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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/13/2026 has been entered.
Response to Amendment
The amendment filed 02/13/2026 has been entered. Claims 1-20 remain pending in the application. Claims 16-19 remain withdrawn. Claims 1-15 and 20 remain rejected. Applicant’s amendments to the Claims have overcome each and every 35 USC 112(b) rejection previously set forth in the Final Office Action mailed 10/15/2025, except where otherwise stated.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 and claim 20, particularly claim 1, are rejected under 35 U.S.C. 101.
The claim(s) is/are directed to a method for performing a cooking process on a food product, which falls into the statutory category of a process.
The claim(s) is/are rejected because the claimed invention is directed to an abstract idea without significantly more.
The limitation “generating a food model based on the cooking recipe information and database information” is a mental process, i.e., a concept that can be performed in the human mind, or a mathematical concept. The claimed “food model” is not specifically directed to a mathematical model, a 3D-model, an informational model, etc., and has been interpreted to broadly encompass different types of models. Regardless, both mental processes, which may include, for example a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis,” (See MPEP 2106.04(a)(2) III A) and mathematical concepts which include mathematical formulas (See MPEP 2106.04(a)(2) I), both constitute abstract ideas, a category of judicial exceptions. The limitation “estimating a thermal property of the food product on the basis of the cooking recipe information and the at least one nutrient information, said thermal property comprising at least one property selected from a group consisting of: a density, a shape, a thermal conductivity, and a heat capacity of the food product” is a mathematical calculation (i.e., a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number) that also constitutes an abstract idea (See MPEP 2106.04(a)(2) I). Additionally, the limitation “defining a cooking parameter of a cooking appliance based on the food model, wherein the cooking parameter comprises a parameter selected from a group consisting of: a cooking temperature, a duration of a cooking program or program segment or program step, and a heating mode” is also a mental process, and, therefore, falls within the category of the judicial exception of abstract ideas.
The judicial exception(s) is/are not integrated into a practical application because the claim limitations, specifically, “performing the cooking process on the food product using the cooking appliance and the cooking parameter, wherein the cooking process is performed by adjusting the cooking parameter of the cooking appliance during the cooking process based on continuous or successive updates in the food model” does not go beyond generally linking the judicial exception(s) to the technical field of performing a cooking process on a food product using a cooking appliance (See MPEP 2106.05(h)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations “obtaining cooking recipe information provided in a cooking recipe, wherein the cooking recipe information comprises at least one recipe information selected from a group consisting of: an ingredient, a preparation step or preparation process, a shape of unprocessed food, a composition, property, or condition of the food product, and a key word or phrase relating to the food product, said ingredient, or a desired final property of the food product” and “obtaining database information from a database, the database information comprising at least one nutrient information selected from a group consisting of: water, fat, carbohydrates, and proteins of the ingredient and/or unprocessed food” are mere data gathering that is considered insignificant extra-solution activity (See MPEP 2106.05(g)). Also, receiving and transmitting data are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (See MPEP 2106.05(d) II). Furthermore, the step of “performing the cooking process on the food product using the cooking appliance and the cooking parameter” is also merely well-understood, routine, conventional activity previously known to the industry that does not integrate the judicial exception into a practical application (See MPEP 2106.05). Dependent claims 2-15 and 20 also do not provide additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claim(s) is/are not eligible subject matter under 35 U.S.C. 101.
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.
Claim(s) 1, 3-5, 11-14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190223474 A1) in view of 2010 ASHRAE Handbook, Ghalavand (WO 2015170244 A1), Watt (Database Design), Chen (US 20180157232 A1), Ohtani (US 20210338008 A1), Narita (US 4463249 A), and Li (CN 108685477 A).
Regarding claim 1, Lee teaches (Paragraph 0013, 0035) generating a thermal finite element analysis (FEA) model of all detected food items in an oven (cooking appliance) based on information obtained by the weight, 3D scanner, thermal imaging sensor, and operator input information. Lee further teaches (Paragraph 0036; Fig. 1 #105) information pertaining to the volume and geometry (shape/thermal property) of the food items being prepared is gathered from the sensors and delivered to the controller 105, wherein the controller 105 utilizes the sensor data to generate a cooking thermal model. Also, Lee teaches (Paragraph 0027) the precision cooking oven 100 initially scans the internal properties of a food item (e.g., density (thermal property)) and thereafter uses these properties to build and run a thermal model of the food that is to be cooked. In addition, Lee teaches (Paragraph 0014, 0027, 0036) the oven initiates and completes a cooking process, wherein throughout the food item cooking process the weight, 3D scanning, and thermal imaging sensors continuously gather sensor information regarding the cooking food, the detected sensor information being utilized to continuously update the cooking time of the food (duration of a cooking program), designated food heating patterns, and cooking power levels (heating mode) for the cooking food items (adjusting cooking parameters of the cooking appliance), wherein, as shown above, the sensor information includes information pertaining to the volume and geometry (shape/thermal property) and density information (thermal property), and wherein, the oven controller utilizes the FEA results to adjust the cooking time, heating pattern, and a cooking power level (i.e., the FEA model is continuously updated, and cooking parameters of the cooking appliance based on the food model are adjusted).
