DETAILED ACTION
Status of Claims
Claims 1-3 and 6 are currently pending and have been examined in this application. This Non-Final Rejection is in response to the amendment submitted on 3/09/2026.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Arguments and Amendments
Applicant’s arguments, filed on 3/09/2026, with respect to the rejection of Claims 1-3, and 6 under 35 USC 103 have been fully considered but they are moot in view of the new grounds of rejection provided below, which was necessitated based on Applicant’s amendments to the claims, which changed the scope of the claims. Examiner notes wherein Applicant’s arguments are directed towards the newly amended claim limitation(s), which are addressed by the newly found prior art, as indicated below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are:
Claim 1:
[operating condition information acquisition unit ] Prong1: operating condition information acquisition unit; Prong 2: acquires operating condition information; Prong 3: Sufficient structure not recited. Drawings: Figure 1 and Figure 5 (diagnostic apparatus 22) Specification: Page 8, [0025] ; Referring back to FIG. 1, the diagnostic apparatus 22 is an information processing apparatus
Claim 1:
[detection result acquisition unit] Prong1: detection result acquisition unit; Prong 2: acquires detection results; Prong 3: Sufficient structure not recited. Drawings: Figure 1 and Figure 5 (diagnostic apparatus 22) Specification: Page 8, [0025] ; Referring back to FIG. 1, the diagnostic apparatus 22 is an information processing apparatus
Claims 1, 2, and 3:
[detection result estimation unit] Prong1: detection result estimation unit; Prong 2: that estimates; Prong 3: Sufficient structure not recited. Drawings: Figure 1 and Figure 5 (diagnostic apparatus 22) Specification: Page 8, [0025] ; Referring back to FIG. 1, the diagnostic apparatus 22 is an information processing apparatus
Claims 1:
[sensor state estimation unit] Prong1: sensor state estimation unit; Prong 2: that estimates a state; Prong 3: Sufficient structure not recited. Drawings: Figure 1 and Figure 5 (diagnostic apparatus 22) Specification: Page 8, [0025] ; Referring back to FIG. 1, the diagnostic apparatus 22 is an information processing apparatus
Claims 1 and 6:
[deterioration estimation unit] Prong1: deterioration estimation unit; Prong 2: that estimates a degree of deterioration; Prong 3: Sufficient structure not recited. Drawings: Figure 1 and Figure 5 (diagnostic apparatus 22) Specification: Page 8, [0025] ; Referring back to FIG. 1, the diagnostic apparatus 22 is an information processing apparatus
Claim 1:
[update unit] Prong1: update unit; Prong 2: that updates the simulation model; Prong 3: Sufficient structure not recited. Drawings: Figure 1 and Figure 5 (diagnostic apparatus 22) Specification: Page 8, [0025] ; Referring back to FIG. 1, the diagnostic apparatus 22 is an information processing apparatus
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(b)
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.
Claims 1 is 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 alternately refers to a plurality of simulation models as “a simulation model" and “the simulation model” in a manner which renders the claim indefinite. Particularly in amended claim 1 at the end of page 2 and beginning of page 3, the claim recites the following: “updates the simulation model used by the detection result estimation unit to a simulation model”. Since multiple simulation models were referred to prior to this limitation, it is not clear which simulation model is being referred and seems to invoke a third simulation model which is not clearly differentiated from the preceding simulation models.
