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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2021-030058, filed on 02/26/2021.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/15/2023, 12/22/2025, and 03/23/2026 were considered by the examiner.
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 1-27 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A data processing apparatus comprising:
a processor, wherein the processor performs
data acquisition processing of acquiring first spectral data of a first subject and second spectral data of a second subject, and
wavelength selection processing of selecting a plurality of specific wavelengths from wavelength ranges of the acquired first spectral data and second spectral data, and in the wavelength selection processing, the plurality of specific wavelengths are selected based on a sensing sensitivity which is a normalized difference in a feature amount between the first spectral data and the second spectral data.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above; the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a process and is therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portion of claim 1 comprises process steps that fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category.
Individually and collectively, the steps:
“data acquisition processing of acquiring first spectral data of a first subject and second spectral data of a second subject”, and
“wavelength selection processing of selecting a plurality of specific wavelengths from wavelength ranges of the acquired first spectral data and second spectral data, and in the wavelength selection processing, the plurality of specific wavelengths are selected based on a sensing sensitivity which is a normalized difference in a feature amount between the first spectral data and the second spectral data”
may be performed as mental processes. Data acquisition processing of acquiring spectral data of subjects is collection information, which may be performed as mental processes. Selecting wavelengths based on acquired spectral data and a sensing sensitivity is analysis, which may be performed as mental processes. The type of high-level information collecting and analyzing data recited in these elements has been found by the Federal Circuit to constitute patent ineligible matter (see Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind).
Similar limitations comprise the mental processes type abstract idea recited by independent claim 15.
Step 2A, Prong Two of the analysis entails determining whether a claim includes additional elements that integrate the recited judicial exception (e.g., abstract idea) into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 1 does not include additional elements that integrate the recited abstract idea into a practical application. Based on the individual and collective limitations of claim 1, applying a broadest reasonable interpretation, the most significant of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” (e.g. “processor”) in any combination appear to integrate the abstract idea to technologically improve any aspect of a system that may be used to implement the highlighted steps such a generic computer.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements (e.g. “processor”) are recited generically and are not utilized as a particularized manner of implementing the abstract idea process steps.
Regarding effectuation of a transformation or reduction of a particular article to a different state or thing, the claim includes no such transformation or reduction. Instead, the claim as a whole entails gathering or otherwise obtaining information (data acquisition processing) and analyzing the information (wavelength selection processing).
Similar limitations such as the “processor” that are recited in independent claim 15 are also generically recited and does not integrate the judicial exception into a practical application.
The above additional elements, considered individually and in combination with the claim elements reciting an abstract idea do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under Step 2B.
Regarding Step 2B, independent claims 1 and 15, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in the relevant art as evidenced by the prior art of record as indicated in the rejections under 35 U.S.C. §103.
Independent claims 1 and 15 are therefore not patent eligible.
Dependent claims 2-14 and 16-27 provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of the independent claims (Step 2A, Prong One).
None of dependent claims 2-18 and 20 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two). Claims 2-3, 9-14, 16-17, and 23-26 further detail the wavelength selection step, which may be performed as mental processes. Further, claims 13-14 recite equations for determining the sensing sensitivity, which is a mathematical calculation. Claims 4, 6-8, 18, and 20-22 further detail the data acquisition processing step. Claims 5 and 19 recites a “display processing”, which is the display of certain results of the analysis, which may be performed as mental processes. Claim 27 recites the additional elements “a non-transitory, computer-readable tangible recording medium” and “a computer”, which are recited generically, and amount to instructions to implement the process steps on a computer (MPEP 2106.05(f)). Dependent claims 2-18 and 16-27 do not pass the “significantly more” test under step 2B for the same reasons as discussed with regards to the independent claims. Further, dependent claims reciting alleged inventive concepts which are themselves abstract ideas are not patentable subject matter (MPEP 2106.05: “As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty.")).
The dependent claims 2-14 and 16-27 therefore are also ineligible 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.
