DETAILED ACTION
This office action is responsive to the amendment filed on 06/23/26. As directed by the amendment: claims 1, 2, 7, and 8-11 have been amended; and no claims have been cancelled nor added. Thus, claims 1-11 are presently pending in this application.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “an image acquisition process for acquiring”, it is submitted that as aforementioned recitation is related to a method step and as the claim is directed toward an apparatus (i.e., “an apparatus for controlling a Baumkuchen baking machine”), a single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) (see MPEP 217.05(p)(II) – Product and Process in the Same Claim). Appropriate correction is required.
Claim 1 recites “a decision process for deciding on a point of time”, it is submitted that as aforementioned recitation is related to a method step and as the claim is directed toward an apparatus (i.e., “an apparatus for controlling a Baumkuchen baking machine”), a single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) (see MPEP 217.05(p)(II) – Product and Process in the Same Claim). Appropriate correction is required.
Claim 1 recites “a process comprising moving the roller from the baking position for the oven to the batter application position and rotating the roller at the batter application position to have the outer peripheral surface of the layered Baumkuchen batter on the roller receive further batter applied thereto:
wherein, in the decision process. the computer is configured to use a learning- enhanced model obtained by machine learning”.
Claim 1 recites “an apparatus … comprising …. The computer being configured to perform the following processes for a plurality of times … a process for moving the roller having layered batter ….”, a single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) (see MPEP 217.05(p)(II) – Product and Process in the Same Claim). Appropriate correction is required.
Claim 2 recites “a determination estimation process for estimating a result of a determination by an operator regarding a doneness of the batter of a Baumkuchen based on an operation by the operator relating the roller of the Baumkuchen baking machine or temperature in the oven”, it is submitted that as aforementioned recitation is related to a method step and as the claim is directed toward an apparatus (i.e., “an apparatus for controlling a Baumkuchen baking machine”), a single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) (see MPEP 217.05(p)(II) – Product and Process in the Same Claim). Appropriate correction is required.
Claim 2 recites “a learning process for generating a learning-enhanced model to be used in the decision process by means of machine learning” and claim 1 (from which claim 2 depends and has been amended as detailed hereafter) recites “in the decision process the computer is configured to use a learning-enhanced model”, it is unclear whether the “a learning-enhanced model” of claim 2 is the same or different than the “a learning-enhanced model” of claim 1. Appropriate correction is required.
Claim 7 recites an oven; a batter container; a roller, and an oven; however, it is submitted that claim 1 from which the instant claim depends recites each of the aforementioned limitations, and as such it is unclear whether the aforementioned limitations are the same or different than those of claim 1 from which the instant claim depends. Appropriate correction is required.
The remaining claims are rejected for at least their respective direct and/or indirect dependency from one of the aforementioned independent claims.
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.
Claims 1, 3, 5, 7, 8, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Tetsuya (JP 2006129781) in view of Otani (JPH089932) and Garden et al. (US 20170290345).
With regard to claims 1, 7, 8, and 10 Tetsuya teaches an apparatus for controlling a Baumkuchen baking machine (FIGS. 2, 3, and 17; DESCRIPTION: “The present invention relates to an improvement of a Baumkuchen baking machine.”) including an oven (B), a batter container (72), and a roller (16) capable of moving between a baking position (“Dough Baking Zone 22, FIG. 16) for the oven (B) and the batter container (72), the control apparatus comprising: a computer (motor/timer) adapted to control movement of the roller (16) having layered batter of a Baumkuchen thereon from a batter application position for applying batter in the batter container (72) to batter on the roller (16) (“Dough Application Zone Z1”) to the baking position (“Dough Baking Zone Z2”) for the oven (B) and movement of the roller (16) from the baking position (Z2) for the oven to the batter application position (Z1)(FIG. 16-17 illustrate movement of roller 16 from dough baking zone Z2 to dough application zone z1) and a process comprising moving the roller (16) from the baking position (“Dough Baking Zone 22, FIG. 16) for the oven (B) to the batter application position (Z1)(FIG. 16-17 illustrate movement of roller 16 from dough baking zone Z2 to dough application zone Z1) and rotating the roller (16) AT THE BATTER APPLICATION POSITION (Z1)(FIG. 16-17 illustrate movement of roller 16 from dough baking zone Z2 to dough application zone Z1) to have the outer peripheral surface of the alyered Baumkuchen batter on the roller (16) receive further batter applied thereo.
