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 .
Response to Amendment
The amendment filed on 03/26/2026 has been entered. As directed by the amendment: claim 1 is amended. Claims 7 – 13 are withdrawn. Thus, claims 1 – 6 are currently pending. Applicant’s arguments regarding the Non-Final Rejection are fully considered (please see “Response to Arguments” section) and the following Final rejection is made herein.
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, 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 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.
Claim(s) 1 and 5 – 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jank et al. (WO 2011/110563 A1), hereinafter Jank, in view of Chae et al. (US 2021/0137311 A1, foreign priority date Nov. 08, 2019) and hereinafter "Chae".
Regarding claim 1, Jank discloses a Baumkuchen baking system, comprising:
a Baumkuchen baking machine (an apparatus for automatically producing a Baumkuchen, see FIG. 1) including an oven (baking chamber 11, (0035 and see FIG.1)), a batter container (a dough container 21, (0039 and see FIG. 1) *Note here- "batter" is interpreted to mean a Baumkuchen cake making dough), a roller (baking roller 20, (0036, and see FIG.1)) capable of moving between a baking position for the oven and the batter container (the baking roller 20 is automatically moveable between baking chamber 11 and dough container 21, (0039 and see FIG. 1)), and a camera adapted to photograph a portion of an outer peripheral surface of layered Baumkuchen batter on the roller (sensor 401 adapted to detect the degree of browning of the baked layer of the Baumkuchen on baking roller 20, (0112 and see FIG. 1)); and
a control unit adapted to control the Baumkuchen baking machine (a control device 200 configured to control the Baumkuchen apparatus, (0044 and see FIG.1)) wherein:
the control unit includes an automatic control unit adapted to determine doneness or baking control based on an image, , of an outer peripheral surface of each layer of Baumkuchen batter currently being baked at a baking position for the oven and use a result of each layer of determination to automatically control baking of each layer of the Baumkuchen batter (the control device 200 automatically controls the baking of the Baumkuchen layer based on the photosensor that detects the of browning of the baked layer of the Baumkuchen on baking roller 20 and influence the rotational speed of the baking roller 20, timing and the distances of the radiant heater to the baking roller 20 and radiation intensity of the heat source to control the baking of each layer of the Baumkuchen batter, (0110 - 0114)).
Jank does not explicitly teach that the senor 401 adapted to detect the degree of browning of the baked layer of the Baumkuchen on the baking roller 20 is a camera adapted to photograph the baked layer; a communication unit adapted to communicate data with a server; the server is capable of accessing a storage unit adapted to store a learning-enhanced model obtained by learning a doneness determination or baking control based on an image of an outer peripheral surface of layered Baumkuchen batter on the roller being baked; the controller using the learning-enhanced model provided by the server; and the learning enhanced model is a data set for performing a process in which the image of the outer peripheral surface of layered Baumkuchen batter on the roller being baked is received as input and a value indicating the determination of doneness or baking control is output.
However, Chae that relaters to an AI cooking device which grasps a degree of doneness preferred by a user for a specific food, and automatically performs optimum cooking for the corresponding food (0007 - 0011), also teaches camera 121 to periodically capture the image of the food being cooked to determine the level of doneness based on the captured image, (0147- 00148 and see FIG.4), a communication unit 110 that is adapted to transmit and receive data (communicate), e.g. a learning model, to and from a server 200, wherein a memory 170 of the AI cooking device is capable of storing input data acquired like, learning data, a learning model, and a learning history, (0044, 0056 and see FIG.4), a processor 180 through the learning processer 130 utilizing the learning model, (0049 and see FIG.4), wherein the learning model is a doneness determination model trained by a deep learning algorithm that determines a level of a doneness of the food based on a periodically captured image of the food by a camera 121 and controls the cooking to finish if the determined level of the doneness is up to a user’s preference, or extend cooking when the level of doneness is not met, (0147 – 0150, 0158 – 0162, 0170 – 0171, claims 1 – 8, see FIGS.5 and 8).
Chae further states that such cooking device that employs a deep learning model that controls the cooking device based on periodically captured images of the food the camera greatly enhances automation, cooking time and user's convenience compared to conventional home food cooking appliances, (0003 – 0011).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to include a communication unit adapted to communicate data with a server; the server is capable of accessing a storage unit adapted to store a learning-enhanced model obtained by learning a doneness determination or baking control based on an image of an outer peripheral surface of layered Baumkuchen batter on the roller being baked; the controller using the learning-enhanced model provided by the server; and the learning enhanced model is a data set for performing a process in which the image of the outer peripheral surface of layered Baumkuchen batter on the roller being baked is received as input and a value indicating the determination of doneness or baking control is output as automation, cooking time and user's convenience can be greatly improved as taught in Chae.
Regarding claim 5, Jank in view of Chae teaches the Baumkuchen baking system according to claim 1, wherein:
the control unit further includes a learning unit adapted to create, as teaching data for learning, data indicating a result of determination of doneness or baking control for each layer of the batter estimated from an operator operation by manual control during baking on the Baumkuchen baking machine (the processor 180 includes a learning processor 130 that trains an image recognition model that determine a doneness class indicating a degree of doneness of the food, from the captured food image, by using a doneness class classification mode, Chae (0154 – 0158)); and
the control unit provides, to the server via the communication unit, the teaching data created by the learning unit or a learning-enhanced model generated through learning using the teaching data (the processor 180 through the communication unit 110 may transmit and receive the learning model data to and from the server 200, Chae (0044 and see FIGS 2 and 4)).
