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 .
Claims 1-16 are pending.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed applications, Application No. 63/327,311, filed 04/04/2022, Application No. 63/386,722, filed 12/09/2022, Application No. 63/480,738, filed 01/20/2023, PCT/US2023/065202 filed 03/31/2023, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Claims 1 and 9 recite “generate, by predictive modeling, a predicted effect data object based at least in part on the dietary recommendation data object and the user-specific biological metrics that are not blood glucose.” The prior-filed applications do not contain support for “biological metrics that are not blood glucose.” The previous applications do not disclose any details regarding the “biological metrics” and do not include any disclosure related to “not blood glucose” or “blood glucose” as recited in the independent claims. Any negative limitation or exclusionary proviso must have basis in the original disclosure. If alternative elements are positively recited in the specification, they may be explicitly excluded in the claims. See In re Johnson, 558 F.2d 1008, 1019, 194 USPQ 187, 196 (CCPA 1977) ("[the] specification, having described the whole, necessarily described the part remaining."). See also Ex parte Grasselli, 231 USPQ 393 (Bd. App. 1983), aff’d mem., 738 F.2d 453 (Fed. Cir. 1984). See MPEP 2173.05(i). As there is no disclosure of the negative limitation in the previous applications, the instant application is not entitled to the benefit of the prior application.
Accordingly, claims 1-16 are not entitled to the benefit of the prior application.
Claim Objections
Claims 6 and 14 are objected to because of the following informalities:
Claims 6 and 14 recite “indices/patterns/metrics - , and the predicted.” The “-“ should be amended to an “–” or en dash.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “generate a food recommendation data object based at least in part on the user-specific social, environmental, and biological metrics and retrieved evidence-based data; generate a food recommendation data object aligned with existing dietary guidelines or patterns based at least in part on retrieved evidence-based data containing multiple dietary quality metrics and/or indices and their listed scoring components; generate a food recommendation data object aligned with expert’s food healthfulness opinions based at least in part on retrieved evidence-based data containing multiple food quality metrics and/or indices and their listed scoring components identified and pooled from the scientific literature on nutritional profiling systems/algorithms, including an assessment of the consensus food rating across multiple expert-driven algorithms.” The instant specification does not include enough detail to allow one of ordinary skill in the art to generating a food recommendation. While the disclosure discusses the data input and the output (including paragraph [0028], [0082] and [0057]), it fails to explain how the actual recommendation is determined. In other words, it is like the data is going into a black box and outputs a result, but as the inventive concept appears to be the recommendations and scores, a disclosure of how these are determined must be included to meet the written description requirements. For instance, paragraph [0057] discusses “food recommendations are made with considerations encompassing multiple scoring systems, each having unique strengths and weaknesses, and these are integrated into a meta-nutritional profiling system, which has both expert-derived consensus food recommendation systems (i.e., a nutrient profiling system – NPS) 2110 that may be paired with de novo scoring systems with health outcome specific data-driven recommendations 2120, which are derived from predictive health model(s) in FIG 6 (element 610) and its derivatives FIGS 17-18.” However, the disclosure is insufficient in its description as to how the score is recommendation is actually determined, especially in view of the scores discusses in paragraph [0134].
Claim 1 further recites “generate a dietary recommendation data object based at least in part on the user-specific biological metrics and retrieved evidence-based data; generate a dietary recommendation data object based on the retrieved evidence-based data containing clinical effect sizes for health outcomes stratified by dietary quality index/measure/guideline; generate a multi-dimensional, visual dietary recommendation data object known as a spider or radar chart based on the user-specific dietary quality and retrieved evidence-based data containing multiple dietary quality metrics and/or indices.” Similar to above, the disclosure does not explain how this is done, rather just provides the input and the output. Paragraphs [0108] and [0109] discuss dietary recommendations and the specification points to Figure 25 to show how a dietary recommendation is generated, but there are no specifics as to how it is generated.
The disclosure also fails to support generating “an uncertainty data object” and “a quality data object.” Paragraphs [0085-0087] and [0103] discuss uncertainty but don’t provide specifics as to how it is determined given the variety of sources and type of data used to determine the results. The specification discusses the quality metric, but does not provide detail as how the analysis is done.
Claims 2-8 are rejected as they depend from claim 1. Claim 9 and its dependents 10-16 are rejected for similar reasons.
Claims 2 and 10 discuss “predictive modeling,” but the specifics of the predictive modeling are not detailed in the specification. The specification discusses that the predictive models are used “to rate foods in terms of both absolute and relative health effects ([0035])” and “directly utilized to generate data-driven nutrient profiling systems tailored to health outcomes and are further processed according to the user to provide health outputs ([0072]),” but the disclosure does not provide enough written description as to how this is done.