Tetsuya does not teach the limitations of the computer being configured to perform: an image acquisition process for acquiring, from a camera photographing a portion of an outer peripheral surface of the layered batter of a Baumkuchen on the roller, a group of images of the outer peripheral surface of the batter rotating together with the roller at the baking position for the oven, the group of images covering at least one entire turn; and a decision process for deciding on a point of time at which the roller is to be moved from the baking position for the oven to the batter application position based on a baked color of the outer peripheral surface of the batter indicated by the group of images of the outer peripheral surface of the batter at the baking position for the oven covering at least one entire turn. However, Otani from the same field of endeavor directed toward the control of baked color in a chikuwa baking machine teaches the aforementioned limitations: “In the method configured as described above, when the main body 1 of the bamboo ring baking machine is driven, each bamboo ring is continuously transferred while rolling by the phase means 2, and the camera sensor 11 burns the outer surface of the bamboo ring. Is detected as color image data, This image data is compared with the stored image data in the determination system to make a stepwise determination such as raw burn, slightly raw burn, optimum, slightly overburn, overburn. The determination output of the detected image data by this determination system is compared with the optimum burn color set by the setting device 15 by the operation amount calculation system 14 to determine the output value of the electric heater, and the heater is operated with the operation amount according to this output value. The electric power controller continuously outputs the electric heater to control the electric heater so as to continuously and evenly and optimally burn the color.”, pg. 2, ln. 41-47.
Therefore, it would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in the Tetsuya reference which operates the roller to be moved from the baking position for the oven to the batter application position, to include the computer being configured to perform: an image acquisition process for acquiring, from a camera photographing a portion of an outer peripheral surface of the layered batter of a Baumkuchen on the roller, a group of images of the outer peripheral surface of the batter rotating together with the roller at the baking position for the oven, the group of images covering at least one entire turn; and a decision process for deciding on a point of time when a food product has reached a desired heating/color based on a baked color of the outer peripheral surface of the batter indicated by the group of images of the outer peripheral surface of the batter, as suggested and taught by Otani, for the purpose of providing a desired heating/color of the subject food product (Otani: pg. 2, ln. 41-47). With regard to the method of claim 8 and 10, as the claims include the same limitations as claim 1 except in method form, to the extent that the prior art apparatus meets the structural limitations of the apparatus as claimed, it will obviously perform the method steps as claimed. Furthermore, it has been held that where the claimed and prior art products are identical or substantially identical in structure or composition, or are produced by identical or substantially identical processes, a prima facie case of either anticipation or obviousness has been established. In re Best, 562 F.2d 1252, 1255, 195 USPQ 430, 433 (CCPA 1977); MPEP 2112.01(I)".
Furthermore, with regard to the limitation of wherein, in the decision process the computer is configured to use a learning- enhanced model obtained by machine learning to decide on the point of time at which the roller is to be moved from the baking position for the oven to the batter application position. the learning enhanced model being data that. when receiving images of the outer peripheral surface of batter as input, enables outputting of the doneness determined based on the baked color indicated by the images, it is submitted that although the primary prior art citation does not explicitly teach the aforementioned limitation, it is submitted that as the claim is directed toward an apparatus, a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim. As that a device according to the combined teachings of the cited prior art would be capable of performing the required intended use and no structural differentiation has been identified, it is the examiner’s determination that this feature does not define the present invention over the cited prior art. See MPEP § 2114. Notwithstanding the foregoing, it is submitted that in view of Garden which is directed to an on-demand robotic food assembly and related systems, devices and methods, Garden teaches a machine learning process to adapt a food assembly process as claimed: “the system or a machine-learning system can be supplied with images of desired or desirable patterns of sauce on flatten pieces of dough or even of pizzas. Additionally or alternatively, the system can be provided with ratings input that represents subjective evaluation of pizzas made via various patterns or paths. Additionally or alternatively, the machine-learning system can be supplied with a number of rules, for example that a pattern or path should result in an equal or roughly equal distribution of sauce, cheese, or other toppings across a surface of the food item (e.g., whole pizza pie). Additionally or alternatively, the machine-learning system can be supplied with a number of rules, for example each individual portion (e.g., slice) of the food item (e.g., pizza) should have an equal or roughly equal distribution of sauce, cheese, or other toppings as every other portion (e.g., slice) of the food item (e.g., pizza). The images and/or ratings and/or rules can be used as training data for training the machine-learning system during a training period or training time. “ Garden, para. [0151].