Regarding claim 6, Jank in view of Chae teaches the Baumkuchen baking system according to claim 1, wherein the control unit acquires, from the server, batter recipe data indicating a combination of batter ingredients and a batter preparation procedure associated with the learning-enhanced model provided by the server, and provides, as output, the batter recipe data to an operator of the Baumkuchen baking machine (the processor 180 may request, search, receive, or utilize data of the learning processor 130 or the memory 170, that communicates with server 200 including identification information identifying the food item or type and doneness classification from the food image by using the image recognition model, Chae (0058, 0159 – 0170 and see FIG.4)).
Claim(s) 2 – 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jank view of
Chae in further view of Thomas et al. (US 2017/0188741 A1) and hereinafter "Thomas".
Regarding claim 2, Jank in view of Chae teaches the Baumkuchen baking system according to claim 1, wherein:
the storage unit accessible to the server stores a plurality of learning-enhanced models (a memory 170 of the AI cooking device is capable of storing input data acquired from a server like, learning data, a learning model, and a learning history, Chae (0044, 0056 and see FIG.4), Thus stores a plurality of learning-enhanced models), and
the automatic control unit determines the doneness or baking control based on the image captured by the camera using the learning-enhanced model provided by the server and associated with the chef data indicating the pastry chef designated by the operator (a processor 180 utilizing the learning model that determines a level of a doneness of the food based on a periodically captured image of the food by a camera 121 and automatically controls the cooking unit to deliver a doneness level that is up to a user’s preference , Chae (0147 – 0150, 0158 – 0162, claims 1 – 8, see FIGS.5 and 8)).
Jank in view of Chae does not explicitly teach that the storage unit further stores, in association with each of the plurality of learning-enhanced models, chef data indicating a pastry chef who has contributed to creation of teaching data used for learning for this particular learning-enhanced model; the control unit further includes a user interface unit adapted to receive a designation of a chef by an operator.
However, Thomas that relates to an electronic cooking assistant and cooking systems (0006), also teaches the storage unit of the server further stores, in association with each of the plurality of learning-enhanced models, chef data indicating a pastry chef who has contributed to creation of teaching data used for learning for this particular learning-enhanced model (the system 100 includes a server 108 that may receive a request for information of cooking an item from a remote device 106 via a network 102 wherein the information includes the name of a chef or recipe contributor, (0031 - 0032 and FIG.1)); the control unit further includes a user interface unit adapted to receive a designation of a chef by an operator (a selection of link 2 with a user interface to enable the user select a chef recipe information for cooking (0041 and FIG.2)).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Baumkuchen baking system server database taught by Jank in view of Chae, to store, in association with each of the plurality of learning-enhanced models, chef data indicating a pastry chef who has contributed to creation of teaching data used for learning for this particular learning-enhanced model and the control unit further includes a user interface unit adapted to receive a designation of a chef by an operator as taught in Thomas in order to provide an option of selecting a chef recipe for baking the Baumkuchen.
Regarding claim 3, Jank in view of Kaiser in further view of Thomas teaches the Baumkuchen baking system according to claim 1, wherein the control unit further includes a remote control unit adapted to provide, in real time, an image captured by the camera of the outer peripheral surface of the Baumkuchen batter currently being baked to a remote terminal with which the remote control unit is capable of communicating data via the communication unit and, in accordance with an operation instruction received from the remote terminal, control baking of each layer of the Baumkuchen batter (the server 108 may receive a request for instructions to cook an item from a remote device 106 via a network 102 and communicate to the remote device 106 over the network 102 through any wireless means instructions to cook an item may be used by the cooking device 104, Thomas (0030 and FIG. 1)).
Regarding claim 4, Jank in view of Kaiser in further view of Thomas teaches the Baumkuchen baking system according to claim 3, wherein the remote control unit uses the learning-enhanced model provided by the server to determine the doneness of or baking control for the Baumkuchen batter based on the image captured by the camera of the outer peripheral surface of the Baumkuchen batter currently being baked at the baking (the remote device 106, via network 102 , can receives enhanced cooking instruction provided by the server 108 and dynamically modify the at least one adaptable cooking instruction based on the at least one variable to be customized, Thomas (0030 - 0031 and see claim 10)) , and provide information about the determination to the remote terminal together with the image in real time (adaptable cooking instruction based on the at least one criteria comprises determining a location, temperature, or pressure of the remote device, Thomas (see claims 13 and 15)).
Response to Arguments
CLAIM OBJECTIONS
Applicant’s arguments, see Remarks, page 8, filed on 03/26/2026, with respect to the objection of the term “a baking position for the oven" in claim 1 are persuasive. As such, the objection has been withdrawn.
CLAIM REJECTIONS - 35 U.S.C. § 103
Applicant argues:
Jank’s photosensor 401 that detects the of browning of the baked layer of the Baumkuchen cannot be equated (interpreted as) the claimed camera adapted to photograph an image of the outer layered Baumkuchen as photosensor is a scalar intensity/degree measurement tool and cannot be construed as a camera that captures an image, and
Kaiser’s machine learning is limited to classification/recognition of type of food being cooked and does not teach the claimed earning enhanced model that adapted to determine doneness or baking control based on an image captured by a camera of the of layer of the Baumkuchen.
The above applicant’s arguments with respect to the obviousness of the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. New prior art Chae is introduced to address the limitations in argument in the rejections herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILNESSA B BELAY whose telephone number is (571)272-3136. The examiner can normally be reached M-F approx. 8:00 am - 5:30 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steven Crabb can be reached at (571)270-5095. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DILNESSA B BELAY/Examiner, Art Unit 3761
/JOHN J NORTON/Primary Examiner, Art Unit 3761
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