DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 17/426336, filed on 07/28/2021.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 10/25/2025 and 03/17/2026 is/are being considered by the Examiner.
Claim Interpretation
NOTE: In order to promote compact prosecution, prior art will be applied for all claim limitations as appropriate, even when the broadest reasonable interpretation (BRI) does not require certain contingent or alternative limitations present in claims. However, this should not be taken as an acknowledgement that the BRI and therefore the scope of claims with such limitations are different than as discussed below.
Regarding claim 11, the method claim contains recitation(s) contingent upon “metadata satisfying a predetermined condition”. However, this recitation is not required to carry out the claimed invention and according to MPEP 2111.04, II, “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” See also Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016). Therefore, the BRI of claim 11 has the following interpretations in the alternative (i.e. only one shown in a rejection for the claim to be met):
“applying the medical image to the selected machine learning model”; or
“not applying the medical image to the selected machine learning model”.
Claims 12 and 18 recite similar contingent alternative limitations as claim 11, and have the same interpretation noted above.
NOTE: Claims 1-10 and 20 is/are an apparatus and therefore the BRI of the claim(s) would require structure capable of performing the contingent limitation(s) (see MPEP 2111.04, II, second paragraph).
Claim Objections
Claims 9 and 18 are objected to because of the following informalities: the second instance of “artifact” needs “an” in front of it. Appropriate correction is required.
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 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation "the matched information". There is insufficient antecedent basis for this limitation in the claim.
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.
Claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 12,136,483. Although the claims at issue are not identical, they are not patentably distinct from each other because of the following.
Regarding claim 11, claim 11 of U.S. Patent No. 12,136,483 recites the same limitations as highlighted below:
Claim 11 of the present application
Claim 11 of U.S. Patent No. 12,136,483
A medical image analysis method executed by one or more processors, the method comprising:
receiving a medical image;
obtaining metadata based on the medical image;
determining, based on the metadata, whether the medical image is suitable for analysis by a machine learning model; and
applying the medical image to the machine learning model when the metadata satisfies a predetermined condition, and [2]
not applying the medical image to the machine learning model when the metadata does not satisfy the predetermined condition. [1]
A medical image analysis method executed by one or more processors, the method comprising:
receiving a medical image;
obtaining metadata based on the medical image; and
determining, based on the metadata, whether the medical image is suitable for analysis by a machine learning model configured to detect abnormality,
wherein the determining comprises determining that the medical image is not suitable for the analysis by the machine learning model in response to information related to at least one item included in the metadata not satisfying a predetermined condition, [1] and
wherein the method further comprises obtaining a new medical image of a patient corresponding to the medical image or performing an operation for obtaining the new medical image of the patient, in response to the information related to at least one item included in the metadata not satisfying the predetermined condition.
Italicized section [1] of claim 11 of the present application is implied by italicized section [1] of claim 11 of U.S. Patent No. 12,136,483. Regarding section [2], as noted above, the BRI of the method claim only requires one of the alternative limitations to be met to meet the claim. Therefore, claim 11 of the present application is anticipated by claim 11 of U.S. Patent No. 12,136,483.
Claims 1, 3, 4, 6, 11, 13, 15, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 12,136,483 in view of Katouzian et al, U.S. Publication No. 2019/0392547.
Regarding claim 1, claim 1 of U.S. Patent No. 12,136,483 recites the same limitations as highlighted below:
Claim 1 of the present application
Claim 1 of U.S. Patent No. 12,136,483
A medical image analysis apparatus comprising:
one or more processors; and a memory configured to store one or more instructions that when executed by the one or more processors cause the one or more processors to execute operations, the operations comprising:
receiving a medical image;
obtaining metadata based on the medical image;
determining, based on the metadata, whether the medical image is suitable for analysis by a machine learning model; and
applying the medical image to the machine learning model when the metadata satisfies a predetermined condition, and [2]
not applying the medical image to the machine learning model when the metadata does not satisfy the predetermined condition. [1]
A medical image analysis apparatus comprising:
one or more processors; and a memory configured to store one or more instructions that when executed by the one or more processors cause the one or more processors to execute operations, the operations comprising:
receiving a medical image;
obtaining metadata based on the medical image; and
determining, based on the metadata, whether the medical image is suitable for analysis by a machine learning model configured to detect abnormality,
wherein the determining comprises determining that the medical image is not suitable for the analysis by the machine learning model in response to information related to at least one item included in the metadata not satisfying a predetermined condition, [1]
and wherein the operations further comprise obtaining a new medical image of a patient corresponding to the medical image, or performing an operation for obtaining the new medical image of the patient, in response to the information related to at least one item included in the metadata not satisfying the predetermined condition.
