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 .
DETAILED ACTION
This Office Action corresponds to application 19/330,895 which was filed on 9/17/2025. Claims 1-18 are currently pending.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite a method (claim 1), an appliance (claim 16), a device (claim 17), and a system (claim 18). These claims fall within at least one of the four categories of patentable subject matter.
Step 2A, Prong One
Claim 1 recites determining user actions, determining first recipe based on actions, generating a second recipe, and storing second recipe.
The recited steps for analyzing data, retrieving data, modifying data, and storing data are acts of information evaluation and retrieval that can be practically performed in the human mind with the knowledge base being interpreted as generic computer components to apply the instructions of the abstract idea. For example, a person can determine a user’s actions, match them to corresponding documents/recipes, and save them. Thus, these steps are an abstract idea in the “mental processes” grouping.
Dependent claims 2-15 recite additional elements of performing mathematical operations such as determining dissimilarity between recipes/documents, using an artificial neural network for determining dissimilarity, selecting recipes/documents from a repository, using a large language model, further defining the monitored user actions to comprise ingredients and appliances, storing user characteristics, and using user characteristics for dissimilarities. These are all further extensions of the abstract idea, the additional abstract idea of mathematical concepts, or mere extra-solution activity. For example, with claim 2 a person can determine or calculate a dissimilarity of two documents being above or below a threshold; or with claim 6 a person can select a document/recipe from a collection of documents.
Claim 16 recites determining user actions, determining first recipe based on actions, generating a second recipe, and storing second recipe.
The recited steps for analyzing data, retrieving data, modifying data, and storing data are acts of information evaluation and retrieval that can be practically performed in the human mind with the sensor and processing device being interpreted as generic computer components to apply the instructions of the abstract idea. For example, a person can determine a user’s actions, match them to corresponding documents/recipes, and save them. Thus, these steps are an abstract idea in the “mental processes” grouping.
Claim 17 recites means to determine dissimilarity between two documents/recipes using an artificial neural network.
The recited steps for determining dissimilarity data are acts of information evaluation and retrieval that can be practically performed in the human mind as well as a mathematical concept, with the neural network being interpreted as generic computer components to apply the instructions of the abstract idea. For example, a person can review documents to determine or calculate the dissimilarity between two documents/recipes. Thus, these steps are an abstract idea in the “mental processes” grouping and “mathematical concepts” grouping.
Claim 18 recites determining user actions, determining first recipe based on actions, generating a second recipe, storing second recipe, and determining dissimilarity between two documents/recipes using an artificial neural network.
The recited steps for analyzing data, retrieving data, modifying data, storing data, and determining dissimilarity data are acts of information evaluation and retrieval that can be practically performed in the human mind and mathematical concepts with the sensor, processing device, and neural network being interpreted as generic computer components to apply the instructions of the abstract idea. For example, a person can determine a user’s actions, match them to corresponding documents/recipes, and save them; additionally, a person can review documents to determine or calculate the dissimilarity between two documents/recipes. Thus, these steps are an abstract idea in the “mental processes” and “mathematical concepts” grouping.
Step 2A, Prong Two
This judicial exception is not integrated into a practical application because the combination of additional elements includes only generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
For claims 1-15, the additional elements include the knowledge base, neural network, repository, and Large Language Model.
For claim 16, the additional elements include the sensor and processing device.
For claim 17, the additional elements include the neural network.
For claim 18, the additional elements include the sensor, processing device, neural network, and storage.
The knowledge base/storage, neural network, repository, Large Language Model, sensor, and processing device are all recited at a high-level of generality (i.e., as a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using the knowledge base/storage, neural network, repository, Large Language Model, sensor, and processing device to perform the steps or the additional elements from the dependent claims amounts to no more than part of the abstract idea, mere extra-solution activity, and mere instructions to apply the exception using a generic computer component. The claims are not patent eligible.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Such claim limitations are “… comprising means adapted to …” in independent claim 17.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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, 6-13, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pichara et al. (US2022/0005376), hereinafter Pichara, in view of Knighton et al. (US20230024191), hereinafter Knighton
Regarding Claim 1:
Pichara teaches:
A method for generating a culinary knowledge base, the method comprising: determining user actions during preparation of a dish (Pichara, [0039], note the chef may vary the recipe and the modified recipe, which is interpreted as determined user actions, may be saved to the database);
determining a first cooking recipe, the first cooking recipe including preparation steps being similar to the user actions (Pichara, figure 6, [0039-0043, 0050], note recipes and recipe variations are stored and linked together; note the modified recipe is similar to the original recipe used that was modified);
generating a second cooking recipe, the second cooking recipe including preparation steps matching the user actions for preparing the dish (Pichara, figure 6, [0039-0043], note recipes and recipe variations are stored and linked together; note modifying the recipe and saving the modified recipe, linked to the existing recipe, is interpreted as generating the second cooking recipe); and
storing the second cooking recipe as a variant of the first cooking recipe (Pichara, [0039, 0053], note storing the modified recipe as a variant of the original recipe).
