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 .
Election/Restrictions
Applicant’s election without traverse of species Group IV in the reply filed on March 30, 2026 is acknowledged. All pending claims not withdrawn (1, 11, 12, 14, 18 and 20) are examined in this non-final office action in response to the species election.
In a subsequent reply, please indicate claim status of non-elected claims as “Withdrawn.”
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
35 USC § 101
Referencing independent claim 18 (representing independent claims 1 and 20), the following adds significantly more to the abstract idea:
A computer-implemented method for identifying and providing paint and applicator combinations based on paint application properties, comprising:
…
identifying an optimization function that is based on:
the at least one emphasis for the at least one of the paint application properties, and
a data structure including quantitative measurements of the at least one of the paint application properties for a plurality of paint and applicator combinations;”
…
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 1, 12, 14, 18 and 20 are rejected under 35 USC 102(a)(1) as being anticipated by Filev, US 2005/0096796 (incorporating by reference Filev et al., US 6,528,109 “Filev ‘109).
Filev teaches all the limitations of claims 1, 12, 14, 18 and 20. In Filev see at least (underlined text is for emphasis):
Regarding claim 18. A computer-implemented method for identifying and providing paint and applicator combinations based on paint application properties, comprising:
[Filev: 0009] The present invention overcomes the problems in the prior art by providing in one embodiment a method of optimizing a painting process for applying a paint layer on an article. The method comprises defining a functional relationship between paint processing parameters and a paint layer property (i.e., the average paint layer thickness) using a neural network. This functional relationship is then used in a paint optimization function that measures a combination of quality control parameters and paint transfer efficiency. Finally, the paint optimization function is optimized by adjusting the paint processing parameters utilizing the functional relationship formed by the neural network ("NN"). The method of the invention is advantageously used to establish a global model of the paint process that can be used to predict for a given combination of environmental factors such as down draft (at the bell zone and reciprocator zone), air temperature, and air humidity, and average fluid flow rate the average film thickness on a particular surface of the vehicle body. This prediction is further used to calculate the optimal average fluid flow rates for the left, vertical and horizontal bells, and the optimal down drafts in the bell and reciprocator zones. Accordingly, the methods of the invention can be used to predict the impact of different combinations of process parameters on the final film thickness.
presenting, at a user interface, one or more paint application properties;
(Filev ‘109: col. 2, lines 53-55) FIGS. 8A, 8B, and 8C are views of screen displays from software used to configure the subsystems for the control logic in FIG. 7.
(Filev ‘109: D13: col. 5, lines 50-53) … FIG. 8C shows that viscosity ASH 5 temperature, ASH 6 humidity, ASH 7 Temperature, C/D A-meter 6, D/D A-meter 5 and D/D A-meter 7 have been included as environmental variables in the subsystem "left".
prompting a user, at the user interface, to provide user input indicating at least one emphasis for at least one of the paint application properties;
(Filev ‘109: D6: col. 3, lines 43-47) Referring to FIG. 3, the film thickness sensor system 32 also includes a computer system 42, which includes a computer having a memory, a processor, a display, and user input mechanism, such as a mouse or keyboard, connected to the robots 34
(Filev ‘109: D13: col. 5, lines 41-45) … FIG. 8A shows one of the screens of this software used to determine what inputs that should be included for a particular subsystem.
The selected bells are B1_1, B1_2, B1_3, B1_5, and B1_6. For bell B1_3, Zones 1 through 6 have been included in the subsystem. Similarly, the software has screens to determine what outputs (film thickness and QMS measuring points)
identifying an optimization function that is based on:
[Filev: 0026] It can be appreciated that the painting process is controlled by a set of adjustable paint processing parameters. The method of this embodiment comprises defining a functional relationship with a neural network between the set of paint processing parameters and a paint layer property. The neural network is characterized as having one or more neural layers that comprise a plurality of neural units that have a plurality of neural network parameters. A paint optimization function is then formed that measures the quality and efficiency of the painting process. The paint optimization function is necessarily a function of the paint layer properties and the paint processing parameters. Finally, the paint optimization function is optimized by adjusting the one or more paint processing parameters utilizing the functional relationship defined by the neural network.
the at least one emphasis for the at least one of the paint application properties, and
[Filev: 0026] …The paint optimization function is necessarily a function of the paint layer properties and the paint processing parameters.
a data structure including quantitative measurements of the at least one of the paint application properties for a plurality of paint and applicator combinations;
[Filev: 0027] A flow chart of the global optimization method of the invention is shown in FIG. 2. In step 60 measured data is sorted with respect to color, automobile style, coat properties, and spray booth. This data is then utilized in step 62 to establish the functional relationships with the neural network ("NN"). Next the optimization is performed in step 64. In particular, optimal fluid flows and downdrafts are calculated. Next, these optimal values for the paint process parameters are sent to the IPQC to adjust the film quality in step 66. Similarly, the optimal values are sent to the Air Flow Control System in step 68. These optimal values are then implemented to improve the paint process in step 70.