Claim(s) 1-8, 15-22, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto et al. (US 20050182328 A1) in view of Nagata (US 20100053375 A1).
Regarding claim 1, Matsumoto teaches A data processing apparatus (Abstract) comprising:
a processor, wherein the processor performs ([0077] lines 1-9, “This central control unit has a CPU, a ROM, and a RAM (none of which are shown). The CPU of the central control unit comprises a microprocessor, etc., and performs the various computing processes (image signal generation process (preprocess), vector conversion process, image forming process, color specification process, process for determining sensitivity functions, process for determining a matrix M, and control of the entire system enabling chromaticity measurement in the visible and invisible range)”)
data acquisition processing (Fig. 1) of acquiring first spectral data of a first subject ([0090] lines 11-9, “First, emitted light L1 of all wavelength ranges emitted from the subject sample 10 is received by the spectroscopic optical part 2a. An entire image P.sub.0 or a partial region E1 of the subject sample 10 may be received as the image received at this point. A continuous emission spectrum .lambda..sup.0.sub.S0 of the received image is decomposed into n (n.gtoreq.3) component lights having mutually different central wavelengths, .lambda..sup.0.sub.S1, .lambda..sup.0.sub.S2, . . . .lambda..sup.0.sub.Sn, by means of n (n.gtoreq.3) optical filters (ST1).”) and second spectral data of a second subject ([0024] lines 1-9, “with this invention's system enabling chromaticity measurement in the visible and invisible range, three or more sensitivity functions are determined based on the correlation between the physical state or chemical state differences to be observed that occur among respective subjects constituting the subject set to which the subject sample belongs, and the differences in waveform occurring among the optical spectra of the respective subjects constituting the subject set”.). The continuous emission spectrum of the partial regions from subjects constituting a subject set are the spectral data of the respective subjects, and
wavelength selection processing of selecting a plurality of specific wavelengths from wavelength ranges of the acquired first spectral data and second spectral data ([0072] lines 1-6, “In this case, the spectroscopic optical part 2a has the same arrangement as that of a normal camera that receives emitted light L1 of all wavelength ranges emitted from a subject sample and performs spectral separation, for example, into three or more (or four or more) component lights having mutually different central wavelengths”), and
in the wavelength selection processing, the plurality of specific wavelengths are selected based on a sensing sensitivity ([0141] “As the wavelength values of the initial sensitivity functions .lambda..sup.0.sub.10, .lambda..sup.0.sub.20, and .lambda..sup.0.sub.30, characteristic wavelengths are selected from the spectral data of the optical spectra. Though in many cases, this characteristic wavelength value is a wavelength value for which the spectral intensity I takes on a maximum value or a minimum value, it is not restricted in particular as long as it is a value that reflects the physical state or chemical state differences to be observed among the respective subjects making up the subject set, and may be a wavelength value at a shoulder portion of an optical spectrum. By determining the above-mentioned wavelength values .lambda..sub.A, .lambda..sub.B, and .lambda..sub.C, bandpass-type initial sensitivity functions .lambda..sup.0.sub.10, .lambda..sup.0.sub.20, and .lambda..sup.0.sub.30 are prepared.”)
Matsumoto does not teach the apparatus comprising: which is a normalized difference in a feature amount between the first spectral data and the second spectral data.
Nagata teaches an analogous apparatus, which is a normalized (spectrum normalization unit 121) difference ([0058] “The magnitude of the deviation amount (deviation surface-area) calculated using RMS indicates how much the characteristic shapes of the spectral data match with each other. Therefore, when the deviation amount is sufficiently small, it can be determined that the compared pixels belong to the same segment.”) in a feature amount between the first spectral data and the second spectral data ([0089] “In the processing illustrated in FIG. 6, the spectral data of each clustered segment by the image clustering unit 122 of FIG. 1 is normalized based on the spectral data relating to the illumination light source stored in the light source DB 140 of FIG. 1. Then, the normalized spectral data is compared with each other, and the segments having a small difference in their normalized spectral data are reintegrated as the same segment.”; Figs. 5A and 5B).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Matsumoto to include the normalized difference in a feature because it would yield predictable results, such as having a normalized comparison between spectral data.
