Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
Claim 21 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 20 provides dual-wavelength emissions. Claim 21 using training data set under either single wavelength emissions, dual-wavelength emissions or both. Claiming the single or dual wavelength emissions in the alternative allows for a case where a dual wavelength emission is provided to the target but the classification only relies upon single wavelength emissions, which appear non-suitable for the task. It isn’t clear if the applicant intended for claim 20 to have either a single wavelength or dual wavelength emission, similar to claim 2 or if claim 21 should remove the limitation regarding the single wavelength data set.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4-10, 12, 13 and 15-21 are rejected under 35 U.S.C. 102(a)(1)& (a)(2) as being anticipated by Boctor et al. (U.S. Patent No. 9,723,995, hereinafter Boctor.
With respect to Claim 1, Boctor discloses [see fig 1a unless otherwise noted] method for identifying biological markers using acoustic frequency response, the method comprising:
obtaining acoustic information [via 24] from a received signal [ultrasound probe 22 receives an ultrasound signal] from one or more chromophores at a target location [24; both the biological materials in column 7, lines 46-58 and the contrast agents, column 7, lines 34-45 contain chromophores];
classifying the acoustic information to provide classified acoustic information based on photoacoustic imaging response using a trained classifier [machine learning to classify as normal or abnormal; column 6, lines 54-63]; and
providing an indication of the classified acoustic information one or more chromophores at at the target location [column 7, lines 34-45 indicates features to the user about the target location in the form of a concentration map].
With respect to Claim 2, Boctor discloses further comprising providing a dual-wavelength emission to the target location. column 5, lines 14-21 shows multiple wavelengths.
With respect to Claim 3, Boctor discloses further comprising training a classifier to produce the trained classifier using a training data set of known photoacoustic-sensitive materials under a single wavelength emission conditions, a dual-wavelength emission conditions, or both. Column 5, lines 14-22 shows that you may use multiple wavelengths and the machine learning method in column 6, lines 54-64 would have to have been trained using a training data set of known photoacoustic-sensitive materials in order for Boctor’s invention to function.
With respect to Claim 5, Boctor discloses that the training data set comprises biological material [tissue types; column 7, lines 46-58] and contrast agents [column 7, lines 34-45, ICG].
With respect to Claim 6, Boctor discloses that the biological material comprises bone [column 8, line 59].
With respect to Claim 7, Boctor discloses that the contrast agents comprise indocyanine green (ICG) [column 7, lines 34-45].
With respect to Claim 8, Boctor discloses comprising determining a concentration of each material within the target location based on the classified acoustic information. Column 7, lines 33-57 uses contrast concentration maps to classify tissue types.
With respect to Claim 9, Boctor discloses further comprising alternative processing steps of the collected acoustic frequency information from two wavelength emission including difference of the log compressed spectral obtained from each wavelength emission [column 11, line 58-column 12, line 26 and column 5, line 22-39].
With respect to Claim 10, Boctor discloses further comprising of the use of a single light pulse containing two wavelengths for characterization of photoacoustic-sensitive materials through analysis of the frequency information of the acoustic response. See column 5, lines 18-20, light pulse with multiple wavelengths.
With respect to Claim 11, Boctor discloses in addition to single or two wavelength emission, the use of a single pulse comprising two wavelengths for excitation. See column 5, lines 18-20
With respect to Claim 12, Boctor discloses a system for identifying biological markers using acoustic frequency response, the system comprising: a hardware processor [28]; a non-transitory computer readable medium [memory; column 5, lines 40-45] that stores instructions that when executed by the hardware processor perform a method comprising: obtaining acoustic information [via 22] from a received signal [from ultrasound probe 22] from one or more chromophores at a target location [24]; classifying the acoustic information to provide classified acoustic information based on photoacoustic imaging response using a trained classifier [column 6, lines 54-63]; and providing an indication of the classified acoustic information of the one or more chromophores at the target location [column 7, lines 34-45 indicates features to the user about the target location in the form of a concentration map].
