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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/21/2025 has been entered.
Response to Arguments
Applicant's arguments filed 07/21/2025 have been fully considered, but they are not fully persuasive. The 35 USC § 101 has been overcome. However, the updated 35 USC § 102 and 103 rejections of claims 1-3, 5-14, 16-19, and 21-22 are applied in light of Applicant's amendments.
The Applicant argues “Respectfully, the applicant asserts that currently amended claim 1 overcomes the rejection as it recites that the dispensing device is a dispensing device of the user, and that according to the determined flavoring information and said flavoring adjustments for said flavoring request, the dispensing device dispenses one or more contained materials while said user is preparing a food and/or a beverage… Radcliffe discloses that the dispensing device belongs to a different entity from the user and that the preparation is also performed by a different entity from the user.” (Remarks 07/21/2025)
In response, the Examiner respectfully disagrees. Radcliffe clearly teaches a dispensing apparatus for food that can be customized by any said user (see the rejection below). In Radcliffe the user is not limited. A user can be a customer or a worker or anyone with access to the customization process. Thus, a user can add an order for the dispensing device while preparing any other food/beverage/condiment, etc. Radcliffe does not limit a user to do or not do other activities while the dispensing device is in operation. Thus, the Examiner does not find the arguments persuasive and maintains the 102 rejection.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-7, 9, 11-13, 16-18, and 21-22 is/are rejected under 35 U.S.C. 102 as being anticipated by U.S. PGPub 20190080384 (hereinafter “Radcliffe”)
As per claim 1, Radcliffe teaches a method for designing food and beverage flavor experiences, comprising:
receiving a flavoring request from one or more a user device and a dispensing device; collecting user experience data and collected flavor profile and recipe data from said one or more of said user device and said dispensing device; analyzing said collected user experience data and collected flavor profile and recipe data; determining flavoring information and flavoring adjustments based on the analysis; synchronizing the determined flavoring information and flavoring adjustments with said user device and said dispensing device; and displaying on said at least one user device and said dispensing device of said user said determined flavoring information and said flavoring adjustments for said flavoring request; wherein said determining comprises analyzing basic flavors to determine a relevant flavor balancing and intensity according to said flavoring request;
Radcliffe 0026-0044: “The first method S100 functions to “optimize” flavor, timing, and a constraint for a food product according to various entered and derived factors related to a patron and to a patron's food order… a patron's taste preferences (e.g., based on patron feedback from a previous food order)… the first method S100 can also be applicable to any other assembled and/or prepared food such as salads, burritos, pizza, ice cream cones, banana splits, ice cream cookie sandwiches, hot dogs, deli sandwiches, falafel, gyros, layered cakes, tacos, crepes, waffles, pancakes, omelets, mixed (alcoholic or non-alcoholic) drinks, smoothies, etc… the robotic sandwich assembly apparatus can include a custom patty grinder that grinds fresh meats (e.g., beef, turkey, lamb, chicken, bison) and/or meat substitutes (e.g., soybeans, chickpeas, black beans) and stamps custom patties responsive to custom patty orders…Block S130 can reduce the total weight of the patty and/or replace a percentage of beef in the patty with a corresponding percentage of turkey, chicken, soybeans, and/or chickpeas”0028-0035: “FIGS. 1 and 2, Blocks of the first method S100 can be implemented on a mobile computing device, such as a smartphone or a tablet. Blocks of the first method S100 can thus receive order information, patron (GPS) location, payment information, patron feedback, etc. through a touchscreen or other user interface on the mobile computing device… Blocks of the first method S100 can be implemented directly on a third-party electronic device (i.e., the patron's mobile computing device) in direct or indirect communication with the robotic sandwich assembly apparatus. In this implementation, the mobile computing device can store—or access from a remote database—past orders submitted by the patron, previous patron feedback, previous payment methods, previous receipts, a social networking profile of the patron, demographic information of the patron, the patron's location, tastes or preferences common to the patron's demographic or location, etc. The mobile computing device can also function as a feedback channel for the patron to submit feedback for the current burger order once the burger has been delivered, and the mobile computing device can store this feedback and/or communicate it to a remote database where it is stored in a personal taste profile of the patron