature extraction part 12 may be configured to acquire the state-quantity fluctuation data D from the storage device m];
calculating an abnormality degree representing an extent of an irregularity of process data of the pieces of process data that is read [0060, Fig. 2 — As shown in FIG. 2, the facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6.; 0085, Fig. 7 — a feature acquisition step (S1), an abnormality-degree-calculation-model construction step (S2), an abnormality degree calculation step (S3), an abnormality determination step (S4), an abnormality-contribution-rate calculation step (S5), and an abnormality cause identification step (S6)];
determining, for each of the pieces of process data output by the corresponding one of the plurality of sensors, whether the abnormality degree that is calculated satisfies a predetermined criterion by using causal relation information defining a combination between a cause and the irregularity of the process data output by each of the plurality of sensors, the irregularity appearing as an influence resulting from the cause [0060, Fig. 2 — As shown in FIG. 2, the facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6.; 0085, Fig. 7 — a feature acquisition step (S1), an abnormality-degree-calculation-model construction step (S2), an abnormality degree calculation step (S3), an abnormality determination step (S4), an abnormality-contribution-rate calculation step (S5), and an abnormality cause identification step (S6); 0067 — abnormality determination part 4 determines whether an abnormality is present in the monitoring target facility 9, based on the abnormality degree Q calculated by the abnormality degree calculation part 3. Specifically, the presence of abnormality may be determined based on a comparison between the abnormality degree Q and an abnormality determination threshold L, for instance, it may be determined as normal (no abnormality is present) if the abnormality degree Q is equal to or less than the abnormality determination threshold L, whereas it may be determined as abnormal (an abnormality is present) if the abnormality degree Q is more than the abnormality determination threshold L.]; and
storing causes of abnormal irregularity actions for handling the causes, wherein a candidate for an operating condition of the production facility for suppressing the abnormal irregularity is obtained based on the stored causes [0072-0073, Fig. 2 — the abnormality-degree-calculation-model construction part 2 is configured to store the abnormality degree calculation model M in the storage device m. The abnormality degree calculation part 3 is connected to the abnormality determination part 4 and input the abnormality degree Q calculated using the abnormality degree calculation model M stored in the storage device m into the abnormality determination part 4… a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the user] and
the obtained candidate is proposed to a user of the production facility [0071-0072, Fig. 6 — a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the user. Thus, the operator can confirm the identification result R of the abnormality cause by checking the display device 16].
But Hayashi fails to clearly specify determining whether the abnormality is accurately determined based on processing data, and performs an action by using the calculated abnormality degree when the abnormality is determined to be accurate.
However, Shinkawa teaches determine whether the abnormality is accurately determined based on processing data, and performs an action by using the calculated abnormality degree when the abnormality is determined to be accurate [0027, 0034 — if at least one of the plurality of wind turbines is determined not to have an abnormality based on the abnormality degree calculated based on the operating condition (multiple parameter values), the validity (accuracy) of that abnormality negative determination is verified based on the statistic calculated from the plurality of abnormality degrees at the same timing. Thus, even if the abnormality degree is not more than the threshold, it is possible to early detect an abnormality, and it is possible to prevent a reduction in operating rate due to failure of the wind turbine and an increase in cost; 0110-0118, Figs. 8-9 — determination part 5 determines whether an abnormality is present in each of the plurality of wind turbines 2, based on the abnormality degree E of each of the wind turbines 2 calculated by the abnormality degree calculation part 4… first validity determination part 62 determines whether the abnormality positive determination Ja being verified is valid (first validity determination), based on the number of wind turbines 2 that is determined to have an abnormality as a result of determination based on the abnormality degree E regarding the one or more other wind turbines 2… the control unit 14 further includes a first notification part 64 that notifies that an abnormality is detected (performs an action) if the first validity determination part 62 determines that the abnormality positive determination Ja of the verification target is valid].
Hayashi and Shinkawa are analogous art. They relate to fault detection/monitoring systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Hayashi, by incorporating the above limitations, as taught by Shinkawa.