Therefore, it would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in the Tetsuya reference, to include in the decision process the computer is configured to use a learning- enhanced model obtained by machine learning to decide on the point of time at which the roller is to be moved from the baking position for the oven to the batter application position. the learning enhanced model being data that. when receiving images of the outer peripheral surface of batter as input, enables outputting of the doneness determined based on the baked color indicated by the images, as suggested and taught by Garden, for the purpose of providing an enhanced assembly operation of a subject workpiece.
With regard to claim 3, Tetsuya teaches when acquiring the images, the computer further acquires at least one of a rotational speed of the layered batter of a Baumkuchen on the roller, a baking time for the outer surface of the batter of a Baumkuchen, and a temperature in the oven and, in the decision process, the computer performs the decision based on at least one of the acquired rotational speed, the acquired baking time for the outer surface of the batter of a Baumkuchen, and the acquired temperature in the oven (“After such preparation, when the internal temperature of the baking furnace (B) rises to a certain target temperature (for example, about 350 ° C.), the baking action of the dough (M) is started. It goes without saying that the dough (M) is contained in the dough plate (72) in advance as a dissolved state from flour, fats and oils, sugar, eggs and the like.”, pg. 7, ln. 30-32), whereas Otani teaches in addition to the baked color of the outer peripheral surface of the batter indicated by the group of images (“The control circuit 12 judges and compares the burn color data of bamboo rings continuously detected by the camera sensor 11 with the stored data in which the image data of the optimum burn color is stored in advance. The operation amount calculation system 14 determines the heater output value of the electric heater by comparing the signal from the determination system 13 with the burn color set by the setting device 15 by the operation amount calculation system 14.”, pg. 2, ln. 34-36).
With regard to claim 5, Tetsuya teaches in the decision process, the computer acquires at least one of a speed of circumferential movement of the outer peripheral surface of the layered batter of a Baumkuchen on the roller (“Of course, the rolling pin (16) rotates (rotates) while the rolling pin holding arm (91) is once stopped in the arc motion. The rotation speed of the brake motor (101) is controlled by an inverter. In addition, the arc movement stop time (dough baking time) in the rolling pin holding arm (91) can be appropriately adjusted and set by a timer.”, pg. 9, ln. 1-3), and a diameter of the outer periphery of the layered batter of a Baumkuchen on the roller, and, in the decision process, the computer performs the decision based on at least one of the acquired speed of circumferential movement and the acquired diameter in addition to the baked color of the outer peripheral surface of the batter indicated by the group of images (Otani: “In the method configured as described above, when the main body 1 of the bamboo ring baking machine is driven, each bamboo ring is continuously transferred while rolling by the phase means 2, and the camera sensor 11 burns the outer surface of the bamboo ring. Is detected as color image data, This image data is compared with the stored image data in the determination system to make a stepwise determination such as raw burn, slightly raw burn, optimum, slightly overburn, overburn. The determination output of the detected image data by this determination system is compared with the optimum burn color set by the setting device 15 by the operation amount calculation system 14 to determine the output value of the electric heater, and the heater is operated with the operation amount according to this output value. The electric power controller continuously outputs the electric heater to control the electric heater so as to continuously and evenly and optimally burn the color.”, pg. 2, ln. 41-47).
Claims 2, 4, 6, 9, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Tetsuya (JP 2006129781), Otani (JPH089932), and Garden et al. (US 20170290345) as detailed above, and further in view of Goldberg (WO 2019/014023).
