Office Action 2: Final Rejection (2024-09-26)
Rejection types: Double Patenting

CLAIMS:
AMENDMENTS TO THE CLAIMS

This listing of claims will replace all prior versions and listings of claims in the application.

1\. (Currently Amended) A method for automatic identification of a measurement item, comprising:acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination;
automatically determining, by a processor of an ultrasound imaging device, a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired;
automatically identifying at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and
obtaining a value of the identified at least one measurement item according to the specified section image.

2\. (Original) The method of claim 1, wherein the measurement item is identified based on a comparative analysis of the gray values of the pixels with a preset data model.

3\. (Original) The method of claim 2, further comprising acquiring a measuring mode used during tissue examination.

4\. (Currently Amended) The method of claim 1, wherein automatically determining, by the processor of the ultrasound imaging device, the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises:generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image;

comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample model, respectively; and
searching a training sample whose characteristic is most similar to the characteristic of the specified section image and determining a section type of the training sample searched out as the section type of the specified section image.

5\. (Currently Amended) The method of claim 3, wherein automatically determining, by the processor of the ultrasound imaging device, the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises:extracting a high intensity portion from the specified section image based on the gray values of the pixels of the specified section image; and
performing an identification on the high intensity portion based on the measuring mode to determine the section type of the specified section image.

6\. (Original) The method of claim 1, wherein obtaining the value of the identified at least one measurement item comprises obtaining the value of the identified at least one measurement item manually, semi-automatically or automatically.

7\. (Original) The method of claim 1, wherein the particular section of the section type corresponds to a direction along which a desired section image is acquired from the particular area of the tissue.

8\. (Currently Amended) The method of claim 1, wherein automatically determining, by the processor of the ultrasound imaging device, the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises:extracting section type characteristics of the specified section image based on which one section type is distinguished with another section type; and
determining the section type of the specified section image based on the extracted section type characteristics.

9\. (Original) The method of claim 1, wherein the determined section type is a head circumference section which contains a skull of a fetus, an abdominal circumference section which contains an abdomen of a fetus or a femur section which contains a thigh bone of a fetus.

10\. (Original) The method of claim 1, wherein the one or more characteristics defined by the gray values of the pixels are a shape, a brightness range or a size define by the gray values of the pixels.

11\. (Currently Amended) An ultrasound imaging apparatus, comprising:a probe which transmits ultrasound waves to a tissue and receives ultrasound echoes;
a signal processor which processes the ultrasound echoes to generate ultrasound image data; and
an image processor which processes the ultrasound image data and generates section images;
wherein the image processor is further configured to:
acquire gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination;
automatically determine a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired;
automatically identify at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and
obtain a value of the identified at least one measurement item according to the specified section image.

12\. (Original) The apparatus of claim 11, wherein the image processor is further configured to identify the measurement item based on a comparative analysis of the gray values of the pixels with a preset data model.

13\. (Original) The apparatus of claim 11, wherein the image processor is further configured to:generate a characteristic of the specified section image based on the gray values of the pixels of the specified section image;
compare the characteristic of the specified section image with characteristic of training samples in a preset training sample mode, respectively; and
search a training sample whose characteristic is most similar to the characteristic of the specified section image and determine a section type of the training sample searched out as the section type of the specified section image.

14\. (Original) The apparatus of claim 11, wherein the image processor is further configured to obtain the value of the identified at least one measurement item manually, semi-automatically or automatically.

15\. (Original) The apparatus of claim 11, wherein the particular section of the section type corresponds to a direction along which a desired section image is acquired from the particular area of the tissue.

16\. (Currently Amended) The apparatus of claim 11, wherein the image processor is further configured to determine the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprising:extracting section type characteristics of the specified section image based on which one section type is distinguished with another section type; and
determining the section type of the specified section image based on the extracted section type characteristics.

17\. (Original) The apparatus of claim 11, wherein the determined section type is a head circumference section which contains a skull of a fetus, an abdominal circumference section which contains an abdomen of a fetus or a femur section which contains a thigh bone of a fetus.


18\. (Original) The apparatus of claim 11, wherein the one or more characteristics defined by the gray values of the pixels are a shape, a brightness range or a size define by the gray values of the pixels.