Regarding claim 2, Matsumoto in view of Nagata teaches The data processing apparatus according to claim 1, wherein the feature amount is spectral reflectance (Nagata: [0041] “The spectral estimation unit 241 performs spectral estimation for estimating the spectral data relating to spectral reflectance based on the multiband image data stored in the multiband image data storage unit 230. Based on the estimated spectral data, the spectral image data is generated.”) or spectral intensity (Nagata: [0044] “FIG. 3 is a graph illustrating an operation of the spectrum normalization unit 121 illustrated in FIG. 1. In FIG. 3, the vertical axis represents relative spectral luminance, and the horizontal axis represents wavelength (nm). The respective curves 301 to 303 have different spectral intensities.”).
Regarding claim 3, Matsumoto in view of Nagata teaches The data processing apparatus according to claim 1, wherein at least one specific wavelength among the plurality of specific wavelengths is a wavelength at which the difference in the feature amount is largest (Nagata: Figs. 5A and 5B; [0065] lines 4-8, “if it is determined in step S105 that the deviation surface-area is not smaller than the first threshold Th1 (i.e., that the deviation surface-area is equal to or greater than the first threshold Th1) (NO in step S105), the processing proceeds to step S110.”). One of ordinary skill in the art would recognize that for the range of measured wavelengths (plurality of specific wavelengths) and where the deviation is greater than the threshold, there exists at least one specific wavelength at which the difference is the largest.
Regarding claim 4, Matsumoto in view of Nagata teaches The data processing apparatus according to claim 1, wherein, in the data acquisition processing, data is acquired from a device (Matsumoto: spectroscopic optical part 2a) that acquires two-dimensional spectrum data of wavelengths (Matsumoto: [0090] First, emitted light L1 of all wavelength ranges emitted from the subject sample 10 is received by the spectroscopic optical part 2a. An entire image P.sub.0 or a partial region E1 of the subject sample 10 may be received as the image received at this point. A continuous emission spectrum .lambda..sup.0.sub.S0 of the received image is decomposed into n (n.gtoreq.3) component lights having mutually different central wavelengths, .lambda..sup.0.sub.S1, .lambda..sup.0.sub.S2, . . . .lambda..sup.0.sub.Sn, by means of n (n.gtoreq.3) optical filters (ST1).) more than the selected plurality of specific wavelengths (Matsumoto: [0177] lines 7-11, “the sensitivity functions may be functions that divide a wavelength range of certain width within the visible range into three regions and incorporate these regions as shown in FIG. 21. Also, the functions may have broad band characteristics as shown in FIG. 22.”). One of ordinary skill in the art would recognize that the spectral data of two-dimensional image (image P.sub.0 or partial region E1) is the two-dimensional spectrum data, and that the wavelengths (“emitted light L1 of all wavelength ranges emitted from the subject sample 10”) are more than the selected plurality of specific wavelengths (wavelength ranges of certain width).
Regarding claim 5, Matsumoto in view of Nagata teaches The data processing apparatus according to claim 4, wherein the processor performs display processing of displaying a visible image (Matsumoto: output image P1) showing the spectrum data on a display (Matsumoto: monitor 5) based on the spectrum data (Matsumoto: Figs. 6-14).