Thus, Lee discloses generating a food model based on thermal property information (e.g., shape, density). Lee obtains this thermal property information via sensor measurements and is silent on estimating the thermal property of the food product on the basis of the cooking recipe information and the at least one nutrient information, wherein cooking recipe information comprises at least one recipe information selected from a group consisting of: an ingredient, a preparation step or preparation process, a shape of unprocessed food, a composition, property, or condition of the food product, and a key word or phrase relating to the food product, said ingredient, or a desired final property of the food product, and wherein nutrient information selected from a group consisting of: water, fat, carbohydrates, and proteins of the ingredient and/or unprocessed food is obtained from a database.
2010 ASHRAE Handbook teaches (Page 19.1, 19.6, 19.7, 19.11) thermal properties of foods, including density, specific heat, and thermal conductivity, can be predicted by using composition data including water, protein, fat, and carbohydrate (nutrient data) of the individual food constituents (ingredients).
While 2010 ASHRAE Handbook does not explicitly state that the nutrient data is stored in a data base, the storage of data in a database is well-known and common practice.
For example, Ghalavand teaches (Paragraph 0015) accessing a database to derive food attributes including calories, fat, protein, carbohydrates, (nutrient data) and other variables according to food type (ingredient) information.
The benefits of providing data in databases is also well known in the art. For example, Watt teaches (Paragraph 3) the processing power of a database allows it to manipulate the data it houses, so it can: sort, match, link, aggregate, skip fields, calculate, arrange, etc. Watt further teaches (Paragraph 4) a database can be linked to websites and client-tracking applications. In addition, Watt teaches (Paragraph 10) database systems allow many users to access the same database at the same time. Additionally, Watt teaches (Paragraph 0013)integration of data within a database system allows for data sharing among those who have access to the system.
The prior art also discloses the advantages of acquiring information for controlling cooking operations in addition or as a substitute to sensor collection.
Narita teaches (Claims 1, 2) a method of operating an electric rice cooker wherein a sensor failure is determined by comparing a measurement signal to a reference signal stored in memory.
Li teaches (Paragraph 0008, 0012) a cooking method, wherein, if a sensor malfunction is detected, a first heating curve that is stored in the cooking appliance, corresponding to the sensor is acquired; and the cooking appliance is controlled according to the first heating curve.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee to estimate the thermal property of the food product (e.g., as a substitute for, or in addition to sensor data or user input) on the basis of at least one nutrient information, wherein nutrient information selected from a group consisting of: water, fat, carbohydrates, and proteins of the ingredient and/or unprocessed food is obtained from a database in view of 2010 ASHRAE Handbook, Ghalavand, Watt, Narita, and Li, since Lee already discloses the use of thermal property information in performing a cooking process, since determining the thermal properties of foods from nutrient data including calories, fat, protein, carbohydrates according to the food ingredients is known in the art from 2010 ASHRAE Handbook, since obtaining nutrient information including calories, fat, protein, and carbohydrates from a database is known in the art as shown by Ghalavand, since the processing power of a database allows it to manipulate the data it houses, so it can: sort, match, link, aggregate, skip fields, calculate, arrange, etc. (Watt, Paragraph 3), since a database can be linked to websites and client-tracking applications (Watt, Paragraph 4), since database systems allow many users to access the same database at the same time (Watt, Paragraph 10), since integration of data within a database system allows for data sharing among those who have access to the system (Watt, Paragraph 13), since information acquired other than by sensor detection (e.g., estimated thermal property information based on nutrient data) can be used to verify sensor failure (Narita, Claims 1, 2), and since information acquired other than by sensor detection (e.g., estimated thermal property information based on nutrient data) can be substitute to control a cooking appliance if a sensor malfunctions (Li, Paragraph 0008, 0012).
Also, the food ingredient information necessary to determine the corresponding nutrient data and calculate the thermal properties can be derived cooking recipe information.
For example, Chen teaches (Paragraph 0003, Claim 8) a method of operating a food preparation device, wherein a processor is used to obtain a recipe; and based on the recipe, determine one or more food items (ingredients).
The prior art also discloses the advantages of acquiring information for controlling cooking operations in addition or as a substitute to sensor collection or user input.
Ohtani (US 20210338008 A1) teaches (Paragraph 0005, 0024, 0033) detecting a cooking process performed by a user and determining whether the cooking process deviates from the cooking process indicated in recipe information in order to appropriately address various cooking errors, including the use or preparation of a wrong ingredient.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee to estimate the thermal property of the food product (e.g., as a substitute for, or in addition to sensor data or user input) on the basis of the cooking recipe information, wherein cooking recipe information comprises at least one recipe information selected from a group consisting of: an ingredient, a preparation step or preparation process, a shape of unprocessed food, a composition, property, or condition of the food product, and a key word or phrase relating to the food product, said ingredient, or a desired final property of the food product, Chen, Narita, Li, and Ohtani since Lee already discloses the use of thermal property information in performing a cooking process, since determining the thermal properties of foods from nutrient data including calories, fat, protein, carbohydrates according to the food ingredients is known in the art from 2010 ASHRAE Handbook as shown above, since obtaining cooking recipe information including ingredients is known in the art as shown by Chen, since enabling users to prepare meals or dishes with minimal user interaction provides convenience to users, and further enables those individuals who are unable to cook (e.g., elderly, handicapped) to prepare meals (Chen, Paragraph 0044), since recipe information can be used to address errors in the cooking process including the use or preparation of a wrong ingredient (Ohtani, Paragraph 0005, 0024, 0033), since information acquired other than by sensor detection (e.g., estimated thermal property information based on cooking recipe information) can be used to verify sensor failure (Narita, Claims 1, 2), and since information acquired other than by sensor detection (e.g., estimated thermal property information based on cooking recipe information) can be substitute to control a cooking appliance if a sensor malfunctions (Li, Paragraph 0008, 0012).
