DETAILED ACTION
Responsive to the response filed April 20, 2026. Applicant's election with traverse of Group I (claims 1, 3-6, and 8-17) in the reply is acknowledged. The traversal is on the ground that the office action did not identify the special technical features for each group and explain why they are not the same or corresponding. This is not found persuasive. The groups are directed to three different methods with differences that are clear to a person skilled in the art just from comparing the independent claims. Each group is directed to a completely different method with technical features that are different and not found in the other methods. For example, Group I contains the limitations directed to the claimed steps of detecting, automatically detecting, classifying, storing, visualizing which are not found in the other groups. Group II contains limitations such as the claimed steps of creating, subdividing, inputting, and determining, which are not found in the other groups. The requirement is still deemed proper and is therefore made FINAL.
Claims 18-27 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to nonelected inventions, there being no allowable generic or linking claim.
Claims 1, 3-6, and 8-17 are pending further examination. 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 § 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.
Claims 1, 3-6, and 8-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schnittman (US 2016/0167226).
As per claim 1, Schnittman teaches a method comprising:
navigating an autonomous mobile robot through an operational area using one or more navigation sensors; detecting information about the surroundings of the robot in the operational area (see at least paragraphs [0094-0097]);
automatically detecting sub-areas within the operational area (see at least paragraphs [0094]);
classifying the detected sub-areas as an area class by means of with a classifier based on the detected information, storing detected sub-areas including the determined area class in an electronic map of the robot (see at least paragraphs [0095-0098]); and
visualizing, via a human-machine interface, the detected sub-areas, wherein a user has an option of entering or changing the area class (see at least paragraph [0099], fig 8);
wherein the classifier, when classifying the detected sub-area, takes into account which objects are detected in the respective sub-area, and/or; wherein a measure for a classification correctness probability of a sub-area is determined and the sub-area is stored in the map depending on the measure (see at least paragraphs [0173-0177]).
As per claim 3, Schnittman teaches wherein classifying the detected sub-area comprises: determining that the measure for the classification correctness probability is correct a measure of the probability of the classification being correct; and storing this the measure for the classification correctness probability for at least one object class in the map (see at least paragraphs [0093, 0096, 0131]).
As per claim 4, Schnittman teaches wherein the classifying of the detected sub-areas comprises the following: determining that the measure for the classification correctness probability is correct; and if the measure for the classification correctness probability satisfies a predetermined condition, repeating the classifying the detected sub-area in a changed position of the robot, with the additional sensor data, illumination of the detected sub-area, or a combination thereof (see at least paragraphs [0093, 0096, 0131]).
As per claim 5, Schnittman teaches wherein classifying the detected sub-area comprises: determining that the measure for the classification correctness probability is correct; and if the measure for the classification correctness probability satisfies a predetermined condition, repeating the classifying the detected sub-area after manipulation of the detected sub-area by moving an object in the sub-area or performing a service task in the sub-area (see at least paragraphs [0093, 0096, 0131]).
As per claim 6, Schnittman teaches wherein classifying the detected sub-area comprises: determining that the measure for the classification correctness probability is correct; and if the measure for the classification correctness probability satisfies a predetermined condition, repeating the classifying the detected sub-area by an external device in communication with the robot (see at least paragraphs [0093, 0096, 0131]).
As per claim 8, Schnittman teaches further comprising visualizing, via a human-machine interface, detected sub-areas for which the measure for the classification correctness probability satisfies a predetermined condition (see at least paragraph [0099], fig 8).
As per claim 9, Schnittman teaches wherein the classifier is updated via an update over a network connection (see at least paragraphs [0155, 0127]).
As per claim 10, Schnittman teaches transmitting at least part of the detected information and the classification of the sub-area based thereon to a higher-level entity; and using the transmitted information as training data for generating and/or optimizing a further classifier (see at least paragraphs [0021, 0123, 0155]).
As per claim 11, Schnittman teaches selecting, based on an area class, an action from a table in which area classes are assigned to actions, wherein the actions comprise: creating a restricted area in the sub-area; allowing processing of subarea; creating a data protection area; or a combination thereof (see at least paragraphs [0105-0106]).
As per claim 12, Schnittman teaches detecting objects in the operational area;
classifying the detected objects as an object class with a classifier based on the detected information(see at least paragraphs [0095-0098]);
inserting a restricted area on a map of the robot around the detected classified object, or inserting restriction lines, which may only be crossed by the robot from one direction, on the robot's map (see at least paragraphs [0115, 0120, 0173).
As per claim 13, Schnittman teaches detecting additional objects in one of the detected sub-areas; expanding the detected sub-area so that space occupied by the detected object is not visualized for the user as a wall or boundary of the room (see at least paragraphs [0004, 0094]).
As per claim 14, Schnittman teaches detecting a wall; and expanding the sub-area by an area occupied by the detected object up to the detected wall and updating the map (see at least paragraph [0120]).
As per claim 15, Schnittman teaches extrapolating a course of the wall next to the detected object to infer the position of the wall behind the object; and extending the sub-area by the area occupied by the object up to the position of the wall behind the object and updating the map (see at least fig 9).
As per claim 16, Schnittman teaches expanding the sub-area by an area that has a typical size for the detected object (see at least paragraphs [0004, 0094]).
As per claim 17, Schnittman teaches performing a service task, which is characterized by one or more parameters, in a sub- area; changing a parameter of the service task depending on the area class of the sub-area (see at least paragraph [0002]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ramsey Refai whose telephone number is (313)446-4867. The examiner can normally be reached M-F 9am-5pm EST.
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RAMSEY REFAI
Primary Examiner
Art Unit 3664
/RAMSEY REFAI/Primary Examiner, Art Unit 3664
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