DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The following office action is a Final Office Action in response to the communications received on 08/01/2025.
Claims 1 and 11 have been amended; and claims 2 and 12 have been canceled; and therefore, claims 1, 3-11 and 13-20 are currently pending in this application.
Claim Rejections - 35 USC § 101
3. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
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, 3-11 and 13-20 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1)
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
(Step 2A) [Wingdings font/0xE0] Prong-One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
— Considering the independent claims (claims 1 and 11) as representative claims, the following claimed limitations recite an abstract idea:
calculate a score for an edible, comprising:
[collect] a performance profile comprising a plurality of logged performance metrics;
determine an edible of interest relating to a user;
receive nourishment information relating to the edible of interest to the user, wherein the nourishment information comprises a plurality of ingredients;
generate the score for an edible of interest using a scoring model:
[collect] data that correlates elements of the performance profile and the nourishment information to an edible score;
[update] the scoring model with feedback from previous iterations of the scoring model;
generate the score for an edible of interest as a function of the [updated] scoring model;
calculate one or more nutrient biodiversity scores as a function of the nourishment information comprising:
evaluating each ingredient of the plurality of ingredients, wherein evaluating each ingredient includes:
extracting at least a nutrient from each ingredient of the plurality of ingredients; and
calculating a nutrient biodiversity score for the at least a nutrient further comprises the use of a biodiversity model that comprises:
collect data that correlates nourishment entry data to nutrient biodiversity data;
[update] the biodiversity model with feedback from previous iterations of the biodiversity model; and
generate the biodiversity score as a function of the [updated] scoring model;
determine a nutritional requirement as a function of at least the nourishment information; and
[present] the nutritional requirement and the one or more nutrient biodiversity scores and the score of an edible of interest.
Accordingly, the limitations identified above recite an abstract idea since the limitations correspond to mental processes, which is part of the enumerated groupings of abstract ideas identified according to the eligibility standard (see MPEP 2106.04(a)).
For instance, the claims correspond to a mental process; such as, an evaluation, an observation and/or a judgement process, wherein (A) a score corresponding to an edible of interest related to a user is calculated using a scoring model based on (i) data collected from a performance profile that comprises a plurality of logged performance metrics, and (ii) nourishment information relating to the edible of interest of the user, the nourishment information comprises a plurality of ingredients; wherein the calculation above involves the use of: (a) data that correlates elements of the performance profile and the nourishment information to an edible score, and (b) feedback data from previous iterations of the scoring model; and furthermore, (B) a nutrient biodiversity score(s) for a nutrient(s)—extracted from an ingredient(s)—is generated using a biodiversity model based on (a) data that correlates nourishment entry data to nutrient biodiversity data, (b) feedback data from previous iteration of the model; and accordingly, once a nutritional requirement is determined as a function of the nourishment information, the user is presented with pertinent results—such as, the nutritional requirement, the one or more nutrient biodiversity scores, and the score of the edible of interest.
(Step 2A) [Wingdings font/0xE0] Prong-Two
The claim(s) recite additional element(s), wherein a computing device that comprises a disapply interface is utilized to facilitate the recited functions/steps regarding: collecting logged data (e.g., retrieving a performance profile comprising a plurality of logged user performance metrics); determining interest relating to a user (e.g., determine an edible of interest relating to a user); collecting further information (e.g., receive nourishment information relating to the edible of interest to the user, wherein the nourishment information comprises a plurality of ingredients); performing calculations using one or more machine-learning algorithms/models in order to generate one or more scores (e.g., “generate the score for an edible of interest utilizing a score machine-learning model and comprises: receiving training data, wherein the training data correlates elements of the performance profile and the nourishment information correlated to an edible score training, iteratively, the score machine-learning model . . . calculate one or more nutrient biodiversity scores as a function of the nourishment information comprising: evaluating each ingredient of the plurality of ingredients, wherein evaluating each ingredient includes: extracting at least a nutrient from each ingredient . . . training, iteratively, the biodiversity machine-learning model using the biodiversity training data, wherein training the biodiversity machine-learning model includes retraining the biodiversity machine-learning model with feedback from previous iterations of the biodiversity machine-learning model; and generating the nutrient biodiversity score as a function of the trained score machine-learning model”); determining one or more results (e.g., “determine a nutritional requirement as a function of at least the nourishment information”); and displaying one or more results (e.g., “display the nutritional requirement and the one or more nutrient biodiversity scores and the score of [the] edible of interest through a display interface”), etc.