Regarding claim 6, Matsumoto in view of Nagata teaches The data processing apparatus according to claim 5, wherein, in the data acquisition processing, a first region of the first subject (Matsumoto: partial region E1) and a second region of the second subject (Matsumoto: partial region E2; [0024] lines 1-9, “with this invention's system enabling chromaticity measurement in the visible and invisible range, three or more sensitivity functions are determined based on the correlation between the physical state or chemical state differences to be observed that occur among respective subjects constituting the subject set to which the subject sample belongs, and the differences in waveform occurring among the optical spectra of the respective subjects constituting the subject set”.) are specified on the display based on a user indication (Matsumoto: Figs. 6-14; [0088] “As the hardware arrangement, the camera part 2 may be arranged as a separate unit, the image processing part 4 may be arranged from a normal, commercially-available personal computer, software for controlling the above-mentioned calculation and processing functions of this invention, and additional hardware, and commercially available units may be used as the monitor 5 and printer 6.”), and the first spectral data and the second spectral data in the first region and the second region are acquired (Matsumoto: Fig. 6, E1 and E2; [0090] lines 1-5, “First, emitted light L1 of all wavelength ranges emitted from the subject sample 10 is received by the spectroscopic optical part 2a. An entire image P.sub.0 or a partial region E1 of the subject sample 10 may be received as the image received at this point”).
Regarding claim 7, Matsumoto in view of Nagata teaches The data processing apparatus according to claim 6, wherein, in the data acquisition processing, representative values of the feature amounts in the first region and the second region are calculated to acquire the first spectral data and the second spectral data (Matsumoto: [0121] “As an example of the image P1 to be output to the monitor 5 and/or printer 6 the pseudo color image RGB.sub.iv, an optical spectrum graph G1 showing profiles LE1 and LE2 of reflectance R at partial regions E1 and E2 of the subject, and a table T1 of the numerical data of L.sub.iv*, a.sub.iv*, b.sub.iv*, H.sub.iv*, C.sub.iv*, r.sub.iv, g.sub.iv, etc., may be displayed in combination as shown in FIG. 6”.).
Regarding claim 8, Matsumoto in view of Nagata teaches The data processing apparatus according to claim 7, wherein the representative value is an average value (Matsumoto: [0183] “An image of each sample was taken by the system enabling chromaticity measurement in the visible and invisible ranges, and an averaged reflectance value of a fixed area (1 cm.sup.2) was determined for a portion of each sample. The results are shown in FIG. 23 and FIG. 24.”), a median value, or a most frequent value.
Regarding claim 15, Matsumoto teaches A data processing method (Abstract) to be executed a processor ([0077] lines 1-9, “This central control unit has a CPU, a ROM, and a RAM (none of which are shown). The CPU of the central control unit comprises a microprocessor, etc., and performs the various computing processes (image signal generation process (preprocess), vector conversion process, image forming process, color specification process, process for determining sensitivity functions, process for determining a matrix M, and control of the entire system enabling chromaticity measurement in the visible and invisible range)”), comprising:
a data acquisition (Fig. 1) step of acquiring first spectral data of a first subject ([0090] lines 11-9, “First, emitted light L1 of all wavelength ranges emitted from the subject sample 10 is received by the spectroscopic optical part 2a. An entire image P.sub.0 or a partial region E1 of the subject sample 10 may be received as the image received at this point. A continuous emission spectrum .lambda..sup.0.sub.S0 of the received image is decomposed into n (n.gtoreq.3) component lights having mutually different central wavelengths, .lambda..sup.0.sub.S1, .lambda..sup.0.sub.S2, . . . .lambda..sup.0.sub.Sn, by means of n (n.gtoreq.3) optical filters (ST1).”) and second spectral data of a second subject ([0024] lines 1-9, “with this invention's system enabling chromaticity measurement in the visible and invisible range, three or more sensitivity functions are determined based on the correlation between the physical state or chemical state differences to be observed that occur among respective subjects constituting the subject set to which the subject sample belongs, and the differences in waveform occurring among the optical spectra of the respective subjects constituting the subject set”.). The continuous emission spectrum of the partial regions from subjects constituting a subject set are the spectral data of the respective subjects; and