and/or added to an aggregate taste profile for multiple patrons, such as in the same region or of a similar demographic… the touchscreen can be installed on an external housing of the robotic sandwich assembly apparatus to receive ingredient selections from the patron and to display order information prior to submission”0064: “ the second method S200 generates and implements a patron's taste profile that is specific to hamburgers. In this example, as the patron frequents a hamburger joint (e.g., a hamburger joint housing a robotic sandwich assembly apparatus as described above) over time, the second method S200 collects data on types of hamburgers selected by the patron, such as plain hamburgers, veggie burgers, cheddar cheese and mushroom burgers, avocado and tomato burgers, etc. and extrapolates a preference for a “type” of burger (e.g., greasy, fresh, loaded with toppings, vegetarian, etc.) preferred by the patron as trends (or patron habits) become apparent in the patron's order history. The second method S200 also collects data on selections for ingredients to add to and to remove from standard hamburgers (e.g., addition of pickles and removal of tomatoes), selections for patty doneness (e.g., rare, well-done), selections for hamburger size (e.g., ⅓lb., ½lb., ¾lb.), and/or selections for total hamburger (or meal) calories, etc. submitted by the patron in previous food orders for hamburgers. The second method S200 additionally or alternatively collects patron feedback on hamburgers provided in fulfilled food orders, such as qualitative feedback reciting “too salty,” “perfect amount of mustard,” “not quite enough food,” “patty too dry,” “perfect toast on the bun,” and/or “burger was a little too wet.” The second method S200 then assembles these data into a taste profile for the patron—or updates the patron's existing taste profile—that specifies one or more flavor profiles preferred by the patron (e.g., sweet, savory, light, greasy, meaty, vegetarian, etc.), absolutely preferences for which ingredients to include and which to exclude from personalized hamburger recipes for the patron, how much to cook a patty and toast a bun, and how much salt, pepper, mustard, ketchup, tomato, relish, onion, lettuce, and/or pickle, etc. to load onto a burger. Then, when the patron initiates a new food order for a new hamburger that was not previously ordered by the patron, the second method S200 can apply the patron's taste profile to a recipe for the new hamburger to align the preparation method and ingredients in the new hamburger with the patron's taste preferences. Similarly, when the patron initiates a new food order for a same hamburger specified in a previously order by the patron, the second method S200 can personalize a recipe for the hamburger with the patron's taste profile to match the flavor of the hamburger to that which was ordered previously by the patron, or the second method S200 can adjust the recipe for the hamburger according to the patron's taste profile to better match the patron's taste preferences over the hamburger that which was previously delivered to the patron.” 0055-0057: “Block S150 functions to upload order-related data to the robotic sandwich assembly apparatus to fulfill the customized burger order at or by the specified delivery time. As described above, the recipe and cooking schedule can be transmitted from the patron's mobile computing device directly to the robotic sandwich assembly apparatus or indirectly routed via a remote network. Various Blocks of the first method S100 can alternatively be implemented on the remote server, which can transmit order-related data directly or indirectly to the robotic sandwich assembly apparatus, such as over the Internet or other network. Once the order is submitted to the robotic sandwich assembly apparatus, the robotic sandwich assembly apparatus can implement the order by assembling and cooking the burger according to the recipe and schedule. Block S150 can further include tracking the patron's burger order as the burger is assembled in the robotic sandwich assembly apparatus. For example, Block S150 can interface with the robotic sandwich assembly apparatus to identify a current stage of the burger, such as meat-grinding, patty-pressing, broiling, bun toasting, topping addition, condiment addition, bagging, and ready-for-pickup. At each stage, Block S150 can notify the patron of his burger's status, such as by pushing a notification to the patron's mobile computing device. For example, Block S150 can push notifications that recite “Patty press complete; beginning broil” and then “Tomatoes added; moving to pickles” to the patron's mobile computing device substantially in real time. In this implementation, Block S150 can further enable the patron to make last-minute changes to his order before each schedule (e.g., patty doneness) or ingredient order is realized. For example, upon receiving the “Tomatoes added; moving to pickles” notification, the patron can access the burger order through the native application executing on the mobile computing device and remove pickles from the order, and Block S150 can receive the change and update the order accordingly.”
