CTNF 18/570,463 CTNF 86904 DETAILED ACTION This action is written in response to the application filed 12/14/23. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Title of Invention Is Not Descriptive 06-11 AIA The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Claim Rejections - 35 USC § 112(b) - Indefiniteness 07-30-02 The following is a quotation of the second paragraph of 35 U.S.C. 112: (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. Claims 1-13 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention. Claim 1 recites “calculate a feature amount using the raw data”. However, the applicant provides no meaningful limitations as to the meaning and scope of “feature amount”. The examiner was unable to identify any applicant definition for this term in the specification, and it is not a widely-used term of art within the field of machine learning. Accordingly, it is unclear whether this means: The literal quantitative or qualitive value of a particular feature in the raw data, eg as suggested by the plain meaning of the term; A summary statistic of a particular feature in the raw data, eg as suggested by spec. [0245]; The examiner notes that this portion of the specification pertains to neural networks, but the recited encoding model and estimation model are not limited to this type of model; Some model-specific value regarding the importance of a particular feature in the raw data to the model or to the “correct answer data”. Because it is not clear which of the above interpretations is applicable, the term is ambiguous, and consequently a person of ordinary skill would not be able to understand the scope of the claim with reasonable certainty. Therefore the claim is indefinite. This rejection applies equally to independent claims 12-13, as well as all pending dependent claims which inherit this deficiency. Claim Rejections - 35 USC § 112(a) - Enablement 07-30-01 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. Claims 1-13 are rejected under 35 U.S.C. 112(a) as failing to comply with the enablement requirement. The claims contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The claim term “feature amount” is central to each independent claim, yet this term is indefinite ( see §112 rejection supra ). Furthermore, the specification does not set forth a method of calculating this value, as required by the independent claims. In determining that the claims do not satisfy the enablement requirement, the examiner has considered each of the factors specified in In re Wands. (858 F.2d 731, 737 (Fed. Cir. 1988).) (A) the breadth of the claims, and (B) the nature of the invention The claims are broad in the sense that the applicant does not specify in the claims what problem is being solved, what data is being modeled or how the data is being modeled. The specification mentions such disparate tasks and field-of-uses as “medical/healthcare and security” ([0002]), gait analysis using sensor data from shoes ([0130]-[0131], [0154]), “internet of things (IoT) device data” ([0015]), and classification of image data ([0023]). The nature of the claimed invention is therefore unclear. (C) the state of the prior art and (D) the level of one of ordinary skill The art cited by the applicant and examiner reflect the state of some of the potential prior art. However, even a skilled machine learning engineer could not reproduce the invention without additional guidance form the applicant, eg regarding what problem is being solved and what kind of models are being used. (E) The level of predictability in the art Although computer science is a highly predictable field, new techniques must be well described before they can be reproduced by others. (F) The amount of direction provided by the inventor The Applicant has provided little guidance on how to accomplish the above-identified critical steps in the claimed invention. (G) The existence of working examples The Applicant provides no working example of the claimed invention. (H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure Experimentation alone would not bring a computer scientist—even a highly skilled one—closer to reproducing the claimed invention without further guidance as to how to implement the feature in question. The examiner has considered the factors above and found that the quantity of experimentation necessary to reproduce the claimed invention is undue. Accordingly, claim 1 is rejected for lack of enablement. This rejection applies equally to independent claims 12-13, as well as to dependent claims 2-11. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-09-fti 07-09 (b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States. 07-15 AIA Claim s 1-7 and 12-13 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Zhang . (Zhang, Chenggang, et al. "An imbalanced data classification algorithm of improved autoencoder neural network." 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) . IEEE, 2016.) Regarding claims 1, 12 and 13, Zhang discloses a training device (and a related method and non-transitory recording medium) comprising: a first memory storing instructions; and a first processor connected to the first memory and configured to execute the instructions to: A memory and a processor are inherent throughout the disclosure. acquire a data set of raw data and correct answer data; P. 97, sec. (IV)(A) Experimental data for classification task. calculate a feature amount using the raw data; P. 96, fig. 1 (reproduced below). PNG media_image1.png 250 330 media_image1.png Greyscale ‘raw data’ :: x n ‘feature amount’ :: L2 construct an encoding model that outputs a code related to the feature amount in response to an input of the raw data and an estimation model that outputs an estimation result related to the raw data in response to an input of the code; and Id. “Stacked Autoencoder neural network”. train the encoding model and the estimation model in such a way that the estimation result matches the correct answer data based on a relationship