a wavelength selection step of selecting a plurality of specific wavelengths from wavelength ranges of the acquired first spectral data and second spectral data ([0072] lines 1-6, “In this case, the spectroscopic optical part 2a has the same arrangement as that of a normal camera that receives emitted light L1 of all wavelength ranges emitted from a subject sample and performs spectral separation, for example, into three or more (or four or more) component lights having mutually different central wavelengths”), in which the plurality of specific wavelengths are selected based on a difference in a feature amount between the first spectral data and the second spectral data ([0057] lines 3-9, “In step S103, simultaneously, the image clustering unit 122 (deviation amount calculation unit 1221) determines the sign of the difference in equation (1), that is, the difference between the spectral data of the reference pixel with the reference pixel number p and the spectral data of the pixel with the pixel number i of the segment determination target.”), wherein, in the wavelength selection processing, the plurality of specific wavelengths are selected based on a sensing sensitivity ([0141] “As the wavelength values of the initial sensitivity functions .lambda..sup.0.sub.10, .lambda..sup.0.sub.20, and .lambda..sup.0.sub.30, characteristic wavelengths are selected from the spectral data of the optical spectra. Though in many cases, this characteristic wavelength value is a wavelength value for which the spectral intensity I takes on a maximum value or a minimum value, it is not restricted in particular as long as it is a value that reflects the physical state or chemical state differences to be observed among the respective subjects making up the subject set, and may be a wavelength value at a shoulder portion of an optical spectrum. By determining the above-mentioned wavelength values .lambda..sub.A, .lambda..sub.B, and .lambda..sub.C, bandpass-type initial sensitivity functions .lambda..sup.0.sub.10, .lambda..sup.0.sub.20, and .lambda..sup.0.sub.30 are prepared.”)
Matsumoto does not teach the method, comprising: which is a normalized difference in a feature amount between the first spectral data and the second spectral data.
Nagata teaches an analogous method, which is a normalized (spectrum normalization unit 121) difference ([0058] “The magnitude of the deviation amount (deviation surface-area) calculated using RMS indicates how much the characteristic shapes of the spectral data match with each other. Therefore, when the deviation amount is sufficiently small, it can be determined that the compared pixels belong to the same segment.”) in a feature amount between the first spectral data and the second spectral data ([0089] “In the processing illustrated in FIG. 6, the spectral data of each clustered segment by the image clustering unit 122 of FIG. 1 is normalized based on the spectral data relating to the illumination light source stored in the light source DB 140 of FIG. 1. Then, the normalized spectral data is compared with each other, and the segments having a small difference in their normalized spectral data are reintegrated as the same segment.”; Figs. 5A and 5B).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Matsumoto to include the normalized difference in a feature because it would yield predictable results, such as having a normalized comparison between spectral data.
Regarding claim 16, Matsumoto in view of Nagata teaches The data processing method according to claim 15, wherein the feature amount is spectral reflectance (Nagata: [0041] “The spectral estimation unit 241 performs spectral estimation for estimating the spectral data relating to spectral reflectance based on the multiband image data stored in the multiband image data storage unit 230. Based on the estimated spectral data, the spectral image data is generated.”) or spectral intensity (Nagata: [0044] “FIG. 3 is a graph illustrating an operation of the spectrum normalization unit 121 illustrated in FIG. 1. In FIG. 3, the vertical axis represents relative spectral luminance, and the horizontal axis represents wavelength (nm). The respective curves 301 to 303 have different spectral intensities.”).
Regarding claim 17, Matsumoto in view of Nagata teaches The data processing method according to claim 15, wherein the at least one specific wavelength among the plurality of specific wavelengths is a wavelength at which the difference in the feature amount is largest (Nagata: Figs. 5A and 5B; [0065] lines 4-8, “if it is determined in step S105 that the deviation surface-area is not smaller than the first threshold Th1 (i.e., that the deviation surface-area is equal to or greater than the first threshold Th1) (NO in step S105), the processing proceeds to step S110.”). One of ordinary skill in the art would recognize that for the range of measured wavelengths (plurality of specific wavelengths) and where the deviation is greater than the threshold, there exists at least one specific wavelength at which the difference is the largest.