Italicized section [1] of claim 1 of the present application is implied by italicized section [1] of claim 1 of U.S. Patent No. 12,136,483.
Regarding section [2], claim 1 of U.S. Patent No. 12,136,483 does not expressively teach these limitations.
However, Katouzian in a similar invention in the same field of endeavor teaches
one or more processors; and a memory configured to store one or more instructions that when executed by the one or more processors cause the one or more processors to execute operations (see Katouzian paragraph [0005]), the operations comprising:
receiving a medical image (see Figure 1, medical image 118); obtaining metadata based on the medical image (see Figure 1, text analytics subsystem 120 and metadata parser and analytics subsystem 122 and paragraph [0051] along with medical image analytics subsystem 130 and paragraph [0052], “…medical image analytics subsystem 130 provides additional logic for applying various machine learning and analytics algorithms to the medical image data itself to extract evidence indicative of answers to one or more of the previously indicated questions (1)-(7), i.e. to extract evidence of the characteristics of the medical image which may be indicative of the type of disease or abnormality specific imaging pipeline to apply to the medical image data”); and determining, based on the metadata, whether the medical image is suitable for analysis by a model (see paragraph [0058], “That is, the machine learning model 124, given all of the retrieved evidence from the various subsystems 120, 122, and 130, computes the probability P, e.g., P(disease(s)| d; dim, mod, mode, v, bp, org, . . . ), of particular disease types or classifications are the focus of the medical image(s) or can be identified in the medical image(s) of the medical data 118. These probability values may then be used to select one or more disease specific imaging pipelines 150 to process the medical image data and generate decision support results 116 to be provided to the user 112 in response to their request 114” and paragraph [0078] which indicates the pipelines are models) as taught in claim 1 of U.S. Patent No. 12,136,483 and further teaches
applying the medical image to the machine learning model when the metadata satisfies a predetermined condition (see Figure 1, logics 131-134 determining modality, mode, view, and organs in each medical image. Paragraph [0054] further indicates that the proper pipeline is chosen based on these factors).
One of ordinary skill in the art before the effective filing date of the invention would have found it obvious to combine the teaching of applying a medical image to selected models and not applying it to other models based on metadata as taught in Katouzian with the system recited in claim 1 of U.S. Patent No. 12,136,483 the motivation being to determine accurately if a subject in the medical image has an ailment thereby proposing proper treatment.
Claim 3 has identical wording as claim 3 of U.S. Patent No. 12,136,483
Regarding claim 4, the claims of U.S. Patent No. 12,136,483 in view of Katouzian teaches all the limitations of claim 1, and further teaches wherein the operations further comprise:
matching the obtained metadata to the medical image (see Katouzian paragraph [0069], “Alternatively, supervised or unsupervised retrieval and/or hashing methods can be used to find similar matches to the medical image in the medical data 118 and then leverage information for detecting the underlying imaging mode, view, organs/anatomical structures”).
Claims of U.S. Patent No. 12,136,483 in view of Katouzian does not expressively teach storing at least one of the matched information or the determined result of whether the medical image is suitable for analysis by a machine learning model.
However, Katouzian goes on to teach storing a corpus of medical data for analysis (see Katouzian Figure 3, corpus 340) and storing a knowledge base of diseases (see Katouzian paragraph [0072]). Therefore, one of ordinary skill in the art before the effective filing date of the invention would have found it obvious as a matter of design choice to similarly store matched information based on metadata as claimed to allow it to be further analyzed at a future date.
