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 .
DETAILED ACTION
Priority
Acknowledgment is made of applicant's claim for foreign priority based on a German application DE 10-2020-126355.4 filed on October 8, 2020.
DETAILED ACTION
Claims 11 - 28 are pending in the application.
Claims 1 and 20 are independent.
Claims 1 - 10 are cancelled.
Claims 13 - 15 and 22 - 24 are objected to as stated below.
This action is Final based on the same 35 U.S.C. §102 and/or 35 U.S.C. §103 prior art reference(s) that was not necessitated by the applicant’s amendment; see MPEP §706.07.
Given the amended claim 16, the objection is rescinded; and given the amendment and arguments of claim 11, the 35 U.S.C. 112(b) rejection is rescinded.
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 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) 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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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 20 limitations invokes 35 U.S.C. 112(f) because it uses generic placeholders, such as “a transport unit," “an acquisition unit,” and “a self-identification unit,” coupled with functional language " transporting the containers,” “to acquire at least one input signal and at least one output,” and “determine a self-identification model of the machine” that are not modified by sufficient structure, material, or acts for performing the claimed function. However, the written description of the specification implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function. A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) limitation: paragraphs 0013 states that the transport unit may be a conveyor or robot or transport vehicle, etc. Par. 0031 states that: “The acquisition unit and/or the self-identification unit may be implemented at least in part as a computer program product comprising machine instructions in the computer system which, when executed, at least partially carry out the method.”
Applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181; or
(c) Amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination
Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of
Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
Claims 11, 12, 18, 19, 20, 21, 27, and 28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by German patent document Poschl Stefan (DE 102018210670 A1), herein “Poschl.”
Regarding claim 11,
Poschl teaches a method for operating a machine in a processing plant for containers, including beverage containers, wherein the containers are processed and/or transported by the machine, the method comprising: (Page 2, Par. 3: “Container treatment plants are well known from the prior art in the beverage processing industry. These usually comprise one or more container treatment machines and transport devices which are arranged one after the other in the transport direction of the containers and which transport the containers between the container treatment machines. Transport devices that transport the containers within one of the container treatment machines are also known. These include, for example, conveyors that transport containers in a mass flow, but also devices that are designed for individual or at least isolated transport of the containers.”)
acquiring at least one input signal and at least one output signal of the machine during the processing and/or the transport; (Page 5, Par. 5: “Provision can furthermore be made for the control unit to comprise a neural network or for the control unit to be assigned a neural network which is designed based on a large number of comparisons between data received by the drone and setpoint values for the operating parameters and / or stored in the memory. or to learn the acceleration and /or the pressure an optimization method for the regulation of the operating parameter.” Page 4, Par. 3: “The regulation of the operating parameter based on the comparison between the data received by the drone and the stored target values is to be understood here in the usual meaning of the term “rules”. This means that the control unit is able to carry out a customary control circuit in which the operating parameter is initially set to an initial value and, depending on the corresponding data and the comparison of this data with the target value, the operating parameter is corrected, if necessary, so that, for example, the measured acceleration corresponds to a target value of the acceleration. The drone then measures the acceleration and pressure again and outputs the corresponding data, and the control unit carries out this comparison again in order to determine whether, on the one hand, the control was successful and, if not, readjusting the operating parameter again.”)
determining a self-identification model (drone and/or control unit that comprises a neural network. Page 6, Par. 5 and 6 first phrases: “If the control unit is arranged in the drone…” and “If the control unit is arranged outside the drone…”) of the machine based on the at least one input signal and the at least one output signal, (Page 7, Par. 1: “The control method according to the invention for an operating parameter of a container treatment system in the beverage processing industry, the container treatment system comprising at least one transport device for transporting the containers and a drone that can be inserted into the transport device, the drone comprising an acceleration sensor that measures an acceleration acting on the drone and a pressure sensor Includes, which measures a pressure acting on the drone in the transport device, includes that the drone transmits data corresponding to a measured acceleration and a measured pressure to a control unit and the control unit at least one operating parameter, the acceleration of the drone and / or the Pressure acting on the drone influences, depending on a comparison between the data received by the drone and target values stored in a memory for the operating parameters and / or the acceleration and / or the pressure.” Page 4, Par. 4: “By using the drone, which measures the acceleration and the pressure acting on the drone, data and measurements can be obtained which correspond to or reflect the actual conditions for the containers within the transport device or within the container treatment system. In this way, the control unit can draw direct conclusions about the actual circumstances within the transport device and the operating parameters or the operating parameters can be regulated in a targeted and effective manner.”)