While Pichara teaches generating and storing recipes, Pichara broadly teaches determining user actions during preparation of the dish. To further support this interpretation Knighton is in the same field of endeavor, data analysis and information retrieval, and Knighton teaches:
determining user actions during preparation of a dish (Knighton, figures 4B and 15, [0076, 0112-0114], note determining user preparation events);
determining a first cooking recipe, the first cooking recipe including preparation steps being similar to the user actions (Knighton, figure 8, [0090], note determining multiple similar recipes based on the user actions);
generating a second cooking recipe, the second cooking recipe including preparation steps matching the user actions for preparing the dish (Knighton, figures 4B and 15, [0076, 0080, 0112-0116], note determining multiple recipes based on the user actions; note predicting the recipe, e.g., second recipe, based on the preparation steps matching the user actions).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
Regarding Claim 6:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
selecting the first cooking recipe from a repository including a plurality of cooking recipes (Pichara, [0036], note selecting recipes from a recipe database) (Knighton, figure 8, [0086-0090], note selecting the recipe from a database).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
Regarding Claim 7:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
generating the first cooking recipe in text form using a Large Language Model (Pichara, [0032], note the use of neural networks) (Knighton, figure 2, [0012, 0042, 0059], note any type of machine learning algorithm or neural network may be used; note the use of a language model).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
Regarding Claim 8:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
providing an ingredient or intermediate product for the dish or an amount or state of the ingredient or intermediate product, as part of the user actions (Pichara, figures 1-4, [0032, 0039], note ingredients) (Knighton, figures 4B and 15, [0054, 0076, 0112-0114], note determining user preparation events; note preparation events include ingredients).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
Regarding Claim 9:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
providing a processing of an ingredient or intermediate product as part of the user actions (Pichara, figures 1-4, [0032, 0039, 0074], note ingredients and processing of the ingredients) (Knighton, figures 4B and 15, [0054, 0076, 0112-0114], note determining user preparation events; note preparation events include ingredients and processing of ingredients).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
Regarding Claim 10:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
providing a use of one or more kitchen appliances as part of the user actions (Pichara, figures 1-4, [0018, 0032, 0039, 0061, 0074], note ingredients and processing of the ingredients; note cooking process may include of kitchen appliances) (Knighton, figures 4-5 and 15, [0043, 0054, 0076, 0112-0114], note determining user preparation events; note preparation events include tools, e.g., kitchen appliances).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
Regarding Claim 11:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
storing the second cooking recipe only when the user expresses contentment with the prepared dish (Pichara, [0039, 0052-0053, 0061], note storing the modified recipe as a variant of the original recipe; note storing user feedback for the modified recipe; note storing the recipe is interpreted as an act of contentment with the prepared dish).