OFFICE ACTION:
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 .
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of U.S. Patent No. 11,717,183. Although the claims at issue are not identical, they are not patentably distinct from each other because both application and patent claim an ultrasound system for automated identification of a measurement item.
U. S. Application No. 18/211,516
U. S. Patent No. 11,717,183
1. A method for automatic identification of a measurement item, comprising: acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination; automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image is acquired; automatically identifying at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and obtaining a value of the identified at least one measurement item according to the specified section image.
9. The method of claim 1, wherein the determined section type comprises a head circumference section which contains a skull of a fetus, an abdominal circumference section which contains an abdomen of a fetus, or a femur section which contains a thigh bone of a fetus.
2. The method of claim 1, wherein the measurement item is identified based on a comparative analysis of the gray values of the pixels with a preset data model.
3. The method of claim 2, further comprising acquiring a measuring mode used during tissue examination.
4. The method of claim 1, wherein determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image; comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample model, respectively; and searching a training sample whose characteristic is most similar to the characteristic of the specified section image and determining a section type of the training sample searched out as the section type of the specified section image.
5. The method of claim 3, wherein determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: extracting a high intensity portion from the specified section image based on the gray values of the pixels of the specified section image; and performing an identification on the high intensity portion based on the measuring mode to determine the section type of the specified section image.
6. The method of claim 1, wherein obtaining the value of the identified at least one measurement item comprises obtaining the value of the identified at least one measurement item manually, semi- automatically or automatically.
7. The method of claim 1, wherein the particular section of the section type corresponds to a direction along which a desired section image is acquired from the particular area of the tissue.
8. The method of claim 1, wherein determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: extracting section type characteristics of the specified section image based on which one section type is distinguished with another section type; and determining the section type of the specified section imaged based on the extracted section type characteristics.
10. The method of claim 1, wherein the one or more characteristics defined by the gray values of the pixels comprise a shape, a brightness range, or a size define by the gray values of the pixels.
11. An ultrasound imaging apparatus, comprising: a probe which transmits ultrasound waves to a tissue and receives ultrasound echoes; a signal processor which processes the ultrasound echoes to generate ultrasound image data; and an image processor which processes the ultrasound image data and generates section images; wherein the image processor is further configured to: acquire gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination; determine a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image is acquired; identify at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and obtain a value of the identified at least one measurement item according to the specified section image.
13. The apparatus of claim 11, wherein the image processor is further configured to: generate a characteristic of the specified section image based on the gray values of the pixels of the specified section image; compare the characteristic of the specified section image with characteristic of training samples in a preset training sample mode, respectively; and search a training sample whose characteristic is most similar to the characteristic of the specified section image and determine a section type of the training sample searched out as the section type of the specified section image.
14. The apparatus of claim 11, wherein the image processor is further configured to obtain the value of the identified at least one measurement item manually, semi-automatically or automatically.
1. A method for automatic identification of a measurement item, comprising: acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination; automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired, wherein the determined section type is a head circumference section which contains a skull of a fetus, an abdominal circumference section which contains an abdomen of a fetus or a femur section which contains a thigh bone of a fetus; obtaining a correspondence between the section type and one or more measurement items which are measurable in an image of the section type; automatically identifying at least one measurement item which is measurable in the specified section image according to the correspondence and the section type of the specified section image; and obtaining a value of the identified at least one measurement item according to the specified section image.
2. The method of claim 1, wherein the measurement item is identified based on a comparative analysis of the gray values of the pixels with a preset data model.
3. The method of claim 2, further comprising acquiring a measuring mode used during tissue examination.
4. The method of claim 1, wherein automatically determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image; comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample model, respectively; and searching a training sample whose characteristic is most similar to the characteristic of the specified section image and determining a section type of the training sample searched out as the section type of the specified section image.
5. The method of claim 3, wherein automatically determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: extracting a high intensity portion from the specified section image based on the gray values of the pixels of the specified section image; and performing an identification on the high intensity portion based on the measuring mode to determine the section type of the specified section image.
6. The method of claim 1, wherein obtaining the value of the identified at least one measurement item comprises obtaining the value of the identified at least one measurement item manually, semi-automatically or automatically.
7. The method of claim 1, wherein the particular section of the section type corresponds to a direction along which a desired section image is acquired from the particular area of the tissue.