Claim 6 has identical wording as claim 7 of U.S. Patent No. 12,136,483.
Furthermore, method claims 11, 13, and 15 correspond to apparatus claims 1, 3, and 6, and are similarly rejected.
Regarding claim 20, claim 13 of U.S. Patent No. 12,136,483 recites the same limitations as highlighted below:
Claim 20 of the present application
Claim 13 of U.S. Patent No. 12,136,483
A non-transitory computer-readable medium storing one or more instructions that when executed by one or more processors cause the one or more processors to execute operations, the operations comprising:
receiving a medical image;
obtaining metadata based on the medical image;
determining, based on the metadata, whether the medical image is suitable for analysis by a machine learning model;
and
applying the medical image to the machine learning model when the metadata satisfies a predetermined condition, [2] and
not applying the medical image to the machine learning model when the metadata does not satisfy the predetermined condition. [1]
A non-transitory computer-readable medium storing one or more instructions that when executed by one or more processors cause the one or more processors to execute operations, the operations comprising:
receiving a medical image;
obtaining metadata based on the medical image; and
determining, based on the metadata, whether the medical image is suitable for analysis by a machine learning model configured to detect abnormality,
wherein the determining comprises determining that the medical image is not suitable for the analysis by the machine learning model in response to information related to at least one item included in the metadata not satisfying a predetermined condition, [1] and
wherein the operations further comprise obtaining a new medical image of a patient corresponding to the medical image, or performing an operation for obtaining the new medical image of the patient, in response to the information related to at least one item included in the metadata not satisfying the predetermined condition.
Italicized section [1] of claim 20 of the present application is implied by italicized section [1] of claim 13 of U.S. Patent No. 12,136,483.
Regarding section [2], claim 13 of U.S. Patent No. 12,136,483 does not expressively teach these limitations.
However, Katouzian in a similar invention in the same field of endeavor teaches
one or more processors; and a memory configured to store one or more instructions that when executed by the one or more processors cause the one or more processors to execute operations (see Katouzian paragraph [0005]), the operations comprising:
receiving a medical image (see Figure 1, medical image 118); obtaining metadata based on the medical image (see Figure 1, text analytics subsystem 120 and metadata parser and analytics subsystem 122 and paragraph [0051] along with medical image analytics subsystem 130 and paragraph [0052], “…medical image analytics subsystem 130 provides additional logic for applying various machine learning and analytics algorithms to the medical image data itself to extract evidence indicative of answers to one or more of the previously indicated questions (1)-(7), i.e. to extract evidence of the characteristics of the medical image which may be indicative of the type of disease or abnormality specific imaging pipeline to apply to the medical image data”); and determining, based on the metadata, whether the medical image is suitable for analysis by a model (see paragraph [0058], “That is, the machine learning model 124, given all of the retrieved evidence from the various subsystems 120, 122, and 130, computes the probability P, e.g., P(disease(s)| d; dim, mod, mode, v, bp, org, . . . ), of particular disease types or classifications are the focus of the medical image(s) or can be identified in the medical image(s) of the medical data 118. These probability values may then be used to select one or more disease specific imaging pipelines 150 to process the medical image data and generate decision support results 116 to be provided to the user 112 in response to their request 114” and paragraph [0078] which indicates the pipelines are models) as taught in claim 1 of U.S. Patent No. 12,136,483 and further teaches
applying the medical image to the machine learning model when the metadata satisfies a predetermined condition (see Figure 1, logics 131-134 determining modality, mode, view, and organs in each medical image. Paragraph [0054] further indicates that the proper pipeline is chosen based on these factors).
One of ordinary skill in the art before the effective filing date of the invention would have found it obvious to combine the teaching of applying a medical image to selected models and not applying it to other models based on metadata as taught in Katouzian with the system recited in claim 1 of U.S. Patent No. 12,136,483 the motivation being to determine accurately if a subject in the medical image has an ailment thereby proposing proper treatment.
Claims 7 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 12,136,483 in view of over Katouzian et al, U.S. Publication No. 2019/0392547 in view of Sommer et al, U.S. Publication No. 2021/0156940.