With regard to claim 2, as the claim is directed toward an apparatus and as the subject structural limitations are taught by the cited prior art, it is submitted that a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim. As that a device according to the combined teachings of the cited prior art would be capable of performing the required intended use and no structural differentiation has been identified, it is the examiner’s determination that this feature does not define the present invention over the cited prior art. See MPEP § 2114. Notwithstanding the foregoing, Tetsuya teaches the invention as claimed; however, the citation does not teach the limitation of the limitation of the computer is configured to further perform: a determination estimation process for estimating a result of a determination by an operator regarding a doneness of the batter of a Baumkuchen based on an operation by the operator relating to the roller or temperature in the oven. However, Otani teaches the aforementioned limitation: “In the method configured as described above, when the main body 1 of the bamboo ring baking machine is driven, each bamboo ring is continuously transferred while rolling by the phase means 2, and the camera sensor 11 burns the outer surface of the bamboo ring. Is detected as color image data, This image data is compared with the stored image data in the determination system to make a stepwise determination such as raw burn, slightly raw burn, optimum, slightly overburn, overburn. The determination output of the detected image data by this determination system is compared with the optimum burn color set by the setting device 15 by the operation amount calculation system 14 to determine the output value of the electric heater, and the heater is operated with the operation amount according to this output value. The electric power controller continuously outputs the electric heater to control the electric heater so as to continuously and evenly and optimally burn the color.”, pg. 2, ln. 41-47. Therefore, it would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in the Tetsuya reference, such that the computer is configured to further perform: a determination estimation process for estimating a result of a determination by an operator regarding a doneness of the batter of a Baumkuchen based on an operation by the operator relating to the roller of the Baumkuchen baking machine, as suggested and taught by Otani, for the purpose of providing a predetermined level of heating/browning.
Tetsuya does not teach a learning process for generating a learning-enhanced model to be used in the decision process by means of machine learning using, as teaching data, the estimated result of the determination by the operator and a group of images of the outer peripheral surface of the batter at the baking position for the oven covering at least one entire turn in a period of time including a time of the determination, Notwithstanding the foregoing, Goldberg which is directed toward the same field of endeavor directed toward a configurable food delivery vehicle and related methods and articles is cited herein for teaching the aforementioned limitation: “The system (e.g. , machine-learning system, machine-vision system) may, for example, determine whether a top of the food item is a desired color or colors and, or consistency, for instance determining whether there is too little, too much or an adequate or desired amount of bubbling of melted cheese, too little, too much or an adequate or desired amount of blackening or charring, too little, too much or an adequate or desired amount of curling of a topping (e.g. , curling of pepperoni slices), too little, too much or an adequate or desired amount of shrinkage of a topping (e.g. , vegetables). The system may, for example, determine whether a bottom of the food item is a desired color or colors, for instance determining whether there is too little, too much or an adequate or desired amount of blackening or charring.”. Therefore, it would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in the Tetsuya reference, to include a learning process for generating a learning-enhanced model to be used in the decision process by means of machine learning using, as teaching data, the estimated result of the determination by the operator and a group of images of the outer peripheral surface of the batter at the baking position for the oven covering at least one entire turn in a period of time including a time of the determination, as suggested and taught by Goldberg, for the purpose of providing a learned model for a cooking operation.
With regard to claim 4, Goldberg teaches in the learning process, the computer generates the learning-enhanced model further using at least one of the rotational speed of the layered batter of a Baumkuchen on the roller, the baking time for the outer surface of the batter of a Baumkuchen, and the temperature in the oven in the period of time including the time of the determination based on the operation by the operator (“As used herein the terms "robot" or "robotic" refer to any device, system, or combination of systems and devices that includes at least one appendage, typically with an end of arm tool or end effector, where the at least one appendage is selectively moveable to perform work or an operation useful in the preparation a food item or packaging of a food item or food product. The robot may be autonomously controlled, for instance based at least in part on information from one or more sensors (e.g., optical sensors used with machine- vision algorithms, position encoders, temperature sensors, moisture or humidity sensors). Alternatively, one or more robots can be remotely controlled by a human operator. Alternatively, one or more robots can be partially remotely controlled by a human operator and partially autonomously controlled.”).