wherein said method further comprises dispensing one or more contained materials within said dispensing device of said user according to said determined flavoring information and said flavoring adjustments for said flavoring request, while said user is preparing a food and/or a beverage; Radcliffe 0100-0112: Block S270 of the second method S200 recites submitting the personalized recipe with the new food order to a robotic food assembly apparatus. Block S270 can similarly recite submitting the personalized recipe with the new food order to a food ordering system. Generally, Block S270 functions like Block S150 of the first method S100 to deliver the food order with the personalized recipe generated in Block S260 to a system or apparatus that handles preparation and delivery of the food item. For example, Block S270 can upload the personalized recipe with the food order to a computer network connected to the robotic food assembly apparatus—described above—which automatically implements the personalized recipe to build the corresponding personalized food item (e.g., a hamburger) for the patron. However, Block S270 can function in any other way to transmit, upload, or otherwise deliver the food order with the personalized recipe to a robotic food assembly apparatus, to a food ordering system, etc. on behalf of the patron…In FIG. 6, customers 304-1, 304-2, and 304-3 are placing orders with an establishment 300 that includes a food creation robot 308-1. For example, the food creation robot 308-1 may be configured to create sandwiches such as hamburgers. Subsystems 312 of the food creation robot 308-1 may therefore include, for example, a subsystem that slices, butters, and toasts hamburger buns, a subsystem that slices and dispenses vegetables, a subsystem that dispenses liquid sauces, a subsystem that dispenses powdered seasonings, a subsystem that grinds and cooks meat, etc. Subsystems 312 are coordinated by a robot control module 316.”
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As per claim 2, Radcliffe teaches all the limitations of claim 1.
In addition, Radcliffe teaches:
wherein user experience data includes at least one of: usage data, and taste data;Radcliffe 0064: “The second method S200 then assembles these data into a taste profile for the patron—or updates the patron's existing taste profile—that specifies one or more flavor profiles preferred by the patron (e.g., sweet, savory, light, greasy, meaty, vegetarian, etc.), absolutely preferences for which ingredients to include and which to exclude from personalized hamburger recipes for the patron, how much to cook a patty and toast a bun, and how much salt, pepper, mustard, ketchup, tomato, relish, onion, lettuce, and/or pickle, etc. to load onto a burger. Then, when the patron initiates a new food order for a new hamburger that was not previously ordered by the patron, the second method S200 can apply the patron's taste profile to a recipe for the new hamburger to align the preparation method and ingredients in the new hamburger with the patron's taste preferences. Similarly, when the patron initiates a new food order for a same hamburger specified in a previously order by the patron, the second method S200 can personalize a recipe for the hamburger with the patron's taste profile to match the flavor of the hamburger to that which was ordered previously by the patron, or the second method S200 can adjust the recipe for the hamburger according to the patron's taste profile to better match the patron's taste preferences over the hamburger that which was previously delivered to the patron. it.”
As per claim 3, Radcliffe teaches all the limitations of claim 1.
In addition, Radcliffe teaches:
wherein flavor profile and recipe data includes at least one of: recipe information, taste and flavor data, food and ingredient chemistry data, flavoring rules, and nutritional information; Radcliffe 0064: “ the second method S200 generates and implements a patron's taste profile that is specific to hamburgers. In this example, as the patron frequents a hamburger joint (e.g., a hamburger joint housing a robotic sandwich assembly apparatus as described above) over time, the second method S200 collects data on types of hamburgers selected by the patron, such as plain hamburgers, veggie burgers, cheddar cheese and mushroom burgers, avocado and tomato burgers, etc. and extrapolates a preference for a “type” of burger (e.g., greasy, fresh, loaded with toppings, vegetarian, etc.) preferred by the patron as trends (or patron habits) become apparent in the patron's order history. The second method S200 also collects data on selections for ingredients to add to and to remove from standard hamburgers (e.g., addition of pickles and removal of tomatoes), selections for patty doneness (e.g., rare, well-done), selections for hamburger size (e.g., ⅓lb., ½lb., ¾lb.), and/or selections for total hamburger (or meal) calories, etc. submitted by the patron in previous food orders for hamburgers. The second method S200 additionally or alternatively collects patron feedback on hamburgers provided in fulfilled food orders, such as qualitative feedback reciting “too salty,” “perfect amount of mustard,” “not quite enough food,” “patty too dry,” “perfect toast on the bun,” and/or “burger was a little too wet.” The second method S200 then assembles these data into a taste profile for the patron—or updates the patron's existing taste profile—that specifies one or more flavor profiles preferred by the patron (e.g., sweet, savory, light, greasy, meaty, vegetarian, etc.), absolutely preferences for which ingredients to include and which to exclude from personalized hamburger recipes for the patron, how much to cook a patty and toast a bun, and how much salt, pepper, mustard, ketchup, tomato, relish, onion, lettuce, and/or pickle, etc. to load onto a burger. Then, when the patron initiates a new food order for a new hamburger that was not previously ordered by the patron, the second method S200 can apply the patron's taste profile to a recipe for the new hamburger to align the preparation method and ingredients in the new hamburger with the patron's taste preferences. Similarly, when the patron initiates a new food order for a same hamburger specified in a previously order by the patron, the second method S200 can personalize a recipe for the hamburger with the patron's taste profile to match the flavor of the hamburger to that which was ordered previously by the patron, or the second method S200 can adjust the recipe for the hamburger according to the patron's taste profile to better match the patron's taste preferences over the hamburger that which was previously delivered to the patron.”