One of ordinary skill in the art would have been motivated to do this modification in order to prevent a reduction in operating rate due to false detection and an increase in cost, as taught by Shinkawa [0027, 0034, 0127].
Regarding claim 11, Hayashi teaches a non-transitory computer readable medium storing an abnormal irregularity cause display program causing a computer [0059-0060, Fig. 2 — facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6. The facility condition monitoring device 1 comprises a computer, for instance, including a CPU (processor, not shown), a storage device m such as a memory, including ROM and RAM, and an external storage device, and a communication interface; 0072-0073 — a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the use… The CPU operates (e.g. computation of data) in accordance with instructions of program (facility condition monitoring program) loaded to a main storage device (non-transitory computer readable medium)] to perform:
reading, from a storage device storing pieces of process data of each equipment in a production facility continuously output by a plurality of sensors included in the production facility, the pieces of process data in a predetermined period of time [0059-0060, Fig. 2 — facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6. The facility condition monitoring device 1 comprises a computer, for instance, including a CPU (processor, not shown), a storage device m such as a memory, including ROM and RAM, and an external storage device, and a communication interface; 0053, Fig. 1 — state-quantity fluctuation data D representative of a time-dependent change of a state quantity measured on the monitoring target facility 9. The monitoring target facility 9 is a facility (apparatus) such as, for instance, a wind turbine power generating apparatus (wind turbine) (see FIG. 1) (power production facility) or an engine (not shown) having a piston reciprocably disposed within a cylinder. The state-quantity fluctuation data D contains a set of sensor values (measurement data) obtained by multiple measurement in a certain period (period of time) with sensors 8; 0075 — the feature extraction part 12 may be performed via the storage device m. Specifically, the state-quantity-fluctuation-data acquisition part 11 may be configured to store the acquired state-quantity fluctuation data D in the storage device m, while the feature extraction part 12 may be configured to acquire the state-quantity fluctuation data D from the storage device m];
calculating an abnormality degree representing an extent of an irregularity of process data of the pieces of process data that is read [0060, Fig. 2 — As shown in FIG. 2, the facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6.; 0085, Fig. 7 — a feature acquisition step (S1), an abnormality-degree-calculation-model construction step (S2), an abnormality degree calculation step (S3), an abnormality determination step (S4), an abnormality-contribution-rate calculation step (S5), and an abnormality cause identification step (S6)];
determining, for each of the pieces of process data output by the corresponding one of the plurality of sensors, whether the abnormality degree that is calculated satisfies a predetermined criterion by using causal relation information defining a combination between a cause and the irregularity of the process data output by each of the plurality of sensors, the irregularity appearing as an influence resulting from the cause [0060, Fig. 2 — As shown in FIG. 2, the facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6.; 0085, Fig. 7 — a feature acquisition step (S1), an abnormality-degree-calculation-model construction step (S2), an abnormality degree calculation step (S3), an abnormality determination step (S4), an abnormality-contribution-rate calculation step (S5), and an abnormality cause identification step (S6); 0067 — abnormality determination part 4 determines whether an abnormality is present in the monitoring target facility 9, based on the abnormality degree Q calculated by the abnormality degree calculation part 3. Specifically, the presence of abnormality may be determined based on a comparison between the abnormality degree Q and an abnormality determination threshold L, for instance, it may be determined as normal (no abnormality is present) if the abnormality degree Q is equal to or less than the abnormality determination threshold L, whereas it may be determined as abnormal (an abnormality is present) if the abnormality degree Q is more than the abnormality determination threshold L.]; and
storing causes of abnormal irregularity actions for handling the causes, wherein a candidate for an operating condition of the production facility for suppressing the abnormal irregularity is obtained based on the stored causes [0072-0073, Fig. 2 — the abnormality-degree-calculation-model construction part 2 is configured to store the abnormality degree calculation model M in the storage device m. The abnormality degree calculation part 3 is connected to the abnormality determination part 4 and input the abnormality degree Q calculated using the abnormality degree calculation model M stored in the storage device m into the abnormality determination part 4… a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the user] and
the obtained candidate is proposed to a user of the production facility [0071-0072, Fig. 6 — a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the user. Thus, the operator can confirm the identification result R of the abnormality cause by checking the display device 16].
