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
The following is a quotation of 35 U.S.C. 112(b):
(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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 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.
With respect to Claim 11, the scope of the intended benefits is not clear. Methods are claimed in terms of method steps.
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, 4-13 and 15-19 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 a target 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]; and
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].
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].
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 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 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.
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.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Breene can be reached at 571-272-4107. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from 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. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALEX T DEVITO/Examiner, Art Unit 2855
/JOHN E BREENE/Supervisory Patent Examiner, Art Unit 2855