With regard to claim 6, Tetsuya teaches using at least one of a speed of circumferential movement of the outer peripheral surface of the layered batter of a Baumkuchen on the roller, and a diameter of the outer periphery of the batter of a Baumkuchen in the period of time including the time of the determination by the operator regarding the doneness (“After such preparation, when the internal temperature of the baking furnace (B) rises to a certain target temperature (for example, about 350 ° C.), the baking action of the dough (M) is started. It goes without saying that the dough (M) is contained in the dough plate (72) in advance as a dissolved state from flour, fats and oils, sugar, eggs and the like.”, pg. 7, ln. 30-32), whereas Otani teaches in addition to the baked color of the outer peripheral surface of the batter indicated by the group of images (“The control circuit 12 judges and compares the burn color data of bamboo rings continuously detected by the camera sensor 11 with the stored data in which the image data of the optimum burn color is stored in advance. The operation amount calculation system 14 determines the heater output value of the electric heater by comparing the signal from the determination system 13 with the burn color set by the setting device 15 by the operation amount calculation system 14.”, pg. 2, ln. 34-36.), and Goldberg teaches in the learning process, the computer generates the learning-enhanced model further being applied toward various cooking parameters (“The machine-learning system may be used to evaluate information (e.g., captured images or image data) captured via one or more machine-vision systems, for example determining what type of food item (e.g., what type of pizza) a given food item is, and assessing whether the food item belongs to a given order and, or matches the ordered food item. For example, the machine-learning system may determine whether the food item is correct (e.g., pizza has the correct toppings, has the correct curst {e.g., gluten versus gluten free), has the correct sauce). For instance, a gluten-free pizza can be visually discerned relative to one that includes gluten in the crust, for instance via a three-dimensional (3D) camera system. Also for example, the machine- learning system may determine whether the food item meets other desired criteria or properties (e.g. , pizza has an adequate distribution of toppings, is evenly cooked, has adequate amounts and not too much charring, desired shape, desired size, desired spices). For instance, height of cheese and, or toppings may be assessed via a three-dimensional (3D) camera system, and the machine-learning system may be used assure that the height is within a range of acceptable heights with an upper and a lower bound, which may have been learned over a training data set. If a food item is incorrect or does not meet various criteria, the food item can either be diverted to be repaired, or can be sent to a waste receptacle and in response a replacement order placed in the queue, perhaps expedited to a point closer to actually being assembled than other orders in the queue, for instance to meet a desired time to delivery guarantee.”).
With regard to claims 9 and 11, Tetsuya teaches a method for controlling a Baumkuchen baking machine (FIGS. 2, 3, and 17; DESCRIPTION: “The present invention relates to an improvement of a Baumkuchen baking machine.”) including an oven (B), a batter container (72), and a roller (16) capable of moving between a baking position (“Dough Baking Zone 22, FIG. 16) for the oven (B) and the batter container (72).
Tetsuya does not teach a determination estimation step in which a computer estimates a result of a determination by an operator regarding a doneness of batter of a Baumkuchen based on an operation by the operator of the Baumkuchen baking machine; the estimated result of the determination by the operator and a group of images of an outer peripheral surface of the batter at the baking position for the oven, the group of images covering at least one entire turn in a period of time including a time of the determination; however, Otani from the same field of endeavor directed toward the control of baked color in a chikuwa baking machine teaches the aforementioned limitations: “In the method configured as described above, when the main body 1 of the bamboo ring baking machine is driven, each bamboo ring is continuously transferred while rolling by the phase means 2, and the camera sensor 11 burns the outer surface of the bamboo ring. Is detected as color image data, This image data is compared with the stored image data in the determination system to make a stepwise determination such as raw burn, slightly raw burn, optimum, slightly overburn, overburn. The determination output of the detected image data by this determination system is compared with the optimum burn color set by the setting device 15 by the operation amount calculation system 14 to determine the output value of the electric heater, and the heater is operated with the operation amount according to this output value. The electric power controller continuously outputs the electric heater to control the electric heater so as to continuously and evenly and optimally burn the color.”, pg. 2, ln. 41-47.
Therefore, it would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in the Tetsuya reference which operates the roller to be moved from the baking position for the oven to the batter application position, to include the computer being configured to perform: estimates a result of a determination by an operator regarding a doneness of batter of a Baumkuchen based on an operation by the operator of the Baumkuchen baking machine; the estimated result of the determination by the operator and a group of images of an outer peripheral surface of the batter at the baking position for the oven, the group of images covering at least one entire turn in a period of time including a time of the determination, as suggested and taught by Otani, for the purpose of providing a desired heating/color of the subject food product (Otani: pg. 2, ln. 41-47).