between the code and the feature amount. P. 96, “The training nature of autoencoder neural network is to optimize reconstruction error using the given samples. Optimization target is Cross Entropy or Mean Square Error. The cost function of autoencoder neural network defined in the study is (4).” Regarding claim 2, Zhang discloses the further limitation wherein the first processor is configured to execute the instructions to construct the estimation model using the code output from the encoding model in response to the input of the raw data and the feature amount calculated using the raw data. P. 96, “In Fig. 1, SAE is network structure that has a connection between layers, but has no connection inside each layer. L1 is visual layer, L2 and L3 is hidden layer, x is input sample, xˆ is output feature, and 1+ is bias neuron.” (cont.) “The training nature of autoencoder neural network is to optimize reconstruction error using the given samples. Optimization target is Cross Entropy or Mean Square Error. The cost function of autoencoder neural network defined in the study is (4).” Regarding claim 3, Zhang discloses the further limitation wherein the model construction means the first processor is configured to execute the instructions to construct a reconstruction model that outputs a reconstructed feature amount related to the original feature amount of the code in response to the input of the code, and wherein P. 96, fig. 1 (reproduced supra ). the first processor is configured to execute the instructions to train the encoding model and the reconstruction model in such a way that the reconstructed feature amount matches the feature amount, and (cont.) “The training nature of autoencoder neural network is to optimize reconstruction error using the given samples. Optimization target is Cross Entropy or Mean Square Error. The cost function of autoencoder neural network defined in the study is (4).” PNG media_image2.png 84 500 media_image2.png Greyscale (cont.) “Here, m represents number of input samples, x the input, y the output.” train the encoding model and the estimation model in such a way that the estimation result matches the correct answer data based on the relationship between the code and the feature amount. Id. Regarding claim 4, Zhang discloses the further limitation wherein the first processor is configured to execute the instructions to construct the estimation model using the reconstructed feature amount output from the reconstruction model in response to the input of the code and the code. P. 96, fig. 1 (reproduced supra ), output layer L3. Regarding claim 5, Zhang discloses the further limitation wherein the first processor is configured to execute the instructions to train the estimation model in such a way that a mutual information amount of the code output from the encoding model in response to the input of the raw data and the feature amount calculated using the raw data is reduced. P. 96, eqn. (4). The examiner notes that “in such a way that the mutual information amount… is reduced” is an intended result of the claimed algorithm. Regarding claim 6, Zhang discloses the further limitation wherein the first processor is configured to execute the instructions to construct a first pre-training model that outputs a first conversion value related to the feature amount in response to the input of the raw data, P. 96, “In Fig. 2, sample x is corrupted to x~ according to probability Dq , which then mapped to y through encoder, which further generates z through decoder that makes it reconstruct x as possible. Lost function LH (x, z) represents reconstruction error [15].”(Emphasis added.) train the first pre-training model in such a way that the first conversion value matches the feature amount, P. 96, eqn. 5. set a model parameter of the first pre-training model after training as an initial value of the encoding model, and P. 96, “It initializes the network weights using the greedy layer-wise training method,”. train the encoding model and the estimation model in such a way that the estimation result matches the correct answer data. See generally p. 96. Regarding claim 7, Zhang discloses the further limitation wherein the first processor is configured to execute the instructions to construct a second pre-training model that outputs a second conversion value related to the raw data in response to an input of the feature amount calculated using the raw data, and wherein P. 97, steps d) “Pretrain DAE” and 3) “Finetune DAE”. train the second pre-training model in such a way that the second conversion value matches the estimation result, See generally p. 96, eqns. (3) and (4) reproduced supra . set a model parameter of the second pre-training model after training as an initial value of the estimation model, and P. 97, steps 1) “Set parameter[s].” train the encoding model and the estimation model in such a way that the estimation result matches the correct answer data. See generally p. 96, eqns. (3) and (4) reproduced supra . Claim Rejections - 35 USC § 103 07-20 AIA The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action: (a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made. The following are the references relied upon in the rejections below: Ross (US 2015/0257679 A1) Zhang (Zhang, Chenggang, et al. "An imbalanced data classification algorithm of improved autoencoder neural network." 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2016.) 