[Filev: 0043] … Additionally, the computer used in the system of the invention is further configured to optimize a paint optimization function that measures the efficiency of the painting process by adjusting the one or more paint processing parameters, the paint optimization function being a function of the paint layer property.
identifying an optimal paint and applicator combination from the plurality of paint and applicator combinations that maximizes the optimization function; and
[Filev: 0034] The method of the present invention optimizes the paint process by establishing a process function that includes both paint uniformity and transfer efficiency. The concept of global process optimization is based on the assumption of a global process model that includes all major components of both control systems. During implementation of the methods of the invention, a number of paint processing parameters are varied both in the step of defining a functional relationship with a neural network between the set of paint processing parameters and a paint layer property and in the step of optimizing the optimization function. Potential parameters include, for example, applicator parameters, environmental parameters, applicator position parameters, paint material parameters, and combinations thereof.
presenting the optimal paint and applicator combination at the user interface.
[Filev: Figs. 8A, 8B and 8C; col. 5, lines 29-60]
Regarding claims 1 and 19: Rejections are based upon the disclosures applied to claim 18 by Filev (Filev ‘109) and further upon disclosures by Filev (Filev ‘109) regarding computer system elements and data structures, e.g. processor(s), storage devices, instruction memory and display: see (Filev ‘109: D6: col. 3, lines 43-47).
Regarding claims 12 and 14: Rejections are based upon the disclosures applied to claims 1 and 18 by Filev (Filev ‘109) and further upon disclosures of Filev (Filev ‘109) regarding appearance desirability:
[Filev: 0028] … These mathematical models are further used to calculate the optimal adjustment of the paint processing parameters through a constrained optimization procedure used in the optimization step (see discussion below.) The feedback control system works in supervisory control mode, i.e., it does not interfere with the local paint automation and process zone controllers but optimizes overall paint process performance by automatically adjusting the targets of the local controllers until desired paint layer properties are achieved. The preferred paint layer properties are the paint film thickness, appearance, or a combination of both.
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.
The factual inquiries 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 11 is rejected under 35 USC 103 as being unpatentable over Filev, US 2005/0096796 (incorporating by reference Filev ‘109 US 6,528,109), in view of Mead et al., US 2004/0163197 “Mead.”
Rejection is based in part upon the teachings applied to claim 1 by Filev (Filev ‘109) and further upon the combination of Filev (Filev ‘109)-Mead. Although Filev’s (Filev ‘109) optimization function pertains to techniques using paint sprayer applicators, Mead on the other hand would have taught Filev techniques using a paint roller or paint brush.
In Mead see at least:
[Mead: 0004] In aircraft manufacture most structural elements are joined with fasteners. For corrosion protection, the surfaces of these structural elements are coated with a corrosion inhibiting coating. After assembly, the structural elements must also have the fastener ends (with retaining nuts attached) coated corrosion inhibiting coatings. Spraying on coatings can be used. However, the corrosion inhibiting coatings typically contain chromium compounds and the use of such sprays may cause environmental problems. Brushes can be used, but there [sic] use is time consuming. Thus the use of roller applicators becomes one of most effective methods from both environmental and time considerations.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Mead, which teach the use of roller applicators are one of the most effective methods from both environmental and time considerations when applying corrosion inhibiting coatings, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Mead to the teachings of Filev (Filev ‘109) would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 5,598,972 (Klien, II et al.) “Optical Spray Paint Optimization System and Method,” discloses: (Abstract) An optical spray paint optimization system can be removably mounted to a spray paint gun, thus enhancing the ability of the user to guide the direction of the spray and also locate the nozzle at an optimum spray distance from the surface being painted. The preferred apparatus uses a diode laser, a beam splitter and a reflecting mirror to generate a reference beam and a gauge beam. The reference beam propagates in a fixed forward direction, but the direction of the gauge beam is adjustable by adjusting the attitude of the reflecting mirror. The reference beam and the gauge beam intersect at a convergence point which can be repositioned to a selected distance from the nozzle of the spray painting system by adjusting the path of the gauge beam, thus allowing the user to spray at the optimum spray distance by locating the convergence point on the surface being painted. The beams also aid in aiming the spray.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT M POND whose telephone number is (571)272-6760. The examiner can normally be reached M-F, 8:30 AM-6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT M POND/Primary Examiner, Art Unit 3688 April 29, 2026
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