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 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) is/are:
“a storage unit for storing…”
“an input unit for receiving…”
An output unit for outputting …” in claim 15.
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
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, 2, 12 and 13 are 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.
With regard to claim 1, the feature "input data including at least partly labeled images comprising one or more partly labeled images of the chemical substance" is unclear. It is not apparent what the difference is between the included and the comprised labeled images. If they are the same, the feature lacks conciseness, if they are different, it is unclear in which way.
The feature "wherein the label comprises a shape, in particular a set of pixels associated a chemical substance in the image and a position of the chemical substance on the at least partly labeled images" is unclear. Without the optional and thus non-limiting feature, the feature "the label comprises a shape" is ambiguous, as it is not understood what exactly is identified, i.e. whether it is the geometrical shape (circle, ellipse) or the region of the substance in the image. Furthermore, if the image depicts separate particles of the chemical substance, it is not apparent if the shape is the one of the overall region with particles, or if only one particle is labelled. The optional feature in itself is also unclear, as the term "associated" is technically vague and thus the set of pixels is not technically defined; furthermore it is not understood how a "label" can comprise a set of pixels. The feature "training while maintaining the provided set of parameters" is not understood. The term "maintained" in the given context is technically vague, and thus it is not apparent whether the values of the set of parameters is kept constant during training or whether these parameters are maintained in another way. The feature "per instance instance" is not clear.
Appropriate correction and/or explanation is required.
With regard to claim 2, the feature "Providing a set of parameters associated with a structure of the segmentation model, different from the set of parameters initially provided, thereby amending the structure of the segmentation model" is not clear for the following reasons: It is unclear what the set of parameters is, since the term "associated with a structure of the segmentation model" is not understood because "associated with" is technically vague. It is further unclear in which way the providing step is amending the structure of the segmentation model.
With regard to claims 12 and 13 the "technical performance parameter value", the "performance model", the "technical performance values and features of the chemical substance" and the "technical performance property" are undefined and thus unclear. It is not understood how the "performance model is parametrized based on" the values and features since it is not apparent what is parameterized and how.
Appropriate correction and/or explanation is required.
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 1-5, 7-11 and 14-15 are rejected under 35 U.S.C. 102(a) as being anticipated by Applicant cited Publication titled “Deep Learning-based Method for SEM Image Segmentation in Mineral characterization, an example from Duvernay Shale Samples I Western Canada Sedimentary Basin” to Chen et al..
With regard to claim 1, Chen discloses a training method for training a segmentation model to perform image segmentation on an image of a chemical substance (abstract, section 2.2), the training method comprising:
receiving input data including at least partly labeled images comprising one or more partly labeled images of the chemical substance, wherein the label comprises a shape, in particular a set of pixels associated a chemical substance in the image and a position of the chemical substance on the at least partly labeled images (section 2.3, paragraph 1: The “mask or region of interest, and labeled” imply shape and position based on the location within the image);
receiving a machine learning model, in particular comprising a convolutional neural network, having a corresponding set of parameters associated with a structure of the machine learning model (section 2.4, Fig. 4 displays a U-Net architecture or set of parameters for the structure of the machine learning model);
training the machine learning algorithm model using the input data to obtain a candidate segmentation model for outputting a prediction indicating a shape and position of the chemical substance on input images received as an input, while maintaining the provided set of parameters (sections 2.4 and 2.5 and Fig. 5 displays masks for segmenting the shape and position for the substances. Section 3.1 discusses training the model.); and
calculating a validation metric for the candidate machine learning algorithm, the validation metric including an intersection over union (IoU) per instance, the IoU per instance being a ratio of an overlapping area to a union per instance instance, the overlapping area being the largest area of overlap between a labeled chemical substance from one of the at least partly labeled images and the prediction by the candidate machine learning algorithm on a corresponding input image corresponding to the one of the at least partly labeled images, and the union being a union of the labeled chemical substance from the one of the at least partly labeled images and the prediction by the candidate machine learning algorithm on the corresponding input image (section 3.2, paragraphs 1-2, The IoU formula is discussed and defined as the area of overlap over the area of union for the segmented areas for testing against the ground truth labeled images).
With regard to claim 2, Chen discloses the training method according to claim 1, further comprising, based on the value of the calculated validation metric, in a new iteration:
providing a set of parameters associated with a structure of the segmentation model, different from the set of parameters initially provided, thereby amending the structure of the segmentation model (Fig. 2, box B shows that if results of the training are not satisfied, the training data and strategy are revised and the process repeats the training. This is interpreted as providing a different set of parameters associated with a structure of the segmentation model); and
repeating the steps of receiving the machine learning model, training the machine learning model and calculating the validation metric for the different set of parameters associated with a structure of the machine learning model (Fig. 2, box B shows that the process repeats with revised training data and strategy whenever results are not satisfied).
