DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-14 are presented for examination.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
3.1 Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Is the claim directed to a statutory category?
Yes. The claims are to a device (claim 1), a method (claim 8) comprising a plurality of method steps.
Step 2A- Prong One
The claim(s) recite(s) a device (claim 1), a of predicting free motion headform (FMH) impact performance using artificial intelligence (claim 8), comprising: The step of: “generating an image by extracting a pre-processed test target image, generated by pre-processing test target design data, using a pre-trained model”; “generating a pre-processed test target distance value by pre-processing the test target design data”; “concatenating i) the image generated by extraction using the pre-trained model and ii) the pre-processed test target distance value”; “predicting impact performance using a neural network in which parameters are updated by learning based on i) an image obtained by preprocessing existing design data and ii) existing impact amount data corresponding to the existing design data”, under the broadest reasonable interpretation fall under a mental process or otherwise a mathematical concept. Therefore, the claims are directed to an abstract idea, by use of generic computer components and thus are clearly directed to an abstract idea, as constructed.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional limitation such as: “a data processing processor”, “a machine learning processor”, “an output processor”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0044-0059], and fig.2) which can be of any type, including general-purpose computer (para [0036]) previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “outputting a predicted impact performance value”, under the broadest reasonable interpretation, reasonable fall under insignificant post-solution activities” that is also well-known, routine and conventional activities and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as previously discussed above with reference to the integration of abstract idea into a practical application, the additional elements of: “a data processing processor”, “a machine learning processor”, “an output processor”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0044-0059], and fig.2) which can be of any type, including general-purpose computer (para [0036]) previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; the step of: “outputting a predicted impact performance value”, under the broadest reasonable interpretation, reasonable fall under insignificant post-solution activities” that is also well-known, routine and conventional activities and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101. Therefore, using computer components amount to no more than mere instructions to perform the abstract, and thus are not sufficient to amount to significantly more than the recited abstract, as constructed.
3.2 Dependent claims 2-7 merely include limitations pertaining to: (claims 2, 9), “wherein: the pre-trained model includes a DenseNet201 model, and the image generated by extraction using the pre-trained model is generated further using global average pooling (GAP)” (mental process or otherwise a mathematical concept). (claims 3, 10); “wherein: the existing design data is a design image of a cross-section of a vehicle, and generating the image includes cropping an outer portion of the image of the existing design data” (mental process or otherwise a mathematical concept); (claims 4, 11); “wherein the existing design data includes material information on a headlining, wherein the material information reflects a material of the headlining in the design image by at least one among a color, a chroma, a brightness, a solid line, and a dotted line” (mental process); (claims 5, 12); “wherein the image obtained by pre-processing existing design data is obtained by cropping an outer portion of the design image corresponding to the existing design data by a same size on the basis of a headform” (mental process); (claims 6, 13); “wherein the image obtained by pre-processing existing design data is an image in which a blank image region excluding the design image is enlarged with respect to the existing design data” (mental process or otherwise a mathematical concept); (claims 7, 14) “wherein the impact performance prediction is learned by a multi-layer perceptron (MLP) learning model or a convolution neural network (CNN) learning model” (mental process or otherwise a mathematical concept); all of which further amount to further mathematical concept and/or mental process similar to that already recited by the independent claims and already addressed above and thus are further not patent eligible under 35 USC 101.
Claim Interpretation
4. 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.
4.1 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.
4.2 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 data processing processor”, “a machine learning processor”, “an output processor” in claim 1.
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 § 103
4. 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.
4.0 Claim(s) 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (USPG_PUB No. 2021/0019563), in view of Wang et al. (USPG_PUB No. 2021/0374547).