As per claim 5, Radcliffe teaches all the limitations of claim 1.
In addition, Radcliffe teaches:
wherein analyzing collected user experience data and collected flavor profile and recipe data further comprises: classifying the user experience data and collected flavor profile as at least one of: a flavor, a food, a dish, a culinary example, and a recipe; Radcliffe 0059-0064: “predicting an absolute food preference of the patron from the first food order in Block S220; predicting a relative taste preference of the patron based on the taste feedback and a recipe of the first food item in Block S230; generating a taste profile of the patron based on the absolute food preference and the relative taste preference in Block S240; from the patron, receiving a selection for a second food item in a new food order in Block S250, the second food item different than the first food item; applying the taste profile of the patron to a recipe for the second food item to generate a personalized recipe in Block S260; and submitting the personalized recipe with the new food order to a robotic food assembly apparatus in Block S270… The second method S200 then assembles these data into a taste profile for the patron—or updates the patron's existing taste profile—that specifies one or more flavor profiles preferred by the patron (e.g., sweet, savory, light, greasy, meaty, vegetarian, etc.), absolutely preferences for which ingredients to include and which to exclude from personalized hamburger recipes for the patron, how much to cook a patty and toast a bun, and how much salt, pepper, mustard, ketchup, tomato, relish, onion, lettuce, and/or pickle, etc. to load onto a burger.”
As per claim 6, Radcliffe teaches all the limitations of claim 5.
In addition, Radcliffe teaches:
classifying the culinary example as at least one of: a positive classification, and a negative classification, based on at least one of: the perceptions of at least a user, and sensory data regarding the culinary example; Radcliffe 0068: “Block S210 can prompt the patron to enter absolute or scaled feedback for one or more parameters corresponding to the previous food order. For example, Block S210 can prompt the patron to select either of a “YES” or “NO” radio button for one or more of the following prompts: “are you full,” “are you still hungry,” “was your burger too greasy,” “was your patty undercooked,” “what your burger too salty,” “did your burger have enough veggies,” “did we add enough mustard,” “did you like the pickles on your burger,” and/or “would your burger have tasted between with poppy seeds on your bun,” etc., as shown in FIG. 4. Block S210 can additionally or alternatively prompt the patron to move a slider bar between “not enough” and “too much” positions or between “not satisfied” and “fully satisfied” positions for one or more of the following prompts: “did you get enough to eat,” “did your burger have enough relish,” “was your bun sufficiently toasted,” “did we properly cook your patty to medium-rare,” and/or “was you burger at the right temperature when you began to consume it,” etc., as shown in FIG. 4. Block S210 can present any of these prompts to the patron through the food ordering application, through an email application, or through a web browser, etc. executing on the patron's mobile computing device or other computing device accessed by the patron. However, Block S210 can collect any other feedback related to the patron's previous food order and in any other way.”
As per claim 7, Radcliffe teaches all the limitations of claim 1.
In addition, Radcliffe teaches:
wherein analyzing collected user experience data and collected flavor profile and recipe data further comprises: detecting at least one of: a flavor pattern, and an ingredient pattern; Radcliffe 0059: “As shown in FIG. 4, a method S200 for personalizing food orders includes: from a patron, receiving a taste feedback for a first food item in a first food order submitted previously by the patron in Block S210; predicting an absolute food preference of the patron from the first food order in Block S220; predicting a relative taste preference of the patron based on the taste feedback and a recipe of the first food item in Block S230; generating a taste profile of the patron based on the absolute food preference and the relative taste preference in Block S240; from the patron, receiving a selection for a second food item in a new food order in Block S250, the second food item different than the first food item; applying the taste profile of the patron to a recipe for the second food item to generate a personalized recipe in Block S260; and submitting the personalized recipe with the new food order to a robotic food assembly apparatus in Block S270.”