Regarding claim 3, Lee teaches (Paragraph 0013) information obtained by the weight, 3D scanner, thermal imaging sensor, and operator input information is utilized by the oven controller to generate a thermal finite element analysis (FEA) model of all detected food items within the oven. Lee further teaches (Paragraph 0014) throughout the food item cooking process the weight, 3D scanning, and thermal imaging sensors continuously gather sensor information regarding the cooking food, the detected sensor information being utilized to continuously update the cooking time of the food, designated food heating patterns, and cooking power levels for the cooking food items, wherein, the oven controller utilizes the FEA results to adjust the cooking time, heating pattern, and a cooking power level (i.e., the FEA model, which is generated using detected parameters from the sensors and user input is continuously updated).
Regarding claim 4, as shown above with regard to claim 3, Lee teaches that the FEA model, which is generated using detected parameters from the sensors is continuously updated. Furthermore, Lee teaches (Paragraph 0012) the 3D scanning sensor is configured to detect the volume and geometry (shape) of all food items situated upon the food placement surface, and the thermal imaging sensor is configured to detect the real-time temperature distribution within the food items situated upon the food placement surface.
Regarding claim 5, as shown above with regard to claim 3, Lee teaches that the FEA model, which is generated using detected parameters from the sensors is continuously updated. Furthermore, Lee teaches (Paragraph 0012) the 3D scanning sensor is configured to detect the volume and geometry (shape) of all food items situated upon the food placement surface.
Regarding claim 11, Lee, as modified above, is silent on the thermal property being calculated by a sum comprising summands of multipliers with different mass fractions selected from among carbohydrate mass fractions, protein mass fractions, fat mass fractions, mineral mass fractions, water mass fractions and air mass fractions.
2010 ASHRAE Handbook teaches (Page 19.1, 19.6, 19.7, 19.11) thermal properties of foods can be predicted by using composition data in conjunction with temperature-dependent mathematical models of thermal properties of the individual food constituents, wherein the density of a food product can be determined using the formula
ρ
=
1
-
ε
∑
x
i
/
ρ
i
where xi is the mass fraction of the food constituents and ρi is density of the food constituents; the specific heat of a food product can be determined using the formula
c
u
=
∑
c
i
x
i
where ci is the specific heat of the individual food components and xi is the mass fraction of the food components; and the thermal conductivity of a food product can be determined using the formula
k
=
∑
x
i
v
k
i
where ki is the thermal conductivity of the individual food components and
x
i
v
is the volume fraction of the food components, wherein food components include water, protein, fat, and carbohydrate.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee to calculate thermal properties by a sum comprising summands of multipliers with different mass fractions selected from among carbohydrate mass fractions, protein mass fractions, fat mass fractions, and water mass fractions as taught by 2010 ASHRAE Handbook since both are directed to determining thermal properties of food items including density, since calculating thermal properties by a sum comprising summands of multipliers with different mass fractions selected from among carbohydrate mass fractions, protein mass fractions, fat mass fractions, and water mass fractions is known in the art as shown by 2010 ASHRAE Handbook, since the thermal properties of foods and beverages strongly depend on chemical composition and temperature, and because many types of food are available, it is nearly impossible to experimentally determine and tabulate the thermal properties of foods and beverages for all possible conditions and composition(2010 ASHRAE Handbook, Page 19.1), since thermal properties of foods can be predicted by using composition data in conjunction with temperature-dependent mathematical models of thermal properties of the individual food constituents (2010 ASHRAE Handbook, Page 19.1), since thermal property information acquired from a calculation can be used for food products not available in the database or if there is an error in the sensor readings or to provide thermal property information without the need for sensors, making the apparatus and its operation simpler, increasing convenience, and since calculated thermal property information could be used when sensors are unavailable or inoperable.
Regarding claim 12, Lee, as modified above, is silent at least one of the summands comprising a multiplier of carbohydrate mass fraction, fat mass fraction, water mass fraction or air mass fraction and
further comprising a multiplier of the thermal property of a same molecule.
