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 .
Status of Claims
Claims 1-20 are currently pending and have been examined.
Claims 1, 4, 5, 7-12, and 15-16 have been amended.
Claims 17-20 have been added.
Claims 1-20 have been rejected.
Priority and Formal Matters
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed for Application No. JP2021-100464 on 11/30/2023.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 6/16/2021 claiming benefit to JP2021-100464.
The preliminary amendments to the claims, received on 11/30/2023 have been received and are accepted.
Claim Rejections - 35 USC § 101
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.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-20 are drawn to a device, method, or manufacture, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a device for gas concentration feature quantity estimation. Independent claim 14 recites a method for gas concentration feature quantity estimation. Independent claim 15 recites a non-transitory recording medium storing a computer readable program for gas concentration feature quantity estimation. Independent claim 16 recites a device for gas concentration feature quantity inference model generation.
These independent claims recite the following steps best characterized as a mental process under MPEP § 2106.04(a)(2)(III) citing the abstract idea grouping for mental processes in general:
acquire time-series pixel group inspection data and a temperature value of a gas
the time-series pixel group inspection data being region-extracted from inspection data of a gas distribution moving image representing an existence region of the gas in a space, and having two or more pixels in a vertical direction and a horizontal direction, respectively, and
calculates a gas concentration feature quantity corresponding to the time-series pixel group inspection data using an inference model
the inference model being machine-learned using time-series pixel group training data of the gas distribution moving image having a same size as the time-series pixel group inspection data, and a gas temperature value and a value of the gas concentration feature quantity corresponding to the time-series pixel group training data as training data
Under the broadest reasonable interpretation of the limitations, these limitations are best characterized as applying a mental process to a generic computing environment - see MPEP § 2106.04(a)(2)(III)(c)(2). Furthermore, the use of a trained machine learning inference model can also be characterized as representing mathematical relationships - see MPEP § 2106.04(a)(2)(I)(A).
Dependent claim 2 recites, in part, wherein the time-series pixel group inspection data includes a smaller number of frame pixels than the number of frame pixels of the gas distribution moving image.
Dependent claim 3 recites, in part, wherein the time-series pixel group inspection data includes three or more and seven or less pixels in the vertical and horizontal directions, respectively.
Dependent claim 4 recites, in part, wherein the time-series pixel group training data is a moving image including vibration noise.
Dependent claim 5 recites, in part, wherein the gas concentration feature quantity is an optical absorption coefficient, the gas concentration feature quantity estimation device further comprising a convertor that converts the optical absorption coefficient corresponding to the time-series pixel group inspection data into a concentration length product value related to a gas type based on a relationship characteristic between the optical absorption coefficient and the concentration length product of the gas type.
Dependent claim 6 recites, in part, wherein the gas concentration feature quantity is a gas concentration length product.
Dependent claim 8 recites, in part, wherein training data of the gas distribution moving image is generated by simulation.
Dependent claim 9 recites, in part, wherein the training data of the gas distribution moving image is generated from a background image and the optical absorption coefficient.
Dependent claim 10 recites, in part, wherein the number of frames in the time-series pixel group inspection data or the time-series pixel group training data is larger than the number of pixels in the vertical or the horizontal direction in each frame.
Dependent claim 11 recites, in part, wherein the gas concentration feature quantity corresponding to the time-series pixel group inspection data is a sequence of numbers including values calculated for each frame of the time-series pixel group inspection data.
Dependent claim 12 recites, in part, wherein the gas concentration feature quantity corresponding to the time-series pixel group inspection data is an average value of values calculated for each frame of the time-series pixel group inspection data.
Dependent claim 17 recites, in part, wherein the time-series pixel group training data is a moving image including vibration noise.
Dependent claim 18 recites, in part, wherein the gas concentration feature quantity is an optical absorption coefficient, the gas concentration feature quantity estimation device further comprising a convertor that converts the optical absorption coefficient corresponding to the time-series pixel group inspection data into a concentration length product value related to a gas type based on a relationship characteristic between the optical absorption coefficient and the concentration length product of the gas type.
Dependent claim 20 recites, in part, wherein training data of the gas distribution moving image is generated by simulation.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claim 1 accordingly, and hence are nonetheless directed towards fundamentally the same mental process abstract idea grouping as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1 and 16 recite a hardware processor. Claim 15 recites a non-transitory recording medium storing a computer readable program.
