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 .
Applicant filed a response dated 7/24/2025 in which claims 1, 11, 54, 61-62, and 67-68 have been amended, claims 2-3, 6, 8-10, 14, 16-20, 22-24, 26-30, 32-33, 35-51, 53, 55, 58-60, and 63-66 have been canceled and new claim 69 has been added. The status identifier of claim 34 should read Previously Presented instead of Currently amended as the claim 34 is not amended. Thus, the claims 1, 4-5, 7, 11-13, 15, 21, 25, 31, 34, 52, 54, 56-57, 61-62, and 67-69 are pending in the application.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/24/2025 has been entered.
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, 4-5, 7, 11-13, 15, 21, 25, 31, 34, 52, 54, 56-57, 61-62, and 67-69 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of providing nutritional products recommendation to the subject without significantly more.
Examiner has identified claim 1 as the representative claim that describes the claimed invention presented in independent claims 1 and 61-62.
The claim 1 recites a series of steps, e.g., a computer system comprising an inventory of nutritional products, a recommendation generation module, and assessment data store, at least one processor, and a memory storing at least one program for execution by the at least one processor, the at least one program comprising instructions for: for a population of subjects comprising at least four subjects A) maintaining, in electronic form, on the memory, the inventory of nutritional products comprising, for each respective nutritional product in a plurality of nutritional products comprising at least 10 nutritional products maintained by the inventory of nutritional products, (i) an available physical quantity of a respective nutritional product in the plurality of nutritional products, (ii) a corresponding plurality of dietary supplements, each respective dietary supplement in the corresponding plurality of dietary supplements is associated with a corresponding nutritional aspect of the respective nutritional product when consumed, wherein the corresponding plurality of dietary supplements comprises at least six dietary supplements, and (iii) a dosage form of the respective nutritional product; B) training a machine learning model, wherein the machine learning model is trained using a historic subject response training set, the inventory of nutritional products, at least one pattern classification algorithm, and at least one regression algorithm, and wherein the training the machine-learning model further comprises: applying the historic subject response training set to a first regression algorithm, in the at least one regression algorithm, in order to identify as output from the first regression algorithm a set of predicted subject assessment responses, a set of predicted subject preferences, and a set of predicted nutritional product selections, wherein the set of predicted subject assessment responses, the set of predicted subject preferences, and the set of predicted nutritional product selections are conditioned on a pallet and uniqueness of a population of subjects associated with the historic subject response training set determined by first regression algorithm, and applying the inventory of nutritional products and the historic subject response training set to a first pattern classification algorithm, in the at least one pattern classification algorithm, in order to in order to identify as output from the first pattern classification algorithm a plurality of classifications of nutritional products in the at least 10 nutritional products maintained by the inventory of nutritional products, wherein the plurality of classifications of nutritional products is condition on an available inventory of each respective nutritional product and a geographic restriction for the respective nutritional product; generating, in electronic form, from an assessment data store, a survey for display at a remote device, wherein the survey comprises a set of prompts formed from an amalgamation of a plurality of prompts; C) generating, in real time responsive to a request from a remote device associated with a subject, from a plurality of prompts stored by the assessment data store, using the set of predicted subject preferences and the set of predicted, a survey for display at the remote device, wherein the survey comprises a set of prompts, comprising at least eight prompts, formed from an amalgamation of the plurality of prompts comprising at least 28 prompts; D) obtaining, in electronic form, via a communication network, a plurality of responses elicited by the survey from the subject associated with the remote device, wherein the plurality of responses comprises at least fifteen responses received sequentially from the subject; E) converting, in real time, using the trained machine learning model, the plurality of responses in to a filtered subset of the plurality of nutritional products based on preventing administration of excessive or redundant dosages of a nutritional product in the plurality of nutritional products by the subject, wherein the converting further comprises, for each respective response, in all or a subset of the plurality of responses, determining a plurality of physiological characteristics associated with the subject, wherein plurality of physiological characteristics comprises at least five physiological characteristics comprising a first physiological characteristics of a body mass index (BMI) of the subject and a second physiological characteristics of a caloric intake of the subject; associating a corresponding set of tags associated with the respective response according to an assessment a response to tag lookup data structure of the recommendation generation module, thereby identifying a first set of tags in a plurality of tags based on the corresponding set of tags associated by the respective response in accordance with the response to tag lookup data structure, wherein the plurality of tags comprises at least 28 tags, and wherein the corresponding set of tags comprising at least 21 tags; translating the first set plurality of tags into a corresponding plurality of votes applying the first set of tags against a plurality of decision rules of the recommendation generation module, wherein: each vote in the corresponding plurality of votes is associated with one or more nutritional products in a plurality nutritional products specified by the respective decision rule, the plurality of decision rules comprises at least 27 decision rules, the plurality of votes comprises at least 35 