But Hayashi fails to clearly specify determining whether the abnormality is accurately determined based on processing data, and performs an action by using the calculated abnormality degree when the abnormality is determined to be accurate.
However, Shinkawa teaches determine whether the abnormality is accurately determined based on processing data, and performs an action by using the calculated abnormality degree when the abnormality is determined to be accurate [0027, 0034 — if at least one of the plurality of wind turbines is determined not to have an abnormality based on the abnormality degree calculated based on the operating condition (multiple parameter values), the validity (accuracy) of that abnormality negative determination is verified based on the statistic calculated from the plurality of abnormality degrees at the same timing. Thus, even if the abnormality degree is not more than the threshold, it is possible to early detect an abnormality, and it is possible to prevent a reduction in operating rate due to failure of the wind turbine and an increase in cost; 0110-0118, Figs. 8-9 — determination part 5 determines whether an abnormality is present in each of the plurality of wind turbines 2, based on the abnormality degree E of each of the wind turbines 2 calculated by the abnormality degree calculation part 4… first validity determination part 62 determines whether the abnormality positive determination Ja being verified is valid (first validity determination), based on the number of wind turbines 2 that is determined to have an abnormality as a result of determination based on the abnormality degree E regarding the one or more other wind turbines 2… the control unit 14 further includes a first notification part 64 that notifies that an abnormality is detected (performs an action) if the first validity determination part 62 determines that the abnormality positive determination Ja of the verification target is valid].
Hayashi and Shinkawa are analogous art. They relate to fault detection/monitoring systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above non-transitory computer readable medium, as taught by Hayashi, by incorporating the above limitations, as taught by Shinkawa.
One of ordinary skill in the art would have been motivated to do this modification in order to prevent a reduction in operating rate due to false detection and an increase in cost, as taught by Shinkawa [0027, 0034, 0127].
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hayashi and Shinkawa in view of the English translation of Intou et al. Japanese Patent Publication No. JPH06309584 (hereinafter Intou), published 1994 and previously provided by Applicant.
Regarding claim 7, the combination of Hayashi and Shinkawa teaches all the limitations of the base claims as outlined above.
Further, Hayashi teaches the step of determining whether the calculated abnormality degree satisfies the predetermined criterion, the processor is configured to perform an action [0060, Fig. 2 — As shown in FIG. 2, the facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6.; 0085, Fig. 7 — s a feature acquisition step (S1), an abnormality-degree-calculation-model construction step (S2), an abnormality degree calculation step (S3), an abnormality determination step (S4), an abnormality-contribution-rate calculation step (S5), and an abnormality cause identification step (S6)], and
in a step of determining the handling, the processor is configured to make the output device output, for the irregularity, a plurality of candidates of a possible cause of the irregularity and accuracy of the possible cause [0071-0072, Figs. 2 and 6 — abnormality cause identification part 6 calculates the score for each abnormality cause by collating features F having top N contribution rates C which largely contribute to the abnormality degree Q obtained by the abnormality-contribution-rate calculation part 5, with the cause-and-effect matrix T (see FIG. 6). If the abnormality cause has a high score, a possibility that this abnormality cause accounts for the detected abnormality is high… an identification result R (diagnosis result) of the abnormality cause by the abnormality cause identification part 6 is reported (provided) to an operator. The identification result R of the abnormality cause may include top Na (1≤Na<N) abnormality causes identified by the abnormality cause identification part 6. Additionally, the identification result R of the abnormality cause may include the above scores corresponding to the top Na abnormality causes as information indicating which feature F is a cause or a very likely cause of the detected abnormality. Instead of the scores, the identification result R of the abnormality cause may include the probability of the abnormality cause that can be calculated based on the contribution rate C. In the example in FIG. 6, a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the user; 0059-0060, Fig. 2 — facility condition monitoring device 1 includes an abnormality-degree-calculation-model construction part 2, an abnormality degree calculation part 3, an abnormality determination part 4, an abnormality-contribution-rate calculation part 5, and an abnormality cause identification part 6. The facility condition monitoring device 1 comprises a computer, for instance, including a CPU (processor, not shown), a storage device m such as a memory, including ROM and RAM, and an external storage device, and a communication interface.].