Claims 7 and 19 recite a wherein the gas distribution moving image is an image captured by an imaging device. Claim 13 recites a wherein the imaging device is an infrared camera. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 1 and 16 recite a hardware processor. Claim 15 recites a non-transitory recording medium storing a computer readable program. The specification provides generic hardware configuration for the computer in ¶ 0190). The use of a computer and corresponding hardware therefore amounts to a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claims 7 and 19 recite a wherein the gas distribution moving image is an image captured by an imaging device. Claim 13 recites a wherein the imaging device is an infrared camera. The additional element of an infrared camera for capturing gas images is well-understood, routine, and conventional. This position is supported by Gade et al., Thermal cameras and applications: a survey, 25 Machine Vision and Applications 245-262 (2014) (treated as a review under MPEP § 2106.07(a)(III)(C) that describes the state of the art and discusses what is well-known and in common use in the relevant industry) teaching on infrared cameras for capturing gas particles in the § 4.2.2 Gas detection on p. 251. Therefore, the infrared camera is not sufficient to amount to significantly more than the recited judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-20 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Morimoto (EP-3372996-B1)[hereinafter Morimoto] in view of Whiting et al. (US Patent Application No. 20200320659)[hereinafter Whiting].
As per claim 1, Morimoto teaches on the following limitations of the claim:
a gas concentration feature quantity estimation device comprising is taught in the Detailed Description in ¶ 0021, ¶ 0040, ¶ 0194, and in the Figures at fig. 5A (teaching on a computer and corresponding software executing a model for predicting gas concentration from time-series pixel analysis)
a hardware processor that acquires time-series pixel group inspection data and is taught in the Detailed Description in ¶ 0022, ¶ 0080, and ¶ 0108 (teaching on a computer and corresponding software receiving from an infrared camera infrared spectroscopy data including chronological pixel of interest and peripheral pixel data)
a temperature value of a gas is taught in the Detailed Description in ¶ 0022 and ¶ 0186 (teaching on receiving from an air temperature sensor gas temperature data)
the time-series pixel group inspection data being region-extracted from inspection data of a gas distribution moving image representing an existence region of the gas in a space, and having two or more pixels in a vertical direction and a horizontal direction, respectively, and is taught in the Detailed Description in ¶ 0080 and in the Figures at fig. 15 (teaching on extracting the pixel of interest and peripheral pixel data including a 5x5 grid of a specific region from a subset of the captured infrared spectroscopy data)
calculates a gas concentration feature quantity corresponding to the time-series pixel group inspection data using an inference model is taught in the Detailed Description in ¶ 0189, ¶ 0195, and in the Figures at fig. 15 (teaching on determining a gas concentration from the input data using a convergence (treated as synonymous to an inference) model)
Morimoto fails to teach the following limitation of claim 1. Whiting, however, does teach the following:
the inference model being machine-learned using time-series pixel group training data of the gas distribution moving image having a same size as the time-series pixel group inspection data, and a gas temperature value and a value of the gas concentration feature quantity corresponding to the time-series pixel group training data as training data is taught in the Detailed Description in ¶ 0089 and ¶ 0093-94 (teaching on a machine learning model trained on historical video pixel and gas temperature data captured under the identical conditions for gas plume analysis)
One of ordinary skill in the art would combine the convergence model for determining a gas concentration of Morimoto with a machine learning from a training data set specific structure for determining said model convergence of Whiting with the motivation of “reduc[ing] the need for significant computing resources storing large databases of image data and the need for skilled personnel to manually determine characteristics of the gaseous plume” (Whiting in the Detailed Description in ¶ 0094).
Independent claims 14, 15, and 16 are rejected under substantially the same rational.
