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 .
Formal Matters
Applicant's response, filed 09 March 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-8 and 11-18 are currently pending and have been examined.
Claims 1 and 11 have been amended.
Claims 9, 10, 19, and 20 have been canceled.
Claims 1-8 and 11-18 have been rejected.
Priority
The instant application claims the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c). Accordingly, the effective filing date for the instant application is 03 November 2020 claiming benefit to US Patent Application 17/088,207.
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 1-8 and 11-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-8 and 11-18 are drawn to a system or method, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a system and independent claim 11 recites a method both for analyzing a nutritional content of an alimentary combination in part performing the steps of receive an input, wherein the input comprises an alimentary combination comprising at least a food-related ailment and at least a preference; compute a plurality of alimentary combination factors as a function of the input and a first [algorithm], wherein: the plurality of alimentary combination factors comprises an ingredient quality indicator and a nutritional content indicator; and the first [algorithm] is trained using alimentary combination training data comprising exemplary alimentary combination factors correlated to exemplary alimentary combinations, wherein the first [algorithm] generates a model that models relationships between input data and the plurality of alimentary combination factors using training data containing correlations between categories of data elements; generate at least a modification pertaining to at least an alimentary combination factor of the plurality of alimentary combination factors using a second [algorithm], wherein the second [algorithm] is trained using modification training data comprising exemplary modifications correlated to the exemplary alimentary combination factors; output the at least a modification by displaying the at least a modification; receive, from the user device, user feedback indicating performance of at least one of the first [algorithm] or the second [algorithm]; and retrain at least one of the first [algorithm] or the second [algorithm] based on the user feedback, trigger event-based retraining of at least one of the first [algorithm] or the second [algorithm] by comparing at least one of outputs or errors to at least one of a threshold or a convergence test to update model parameters of a [algorithm] of the first [algorithm] or the [algorithm].
Dependent claims 2 and 12 recite, in part, wherein the input further comprises a desired level of preparation.
Dependent claims 3 and 13 recite, in part, suggest at least a customized instruction as a function of the desired level of preparation.
Dependent claims 4 and 14 recite, in part, wherein: the desired level of preparation comprises a self-sufficient option; and the at least a customized instruction comprises at least a customized instruction pertaining to handling one or more delivered ingredients.
Dependent claims 5 and 15 recite, in part, wherein computing the plurality of alimentary combination factors comprises: receiving at least an instruction comprising at least an ingredient and at least a cooking method pertaining to preparing the alimentary combination; analyzing a nutritional content as a function of the at least an instruction; and computing the nutritional content indicator as a function of the analysis.
Dependent claims 6 and 16 recite, in part, compare a first alimentary combination against a second alimentary combination by matching a first instruction pertaining to preparing the first alimentary combination against a second instruction pertaining to preparing the second alimentary combination; and pair the first alimentary combination with the second alimentary combination as a function of the match.
Dependent claims 7 and 17 recite, in part, wherein generating the at least a modification comprises: generating a first modification pertaining to the at least a cooking method; and generating a second modification pertaining to the at least an ingredient; thereby improving a nutritional value of the alimentary combination.
Dependent claims 8 and 18 recite, in part, generate an alert as a function of the at least a food-related ailment.
These steps of generating a meal plan based on user preferences and health goals, historical user data, and other meal planning factors amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982)).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 1 recites a processor; and a memory communicatively connected to the processor. Claim 11 recites a processor. Claims 1 and 11 recite a user device. Claims 9 and 19 recite a graphical user interface of the user device. The specification discloses that the user device and computer may be any device a user may use (see the instant specification in ¶ 0015. And ¶ 0061). The computer device and corresponding hardware is therefore only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Claims 1 and 11 recite a machine learning process. The specification does not have specific hardware requirements for the machine learning and discloses generic algorithm structures (see the instant specification in ¶ 0026-36). Therefore, the use of the first and second machine learning processes for analyzing a nutritional content of an alimentary combination is only recited as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claims 1 and 11 recite store the generated machine-learning model in the memory for producing outputs given new inputs. The limitations are only recited as a tool which only serves as output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
Claims 1 and 11 recite displaying data in a graphical user interface of a user device. The limitations are only recited as a tool which only serves as display/output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 1 recites a processor; and a memory communicatively connected to the processor. Claim 11 recites a processor. Claims 1 and 11 recite a user device. Claims 9 and 19 recite a graphical user interface of the user device. Claims 1 and 11 recite a machine learning process. Claims 1 and 11 recite displaying data in a graphical user interface of a user device.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claims 1 and 11 recite store the generated machine-learning model in the memory for producing outputs given new inputs. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Claims 1 and 11 recite displaying data. The courts have decided that presenting generated data as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example iv. presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-8 and 11-18 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Claims 1-8 and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Murdoch et al. (US Patent Application No. 20200098466)[hereinafter Murdoch] in view of Sedghi et al., Machine Learning in Event-Triggered Control: Recent Advances and Open Issues, IEEE Access (Sept 27, 2020)[hereinafter Sedghi].