5.1 In considering claims 1 and 8, Lee et al. teaches a free motion headform (FMH) impact performance prediction device using artificial intelligence (see title and abstract), the FMH impact performance prediction device comprising:
a data processing processor (100) configured to: generate an image by extracting a pre-processed test target image, generated by pre-processing test target design data, using a pre-trained model (fig.2, 6-8, para [0007] In accordance with an aspect of the disclosure, an apparatus for crash performance prediction of a vehicle includes: a data processor configured to generate a test target overlap image by pre-processing test target design data, [0012] The crash performance prediction model includes a weight data updated by machine learning at least one of the overlap image generated by pre-processing the existing design data, and the impact value and the impact rating value generated by pre-processing the existing impulse data. [0026] The crash performance prediction model is learned by a multi-layer perceptron (MLP) learning model or a convolutional neural network (CNN) learning model, or transfer learned based on the pre-trained model. [0083] The feature extraction layer 1132 extracts a feature in the image by analyzing the transferred overlap image and transmits the feature in the prediction layer 1133.), and generate a pre-processed test target distance value by pre-processing the test target design data (para [0017] In accordance with another aspect of the disclosure, a method for crash performance prediction of a vehicle includes: generating a test target overlap image by pre-processing test target design data, the test target design data including an existing design data of the vehicle, and further performing pre-processing on an existing impulse data corresponding to the existing design data); a machine learning processor (110) (see para [0026] The crash performance prediction model is learned by a multi-layer perceptron (MLP) learning model or a convolutional neural network (CNN) learning model, or transfer learned based on the pre-trained model.) configured to: concatenate i) the image generated by extraction using the pre-trained model and ii) the pre-processed test target distance value (fig.1, 7-8, para [0024] the prediction model includes a first crash performance prediction model generated based on the overlap image and the impact value, and a second crash performance prediction model generated based on the overlap image and the impact rating. [0026] The crash performance prediction model is learned by a multi-layer perceptron (MLP) learning model or a convolutional neural network (CNN) learning model, or transfer learned based on the pre-trained model.), and predict impact performance using a neural network in which parameters are updated by learning based on i) an image obtained by pre-processing existing design data and ii) existing impact amount data corresponding to the existing design data (fig.7-8, para [0014] the crash performance prediction model includes a first crash performance prediction model generated based on the overlap image and the impact value, and a second crash performance prediction model generated based on the overlap image and the impact rating value. [0016] The crash performance prediction model is learned by a multi-layer perceptron (MLP) learning model or a convolutional neural network (CNN) learning model, or transfer learned based on the pre-trained model. [0022] The crash performance prediction model includes a weight data updated by machine learning at least one of the overlap image generated by pre-processing the existing design data, and the impact value and the impact rating value generated by pre-processing the existing impulse data. [0024] also see para [0086] The crash performance prediction model may be classified into a first performance prediction model generated based on the overlap image and the impact value, and a second performance prediction model generated based on the overlap image and the impact rating value. The crash performance prediction model may be implemented as a model that outputs both an impact value and an impact rating value at the same time, and outputs both a predicted impact value and a predicted impact rating value..); and an output processor (120) configured to output a value predicted by the machine learning processor (see fig.2, 8, para [0015] The output device is configured to output the predicted impact value through the first crash performance prediction model and the prediction predicted impact rating value through the second crash performance prediction model. [0025] The outputting includes outputting the predicted impact value through the first crash performance prediction model and the predicted impact rating value through the second crash performance prediction model.). While Lee et al. does not specifically state that the specific image and the pre-processed test target value are concatenate, he provides for combining several images and data values to arrive at a final image data (see fig.1 learning step in which the design drawing and HIC value were combined to arrive at the ML model, see further fig.4, 7-8), and thus would clearly be obvious to a person of skilled in the art. Nonetheless, Wang et al. provides a method in which a determined sets of featured are concatenated together by way of a training model 202 to form a features 218 (see fig.2, para [0076], [0095], concatenate the features to generate a feature set).
Lee et al. and Wang et al. are analogous art because they are from the same field of endeavor and that the model analyzes by Wang et al. is similar to that of Lee et al. Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Wang et al. with that of Lee et al. because Wang et al. teaches the improvement of reliability, safety and performance (see para [0229]).
5.2 As per claims 2 and 9, the combined teachings of Lee et al. and Wang et al. teaches that wherein: the pre-trained model includes a DenseNet201 model (see Lee et al. para [0091] The crash performance prediction model employed by the apparatus for crash performance prediction may perform transfer learning based on a pre-trained model such as a ResNet, DenseNet, or Inception model among a multi-layer perceptron (MLP) or a convolutional neural network (CNN).), and the image generated by extraction using the pre-trained model is generated further using global average pooling (GAP) (see Wang et al. para [0096], In at least one embodiment, a pre-trained residual neural network is utilized as a backbone, followed by a global average pooling layer and a fully-connected layer for final classification.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Wang et al. with that of Lee et al. because Wang et al. teaches the improvement of reliability, safety and performance (see para [0229]).
5.3 With regards to claims 3 and 10, the combined teachings of Lee et al. and Wang et al. teaches that wherein: the existing design data is a design image of a cross-section of a vehicle (see Lee et al. para [0008] The existing design data includes first design data and second design data, the first design data is a design image of the longitudinal section of the vehicle, the second design data is a design image for the vehicle cross section, [0046], and the overlap image refers to data generated according to a result of performing a pre-processing based on design drawings obtained from different aspects such as a cross section and a longitudinal section of the vehicle.), and generating the image includes cropping an outer portion of the image of the existing design data (Lee et al. para [0010] the image is provided by cropping design images corresponding to each of the first design data and the second design data with the same size based on the impact point and by overlapping the cropped design images. [0020] The outputting includes: cropping design images corresponding to each of the first design data and the second design data with the same size based on the impact point and overlapping the cropped design images). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Wang et al. with that of Lee et al. because Wang et al. teaches the improvement of reliability, safety and performance (see para [0229]).