As per claim 2, the combination of Morimoto and Whiting discloses all of the limitations of claim 1. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 1, wherein the time-series pixel group inspection data includes a smaller number of frame pixels than the number of frame pixels of the gas distribution moving image is taught in the Detailed Description in ¶ 0040, ¶ 0080, and in the Figures at fig. 15 (teaching on extracting the pixel of interest and peripheral pixel data including a 5x5 grid of a specific region from a subset of the captured infrared spectroscopy data)
As per claim 3, the combination of Morimoto and Whiting discloses all of the limitations of claim 2. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 2, wherein the time-series pixel group inspection data includes three or more and seven or less pixels in the vertical and horizontal directions, respectively is taught in the Detailed Description in ¶ 0040, ¶ 0080 and in the Figures at fig. 15 (teaching on extracting the pixel of interest and peripheral pixel data including a 5x5 grid of a specific region from a subset of the captured infrared spectroscopy data)
As per claim 4, the combination of Morimoto and Whiting discloses all of the limitations of claim 1. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 1, wherein the time-series pixel group training data is a moving image including vibration noise is taught in the Detailed Description in ¶ 0022, ¶ 0080, ¶ 0108, and ¶ 0144 (teaching on a computer and corresponding software receiving from an infrared camera including shaking motions (treated as synonymous to vibration noise) infrared spectroscopy data)
As per claim 5, the combination of Morimoto and Whiting discloses all of the limitations of claim 1. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 1, wherein the gas concentration feature quantity is an optical absorption coefficient, the gas concentration feature quantity estimation device further comprising a convertor that converts the optical absorption coefficient corresponding to the time-series pixel group inspection data into a concentration length product value related to a gas type based on a relationship characteristic between the optical absorption coefficient and the concentration length product of the gas type is taught in the Detailed Description in ¶ 0189, ¶ 0195, and in the Figures at fig. 15 (teaching on determining a gas concentration from the input data using a convergence (treated as synonymous to an inference) model taking into account a gas spectral transmittance determined from a gas spectral absorption coefficient for the concentration length)
As per claim 6, the combination of Morimoto and Whiting discloses all of the limitations of claim 1. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 1, wherein the gas concentration feature quantity is a gas concentration length product is taught in the Detailed Description in ¶ 0189, ¶ 0195, and in the Figures at fig. 15 (teaching on determining a gas concentration from the input data using a convergence (treated as synonymous to an inference) model taking into account a gas spectral transmittance determined from a gas spectral absorption coefficient for the concentration length)
As per claim 7, the combination of Morimoto and Whiting discloses all of the limitations of claim 1. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 1, wherein the gas distribution moving image is an image captured by an imaging device is taught in the Detailed Description in ¶ 0022, ¶ 0080, and ¶ 0108 (teaching on a computer and corresponding software receiving from an infrared camera infrared spectroscopy data including chronological pixel of interest and peripheral pixel data)
As per claim 8, the combination of Morimoto and Whiting discloses all of the limitations of claim 1. Morimoto fails to teach the following; Whiting, however, does disclose:
the gas concentration feature quantity estimation device according to claim 1, wherein training data of the gas distribution moving image is generated by simulation is taught in the Detailed Description in ¶ 0037, ¶ 0089, and ¶ 0093-94 (teaching on a machine learning model trained on historical plume simulation video pixel and gas temperature data captured under the identical conditions for gas plume analysis)
One of ordinary skill in the art would combine the convergence model for determining a gas concentration of Morimoto with a machine learning from a training data set specific structure for determining said model convergence of Whiting with the motivation of “reduc[ing] the need for significant computing resources storing large databases of image data and the need for skilled personnel to manually determine characteristics of the gaseous plume” (Whiting in the Detailed Description in ¶ 0094).
As per claim 9, the combination of Morimoto and Whiting discloses all of the limitations of claim 1. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 1, wherein the ... data of the gas distribution moving image is generated from a background image and the optical absorption coefficient is taught in the Detailed Description in ¶ 0080, ¶ 0108, ¶ 0189, ¶ 0195, and in the Figures at fig. 15 (teaching on determining a gas concentration from the larger video input data using a convergence (treated as synonymous to an inference) model taking into account a gas spectral transmittance determined from a gas spectral absorption coefficient for the concentration length)
Morimoto fails to teach the following; Whiting, however, does disclose:
training data is taught in the Detailed Description in ¶ 0037, ¶ 0089, and ¶ 0093-94 (teaching on a machine learning model trained on historical video pixel and gas temperature data)
One of ordinary skill in the art would combine the convergence model for determining a gas concentration of Morimoto with a machine learning from a training data set specific structure for determining said model convergence of Whiting with the motivation of “reduc[ing] the need for significant computing resources storing large databases of image data and the need for skilled personnel to manually determine characteristics of the gaseous plume” (Whiting in the Detailed Description in ¶ 0094).