07-21-aia AIA Claim s 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang and Ross . Regarding claim 8, Zhang discloses the further limitation comprising an estimation system comprising an estimation device in which an encoding model and an estimation model constructed by the training device according to claim 1 are implemented, the system comprising: … a second memory storing instructions, and a second processor connected to the second memory and configured to execute the instructions to A memory and a processor are inherent throughout the Zhang disclosure. input raw data measured by the measurement instrument to the encoding model, and P. 97, sec. (IV)(A) Experimental data for classification task. transmit a code output from the encoding model in response to an input of the raw data, and P. 96, fig. 1 (reproduced supra ), output from L2. the estimation device that includes the estimation model, a third memory storing instructions, and a third processor connected to the third memory and configured to execute the instructions to … input the received code to the estimation model, and P. 96, fig. 1 (reproduced supra ), input to L3. output an estimation result output from the estimation model in response to an input of the code. P. 96, fig. 1 (reproduced supra ), output from L3. Ross discloses the following further limitation which Zhang does not disclose: a measurement device that includes at least one measurement instrument and the encoding model, … Abstract: “a pair of sensor insoles that include a plurality of force sensors [and] an accelerometer”. See also fig. 1 (reproduced below). PNG media_image3.png 682 380 media_image3.png Greyscale receive the code transmitted from the measurement device, Abstract “a transmitter for transmitting data to a portable electronic device”. At the time of filing, it would have been obvious to a person of ordinary skill to apply the classification system of Zhang to gait data, as measured and transmitted by Ross, because this would provide for the diagnosis of problems with a subject’s gait, if present. Regarding claim 9, Zhang discloses the further limitation wherein the second processor of the measurement device is configured to execute the instructions to calculate a feature amount using the raw data, and P. 96, fig. 1, (reproduced supra ) layer L2. The examiner notes that a processor is inherent throughout Zhang (as well as Ross). transmit the code output from the encoding model in response to the input of the raw data and the feature amount calculated using the raw data to the estimation device, and wherein P. 96, fig. 1, output from layer L2. the third processor of the estimation device is configured to execute the instructions to receive the code and the feature amount transmitted from the measurement device, P. 96, fig. 1, input to layer L3. input the received code and the received feature amount to the estimation model, and P. 96, eqns. (3) and (4). output the estimation result output from the estimation model in response to inputs of the code and the feature amount. P. 96, fig. 1, output from layer L3. Regarding claim 10, Zhang discloses the further limitation wherein the estimation device includes a reconstruction model that outputs a reconstructed feature amount related to the original feature amount of the code in response to the input of the code, and wherein the third processor of the estimation device is configured to execute the instructions to input, to the estimation model, a reconstructed feature amount output from the reconstruction model in response to the input of the code, and See generally p. 96, describing autoencoder models. output the estimation result output from the estimation model in response to an input of the reconstructed feature amount. P. 96, fig. 1, output from L3. Regarding claim 11, Ross discloses the further limitation wherein the measurement device is worn by a user, wherein the second processor of the measurement device is configured to execute the instructions to measure sensor data related to a motion of the user, and Abstract: “a pair of sensor insoles that include a plurality of force sensors [and] an accelerometer”. See also fig. 1 (reproduced below). PNG media_image3.png 682 380 media_image3.png Greyscale transmit the code output from the encoding model to the estimation device in response to an input of the measured sensor data, and Abstract “a transmitter for transmitting data to a portable electronic device”. Zhang discloses the following further limitation: wherein the third processor of the estimation device is configured to execute the instructions to receive the code transmitted from the measurement device and transmit information related to the estimation result to a mobile terminal having a display means with which the estimation result can be visually recognizable by the user. See generally p. 96, describing autoencoder classification models. Additional Relevant Prior Art The following references were identified by the Examiner as being relevant to the disclosed invention, but are not relied upon in any particular prior art rejection: Sarkodie-Gyan discloses a technique for the automated diagnosis of gait problems using sensor data ( eg accelerometers and gyroscopes). (US 2014/0100494 A1) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Vincent Gonzales whose telephone number is (571) 270-3837. The examiner can normally be reached on Monday-Friday 7 a.m. to 4 p.m. MT. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang, can be reached at (571) 270-7092. Information regarding the status of an application may be obtained from the USPTO 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. /Vincent Gonzales/Primary Examiner, Art Unit 2124 Application/Control Number: 18/570,463 Page 2 Art Unit: 2124 Application/Control Number: 18/570,463 Page 3 Art Unit: 2124 Application/Control Number: 18/570,463 Page 4 Art Unit: 2124 Application/Control Number: 18/570,463 Page 5 Art Unit: 2124 Application/Control Number: 18/570,463 Page 6 Art Unit: 2124 Application/Control Number: 18/570,463 Page 7 Art Unit: 2124 Application/Control Number: 18/570,463 Page 8 Art Unit: 2124 Application/Control Number: 18/570,463 Page 9 Art Unit: 2124 Application/Control Number: 18/570,463 Page 10 Art Unit: 2124 Application/Control Number: 18/570,463 Page 11 Art Unit: 2124 Application/Control Number: 18/570,463 Page 12 Art Unit: 2124 Application/Control Number: 18/570,463 Page 13 Art Unit: 2124 Application/Control Number: 18/570,463 Page 14 Art Unit: 2124
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