8. The method of claim 1, wherein automatically determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: extracting section type characteristics of the specified section image based on which one section type is distinguished with another section type; and determining the section type of the specified section imaged based on the extracted section type characteristics.
9. The method of claim 1, wherein the one or more characteristics defined by the gray values of the pixels are a shape, a brightness range or a size define by the gray values of the pixels.
10. A method for automatic identification of a measurement item, comprising: acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination; automatically identifying at least one measurement item which is measurable in the specified section image; determining an object corresponding to the identified at least one measurement item in the specified section image, wherein determining the object corresponding to the identified at least one measurement item in the specified section image comprises: automatically determining the object corresponding to the identified at least one measurement item in the specified section image; or detecting a trace operation of an operator on the specified section image, and determining the object corresponding to the identified at least one measurement item in the specified section image according to the detected trace operation; and obtaining a value of the identified at least one measurement item by performing a measurement on the determined object.
11. The method of claim 10, wherein automatically identifying the at least one measurement item which is measurable in the specified section image comprises: generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image; comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample model, respectively; and searching a training sample whose characteristic is most similar to the characteristic of the specified section image and outputting a measurement item corresponding to the training sample searched out as the at least one measurement item which is measurable in the specified section image.
13. The method of claim 10, wherein obtaining the value of the identified at least one measurement item comprises obtaining the value of the identified at least one measurement item manually, semi-automatically or automatically.
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-24 of U.S. Patent No. 10,898,109. Although the claims at issue are not identical, they are not patentably distinct from each other because both application and patent claim an ultrasound system for automated identification of a measurement item.
U. S. Application No. 18/211,516
U. S. Patent No. 10,898,109
1. A method for automatic identification of a measurement item, comprising: acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination; automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image is acquired; automatically identifying at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and obtaining a value of the identified at least one measurement item according to the specified section image.
2. The method of claim 1, wherein the measurement item is identified based on a comparative analysis of the gray values of the pixels with a preset data model.
3. The method of claim 2, further comprising acquiring a measuring mode used during tissue examination.
4. The method of claim 1, wherein determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image; comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample model, respectively; and searching a training sample whose characteristic is most similar to the characteristic of the specified section image and determining a section type of the training sample searched out as the section type of the specified section image.
5. The method of claim 3, wherein determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises: extracting a high intensity portion from the specified section image based on the gray values of the pixels of the specified section image; and performing an identification on the high intensity portion based on the measuring mode to determine the section type of the specified section image.
6. The method of claim 1, wherein obtaining the value of the identified at least one measurement item comprises obtaining the value of the identified at least one measurement item manually, semi- automatically or automatically.
1. A method for automatic identification of a measurement item, comprising: acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination; automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired; obtaining a correspondence between the section type and one or more measurement items which are measurable in an image of the section type; automatically identifying at least one measurement item which is measurable in the specified section image according to the correspondence and the section type of the specified section image; detecting an object corresponding to the identified at least one measurement item in the specified section image; and obtaining a value of the identified at least one measurement item by performing a measurement on the detected object.
2. The method of claim 1, wherein the measurement item is identified based on a comparative analysis of the gray values of the pixels with a preset data mode.
3. The method of claim 2, further comprising acquiring a measuring mode used during tissue examination.
4. The method of claim 1, wherein automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels comprises: generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image; comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample mode, respectively; and searching a training sample whose characteristic is most similar to the characteristic of the specified section image and determining section type of the training sample searched out as the section type of the specified section image.
7. The method of claim 3, wherein automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels comprises: extracting high intensity portions from the specified section image based on the gray values of the pixels of the specified section image; and performing an identification on the high intensity portions based on the measuring mode to determine the section type of the specified section image.
10. The method of claim 1, wherein obtaining a value of the identified at least one measurement item comprises obtaining a value of the identified at least one measurement item manually, semi-automatically or automatically.
Response to Arguments
Applicant’s arguments, see Remarks, filed 06/14/24, with respect to 103 rejection of claims 1-18 have been fully considered and are persuasive.  The 103 rejection of claims 1-18 has been withdrawn.
The double patenting rejection is maintained and is made FINAL.
Conclusion
THIS ACTION IS MADE FINAL.  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 extension fee 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.