As per claim 10, the combination of Morimoto and Whiting discloses all of the limitations of claim 1. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 1, wherein the number of frames in the time-series pixel group inspection data or the time-series pixel group training data is larger than the number of pixels in the vertical or the horizontal direction in each frame is taught in the Detailed Description in ¶ 0040-41, ¶ 0080, and in the Figures at fig. 15 (teaching on extracting the pixel of interest and peripheral pixel data including a 5x5 grid of a specific region from a subset (here - a 25x25 pixel) of the captured infrared spectroscopy data)
As per claim 11, the combination of Morimoto and Whiting discloses all of the limitations of claim 1. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 1, wherein the gas concentration feature quantity corresponding to the time-series pixel group inspection data is a sequence of numbers including values calculated for each frame of the time-series pixel group inspection data is taught in the Detailed Description in ¶ 0041, ¶ 0214-215, the Claims in claim 4, and in the Figures at fig. 15 (teaching on calculating an average absolute value of the with and without gas background for the chronological per frame pixel data when determining the gas concentration value)
As per claim 12, the combination of Morimoto and Whiting discloses all of the limitations of claim 1. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 1, wherein the gas concentration feature quantity corresponding to the time-series pixel group inspection data is an average value of values calculated for each frame of the time-series pixel group inspection data is taught in the Detailed Description in ¶ 0041, ¶ 0214-215, the Claims in claim 4, and in the Figures at fig. 15 (teaching on calculating an average absolute value of the with and without gas background for the chronological per frame pixel data when determining the gas concentration value)
As per claim 13, the combination of Morimoto and Whiting discloses all of the limitations of claim 7. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 7, wherein the imaging device is an infrared camera is taught in the Detailed Description in ¶ 0022, ¶ 0080, and ¶ 0108 (teaching on a computer and corresponding software receiving from an infrared camera)
As per claim 17, the combination of Morimoto and Whiting discloses all of the limitations of claim 2. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 2, wherein the time-series pixel group training data is a moving image including vibration noise is taught in the Detailed Description in ¶ 0022, ¶ 0080, ¶ 0108, and ¶ 0144 (teaching on a computer and corresponding software receiving from an infrared camera including shaking motions (treated as synonymous to vibration noise) infrared spectroscopy data)
As per claim 18, the combination of Morimoto and Whiting discloses all of the limitations of claim 2. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 2, wherein the gas concentration feature quantity is an optical absorption coefficient, the gas concentration feature quantity estimation device further comprising a convertor that converts the optical absorption coefficient corresponding to the time-series pixel group inspection data into a concentration length product value related to a gas type based on a relationship characteristic between the optical absorption coefficient and the concentration length product of the gas type is taught in the Detailed Description in ¶ 0189, ¶ 0195, and in the Figures at fig. 15 (teaching on determining a gas concentration from the input data using a convergence (treated as synonymous to an inference) model taking into account a gas spectral transmittance determined from a gas spectral absorption coefficient for the concentration length)
As per claim 19, the combination of Morimoto and Whiting discloses all of the limitations of claim 2. Morimoto also discloses the following:
the gas concentration feature quantity estimation device according to claim 2, wherein the gas distribution moving image is an image captured by an imaging device is taught in the Detailed Description in ¶ 0022, ¶ 0080, and ¶ 0108 (teaching on a computer and corresponding software receiving from an infrared camera infrared spectroscopy data including chronological pixel of interest and peripheral pixel data)
As per claim 20, the combination of Morimoto and Whiting discloses all of the limitations of claim 2. Morimoto fails to teach the following; Whiting, however, does disclose:
the gas concentration feature quantity estimation device according to claim 2, wherein training data of the gas distribution moving image is generated by simulation is taught in the Detailed Description in ¶ 0037, ¶ 0089, and ¶ 0093-94 (teaching on a machine learning model trained on historical plume simulation video pixel and gas temperature data captured under the identical conditions for gas plume analysis)
One of ordinary skill in the art would combine the convergence model for determining a gas concentration of Morimoto with a machine learning from a training data set specific structure for determining said model convergence of Whiting with the motivation of “reduc[ing] the need for significant computing resources storing large databases of image data and the need for skilled personnel to manually determine characteristics of the gaseous plume” (Whiting in the Detailed Description in ¶ 0094).
Conclusion
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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