Examiner’s Note:
The examiner strongly suggests providing a clearer definition for “a simulation model” at the end of claim 1 which differentiates and/or clarifies which simulation model is being referred to at the end of the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Kasai (US 20190091861A1) as modified by Rosenberg (US 10335962 B1) in view of Hiruta (US 20160169771 A1)
Claim 1:
Kasai teaches the following limitations:
A diagnostic system comprising: an operating condition information acquisition unit that acquires operating condition information capable of identifying an operating condition of equipment having a movable part that is driven by receiving a driving force; (Kasai - [0180] More specifically, according to the present embodiment, in the supporting arm apparatus 10, the state of the joint section 130 is detected by the joint state detecting section 132. Further, in the control apparatus 20, on the basis of the state of the joint section 130, the purpose of motion, and the constraint condition, various kinds of operations related to the whole body cooperative control using the generalized inverse dynamics for controlling driving of the arm section 120 are performed, and torque command value τ serving as the operation result are calculated. …) a detection result acquisition unit that acquires detection results of a plurality of sensors installed in the equipment; (Kasai - [0142] The joint state detecting section 132 detects the state of the joint section 130. … the torque detecting section 134 correspond to the encoder 427 of the actuator 430 illustrated in FIG. 3 and the torque sensors 428 and 428a illustrated in FIGS. 4A and 4B. The joint state detecting section 132 transmits the detected state of the joint section 130 to the control apparatus 20. ; [0183] … (d) A controller is notified of the values detected by the sensors of (a) and (b) above as control observation values. )
the detection result of a particular sensor of the plurality of sensors at the second point of time; a sensor state estimation unit that estimates a state of the particular sensor by (Kasai – [0186] … Then, the predicted values are compared with the detection values (sensor values) of the encoder 427 and the torque sensor 428 to perform trouble detection on the encoder 427 and the torque sensor 428. ; [0187] This makes it possible to predict, from one sensor value of the actuator 430, another sensor value of the same actuator 430 for each of the plurality of actuators 430 installed in the supporting arm even in the state in which unexpected external force is applied to the supporting arm.) comparing the detection result of the particular sensor at the second point of time, which is estimated, with the detection result of the particular sensor of the plurality of sensors acquired at the second point of time; (Kasai – [0188] Specifically, the use of the predicted values allows sensor trouble to be sensed as follows. (A) From the torque sensor value of the actuator 430, the angle sensor value of the same actuator 430 is estimated, and trouble/malfunction is sensed depending on whether it agrees with the actually measured data. … ; [0190] The sensor trouble determination section 266 compares the encoder prediction value with a detection value (actually measured value) of the encoder 427 to perform trouble determination on the encoder 427. In addition, the sensor trouble determination section 266 compares the torque prediction value with a detection value (actually measured value) of the torque sensor 428 to perform trouble determination on the torque sensor 428. …)
a deterioration estimation unit that estimates a degree of deterioration of the equipment based on the detection results of the plurality of sensors; and
(Kasai – [0188] Specifically, the use of the predicted values allows sensor trouble to be sensed as follows. (A) From the torque sensor value of the actuator 430, the angle sensor value of the same actuator 430 is estimated, and trouble/malfunction is sensed depending on whether it agrees with the actually measured data. … ; [0190] The sensor trouble determination section 266 compares the encoder prediction value with a detection value (actually measured value) of the encoder 427 to perform trouble determination on the encoder 427. In addition, the sensor trouble determination section 266 compares the torque prediction value with a detection value (actually measured value) of the torque sensor 428 to perform trouble determination on the torque sensor 428. …)
Kasai does not explicitly teach the following limitations, however Rosenberg teaches:
a detection result estimation unit that estimates, by inputting the operating condition information acquired at a first point of time and the detection results of the plurality of sensors acquired at the first point of time to a simulation model,
(Rosenberg - [page 21, column 21, lines 14- 24] The actual performance of the robot obtained from the sensor data and the simulation results of the robot performance may be compared and calculated for discrepancy 713. Based on the calculated discrepancy, fault may be detected (operation 715) and diagnosed. Fault detection and diagnosis may follow the operations described in FIGS. 4 and 5. The robot commands may or may not be updated based on the detected fault. For instance, if the diagnosis reveals a fault that requires additional operations for the robot to perform, the robot commands are updated to reflect those adjustments.)
