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 13 and 22 are 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 13 recites limitation “a region corresponding to a lesion” in line 3. It is unclear the above lesion is a newly introduced different lesion or the same lesion as recited in claim 10 line 7, since claim 13 is dependent on claim 10.
Thus, the above limitation renders claim indefinite. For the purpose of examination, the above limitation is interpreted as the same lesion recited in claim 10.
Claim 22 recites limitation “a region corresponding to a lesion” in line 2. It is unclear the above lesion is a newly introduced different lesion or the same lesion as recited in claim 19 line 5, since claim 22 is dependent on claim 19.
Thus, the above limitation renders claim indefinite. For the purpose of examination, the above limitation is interpreted as the same lesion recited in claim 19.
Therefore, claim 13 and 22 are rejected under 35 U.S.C. 112(b), as being indefinite.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 19 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 and 6 of U.S. Patent No. 12,527,551 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because each and every limitation recited in the above claims of instant application 19/430,501 are being unpatentable over US 12,527,551 B2, which is detailed as following:
Instant application 19/187,415
Reference patent US 12,527,551 B2
[claim 19]
A method performed by one or more processors, the method comprising:
generating a plurality of types of ultrasound information based on transmission and reception of ultrasound waves with respect to a subject,
the plurality of types of ultrasound information including ultrasound image data generated in one or more imaging modes;
detecting and measuring a lesion based on the plurality of types of ultrasound information to obtain a measurement result; and
analyzing a state of the lesion based on the measurement result by applying at least a part of the plurality of types of ultrasound information to at least one learning model constructed by machine learning in advance, and
generating analysis information estimating a prognosis of the lesion.
[claim 1]
one or more processors configured to:
(although claim 1 is an apparatus claim, it also claims computer-implemented process)
generate a plurality of types of ultrasound information based on the reception signal,
an ultrasound transmission and reception unit configured to transmit and receive ultrasound waves to and from a subject and generate a reception signal; (this limitation is also claimed to describe the process of ultrasound transmission and reception.)
the ultrasound information including a plurality of types of ultrasound image data generated in different modes;
detect and measure a lesion based on the plurality of types of ultrasound information, to obtain a measurement result; and
analyze a state of the lesion based on the measurement result and by applying the plurality of types of ultrasound image data generated in the different modes to a learning model constructed by machine learning in advance for the different modes, and
generate analysis information estimating a prognosis of the lesion.
[claim 20]
The method according to claim 19,
wherein analyzing the state of the lesion includes
using at least one of: (a) a look-up table storing calculation results based on statistical information or a medical guideline for a mass lesion, or
(b) a statistical arithmetic calculation.
[claim 6]
The ultrasound diagnostic apparatus according to claim 1,
wherein the one or more processors are further configured to generate progression degree prediction information regarding a progression degree prediction of lesion,
by using (a) a look-up table that stores a result based on a statistical calculation result,
(b) a statistical arithmetic calculation,
Therefore, claim 19 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 and 6 of U.S. Patent No. 12,527,551 B2.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim 10, 11, 14 – 16, 19, 20 and 22 – 25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Noguchi et al. (US 2020/0170624 A1; published on 06/04/2020) (hereinafter "Noguchi").