However, the claimed additional element(s) fail to integrate the abstract idea into a practical application since the additional element(s) are utilized merely as a tool to facilitate the abstract idea. Thus, when each claim is considered as a whole, the additional element(s) fail to integrate the abstract idea into a practical application since they fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
The observations above confirm that the claims are indeed directed to an abstract idea.
(Step 2B)
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions. Accordingly, none of the current claims, when considered as a whole, recites an element—or a combination of elements—directed to an inventive concept.
It is also worth note, per the original disclosure, that the claimed system/method is directed to a conventional and generic arrangement of the additional elements. For instance, the specification describes a system that utilizes one or more commercially available conventional computing devices (e.g., a smartphone, a laptop computer, a desktop computer, etc.) that communicate with an online server via the conventional communication network—such as, the Internet (e.g., see [0009]; [0014]); and wherein, based on the analysis of one or more parameters collected regarding a user and/or food items (e.g., performance metrics, sensor/medical data, one or more ingredients/meals), the system generates one or more results (e.g., one or more nutrient amounts, a biodiversity score(s) for a nutrient(s), etc.); and thereby, the system presents pertinent information to the user, etc. (see ([0014] to [0027]).
In addition, the utilization of the conventional computer/network technology to facilitate the presentation of pertinent information—such as nutritional information—to a user(s), based on the analysis of collected data related to one or more users and/or food items, etc., is directed to a well-understood, routine or conventional activity in the art (e.g., US 2014/0349256; US 2013/0216982; US 2013/0280681; US 2005/0113650, etc.).
Note also that the use of two or more machine learning models, including an arrangement in which the output(s) from a first machine-learning model is utilizes as the input(s) to the second machine-learning model, etc., is also part of the conventional computer network technology (e.g., US 2008/0154651; US 2007/0005568, etc.).
The above observation confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 3-10 and 13-20). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology).
● Applicant’s arguments directed to section §101 have been fully considered (i.e., the arguments filed on 08/01/2025).
As an initial matter, the group certain metamethods of organizing human activity, is no longer applicable to the current claims. Particularly, given the current claim amendment, which requires various score calculation steps, the abstract idea group mental processes is considered to be the most relevant. Consequently, Applicant’s arguments directed to certain metamethods of organizing human activity is not relevant to challenge the current eligibility analysis.
Applicant has attempted to challenge the Office’s findings while referring to parts of the MPEP; however, the arguments are not persuasive at least for the following reasons:
Firstly, regarding a mental process, Applicant appears to be attempting to challenge the Office’s findings while relying on the claimed additional elements. For instance, Applicant asserts, “in the context of a complex computational process, using two machine learning models improves and enhances the results of determining a nutritional requirement. Using the output of the first score machine learning model as an input to the second machine learning model enhances computational efficiency and in turn improves the accuracy of the nutritional requirement for a user. The process cannot be performed manually or with pen and paper at the scale nor the speed required in moder data processing systems, not even in their most simplistic form” (emphasis added).
However, the inquiry per prong-one of Step 2A does not require one to consider any of the claimed computer-elements (e.g., the machine learning algorithms and/or the processing system, etc.). Instead, the inquiry requires one to identify just the abstract idea recited in the claim, along with a relevant explanation; e.g., see MPEP 2106.07(a), (emphasis added).
For Step 2A Prong One, the rejection should identify the judicial exception by referring to what is recited (i.e., set forth or described) in the claim and explain why it is considered an exception . . . the rejection should identify the abstract idea as it is recited (i.e., set forth or described) in the claim and explain why it is an abstract idea.
Thus, Applicant’s attempt to challenge the Office’s findings presented under prong-one of Step 2A, while relying on the claimed computer-elements, is not persuasive.
In addition, the eligibility test, per prong-one of Step 2A, does not require one to evaluate whether a human can operate at speed/scale comparable to a computer. Instead, the test requires one to evaluate whether a human can perform, mentally and/or using a pen and paper, the recited abstract idea. For instance, regarding the various calculation steps, a human—such as a nutritionist or a dietitian—can use one or more models (e.g., one or more models in the form of: tables that correlate logged performance metrics and nourishment information to one or more edible scores, and/or nourishment entry data to nutrient biodiversity data; one or more mathematical equations/formulas for calculating one or more relevant scores, etc.) in order to calculate various scores, including: a score for an edible item, a nutrient biodiversity score, etc. Of course, after completing the various calculations above, the nutritionist/dietitian can present—using a pen and paper—one or more results to the user; wherein such results include: a nutritional requirement, each of the one or more nutrient biodiversity scores, the score for the edible item(s), etc.