But the combination of Hayashi and Shinkawa fails to clearly specify multiply each of a plurality of types of pieces of the process data is multiplied by a coefficient in accordance with a type of the process data or a coefficient based on a magnitude of the abnormality degree to obtain accuracy of the cause of the irregularity.
However, Intou teaches multiply each of a plurality of types of pieces of the process data is multiplied by a coefficient in accordance with a type of the process data or a coefficient based on a magnitude of the abnormality degree to obtain accuracy of the cause of the irregularity [0037 — If there are multiple event occurrence paths in the causal relationship tree, for example, if there are multiple lower-level events whose degree of event occurrence exceeds the threshold, which path has priority in step 64 is determined by calculation. In each layer, the higher product (multiplied by) of the degree of occurrence of the event and the degree of relation is preferentially adopted, and the route is obtained (step 65). After the search for the first route is completed in in is way. The second event occurrence route is obtained by adopting the one having the highest product of the degree of occurrence of the event and the degree of relation (step 67).].
Hayashi, Shinkawa and Intou are analogous art. They relate to fault detection/monitoring systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above device, as taught by the combination of Hayashi and Shinkawa, by incorporating the above limitations, as taught by Intou.
One of ordinary skill in the art would have been motivated to do this modification so that an irregularity that occurs more frequently — and would therefore have a greater effect — is given a more weight, as suggested by Intou [0037].
Claim(s) 8 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hayashi and Shinkawa in view of Intou and further in view of Golash et al. U.S. Patent Publication No. 20170308422 (hereinafter Golash).
Regarding claim 8, the combination of Hayashi and Shinkawa teaches all the limitations of the base claims as outlined above.
Further, Hayashi teaches the processor is configured to make the output device output information [0072-0073, Fig. 2 — the abnormality-degree-calculation-model construction part 2 is configured to store the abnormality degree calculation model M in the storage device m. The abnormality degree calculation part 3 is connected to the abnormality determination part 4 and input the abnormality degree Q calculated using the abnormality degree calculation model M stored in the storage device m into the abnormality determination part 4… a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the user].
But the combination of Hayashi and Shinkawa fails to clearly specify a logic tree where, by using the irregularity as a root and the cause of the irregularity as a leaf, events appearing in a course from the cause to the irregularity are connected in a hierarchical manner and make the output device output the information indicating the handling to be taken for the cause in association with the cause.
However, Intou teaches make the output device output the information indicating the handling to be taken for the cause in association with the cause [0005 — the cause is diagnosed, the name of the cause and corresponding operation guidance (handling) are displayed; 0017 — the history data of the corresponding point is retrieved from the history database, and together with the cause and operation guidance, related system diagrams, trend graphs, and other monitoring data are displayed on the display device; 0047 — At the same time, the guidance message correspond to the message ID of the event identified as the cause is retrieved from guidance database 29 and displayed on the display device 33 together with the cause].
Hayashi, Shinkawa and Intou are analogous art. They relate to fault detection/monitoring systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above device, as taught by the combination of Hayashi and Shinkawa, by incorporating the above limitations, as taught by Intou.
One of ordinary skill in the art would have been motivated to do this modification in order to assist an operator with an appropriate coping method for a related cause, as suggested by Intou [0002, 0005, 0017, 0047].
But the combination of Hayashi, Shinkawa and Intou fails to clearly specify a logic tree where, by using the irregularity as a root and the cause of the irregularity as a leaf, events appearing in a course from the cause to the irregularity are connected in a hierarchical manner.
