DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Claim Rejections - 35 USC § 103
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 of this title, 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.
Claims 16-20, 24-25, 29-32 and 34-35 are rejected under 35 U.S.C. 103 as being unpatentable over Hansen et al. US2015/0248719 hereinafter referred to as Hansen in view of Saito US2013/0188090.
As per Claim 16, Hansen teaches a method for providing image-originated search results, the method comprising:
receiving image data comprising a sequence of image frames; (Hansen, Paragraph [0065], “In some embodiments, the object to be searched can be indicated by a user in image data collected on the personal electronic device. For example, where the search is performed by first collecting video, the video can be analyzed to identify a plurality of objects in the video and then the user can identify the object to be searched by selecting an object in the video.”)
extracting, from the image data, visual search information for the moving object; (Hansen, Paragraph [0059], “In one embodiment, the database is created using a neural net to identify 3D model features or combinations of 3D model features that most quickly and/or accurately identify an object and/or its category. In this embodiment, identifiable features are determined using a neural net, where the neural net is provided with multiple instances of objects in different conditions or settings and the neural net is programmed to vary parameters of the identifiable features to determine one or more sets of particular parameters that can be used to uniquely identify particular objects. In some embodiments, the identifiable feature may be an extractable feature from image data”)
triggering, based on the visual search information for the moving object, a product search process, wherein the product search process further comprises matching a set of visual features from the visual search information for the moving object to a set of product images in a database; and (Hansen, Paragraph [0040], “Some embodiments of the invention relate to identifying objects from a 3D model captured from a personal electronic device. The 3D model generated from imagery captured by the portable electronic device may be compared to a database of known objects. The comparison matches features of the 3D model of the target object (i e, unknown object) with known features of known objects. In a preferred embodiment, the comparison includes comparing at least one three dimensional feature of the target object with a three dimensional feature of a known object. The database search can include scanning a plurality of identified three-dimensional features of the target object with known features of the object data stored in the database”)
returning, to a user device, one or more matching product images associated with a product for presentation in a graphical user interface (GUI) displayed at the user device. (Hansen, Paragraph [0050]-[0052], “The search result can generate an identification of a specific product or a category of products, or both. The search result can include additional information from the database and/or provide links to web documents with additional information associated with the identified object. The search result may also include an advertisement contextually related to the object, identifying information, and/or additional information associated with the object in the database”)
Hansen does not explicitly teach identifying, based on processing the sequence of image frames in the image data, a moving object present in the image data for a threshold period of time;
Saito teaches identifying, based on processing the sequence of image frames in the image data, a moving object present in the image data for a threshold period of time; (Saito, Paragraph [0068], “After displaying the image capture wait screen, the control unit 216 waits until the motion of the object of point A is detected by the object detection unit 204 (step S132). The motion of the object of point A may be detected as follows. Whether or not the motion level of the object at point A exceeds the threshold value (corresponding to the motion level selected in step S130) is determined, and the motion is detected in the case where the motion level exceeds the threshold value”, threshold of time is considered to be 0 as in the object is in frame)
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Saito into Hansen because by utilizing thresholds and camera focus to perform object detection and motion levels will enhance Hansen by providing means to improve the functionality of the camera of Hansen.
Therefore it would have been obvious to one of ordinary skill to combine the two references to obtain the invention in Claim 16.
As per Claim 17, Hansen in view of Saito teaches the method of claim 16, wherein the image data comprises video data. (Hansen, Paragraph [0065], “In some embodiments, the object to be searched can be indicated by a user in image data collected on the personal electronic device. For example, where the search is performed by first collecting video, the video can be analyzed to identify a plurality of objects in the video and then the user can identify the object to be searched by selecting an object in the video.”)
The rationale applied to the rejection of claim 16 has been incorporated herein.
As per Claim 18, Hansen in view of Saito teaches the method of claim 16, wherein the visual search information for the moving object comprises shape data for one or more portions of the moving object. (Hansen, Paragraph [0039])
The rationale applied to the rejection of claim 16 has been incorporated herein.
As per Claim 19, Hansen in view of Saito teaches the method of claim 16, wherein the visual search information for the moving object comprises size data for one or more portions of the moving object. (Hansen, Paragraph [0039], [0069], “In some embodiments, the object can be identified in part by estimating the actual size of the object being searched. The actual size can be estimated by identifying a feature of known size in the imaging data. The estimated actual size of an object may provide important data for distinguishing between different species of the same type of object and for improving the accuracy and/or speed of the search and/or for reducing the computational power needed to provide an accurate match”)
The rationale applied to the rejection of claim 16 has been incorporated herein.
As per Claim 20, Hansen in view of Saito teaches the method of claim 16, wherein identifying the moving object present in the image data comprises applying object detection techniques to identify the moving object as an object of interest relative to other objects present in the image data for the threshold period of time. (Hansen, Paragraph [0040] and Saito, Paragraph [0068])
The rationale applied to the rejection of claim 16 has been incorporated herein.
As per Claim 24, Hansen in view of Saito teaches the method of claim 16, further comprising ranking the one or more matching product images based on respective confidence values, wherein returning the one or more matching product images for presentation at the user device causes the user device to present the one or more matching product images based on the ranking. (Hansen, Paragraph [0050]-[0052], “The search result can generate an identification of a specific product or a category of products, or both. The search result can include additional information from the database and/or provide links to web documents with additional information associated with the identified object. The search result may also include an advertisement contextually related to the object, identifying information, and/or additional information associated with the object in the database” There may be one result and therefore the results will be ranked when shown)
The rationale applied to the rejection of claim 16 has been incorporated herein.
As per Claim 25, Hansen in view of Saito teaches the method of claim 16, wherein returning the one or more matching product images for presentation at the user device comprises: selecting a subset of the one or more matching product images that are determined to be best search results; and causing the user device to present the subset of the one or more matching product images in the GUI. (Hansen, Paragraph [0050]-[0052], “The search result can generate an identification of a specific product or a category of products, or both. The search result can include additional information from the database and/or provide links to web documents with additional information associated with the identified object. The search result may also include an advertisement contextually related to the object, identifying information, and/or additional information associated with the object in the database”
The rationale applied to the rejection of claim 16 has been incorporated herein.
As per Claim 29, Claim 29 claims a computing device comprising: a processor; an imaging system coupled to the processor; a display element; and memory including instructions that, when executed by the processor, enable the computing device performing the method as claimed in Claim 1. Therefore the rejection and rationale are analogous to that made in Claim 16.
As per Claim 30, Claim 30 claims the same limitation as Claim 18 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 18.
As per Claim 31, Claim 31 claims the same limitation as Claim 19 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 19.
As per Claim 32, Claim 32 claims the same limitation as Claim 20 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 20.
As per Claim 34, Claim 34 claims the same limitation as Claim 24 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 24.
As per Claim 35, Claim 35 claims the same limitation as Claim 25 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 25.
Allowable Subject Matter
Claims 26-28 are allowed.
Claims 21-23 and 33 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
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/MING Y HON/Primary Examiner, Art Unit 2666
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