As per claim 1, Murdoch teaches on the following limitations of the claim:
a system for analyzing a nutritional content of an alimentary combination, the system comprising is taught in the Detailed Description in ¶ 0066, ¶ 0080, and ¶ 0178 (teaching on a machine learning based system for generating a user menu based on user preferences and nutritional requirements);
a processor; and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to is taught in the Detailed Description in ¶ 0233, ¶ 0260, and in the Figures at fig. 35 (teaching on a user device and central processor with corresponding processor and memory);
receive an input from a user device, wherein the input comprises an alimentary combination comprising at least a food-related ailment and at least a preference is taught in the Detailed Description in ¶ 0077, ¶ 0079, and ¶ 0417 (teaching on receiving a user profile from a profile database wherein the profile includes user nutrient target and user food preferences entered by the user at a user device);
compute a plurality of alimentary combination factors as a function of the input and a first machine-learning process, wherein is taught in the Detailed Description in ¶ 0080, ¶ 0212, and in the Figures at fig. 16 reference character 1602 (teaching on determining a meal plan (treated as synonymous to a plurality of alimentary combination factors) via a meal plan optimization unsupervised machine learning model);
the plurality of alimentary combination factors comprises an ingredient quality indicator and a nutritional content indicator; and is taught in the Detailed Description in ¶ 0147, ¶ 0154, and ¶ 0166 (teaching on the meal plan optimization includes an ingredient/food option regarding source/brand (treated as synonymous to ingredient quality indicator) and the nutritional attributes of the food);
the first machine-learning process is trained using alimentary combination training data comprising exemplary alimentary combination factors correlated to exemplary alimentary combinations wherein the first machine-learning process generates a machine-learning model that models relationships between input data and the plurality of alimentary combination factors using training data containing correlations between categories of data elements, and stores the generated machine-learning model in the memory for producing outputs given new inputs is taught in the Detailed Description in ¶ 0175, ¶ 0178, and in the Figures at fig. 16 reference character 1608 (teaching on the non-supervised machine learning meal plan optimization model being trained on historical data of similar patient and corresponding meal plans (treated as synonymous to exemplary data) - Examiner notes that one of ordinary skill in the art would readily recognize that a model represents a relationship between the inputs and the outputs and when new inputs are provided, the model outputs new outputs);
generate at least a modification pertaining to at least an alimentary combination factor of the plurality of alimentary combination factors using a second machine-learning process, wherein the second machine-learning process is trained using modification training data comprising exemplary modifications correlated to the exemplary alimentary combination factors; and is taught in the Detailed Description in ¶ 0066-67, ¶ 0179, ¶ 0212, and in the Figures at fig. 16 reference character 1608 (teaching on receiving a user feedback (treated as synonymous to a modification) to incorporate and update the meal plan according to a second supervised machine learning model);
output the at least a modification by displaying the at least a modification in a graphical user interface of a user device is taught in the Detailed Description in ¶ 0066, ¶ 0207 and in the Figures at fig. 11 (teaching on outputting the menu that reflects the user feedback via menu modifications to the user interface);
receive, from the user device, user feedback indicating performance of at least one of the first machine-learning process or the second machine-learning process; and retrain at least one of the first machine-learning process or the second machine-learning process based on the user feedback is taught in the Detailed Description in ¶ 0066-67, ¶ 0179, ¶ 0212, and in the Figures at fig. 16 reference character 1608 (teaching on receiving a user feedback (treated as synonymous to a modification) from a user interface to incorporate and update the meal plan according to a second supervised machine learning model).