As per claim 9, Radcliffe teaches all the limitations of claim 1.
In addition, Radcliffe teaches:
analyzing and calculating an ingredient and flavor ratio for a set of two or more components, wherein the set of two or more components includes at least two of: an ingredient, a flavor, and a food; Radcliffe 0079: “Block S230 can also identify preferred combinations (i.e., ratios) of ingredients in a food item from qualitative feedback collected from the patron in Block S210. In one example in which Block S210 receives a slider bar adjustment between a “not enough” position and a “too much” positions for a prompt reciting “did your burger have the right amount of ‘gameyness,”’ Block S230 can assign a spectrum of ratios of ground lamb to ground beef in hamburger patties to slider positions along the slider bar, such as a lamb-to-beef ratio of 1:0 for the extreme “not enough” position, a lamb-to-beef ratio of 0:1 for the extreme “too much” position, a lamb-to-beef ratio of 1:1 for the center between the two extreme positions, and a linear change between the 1:0 and 0:1 positions therebetween. Block S230 can thus extrapolate a preference for a patty meat blend from qualitative feedback provided by the patron.”
As per claim 11, Radcliffe teaches all the limitations of claim 9.
In addition, Radcliffe teaches:
calculating a ratio of total aggregated flavors and combinations of ingredients and flavors within at least one of: a dish, and a recipe; Radcliffe 0079: “Block S230 can also identify preferred combinations (i.e., ratios) of ingredients in a food item from qualitative feedback collected from the patron in Block S210. In one example in which Block S210 receives a slider bar adjustment between a “not enough” position and a “too much” positions for a prompt reciting “did your burger have the right amount of ‘gameyness,”’ Block S230 can assign a spectrum of ratios of ground lamb to ground beef in hamburger patties to slider positions along the slider bar, such as a lamb-to-beef ratio of 1:0 for the extreme “not enough” position, a lamb-to-beef ratio of 0:1 for the extreme “too much” position, a lamb-to-beef ratio of 1:1 for the center between the two extreme positions, and a linear change between the 1:0 and 0:1 positions therebetween. Block S230 can thus extrapolate a preference for a patty meat blend from qualitative feedback provided by the patron.”
As per claim 12, Radcliffe teaches all the limitations of claim 1.
In addition, Radcliffe teaches:
generating one or more personalized suggestions, wherein a personalized suggestion is an ingredient quantity suggestion; Radcliffe 0036: “Block S110 can automatically select all or a portion of the ingredients in the patron's new order. In one implementation, in response to an “I'm hungry,” “I want a burger,” or other input by the patron into the native application, Block S110 accesses a past burger order by the patron and modifies (e.g., adjusts ingredient quantities and doneness specifications) the past order according to the patron's order feedback in order to generate a selection of burger ingredients and preferences that better meets the patrons taste, nutritional, and/or other preferences. In another implementation, Block S110 accesses past patron orders, past patron order feedback, a time of day, a recent social networking communication or post, patron trends or habits, patron demographic and location, and/or any other patron or order information to generate a selection of burger ingredients and preferences particular to the patron at the particular time. In this implementation, Block S110 can thus implement a burger recommendation service to recommend particular burger ingredients and/or preferences to meet a patron's current dietary needs.”
As per claim 13, Radcliffe teaches all the limitations of claim 1.
In addition, Radcliffe teaches:
calculating the relative intensity of each ingredient included in a recipe; Radcliffe 0045: “prior feedback from the patron suggests that the patron has a strong liking for tomato and lettuce, though he exhibits a slight preference for lettuce over tomato. Furthermore, prior feedback suggests that the patron is particularly sensitive to doneness, and Block S130 can thus set a threshold change in patron-selected patty doneness to 5% rather than 10%, which is standard for customers with less sensitivity to doneness. As in the previous example, Block S130 can determine that the patron's total burger calorie selection is less than a predicted total number of calories for the burger with the patron's selected ingredients and ingredient quantities. In response, rather than increasing patty doneness, Block S130 can reduce patty size and replace a lost patty weight (before or after cooking) with lettuce and tomato at 1.3:1 ratio of lettuce to tomato based on the patron's slight preference for lettuce in order to meet the patron's taste preferences received in Block S110 and nutrition preference and hunger level received in Block S120.”