Regarding Claim 12:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
storing the second cooking recipe with a reference to characteristics of the user (Pichara, [0026, 0039, 0052-0053, 0061], note storing the modified recipe as a variant of the original recipe; note storing user feedback for the modified recipe includes characteristics of the user) (Knighton, figures 4-5 and 15, [0043, 0054, 0076, 0086-0090, 0112-0114], note determining user preparation events; note preparation events include ingredients and tools, e.g., kitchen appliances; note the use of ingredients and tools are interpreted as a user characteristic; note determining dissimilarity based on user characteristics such as frequency of cooking a recipe).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
Regarding Claim 13:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
providing a dissimilarity of associated user characteristics as part of a dissimilarity between cooking recipes (Pichara, [0026, 0039, 0052-0053, 0061], note storing the modified recipe as a variant of the original recipe; note storing user feedback for the modified recipe includes characteristics of the user; note determining dissimilarity includes user characteristics) (Knighton, figures 4-5 and 15, [0043, 0054, 0076, 0086-0090, 0112-0114], note determining user preparation events; note preparation events include ingredients and tools, e.g., kitchen appliances; note the use of ingredients and tools are interpreted as a user characteristic; note determining dissimilarity based on user characteristics such as frequency of cooking a recipe).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
Claim 16 discloses substantially the same limitations as claim 1 respectively, except claim 16 is directed to a system comprising a sensor and processing device comprising a processor (Pichara, figure 1, [0015, 0085], note smart cooking device and processor) (Knighton, figures 16-17, abstract, note use of sensor and processor) while claim 1 is directed to a method. Therefore claim 16 is rejected under the same rationale set forth for claim 1.
Regarding Claim 17:
Pichara teaches:
A service device, comprising means adapted to determine a dissimilarity between two cooking recipes based on an artificial neural network (Pichara, [0052-0053], note using an artificial neural network and determining how similar recipes are based on closeness, e.g., distance, in feature space).
While Pichara teaches determining dissimilarity between two cooking recipes, Pichara broadly teaches dissimilarity. To further support this interpretation Knighton is in the same field of endeavor, data analysis and information retrieval, and Knighton teaches:
means adapted to determine a dissimilarity between two cooking recipes (Knighton, figures 4-5 and 15, [0043, 0054, 0076, 0086-0090, 0112-0114], note determining dissimilarity based on user characteristics such as frequency of cooking a recipe) based on an artificial neural network (Knighton, figure 2, [0012, 0042, 0059], note any type of machine learning algorithm or neural network may be used; note the use of a language model).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
Regarding Claim 18:
Pichara teaches:
A system for generating a culinary knowledge base, the system comprising: at least one kitchen appliance according to claim 16 (Pichara, figure 1, [0015, 0039-0043, 0050-0053, 0085], note smart cooking device);
a service device adapted to determine a dissimilarity between two cooking recipes based on an artificial neural network (Pichara, [0052-0053], note using an artificial neural network and determining how similar recipes are based on closeness, e.g., distance, in feature space); and
a storage device for a plurality of cooking recipes (Pichara, figure 1, [0039, 0053], note storing the modified recipe as a variant of the original recipe).
While Pichara teaches determining dissimilarity between two cooking recipes, Pichara broadly teaches dissimilarity. To further support this interpretation Knighton is in the same field of endeavor, data analysis and information retrieval, and Knighton teaches:
at least one kitchen appliance according to claim 16 (Knighton, figures 1 and 17, [0076, 0080, 0086-0090, 0112-0116], note cooking device)
a service device adapted to determine a dissimilarity between two cooking recipes based on an artificial neural network (Knighton, figures 4-5 and 15, [0043, 0054, 0076, 0086-0090, 0112-0114], note determining dissimilarity based on user characteristics such as frequency of cooking a recipe) based on an artificial neural network (Knighton, figure 2, [0012, 0042, 0059], note any type of machine learning algorithm or neural network may be used; note the use of a language model).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
Claim Rejections - 35 USC § 103
Claim(s) 2-5 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pichara in view of Knighton and Hunt (US2005/0160114).
Regarding Claim 2:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
determining that a dissimilarity between the first and second cooking recipes is lower than a first predetermined threshold (Pichara, [0037-0038, 0052-0053], note determining similarity scores based on differences between recipes and target recipes) (Knighton, [0086-0088], note determining probability of multiple recipes).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
While Pichara as modified teaches determining cooking recipes, Pichara as modified doesn’t specifically state the use of a threshold. However, Hunt is in the same field of endeavor, data analysis, and Hunt teaches:
determining that a dissimilarity between the first and second cooking recipes is lower than a first predetermined threshold (Hunt, abstract, figures 2-3 [0024, 0040, 0056], note determining similarity of recipes exceeding or not exceeding a threshold. When combined with the previously cited references this would be for the cooking recipes as taught by Pichara and Knighton).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using analysis of large amounts of data to provide the most likely results (Hunt, [0003]).