The observation above demonstrates that the underlying patent-ineligible claimed invention can indeed be performed mentally and/or using a pen and paper. Also see Versata Dev. Grp. v. SAP Am., Inc., 793 F.3d 1306, 1335 (Fed. Cir. 2015) (“Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind”, emphasis added). Consequently, Applicant’s arguments are not persuasive.
Secondly, while attempting to summarize sections from the MPEP regarding prong-two of Step 2A, Applicant asserts that “the amended claim 1 in the instant application contain limitations that amount to more than mere instructions to apply an exception at least because the limitations include steps such as receiving data that includes data inputted by a user, but also the training of at least two machine learning models, wherein the output of the first machine learning model is an input to the second machine learning model. It is extremely beneficial to use the output of one machine learning model as input to another. This ensemble learning leads to improved overall performance by leveraging the strengths of multiple models. Each of the two models in this case capture different aspects or nuances of the data (at least edible of interest and nourishment information) and combining their outputs allows the final model to learn from a more comprehensive representation of the data, leading to better predictions. By combining the strengths of different models, stacking can often achieve higher accuracy than using any single model alone. Applicant submits that the combination of additional elements, at least the use of the two machine ‘stacked’ machine learning models are meaningful limitation as they confine the idea of calculating a score of an edible to a particular and practical application” (emphasis added). Applicant has also cited some paragraphs from the specification ([0028], [0042], [0043]) in order to support the above assertion (e.g., see pages 15-17 of Applicant’s arguments).
However, Applicant appears to fail to address the core issue regarding prong-two of Step 2A. Particularly, except for the assertions made regarding the benefits of using multiple machine-learning models, including the alleged “high” accuracy being achieved when the output of a first machine-learning (first-ML) model is used as an input to a second machine-learning (second-ML) model, Applicant does not demonstrate whether any of the current claims (and/or the disclosure as a whole) is providing a technological improvement over the existing computer/network technology. It is worth to note that the use of two or more machine-learning models, including an arrangement where the output from a first-ML model is provided as an input to a second-ML model, etc., is already part of the existing computer/network technology. In fact, the exemplary refences, which are presented as part of the Step 2B analysis, already supports the
above fact. For instance, Kenefick (US 2008/0154651), which is a publication available to the public for more than a decade prior to Applicant’s claimed (and disclosed) system/method, teaches a system directed to the existing computer/network technology. This system implements multiple predictive models for an intelligent decision making process ([0002]), wherein the predictive models are already in the form of existing machine-learning algorithms ([0044]); and the system also implements an arrangement in which the output from a first-ML model is provided as an input to a second-ML model, including performing one or more iterations in order to enhnce the accuracy of the parameters being generated ([0051], [0052]); and furthermore, based on updated/new data being gathered, the models are updated or retrained in order to refine the system’s capabilities ([0068]).
Similarly, Angelo (US 2007/0005568), also a publication available to the public for more than a decade prior to Applicant’s claimed (disclosed) system/method, describes such a system directed to the existing computer/network technology; and this system also implements two or more machine-learning models. Particularly, Angelo is attempting to make a more accurate prediction regarding the repository, which the system should use to generate query results to the user (e.g., see [0026], [0029]); and this system also uses various types of machine-learning models ([0046]); and furthermore, besides analyzing information regarding the user and/or the user’s search query ([0059]), Angelo also implements an arrangement in which the output from a first model is provided as an input to a second model ([0063], [0064]).
Although no evidence is necessarily required, per prong-two of Step 2A, the references above already confirm that the claimed (if any) and/or the disclosed use of such two or more machine-learning models, including a scheme in which the output from the first-ML model is provided as an input to the second-ML model, etc., certainly does not constitute a technological improvement. Instead, the current claims (and the original disclosure) are demonstrating that the claimed (and disclosed) system/method is utilizing the existing computer/network technology—merely as a tool—to facilitate the claimed abstract idea (e.g., facilitating the process of calculating one or more scores, etc.). Consequently, Applicant’s arguments are not persuasive. Of course, it is also worth to note that none of the current claims necessarily implements such a scheme in which the output from a first-ML model is used as an input to a second-ML model. Instead, each of the current claims is reciting the process of generating, as a function of the trained score machine-learning model, (i) the score for an edible of interest and (ii) the nutrient biodiversity score. Particularly, there is no instance in which an output of a first-ML model is provided as an input to a second-ML model.
