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 .
Election/Restrictions
Claim 30 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 2/10/26.
Claim Rejections - 35 USC § 102
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.
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 16-17, 22 and 26-29 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bhogal (20180292092).
Bhogal teaches a method for determining a cooking end time of food located in a cooking chamber (par. 0054) of a household cooking appliance (par. 0039), the method comprising:
creating at the beginning of a cooking process (par. 0089; par. 0080) a lightness value-separable image of the cooking chamber (par. 0113 racks vs tray vs food; par. 0044 filter)
carrying out a segmentation of the lightness value-separable image based on color coordinates thereof by way of cluster analysis (par. 0113 last 9 lines) to produce food pixels associated with the food and surrounding pixels associated with a surrounding area of the food (par. 0113 racks vs tray vs food)
offering a user an opportunity to enter a target degree of browning (par. 0069 lines 14-18 brownness)
recording during the cooking process images of the cooking chamber at intervals (par. 0114)
computing in these images based on the food pixels a respective actual degree of browning (par. 0114; par. 0144)
comparing the actual degree of browning with the target degree of browning (par. 0114; par. 0144) and
treating the food in the cooking chamber until the actual degree of browning has at least approximately reached the target degree of browning (par. 0114; par. 0144)
Claim 17, recording at the beginning of the cooking process an RGB image of the cooking chamber (par. 0080 color image; par. 0042) and converting the RGB image into the lightness value-separable image (par. 0113).
Claim 22, further comprising repeating the segmentation in the course of the cooking process (par. 0114; par. 0144)
Claim 26, wherein the user is offered the opportunity of entering the target degree of browning based on target degrees of browning described on a character basis (par. 0069).
Claim 27, further comprising offering real images of the food or of a similar food with different degrees of browning retrieved from a database for selection of the target degree of browning (par. 0069).
Claim 28, the user is offered the opportunity of entering the target degree of browning by displaying the originally recorded image and displaying the food in the image as browned with the target degree of browning upon selection of the target degree of browning by the user (par. 0069; 0114).
Claim 29, wherein the food is treated in the cooking chamber until a distance between the target degree of browning in a color space from a current actual browning value has passed through a minimum (par. 0114; minimum being target; par. 0135).
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 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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 18-21, 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Bhogal (20180292092).
With respect to claim 18, Bhogal teaches the image including color images, the camera can single out given light bands (par. 0044) by an image analysis module (par. 0057) including cluster and/or grouping of pixels (par. 0113). Foodstuff features that can be extracted from the image (e.g., via image analysis module 80) can include foodstuff color (e.g., average color, color gradients, etc.) or any other suitable feature that can be visually determined (par. 0113). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to teach substitute one method of segmenting as taught by Bhogal with converting the RGB image into an L*a*b* image, with L* being a lightness component and a* and b* being color components; and segmenting the L*a*b* image based on the color components a* and b* since Bhogal teaches the segmenting and filtering of light bands for its art recognized and applicants intended purpose of analyzing images to extract food cooking progress relative a food parameter such as in the instant a degree of current browning relative a target browning (par. 0114, 0144).
Claim 19, Bhogal teaches the image including color images, the camera can single out given light bands (par. 0044) by an image analysis module (par. 0057) including cluster and/or grouping of pixels (par. 0113). Foodstuff features that can be extracted from the image (e.g., via image analysis module 80) can include foodstuff color (e.g., average color, color gradients, etc.) or any other suitable feature that can be visually determined (par. 0113). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to teach a type of algorithm, such as in the instant case, a k-means-like algorithm for its art recognized purpose of achieving a same segmentation carried out via the cluster analysis as taught by Bhogal for recognizing foodstuff features from the image generic algorithms, or using per-pixel classification, pixel grouping and/or clustering (e.g., to identify like pixels), and/or cluster classification (e.g., to determine whether a group of pixels is representative of a rack, tray, foodstuff, etc.) (par. 0113) as further taught by Bhogal by analyzing images to extract food cooking progress relative a food parameter such as in the instant a degree of current browning relative a target browning (par. 0114, 0144).
Bhogal teaches alternative to foodstuff detection, the cooking instructions can be determined in anticipation of a food insertion (par. 0089). Thus since Bhogal teaches user selection and a database which stores information and data related to cooking sessions (par. 0058). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to teach the segmentation by the k-means-like algorithm by an opening operation for its art recognized and applicants intended purpose of analyzing images to extract food cooking progress relative a food parameter such as in the instant a degree of current browning relative a target browning (par. 0114, 0144).