Murdoch fails to teach the following limitation of claim 1. Sedghi, however, does teach the following:
wherein the processor is configured to trigger event-based retraining of at least one of the first machine-learning process or the second machine-learning process by comparing at least one of outputs or errors to at least one of a threshold or a convergence test to update model parameters of a machine learning model of the first machine-learning process or the second machine-learning process is taught in the § A. Event-Triggered and Self-Triggered control on p. 3, B. Neural Networks (NNS) on p. 6, and § A. Reinforcement Learning - Critic-Only on p. 8 (teaching on a plurality of model maintenance approaches including event-based triggers of error convergence or error threshold);
One of ordinary skill in the art before the effective filing date would combine the meal planning machine learning model updating with user input preferences of Murdoch with the event-based updating triggers of Sedghi with the motivation of “acting only when the relevant information is available or needs to be communicated” (Sedghi in the § § A. Event-Triggered and Self-Triggered control on p. 3).
Independent claim 11 is rejected under the same rational.
As per claim 2, the combination of Murdoch and Sedghi discloses all of the limitations of claim 1. Murdoch also discloses the following:
the system of claim 1, wherein the input further comprises a desired level of preparation is taught in the Detailed Description in ¶ 0166, ¶ 0102, and ¶ 0226 (teaching on the user preference include the source of the food being an ingredient level or restaurant level (treated as synonymous to a preparation level) preferences or the time required to prepare the meal).
Dependent claim 12 is rejected under the same rational.
As per claim 3, the combination of Murdoch and Sedghi discloses all of the limitations of claim 2. Murdoch also discloses the following:
the system of claim 2, wherein the processor is further configured to suggest at least a customized instruction as a function of the desired level of preparation is taught in the Detailed Description in ¶ 0166, ¶ 0102, and ¶ 0226 (teaching on the user preference include the source of the food being an ingredient level or restaurant level (treated as synonymous to a preparation level) preferences or the time required to prepare the meal (treated as synonymous to a "self-sufficient option") as well as a delivery options for the food (treated as synonymous to handling of the delivered ingredients)).
Dependent claim 13 is rejected under the same rational.
As per claim 4, the combination of Murdoch and Sedghi discloses all of the limitations of claim 3. Murdoch also discloses the following:
the system of claim 3, wherein: the desired level of preparation comprises a self-sufficient option; and the at least a customized instruction comprises at least a customized instruction pertaining to handling one or more delivered ingredients is taught in the Detailed Description in ¶ 0166, ¶ 0102, and ¶ 0226 (teaching on the user preference include the source of the food being an ingredient level or restaurant level (treated as synonymous to a preparation level) preferences or the time required to prepare the meal (treated as synonymous to a "self-sufficient option") as well as a delivery options for the food (treated as synonymous to handling of the delivered ingredients)).
Dependent claim 14 is rejected under the same rational.
As per claim 5, the combination of Murdoch and Sedghi discloses all of the limitations of claim 1. Murdoch also discloses the following:
the system of claim 1, wherein computing the plurality of alimentary combination factors comprises: receiving at least an instruction comprising at least an ingredient and at least a cooking method pertaining to preparing the alimentary combination is taught in the Detailed Description in ¶ 0166, ¶ 0102, and ¶ 0226 (teaching on the user preference include the source of the food being an ingredient level or restaurant level (treated as synonymous to a preparation method) preferences or the time required to prepare the meal);
analyzing a nutritional content as a function of the at least an instruction; and computing the nutritional content indicator as a function of the analysis is taught in the Detailed Description in ¶ 0166, ¶ 0102, ¶ 0203, and ¶ 0226 (teaching on analyzing the meal preferences for ingredient/food item level nutritional content in kcals for the kcal target evaluation).
Dependent claim 15 is rejected under the same rational.