With respect to Claim 13, Boctor discloses that the method further comprises providing a single wavelength radiation emission or a dual-wavelength emission to the target location. See column 5, lines 14-21.
With respect to Claim 15, Boctor discloses that the method further comprises training a classifier to produce the trained classifier using a training data set of known photoacoustic-sensitive materials under a single wavelength emission conditions, a dual-wavelength emission conditions, or both.
Column 5, lines 14-22 shows that you may use multiple wavelengths and the machine learning method in column 6, lines 54-64 would have to have been trained using a training data set of known photoacoustic-sensitive materials in order for Boctor’s invention to function.
With respect to Claim 16, Boctor discloses that the training data set comprises biological material [tissue types; column 7, lines 46-58] and contrast agents [column 7, lines 34-45, ICG].
With respect to Claim 17, Boctor discloses that the biological material comprises bone [column 8, line 59].
With respect to Claim 18, Boctor discloses that the contrast agents comprise indocyanine green (ICG) [column 7, lines 34-45].
With respect to Claim 19, Boctor discloses that the method further comprises determining a concentration of each material within the target location based on the classified acoustic information. Column 7, lines 33-57 uses contrast concentration maps to classify tissue types.
With respect to Claim 20, Boctor discloses a method for identifying biological markers using acoustic frequency response, the method comprising: providing a dual-wavelength emission [column 5, lines 12-21 shows multiple wavelengths] to the target location [24]; obtaining acoustic information from a received signal from a target location [ultrasound probe receives an ultrasound signal from location 24]; classifying the acoustic information to provide classified acoustic information based on photoacoustic imaging response using a trained classifier [machine learning to classify as normal or abnormal; column 6, lines 54-63]; providing an indication of the classified acoustic information at the target location [column 7, lines 34-45 indicates features to the user about the target location in the form of a concentration map]; and alternatively processing the classified acoustic frequency information by obtaining a difference of the spectra obtained from each wavelength emission and obtaining a difference of a log compressed spectral obtained from each wavelength emission; and cross correlation between the spectra obtained from each wavelength emission [column 11, line 58-column 12, line 26 and column 5, line 22-39].
With respect to Claim 21, Boctor discloses further comprising training a classifier to produce the trained classifier using a training data set of known photoacoustic-sensitive materials under a single wavelength emission conditions, a dual-wavelength emission conditions, or both. Column 5, lines 14-22 shows that you may use multiple wavelengths and the machine learning method in column 6, lines 54-64 would have to have been trained using a training data set of known photoacoustic-sensitive materials in order for Boctor’s invention to function.
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 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Boctor in view of Semmlow (U.S. Publication No. 2010/0094152, hereinafter Semmlow).
With respect to Claims 3 and 14, Boctor uses support vector machine learning [column 6, lines 54-64] rather than a neural network, as claimed.
Semmlow shows that both support vector and neural networks are common, well-known types of machine learning methods used to analyze photoacoustic signals. See para 60.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Boctor to use any known type of trained classifier, including a neural network.
Response to Arguments
Applicant's arguments filed 14 May 2026 have been fully considered but they are not persuasive.
On page 6, the applicant argues that Boctor does not discloses obtaining acoustic information from a received signal from one or more chromophores at a target location.
Boctor teaches using photoacoustics to analyze biological materials using contrast agents. The applicant doesn’t dispute this as there is no argument against the rejection of claims 5-7. Chromophores are just molecules that absorb light. Boctor uses the exact same chromophores as the applicant. See applicant’s specification para 45 indicating that the biological materials and contrast agents are chromophores. If Boctor’s biological materials and contract agents didn’t absorb the light delivered by 32, the device wouldn’t function.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX T DEVITO whose telephone number is (571)270-7551. The examiner can normally be reached 12pm- 8 pm EST M-S.
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/ALEX T DEVITO/Examiner, Art Unit 2855 /JOHN E BREENE/Supervisory Patent Examiner, Art Unit 2855
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