wherein the self-identification model reproduces at least one current operating point of the machine; (acceleration; Note – There are 112 instances of acceleration measured and/or interpreted by the drone or control unit. Page 8, Par. 1: “It can further be provided that the control unit comprises a neural network, or the control unit is assigned a neural network, which is based on a large number of comparisons between data received by the drone and target values for the operating parameters and / or the data stored in the memory Acceleration and /or pressure…” See also Page 9, Par. 3.)
automatically configuring or optimising, using the self-identification model, at least one machine parameter of the machine and/or of a downstream machine; (Page 8, Par. 1: “It can further be provided that the control unit comprises a neural network, or the control unit is assigned a neural network, which is based on a large number of comparisons between data received by the drone and target values for the operating parameters and / or the data stored in the memory Acceleration and / or pressure learns an optimization method for regulating the operating parameter and regulating the operating parameter using the Optimization procedure optimized. In this way, the time required for regulating the operating parameter or the number of necessary regulation cycles can advantageously be reduced.” Page 5, last paragraph: “From high success values or success rates, the neural network can then learn that the corresponding changes in the operating parameter prove to be particularly advantageous given the ratios of acceleration and / or pressure or other variables that act on the container, and this in the optimization of the Take control of the operating parameters into account.” See also Page 18, last paragraph. Page 20, Par. 6: “Control procedure according to one of the Claims 8 to 11, wherein the control unit (102) comprises a neural network or the control unit (102) is assigned a neural network which is based on a multiplicity of comparisons between data received by the drone (101) and target values for the operating parameters and stored in the memory/ or the acceleration and / or the pressure learns an optimization method for the regulation of the operating parameter and optimizes the regulation of the operating parameter on the basis of the optimization method.”)
and automatically performing, using the self-identification model, a diagnosis of the machine. (Page 15, Par. 2: “First, it can be provided that the drone 101 which measures the acceleration (torque) acting on it during rotation and transmits the corresponding data to the control unit, which can then determine whether the rotational speed reached or the acting torques need to be regulated and, for example, increased or decreased. Additionally or alternatively, the drone can be used by the centering device 391 measure the contact pressure F caused on them (corresponding to a contact pressure) and transmit the corresponding data to the control unit. Depending on this data, the control unit can then determine whether the force F is too large or too small and an operating parameter of the centering device regulate accordingly. For example, the control unit can increase or decrease a torque of a drive of the centering device that ultimately causes the contact pressure F.”)
Regarding claim 12,
The previously cited references teach the limitations of claim 11 which claim 12 depends. Poschl also teaches that the self-identification model is continuously determined during operation of the machine. (Pag 7, Par. 3: “In one embodiment, the drone measures the acceleration and / or the pressure continuously”)
Regarding claim 18,
The previously cited references teach the limitations of claim 11 which claim 18 depends. Poschl also teaches that the at least one machine parameter
comprises a control parameter, an amount of plastic supplied, an amount of energy, a trajectory, a speed and/or an action time. (Page 9, Par. 5: “For this purpose the control unit 102 with a data line 132 with the transport device 103or connected to the components to be controlled (drives, centering device, stand plate or the like) and can control the operating parameters, such as the speed of the drive or the contact pressure of the centering device or the speed of the turntable or the like.”)
Regarding claim 19,
The previously cited references teach the limitations of claim 11 which claim 19 depends. Poschl also teaches that the wherein the machine comprises a container
making machine, a filler, a capper, a rinser, a labeller, a container inspection machine, a direct printing machine, a conveyor, a palletiser, a packaging machine, a robot, an autonomous transport vehicle, and/or a pump. (Page 14, Par. 3: “ 3c shows a more specific embodiment in which the container 130 along with the drones along the arrow shown on two conveyor belts 361 and 362 be transported. The conveyor belts are each driven by individual drives 371 and 372 driven. Such conveyor belts are usually used to transport containers 130 distance if the conveyor belt is downstream in the direction of transport 362 moves faster than the conveyor belt 361 , Similarly, the distance between the containers can be reduced when the conveyor belt 362 causes a slower transport (correspondingly slower speed) than the first conveyor belt 361” See also Page 3, Par. 1: “…often only recognized at the end of the container treatment after passing through a suitable inspection unit, which can result in considerable rejects…”)
Regarding claims 20, 21, 27, and 28, they are directed to a system or machine apparatus(es) to implement the method of steps set forth in claims 11, 12, 18, and 19, respectively. Poschl teaches the claimed method of steps in claims 11, 12, 18, and 19. Therefore, Poschl teaches the machine apparatus(es) to implement the claimed method of steps in claims 20, 21, 27, and 28.