APPLICANT REBUTTAL: REMARKS
Please reconsider the present application in view of the above amendments and the following remarks. Applicant thanks the Examiner for carefully considering the present application.
I.
Disposition of Claims
Claim 1 has been amended. Support can be found at least in paragraphs [0014], [0042] and [0073] of the publication document.
Claim 11 has been amended in the manner similar to claim 1.
Claims 4-5, 8, and 16 have been amended in light of the amendments to their base claim and/or to improve their form.
No new matters are added.
II.
Double-Patenting Rejection
In section 2 of the Office Action, claims 1-18 were rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of US Patent No. 11,717,183 and 1-24 of US Patent No.10,898,109.
Applicant respectfully submits that the independent claims have been amended such that they are now distinct and maintain a clear demarcation over the claims 1-14 of US Patent No. 11,717,183 and 1-24 of US Patent No.10,898,109. As such, Applicant respectfully requests withdrawal of the rejection. III.
Rejections under 35 U.S.C.
Claims 1-18 are rejected under 35 U.S.C. § 103 as being unpatentable over Weng (US5588435) in view of Yamauchi (US2002102023).
Amended claim 1 recites, among other elements:
automatically determining, by a processor of an ultrasound imaging device, a
section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired
(Feature A)
automatically identifying at least one measurement item which is measurable in
the specified section image according to the section type of the specified section image
(Feature B)
>   Regarding Feature A
In section 7 of the Office Action, the Examiner conceded that Weng does not disclose automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired.
That is, Weng fails to disclose Feature A of the amended claim 1.
In section 8 of the Office Action, it was asserted that Yamauchi disclose automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired (which is similar to Feature A of the amended claim 1). Applicant respectfully disagrees.
Yamauchi discloses
"After
the image generating unit 110 and the
image normalizing
unit
generate a new ultrasound image,
the automatic contour extracting unit 120 automatically extracts a contour of an object to be examined"
(see paragraph [0103]). That is, the automatic contour extracting unit is used to extract a contour from the ultrasound image.
However, Yamauchi fails to disclose that the automatic contour extracting unit can automatically determine the section type of the object in the ultrasound image.
On the contrary, applicant respectfully submits that the type of the object in the ultrasound image has already been determined before the automatic contour extracting unit begins to operate. Specifically, paragraphs [0170], [0175] and [0178] of Yamauchi disclose: "This
image normalizing unit 211 is uni
q
ue in that it performs different normalization for each object to be examined,
and that it selects a conversion operation best suited for the object and performs the selected conversion operation to normalize an ultrasound image of the object. The image normalizing unit 211 includes a control judging unit 211a, a standard storing unit 211b, and a density converting unit 211c", "On receiving an ultrasound image from the image generating unit 110,
the control
Judging
unit 211a specifies an object to be examined and a region to be normalized in accordance with either an instruction given by the operator beforehand",
and "This image normalizing unit 211 performs not fixed normalization but flexible normalization by
selecting normalization best suited to an object to be examined.
This allows
subse
q
uent image processing
,
such as contour extraction,
to be accurately
performed at higher speed". That is, the object to be processed in the image has already been specified and selected according to the instruction given by the operator before the automatic contour extracting unit extracts the contour of the object.
Therefore, Yamauchi fails to disclose that the automatic contour extracting unit can automatically determine the section type of the object in the ultrasound image.
In view of the above, it is submitted that Yamauchi fails to disclose "automatically determining, by a processor of an ultrasound imaging device, a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired," as recited in the amended claim 1.
> Regarding Feature B
In section 6 of the Office Action, it was asserted that Weng discloses identifying at least one measurement item corresponding to the specified section image based on the gray values of the pixels and measuring a measurement parameter of the specified section image based on the measurement item identified. (which is similar to Feature B of the amended claim 1). Applicant respectfully submits that Weng fails to disclose Feature B of the amended claim 1 for the following reasons.
Applicant respectfully submits that the "measurement item" refers to the items to be measured during the ultrasound examination, and different examination modes may include different measurement items. For example, in an abdomen mode, the common measurement items include the size of the liver, gallbladder, spleen and kidneys, etc. In an obstetric mode, head circumference (HC), biparietal diameter (BPD), abdominal circumference (AC) and femur length (FL) are necessary measurement items for each examination.
Weng merely teaches automatically measuring values of designated parameters. However, "automatically measuring something" is totally different from "automatically identifying what is/are to be measured". As a matter of fact, Weng (see col. 2, lines 30-40) explicitly discloses "The user then designates a general geometry feature of the displayed body structure and at least one measurement parameter associated with the designated geometry feature. For curved, closed structures such as the head or abdomen, the measurement parameters may, for example, be the HC, AC, OFD, or BPD". That is, in Weng, the measurement parameter(s) (which corresponds to the measurement items of the present invention) is designated by the user, rather than being identified automatically. In other words, in Weng, the user designates what is/are to
be measured, and then the processor automatically measures the values of the designated measurement parameter(s).
Therefore, it is submitted that Weng fails to disclose "automatically identifying at least one measurement item which is measurable in the specified section image according to the section type of the specified section image" as recited in the amended claim 1.
Furthermore, it is submitted that Yamauchi also fails to cure the above deficiencies of Weng.
In view of the foregoing, it is submitted that the cited references, considered separately or in combination, fail to disclose, teach or suggest Feature A and Feature B of amended claim 1, and amended claim 1 is therefore patentable over the cited references.
In view of the above, Applicant respectfully submits that amended claim 1 is patentable over the cited references. Based on the same reasons, amended claim 11 is also patentable over the cited references. Their respectively associated dependent claims are patentable by virtue of their dependency. Accordingly, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. §103.
CONCLUSION