As per claim 16, Radcliffe teaches all the limitations of claim 1.
In addition, Radcliffe teaches:
adjusting the analysis of collected user experience data and collected flavor profile and recipe data to learn one or more ingredients which may be substituted into one or more recipes without altering the culinary experience of the one or more recipes; Radcliffe 0044-0045: “Block S130 can increase patty cook time (i.e., doneness) to reduce the grease (and calorie from fat content) of the burger. Furthermore, if the adjusted patty doneness (related to cooking time and temperate) approaches a boundary of patty doneness and the total burger calorie content is still excessive, Block S130 can reduce the total weight of the patty and/or replace a percentage of beef in the patty with a corresponding percentage of turkey, chicken, soybeans, and/or chickpeas. For example, if the patron specifies 20% done, Block S130 can set an upper limit of 30% doneness to meet the patron's nutritional preferences without substantially sacrificing the taste of the burger, which can be based on previous feedback from the patron, feedback from other customers in the patron's area, and/or feedback from other customers of a demographic or tastes similar to that of the patron. Similarly, if the patron specifies 50% beef, Block S130 can set a lower limit of 40% beef to meat the patron's nutritional preferences without substantially sacrificing taste. Additionally or alternatively, Block S130 can proportionally (e.g., linearly, exponentially) reduce mayonnaise amount, increase cook time, and/or adjust patty content… rather than increasing patty doneness, Block S130 can reduce patty size and replace a lost patty weight (before or after cooking) with lettuce and tomato at 1.3:1 ratio of lettuce to tomato based on the patron's slight preference for lettuce in order to meet the patron's taste preferences received in Block S110 and nutrition preference and hunger level received in Block S120.”
As per claim 17, Radcliffe teaches all the limitations of claim 1.
In addition, Radcliffe teaches:
applying at least one of: a recipe modification analysis, and an ingredient substitution analysis to identify one or more replacement ingredients; Radcliffe 0044-0045: “Block S130 can increase patty cook time (i.e., doneness) to reduce the grease (and calorie from fat content) of the burger. Furthermore, if the adjusted patty doneness (related to cooking time and temperate) approaches a boundary of patty doneness and the total burger calorie content is still excessive, Block S130 can reduce the total weight of the patty and/or replace a percentage of beef in the patty with a corresponding percentage of turkey, chicken, soybeans, and/or chickpeas. For example, if the patron specifies 20% done, Block S130 can set an upper limit of 30% doneness to meet the patron's nutritional preferences without substantially sacrificing the taste of the burger, which can be based on previous feedback from the patron, feedback from other customers in the patron's area, and/or feedback from other customers of a demographic or tastes similar to that of the patron. Similarly, if the patron specifies 50% beef, Block S130 can set a lower limit of 40% beef to meat the patron's nutritional preferences without substantially sacrificing taste. Additionally or alternatively, Block S130 can proportionally (e.g., linearly, exponentially) reduce mayonnaise amount, increase cook time, and/or adjust patty content… rather than increasing patty doneness, Block S130 can reduce patty size and replace a lost patty weight (before or after cooking) with lettuce and tomato at 1.3:1 ratio of lettuce to tomato based on the patron's slight preference for lettuce in order to meet the patron's taste preferences received in Block S110 and nutrition preference and hunger level received in Block S120.”
As per claim 18, Radcliffe teaches all the limitations of claim 1.
In addition, Radcliffe teaches:
generating a culinary profile, wherein the culinary profile indicates a user's preferences for at least one of: a flavor, an ingredient, a combination of flavors, and a combination of ingredients; Radcliffe 0094: “Block S260 can access the patron's taste profile from local memory within the patron's mobile computing device as the patron interfaces with a native food ordering application executing thereon to build an order. Block S260 can similarly retrieve the taste profile from a remote server, such as based on a user profile logged-in to the native food ordering application executing on the patron's mobile computing device. Alternatively, Block S260 can access or retrieve the taste profile and apply the taste profile to a new food order accordingly by matching identifying information extracted from a system of payment supplied by the patron to a particular taste profile stored in a taste profile database. However, Block S260 can access and select the taste profile for the patron in any other suitable way.
