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.
Response to Arguments
Applicant’s arguments, filed 3/20/26, 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(b) (page 7) are persuasive and the claims are no longer rejected for these specific reasons. Note that new grounds of rejection based on 35 U.S.C. § 112(b) are presented below.
Applicant’s arguments with regard to the rejection under 35 U.S.C. § 101 (page 8) is moot because the claims are no longer rejected under that statute.
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(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 6-11 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 6 recites the limitation 'wherein the production facility is controlled based on the stored obtained candidate obtained from the identified irregularity cause'. This constitutes new matter because it is not described in the application as originally filed. As indicated by Applicant (page 8), paragraph [0014] states that ‘identifying device l may obtain, for example, a candidate for an operating condition for suppressing the abnormal irregularity based on a table that stores causes of the abnormal irregularity actions for handling the causes and the identified cause and may propose the candidate to the user’. However, proposing the candidate to a user is different than controlling the facility based on the candidate. See also MPEP 2163 I A.
Claims 10 and 11 recite similar language to claim 6 and are rejected based on the same rationale as for claim 6.
The respective dependent claims are also rejected under 35 U.S.C. § 112(a) as they inherit all of the characteristics of the claim from which they depend.
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) 7-9 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 7 recites ‘the step of determining the handling’ that lacks an antecedent basis.
Claim 8 recites ‘the step of determining the handling’ that lacks an antecedent basis.
Claim 9 recites ‘the step of determining the handling’ that lacks an antecedent basis.
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 § 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 Liu et al. U.S. Patent Publication No. 20170269983 (hereinafter Liu).
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
obtain a previously stored candidate for an operating condition of the production facility for suppressing the abnormal irregularity [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 Hayashi fails to clearly specify the facility is controlled based on the stored obtained candidate obtained from the identified irregularity cause.
However, Liu teaches the facility is controlled based on the stored obtained candidate obtained from the identified irregularity cause [0034-0035, Fig. 3 — At 305, the device object repository 240 is queried based on the analyzed result (for example, failure report) in order to obtain the recommended fix solution. The failure is attempted to be fixed according to the recommended fix solution at 306. At 307, it is determined whether the failure has been fixed. If so, the task is closed automatically at 308].
Hayashi and Liu 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 Liu.
One of ordinary skill in the art would have been motivated to do this modification in order to actually control the facility to apply a stored candidate/fix solution to actually suppress the abnormality, as suggested by Liu [0034-0035], therefore improving the performance of the facility.
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
obtain a previously stored candidate for an operating condition of the production facility for suppressing the abnormal irregularity [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 Hayashi fails to clearly specify the facility is controlled based on the stored obtained candidate obtained from the identified irregularity cause.
However, Liu teaches the facility is controlled based on the stored obtained candidate obtained from the identified irregularity cause [0034-0035, Fig. 3 — At 305, the device object repository 240 is queried based on the analyzed result (for example, failure report) in order to obtain the recommended fix solution. The failure is attempted to be fixed according to the recommended fix solution at 306. At 307, it is determined whether the failure has been fixed. If so, the task is closed automatically at 308].
Hayashi and Liu 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 Liu.
One of ordinary skill in the art would have been motivated to do this modification in order to actually control the facility to apply a stored candidate/fix solution to actually suppress the abnormality, as suggested by Liu [0034-0035], therefore improving the performance of the facility.
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
obtain a previously stored candidate for an operating condition of the production facility for suppressing the abnormal irregularity [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 Hayashi fails to clearly specify the facility is controlled based on the stored obtained candidate obtained from the identified irregularity cause.
However, Liu teaches the facility is controlled based on the stored obtained candidate obtained from the identified irregularity cause [0034-0035, Fig. 3 — At 305, the device object repository 240 is queried based on the analyzed result (for example, failure report) in order to obtain the recommended fix solution. The failure is attempted to be fixed according to the recommended fix solution at 306. At 307, it is determined whether the failure has been fixed. If so, the task is closed automatically at 308].
Hayashi and Liu 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 Liu.
One of ordinary skill in the art would have been motivated to do this modification in order to actually control the facility to apply a stored candidate/fix solution to actually suppress the abnormality, as suggested by Liu [0034-0035], therefore improving the performance of the facility.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hayashi and Liu 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 Liu 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 the 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.].
Further, Liu teaches the step of determining the handling [0024, 0039 — determine a matching degree between failure information in the failure report and the historical failure information; and in response to the matching degree being above a predetermined threshold, obtain the fix solution (handling) from the device object repository].
But the combination of Hayashi and Liu 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, Liu 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 Liu, 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 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hayashi and Liu in view of Golash et al. U.S. Patent Publication No. 20170308422 (hereinafter Golash).
Regarding claim 8, the combination of Hayashi and Liu 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, Liu teaches the step of determining the handling, the processor is configured to make the output device output the information indicating the handling to be taken for the cause in association with the cause [0024, 0039 — determine a matching degree between failure information in the failure report and the historical failure information; and in response to the matching degree being above a predetermined threshold, obtain the fix solution (handling) from the device object repository].
But the combination of Hayashi and Liu 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, Liu 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 and Liu, 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].
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Hayashi, Liu and Intou in view of Golash.
Regarding claim 9, the combination of Hayashi, Liu 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, Liu teaches the step of determining the handling, the processor is configured to make the output device output the information indicating the handling to be taken for the cause in association with the cause [0024, 0039 — determine a matching degree between failure information in the failure report and the historical failure information; and in response to the matching degree being above a predetermined threshold, obtain the fix solution (handling) from the device object repository].
But the combination of Hayashi, Liu 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, Liu, 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, Liu 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].
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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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