NEXT OFFICE ACTION: 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 .
Information Disclosure Statement
The information disclosure statement filed 06/19/23 has been considered, except reference cited in page 3, no. 10. US 2006270938 to Noma et al. The cited reference is not correct and the publication number is not related to Noma et al. as such the reference US2006270938 to Noma et al is not considered.
Allowable Subject Matter
Claims 1-18 are allowed.
The following is an examiner’s statement of reasons for allowance: the closest prior art of record are U. S. Publication No. 2009/0093717 to Carneiro et al.; U. S. Patent No. 5,588,435 to Weng et al. and U. S. Publication No. 2006/0173317 to Lee none of the prior art alone or in combination teaches the limitations of the independent claim specifically “automatically determining, by a processor of an ultrasound imaging device, a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired; automatically identifying at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and obtaining a value of the identified at least one measurement item according to the specified section image”.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”

CITED REFERENCE SUMMARIES: Ref US11717183: Claims:
1. A method for automatic identification of a measurement item, comprising:
acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination;
automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired, wherein the determined section type is a head circumference section which contains a skull of a fetus, an abdominal circumference section which contains an abdomen of a fetus or a femur section which contains a thigh bone of a fetus;
obtaining a correspondence between the section type and one or more measurement items which are measurable in an image of the section type;
automatically identifying at least one measurement item which is measurable in the specified section image according to the correspondence and the section type of the specified section image; and
obtaining a value of the identified at least one measurement item according to the specified section image.
2. The method of claim 1, wherein the measurement item is identified based on a comparative analysis of the gray values of the pixels with a preset data model.
3. The method of claim 2, further comprising acquiring a measuring mode used during tissue examination.
4. The method of claim 1, wherein automatically determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises:
generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image;
comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample model, respectively; and
searching a training sample whose characteristic is most similar to the characteristic of the specified section image and determining a section type of the training sample searched out as the section type of the specified section image.
5. The method of claim 3, wherein automatically determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises:
extracting a high intensity portion from the specified section image based on the gray values of the pixels of the specified section image; and
performing an identification on the high intensity portion based on the measuring mode to determine the section type of the specified section image.
6. The method of claim 1, wherein obtaining the value of the identified at least one measurement item comprises obtaining the value of the identified at least one measurement item manually, semi-automatically or automatically.
7. The method of claim 1, wherein the particular section of the section type corresponds to a direction along which a desired section image is acquired from the particular area of the tissue.
8. The method of claim 1, wherein automatically determining the section type of the specified section image based on the one or more characteristics defined by the gray values of the pixels comprises:
extracting section type characteristics of the specified section image based on which one section type is distinguished with another section type; and
determining the section type of the specified section imaged based on the extracted section type characteristics.
9. The method of claim 1, wherein the one or more characteristics defined by the gray values of the pixels are a shape, a brightness range or a size define by the gray values of the pixels.
10. A method for automatic identification of a measurement item, comprising:
acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination;
automatically identifying at least one measurement item which is measurable in the specified section image;
determining an object corresponding to the identified at least one measurement item in the specified section image, wherein determining the object corresponding to the identified at least one measurement item in the specified section image comprises:
automatically determining the object corresponding to the identified at least one measurement item in the specified section image; or
detecting a trace operation of an operator on the specified section image, and determining the object corresponding to the identified at least one measurement item in the specified section image according to the detected trace operation; and