With regard to claim 3, Chen discloses the training method according to claim 1, further comprising:
storing and/or outputting a current candidate machine learning algorithm as a trained machine learning algorithm for performing image segmentation if the calculated validation metric is determined as being greater than or equal to a predetermined validation threshold; and/or storing and/or outputting the candidate machine learning algorithm with the highest validation metric amongst candidate machine learning algorithms from multiple iterations (Fig. 2, box B shows that if results of the training are not satisfied, the training data and strategy are revised and the process repeats the training. This is interpreted as providing a different set of parameters associated with a structure of the segmentation model. The results being satisfied is interpreted as meeting a threshold level of validation metric).
With regard to claim 4, Chen discloses the training method according to claim 1, wherein the segmentation model is configured to take an image, perform a learned transformation of the image, wherein performing the learned transformation refers to, and output a list of shapes in the image (section 2.4, The training performed operates to generate semantic segmentation or an output of identified shapes);
wherein the list of shapes refers to shapes identified in the image wherein the machine learning algorithm has a free parameter, in particular a weight that is optimized by heuristic optimization during the training (section 2.5, Weight function, The weights are adjusted based on the predicted score. Weights are used and adjusted in order to enhance feature extraction for identifying semantic segmentation).
With regard to claim 5, Chen discloses the training method according to claim 1, wherein the machine learning model is one of the following machine learning models: U-Net or Mask-RCNN (region based convolutional neural network) (sections 3.1, paragraph 2, “U-Net Training”).
With regard to claim 7, Chen discloses the training method according to claim 1, wherein the at least partly labeled images of the chemical substance are scanning electron microscope (SEM) images (Abstract).
With regard to claim 8, Chen discloses the training method according to claim 1, further including calculating IoU scores using multiple values of an IoU metric and determining a selected value of the IoU metric, the selected value of the IoU metric being the value out of the multiple values of the IoU metric leading to the highest IoU score (Fig. 8 and section 3.2, The IoU calculation is disclosed to be performed repeatedly).
With regard to claim 9, Chen discloses the training method according to claim 8, wherein the validation metric corresponds to the IoU per instance score calculated with the selected value of the IoU metric (Section 3.2, The IoU validation metric is calculated and scored for an overall prediction).
With regard to claim 10, Chen discloses a segmentation method for performing segmentation of data representing a chemical substance using a trained machine learning algorithm trained according to the training method of claim 1, the segmentation method including:
receiving at least partially unlabeled data to be segmented, the at least partially unlabeled data including an image of the chemical substance (section 2.3, paragraph 1: The “mask or region of interest, and labeled” imply shape and position based on the location within the image);
inputting the at least partially unlabeled data into the trained machine learning algorithm (section 2.4, Fig. 4 displays a U-Net architecture or set of parameters for the structure of the machine learning model); and
outputting, by the trained machine learning algorithm, label data indicating a shape and position of the chemical substance on the image of the chemical substance (section 3.2, paragraphs 1-2, and Figs. 5, 6, 7 and 9, Segmented images are output indicating the semantic segmentation. The IoU formula is discussed and defined as the area of overlap over the area of union for the segmented areas for testing against the ground truth labeled images).
With regard to claim 11, Chen discloses the segmentation method according to claim 10, further including:
using the label data, performing an image analysis to determine features of the represented chemical substance (section 3.2, paragraphs 1-2, and Figs. 5, 6, 7 and 9, Segmented images are output indicating the semantic segmentation. The IoU formula is discussed and defined as the area of overlap over the area of union for the segmented areas for testing against the ground truth labeled images).
With regard to claim 14, Chen discloses a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to claim 1 (section 5, “this method allows for the automated computer-based…” A computer program product is considered necessary to perform the machine learning steps of the described process).
With regard to claim 15, Chen, the discussions of claims 1 and 14 apply. A computer with memory for storing the machine learning model and a processor are considered necessary to perform the described process of the method recited in claim 1 and in Chen. See section 5, “this method allows for the automated computer-based…” A computer program product is considered necessary to perform the machine learning steps of the described process.
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 6 is rejected under 35 U.S.C. 103 as being unpatentable over the Applicant cited Publication titled “Deep Learning-based Method for SEM Image Segmentation in Mineral characterization, an example from Duvernay Shale Samples I Western Canada Sedimentary Basin” to Chen et al.
With regard to claim 6, Chen discloses the training method according to claim 1, but does not explicitly disclose wherein the chemical substance is a particle made of a cathode active material, nickel, cobalt and/or manganese. Chen discloses clay mineral and shale, but does not explicitly disclose the specific minerals claimed. However one of ordinary skill in the art will readily recognize that the image segmentation applied could be used for any material as a matter of choice and that the system would function the same. Therefore it would have been obvious to one of ordinary skill in the art before time of filing that images of the listed chemical substances would be effectively processed by the system of Chen.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WESLEY J TUCKER whose telephone number is (571)272-7427. The examiner can normally be reached 9AM-5PM Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JOHN VILLECCO can be reached at 571-272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WESLEY J TUCKER/Primary Examiner, Art Unit 2661
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