Claims 5 and 13 are rejected as they depend from claims 2 and 10 respectively.
Claims 5 and 13 recite “wherein multiple food scoring systems are aggregated either from expert opinions represented as nutritional profiling systems from a published scientific article, from a data-driven modeling approach of claim 2, and/or a hybrid expert-data driven food scoring system, to generate the food recommendation data object comprised of an expert consensus evaluation, which can be defined by created a summary distribution of health scores for any given food.” Similar to the above claims, the disclosure does not provide sufficient written description as to how this is done. Multiple paragraphs, including at least [0121] and [0134], discuss using scores, but doesn’t disclose how they are combined or standardized to arrive an output score or generate the food recommendation.
Claims 7 and 15 recite “wherein to generate the food recommendation data object, the processor uses data-driven scoring systems, and/or expert-consensus based scoring systems, which are defined by aggregating individual expert opinions about what constitutes a healthy food codified into a nutritional profiling algorithm, by combining them into a meta-nutritional profiling system.” The instant disclosure does not provide sufficient written description as to how this is done. Paragraphs [0134-0135] discuss aggregating food scoring systems and communicating uncertainty, but with the various types of data it is not described how this is determined.
Claim Rejections - 35 USC § 112(b)
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 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 9 include multiple recitations of ““generate a food recommendation data object…,” and while they are based on different factors, it is unclear if it’s the same food recommendation data object or a different object as each recitation begins with “a.” If they are different, Examiner recommends distinguishing between them. If they are the same, it is recommended that after the first instance of “generate a food recommendation data object” that the following limitations be amended to ““generate the food recommendation data object.”
Claims 2-8 and 10-16 are rejected as they depend from claim 1.
Claims 1 and 9 include multiple recitations of ““generate a dietary recommendation data object…,” and while they are based on different factors, it is unclear if it’s the same dietary recommendation data object or a different object as each recitation begins with “a.” If they are different, Examiner recommends distinguishing between them. If they are the same, it is recommended that after the first instance of “generate a dietary recommendation data object” that the following limitations be amended to ““generate the dietary recommendation data object.” The “uncertainty data object” has results in the same problem as the objects described above and it also rejected as being unclear.
Claims 2-8 and 10-16 are rejected as they depend from claim 1.
Claims 2 and 10 recite “wherein the predictive modeling is conducted by back propagating model parameters selected from the top, most accurate model(s) identified through a process of evaluating multiple models on the basis of their predictive accuracy, and its uncertainty, via variations on methods such as cross-validation, such as leave-one-out, leave-future-out, leave-group-out, 5-fold, 10-fold, and quantified via metrics such as expected log posterior predictive density, Watanabe Akaike information criteria, root mean squared error, R2, adjusted R2, Brier scores, calibration assessed with stable reliability diagrams and CORP, net reclassification index, etc simultaneously through a food composition database and generating both absolute and relative health-effect predictions and food scorings.”
The term “top, most accurate model(s)” in claims 2 and 10 is a relative term which renders the claim indefinite. The term “top, most accurate model(s)” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. While the claims recite “the predictive modeling is conducted by back propagating model parameters selected from the top, most accurate model(s) identified through a process of evaluating multiple models on the basis of their predictive accuracy, and its uncertainty, via variations on methods such as cross-validation…,” the “top, most accurate model(s)” is still relative as it is unknown how it is determined since the remaining of the claim has additional indefiniteness issues.
Additionally, the claims 2 and 10 repeatedly recite “such as” rendering the claim indefinite. It is unclear if the elements following “such as” are required or are included for exemplary purposes. For purposes of examination, it will be interpreted as explanatory.
Claims 2 and 10 also recite “with stable reliability diagrams and CORP, net reclassification index, etc simultaneously….” The use of “etc” renders the claim indefinite as it leaves the limitation open-ended as it does not limit the scope of the claim.
Claims 5 and 13 are rejected as they depend from claims 2 and 10 respectively.
Claims 3 and 11 recite “wherein said back propagating is performed through posterior predictive distributions from selected model(s), thus including an assessment of both within and between model uncertainty, or from summary statistics of the model parameters.” It is unclear if the portion of the limitation reciting “…thus including an assessment of both within and between model uncertainty, or from summary statistics of the model parameters” is included to explain the preceding limitation or is actually further limiting the claim. For purposes of examination, it will be interpreted as explanatory.