votes, each respective decision rule in the plurality of decision rules is (i) associated with one or more corresponding tags in a second set of tags in the plurality of tags and (ii) defines one or more unique logical operations that collectively specify a condition for implementing the respective decision rule, the one or more unique logical operations comprise at least five logical operations, at least one tag in the second set of tags is incorporated into two or more decision rules in the plurality of decision rules, each tag in the second set of tags is incorporated into at least one decision rule in the plurality of decision rules, and the first set of tags is a subset comprising less then all of the second set of tags, wherein: when the first set of tags satisfies the condition associated with a respective decision rule in the plurality of decision rules, the respective decision rule is implemented, thereby casting a vote in the plurality of votes for one or more nutritional products, in the plurality of nutritional products specified by the respective decision rule, and when the first set of tags fails to satisfy the condition associated with the respective decision rule, the respective decision rule is not implemented, thereby casting no vote in the plurality of votes, thereby causing two or more nutritional products in the plurality of nutritional products to have one or more votes in the plurality of votes upon translating all the tags in the first set of tags; identifying a subset of the plurality of nutritional products on the basis of the votes in the plurality of votes received by respective nutritional products in the plurality of nutritional products, wherein the subset of the plurality of nutritional products (i) comprises at least 10 nutritional products and (ii) consists of those nutritional products in the plurality of nutritional products that each received a sufficient number of votes to satisfy a nutritional product selection criterion; filtering the subset of the plurality of nutritional products against (i) a plurality of periodic nutritional limits and (ii) the plurality of classifications of nutritional products, wherein each respective periodic nutritional limit in the plurality of periodic nutritional limits specifies a corresponding maximum amount of a corresponding dietary supplement consumed in a corresponding period of time based on the plurality of physiological characteristics of the subject determined from the plurality of responses, and the recommendation generation module (i) independently sums, for each respective dietary supplement associated with each periodic nutritional limit in the plurality of period nutritional limits, the corresponding amount of respective dietary supplement in the subset of the plurality of nutritional products, and removes from the subset of the plurality of nutritional products one or more doses of one or more nutritional products in the subset of the plurality of nutritional products when one or more periodic nutritional limits in the plurality of periodic nutritional limits is determined by the recommendation to have been exceeded in order to prevent the one or more periodic nutritional limits from being exceeded, and (ii) removes from the subset of the plurality of nutritional products one or more nutritional products associated with a first classification of nutritional products in the plurality of classifications of nutritional products determined from the plurality of responses, thereby forming a filtered subset of the plurality of nutritional products comprising at least five nutritional products; F) generating, by the recommendation generation module, for display at the remote device, a report configured to provide a dietary recommendation to the subject based on the filtered subset of the plurality of nutritional products, wherein the report comprises (i) a listing of a first selection of two or more nutritional products in the filtered subset of the plurality of nutritional products, (ii) a reason for inclusion of one or more nutritional products in the filtered subset of the plurality of nutritional products, and (iii) an interactable graphical user interface feature allowing for modification of the first selection of two or more nutritional products in the filtered subset of the plurality of nutritional products at the remote device by substituting a first product in the filtered subset of the plurality of nutritional products with a second product in the two or more nutritional products or adding the first product to the two or more nutritional products in the filtered subset of the plurality of nutritional products; G) communicating, via the communication network, in electronic form, the repost for the subject to the remote device; and H) receiving, via the communication network, responsive to the communicating, via the interactable graphical user interface feature, an indication of an input for the first selection of the substituting the first product in the filtered subset of the plurality of nutritional products with the second product in the two or more nutritional products or the adding the first product to the two or more nutritional products in the filtered subset of the plurality of nutritional products. These limitations (with the exception of italicized limitations) describe the abstract idea of providing a dietary recommendation to the subject which correspond to a Certain Methods of Organizing Human Activity. The additional elements of a computer system, a recommendation generation module, assessment data store, one processor and a memory storing at least one program for execution by the at least one processor, the at least one program, electronic form, a remote device, a communication network, a machine learning algorithm, a machine learning model, one pattern classification algorithm, one regression algorithm, graphical user interface, and data structure do not restrict the claim from reciting an abstract idea. Thus, the claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a computer system, a recommendation generation module, assessment data store, one processor and a memory storing at least one program for execution by the at least one processor, the at least one program, electronic form, a remote device, a communication network, a machine learning algorithm, and data structure are recited at a high level of generality and do not meaningfully apply the abstract idea and thus do not transform the abstract idea into a patent eligible subject matter. Thus, the claim 1 is directed to an abstract idea (Step 2A-Prong 2: NO).
The claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a computer system, a recommendation generation module, assessment data store, one processor and a memory storing at least one program for execution by the at least one processor, the at least one program, electronic form, a remote device, a communication network, a machine learning algorithm, and data structure do not apply the abstract idea in a meaningful way and thus do not amount to add significantly more. Thus, the claim 1 is directed to an abstract idea (Step 2B: NO).
Similar arguments can be extended to other independent claims 61-62 and hence rejected on similar grounds as claim 1.
Dependent claims 4-5, 7, 11-13, 15, 21, 25, 31, 34, 52, 54, 56-57, and 67-69 further define the abstract idea that is present in the independent claims 1 and 61-62 and thus corresponds to Certain Methods of Organizing Human Activity and hence are abstract in nature for the reason presented above. Dependent claims do not include additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claims 1, 4-5, 7, 11-13, 15, 21, 25, 31, 34, 52, 54, 56-57, 61-62, and 67-69 are not patent-eligible.
Response to Arguments
Applicant's arguments filed dated 7/24/2025 have been fully considered but they are not persuasive due to the following reasons:
With respect to the rejection of claims 1, 4-5, 7, 11-13, 15, 21, 25, 31, 34, 52, 54, 56-57, 61-61, and 65-68 under 35 U.S.C. 101, Applicant states that the claims are directed to the technical operation of a machine learning model that processes data to detect correlations between available nutritional products and generate outputs in the form of classification of nutritional products used to create filtered subsets of nutritional products for presentation to a subject. These processes are inherently computational and algorithmic, focusing on the training and application of the machine learning model in real time based on responses to prompts presented to the subject.
Examiner respectfully disagrees and notes that under Step 2A, Prong One, the claim limitations are initially considered in the absence of additional elements to determine if the claim recites an abstract idea. The additional elements are then considered to determine if the additional elements restrict the claim from reciting an abstract idea irrespective of technical operation. In this case, it was determined that the claim recites an abstract idea and the additional elements do not restrict the claim from reciting an abstract idea. The additional elements and its technical operation are considered in further detail under Step 2A, Prong 2 and Step 2B.
Applicant further states that under Step 2A, Prong 2, the claims focuses on a technological improvement by specifying a particular way of running a computer-implemented process through defined training steps and data collected and evaluated using particular prompts and user responses. Applicant then cites Examples 47 and 48 to show technical improvements over traditional methods. Applicant then cites Recentive Analytics case and concludes that such a bifurcated model training is not conventional, and leads to a trained model that is uniquely suited to recommending nutritional products.
Examiner respectfully disagrees and notes that the additional elements are recited at a high level and it amounts to merely applying the abstract idea. There is no technical improvement when the additional elements implements the abstract idea. Training a machine learning model with historical data and the use of various algorithm to output a report to provide the dietary recommendation to the subject are merely use of additional elements as a tool to apply the abstract idea. The machine learning model does not predict something that would not have been possible without the use of a model. There is nothing in the claim that may represent an inventive concept or predictive nature of the model that go beyond simply a qualified person who is able to recommend these nutritional products based on the available data about the subject. This step would be equivalent to filtering/sorting the various nutritional products by the trained machine learning model. The scope of the claimed invention is very different than what is present in Example 47 and 48 and the Recentive Analytics decision. Thus, the arguments presented with respect to Example 47 and 48 and the Recentive Analytics decision are not persuasive.
Applicant also states that under Step 2B, the present independent claims are patent-eligible under Step 2A of the two-part Alice/Mayo test, and therefore step 2B does not need to be applied. Moreover, the present claims include an inventive concept and are patent-eligible under Step 2B of the two-part Alice/Mayo test. The non-conventional and specific arrangement of steps provides a technical improvement in the field, aligning with the principles set forth in Berkheim. Applicant then cites Example 47 and 48 and states that the present claims mirror these inventive features by integrating a trained machine learning model into a process that solves technical problems in nutritional product recommendations.
Examiner respectfully disagrees and notes that the additional elements simply applies the abstract idea and does not offer any technical/technology improvement or provide an inventive concept. The additional elements do not offer an inventive concept and thus the additional elements do not amount to add significantly more when implementing the abstract idea.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAJESH KHATTAR whose telephone number is (571)272-7981. The examiner can normally be reached M-F 8AM-5PM.
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RAJESH KHATTAR
Primary Examiner
Art Unit 3684
/RAJESH KHATTAR/Primary Examiner, Art Unit 3684