5.4 Regarding claims 4 and 11, the combined teachings of Lee et al. and Wang et al. teaches that wherein the existing design data includes material information on a headlining, wherein the material information reflects a material of the headlining in the design image by at least one among a color, a chroma, a brightness, a solid line, and a dotted line (see Lee et al. para [0009], [0019] The existing design data includes the material information of the vehicle, the material information is reflected in at least one of color, saturation, lightness, solid line and dotted line in the design image. [0064] In addition, the design data may include material information of the vehicle. For example, the image output based on the design data may be divided into various colors. Here, the color may reflect material information of the vehicle. For example, the glass material may be green, the metal material may be blue, and the plastic material may be red. The vehicle is made of various materials, and the resistance to impact varies depending on the material. In order to reflect this information, the design data may include color information reflecting material information of the vehicle. In addition to being expressed in color, the material information may be expressed by allowing the materials of the vehicle to be distinguished from each other by using different characteristics: saturation and, brightness, solid line and dotted line, etc.). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Wang et al. with that of Lee et al. because Wang et al. teaches the improvement of reliability, safety and performance (see para [0229]).
5.5 As per claims 5 and 12, the combined teachings of Lee et al. and Wang et al. teaches that wherein the image obtained by pre-processing existing design data is obtained by cropping an outer portion of the design image corresponding to the existing design data by a same size on the basis of a headform (see Lee et al. para 0010] The overlap image is provided by cropping design images corresponding to each of the first design data and the second design data with the same size based on the impact point and by overlapping the cropped design images. [0020] The outputting includes: cropping design images corresponding to each of the first design data and the second design data with the same size based on the impact point and overlapping the cropped design images. See further Lee et al. [0066], [0069]). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Wang et al. with that of Lee et al. because Wang et al. teaches the improvement of reliability, safety and performance (see para [0229]).
5.6 With regards to claims 6 and 13, the combined teachings of Lee et al. and Wang et al. teaches that wherein the image obtained by pre-processing existing design data is an image in which a blank image region excluding the design image is enlarged with respect to the existing design data (see Lee et al. para [0066], the overlap image 2023 may be an image in which different images of the first design data 2021 and the second design data 2022 are enlarged and overlapped. Here, an image blank may be added to a part of the image in order to prevent loss of the output image. In other words, by adding an image blank, the image blank of design image does not occur when the design images corresponding to each of the first design data and the second design data share the same impact point, and each image is cropped to the same size and generated as an overlapped image. [0067], Therefore, the image blank area of the entire image of the first design data may be enlarged. Accordingly, the first partial image may be overlapped with the second partial image with the same image size without losing any portion. Lee et al. Fig. (606)). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Wang et al. with that of Lee et al. because Wang et al. teaches the improvement of reliability, safety and performance (see para [0229]).
5.7 As per claims 7 and 14, the combined teachings of Lee et al. and Wang et al. teaches that wherein the impact performance prediction is learned by a multi-layer perceptron (MLP) learning model or a convolution neural network (CNN) learning model (see Lee et al. para [0016] The crash performance prediction model is learned by a multi-layer perceptron (MLP) learning model or a convolutional neural network (CNN) learning model, or transfer learned based on the pre-trained model. [0026] The crash performance prediction model is learned by a multi-layer perceptron (MLP) learning model or a convolutional neural network (CNN) learning model, or transfer learned based on the pre-trained model. [0091] The crash performance prediction model employed by the apparatus for crash performance prediction may perform transfer learning based on a pre-trained model such as a ResNet, DenseNet, or Inception model among a multi-layer perceptron (MLP) or a convolutional neural network (CNN).). Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine the method of Wang et al. with that of Lee et al. because Wang et al. teaches the improvement of reliability, safety and performance (see para [0229]).
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
6.1 Unterthiner et al. (USPG_PUB No. 2021/0256422) teaches systems and methods for predicting machine learning model performance from the model parameter values, including for use in making improved decisions with regard to early stopping of training procedures.
6.2 Hua et al. (USPG_PUB No. 2020/0257961) teaches a neural architecture search using a performance prediction neural network.
6.3 Li et al. (USPG_PUB No. 2022/0085878) teaches a method for free space optical communication performance prediction.
7. claims 1-14 are rejected and this action is non-final. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMERSON C PUENTE can be reached at 571-272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 September 22, 2026
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