Tetsuya does not teach a learning step in which the computer generates a learning-enhanced model to be used in a decision process by means of machine learning using, as teaching data, wherein the learning-enhanced model is data to be used in the decision process in which the computer decides on a point of time at which the roller is to be moved from the baking position for the oven to the batter application position based on a baked color of the outer peripheral surface of the batter indicated by the group of images of the outer peripheral surface of the batter at the baking position for the oven covering at least one entire turn; however, Goldberg which is directed toward the same field of endeavor directed toward a configurable food delivery vehicle and related methods and articles is cited herein for teaching the aforementioned limitation: “The system (e.g. , machine-learning system, machine-vision system) may, for example, determine whether a top of the food item is a desired color or colors and, or consistency, for instance determining whether there is too little, too much or an adequate or desired amount of bubbling of melted cheese, too little, too much or an adequate or desired amount of blackening or charring, too little, too much or an adequate or desired amount of curling of a topping (e.g. , curling of pepperoni slices), too little, too much or an adequate or desired amount of shrinkage of a topping (e.g. , vegetables). The system may, for example, determine whether a bottom of the food item is a desired color or colors, for instance determining whether there is too little, too much or an adequate or desired amount of blackening or charring.”.
Therefore, it would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in the Tetsuya reference, to include a learning step in which the computer generates a learning-enhanced model to be used in a decision process by means of machine learning using, as teaching data, wherein the learning-enhanced model is data to be used in the decision process in which the computer decides on a point of time at which the roller is to be moved from the baking position for the oven to the batter application position based on a baked color of the outer peripheral surface of the batter indicated by the group of images of the outer peripheral surface of the batter at the baking position for the oven covering at least one entire turn, as suggested and taught by Goldberg, for the purpose of providing a learned model for a cooking operation.
Response to Arguments
Applicant's arguments filed 06/23/26 have been fully considered and are addressed hereafter.
At pg. 12 of the response the Applicant contends: “Otani does not describe the layter-by-layer Baumkuchen process now recited in claim 1 (i.e., in which the roller is repeatedly moved between a batter application position and a baking position, images covering at least one entire turn are obtained for the then-current outer layer, and a decision is made whether that layer has reached the proper doneness to begin the next application cycle….” It is submitted that claim 1 is directed toward an apparatus, and as such, the newly amended (and some existing process limitations which were rejected in the prior office action for definiteness) render the claims indefinite. As indicated in the prior office action (and again herein), method steps which are included within apparatus render the claim indefinite as a single claim which claims both an apparatus and the method steps of using the apparatus is indefinite under 35 U.S.C. 112(b) (see MPEP 217.05(p)(II) – Product and Process in the Same Claim). As an additional note, it must be stressed that a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim. As that a device according to the combined teachings of the cited prior art would be capable of performing the required intended use and no structural differentiation has been identified, it is the examiner’s determination that this feature does not define the present invention over the cited prior art. See MPEP § 2114.
With regard to the Applicant’s arguments regarding the apparatus of the instant patent application being directed toward Baumkuchn in contrast to Otani which is directed toward Chikuwa, it is respectfully submitted that the primary citation (Fujinami) is directed toward a Baumkuchen machine, and the secondary citation (Otani) is cited for deficiencies identified in the prior art rejections of the instant office action. Notwithstanding the foregoing, it must be stressed that “Baumkuchn” is essentially a workpiece upon which the apparatus of the instant patent application works upon, and as such, the inclusion of a material or workpiece worked upon by a claimed apparatus does not impartment patentable to that apparatus – patentability requires a positive recitation of structural elements rather thatn the object or article the machine operates upon.
To overcome the various rejections of the instant patent application, the Applicant may consider amending the claims to positively recite the structural limitations of the subject device being claimed therein.
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 JOSEPH W ISKRA whose telephone number is (313) 446-4866. The examiner can normally be reached on M-F: 09:00-17:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, IBRAHIME ABRAHAM can be reached on 571-270-5569. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEPH W ISKRA/Examiner, Art Unit 3761
/IBRAHIME A ABRAHAM/Supervisory Patent Examiner, Art Unit 3761
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