Regarding claim 10, Noguchi discloses a non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a process ("The CPU 101 executes programs stored in the main storage device 102. The CPU 101 operates as function units (modules) that realize specific functions, respectively, by executing processes in accordance with the programs." [0025]) comprising:
generating a plurality of types of ultrasound information based on transmission and reception of ultrasound waves with respect to a subject ("The ultrasound diagnostic apparatus 100 outputs ultrasound to a subject, and generates a tomographic image (echo image) from the reflected ultrasound signal (echo signal)." [0023]), the plurality of types of ultrasound information including ultrasound image data generated in one or more imaging modes ("The image generator 121 generates a tomographic image by performing a scanning conversion process on the RF signal frame data." [0040]);
detecting and measuring a lesion based on the plurality of types of ultrasound information to obtain a measurement result ("The lesion detector 122 performs a detection process to detect a lesion from the tomographic image, and outputs the detection result as detection information. The lesion detector 122 stores, in the secondary storage device 103, the detection information corresponding to the tomographic image." [0041]; "The lesion analyzer 700 calculates the width and height of the lesion based on the border of the lesion ... The lesion analyzer 700 further analyzes the shape of the lesion border." [0082]); and
analyzing a state of the lesion based on the measurement result ("The lesion analyzer 700 determines whether a legion is benign or malignant, and the category thereof using lesion analysis results such as lesion size, lesion aspect ratio, and border shape, the lesion images, and the like." [0085]) by applying at least a part of the plurality of types of ultrasound information to at least one learning model constructed by machine learning in advance ("Examples of the analysis method includes a method using an estimation model generated based on a machine learning algorithm … In a case where a machine learning algorithm with teacher data is used, data composed of tomographic images, lesion detection results, and lesion analysis results may be used as learning data." [0085]), and generating analysis information estimating a prognosis of the lesion ("The lesion analyzer 700 determines whether a legion is benign or malignant, and the category thereof using lesion analysis results such as lesion size, lesion aspect ratio, and border shape, the lesion images, and the like." [0085]; "The discriminator 901 outputs the discrimination result regarding the lesion being benign or malignant, and the category of the lesion." [0088]).
Regarding claim 11, Noguchi discloses all claim limitations, as applied in claim 10, and further discloses wherein analyzing the state of the lesion includes using: (b) a statistical arithmetic calculation ("… such as the area and aspect ratio of the region corresponding to each possible lesion, the average value of the diagnosis map, the likelihood calculated by inputting the diagnosis map to a discriminator generated by machine learning, and the like." [0067]).
Regarding claim 13, Noguchi discloses all claim limitations, as applied in claim 10, and further discloses wherein the process further comprises: displaying, on a display unit ("The display unit 123 generates display data to present the tomographic image, the lesion detection result, and the like." [0042]), a region corresponding to a lesion by enclosing the region with a line ("(Display 1) presents a lesion 205 in the tomographic image 200 using the contour form." [[0073]), together with at least one of a lesion type, a category, or a stage as text information ("The analysis result display field 1030 is a field to display the analysis information corresponding to the tomographic image ..." [0099];l see Fig.10A, benign or malignant is shown in field 1030).
Regarding claim 14, Noguchi discloses all claim limitations, as applied in claim 10, and further discloses wherein the process further comprises: receiving an input for correcting a diagnostic result based on a doctor's judgment ("For example, in a case where the profile of the lesion is to be corrected, the user operates the detection result display field 1020 as illustrated in FIG. 10B. Specifically, the user sets control points to specify the profile of the lesion." [0102]); and
re-processing at least one of probability calculation or prognosis estimation based on the corrected diagnostic result ("The display unit 123 updates the detection information based on an input of the user. In this case, the display unit 123 may input the detection information back into the lesion analyzer 700 to analyze a lesion again." [0102]).
Regarding claim 15, Noguchi discloses all claim limitations, as applied in claim 10, and further discloses wherein the at least a part of the plurality of types of ultrasound information applied to the one learning model includes the ultrasound image data generated in the one or more imaging modes ("The image generator 121 generates a tomographic image by performing a scanning conversion process on the RF signal frame data." [0040]; "The lesion analyzer 700 inputs a tomographic image and a diagnosis map to the CNN 900 and calculates a feature amount." [0088]).
Regarding claim 16, Noguchi discloses all claim limitations, as applied in claim 10, and further discloses wherein analyzing the state of the lesion includes applying at least one of the measurement result or quantitative feature values derived from the measurement result to the at least one learning model ("The lesion analyzer 700 calculates the width and height of the lesion based on the border of the lesion ... The lesion analyzer 700 further analyzes the shape of the lesion border." [0082]; "Next, the lesion analyzer 700 inputs the feature amount, analysis information, and subject information to the discriminator 901." [0088]).