Regarding claim 7, claims of U.S. Patent No. 12,136,483 in view of over Katouzian teaches all the limitations of claim 1, and further teaches wherein the obtaining comprises predicting the metadata from the medical image using a prediction model (see Katouzian Figure 1, medical image analytics subsystem 130 and paragraph [0052], “In addition to the text analytics and the metadata parsing and analytics applied by the subsystems 120 and 122, medical image analytics subsystem 130 provides additional logic for applying various machine learning and analytics algorithms to the medical image data itself to extract evidence indicative of answers to one or more of the previously indicated questions (1)-(7), i.e. to extract evidence of the characteristics of the medical image which may be indicative of the type of disease or abnormality specific imaging pipeline to apply to the medical image data”).
Claims of U.S. Patent No. 12,136,483 in view of over Katouzian does not expressively teach wherein the metadata comprises information related to presence of an artifact in the medical image.
However, Sommer in a similar invention in the same field of endeavor teaches a medical image analysis apparatus (see Sommer Figure 1) configured to obtain metadata about a medical image (see paragraph [0067]) as taught in claims of U.S. Patent No. 12,136,483 in view of over Katouzian wherein
the metadata comprises information related to presence of an artifact in the medical image (see paragraph [0067]).
One of ordinary skill in the art before the effective filing date of the invention would have found it obvious to combine the teaching of having metadata indicating presence of an artifact as taught in Sommer with the system taught in claims of U.S. Patent No. 12,136,483 in view of over Katouzian, the motivation being to ensure such artifacts are not taken as indications of anomalous regions inside a human body.
Furthermore, method claim 16 corresponds to apparatus claims 7, and is similarly rejected.
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.
Claim(s) 1, 3, 4, 6, 11, 13, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Katouzian et al, U.S. Publication No. 2019/0392547.
Regarding claim 1, Katouzian teaches a medical image analysis apparatus comprising:
one or more processors; and a memory configured to store one or more instructions that when executed by the one or more processors cause the one or more processors to execute operations (see Katouzian paragraph [0005]), the operations comprising:
receiving a medical image (see Figure 1, medical image 118);
obtaining metadata based on the medical image (see Figure 1, text analytics subsystem 120 and metadata parser and analytics subsystem 122 and paragraph [0051] along with medical image analytics subsystem 130 and paragraph [0052], “…medical image analytics subsystem 130 provides additional logic for applying various machine learning and analytics algorithms to the medical image data itself to extract evidence indicative of answers to one or more of the previously indicated questions (1)-(7), i.e. to extract evidence of the characteristics of the medical image which may be indicative of the type of disease or abnormality specific imaging pipeline to apply to the medical image data”);
determining, based on the metadata, whether the medical image is suitable for analysis by a model (see paragraph [0058], “That is, the machine learning model 124, given all of the retrieved evidence from the various subsystems 120, 122, and 130, computes the probability P, e.g., P(disease(s)| d; dim, mod, mode, v, bp, org, . . . ), of particular disease types or classifications are the focus of the medical image(s) or can be identified in the medical image(s) of the medical data 118. These probability values may then be used to select one or more disease specific imaging pipelines 150 to process the medical image data and generate decision support results 116 to be provided to the user 112 in response to their request 114” and
applying the medical image to the model when the metadata satisfies a predetermined condition, and not applying the medical image to the model when the metadata does not satisfy the predetermined condition (see Figure 1, logics 131-134 determining modality, mode, view, and organs in each medical image. Paragraph [0054] further indicates that the proper pipeline is chosen based on these factors and therefore some are used and some are not).
Katouzian does not expressively teach wherein the model is a machine learning model. However, one of ordinary skill in the art before the effective filing date of the invention would have found it obvious as a matter of simple substitution to replace the generic model of Katouzian with a machine learning model as claimed to yield the predictable results of successfully determining abnormalities in medical images.
Independent claims 11 and 20 recite similar limitations as claim 1, and are rejected under similar rationale.