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.
Claims 16, 17, 25, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Poschl in view of Barker et al. (US Patent No. 11016468), herein “Barker.”
Regarding claim 16,
The previously cited references teach the limitations of claim 11 which claim 16 depends. Poschl does not teaches inferring or prediction of operational changes However, Barker does teach further comprising inferring, using the self-identification model, operational changes in the machine to respond thereto by automatically configuring or optimising the at least one machine parameter and/or automatically diagnosing the machine. (Col. 10, line 59 – Col. 11, line 2: “For a non-limiting example, if the sensor 104 measures temperature in an electronic control or master controller of a machine and the amount of weigh given to the operating parameter 130 may be greater than the vibration operating parameter 130 in making a predictive determination 146 (such as a predicted time of failure determination 148) that the machine is in need of maintenance of servicing or in making a prediction determination of the likelihood that the machine will need maintenance over a certain period of time or the possibility that the machine will fail if the machine continues to operate.” Col. 14, lines 20 – 32: “In a preferred embodiment of the invention, the analysis module 110 operates to perform an analysis of current predictive determinations 146 made for all of the machines 10 functioning in an entire industrial operation 174 and makes a recommendation 168 for scheduling that provides an operator 12 with the most optimized time to perform maintenance. For example, the analysis module operates to make a mathematical analysis, such as a predictive analysis, using current predictive determinations and identifies which machine most requires maintenance (a critical machine) to prevent failure or operational errors occurring in the relative near future, such as in a predictive time of failure determination.” See also the full paragraph of Col. 10, line 43 – Col. 11, line 33; Col. 12, lines 49 – 59 (system includes a neural network); Col. 13, lines 24 – 36 (mathematical analysis for prediction.) See also background information: Col. 5, lines 62 - 67: “As used herein the term “industrial operation” includes manufacturing operations, assembly operations, transporting operations, and production operations having a plurality of machines, such as an assembly line, a production line, a transportation line (conveyor line), a manufacturing line, a packaging line, and the like;” and full paragraph Col. 5, line 37 – Col. 6, line 45.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the drone and/or control unit that comprises a model, defined as calculating a function from measure inputs, and may also consist of a neural network that can learn and optimize the operating parameter of the machinery as in Poschl with using software such as a neural network that predicts (infers) and optimizes the operational parameter of the machine as in Barker in order to prevent failure or operational errors. (Col. 14, lines 20 – 32)
Regarding claim 17,
The previously cited references teach the limitations of claim 16 which claim 17 depends. Barker also teaches that the operational changes comprises a
wear, a changed container throughput, and/or a changed manipulation mass of the machine, further comprising: changing the determined self-identification model such that the at least one machine parameter of the machine and/or of the downstream machine is automatically adjusted with the changed self-identification model. (Col. 18, lines 11 – 23: “Further, using the predictive determination, the monitoring system operates to make a recommendation as to scheduling which industrial operation should be used to perform an industrial operation that minimizes the likelihood of a malfunction or an interruption of the industrial operation. It should also now be apparent that as additional historical data is collected, the operational baseline for a machine as well as predictive determinations and recommendations will automatically be updated and will increasingly become more accurate as the monitoring system continues to learn how a machine operates under different environmental operating conditions and over operating times.)
Regarding claims 25 and 26, they are directed to a system or machine apparatus(es) to implement the method of steps set forth in claims 16 and 17, respectively. Poschl and Barker teach the claimed method of steps in claims 16 and 17. Therefore, Poschl and Barker teach the machine apparatus(es) to implement the claimed method of steps in claims 25 and 26.