Claims 21-22 are directed to the CRM and system for performing the method of claim 1 above. Since Radcliffe teaches the CRM and system, the same art and rationale apply.
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 8 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20190080384 (hereinafter “Radcliffe”) et al., in view of U.S. PGPub 20210150543 to (hereinafter “Sodhi”) et al.
As per claim 8, Radcliffe teaches all the limitations of claim 7.
In addition, Radcliffe teaches:
creating a plurality of records, wherein each record of the plurality of records includes at least one of: general information, a description of a recipe's calculated flavor, an aggregation of ingredients' chemical structures, a calculated flavor, a perceived flavor, a nutritional value, an engineered feature of a recipe, an overall community ranking, an overall popularity ranking, source reliability information, a list of ingredients, an ingredient name, an ingredient quantity, a preparation method, and a chemical structure; Radcliffe 0059-0064: “predicting an absolute food preference of the patron from the first food order in Block S220; predicting a relative taste preference of the patron based on the taste feedback and a recipe of the first food item in Block S230; generating a taste profile of the patron based on the absolute food preference and the relative taste preference in Block S240; from the patron, receiving a selection for a second food item in a new food order in Block S250, the second food item different than the first food item; applying the taste profile of the patron to a recipe for the second food item to generate a personalized recipe in Block S260; and submitting the personalized recipe with the new food order to a robotic food assembly apparatus in Block S270… The second method S200 then assembles these data into a taste profile for the patron—or updates the patron's existing taste profile—that specifies one or more flavor profiles preferred by the patron (e.g., sweet, savory, light, greasy, meaty, vegetarian, etc.), absolutely preferences for which ingredients to include and which to exclude from personalized hamburger recipes for the patron, how much to cook a patty and toast a bun, and how much salt, pepper, mustard, ketchup, tomato, relish, onion, lettuce, and/or pickle, etc. to load onto a burger.”
Radcliffe may not explicitly teach the following. However, Sodhi teaches:
executing a learning process over the plurality of records to identify at least a pattern; and assigning a score to each pattern learned; Sodhi 0035-0042: “one or more components of a taste profile, such as scores assigned to food items and/or food characteristics of food items, can be generated by a trained machine learning module, such as the trained machine learning module 360, described with respect to FIG. 3 below… the taste profile system can implement machine learning processes to assign a preference score of 8.5 to food items that include hot spices, such as red chili peppers… the array of data that can be input into the trained machine learning module 360 can include events 310 (e.g., calendar data); web activity 320 (e.g., user social media posts and user web browsing history); purchasing activity 330 (e.g., frequencies and/or quantities of purchased foods and/or food ingredients); IoT data 340 (e.g., camera images and/or audio recordings, information about quantities of food items and/or food ingredients obtained by smart refrigerators and smart containers); and contextual data 350 (e.g., menu information corresponding to a restaurant where the user is located and/or information regarding a time, date, season, location, and/or weather conditions corresponding to the user's dining activity). By using this array of data to generate a taste profile, embodiments of the present disclosure can account for a variety user activities that can indicate specific food preferences of the user. Accordingly, this array of data can allow the taste profile system to generate accurate food recommendations.
Radcliffe and Sodhi are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Radcliffe with the aforementioned teachings from Sodhi with a reasonable expectation of success, by adding steps that allow the software to utilize machine learning with the motivation to more efficiently and accurately organize and analyze data [Sodhi 0042].
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20190080384 (hereinafter “Radcliffe”) et al., in view of Official Notice.
As per claim 10, Radcliffe teaches all the limitations of claim 9.
Radcliffe may not explicitly teach the following:
generating, for a set of two or more components, at least one of: an average, a median, and a standard deviation; However, Official Notice is taken that generating an average, a median, and/or a standard deviation was old and well known type of mathematical analysis in the art at the time of Applicant's invention, which would have been obvious to include in food/recipe analysis because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20190080384 (hereinafter “Radcliffe”) et al., in view of U.S. PGPub 20170235734 to (hereinafter “Byron”) et al.
As per claim 14, Radcliffe teaches all the limitations of claim 1.