Regarding Claim 3:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
determining that a dissimilarity between the first and second cooking recipe is greater than a second predetermined threshold (Pichara, [0037-0038, 0052-0053], note determining similarity scores based on differences between recipes and target recipes) (Knighton, [0086-0088], note determining probability of multiple recipes).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
While Pichara as modified teaches determining cooking recipes, Pichara as modified doesn’t specifically state the use of a threshold. However, Hunt is in the same field of endeavor, data analysis, and Hunt teaches:
determining that a dissimilarity between the first and second cooking recipe is greater than a second predetermined threshold (Hunt, abstract, figures 2-3 [0024, 0040, 0056], note determining similarity of recipes exceeding or not exceeding a threshold. When combined with the previously cited references this would be for the cooking recipes as taught by Pichara and Knighton).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using analysis of large amounts of data to provide the most likely results (Hunt, [0003]).
Regarding Claim 4:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
associating each of the first and second cooking recipes with a respective point in a latent space of an artificial neural network, and determining a dissimilarity between the first and second cooking recipes based on a distance between the associated points in the latent space (Pichara, [0052-0053], note using an artificial neural network and determining how similar recipes are based on closeness, e.g., distance, in feature space).
Regarding Claim 5:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
associating each of the first and second cooking recipes with a respective point in a latent space of an artificial neural network, and determining a dissimilarity between the first and second cooking recipes based on a distance between the associated points in the latent space (Pichara, [0052-0053], note using an artificial neural network and determining how similar recipes are based on closeness, e.g., distance, in feature space).
Regarding Claim 14:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
determining that a dissimilarity between the first and second cooking recipes and providing the dissimilarity of associated user characteristics as part of the dissimilarity between the first and second cooking recipes (Pichara, [0026, 0037-0039, 0052-0053, 0061], note determining similarity scores based on differences between recipes and target recipes; note determining dissimilarity includes user characteristics) (Knighton, figures 4-5 and 15, [0043, 0054, 0076, 0086-0090, 0112-0114], note determining dissimilarity between recipes; note dissimilarity may be based on user characteristics such as frequency of cooking a recipe).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
While Pichara as modified teaches determining cooking recipes, Pichara as modified doesn’t specifically state the use of a threshold. However, Hunt is in the same field of endeavor, data analysis, and Hunt teaches:
determining that a dissimilarity between the first and second cooking recipes is lower than a first predetermined threshold (Hunt, abstract, figures 2-3 [0024, 0040, 0056], note determining similarity of recipes exceeding or not exceeding a threshold. When combined with the previously cited references this would be for the cooking recipes as taught by Pichara and Knighton).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using analysis of large amounts of data to provide the most likely results (Hunt, [0003]).
Regarding Claim 15:
Pichara as modified shows the method as disclosed above;
Pichara as modified further teaches:
determining that a dissimilarity between the first and second cooking and providing the dissimilarity of associated user characteristics as part of the dissimilarity between the first and second cooking recipes (Pichara, [0026, 0037-0039, 0052-0053, 0061], note determining similarity scores based on differences between recipes and target recipes; note determining dissimilarity includes user characteristics) (Knighton, figures 4-5 and 15, [0043, 0054, 0076, 0086-0090, 0112-0114], note determining dissimilarity between recipes; note dissimilarity may be based on user characteristics such as frequency of cooking a recipe).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using user actions to determine the predicted result, e.g., recipe.
While Pichara as modified teaches determining cooking recipes, Pichara as modified doesn’t specifically state the use of a threshold. However, Hunt is in the same field of endeavor, data analysis, and Hunt teaches:
determining that a dissimilarity between the first and second cooking recipe is greater than a second predetermined threshold (Hunt, abstract, figures 2-3 [0024, 0040, 0056], note determining similarity of recipes exceeding or not exceeding a threshold. When combined with the previously cited references this would be for the cooking recipes as taught by Pichara and Knighton).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Knighton because all references are directed to data analysis and information retrieval and because Knighton would expand upon the teachings of the previously cited references in data analysis which would improve the performance and usability of the system by using analysis of large amounts of data to provide the most likely results (Hunt, [0003]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bhogal et al. (US2024/0188757) teaches determining user actions for a recipe.
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/JOHN J MORRIS/Examiner, Art Unit 2151 6/23/2026
/James Trujillo/Supervisory Patent Examiner, Art Unit 2151
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