Response to Arguments
The examiner respectfully traverses applicant’s arguments. In the remarks, applicant argues that Poschl et al. does not teach: “a self-identification model of the machine based on the at least one input signal and the at least one output signal, wherein the self-identification model reproduces at least one current operating point of the machine.” Applicant argues that Poschl only discloses a control unit or neural network that does not anticipate a “model” or “a self-identification model.” Examiner is not persuaded by this argument. By applicant’s own definition “a self-identification model is a mathematical representation that identifies the behavior of the system itself (e.g., modeling the transfer function between the machine's input and output).” [Emphasis added by Examiner] By this definition, Poschl teaches a self-identification model. Poschl in page 10, paragraph 3 states:
“…the data need not be identical to or corresponding to the measured acceleration and the measured pressure. Rather, the drone can also transmit data to the control unit 112 process[or] the measured acceleration or the measured pressure. For example, the force acting on the drone can be calculated from a measured acceleration and this can then be transmitted to the control unit in the form of data. Furthermore, the drone can use the acceleration measured over a certain time, for example, to create a speed profile of its movement and this speed profile instead of the acceleration to the control unit 102 transfer. The same applies to the measured pressure. For example, the drone can combine measured values for the pressure that acts on the drone from all sides into a pressure function that indicates the pressure as a function of the location in question on the surface of the drone to which this pressure acts. This can be used to determine whether the pressure is acting from above or below, for example in a centering device and during clamping of the drone between a stand plate and the centering device, or whether the dynamic pressure in a mass transporter is the same from all directions. Corresponding data can then be sent to the control unit 112 be transmitted.
This paragraph clearly defines a mathematical representation that identifies the behavior of the system as defined by the applicant. Doing calculations as describe on page 10 is a mathematical act or function. The drone that includes the control unit and processor, processes measured values, such as pressure and acceleration, and creates speed and acceleration profiles of the drone moving through the processing plant. As stated above in the cited paragraph: “the drone can combine measured values for the pressure that acts on the drone from all sides into a pressure function that indicates the pressure as a function of the location in question on the surface of the drone to which this pressure acts” Doing calculations and creating a function using measured values of pressure related to location by a processor is by definition, according to the applicant, mathematical modeling.
The following paragraph further defines the procedures when there is a discrepancy between the measured values, such as acceleration, and target values, wherein the system can be controlled based on the discrepancy. This is on point with the instant applications claim limitations and applicant’s arguments that Poschl clearly teaches configuration or optimization using the model (defined by Poschl as a processor calculating functions for measured input parameters).
Thus the 35 USC 102 rejection under Poschl is maintained and this is a final office action.
Allowable Subject Matter
Claims 13 – 15 and 22 - 24 are 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 pending resolving all intervening issues such as the 35 U.S.C. §112(b) rejections above. Reasons for allowance will be held in abeyance pending final recitation of the claims. The prior art does not disclose the elements of claim 11 or claim 20 and the elements that the self-identification model comprises one or more self-identification equations, including a linear inhomogeneous differential equation and/or a difference equation. Claims 14 and 15 depend on claim 13, and claims 23 and 24 depend on claim 22 and are also objected to.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Das (US PG Pub. No. 20190354091) may also teach the elements of claim 16; and teaches monitoring parameters and machines of manufacturing plastics (Par. 0037). Das also teaches prediction (inferring) operational changes to the machine. (claim 1: “health parameters of the machine components and sensors from motor encoders; a cloud based server communicatively coupled with a plurality of automatic industrial machines, and wherein the server is configured for collecting the plurality of measured parameters from the plurality of sensors present in the add-on module coupled with each automatic industrial machine; a predictive diagnosis and maintenance engine provided in the server and run on a hardware processor, and wherein the predictive diagnosis and maintenance engine is configured for predicting a plurality of impending malfunctions and breakdowns of one or more machine components provided in each automatic industrial machine by a continuous monitoring and evaluation of the measured parameters; and a navigation engine provided in the server and run on a hardware processor, and wherein the navigation engine is configured for directing a motor controller provided in each add-on module to navigate the automated industrial machine to a desired location.”)
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 CHAD G ERDMAN whose telephone number is (571)270-0177. The examiner can normally be reached Mon - Fri 7am - 3pm or 4pm EST..
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, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAD G ERDMAN/Primary Examiner, Art Unit 2116