Radcliffe may not explicitly teach the following. However, Byron teaches:
computing Byron 0025: “a template recipe and a list of substitute ingredients may be provided as input. Then, the template recipe may be analyzed to determine a taste profile corresponding with the template recipe. The ingredients and proportions of the ingredients may be analyzed to compute taste scores (i.e., taste magnitudes) for the five tastes (i.e., saltiness, sweetness, sourness, bitterness, and umami). For each ingredient and taste, a numeric score may be calculated. Nutrients and compounds responsible for each taste may be identified and the intensity may be retrieved from a database or other data store. A taste score may be calculated by taking the sum of the concentration multiplied by the intensity. For example, saltiness comes from potassium and sodium, sweetness comes from sucrose and other carbohydrates, etc. Furthermore, various substances have known intensities, such as sweeteners that may be compared in intensity relative to sucrose…0035-0038: The ingredients list and proportions may then be analyzed to determine a score for each taste (i.e., saltiness, sweetness, sourness, bitterness, and umami). For each ingredient a taste score may be calculated based on the nutrients and compounds within each ingredient and known taste intensities… an ingredient's sweetness may be calculated based on the type of ingredient (e.g., an artificial sweetener) which may have a predetermined intensity (e.g., a multiplier). The intensity of various ingredients may be stored in a database (e.g., 114 (FIG. 1)) as a table that may be searched. Once the intensity is found, then the concentration of the nutrient or compound within the ingredient may be determined. The concentration may also be stored in tables in a database (e.g., 114 (FIG. 1)) or searched elsewhere based on the ingredient type and quantity required by the recipe. Based on the intensity and concentration, a score for the taste with respect to that single ingredient may be determined (e.g., an artificial sweetener may have a sweetness score of 25). The calculation for the taste score may be made by multiplying the sum of the concentration by the intensity… Thus, a numeric score may be obtained for each of the five tastes resulting in a taste profile for the template recipe consisting of five numeric taste scores (e.g., saltiness score=23, sweetness score=84, sourness score=3, bitterness score=4, and umami score=2).
Radcliffe and Byron are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Radcliffe with the aforementioned teachings from Byron with a reasonable expectation of success, by adding steps that allow the software to utilize computing metrics with the motivation to more efficiently and accurately organize and analyze data [Byron 0035].
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20190080384 (hereinafter “Radcliffe”) et al., in view of U.S. PGPub 20190370916 to (hereinafter “Surkin”) et al.
As per claim 19, Radcliffe teaches all the limitations of claim 1.
Radcliffe may not explicitly teach the following. However, Surkin teaches:
detecting an embedded-name in a preferred-data source object, wherein the preferred-data source object is an audio; determining the embedded-name in the preferred-data source object is a substring of the one or more name entries; and based on determining that the embedded-name is a substring, selecting the embedded-name as the preferred-data; Surkin 0026: “The following disclosure describes the present invention according to several embodiments directed at methods, systems, and apparatuses related to generating, managing, and utilizing personalized electronic food profiles to facilitate hyper-personalized dining experiences. More specifically technology is described herein for providing a personalized dining experience to an individual or a group by combining guest(s) food profile data with existing and user-generated transactions and content, both internal and third-party. Food profile data may be entered by the user as items and attributes. An item refers to an ingredient or dietary restriction or preference (e.g., “almonds,” “gluten,” “vegan,” etc.) an attribute describes the associated dietary restriction (e.g., “ingredients cannot be made in a facility that processes peanuts” or “no cross-contact”). This allows various dietary information to be supplied such as ingredients, groups of ingredients, nutrients, dietary rules, portion sizes, and common diets. Items in a user's profile may be categorized for context and level of severity. User generated transactions include any dining event, such as a catering order, restaurant reservation, event invitation, dinner party, or more. User generated content includes items like ratings, reviews, recommendations, shares, images, and other social media and online sources. The system can combine this data for one or more individuals and provide personalized recommendations for dining experiences, and also provides private cooks, organizers, and hospitality and other types of businesses with actionable data to further personalize the dining experience. The system is a universal electronic food profile that can simultaneously communicate with any other internet-connected system.”
Radcliffe and Surkin are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Radcliffe with the aforementioned teachings from Surkin with a reasonable expectation of success, by adding steps that allow the software to utilize clustering information with the motivation to more efficiently and accurately organize and analyze data [Surkin 0026].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Eric Stamber, can be reached at (571) 272-6724. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/Primary Examiner, Art Unit 3625