DETAILED OFFICE 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.
Claims 1-20 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 1, for example, recites a) “wherein the item of object information is determined via a control device by evaluating camera data from at least one camera image” and b) “determining, via the tracking device, an item of pose information… and using the item of pose information to determine the item of object information”. It appears that there are two instances of determining the item of object information. It is unclear if the item of object information is either of a) or b) or both. Clarification/explanation is respectfully requested.
Claim 1 also recites the terms “item of object information” and “item of pose information”. It is confusing as to if the word “item” is any different than “information”. In other words, it is unclear if the scope of “object information” itself is any different than “item of object information”. If so, please explain the difference by referring to the corresponding sections from the Specification.
Claim 1 recites “camera data from at least one camera image”. It is unclear what the difference between “camera data” and “camera image”.
With respect to claim 13, arguments analogous to those presented for claim 1, are applicable.
Claim 2 recites “determines at least one object class of the at least one object with an associated item of reliability information as output data”. It is confusing as to what is determined as the “output”. Also, it is unclear if the camera data is being used in determining this output data.
Claim 3 recites “additional input data”. It is unclear what this input data is additional to.
Claim 4 recites “at least one of an orientation of the at least one object or a position of the at least one object is also determined”. It is confusing if they are referring to the same position and orientation recited in claim 1. If so, it should be amended to say “the position” and “the orientation”.
Claim 4 recites “checking a plausibility of an evaluation result of the trained evaluation function”. The function appears to generate multiple results. If so, it is unclear which result is being checked for plausibility.
Claims 7 and 18 recite “defines a fundamental truth”. It is unclear what this term represents/means.
Claim 8 recites “if an item of identification information.. not present, the training dataset is supplemented”. It is unclear if the not present object information is updated using the training dataset or something other than the objection information is updated.
Claim 9 recites “camera data” and “the camera data”. It is unclear if they are all referring to the same “camera data” recited in claim 1.
Claims 11 and 19 recite “in case of a tracking device which measures…”. It is unclear what this “in case of” condition represent.
Claims 11 and 19 recite “a tracking device” and “the tracking device”. It is unclear if they are all referring to the same “the tracking device” recited in claim 1.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pavek et al. USPGPUB 2024/0170142 (hereinafter Pavek).
With respect to claim 1, Pavek teaches a method for determining items of object information in at least one of a medical examination or treatment arrangement with at least one object (fig. 3),
wherein the at least one object is captured via at least one camera and an item of object information includes at least one of identification information or positional information relating to the at least one object (identifier for identifying medical devices in paragraph 48),
wherein the item of object information is determined via a control device by evaluating camera data from at least one camera image obtained by the at least one camera (paragraph 48),
wherein the at least one object is equipped with a tracking device (paragraph 49), and wherein the method comprises:
determining, via the tracking device, an item of pose information describing at least one of a position or an orientation of the at least one object (paragraphs 49 & 64); and
using the item of pose information to determine the item of object information (paragraphs 49 & 64).
With respect to claim 2, Pavek teaches the method as claimed in claim 1, wherein a trained evaluation function is used for evaluating the camera data and determines at least one object class of the at least one object with an associated item of reliability information as output data (paragraphs 78-80 & 85).
With respect to claim 3, Pavek teaches the method as claimed in claim 2, wherein the trained evaluation function uses at least part of the item of pose information as additional input data (paragraphs 49 & 64).
With respect to claim 4, Pavek teaches the method as claimed in claim 2, wherein, in addition to the at least one object class, at least one of an orientation of the at least one object or a position of the at least one object is also determined, via the trained evaluation function, as positional information which is then compared with the at least one of the orientation of the at least one object or the position of the at least one object described by the item of pose information for checking a plausibility of an evaluation result of the trained evaluation function (training using the data gathered by the machine learning algorithm in paragraphs 78-80 & 85).
With respect to claim 5, Pavek teaches the method as claimed in claim 2, wherein the tracking device has an identification feature which is assigned to the at least one object and by which an item of identification information relating to the at least one object is determined, the item of identification information being used to check a plausibility of the at least one object class that has been determined (training using the data gathered by the machine learning algorithm in paragraphs 78-80 & 85).
With respect to claim 6, Pavek teaches the method as claimed in claim 4, wherein the reliability information is adapted as a function of at least one plausibility check result, or in the event that at least one plausibility check fails, at least one assigned measure is carried out (taking action based on the result in paragraph 85).
With respect to claim 7, Pavek teaches the method as claimed in claim 3, wherein at least one of the item of pose information or the item of identification information is stored together with assigned camera data as a training dataset, and wherein the at least one of the item of pose information or the item of identification information defines a fundamental truth and is used for training at least the trained evaluation function (training using the data gathered by the machine learning algorithm in paragraphs 78-80 & 85).
With respect to claim 8, Pavek teaches the method as claimed in claim 7, wherein if an item of identification information relating to the at least one object is not present, the training dataset is supplemented in respect of the fundamental truth by at least one information item that is provided by a user (paragraphs 143~144).