a model storage unit that stores data for a plurality of simulation models adapted to a plurality of degrees of deterioration of the equipment;
(Rosenberg - [page 15, column 10, lines 28- 55] The USM module 308 may be configured to store and/or update the models of the individual components and modules of the physical system 202 of the robot, and may also include functional models of one or more different modular parts of the physical system (e.g., the physical system 202, sensor system 204 and power system 212) and the interactions among them. …The models can be used to predict or calculate an output performance of the robot according to an input data. … the models may enable exact description of the effects of factors such as the change in temperature or wear and tear on frictional resistance, the effect of bumping into some external object, or the effect of a failing structural element upon the strength of a joint. ; [page 21, column 22, lines 2- 8] Once the cause of the anomaly is identified, the nature and magnitude of the fault can be determined through further diagnostic exercises, including exercises described herein. The FDD system 206 may also serve various additional functions. The FDD system may be configured to self-validate the diagnosis, or modify/update the simulations calculated by the robot 100 for continued operations.
Kasai in combination with Rosenberg does not explicitly teach the following limitations, however Hiruta teaches:
an update unit that reads, from the plurality of simulation models stored in the model storage unit, a simulation model corresponding to the degree of deterioration of the equipment estimated by the deterioration estimation unit and updates the simulation model used by the detection result estimation unit to a simulation model, before the sensor state estimation unit estimates the state of the particular sensor, (Hiruta – [0008] … a data storage unit that stores a diagnostic model that is a requirement to diagnose an abnormal state of the machine, and diagnose the abnormal′ state of the diagnosed machine based on the diagnostic model, a first distribution creation unit configured to create first distribution that is data frequency of each sensor based on the sensor data group … ; [0055] The diagnosing unit 130 performs a machine abnormality diagnosis of each machine and each diagnostic model based on data stored in the storage unit 120.) wherein the sensor state estimation unit estimates the state of the particular sensor when the degree of deterioration of the equipment estimated by the deterioration estimation unit is equal to or greater than a predetermined threshold value. (Hiruta – [0009] Further, in the condition monitoring apparatus according to the present invention, the diagnostic model includes a sensor group used to diagnoses, an diagnostic algorithm to be used, a condition division requirement including sensor data for extracting a condition of the diagnostic target machine and a threshold value requirement of the sensor data, and a determination requirement that is a threshold value as an index to express an abnormal state level calculated by the diagnostic algorithm. ; [0068] The second distribution creation unit 150 creates, for each machine and each diagnostic model, frequency distribution (second frequency distribution) and probability distribution (second probability distribution) of all pieces of sensor data that have a determination result of “1” (abnormal state) in the diagnostic period.)
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Kasai to provide a method for combining the operating condition information of the robot with the sensor readings and then inputting this data into a simulation model for the purpose of estimating or predicting future sensor results as taught in Rosenberg and to further provide a means of accessing machine models of which simulate the current state of the equipment as taught in Hiruta . Having the ability to generate predictions or estimations about the future state of sensors to further compare the machine’s state to stored machine models allows the system to compare these estimations with live sensor data and more accurately assess the health and/or level of deterioration for sensors that are in active use on the machine.
Claim 2:
Kasai teaches the following limitations:
The diagnosis system according to claim 1, wherein the equipment includes a plurality of movable parts, and (Kasai - [0179] As described above, according to the present embodiment, the arm section 120 having the multi-link structure in the supporting arm apparatus 10 has at least 6 or more degrees of freedom, and driving of each of the plurality of joint sections 130 configuring the arm section 120 is controlled by the drive control section 111. …) the detection result estimation unit estimates the detection result of the particular sensor at the second point of time by inputting operating conditions of the plurality of movable parts to the simulation model. (Kasai – [0186] … Then, the predicted values are compared with the detection values (sensor values) of the encoder 427 and the torque sensor 428 to perform trouble detection on the encoder 427 and the torque sensor 428. ; [0187] This makes it possible to predict, from one sensor value of the actuator 430, another sensor value of the same actuator 430 for each of the plurality of actuators 430 installed in the supporting arm even in the state in which unexpected external force is applied to the supporting arm.)