2010 ASHRAE Handbook teaches (Page 19.1, 19.6, 19.7, 19.11) thermal properties of foods can be predicted by using composition data in conjunction with temperature-dependent mathematical models of thermal properties of the individual food constituents, wherein the density of a food product can be determined using the formula
ρ
=
1
-
ε
∑
x
i
/
ρ
i
where xi is the mass fraction of the food constituents and ρi is density of the food constituents; the specific heat of a food product can be determined using the formula
c
u
=
∑
c
i
x
i
where ci is the specific heat of the individual food components and xi is the mass fraction of the food components; and the thermal conductivity of a food product can be determined using the formula
k
=
∑
x
i
v
k
i
where ki is the thermal conductivity of the individual food components and
x
i
v
is the volume fraction of the food components, wherein food components include water, protein, fat, and carbohydrate.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee, as modified above, for at least one of the summands to comprise a multiplier of carbohydrate mass fraction, fat mass fraction, water mass fraction or air mass fraction and to further comprise a multiplier of the thermal property of a same molecule as taught by 2010 ASHRAE Handbook since both are directed to determining thermal properties of food items including density, since calculating thermal properties wherein at least one of the summands comprise a multiplier of carbohydrate mass fraction, fat mass fraction, water mass fraction or air mass fraction and further comprises a multiplier of the thermal property of a same molecule is known in the art as shown by 2010 ASHRAE Handbook, since the thermal properties of foods and beverages strongly depend on chemical composition and temperature, and because many types of food are available, it is nearly impossible to experimentally determine and tabulate the thermal properties of foods and beverages for all possible conditions and composition(2010 ASHRAE Handbook, Page 19.1), since thermal properties of foods can be predicted by using composition data in conjunction with temperature-dependent mathematical models of thermal properties of the individual food constituents (2010 ASHRAE Handbook, Page 19.1), since thermal property information acquired from a calculation can be used for food products not available in the database or if there is an error in the sensor readings or to provide thermal property information without the need for sensors, making the apparatus and its operation simpler, increasing convenience, and since calculated thermal property information could be used when sensors are unavailable or inoperable.
Regarding claim 13, Lee, as modified above, is silent on at least one thermal property of a water molecule and/or of an air gas mixture being a parabolic function over a temperature range and
at least one thermal property of a fat molecule and/or carbohydrate molecule being a linear function over the temperature range.
2010 ASHRAE Handbook teaches (Page 19.1, 19.2) thermal property models for water in the range of -40 ≤ t ≤ 150 °C, wherein thermal conductivity, density, and specific heat are functions of t2 (i.e., parabolic) and thermal property models for fat and carbohydrate in the range of -40 ≤ t ≤ 150 °C, wherein density is a function of t (i.e., linear).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee, as modified above, for at least one thermal property of water to be a parabolic function over a temperature range and at least one thermal property of a fat molecule and/or carbohydrate to be a linear function over the temperature range as taught by 2010 ASHRAE Handbook since both are directed to determining thermal properties of food items including density, since determining at least one thermal property of water with a parabolic function over a temperature range and at least one thermal property of a fat molecule and/or carbohydrate with a linear function over the temperature range is known in the art as shown by 2010 ASHRAE Handbook, since one of ordinary skill in the art would recognize that functions that accurately describe the thermal properties would be desirable for accurate results, since the thermal properties of foods and beverages strongly depend on chemical composition and temperature, and because many types of food are available, it is nearly impossible to experimentally determine and tabulate the thermal properties of foods and beverages for all possible conditions and composition(2010 ASHRAE Handbook, Page 19.1), since thermal properties of foods can be predicted by using composition data in conjunction with temperature-dependent mathematical models of thermal properties of the individual food constituents (2010 ASHRAE Handbook, Page 19.1), since thermal property information acquired from a calculation can be used for food products not available in the database or if there is an error in the sensor readings or to provide thermal property information without the need for sensors, making the apparatus and its operation simpler, increasing convenience, and since calculated thermal property information could be used when sensors are unavailable or inoperable.
Regarding claim 14, Lee teaches (Paragraph 0008, 0013, 0014) the provision of a precision cooking oven (specific cooking appliance) for the preparation of food products, wherein information obtained by the weight, 3D scanner, thermal imaging sensor, and operator input information is utilized by the oven controller to generate a thermal finite element analysis (FEA) model of all detected food items within the oven and oven controller utilizes the results of the FEA model to calculate a cooking time, heating pattern, and a cooking power level for the cooking of the food items (i.e. the food model is adapted to the cooking oven (specific cooking appliance)).
Lee is silent on the assistance of a database, however, the assistance of a database in adaptation of a food model is obvious in view of the prior art for the reasons stated above with regard to claim 1.
Regarding claim 20, Lee teaches (Paragraph 0034) in an exemplary embodiments of the present invention the precision laser cooking oven 100 has an autonomous cooking mode, wherein, instead of an oven operator cooking by trial and error, the controller 105 within the oven can identify the properties of internally placed food items that are to be cooked, and these properties can be utilized to perform a FEA thermal analysis with traveling heat loads in order to determine the time and power consumption that will be needed to achieve any desired cooking results that have been provided by the system operator.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190223474 A1) in view of 2010 ASHRAE Handbook, Ghalavand (WO 2015170244 A1), Watt (Database Design), Chen (US 20180157232 A1), Ohtani (US 20210338008 A1), Narita (US 4463249 A), and Li (CN 108685477 A), and further in view of Han (US 20180008079 A1).