Regarding claim 19, Noguchi discloses a method performed by one or more processors ("The CPU 101 executes programs stored in the main storage device 102. The CPU 101 operates as function units (modules) that realize specific functions, respectively, by executing processes in accordance with the programs." [0025]), the method comprising:
generating a plurality of types of ultrasound information based on transmission and reception of ultrasound waves with respect to a subject ("The ultrasound diagnostic apparatus 100 outputs ultrasound to a subject, and generates a tomographic image (echo image) from the reflected ultrasound signal (echo signal)." [0023]), the plurality of types of ultrasound information including ultrasound image data generated in one or more imaging modes ("The image generator 121 generates a tomographic image by performing a scanning conversion process on the RF signal frame data." [0040]);
detecting and measuring a lesion based on the plurality of types of ultrasound information to obtain a measurement result ("The lesion detector 122 performs a detection process to detect a lesion from the tomographic image, and outputs the detection result as detection information. The lesion detector 122 stores, in the secondary storage device 103, the detection information corresponding to the tomographic image." [0041]; "The lesion analyzer 700 calculates the width and height of the lesion based on the border of the lesion ... The lesion analyzer 700 further analyzes the shape of the lesion border." [0082]); and
analyzing a state of the lesion based on the measurement result ("The lesion analyzer 700 determines whether a legion is benign or malignant, and the category thereof using lesion analysis results such as lesion size, lesion aspect ratio, and border shape, the lesion images, and the like." [0085]) by applying at least a part of the plurality of types of ultrasound information to at least one learning model constructed by machine learning in advance ("Examples of the analysis method includes a method using an estimation model generated based on a machine learning algorithm … In a case where a machine learning algorithm with teacher data is used, data composed of tomographic images, lesion detection results, and lesion analysis results may be used as learning data." [0085]), and generating analysis information estimating a prognosis of the lesion ("The lesion analyzer 700 determines whether a legion is benign or malignant, and the category thereof using lesion analysis results such as lesion size, lesion aspect ratio, and border shape, the lesion images, and the like." [0085]; "The discriminator 901 outputs the discrimination result regarding the lesion being benign or malignant, and the category of the lesion." [0088]).
Regarding claim 20, Noguchi discloses all claim limitations, as applied in claim 19, and further discloses wherein analyzing the state of the lesion includes using: (b) a statistical arithmetic calculation ("… such as the area and aspect ratio of the region corresponding to each possible lesion, the average value of the diagnosis map, the likelihood calculated by inputting the diagnosis map to a discriminator generated by machine learning, and the like." [0067]).
Regarding claim 22, Noguchi discloses all claim limitations, as applied in claim 19, and further discloses wherein the method further comprises: displaying, on a display unit ("The display unit 123 generates display data to present the tomographic image, the lesion detection result, and the like." [0042]), a region corresponding to a lesion by enclosing the region with a line ("(Display 1) presents a lesion 205 in the tomographic image 200 using the contour form." [[0073]), together with at least one of a lesion type, a category, or a stage as text information ("The analysis result display field 1030 is a field to display the analysis information corresponding to the tomographic image ..." [0099];l see Fig.10A, benign or malignant is shown in field 1030).
Regarding claim 23, Noguchi discloses all claim limitations, as applied in claim 19, and further discloses wherein the method further comprises: receiving an input for correcting a diagnostic result based on a doctor's judgment ("For example, in a case where the profile of the lesion is to be corrected, the user operates the detection result display field 1020 as illustrated in FIG. 10B. Specifically, the user sets control points to specify the profile of the lesion." [0102]); and
re-processing at least one of probability calculation or prognosis estimation based on the corrected diagnostic result ("The display unit 123 updates the detection information based on an input of the user. In this case, the display unit 123 may input the detection information back into the lesion analyzer 700 to analyze a lesion again." [0102]).
Regarding claim 24, Noguchi discloses all claim limitations, as applied in claim 19, and further discloses wherein the at least a part of the plurality of types of ultrasound information applied to the one learning model includes the ultrasound image data generated in the one or more imaging modes ("The image generator 121 generates a tomographic image by performing a scanning conversion process on the RF signal frame data." [0040]; "The lesion analyzer 700 inputs a tomographic image and a diagnosis map to the CNN 900 and calculates a feature amount." [0088]).
Regarding claim 25, Noguchi discloses all claim limitations, as applied in claim 19, and further discloses wherein analyzing the state of the lesion includes applying at least one of the measurement result or quantitative feature values derived from the measurement result to the at least one learning model ("The lesion analyzer 700 calculates the width and height of the lesion based on the border of the lesion ... The lesion analyzer 700 further analyzes the shape of the lesion border." [0082]; "Next, the lesion analyzer 700 inputs the feature amount, analysis information, and subject information to the discriminator 901." [0088]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 12 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Noguchi, as applied in claim 10 and 19 respectively, and further in view of Grimm et al. (US 2018/0061077 A1; published on 03/01/2018) (hereinafter "Grimm").