obtaining a value of the identified at least one measurement item by performing a measurement on the determined object.
11. The method of claim 10, wherein automatically identifying the at least one measurement item which is measurable in the specified section image comprises:
generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image;
comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample model, respectively; and
searching a training sample whose characteristic is most similar to the characteristic of the specified section image and outputting a measurement item corresponding to the training sample searched out as the at least one measurement item which is measurable in the specified section image.
12. The method of claim 11, wherein the characteristic of the specified section image comprises an eigenvalue or an eigenvector calculated based on the values of the pixels.
13. The method of claim 10, wherein obtaining the value of the identified at least one measurement item comprises obtaining the value of the identified at least one measurement item manually, semi-automatically or automatically.
14. A method for automatic identification of a measurement item, comprising:
acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination;
automatically identifying at least one measurement item which is measurable in the specified section image, wherein automatically identifying the at least one measurement item which is measurable in the specified section image comprises:
generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image, wherein the characteristic of the specified section image comprises an eigenvalue or an eigenvector calculated based on the gray values of the pixels;
comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample model, respectively; and
searching a training sample whose characteristic is most similar to the characteristic of the specified section image and outputting a measurement item corresponding to the training sample searched out as the at least one measurement item which is measurable in the specified section image;

determining an object corresponding to the identified at least one measurement item in the specified section image; and
obtaining a value of the identified at least one measurement item by performing a measurement on the determined object.
Ref US10898109: Claims:
1. A method for automatic identification of a measurement item, comprising:
acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination;
automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired;
obtaining a correspondence between the section type and one or more measurement items which are measurable in an image of the section type;
automatically identifying at least one measurement item which is measurable in the specified section image according to the correspondence and the section type of the specified section image;
detecting an object corresponding to the identified at least one measurement item in the specified section image; and
obtaining a value of the identified at least one measurement item by performing a measurement on the detected object.
2. The method of claim 1, wherein the measurement item is identified based on a comparative analysis of the gray values of the pixels with a preset data mode.
3. The method of claim 2, further comprising acquiring a measuring mode used during tissue examination.
4. The method of claim 1, wherein automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels comprises:
generating a characteristic of the specified section image based on the gray values of the pixels of the specified section image;
comparing the characteristic of the specified section image with characteristics of training samples in a preset training sample mode, respectively; and
searching a training sample whose characteristic is most similar to the characteristic of the specified section image and determining section type of the training sample searched out as the section type of the specified section image.
5. The method of claim 4, wherein the characteristic of the training sample is a projection coefficient of an eigenvector of the training sample on a mean value of the training sample, and the characteristic of the specified section image is a projection coefficient of an eigenvector of the specified section image on the mean value of the training sample.
6. The method of claim 5, wherein the characteristic of the specified section image is calculated by the following formula:
  w=E T(I test −m)
wherein Itest is the eigenvector of the specified section image, m is the mean value of the training sample, T represents matrix transpose, E is an orthogonalized eigenvector, and w is the projection coefficient of the eigenvector of the specified section image on the mean value of the training sample.
7. The method of claim 3, wherein automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels comprises:
extracting high intensity portions from the specified section image based on the gray values of the pixels of the specified section image; and
performing an identification on the high intensity portions based on the measuring mode to determine the section type of the specified section image.
8. The method of claim 7, wherein extracting high intensity portions from the specified section image comprises:
categorizing the gray values of the pixels of the specified section image into a plurality of categories using a cluster segmentation method;
keeping pixels which have gray values of one or more categories with maximum gray values unchanged while assigning zero to pixels which have gray values of other categories to obtain a characteristic image; and
identifying connected regions in the characteristic image and determining one or more connected regions with maximum intensity to obtain the high intensity portions.
9. The method of claim 8, wherein performing an identification on the high intensity portions based on the measuring mode comprises:
analyzing intensities and shapes of the connected regions identified; and
determining the section type of the specified section image based on the measuring mode and results of the analysis.
10. The method of claim 1, wherein obtaining a value of the identified at least one measurement item comprises obtaining a value of the identified at least one measurement item manually, semi-automatically or automatically.
11. The method of claim 1, wherein the particular section of the section type corresponds to a direction along which a desired section image is acquired from the particular area of the tissue.
12. The method of claim 1, wherein automatically determining a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels comprises:
