DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
1. Claim(s) 7-9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tokorozuki et al. (US 2017/0336775).
Tokorozuki et al. discloses an abnormality detection apparatus detecting an abnormality of a production device, comprising:
a communication unit (FIG. 1: COMMUNICATION NETWORK) that receives operating data that is transmitted from a control device performing operation control of the production device, the operating data the operation control (FIG. 1: PLC (a programing logic controller) controls the operation of the manufacturing devices ME1-MEn);
an acquisition unit that acquires time-series sensor value data output from a sensor detecting a physical quantity related to operation of the production device or a product manufactured by the production device (FIG. 1: The sensors SEN1-SEN10 detect the physical quantity related to the operation of the manufacturing devices ME1-MEn); and
a processing unit that calculates statistics of the sensor value data acquired by the acquisition unit and determines a presence or an absence of an abnormality of the manufacturing device based on the statistics calculated and a threshold depending on the operating data acquired (FIGs. 1 and 3: The measured physical quantities related to the operation of the manufacturing devices from the sensors are acquired by the abnormality detection devices for determining the presence/absence of an abnormality) ,
wherein the communication unit (FIG. 1: COMMUNICATION NETWORK) transmits the operating data and the sensor value data to an external diagnostic apparatus that diagnoses a condition of the production device (FIG. 1: Management apparatus CS), receives a diagnostic result transmitted from the diagnostic apparatus and transmits a determination result from the processing unit, the statistics and the diagnostic result received to the control device (FIGs. 1 and 13: The communication network NW transmits data between the abnormality detection devices FDD1-n and the management apparatus CS. Paragraph [0045]: The learned models MDL[1]-[x] in the management apparatus CS determine whether the abnormality is present).
Claim(s) 1, 3, 6 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tokorozuki et al. (US 2017/0336775).
Regarding to claim 1:
Tokorozuki et al. discloses an abnormality detection system including an abnormality detection apparatus detecting an abnormality of a manufacturing device and a diagnostic apparatus diagnosing a condition of the production device, comprising:
a control device that performs operation control of the manufacturing device and transmits operating data of the operation control to the abnormality detection apparatus (FIG. 1: PLC (a programing logic controller) controls the operation of the manufacturing devices ME1-MEn);
a sensor that detects a physical quantity related to operation of the manufacturing device or a product manufactured by the manufacturing device and outputs time-series sensor value data indicating the physical quantity detected to the abnormality detection apparatus (FIG. 1: The sensors SEN1m-SEN10 to SENnm-SENn0 detect the physical quantity related to the operation of the manufacturing devices ME1-MEn); and
a database that accumulates information including the operating data, the sensor value data and a condition of the production device (FIG. 1: Database DB1), wherein
the abnormality detection apparatus (FIG. 1: The abnormality detection devices FDD1-FDDn) includes a first communication unit that receives the operating data transmitted from the control device, an acquisition unit that acquires the sensor value data output from the sensor (FIG. 1: The sensor values DTG1m-10 and DTGnm-n0), and a processing unit that calculates statistics of the sensor value data acquired by the acquisition unit and determines a presence or an absence of an abnormality of the manufacturing device based on the statistics calculated and a threshold depending on the operating data received, the first communication unit transmits the operating data and the sensor value data to the diagnostic apparatus (FIGs. 2-3 show the abnormality detection device FDD receives the sensor values SEN to determine the presence/absence of an abnormality by the abnormality detection unit FDU, and output the detection result),
the diagnostic apparatus(FIG. 1: Management apparatus CS) includes a second communication unit that receives the operating data and the sensor value data that are transmitted from the abnormality detection apparatus, and a diagnostic processing unit that diagnoses a condition of the manufacturing device based on the operating data and the sensor value data that are received by the second communication unit and information accumulated in the database (Paragraph [0045]: The learned models MDL[1]-[x] in the management apparatus CS determine whether the abnormality is present based on the information from the sensors SEN and the information from the database DB1), the second communication unit transmits a diagnostic result from the diagnostic processing unit to the abnormality detection apparatus, and the first communication unit of the abnormality detection apparatus receives the diagnostic result from the diagnostic processing unit, and transmits a determination result from the processing unit, the statistics and the diagnostic result from the diagnostic processing unit to the control device (FIGs. 1 and 13: The communication network NW transmits data/determination results between the abnormality detection devices FDD1-n and the management apparatus CS).
Regarding to claim 3: wherein the diagnostic apparatus has a learning model (FIG. 1: The learned models MDL[1]-[x]) that outputs a feature of the manufacturing device or a product if the sensor value data is input, and the diagnostic processing unit diagnoses a condition of the manufacturing device by comparing a feature outputted acquired by inputting the sensor value data received into the learning model and a feature acquired by inputting the sensor value data accumulated in the database to the learning model (Paragraph [0045]: The learned models MDL[1]-[x] in the management apparatus CS determine whether the abnormality is present based on the information from the sensors SEN and the information from the database DB1).
Regarding to claim 6: wherein the abnormality detection system is configured to detect an abnormality of a molding machine (Using the claimed/prior abnormality detection system for detecting an abnormality in a molding machine is considered as an intended use of such claimed/prior abnormality detection system and that intended use does not carry patentable weight).
Allowable Subject Matter
Claim 4 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The primary reasons for the indication of the allowability of the claim is the inclusions therein, in combination as currently claimed, of the limitation that wherein the second communication unit receives feedback information indicating validity of the diagnostic result from the diagnostic processing unit or indicating a correct diagnostic result, and the diagnostic processing unit additionally trains the learning model based on the feedback information received or updates a diagnostic criterion based on the feature is neither disclosed nor taught by the cited prior art of record, alone or in combination.
Response to Arguments
Applicant's arguments filed 3/27/2026 have been fully considered but they are not persuasive.
In response to Applicant’s Remarks, the Examiner cites that, in Tokorozuki et al., FIG. 13, the management apparatus CS’ with the function of abnormality determination, acts as an external diagnostic apparatus; as a result. In addition, FIGs. 1 and 13 show the communication between the manufacturing devices and the communication network, data in such communication thus reads on the claimed operating data. Furthermore, the communication network receives the abnormality notification from the management apparatus CS’ and transmits it to the manufacturing device via PLC (FIG. 13).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAM S NGUYEN whose telephone number is (571)272-2151.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DOUGLAS RODRIGUEZ, can be reached on 571-431-0716. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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