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 Objections
Claim 1 is objected to because of the following informalities: in line 5 the phrase object the model does not make grammatic sense. Appropriate correction is required.
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) 1-5, 8, 10-17, 20 and 22-24 are is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (11809976) in view of Ravichandran et al (12406469).
Regarding claims 1, 13, Li discloses an apparatus and method for generating an authorized payload to update a model configured to identify at least one object, the method comprising:
capturing, by a data capture device, one or more images comprising image data of an unidentified object within a field of view of the data capture device (col.1, lines 14-17 i.e. classify images), wherein the at least one object (step 403) the model is configured to identify with a predetermined level of confidence does not include the unidentified object (step 406 uncertainty metric is equivalent to confidence if the object is in the model);
generating, based on analyzing the image data, a payload (features in step 410) including: image associated data including a representation of at least a portion of the unidentified object (col.6, lines 24-42), an object identifier corresponding to an object of the at least one object the model is configured to identify (i.e. a streetlight).
Although Li fails to teach an indication the payload is at least one of authorized or not authorized; responsive to the payload being authorized, performing a model update operation; and responsive to the payload not being authorized, refraining from performing the model update operation, however Ravichandran et al do. See Ravichandran col.24, lines 53-61 where it is noted that only authorized data can be used in model updates or changes. Since both systems teach the recognition of images and attempts to classify them, it would have been obvious to one of ordinary skill before the time of filing to include some form of identification or authorization of the user to make sure the models are only updated by trusted sources.
Regarding claims 2 and 14 note Li wherein the object identifier is based upon one or more of an electronic identifier or a visual identifier included in at least the portion of image (recognized color in col.6, lines 24+).
Regarding claims 3 and 15 Ravichandran teaches the indication the payload is authorized is based upon one or more of: the data capture device, a location of data capture device, an operation of the data capture device, a user of the data capture device (Ravi col.24, lines 53+), a user input at the data capture device, or an authorization element in the image associated data.
Regarding claims 4 and 16, Li teach the model update operation includes one or more of: storing at least a portion of the image associated data on a memory as model update data, updating the model using at least the portion of the image associated data to identify the unidentified object as the object corresponding to the object identifier, creating an association between the object identifier and the image data and/or image associated data (classification is an association step 404 in figure 4), or refraining from performing for at least a portion of time one or more operations associated with ticket-switching that is triggered by a subsequent payload associated with the unidentified object.
Regarding claims 5 and 17, Li teaches wherein the payload includes one or more of: a feature of the unidentified object or a feature of the object (figure 410 shows features are included in the data sent (i.e. payload)) corresponding to the object identifier.
Regarding claims 10-12 and 22-24, Li teaches capturing by the data capture device, one or more additional images of the unidentified object; generating a second payload based upon the one or more additional images; and based upon the second payload and previously receiving the authorized payload associated with the unidentified object, perform the model update operation. Li is able to process many images as the capturing of images is a repeatable action. Also as noted before Li’s system will assign layers of classification and if the uncertainty metric is too high no updates will be made. See col.17, lines 47+
Regarding claims 8 and 20, Ravichandran teaches receiving information indicating the payload is not authorized (i.e. unauthorized user); responsive to receiving the information, generating information to authorize the payload (authorizing the user); and providing, to a computing device, the information to authorize the payload (col.24, lines 53+ where to become authorized some type of payload must be authenticated where it be ID, badge, PIN or other key information).
Claim(s) 6, 7, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (11809976) in view of Ravichandran et al (12406469) as applied to the claims above, and further in view of Chakravarty et al. 11961279.
As for claims 6 and 18, do not mention that the unidentified object is one object, of the at least one object, the model is configured to identify, with updated packaging. However, Chakravarty et al do note that the image models can add new objects (paragraph 73) and that machine learning will produce the ability to recognize based on changes to the same object. Therefore, it would have been obvious to one of ordinary skill in the art before the time of filing to include new packages into the learning model as they come out so more accurate recognition of object and more of them can be utilized.
As for claims 7 and 19 Chakravarty teaches the region of interest in the one or more images; and cropping the one or more images to include the region of interest. (col.22, lines 62-67 and col.18, lines 54-62). Clearly it is well known to crop images to allow for computer resources to process on the area only containing the object.
Allowable Subject Matter
Claims 9 and 21 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.
The following is a statement of reasons for the indication of allowable subject matter: the prior art fails to teach the specific limitations of the claims which taken with all the claim it depends from is not considered obvious over the prior art for the specificity of the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hoffman et al is cited as teaching adding new objects to trainable image models (US PG Pub 20250308225.
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/CHRISTOPHER S KELLEY/ Supervisory Patent Examiner, Art Unit 2482
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