extracting section type characteristics of the specified section image based on which one section type is distinguished with another section type; and
determining the section type of the specified section imaged based on the extracted section type characteristics.
13. The method of claim 1, wherein the determined section type is a head circumference section which contains a skull of a fetus, an abdominal circumference section which contains an abdomen of a fetus or a femur section which contains a thigh bone of a fetus.
14. The method of claim 1, wherein the one or more characteristics defined by the gray values of the pixels are a shape, a brightness range or a size define by the gray values of the pixels.
15. An ultrasound imaging apparatus, comprising:
a probe which transmits ultrasound waves to a tissue and receives ultrasound echoes;
a signal processor which processes the ultrasound echoes to generate ultrasound image data; and
an image processor which processes the ultrasound image data and generates section images;
wherein the image processor is further configured to:
acquire gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination;
automatically determine a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired;
obtain a correspondence between the section type and one or more measurement items which are measurable in an image of the section type;
automatically identify at least one measurement item which is measurable in the specified section image according to the correspondence and the section type of the specified section image;
detect an object corresponding to the identified at least one measurement item in the specified section image; and
obtain a value of the identified at least one measurement item by performing a measurement on the detected object.
16. The apparatus of claim 15, wherein the image processor is further configured to identify the measurement item based on a comparative analysis of the gray values of the pixels with a preset data mode.
17. The apparatus of claim 16, wherein the image processor is further configured to acquire a measuring mode used during tissue examination.
18. The apparatus of claim 15, wherein the image processor is further configured to:
generate a characteristic of the specified section image based on the gray values of the pixels of the specified section image;
compare the characteristic of the specified section image with characteristics of training samples in a preset training sample mode, respectively; and
search a training sample whose characteristic is most similar to the characteristic of the specified section image, and determine section type of the training sample searched out as the section type of the specified section image.
19. The apparatus of claim 18, wherein the characteristic of the training sample is a projection coefficient of an eigenvector of the training sample on a mean value of the training sample, and the characteristic of the specified section image is a projection coefficient of an eigenvector of the specified section image on the mean value of the training sample.
20. The apparatus of claim 19, wherein the image processor is configured to generate the characteristic of the specified section image using the following formula:
  W=E T(I test −M)
wherein Itest is the eigenvector of the specified section image, m is the mean value of the training sample, T represents matrix transpose, E is an orthogonalized eigenvector, and w is the projection coefficient of the eigenvector of the specified section image on the mean value of the training sample.
21. The apparatus of claim 17, wherein the image processor is further configured to:
extract high intensity portions from the specified section image based on the gray values of the pixels of the specified section image;
perform an identification on the high intensity portions based on the measuring mode to determine the section type of the specified section image.
22. The apparatus of claim 21, wherein the image processor is further configured to:
categorize the gray values of the pixels of the specified section image into a plurality of categories using a cluster segmentation method;
keep pixels which have gray values of one or more categories with maximum gray values unchanged while assigning zero to pixels which have gray values of other categories to obtain a characteristic image; and
identify connected regions in the characteristic image and determine one or more connected regions with maximum intensity to obtain the high intensity portions.
23. The apparatus of claim 22, wherein the image processor is further configured to:
analyze intensities and shapes of the connected regions identified; and
determine the section type of the specified section image based on the measuring mode and results of the analysis.
24. The apparatus of claim 22, wherein the image processor is further configured to perform a pre-processing on the specified section image to increase boundary continuity before extracting the high intensity portions in the specified section image, and a post-processing on the characteristic image to remove noise to increase boundary continuity after the characteristic image is obtained.
Ref APP-18_211516: Claims:
A method for automatic identification of a measurement item, comprising: acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination; automatically determining, by a processor of an ultrasound imaging device, a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired; automatically identifying at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and obtaining a value of the identified at least one measurement item according to the specified section image.
acquiring gray values of pixels of a specified section image, wherein the gray values of the pixels correspond to ultrasound echoes generated by reflection of ultrasound waves by a tissue under examination;
automatically determining, by a processor of an ultrasound imaging device, a section type of the specified section image based on one or more characteristics defined by the gray values of the pixels, the section type identifying a particular section of a particular area of the tissue from which the specified section image was acquired;
automatically identifying at least one measurement item which is measurable in the specified section image according to the section type of the specified section image; and
obtaining a value of the identified at least one measurement item according to the specified section image.
The method of claim 1, wherein the measurement item is identified based on a comparative analysi
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