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 § 112
The following is a quotation of the first paragraph 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 9 is 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The applicant amended the claim to indicate that the additional information comprises each of a number of conditions. The issue stems from stating that it is each of a completely overfilled state, and a completely overfilled state. The specification as filed says that the information can be a completely overfilled state, and/or a completely overfilled state. The or is important because that would mean that both are not necessarily present or required, which is different than the claimed “each”. Therefore, it is not clear the applicant had possession of the invention that requires “each”.
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, 6-9, 11-13, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gray et al. (U.S. Patent Number 5,864,600; hereinafter referred to as Gray), further in view of Heuft (U.S. Patent Number 8,538,714), and further in view of Armstrong et al. (U.S. Patent Application Publication Number 2020/0193620; hereinafter referred to as Armstrong).
With respect to claim 1, Gray discloses and illustrates a method of checking a fill level of containers (containers 116), wherein the containers are transported by a transporter (conveyor 114) as a container mass flow (direction of motion 112) and measurement data of the containers are captured by a sensor unit (detector array 104; see column 2, lines 1 through 7), and wherein the measurement data are evaluated by an evaluation unit (controller 106; see column 2, lines 16 through 37), thereby determining the respective fill level of the containers (column 1 lines 10 through 34), wherein the measurement data are evaluated by the evaluation unit using artificial intelligence so as to determine the fill level (see column 10, lines 59 through 63). Gray fails to disclose wherein the sensor unit comprises different sensors, each operating with a different measurement method, and wherein the containers are captured as the measurement data by the different sensors is not explicitly disclosed in Gray. Gray only discloses radiation from an x-ray source and a detection array to detect said radiation. However, Heuft teaches that the fill level can be measured by x-ray absorption as known in the art (see column 1, lines 21 through 52). Further, Heuft also teaches the use of a camera in order to capture data (see column 1, lines 53 through 67). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to utilize the camera taught in Heuft with the system in Gray as the sensor in the Gray system in order to provide a quality control for the system measurements as Heuft teaches that a multi sensor system is capable of producing images with the camera and the radiation and overlay the images for quality control. The camera is disclosed to be a black and white or color camera, thus a camera and an x-ray source are different measurement methods. Further still, Heuft teaches that the use of a multisensory camera with various imaging sensors operating on different physical principals is also known, thus multiple sensors operating with different methods is disclosed to be known to those of ordinary skill in the art and the benefits of such sensor systems are disclosed in at least column 1, lines 53 through 67 of Heuft. However, neither Gray nor Heuft disclose wherein the evaluation unit using artificial intelligence is trained with training data sets, each comprising training measurement data of a training container, and further comprising additional information, wherein the additional information comprises at least one of a desired fill level, a completely overfilled state, and a completely underfilled state of the training container captured as the training measurement data, and comprises evaluability information about the training measurement data wherein the training measurement data of a plurality of training containers is captured establish the training data sets therefrom, wherein each of the plurality of training containers is a different container type and/or grade. However, Armstrong teaches an artificial neural network that is used to predict the current state of a process based upon sensor measurements of the process variables at previous times (Please see at least the abstract of Armstrong). Armstrong further provides additional information including a desired fill level (a true fill level is disclosed in at least paragraph [0048] of Armstrong) Armstrong further discloses evaluability information about the training measurement data (see at least paragraphs [0048] and [0049] of Armstrong). Gray does disclose the use of a standard container (See Gray column 2, lines 64 through 67) and using a neural network for the data obtained (see Gray column 10, lines 59 through 63). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to use the training sets disclosed in Armstrong in order to create the needed data sets to train the neural network from Gray as the additional data sets would provide for optimum measurement results and give the specifics needed to know how the neural networks disclosed in Gray might actually be properly used and can determine if errors are present and compensation for said errors. Further, Armstrong discloses that the images obtained can be for different container types (see Armstrong paragraph [0028]). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to obtain training sets for different container types as taught by Armstrong in order to obtain a more robust training set for a system such as disclosed in Gary.
With respect to claims 2, the method according to claim 1, wherein the evaluation unit using artificial intelligence comprises a deep neural network, wherein the measurement data are evaluated with the deep neural network so as to determine the fill level is not explicitly disclosed by Gray. Gray discloses the use of a neural network, versus specifying a deep neural network. However, a deep neural network is a type of neural network. Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to use a deep neural network in the Gray system since a deep neural network is a network in which layers of data are used in the learning sets, thus the use of a deep neural network would provide a better amount of data to characterize the containers so that more accurate measurements can be made in comparison to a standard neural network.