Regarding claim 3, Katouzian teaches all the limitations of claim 1, and further teaches wherein the metadata comprises at least one of information related to an object included in the medical image (see Katouzian Figure 1, organ/structure detection logic 131), information related to an imaging environment of the medical image (see Katouzian Figure 1, logics 132-134), information related to a type of the medical image (see Katouzian Figure 1, logics 132-134), or information related to a display method of the medical image.
Method claim 13 recites similar limitations as claim 3, and is rejected under similar rationale.
Regarding claim 4, Katouzian teaches all the limitations of claim 1, and further teaches wherein the operations further comprise:
matching the obtained metadata to the medical image (see Katouzian paragraph [0069], “Alternatively, supervised or unsupervised retrieval and/or hashing methods can be used to find similar matches to the medical image in the medical data 118 and then leverage information for detecting the underlying imaging mode, view, organs/anatomical structures”).
Katouzian does not expressively teach storing at least one of the matched information or the determined result of whether the medical image is suitable for analysis by a machine learning model.
However, Katouzian goes on to teach storing a corpus of medical data for analysis (see Katouzian Figure 3, corpus 340) and storing a knowledge base of diseases (see Katouzian paragraph [0072]). Therefore, one of ordinary skill in the art before the effective filing date of the invention would have found it obvious as a matter of design choice to similarly store matched information based on metadata as claimed to allow it to be further analyzed at a future date.
Regarding claim 6, Katouzian teaches all the limitations of claim 1, and further teaches wherein the obtaining comprises:
generating a first metadata predicted for the medical image using a prediction model (see Katouzian Figure 1, medical image analytics subsystem 130 and paragraph [0052], “In addition to the text analytics and the metadata parsing and analytics applied by the subsystems 120 and 122, medical image analytics subsystem 130 provides additional logic for applying various machine learning and analytics algorithms to the medical image data itself to extract evidence indicative of answers to one or more of the previously indicated questions (1)-(7), i.e. to extract evidence of the characteristics of the medical image which may be indicative of the type of disease or abnormality specific imaging pipeline to apply to the medical image data”); and
obtaining a second metadata stored corresponding to the medical image; and selecting, as the metadata, one of the first metadata and the second metadata. (se Figure 1, clinical notes to text analytics subsystem 120 going to hint 126 and modality 138 of model 124 and paragraph [0056]).
Method claim 15 recites similar limitations as claim 6, and is rejected under similar rationale.
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Katouzian et al, U.S. Publication No. 2019/0392547 in view of Sommer et al, U.S. Publication No. 2021/0156940.
Regarding claim 7, Katouzian teaches all the limitations of claim 1, and further teaches wherein the obtaining comprises predicting the metadata from the medical image using a prediction model (see Katouzian Figure 1, medical image analytics subsystem 130 and paragraph [0052], “In addition to the text analytics and the metadata parsing and analytics applied by the subsystems 120 and 122, medical image analytics subsystem 130 provides additional logic for applying various machine learning and analytics algorithms to the medical image data itself to extract evidence indicative of answers to one or more of the previously indicated questions (1)-(7), i.e. to extract evidence of the characteristics of the medical image which may be indicative of the type of disease or abnormality specific imaging pipeline to apply to the medical image data”).
Katouzian does not expressively teach wherein the metadata comprises information related to presence of an artifact in the medical image.
However, Sommer in a similar invention in the same field of endeavor teaches a medical image analysis apparatus (see Sommer Figure 1) configured to obtain metadata about a medical image (see paragraph [0067]) as taught in Katouzian wherein
the metadata comprises information related to presence of an artifact in the medical image (see paragraph [0067]).
One of ordinary skill in the art before the effective filing date of the invention would have found it obvious to combine the teaching of having metadata indicating presence of an artifact as taught in Sommer with the system taught in Katouzian, the motivation being to ensure such artifacts are not taken as indications of anomalous regions inside a human body.
Method claim 16 recites similar limitations as claim 17, and is rejected under similar rationale.
Allowable Subject Matter
Claims 2, 5, 8-10, 12, 14, and 17-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CASEY L KRETZER whose telephone number is (571)272-5639. The examiner can normally be reached M-F 10:00-7:00 PM Pacific Time.
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/CASEY L KRETZER/Primary Examiner, Art Unit 2635
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