Regarding claim 2, Lee, as modified above, is silent on the database information comprising a change of the cooking parameter and/or a property of the ingredient and/or of the food product during the cooking process and/or during the process of preparation of the food product.
Han teaches (Paragraph 0002, 0640, 0641) a method of operating a cooking apparatus, wherein a storage 1060 of a cooking apparatus 1000 may store a preview database to generate a preview of a food model, wherein the preview database may include data on changes in shapes and colors of food (change of the food product during the process of preparation) with respect to the ingredient and color of the food model, the heating device, the cooking temperature, and the cooking time.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method Lee, as modified above, to configure the database to comprise information about a change of the food product during the process of preparation as taught by Han since both are directed to methods of food production using apparatuses capable of processing data and producing food models, since including information about a change of the food product during the process of preparation in a database is known in the art as shown by Han, since a controller 1010 may estimate the shape of a cooked food based on the ingredient and color of the food model, the heating device, the cooking temperature, and the cooking time by using the preview database stored in the storage 1060 and display the estimated food shape of food on the user interface 1020 (Han, Paragraph 0643) which can be useful to users to understand what the results of cooking will look like prior to cooking, since information of changes in shape and color from the food preparation process can allow users to produce food products that have a desired appearance, and since information on changes of the food product during the process of preparation can be used to verify if the preparation process is being performed correctly by comparing actually results to those provided in the database.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190223474 A1) in view of 2010 ASHRAE Handbook, Ghalavand (WO 2015170244 A1), Watt (Database Design), Chen (US 20180157232 A1), Ohtani (US 20210338008 A1), Narita (US 4463249 A), and Li (CN 108685477 A), and further in view of Zanoni et al. (Modelling of browning kinetics of bread crust during baking).
Regarding claim 6, Lee teaches (Paragraph 0013) information obtained by the weight, 3D scanner, thermal imaging sensor, and operator input information is utilized by the oven controller to generate a thermal finite element analysis (FEA) model of all detected food items within the oven. Lee further teaches (Paragraph 0014) throughout the food item cooking process the weight, 3D scanning, and thermal imaging sensors continuously gather sensor information regarding the cooking food, the detected sensor information being utilized to continuously update the cooking time of the food, designated food heating patterns, and cooking power levels for the cooking food items, wherein, the oven controller utilizes the FEA results to adjust the cooking time, heating pattern, and a cooking power level (i.e., the FEA model is continuously updated).
Lee is silent on the food model being generated and/or adapted or updated during the cooking process in consideration of information about an ingredient which influences a desired effect or change in the food product prior to or during the cooking process, said effect or change being selected from among denaturation of proteins, enzymatic activity, hydrolysis of connective tissue, formation of crumb structure and crust, browning kinetics, drying, breakdown of cell walls, leavening, and/or killing of bacteria.
Zanoni et al. teaches (page 605, 606) a kinetic model for browning of bread crust, correlated to a baking model, which can be used as an index of the degree of baking of the crust, wherein a study of browning kinetics was carried out using ground, dried bread crumb, and browning tests were carried out on the dried crumb under different temperature conditions to produce a model to predict crust browning during baking.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee, as modified above, to generate the food model in consideration of information about an ingredient which influences a desired effect or change in the food product prior to or during the cooking process, said effect or change being formation of crumb structure and crust and/or browning kinetics in view of Zanoni et al. since both are directed to modelling food being cooked, since generating a food model in consideration of information about an ingredient which influences a desired effect or change in the food product prior to or during the cooking process, said effect or change being formation of crumb structure and crust and/or browning kinetics is known in the art as shown by Zanoni et al., since the crust browning model proved valid in predicting the experimental trend of browning in the range of crust colour which includes traditional values for commercial breads (Zanoni et al., page 608), thus providing a way to predict and control browning to a desired amount, and since surface colour is an important characteristic of baked products because it, together with texture and aroma, contributes to consumer preference (Zanoni et al., page 608).
Furthermore, while Zanoni et al. is silent on the model being generated continuously or at discrete intervals, as shown above, Lee teaches continuously updating the model, and it would be obvious to one of ordinary skill in the art to continuously update process in consideration of information about an ingredient which influences a desired effect or change in the food product prior to or during the cooking process, said effect or change being formation of crumb structure and crust and/or browning kinetics since continuously updating the model (e.g. by factoring in relevant sensor measurements during the cooking process) would ensure that the model was accurate to the particular food and conditions being used currently in food production, ensuring higher accuracy and better results for the users.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190223474 A1) in view of 2010 ASHRAE Handbook, Ghalavand (WO 2015170244 A1), Watt (Database Design), Chen (US 20180157232 A1), Ohtani (US 20210338008 A1), Narita (US 4463249 A), and Li (CN 108685477 A), and further in view of Fan et al. (A model for the oven rise of dough during baking).