With respect to claim 3, the method according to claim 1, wherein the sensor unit comprises a camera, with which the containers are captured as image data, and wherein the measurement data comprises the image data is not explicitly disclosed in Gray. Gray only discloses radiation from an x-ray source and a detection array to detect said radiation. However, Heuft teaches that the fill level can be measured by x-ray absorption as known in the art (see column 1, lines 21 through 52). Heuft also teaches the use of a camera in order to capture data (see column 1, lines 53 through 67). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to utilize the camera system taught in Heuft as the sensor arrangement with the system in Gray in order to provide a quality control for the system in that Heuft discloses that a multi sensor system is capable of producing images with the camera and the radiation and overlay the images for quality control.
With respect to claim 6, the method according to claim 5, wherein the training measurement data is at least partially evaluated by a user, thereby manually determining additional information is not disclosed in Gray or Heuft. Gary does disclose prompting a user to place a test container on the conveyor (see at least Gray column 7, lines 24 through 27) However, Armstrong teaches using human classifiers for determination of fill level (see Armstrong paragraph [0032]), and determination of data by human analysis (see Armstrong paragraph [0072]). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to use the teachings of Armstrong to improve upon the system of Gray to eliminate the known errors and set backs of the previous types of systems. Using the method disclosed in Armstrong with the systems of Gray and/or Hess would provide a much more accurate training set with data sets tailored to specific needs as addressed by the user interaction.
With respect to claim 7, the method according to claim 5, wherein the training measurement data is at least partially evaluated by a further evaluation unit using a conventionally operating evaluation method while automatically determining the additional information isn’t disclosed in Gray or Heuft. However, Armstrong teaches that values are initially weighted in the system and measurements are run in order to create a data set with unsupervised learning (see Armstrong paragraph [0040]). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to utilize the method of Armstrong in order to compensate for the possible errors in the method in Gray to ensure more accurate measurement.
With respect to claim 8, the method according to claim 5, wherein the training measurement data of the training container is captured by another sensor unit is not explicitly disclosed by Gray or Heuft. Armstrong, however, teaches a system that utilizes a plurality of sensors wherein each sensor measures a specific parameter (See at least Armstrong paragraph [0026]). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to use a sensor to measure the standard container versus a sensor to measure the other data is contemplated by Armstrong in order to, for example, compensate for various types of sensor measurements thus providing a better trained system so that more complete measurements can be made.
With respect to claim 9, the method according to claim 5, further comprising additional information, wherein the additional information comprises each of a desired fill level, a completely overfilled state, and a completely underfilled state of the training container captured as the training measurement data, and/or comprises evaluability information about the training measurement data is disclosed in at least paragraph [0048] and [0049] of Armstrong the loss function can penalize incorrect outputs based on a rewards matrix. The loss function can receive the rewards matrix as an input (and/or can receive the matrix in any other suitable manner). The rewards matrix preferably defines penalties for incorrect outputs (e.g., wherein errors of different types and/or severities can be penalized differently, such as based on costs and/or other undesirable consequences resulting from occurrence of the error).
With respect to claim 11, Gray discloses and illustrates a device for checking a fill level of containers, wherein measurement data are captured and evaluated to determine fill level (see column 2, lines 16 through 37), the device comprising a transporter (conveyor 114) configured to transport the containers (containers 116) as a container mass flow (direction of motion 112), sensor unit (detector array 104) configured to capture the measurement data of the containers (see column 2, lines 1 through 7), and an evaluation unit (controller 106) configured to evaluate the measurement data to determine the fill level of each of the containers (see column 2, lines 16 through 37) wherein the evaluation unit is configured to evaluate the measurement data using artificial intelligence to determine the fill level (see column 10, lines 59 through 63). Gray fails to disclose wherein the sensor unit comprises different sensors, each operating with a different measurement method, and wherein the containers are captured as the measurement data by the different sensors is not explicitly disclosed in Gray. Gray only discloses radiation from an x-ray source and a detection array to detect said radiation. However, Heuft teaches that the fill level can be measured by x-ray absorption as known in the art (see column 1, lines 21 through 52). Further, Heuft also discloses teaches the use of a camera in order to capture data (see column 1, lines 53 through 67). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to utilize the camera system taught in Heuft as the sensors with the system in Gray in order to provide a quality control for the system in that Heuft teaches that a multi sensor system is capable of producing images with the camera and the radiation and overlay the images for quality control. The camera is disclosed to be a black and white or color camera, thus a camera and an x-ray source are different measurement methods. Further still, Heuft teaches that the use of a multisensory camera with various imaging sensors operating on different physical principals is also known, thus multiple sensors operating with different methods is disclosed to be known to those of ordinary skill in the art and the benefits of such sensor systems are disclosed in at least column 1, lines 53 through 67 of Heuft. However, neither Gray nor Heuft disclose wherein the evaluation unit using artificial intelligence is trained with training data sets, each comprising training measurement data of a training container and further comprising additional information, wherein the additional information comprises at least one of a desired fill level, a completely overfilled state, and a completely underfilled state of the training container captured as the training measurement data, and comprises evaluability information about the training measurement data wherein the training measurement data of a plurality of training containers is captured establish the training data sets therefrom, wherein each of the plurality of training containers is a different container type and/or grade. However, Armstrong teaches an artificial neural network that is used to predict the current state of a process based upon sensor measurements of the process variables at previous times (Please see at least the abstract of Armstrong). Armstrong further provides additional information including a desired fill level (a true fill level is disclosed in at least paragraph [0048] of Armstrong) Armstrong further discloses evaluability information about the training measurement data (see at least paragraphs [0048] and [0049] of Armstrong). Gray does disclose the use of a standard container (See Gray column 2, lines 64 through 67) and using a neural network for the data obtained (see Gray column 10, lines 59 through 63). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to use the training sets disclosed in Armstrong in order to create the needed data sets to train the neural network from Gray as the additional data sets would provide for optimum measurement results and give the specifics needed to know how the neural networks disclosed in Gray might actually be properly used and can determine if errors are present and compensation for said errors. Further, Armstrong discloses that the images obtained can be for different container types (see Armstrong paragraph [0028]). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to obtain training sets for different container types as taught by Armstrong in order to obtain a more robust training set for a system such as disclosed in Gary.
With respect to claim 12, the device according to claim 11, wherein the artificial intelligence comprises a deep neural network, to evaluate the measurement data for determining fill level is not explicitly disclosed. Gray discloses the use of a neural network, versus specifying a deep neural network. Gray discloses the use of a neural network, versus specifying a deep neural network. However, a deep neural network is a type of neural network. Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to use a deep neural network in the Gray system since a deep neural network is a network in which layers of data are used in the learning sets, thus the use of a deep neural network would provide a better amount of data to characterize the containers so that more accurate measurements can be made in comparison to a standard neural network.
With respect to claim 13, the device according to claim 11, wherein the sensor unit comprises a camera, to capture the containers as image data, and wherein the measurement data comprises the image data is not explicitly disclosed in Gray. Gray only discloses radiation from an x-ray source and a detection array to detect said radiation. However, Heuft teaches that the fill level can be measured by x-ray absorption as known in the art (see column 1, lines 21 through 52). However, Heuft also teaches the use of a camera in order to capture data (see column 1, lines 53 through 67). Therefore, it would have been obvious to one skilled in the art at the time the invention was filed to utilize the camera taught in Heuft as the sensors with the system in Gray in order to provide a quality control for the system in that Heuft teaches that a multi sensor system is capable of producing images with the camera and the radiation and overlay the images for quality control.
With respect to claim 15, the method according to claim 1, wherein the containers are beverage industry bottles is disclosed in Gray as Gray discloses that there are containers, which are beverage cans (see Gray column 4, line 1), but Gray also discloses that containers can be cans or bottles (see at least Gray column 1, lines 44 through 46).
Response to Arguments
Applicant's arguments filed 06 May 2026 have been fully considered but they are not persuasive.
First, applicant is arguing that Armstrong is non analogous art. MPEP 2141.01(a) states that a reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention). With this in mind, Armstrong and the present invention both pertaining to fill level measurement. Further, both Armstrong and the present invention are related to solving the problem of obtaining reliable fill level measurements. Therefore, Armstrong is analogous art since it is in the same field of endeavor and it is reasonably pertinent to the problem faced by the inventor.
Second, the applicant argues Armstrong is not directed to measuring a fill level of liquids. MPEP 2145 notes that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a fill level of liquids ) are not recited in the rejected claim(s). Therefore, the argument about specific fill level of liquids is not proper.
Finally, the applicant argues limitations of the claims in view of what either Armstrong or Gray lack individually. MPEP 2145 notes one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. There is no argument as to what the combination of the references lack or do not teach, which amounts to a piecemeal analysis of the references individually. A piecemeal analysis of the rejections is not a proper argument against the rejection as given. Therefore, the arguments by the applicant are not persuasive.
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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RODNEY T FRANK whose telephone number is (571)272-2193. The examiner can normally be reached M-F 9am-5:30pm.
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, Peter Macchiarolo can be reached at (571) 272-2375. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
RODNEY T. FRANK
Examiner
Art Unit 2855
/PETER J MACCHIAROLO/Supervisory Patent Examiner, Art Unit 2855
June 3, 2026
Prosecution Insights