However, Golash teaches a logic tree where, by using the irregularity as a root and the cause of the irregularity as a leaf, events appearing in a course from the cause to the irregularity are connected in a hierarchical manner [0058-0061, Fig. 6 — Debugging module 110 may then perform debugging steps 602-616 in FIG. 6; 0066-0067, Fig. 6 — debugging module 110 may determine that that the networking malfunction resulted at least in part from the potential cause by traversing the tree data structure that includes and/or represents the set of debugging steps… debugging module 110 may continue executing the debugging steps until reaching a leaf node (e.g., a node with no children) within the tree data structure. Upon reaching such a leaf node, debugging module 110 may determine that the hardware component involved in the debugging step represented by that leaf node is the root cause of the networking malfunction.].
Hayashi, Shinkawa, Intou and Golash are analogous art. They relate to fault detection/monitoring systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above device, as taught by the combination of Hayashi, Shinkawa, and Intou by incorporating the above limitations, as taught by Golash.
One of ordinary skill in the art would have been motivated to do this modification to improve methods, systems, and apparatuses for debugging, as suggested by Golash [0001-0003].
Regarding claim 9, the combination of Hayashi, Shinkawa and Intou teaches all the limitations of the base claims as outlined above.
Further, Hayashi teaches the processor is configured to make the output device output information [0072-0073, Fig. 2 — the abnormality-degree-calculation-model construction part 2 is configured to store the abnormality degree calculation model M in the storage device m. The abnormality degree calculation part 3 is connected to the abnormality determination part 4 and input the abnormality degree Q calculated using the abnormality degree calculation model M stored in the storage device m into the abnormality determination part 4… a relationship between the abnormality cause and its probability (FIG. 6) is displayed on a screen of a display device 16 such as a display to present the diagnosis result to the user].
Further, Intou teaches make the output device output the information indicating the handling to be taken for the cause in association with the cause [0005 — the cause is diagnosed, the name of the cause and corresponding operation guidance (handling) are displayed; 0017 — the history data of the corresponding point is retrieved from the history database, and together with the cause and operation guidance, related system diagrams, trend graphs, and other monitoring data are displayed on the display device; 0047 — At the same time, the guidance message correspond to the message ID of the event identified as the cause is retrieved from guidance database 29 and displayed on the display device 33 together with the cause].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above device, as taught by the combination of Hayashi and Shinkawa, by incorporating the above limitations, as taught by Intou.
One of ordinary skill in the art would have been motivated to do this modification in order to assist an operator with an appropriate coping method for a related cause, as suggested by Intou [0002, 0005, 0017, 0047].
But the combination of Hayashi, Shinkawa and Intou fails to clearly specify a logic tree where, by using the irregularity as a root and the cause of the irregularity as a leaf, events appearing in a course from the cause to the irregularity are connected in a hierarchical manner.
However, Golash teaches a logic tree where, by using the irregularity as a root and the cause of the irregularity as a leaf, events appearing in a course from the cause to the irregularity are connected in a hierarchical manner [0058-0061, Fig. 6 — Debugging module 110 may then perform debugging steps 602-616 in FIG. 6; 0066-0067, Fig. 6 — debugging module 110 may determine that that the networking malfunction resulted at least in part from the potential cause by traversing the tree data structure that includes and/or represents the set of debugging steps… debugging module 110 may continue executing the debugging steps until reaching a leaf node (e.g., a node with no children) within the tree data structure. Upon reaching such a leaf node, debugging module 110 may determine that the hardware component involved in the debugging step represented by that leaf node is the root cause of the networking malfunction.].
Hayashi, Shinkawa, Intou and Golash are analogous art. They relate to fault detection/monitoring systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above device, as taught by the combination of Hayashi, Shinkawa and Intou, by incorporating the above limitations, as taught by Golash.
One of ordinary skill in the art would have been motivated to do this modification to improve methods, systems, and apparatuses for debugging, as suggested by Golash [0001-0003].
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hubauer et al. U.S. Patent Publication No. 20190079506 discloses a smart embedded control system that utilizes fault trees for fault analysis.
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERNARD G. LINDSAY whose telephone number is (571)270-0665. The examiner can normally be reached Monday through Friday from 8:30 AM to 5:30 PM EST.
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119
Prosecution Insights