Regarding claim 7, Lee teaches (Paragraph 0013) information obtained by the weight, 3D scanner, thermal imaging sensor, and operator input information is utilized by the oven controller to generate a thermal finite element analysis (FEA) model of all detected food items within the oven. Lee further teaches (Paragraph 0014) throughout the food item cooking process the weight, 3D scanning, and thermal imaging sensors continuously gather sensor information regarding the cooking food, the detected sensor information being utilized to continuously update the cooking time of the food, designated food heating patterns, and cooking power levels for the cooking food items, wherein, the oven controller utilizes the FEA results to adjust the cooking time, heating pattern, and a cooking power level (i.e., the FEA model is continuously updated).
Lee is silent on the food model being and/or adapted or updated during the cooking process in consideration of information about an amount or ratio of at least one gas incorporated in the food product due to a result or an effect of a preparation step or process and/or due to an influence of an ingredient and/or due to a chemical process, an amount or percentage or mass fraction of the at least one gas being continuously adapted or updated during the cooking process.
Fan et al. teaches (Abstract, Page 69; Conclusions, Page 76; Elements in Model, page 71) a model for dough expansion during oven rise, wherein the dynamics of oven rise of dough during baking were simulated by considering the growth of gas cells as a result of the thermally-induced release of CO2 and water vapour from the aqueous dough phase, wherein Xc, the concentration of CO2 in the aqueous dough, with units of kg CO2/kg dough (ratio), is a model parameter.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee, as modified above, to generate, adapt or update the food model in consideration of information about an amount or ratio of at least one gas incorporated in the food product due to a result or an effect of a preparation step or process in view of Fan et al., since both are directed to modelling food being cooked, since generating or adapting or updating a food model in consideration of information about an amount or ratio of at least one gas incorporated in the food product due to a result or an effect of a preparation step or process is known in the art as shown by Fan et al., since oven rise in the early stages of baking plays a significant role in determining the resultant loaf volume and bread quality (Fan et al., Introduction, Page 69), since CO2 plays a major role in the low temperature expansion (Fan et al., Results, Page 74), and since variations in initial CO2 concentration in dough have an important influence on the rate of cell growth during the whole period of oven rise (Fan et al., Results, Page 74), thus making the ratio of CO2 important in modeling bread products for accurate information to provide users.
Furthermore, while Fan et al. is silent on the model being generated or updated or adapted continuously or at discrete intervals, as shown above, Lee teaches continuously updating the model, and it would be obvious to one of ordinary skill in the art to continuously update process in consideration of information about an amount or ratio of at least one gas incorporated in the food product due to a result or an effect of a preparation step or process since continuously updating the model (e.g. by factoring in relevant sensor measurements during the cooking process) would ensure that the model was accurate to the particular food and conditions being used currently in food production, ensuring higher accuracy and better results for the users.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190223474 A1) in view of 2010 ASHRAE Handbook, Ghalavand (WO 2015170244 A1), Watt (Database Design), Chen (US 20180157232 A1), Ohtani (US 20210338008 A1), Narita (US 4463249 A), Li (CN 108685477 A), and Fan et al. (A model for the oven rise of dough during baking), and further in view of Jeong (KR 20180128231 A) and Newberry (The secret life of gas bubbles and their role in bread doughs).
Regarding claim 8, Lee, as modified above, is silent on the amount or percentage or mass fraction of the at least one gas being considered in at least one formula calculating the at least one estimated thermal property of the food product.
Jeong teaches (Paragraph 0001, 0032) a method for producing a rice cake, wherein dough is fermented, and the volume of the dough increases and the density decreases through the fermentation process, wherein the decrease in density is due to the generation of carbon dioxide, which increases the size of the pores.
Newberry teaches (Paragraph 2) in the production of bread products; dough density decreases as the yeast produces more carbon dioxide and the dough expands.
Thus, it is known in the art that the amount of carbon dioxide gas influences the density (thermal property) of dough based food products.
While Jeong and Newberry do not articulate an exact formula for calculating the density from the amount of carbon dioxide gas, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee to consider the amount of carbon dioxide in a formula calculating the density of the food product, since each of Lee, Jeong, and Newberry is directed to the production of food products with a particular density, since the effect of the amount of carbon dioxide on the density of a food product, particularly a dough based food product, is known in the art as shown by Jeong and Newberry, since establishing a formula (such as by conducting routine experimentation well within the understanding of one of ordinary skill in the art) directly relating the amount of carbon dioxide to the density would give a more exact determination of the effect of carbon dioxide on density, since determining the effect of carbon dioxide on the density would account for changes in density to dough products due to fermentation, which would influence the quality of the final product of the cooking process and would otherwise not be accounted for by measuring the dough density prior to processing, since density information acquired from a formula calculation can be used to correct information if there is an error in the sensor readings or to provide density information without the need for sensors, making the apparatus and its operation simpler, increasing convenience, and since density information calculated from a formula could be used when sensors are unavailable or inoperable.
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190223474 A1) in view of 2010 ASHRAE Handbook, Ghalavand (WO 2015170244 A1), Watt (Database Design), Chen (US 20180157232 A1), Ohtani (US 20210338008 A1), Narita (US 4463249 A), and Li (CN 108685477 A), and further in view of Lee (US 8145854 B1).