With respect to claim 9, Pavek teaches the method as claimed in claim 1, wherein the tracking device is a pose marking device, and wherein the item of pose information is determined from camera data relating to the pose marking device during the evaluating of the camera data (paragraphs 49 & 64).
With respect to claim 10, Pavek teaches the method as claimed in claim 9, wherein the pose marking device is attached to or arranged on the at least one object such that at least one reference direction of the pose marking device corresponds to a distinct object direction of the at least one object, wherein said at least one reference direction is unambiguously identifiable by the evaluating, and wherein an item of assignment information describing the correspondence is stored in the control device for use during the evaluating (paragraphs 49 & 64).
With respect to claim 11, Pavek teaches the method as claimed in claim 1, wherein in case of a tracking device which measures actively, the tracking device and the at least one camera are at least one of registered with each other or calibrated with a coordinate system usable by both (paragraph 141).
With respect to claim 12, Pavek teaches the method as claimed in claim 1, wherein the at least one camera and the control device form part of a control system of a medical technology device that is operable at least partly autonomously, and wherein the item of object information is used to determine at least one control measure for the medical technology device (figs. 3 & 4).
With respect to claim 13, Pavek discloses an arrangement comprising:
at least one object (figs. 3 & 4);
at least one camera configured to capture the at least one object (paragraphs 47 & 48); and
a control device including an evaluation unit configured to determine an item of object information by evaluating camera data from at least one camera image obtained by the at least one camera (paragraphs 47 & 48), wherein
the item of object information includes at least one of an item of identification or an item of positional information relating to the at least one object (paragraphs 47 & 48),
the at least one object is equipped with a tracking device and an item of pose information which at least one of describes a position or based on which orientation of the at least one object is determined via the tracking device (paragraphs 49 & 64), and
the evaluation unit is configured to use the item of pose information for determining the item of object information (paragraphs 49 & 64).
With respect to claim 14, Pavek discloses the non-transitory computer-readable storage medium storing computer-readable instructions that, when executed at a control device of at least one of a medical examination or treatment arrangement, cause the at least one of the medical examination or treatment arrangement to perform the method of claim 1 (program/software running in fig. 4).
With respect to claim 15, Pavek teaches the method of claim 6, wherein the at least one assigned measure includes at least one of an indication to a user or a modification of at least one of the item of object information or of a utility function which uses the item of object information (paragraph 141).
With respect to claim 16, Pavek teaches the method as claimed in claim 4, wherein the tracking device has an identification feature, which is assigned to the at least one object and by which an item of identification information relating to the at least one object is determined, the item of identification information being used to check a plausibility of the at least one object class that has been determined (paragraphs 47, 48 and 51).
With respect to claim 17, Pavek teaches the method as claimed in claim 5, wherein the reliability information is adapted as a function of at least one plausibility check result, or in the event that at least one plausibility check fails, at least one assigned measure is carried out (taking action based on the result in paragraph 85).
With respect to claim 18, Pavek teaches the method as claimed in claim 5, wherein at least one of the item of pose information or the item of identification information is stored together with assigned camera data as a training dataset, and wherein the at least one of the item of pose information or the item of identification information defines a fundamental truth, and is used for training the trained evaluation function (training using the data gathered by the machine learning algorithm in paragraphs 78-80 & 85).
With respect to claim 19, Pavek teaches the method as claimed in claim 5, wherein in case of a tracking device which measures actively, the tracking device and the at least one camera are at least one of registered with each other or calibrated with a coordinate system usable by both (paragraph 141).
With respect to claim 20, Pavek teaches the method as claimed in claim 5, wherein the at least one camera and the control device form part of a control system of a medical technology device that is operable at least partly autonomously, and wherein the item of object information is used to determine at least one control measure for the medical technology device (figs. 3 & 4).
Claims 1, 13 and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by DeBusk et al. USPGPUB 2016/0110780 (hereinafter DeBusk).
With respect to claim 1, DeBusk teaches a method for determining items of object information in at least one of a medical examination or treatment arrangement with at least one object (Abstract),
wherein the at least one object is captured via at least one camera and an item of object information includes at least one of identification information or positional information relating to the at least one object (identifier uniquely identifies items in paragraph 39),
wherein the item of object information is determined via a control device by evaluating camera data from at least one camera image obtained by the at least one camera (paragraphs 39 & 40 for a camera reading a barcode or a QR code),
wherein the at least one object is equipped with a tracking device (RFID tags in paragraphs 36-38), and wherein the method comprises:
determining, via the tracking device, an item of pose information describing at least one of a position or an orientation of the at least one object (position information based on RFID tracker in paragraphs 36-38); and
using the item of pose information to determine the item of object information (paragraphs 36-38).
With respect to claims 13 and 14, arguments analogous to those presented for claim 1, are applicable.
Conclusion
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/CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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