Claim 3:
Kasai teaches the following limitations:
The diagnosis system according to claim 1, wherein the detection result estimation unit estimates the detection result of the particular sensor at the second point of time
(Kasai – [0186] … Then, the predicted values are compared with the detection values (sensor values) of the encoder 427 and the torque sensor 428 to perform trouble detection on the encoder 427 and the torque sensor 428. ; [0187] This makes it possible to predict, from one sensor value of the actuator 430, another sensor value of the same actuator 430 for each of the plurality of actuators 430 installed in the supporting arm even in the state in which unexpected external force is applied to the supporting arm.)
Kasai does not explicitly teach the following limitations, however Rosenberg teaches:
by inputting the detection results of the sensors, of the plurality of the sensors, other than particular sensor to the simulation model.
(Rosenberg - [page 11, column 2, lines 52- 58] …(b) calculate discrepancy measurements between (i) sensor data collected from a plurality of sensors during the operation of the robot and (ii) an output of the simulation models based on the one or more input control signals, wherein discrepancy measurements beyond a pre-determined threshold are indicative of fault;
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Kasai to provide a method for combining the operating condition information of the robot with the sensor readings and then inputting this data into a simulation model for the purpose of estimating or predicting future sensor results as taught in Rosenberg. Having the ability to generate predictions or estimations about the future state of sensors allows the system to compare these estimations with live sensor data and more accurately assess the health and/or level of deterioration for sensors that are in active use on the robot.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Kasai (US 20190091861A1) as modified by Rosenberg (US 10335962 B1) in view of Hiruta (US 20160169771 A1) in further view of Huang (US 20210086361 A1)
Claim 6:
Kasai in combination with Rosenberg and Hiruta does not explicitly teach the following limitations, however Huang teaches:
The diagnosis system according to claim 1 wherein the plurality of sensors detect a vibration at respective positions of sensor installation, and the deterioration estimation unit estimates the degree of deterioration of the equipment by comparing a vibration convergence time at the sensor installation position calculated based on the detection results of the plurality of sensors with a vibration convergence time estimated based on the operating condition information.
(Huang - [0040] In fluctuation-based anomaly detection, example implementations involve an anomaly detection method for the robotic arms based on their fluctuations in the vibration measurements. The anomaly indicator is defined to characterize the noisy level of the fluctuations by using a thresholding mechanism. For different types of operations, the normal ranges of the fluctuation are learned respectively using normal vibration measurements. A thresholding mechanism is then applied to count the number of peaks in a vibration measurement (associated to one operation) that beyond the pre-learned normal range. The anomaly indicator is the peak density which is the number of peaks normalized by the time duration of the vibration measurement. There are two phases involved in the fluctuation-based anomaly detection: the learning phase and the application phase.)
Therefore, prior to the effective filing date of the claimed invention, it would have been
obvious to one of ordinary skill in the art to modify Kasai, Rosenberg, and Hiruta to provide a method for estimating a degree of deterioration based on the time convergence of vibration data provided by sensors as taught in Huang. Having the ability to generate deterioration estimations based on time convergence vibration data allows the system to more accurately assess the health and/or level of deterioration present in the robot.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
Yoshida (US 20190354080 A1) describes an abnormality detector which includes a signal output unit for detecting a sign of an abnormality based on a physical quantity acquired from a manufacturing machine and outputting a signal; and a machine learning device including state observation unit for observing, as a state variable representing a present state related to an operation of the manufacturing machine.
Okada (US 20200042836 A1) describes a device that stores model definition information defining a statistical model, a data acquisition unit that acquires data from a sensor signal in a first mode and acquires data for generating a new statistical model from a sensor signal in a second mode according to a condition different from the first condition and a determination unit that determines whether the data acquired satisfies a requirement defined by the model definition information.
Natsumeda (US 20190265088 A1) describes a system analysis apparatus which includes sensor history information, based on sensor values output by a plurality of sensors provided in a system. The sensor history information represents in a time series whether or not the sensor value(s) output by each of the plurality of sensors is abnormal, and/or whether or not a relationship between the sensor values output by different sensors is abnormal.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN LINDSAY OSTROW whose telephone number is (703)756-1854. The examiner can normally be reached M-F 8 - 5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Mott can be reached on (571) 270 5376. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALAN LINDSAY OSTROW/ Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
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