Regarding claim 18, Matsumoto in view of Nagata teaches The data processing method according to claim 15, wherein, in the data acquisition step, data is acquired from a device (Matsumoto: spectroscopic optical part 2a) that acquires spectrum data of wavelengths (Matsumoto: [0090] First, emitted light L1 of all wavelength ranges emitted from the subject sample 10 is received by the spectroscopic optical part 2a. An entire image P.sub.0 or a partial region E1 of the subject sample 10 may be received as the image received at this point. A continuous emission spectrum .lambda..sup.0.sub.S0 of the received image is decomposed into n (n.gtoreq.3) component lights having mutually different central wavelengths, .lambda..sup.0.sub.S1, .lambda..sup.0.sub.S2, . . . .lambda..sup.0.sub.Sn, by means of n (n.gtoreq.3) optical filters (ST1).) more than the selected plurality of specific wavelengths (Matsumoto: [0177] lines 7-11, “the sensitivity functions may be functions that divide a wavelength range of certain width within the visible range into three regions and incorporate these regions as shown in FIG. 21. Also, the functions may have broad band characteristics as shown in FIG. 22.”). One of ordinary skill in the art would recognize that the spectral data of two-dimensional image (image P.sub.0 or partial region E1) is the two-dimensional spectrum data, and that the wavelengths (“emitted light L1 of all wavelength ranges emitted from the subject sample 10”) are more than the selected plurality of specific wavelengths (wavelength ranges of certain width).
Regarding claim 19, Matsumoto in view of Nagata teaches The data processing method according to claim 18, further comprising: a step of displaying a visible image (Matsumoto: output image P1) showing the spectrum data on a display (Matsumoto: monitor 5) based on the spectrum data (Matsumoto: Figs. 6-14).
Regarding claim 20, Matsumoto in view of Nagata teaches The data processing method according to claim 19, wherein, in the data acquisition step, a first region of the first subject (Matsumoto: partial region E1) and a second region of the second subject (Matsumoto: partial region E2; [0024] lines 1-9, “with this invention's system enabling chromaticity measurement in the visible and invisible range, three or more sensitivity functions are determined based on the correlation between the physical state or chemical state differences to be observed that occur among respective subjects constituting the subject set to which the subject sample belongs, and the differences in waveform occurring among the optical spectra of the respective subjects constituting the subject set”.) are specified on the display based on a user indication (Matsumoto: Figs. 6-14; [0088] “As the hardware arrangement, the camera part 2 may be arranged as a separate unit, the image processing part 4 may be arranged from a normal, commercially-available personal computer, software for controlling the above-mentioned calculation and processing functions of this invention, and additional hardware, and commercially available units may be used as the monitor 5 and printer 6.”), and the first spectral data and the second spectral data in the first region and the second region are acquired (Matsumoto: Fig. 6, E1 and E2; [0090] lines 1-5, “First, emitted light L1 of all wavelength ranges emitted from the subject sample 10 is received by the spectroscopic optical part 2a. An entire image P.sub.0 or a partial region E1 of the subject sample 10 may be received as the image received at this point”).
Regarding claim 21, Matsumoto in view of Nagata teaches The data processing method according to claim 20, wherein, in the data acquisition step, representative values of the feature amounts in the first region and the second region are calculated to acquire the first spectral data and the second spectral data (Matsumoto: [0121] “As an example of the image P1 to be output to the monitor 5 and/or printer 6 the pseudo color image RGB.sub.iv, an optical spectrum graph G1 showing profiles LE1 and LE2 of reflectance R at partial regions E1 and E2 of the subject, and a table T1 of the numerical data of L.sub.iv*, a.sub.iv*, b.sub.iv*, H.sub.iv*, C.sub.iv*, r.sub.iv, g.sub.iv, etc., may be displayed in combination as shown in FIG. 6”.).
Regarding claim 22, Matsumoto in view of Nagata teaches The data processing method according to claim 21, wherein the representative value is an average value (Matsumoto: [0183] “An image of each sample was taken by the system enabling chromaticity measurement in the visible and invisible ranges, and an averaged reflectance value of a fixed area (1 cm.sup.2) was determined for a portion of each sample. The results are shown in FIG. 23 and FIG. 24.”), a median value, or a most frequent value.