As per claim 6, the combination of Murdoch and Sedghi discloses all of the limitations of claim 5. Murdoch also discloses the following:
the system of claim 5, wherein the processor is further configured to: compare a first alimentary combination against a second alimentary combination by matching a first instruction pertaining to preparing the first alimentary combination against a second instruction pertaining to preparing the second alimentary combination; and pair the first alimentary combination with the second alimentary combination as a function of the match is taught in the Detailed Description in ¶ 0103, ¶ 0238, and in the Figures in fig. 20 (teaching on comparing the current user's preferences and generated meal plan with other historical user preferences and generated meal plans that best match the current user to generate modifications for the user based on the match (treated as synonymous to pairing the alimentary combinations)).
Dependent claim 16 is rejected under the same rational.
As per claim 7, the combination of Murdoch and Sedghi discloses all of the limitations of claim 5. Murdoch also discloses the following:
the system of claim 5, wherein generating the at least a modification comprises: generating a first modification pertaining to the at least a cooking method; and is taught in the Detailed Description in ¶ 0166, ¶ 0102, and ¶ 0226 (teaching on modifying the meal plan according to user preference including the source of the food being an ingredient level or restaurant level (treated as synonymous to a preparation method) preferences or the time required to prepare the meal);
generating a second modification pertaining to the at least an ingredient; thereby improving a nutritional value of the alimentary combination is taught in the Detailed Description in ¶ 0166, ¶ 0102, ¶ 0203, and ¶ 0226 (teaching on analyzing the meal preferences for ingredient/food item level nutritional content in kcals for the kcal target evaluation and modifying the plan to meet the target).
Dependent claim 17 is rejected under the same rational.
As per claim 8, the combination of Murdoch and Sedghi discloses all of the limitations of claim 1. Murdoch also discloses the following:
the system of claim 1, wherein the processor is further configured to generate an alert as a function of the at least a food-related ailment is taught in the Detailed Description in ¶ 0207 (teaching on generating a warning flag related to the meal plan).
Dependent claim 18 is rejected under the same rational.
Response to Arguments
Applicant's arguments filed 09 March 2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that the instant claims do not recite certain methods of organizing human activity abstraction under Step 2A Prong 1 as the claims recite specific technical operations performed by a computing device for generating, storing, and retaining machine learning models. Examiner disagrees. The use of electronic means for performing the abstract idea is not enough to overcome Step 2A Prong 1 (2019 Revised Patent Subject Matter Eligibility Guidance, 84 FED. REG. 4 (January 7, 2019) at p. 8 footnote 54 further citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316-18 (Fed. Cir. 2016) where the electronic implementation of human activity was not adequate to overcome Step 2A Prong 1). Generating generic models, saving feature weights from a model and inputs/outputs from the models, and triggering a retraining of a model under a conditional statement are all are implemented on generic computer hardware. Examiner notes that the conditional trigger for retraining a model is considered abstract requiring mere mathematical relationships.
Next, Applicant asserts that the claims do not recite a mental process. Examiner has not relied on said judicial exception for the rejection.
Next, Applicant asserts that the claims are directed towards a technical improvement under Step 2A Prong 2 via an improvement to machine learning. Examiner disagrees. There is no evidence in the specification that the improvement exists outside the abstract idea. The specification states “Users are often confronted with deciding which restaurant would be more convenient to patronize and order food. After identifying an establishment, maybe based on the proximity to the user’s location, time is then wasted in dealing with selecting the food and placing an order as the user may have dietary restrictions, for example, which may extend the ordering activity further wasting more of the user’s precious time” (Background in ¶ 0003). an improvement to the abstract idea of food recommendation does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Merely adding generic computer components to perform the method is not sufficient.
Next, Applicant asserts that the instant claims are similar to that of Ex parte Desjardins et al.. Examiner disagrees. As stated above, there is no discussion in the specification that the training and maintenance of the machine learning algorithms is an improvement over the prior art. Examiner notes this is further evidenced by the abundant embodiments offered for the models and maintenance processes that may be implemented to solve the problem with the abstract idea.
Finally, Applicant asserts that the steps Examiner has identified as abstract are not well understood, routine, and conventional. Without commenting on the inventiveness of the abstraction, the consideration under Step 2B is if the additional elements, alone or in combination, are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Applicant’s arguments filed 09 March 2026 with respect to 35 USC § 103 have been considered and are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Sedghi, as per the rejection above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857