Regarding claim 12, Noguchi teaches all claim limitations, as applied in claim 10, except wherein analyzing the state of the lesion includes comprehensively judging the analysis information and information obtained from at least one other diagnostic apparatus different from an ultrasound diagnostic apparatus, using multimodal artificial intelligence.
However, in the same field of endeavor, Grimm teaches wherein analyzing the state of the lesion includes comprehensively judging the analysis information and information obtained from at least one other diagnostic apparatus different from an ultrasound diagnostic apparatus ("In the example of the process flow shown in FIG. 1, measurement data MD1, MD2, MD3, MD4, MD5 from different measurements Ia, Ib, Ic, Id, Ie is firstly transferred in method step II. These measurements Ia, Ib, Ic, Id, Ie may for example be performed on different devices, e.g. in method step Ia on an MRT device, in method step Ib on a CT device, in method step Ic on an ultrasound device ..." [0112]; "The high-dimensional first parameter space PR1, in which the value tuple TP1 which is formed from the individual measurement values of the measurement data MD1, MD2, MD3, MD4, MD5, is then analyzed in step III." [0115]), using multimodal artificial intelligence ("In particular, the said high-dimensional, multi-parametric patient data or medical measurement data can also be analyzed with the aid of machine learning techniques ... to classification into clinically relevant classes (e.g. benign, malignant tumor with expected tumor genotype) ..." [0101]).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to modify machine learning based lesion analysis as taught by Noguchi with additional image data from different devices in machine learning model for analysis as taught by Grimm. By using multimodal measurements-based machine learning model, it is possible to "simplify the consideration of a variety of measurement data in diagnostics" (see Grimm; [0006]).
Regarding claim 21, Noguchi teaches all claim limitations, as applied in claim 19, except wherein analyzing the state of the lesion includes comprehensively judging the analysis information and information obtained from at least one other diagnostic apparatus different from an ultrasound diagnostic apparatus, using multimodal artificial intelligence.
However, in the same field of endeavor, Grimm teaches wherein analyzing the state of the lesion includes comprehensively judging the analysis information and information obtained from at least one other diagnostic apparatus different from an ultrasound diagnostic apparatus ("In the example of the process flow shown in FIG. 1, measurement data MD1, MD2, MD3, MD4, MD5 from different measurements Ia, Ib, Ic, Id, Ie is firstly transferred in method step II. These measurements Ia, Ib, Ic, Id, Ie may for example be performed on different devices, e.g. in method step Ia on an MRT device, in method step Ib on a CT device, in method step Ic on an ultrasound device ..." [0112]; "The high-dimensional first parameter space PR1, in which the value tuple TP1 which is formed from the individual measurement values of the measurement data MD1, MD2, MD3, MD4, MD5, is then analyzed in step III." [0115]), using multimodal artificial intelligence ("In particular, the said high-dimensional, multi-parametric patient data or medical measurement data can also be analyzed with the aid of machine learning techniques ... to classification into clinically relevant classes (e.g. benign, malignant tumor with expected tumor genotype) ..." [0101]).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to modify machine learning based lesion analysis as taught by Noguchi with additional image data from different devices in machine learning model for analysis as taught by Grimm. By using multimodal measurements-based machine learning model, it is possible to "simplify the consideration of a variety of measurement data in diagnostics" (see Grimm; [0006]).
Claim 17, 18, 26 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Noguchi, as applied in claim 16, 10, 25 and 19 respectively, and further in view of Addanki (US 2022/0254490 A1; published on 08/11/2022).
Regarding claim 17, Noguchi teaches all claim limitations, as applied in claim 16, and Noguchi further teaches wherein the at least one learning model includes: (a) a convolutional neural network configured to analyze the ultrasound image data ("The lesion analyzer 700 includes a convolutional neural network (CNN) 900 …" [0087]).
Although Noguchi fails to teach using (b) a recurrent neural network or a long short-term memory network configured to analyze time-series information of the lesion, Noguchi encourages using multiple machine learning algorithms ("The estimation model may be generated by combining a plurality of machine learning algorithms." [0085]).