Claims 5 and 13 recite “wherein multiple food scoring systems are aggregated either from expert opinions represented as nutritional profiling systems from a published scientific article, from a data-driven modeling approach of claim 2, and/or a hybrid expert-data driven food scoring system, to generate the food recommendation data object comprised of an expert consensus evaluation, which can be defined by created a summary distribution of health scores for any given food.” It is unclear if the recitation of “…which can be defined by created a summary distribution of health scores for any given food” is required or merely included for exemplary purposes. For purposes of examination, it will be interpreted as explanatory.
Appropriate correction is required.
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 1-16 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.
Claims 1-8 are drawn to a system for providing a food and/or dietary recommendation and a predicted effects profile, which is within the four statutory categories (i.e. machine). Claims 9-16 are drawn to a method for providing a food and/or dietary recommendation and a predicted effects profile, which is within the four statutory categories (i.e. process).
Claims 1-8 (Group I) recite a system for providing a food and/or dietary recommendation and a predicted effects profile, the system comprising:
at least one processor (apply it, MPEP § 2106.05(f));
a communication interface communicatively coupled to the at least one processor (apply it, MPEP § 2106.05(f)); and
a memory device storing executable code that, when executed, causes the processor to (apply it, MPEP § 2106.05(f)):
receive, from a user device (apply it, MPEP § 2106.05(f)), user-specific personal data comprising user-specific biological metrics;
receive, from the user device (apply it, MPEP § 2106.05(f)), user selection inputs comprising user-specific dietary and food consumption data;
receive, from the user device (apply it, MPEP § 2106.05(f)), user selection inputs comprising user-specific health outcome and prediction model preference data;
automatically generate and store, in a system-internal database (insignificant extra-solution activity, MPEP § 2106.05(g) and apply it, MPEP § 2106.05(f));
access at least one external database and retrieve therefrom evidence-based data (insignificant extra-solution activity, MPEP § 2106.05(g)) associated with at least portions of the user-specific health and consumption profile;
generate a food recommendation data object based at least in part on the user-specific social, environmental, and biological metrics and retrieved evidence-based data;
generate a food recommendation data object aligned with existing dietary guidelines or patterns based at least in part on retrieved evidence-based data containing multiple dietary quality metrics and/or indices and their listed scoring components;
generate a food recommendation data object aligned with expert’s food healthfulness opinions based at least in part on retrieved evidence-based data containing multiple food quality metrics and/or indices and their listed scoring components identified and pooled from the scientific literature on nutritional profiling systems/algorithms, including an assessment of the consensus food rating across multiple expert-driven algorithms;
generate a dietary recommendation data object based at least in part on the user-specific biological metrics and retrieved evidence-based data;
generate a dietary recommendation data object based on the retrieved evidence-based data containing clinical effect sizes for health outcomes stratified by dietary quality index/measure/guideline;
generate a multi-dimensional, visual dietary recommendation data object known as a spider or radar chart based on the user-specific dietary quality and retrieved evidence-based data containing multiple dietary quality metrics and/or indices;
generate, by predictive modeling, a predicted effect data object based at least in part on the dietary recommendation data object and the user-specific biological metrics that are not blood glucose;
generate, by either predictive modeling and/or via a prior belief elicitation method, an uncertainty data object based on a survey question and/or framework coded into a prior distribution of effect size or relative variable importance, the degree of missingness in the food and or diet data, the variation/distribution in prediction results across multiple predictive models, the distribution of predictions resulting from the same model, variation in the target prediction task, and/or variation in individuals and subgroups;
generate, by an expert-belief elicitation method using nutritional profiling algorithms that reflect various expert beliefs on food healthfulness, an uncertainty data object based the variation/distribution in prediction results across multiple predictive models, the distribution of predictions resulting from the same model, variation in the target prediction task, and/or variation in individuals and subgroups;
generate, by either predictive modeling and/or via a prior belief elicitation method, a quality data object based on a survey question coded into a prior distribution of effect size or importance, a measure to quantify the degree of accuracy of the prediction, the variation in predicted results across multiple predictive models ranked by their predictive accuracy, and/or based on variation in accuracy among individuals and subgroups; and
transmit for display, at least in part, on the user device (apply it, MPEP § 2106.05(f) and insignificant extra-solution activity, MPEP § 2106.05(g)):
the food recommendation data object;
the dietary recommendation data object
the predicted effect data object;
the uncertainty data object; and
the quality data object.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity because it recites fundamental economic practices, commercial or legal interactions, and/or managing personal behavior or relationships or interactions between people. Any limitations not identified above as part of the abstract idea are underlined and are considered “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 9-16 is identical as the abstract idea for Claims 1-8 (Group I), because the only difference is the Groups are directed towards different statutory categories.