Regarding claim 27, Matsumoto in view of Nagata teaches A non-transitory, computer-readable tangible recording medium on which a program for causing, when read by a computer, the computer to execute the data processing method according to claim 15 is recorded (Matsumoto: [0124] The respective structural units (respective devices) of FIG. 1, which configure the above-described image processing apparatus 100, and the respective steps of FIGS. 4 and 6, which illustrate the image processing method performed by the image processing apparatus 100, can be realized by executing a program stored on a central processing unit (CPU), random access memory (RAM), read only memory (ROM) and the like of a computer. This program and a computer-readable recording medium on which such a program is recorded are also included in the present invention.).
Allowable Subject Matter
Claims 9-14 and 23-26 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and rewritten to overcome the rejection under 35 USC 101 (see details above).
The closest are was considered.
Regarding claim 9, none of the prior art teaches the data processing apparatus, wherein, in the wavelength selection processing, a first wavelength at which the difference in the feature amount between the first spectral data and the second spectral data is largest, and a second wavelength at which the difference in the feature amount between the first spectral data and the second spectral data is largest or maximum in a different wavelength range separated from the first wavelength by a wavelength difference equal to or larger than a predetermined difference are selected as the specific wavelengths.
Claim 10 provides further limitations to claim 9, on which it depends.
Regarding claim 11, none of the prior art teaches the data processing apparatus, wherein, in the wavelength selection processing, in a case in which a reference wavelength at which the feature amounts of the acquired first spectral data and second spectral data match each other is present, a third wavelength at which the difference in the feature amount between the first spectral data and the second spectral data is largest on a short wave side with respect to the reference wavelength, and a fourth wavelength at which the difference in the feature amount between the first spectral data and the second spectral data is largest on a long wave side with respect to the reference wavelength are selected as the specific wavelengths.
Regarding claim 12, none of the prior art teaches the data processing apparatus, wherein, in the wavelength selection processing, in a case in which two or more reference wavelengths at which the feature amounts of the acquired first spectral data and second spectral data match each other are present, a fifth wavelength at which the difference in the feature amount between the first spectral data and the second spectral data is largest between the two or more reference wavelengths is selected as one of the plurality of specific wavelengths.
Regarding claim 13, none of the prior art teaches the data processing apparatus, wherein the sensing sensitivity is calculated by the following expression
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60
438
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Regarding claim 14, none of the prior art teaches the data processing apparatus, wherein the sensing sensitivity is calculated by the following expression
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56
606
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Regarding claim 23, none of the prior art teaches the method, wherein, in the wavelength selection step, a first wavelength at which the difference in the feature amount between the first spectral data and the second spectral data is largest, and a second wavelength at which the difference in the feature amount between the first spectral data and the second spectral data is largest or maximum in a different wavelength range separated from the first wavelength by a wavelength difference equal to or larger than a predetermined difference are selected as the specific wavelengths, respectively.
Claim 24 provides further limitations to claim 23, on which it depends.
Regarding claim 25, none of the prior art teaches the method, wherein, in the wavelength selection step, in a case in which a reference wavelength at which the feature amounts of the acquired first spectral data and second spectral data match each other is present, a third wavelength at which the difference in the feature amount between the first spectral data and the second spectral data is largest on a short wave side with respect to the reference wavelength, and a fourth wavelength at which the difference in the feature amount between the first spectral data and the second spectral data is largest on a long wave side with respect to the reference wavelength are selected as the specific wavelengths, respectively.
Regarding claim 26, none of the prior art teaches the method, wherein, in the wavelength selection step, in a case in which two or more reference wavelengths at which the feature amounts of the acquired first spectral data and second spectral data match each other are present, a fifth wavelength at which the difference in the feature amount between the first spectral data and the second spectral data is largest between the two or more reference wavelengths is selected as one of the plurality of specific wavelengths.
Conclusion
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/B.B.G./Examiner,
Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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