Regarding claim 9, Lee (US 20190223474 A1) is silent on at least one keyword being extracted from the cooking recipe and the food model being generated on the basis of the cooking recipe and at least one element of the database associated with the keyword, the database including at least one concordance list in which the keyword is associated with at least one element of the database.
As shown above with regard to claim 1, 2010 ASHRAE Handbook teaches (Page 19.1, 19.6, 19.7, 19.11) thermal properties of foods, including density, specific heat, and thermal conductivity, can be predicted by using composition data including water, protein, fat, and carbohydrate (nutrient data) of the individual food constituents (ingredients).
While 2010 ASHRAE Handbook does not explicitly state that the nutrient data is stored in a data base, the storage of data in a database is well-known and common practice.
For example, Ghalavand teaches (Paragraph 0015) accessing a database to derive food attributes including calories, fat, protein, carbohydrates, (nutrient data) and other variables according to food type (ingredient) information.
Also, the food ingredient information necessary to determine the corresponding nutrient data and calculate the thermal properties can be derived cooking recipe information.
For example, Chen teaches (Paragraph 0003, Claim 8) a method of operating a food preparation device, wherein a processor is used to obtain a recipe; and based on the recipe, determine one or more food items (ingredients).
Also, Watt teaches (Paragraph 3) the processing power of a database allows it to manipulate the data it houses, so it can perform functions including matching, linking, and aggregating.
Lee (US 8145854 B1) teaches (Col. 4, lines 6-13) Recipe Processing Software that converts natural language recipes to food preparation machine instructions, wherein a grammar parser parses ingredient lists to extract the ingredients (keywords) and quantities needed for a given recipe.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee (US 20190223474 A1), as modified above, to extract keywords, (e.g., ingredients) from the cooking recipe to generate a food model on the basis of the cooking recipe and at least one element of the database associated with the keyword, the database including at least one concordance list in which the keyword is associated with at least one element of the database in view of Lee (US 8145854 B1), since both are directed to methods of food preparation, since extracting keywords, particularly ingredients, from a cooking recipe is known in the art as shown by Lee (US 8145854 B1), since generating a food model based on a cooking recipe and at least one element of a database associated with the recipe information (e.g., an ingredient and nutrient data of the ingredient) is obvious for the reasons stated above with regard to claim 1, since extracting keywords from a recipe removes the need for an operator to manually enter ingredients, providing user convenience and remove the risk of human error, since extracting keywords from a recipe allows the all the ingredients to be pre-selected, removing the need for the user to determine which ingredients will be needed or work best for a particular dish or meal, since enabling users to prepare meals or dishes with minimal user interaction provides convenience to users, and further enables those individuals who are unable to cook (e.g., elderly, handicapped) to prepare meals (Chen, Paragraph 0044), since recipe information can be used to address errors in the cooking process including the use or preparation of a wrong ingredient (Ohtani, Paragraph 0005, 0024, 0033), since associating ingredient keywords with elements of a database in concordance lists allows the apparatus to automatically determine the appropriate database elements, providing convenience to the user and preventing potential human error in selecting the appropriate thermal properties, and since the processing power of a database allows it to manipulate the data it houses, so it can perform functions including matching, linking, and aggregating (Watt, Paragraph 3).
Regarding claim 10, Lee (US 20190223474 A1), as modified above, is silent on at least one phrase being extracted from the cooking recipe, wherein said phrase is associated with at least one element of the database, and wherein said phrase is tabled in a concordance list and associated therein with the at least one element of the database and/or is associated with at least one cooking parameter.
Lee (US 8145854 B1) teaches (Col. 4, lines 6-13, 63-65; Col. 5, lines 16-21) Recipe Processing Software that converts natural language recipes to food preparation machine instructions, wherein each food preparation command has an entry in a local database containing rules for the semantic roles involved in conducting each command, wherein, for example, the database entry for the command "bake" contains a rule indicating that the noun phrase objects of the sentence should be heated to the temperature specified by a prepositional phrase in the sentence that contains a temperature (cooking parameter), or to a default temperature if no temperature is specified in the sentence. Lee (US 8145854 B1) further teaches (Col. 6, lines 51-53) the parser also identifies the prepositional phrases to indicate the temperature (cooking parameter) to bake at and the condition to stop baking.
Also, Watt teaches (Paragraph 3) the processing power of a database allows it to manipulate the data it houses, so it can perform functions including matching, linking, and aggregating.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee (US 20190223474 A1) to extract phrases from a recipe, associated with database elements and keywords in view of Lee (US 8145854 B1), since both are directed to methods of food preparation, since extracting phrases associated with database elements and cooking parameters from a cooking recipe is known in the art as shown by Lee (US 8145854 B1), since extracting phrases related to functions like baking at a particular temperature from a recipe removes the need for an operator to manually select the operating conditions, providing user convenience and remove the risk of human error, and since associating phrases with database elements and cooking parameters allows food items to be cooked according to recipe specifications, without the need for a user to individually determine appropriate cooking parameters, since enabling users to prepare meals or dishes with minimal user interaction provides convenience to users, and further enables those individuals who are unable to cook (e.g., elderly, handicapped) to prepare meals (Chen, Paragraph 0044), since recipe information can be used to address errors in the cooking process including the use or preparation of a wrong ingredient (Ohtani, Paragraph 0005, 0024, 0033), and since the processing power of a database allows it to manipulate the data it houses, so it can perform functions including matching, linking, and aggregating (Watt, Paragraph 3).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20190223474 A1) in view of 2010 ASHRAE Handbook, Ghalavand (WO 2015170244 A1), Watt (Database Design), Chen (US 20180157232 A1), Ohtani (US 20210338008 A1), Narita (US 4463249 A), and Li (CN 108685477 A), and further in view of Slagg (US 4840239 A).