Dependent Claims 2-8 and 10-16 include other limitations, for example Claims 2 and 10 recite wherein the predictive modeling is conducted by back propagating model parameters selected from the top, most accurate model(s) identified through a process of evaluating multiple models on the basis of their predictive accuracy, and its uncertainty, via variations on methods such as cross-validation, such as leave-one-out, leave-future-out, leave-group-out, 5-fold, 10-fold, and quantified via metrics such as expected log posterior predictive density, Watanabe Akaike information criteria, root mean squared error, R2, adjusted R2, Brier scores, calibration assessed with stable reliability diagrams and CORP, net reclassification index, etc simultaneously through a food composition database and generating both absolute and relative health-effect predictions and food scorings, Claims 3 and 11 recite wherein said back propagating is performed through posterior predictive distributions from selected model(s), thus including an assessment of both within and between model uncertainty, or from summary statistics of the model parameters, Claims 4 and 12 recite wherein at least one predictive health model is used to quantitatively compare and prioritize multiple competing lifestyle interventions in terms of their capacity to affect a future health state, Claims 5 and 13 recite wherein multiple food scoring systems are aggregated either from expert opinions represented as nutritional profiling systems from a published scientific article, from a data-driven modeling approach of claim 2, and/or a hybrid expert-data driven food scoring system, to generate the food recommendation data object comprised of an expert consensus evaluation, which can be defined by created a summary distribution of health scores for any given food, Claims 6 and 14 recite
wherein a respective uncertainty is transmitted with each of the food recommendation data object – expressed as a statistical distribution and summary statistics derived thereof representing variations in food quality or healthfulness across multiple food health or nutrition profiling systems, the dietary recommendation data object – expressed as a statistical distribution and summary statistics derived thereof representing variations in diet quality across multiple diet quality indices/patterns/metrics –, and the predicted effect data object – expressed as a statistical distribution and summary statistics derived thereof representing variations in expected effects, Claims 7 and 15 recite wherein to generate the food recommendation data object, the processor uses data-driven scoring systems, and/or expert-consensus based scoring systems, which are defined by aggregating individual expert opinions about what constitutes a healthy food codified into a nutritional profiling algorithm, by combining them into a meta-nutritional profiling system, Claims 8 and 16 recite wherein to generate the food recommendation data object, the dietary recommendation data object, and the predicted effect data object, the processor utilizes user-specific relative beliefs, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 9.
Furthermore, Claims 1-16 are not integrated into a practical application because the additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of at least one processor, a communication interface communicatively coupled to the at least one processor, a memory device storing executable code, a user device, store data in a system-internal database, and transmitting data for display, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs [0105], [0110], [0145] of the present Specification, see MPEP 2106.05(f); and
add insignificant extra-solution activity to the abstract idea – for example, the recitation of retrieving data, which amounts to mere data gathering, and/or the recitation of storing, retrieving and transmitting data, which amounts to an insignificant application, see MPEP 2106.05(g).
Furthermore, the Claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e. the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
paragraphs[0105], [0110] and [0145] of the Specification discloses that the additional elements (i.e. at least one processor, a communication interface communicatively coupled to the at least one processor, a memory device storing executable code, a user device, store data in a system-internal database, and transmitting data for display) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. storing, retrieving and transmitting data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives nutrition and dietary data, and transmits the data to a user device over a network, for example the Internet;
Electronic recordkeeping, e.g. see Alice Corp v. CLS Bank – similarly, the current invention merely recites the storing of data on a database and/or electronic memory; and
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites storing executable code and user-specific health and consumption profile data in a database and/or electronic memory, and retrieving the evidence-based data from storage in order to make recommendations.
Dependent Claims 2-8 and 10-16 include other limitations, but none of these functions are deemed significantly more than the abstract idea because they do not any additional elements beyond those recited in independent claims 1 and 9.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above-identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-16 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free from Prior Art
Examiner notes that the claims are free from any prior art rejections. The combination of limitations found in independent claims 1 and 9 is not disclosed or rendered obvious by the prior art of record based on the current interpretation of the claims.
The closest prior art of record includes Kroll (U.S. Pub. No. 2010/0079291 A1) which discloses a weight management system using a food scoring system and nutrition recommendations.
However, the prior art does not disclose or render obvious the combination of limitations found in claims 1 and 9. Claims 2-8 and 10-16 are not subject to art rejections as they depend from claims 1 and 9, respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachelle Reichert whose telephone number is (303)297-4782. The examiner can normally be reached M-F 9-5 MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham can be reached at (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686