In addition, in the same field of endeavor, Addanki teaches wherein the at least one learning model includes: (a) a convolutional neural network configured to analyze the ultrasound image data ("For example, the machine learning model can include a neural network (NN), such as a convolutional neural network (CNN) …" [0009]); and
(b) a recurrent neural network or a long short-term memory network configured to analyze time-series information of the lesion ("… recurrent neural network (RNN), long/short term memory network (LSTM) … and so forth." [0009]).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to modify machine learning based lesion analysis as taught by Noguchi with additional image data over time in machine learning model for analysis as taught by Addanki. Doing so would make it possible "to enable medical service providers to tailor therapeutic strategies to individual patients depending on their respective disease characteristics and rate of progression" (see Addanki; [0041]).
Regarding claim 18, Noguchi teaches all claim limitations, as applied in claim 10, except wherein estimating the prognosis of the lesion includes predicting a progression of the lesion based on continuous observation data of the lesion over time.
However, in the same field of endeavor, Addanki teaches wherein estimating the prognosis of the lesion includes predicting a progression of the lesion based on continuous observation data of the lesion over time ("The additional image data can represent diseased tissue at a later point in time relative to initial image data analyzed by the data processing device 120. The additional image data can enable the data processing device 120 to update the prediction of the rate of disease progression for the patient over time." [0045]).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to modify machine learning based lesion analysis as taught by Noguchi with additional image data over time in machine learning model for analysis as taught by Addanki. Doing so would make it possible "to enable medical service providers to tailor therapeutic strategies to individual patients depending on their respective disease characteristics and rate of progression" (see Addanki; [0041]).
Regarding claim 26, Noguchi teaches all claim limitations, as applied in claim 25, and Noguchi further teaches wherein the at least one learning model includes: (a) a convolutional neural network configured to analyze the ultrasound image data ("The lesion analyzer 700 includes a convolutional neural network (CNN) 900 …" [0087]).
Although Noguchi fails to teach using (b) a recurrent neural network or a long short-term memory network configured to analyze time-series information of the lesion, Noguchi encourages using multiple machine learning algorithms ("The estimation model may be generated by combining a plurality of machine learning algorithms." [0085]).
In addition, in the same field of endeavor, Addanki teaches wherein the at least one learning model includes: (a) a convolutional neural network configured to analyze the ultrasound image data ("For example, the machine learning model can include a neural network (NN), such as a convolutional neural network (CNN) …" [0009]); and
(b) a recurrent neural network or a long short-term memory network configured to analyze time-series information of the lesion ("… recurrent neural network (RNN), long/short term memory network (LSTM) … and so forth." [0009]).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to modify machine learning based lesion analysis as taught by Noguchi with additional image data over time in machine learning model for analysis as taught by Addanki. Doing so would make it possible "to enable medical service providers to tailor therapeutic strategies to individual patients depending on their respective disease characteristics and rate of progression" (see Addanki; [0041]).
Regarding claim 27, Noguchi teaches all claim limitations, as applied in claim 19, except wherein estimating the prognosis of the lesion includes predicting a progression of the lesion based on continuous observation data of the lesion over time.
However, in the same field of endeavor, Addanki teaches wherein estimating the prognosis of the lesion includes predicting a progression of the lesion based on continuous observation data of the lesion over time ("The additional image data can represent diseased tissue at a later point in time relative to initial image data analyzed by the data processing device 120. The additional image data can enable the data processing device 120 to update the prediction of the rate of disease progression for the patient over time." [0045]).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to modify machine learning based lesion analysis as taught by Noguchi with additional image data over time in machine learning model for analysis as taught by Addanki. Doing so would make it possible "to enable medical service providers to tailor therapeutic strategies to individual patients depending on their respective disease characteristics and rate of progression" (see Addanki; [0041]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Matsumura et al. (US 2006/0052702 A1; published on 03/09/2006) (hereinafter "Matsumura") teach a lesion detection and analysis apparatus and method. Different modes of ultrasound imaging is used to generage images for lesion analysis.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAO SHENG whose telephone number is (571)272-8059. The examiner can normally be reached Monday to Friday, 8:30 am to 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne M. Kozak can be reached at (571) 270-0552. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAO SHENG/ Primary Examiner, Art Unit 3797
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