Thirdly, unlike Applicant’s assertion, neither Applicant’s current arguments nor the current claim amendment moots the Office’s findings presented under Step 2B. In this read, while referring to Berkheimer v. HP Inc., 881 F.3d 1360, 1369 (Fed. Cir. 2018), Applicant is asserting that “claim 1 as amended recites: ‘generate the score for an edible of interest utilizing a score machine-learning model and comprises: receiving training data, wherein the training data correlates elements of the performance profile and the nourishment information correlated to an edible score training . . . and generating the nutrient biodiversity score as a function of the trained score machine-learning model’ improves decision-making on nutritional information, which constitutes a significant improvement based on what is commonly practiced in the nutritional industries” (emphasis added).
However, Applicant appears to be mistaking the claimed new concept as a form of technological improvement. It is worth to note that the current claims may recite a new concept—such as, a new nutrition analysis procedure and/or decision-making procedure. However, this does not necessarily mean that the claimed (and/or the disclosed) system/method is implementing an inventive concept; rather, it only means that the claims are reciting a new abstract idea, which the prior art does not contemplate. However, the above does necessarily not imply eligibility per Step 2B since a new abstract idea is still an abstract idea; see MPEP 2106.06 (I) (emphasis added),
Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the §101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a §101 inventive concept is thus distinct from demonstrating §102 novelty."). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103 . . . patentability of the claimed invention under 35 U.S.C.102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C.101.
In contrast, the inquiry per Step 2B does not rely just on the new abstract idea. Instead, while considering each of the claims as a whole, Step 2B determines whether the claim is directed to a non-generic and non-conventional arrangement of the additional elements. Of course, given the fact that the claimed (and the disclosed) system/method is relying merely on the conventional computer/network technology, each of the claims—when considered as a whole—is directed to the conventional and generic arrangement of the additional elements. Thus, the finding above confirms that none of the claims implements an inventive concept. Of course, the lack of technological improvement (i.e., the finding per prong-one of Step 2A) also confirms the lack of an inventive concept; see MPEP 2106.05(a) (emphasis added),
While improvements were evaluated in Alice Corp. as relevant to the search for an inventive concept (Step 2B), several decisions of the Federal Circuit have also evaluated this consideration when determining whether a claim was directed to an abstract idea (Step 2A). See, e.g., Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-16, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016); Visual Memory, LLC v. NVIDIA Corp., 867 F.3d 1253, 1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017). Thus, an examiner should evaluate whether a claim contains an improvement to the functioning of a computer or to any other technology or technical field at Step 2A Prong Two and Step 2B, as well as when considering whether the claim has such self-evident eligibility that it qualifies for the streamlined analysis.
Thus, at least for the reasons discussed above, the Office concludes that none of the current claims—when considered as a whole—implements an inventive concept that amounts to “significantly more” than an abstract idea.
Claim Rejections - 35 USC § 112
4. The following is a quotation of 35 U.S.C.112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C.112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
● Claims 1, 3-11 and 13-20 are rejected under 35 U.S.C.112(b), or second paragraph (pre-AIA ), as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
(a) Each of claims 1 and 11 recites, “generat[ing] . . . the score for an edible of interest” (e.g., see lines 7 and 16 of claim 1; and line 9 of claim 11, emphasis added).
However, it is unclear whether the limitation, “an edible of interest”, above is referring the same limitation, “edible of interest”, obtained in the preceding determining step (e.g., “determin[ing] an edible of interest relating to the user”). s
(b) Each of claims 1 and 11 recites, “generating the nutrient biodiversity score as a function of the trained score machine-learning model” (emphasis added). However, it is unclear whether the limitation “the trained score machine-learning model” above is intended to mean “the trained biodiversity machine-learning model” (emphasis added).
(c) Each of claims 1 and 11 recites, “display[ing] . . . nutrient biodiversity scores and the score of an edible of interest through a display interface” (emphasis added).
However, it is unclear whether the limitation, “an edible of interest”, above is referring to a new “edible of interest” or to the same “edible of interest” recited in the preceding lines.
(d) Claim 11 further recites, “generating the score of an edible as a function of the trained score machine-learning model” (emphasis added).
However, it is unclear whether the limitation, “an edible”, above is intended to implying, “the edible of interest” (emphasis added).
In addition, Applicant is strongly recommended to evaluate the terms recited per each of the current claims and make appropriate corrections if additional discrepancies are noted.
Prior Art
5. Considering each of claims 1 and 11 as a whole (including the respective dependent claims), the prior art does not teach or suggest the current claims (regarding the state of the prior art, see the office-action dated 03/13/2025).
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this final office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 filled 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUK A GEBREMICHAEL whose telephone number is (571) 270-3079. The examiner can normally be reached on 7:00AM-3:00PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DAVID LEWIS can be reached on (571) 272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715