Regarding claim 15, Lee, as modified above, is silent on an amount of an ingredient provided without weight specification being converted into a standardized weight.
Slagg teaches (Col. 1, lines 6-10; Col. 12, lines 48-67; Col. 13, lines 1-6) a scale that displays recipe ingredients or other materials placed on its pan in terms of their volume and it also functions conventionally to display the weight of any substance in terms of metric or English units of measurement, wherein a volume of a particular ingredient called for in the recipe may be entered and have the weight of that volume of the ingredient displayed instead of its volume, and the processor must access the V/W ratio or conversion factor for the particular material and use the factor to calculate the weight of the designated volume of one specific material and display this weight after having been informed of the volume of the ingredient.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee, as modified above, to convert an amount of an ingredient provided without weight specification into a standardized weight as taught by Slagg, since both are directed to methods of food production, since converting an amount of an ingredient provided without weight specification into a standardized weight is known in the art as shown by Slagg, since measuring ingredients in terms of volume is often inaccurate and can result in baked or cooked products sometimes tasting, looking or feeling different from one production of the recipe to another (Slagg, Col. 1, lines 18-22), and since providing a volumetric-to-gravimetric converter can substantially reduce and even, in some cases, eliminate the need for volumetric measuring devices for producing a baked or cooked product in accordance with a recipe (Slagg, Col. 2, lines 56-63).
Response to Arguments
Applicant's arguments filed 02/13/2026 regarding the 35 USC 101 rejection of claims 1-15 have been fully considered but they are not persuasive. The Examiner acknowledges that claim 21, now canceled, was not explicitly listed as rejected under 35 USC 101. This was a typographical error that has been corrected. All remaining elected claims 1-15 and 20 are rejected under 35 USC 101.
Regarding the Applicant’s argument that the limitations "defining a cooking parameter of a cooking appliance based on the food model" and "performing the cooking process on the food product using the cooking appliance and the cooking parameter, wherein the cooking process is performed by adjusting the cooking parameter of the cooking appliance during the cooking process based on continuous or successive updates in the food model" integrate the asserted abstract ideas of claim 1 into practical application, the Examiner maintains that defining a cooking parameter of a cooking appliance based on the food model” is also a mental process, and, therefore, falls within the category of the judicial exception of abstract ideas, and “performing the cooking process on the food product using the cooking appliance and the cooking parameter, wherein the cooking process is performed by adjusting the cooking parameter of the cooking appliance during the cooking process based on continuous or successive updates in the food model” does not go beyond generally linking the judicial exception(s) to the technical field of performing a cooking process on a food product using a cooking appliance (See MPEP 2106.05(h)).
Applicant’s arguments, see pages 7-10, filed 02/13/2026, with respect to the rejection(s) of claim(s) 1-15 and 20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, these arguments have been made in view of amendments to the claims, and, upon further consideration, a new ground(s) of rejection is made over Lee (US 20190223474 A1) in view of 2010 ASHRAE Handbook, Ghalavand (WO 2015170244 A1), Watt (Database Design), Chen (US 20180157232 A1), Ohtani (US 20210338008 A1), Narita (US 4463249 A), and Li (CN 108685477 A).
Regarding the Applicant’s argument that Lee ‘274 and Chen do not teach or suggest generating an FEA food model based on its recipe information, particularly by estimating a thermal property based on the cooking recipe information and nutrient information, the Examiner maintains that the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). In this case, Lee teaches generating a food model based on thermal properties including shape and density, 2010 ASHRAE Handbook teaches using nutrient information for particular ingredients to determine thermal properties including density and Chen teaches determining particular ingredients from a cooking recipe. Therefore, for the reasons stated above with regard to claim 1, in view of the prior art, the claimed invention is obvious.
In response to the Applicant’s argument that none of the cited references teaches or suggests that recipe information can be used to correct information incorrectly entered by a user preventing errors in the model and food production or recipe information could be obtained by the processor to prevent users from having to enter the information directly, increasing convenience, and since recipe information can include a wide variety of options for the food model and cooking operation to satisfy a variety of consumer tastes and preferences, the Examiner notes that prior art is now directly cited to further demonstrate such features as stated above with regard to claim 1.
Therefore, claim 1 and all dependent claims remain rejected under 35 USC 103.
Conclusion
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/AUSTIN PARKER TAYLOR/Examiner, Art Unit 1792
/ERIK KASHNIKOW/Supervisory Patent Examiner, Art Unit 1792