Claim 21, Bhogal teaches the image including color images, the camera can single out given light bands (par. 0044) by an image analysis module (par. 0057) including cluster and/or grouping of pixels (par. 0113). Foodstuff features that can be extracted from the image (e.g., via image analysis module 80) can include foodstuff color (e.g., average color, color gradients, etc.) or any other suitable feature that can be visually determined (par. 0113). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to teach the segmentation by the k-means-like algorithm by a user-guided region growing algorithm for its art recognized purpose of achieving a same segmentation carried out via the cluster analysis as taught by Bhogal for recognizing foodstuff features from the image generic algorithms, or using per-pixel classification, pixel grouping and/or clustering (e.g., to identify like pixels), and/or cluster classification (e.g., to determine whether a group of pixels is representative of a rack, tray, foodstuff, etc.) (par. 0113) as further taught by Bhogal by analyzing images to extract food cooking progress relative a food parameter such as in the instant a degree of current browning relative a target browning (par. 0114, 0144).
Claim 23, Bhogal teaches computing a predicted browning for a current food (par. 0114) based on color space coordinates of food pixels (par. 0057 pixel to pixel area maps; par. 0113 pixel clustering/grouping) of an initially recorded lightness value separable image (par. 0113 last 9 lines) and with the aid of real browning information stored in a database for different foods (par. 0057, 0058) and offering the user an opportunity to enter the target degree of browning with the aid of color fields having colors which correspond to spaced points of the predicted browning (par. 0069).
Though silent to determining based on a predicted browning curve for a current food based on averaged color space coordinates of food pixels. Since Bhogal teaches the target food parameter including browning (par. 0069). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to teach a browning curve with respect to the taught “graphs” and achieving a same desired user interface which provides images to a user of browning levels in graphical representation and associated target food conditions be set as desired by Bhogal (par. 0069).
Claim 24, It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to teach the predicted browning curve is computed by creating for individual points of the predicted browning curve a linear equation system that links the individual points via matrix factors to an initial averaged color space coordinates of the food pixels, and further comprising determining the matrix factors using a regression analysis from the averaged color space coordinates of the food pixels of the real browning curves stored in the database since Bhogal teaches the image including color images, the camera can single out given light bands (par. 0044) by an image analysis module (par. 0057) including cluster and/or grouping of pixels (par. 0113). Foodstuff features that can be extracted from the image (e.g., via image analysis module 80) can include foodstuff color (e.g., average color, color gradients, etc.) or any other suitable feature that can be visually determined (par. 0113). For its art recognized purpose of achieving a same segmentation carried out via the cluster analysis as taught by Bhogal for recognizing foodstuff features from the image generic algorithms, or using per-pixel classification, pixel grouping and/or clustering (e.g., to identify like pixels), and/or cluster classification (e.g., to determine whether a group of pixels is representative of a rack, tray, foodstuff, etc.) (par. 0113) as further taught by Bhogal by analyzing images to extract food cooking progress relative a food parameter such as in the instant a degree of current browning relative a target browning (par. 0114, 0144).
Claim 25, Bhogal teaches after the beginning of the cooking process, recording images of the current food at predetermined intervals (par. 0114), determining the actual degree of browning of the food from the images with the aid of the food pixels, (par. 0114) recomputing the predicted browning for the current food based on the actual degree of browning (par. 0114) and adapting the target degree of browning from the predicted browning curve (par. 0114).
Though silent to a browning curve. Bhogal teaches the image including color images, the camera can single out given light bands (par. 0044) by an image analysis module (par. 0057) including cluster and/or grouping of pixels (par. 0113). Foodstuff features that can be extracted from the image (e.g., via image analysis module 80) can include foodstuff color (e.g., average color, color gradients, etc.) or any other suitable feature that can be visually determined (par. 0113).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to teach a browning curve for its art recognized purpose of achieving a same segmentation carried out via the cluster analysis as taught by Bhogal for its intended purpose of analyzing images to extract food cooking progress relative a food parameter such as in the instant a degree of current browning relative a target browning (par. 0114, 0144).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20170074522, 20160192446, 20150289324, 20130306627, 20130186887 directed to food image/camera sensor cooking control.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Steven Leff whose telephone number is (571) 272-6527. The examiner can normally be reached on Mon-Fri 8:30 - 5:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erik Kashnikow can be reached at (571) 270-3475. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEVEN N LEFF/Primary Examiner, Art Unit 1792
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