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 .
Response to Amendment
The previous double patenting rejection remains.
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
Notice to Applicant
In the amendment dated 04/01/2026, the following has occurred: claims 21, 32, and 40 have been amended; claims 1-20 have been canceled; claims 22-31 and 33-39 remain unchanged; and no new claims have been added.
Claims 21-40 are pending.
Effective Filing Date: 05/24/2022
Response to Arguments
Double Patenting:
Applicant argues that the claims are not similar and that Examiner has not met the burden of aligning the two claim sets. Examiner however respectfully disagrees as page 4 of the previous non-final office action aligns the claim sets of the ‘539 patent and the present application.
35 U.S.C. 101 Rejections:
Step 2A, Prong One:
Applicant argues that the claims do not recite an abstract idea under Step 2A, Prong One. Applicant further states that the steps of “receiving multi-domain data…”, “identifying a trigger event…”, and “generating an episode…generating a machine learning model…” are not abstract elements. Examiner however respectfully disagrees as these steps can indeed be reciting certain methods of organizing human activity. Applicant further argues that the amended claims are also not directed towards an abstract idea, Examiner however respectfully disagrees and directs Applicant to the updated 101 rejection section.
Applicant further argues that the claims are similar to DDR. Examiner however respectfully disagrees. Applicant further states that there is segmentation of data synthesized from multiple sources and that generation of the model is rooted in a computer technology. Examiner however respectfully disagrees as the data segmentation, as outlined in paragraphs [0075] - [0077] of the specification, is something that is occurring and not necessarily something which is being stated as part of the technical solution. Furthermore, generation of a machine learning model is not necessarily rooting claims to a particular technological environment as that concept can also be classified as generating a model, and the model being a machine learning model is either “apply it” or “generally linking the abstract idea to a particular technology”. In the case of the present claims, Examiner has directed the machine learning elements towards “apply it”.
Lastly, Applicant argues with respect to the Ex parte Hannun case and states that the present claims include “receiving”, “normalizing”, and “identifying” steps which makes data more useful for identifying events and generating a model. Examiner however respectfully disagrees. Examiner has not directed the claim limitations towards being mental processes, rather Examiner has directed the claims to an abstract idea under certain methods of organizing human activity. Examiner’s characterization of the abstract idea is different than the characterization in Ex parte Hannun.
Step 2A, Prong Two:
Applicant argues that the claims are directed towards a practical application and cites Ex parte Desjardins. The Examiner respectfully submits that there is no improvement to the claimed machine learning as there is in Desjardins. As found by the Panel, the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" represents “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application-“improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel.
Applicant’s claims do not provide such an improvement. Initially, there is no training within most of the claims so this argument falls on its face from the outset. Even assuming there was, there is no indication in the cited portion of the Specification that the claimed invention provides an improvement as to how model is trained. Improving the accuracy of a machine learning model by supplying it with specific data is not an improvement to how the model is trained within the meaning of Desjardins (see quotations from Recentive, infra). This is how all machine learning models are optimized (i.e., select training data, train the model, compare the output to validation data, receive feedback, adjust the parameters of the training data according to the comparison/feedback, and repeat until an accuracy threshold is met). Put another way, the particular way the machine learning model of applicant’s invention uses the data to train itself is not improved, which is the holding of Desjardins. Applicant is merely improving the accuracy of the model by optimizing the data selected/used by the model. Improving the accuracy of a model is not an improvement by any measure in MPEP 2106.
Examiner’s position is also supported by the decision in Recentive Analytics, Inc. v. Fox Corp. Recentive held that non-specifically claimed training of an [AI/ML] algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12.
Applicant also argues that Examiner mischaracterizes the “using at least one set….” limitation as “apply it”. Examiner however would like to point out that only the machine learning model was an additional element in that limitation, while the remaining portion of the claim limitation is part of the abstract idea. Therefore there assertation of the entire limitation being incorrectly directed towards an abstract idea under “apply it” is incorrect.
Step 2B:
Applicant points to BASCOM and states that present claims do not preempt all ways of performing the alleged abstract idea. Initially, the claims are not similar to those of BASCOM as there is no analogous determination of being significantly more. The claims of BASCOM proved to be significantly more as the claims recited a discrete implementation of the abstract idea of filtering content, as the filtering within the context of the internet browser provided a particular implementation. The present claims are dissimilar as data is being sourced from multiple sources and an analysis is being performed on that data and a model is being adjusted. Applying a machine learning model does not preempt a discrete implementation.
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a non-statutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based e-Terminal Disclaimer may be filled out completely online using web-screens. An e-Terminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about e-Terminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-40 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-12 of U.S. Patent No. 11,978,539 in view of U.S. 2019/0251457 (the Bryne et al. reference). Claims 21, 30-31, and 40 of the present claims are similar to claims 1, 6-7, and 12 of the granted patent with two additional limitations: “using the at least one set of observations and the one or more driving factors, generating a machine learning model for outputting individualized healthcare recommendations for a user” which is taught using the Bryne et al. reference in claim 15 of Bryne et al. and “adjusting the machine learning model based on the one or more outcomes metrics” which is taught in paragraph [0085] of Bryne et al. Claims 22 and 32 of the present claims are similar to claims 2 and 8 of the granted patent. Claims 23 and 33 of the present claims are similar to claims 3 and 9 of the granted patent. Claims 24 and 34 of the present claims are similar to paragraph [0135] of Bryne et al. Claims 25 and 35 of the present claims are similar to claims 4 and 10 of the granted patent. Claims 26 and 36 of the present claims are similar to claims 5 and 11 of the granted patent. Claims 27 and 37 of the present claims are similar to paragraphs [0132] – [0133] of Bryne et al. Claims 28 and 38 of the present claims are similar to paragraphs [0024] – [0044] of Bryne et al. Claims 29 and 39 of the present claims are similar to paragraphs [0056] and [0132] of Bryne et al.
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 21-40 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 21-29 and claim 30 are drawn to mediums and claims 31-39 and claim 40 are drawn to methods, each of which is within the four statutory categories. Claims 21-40 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 21 recites a non-transitory computer readable medium including instructions that are executable by one or more processors to cause a system to perform a method for multi- domain data segmentation, and automatically generating and refining hypotheses, the method comprising:
1) receiving multi-domain data from a plurality of data sources;
2) normalizing the multi-domain data;
3) identifying a trigger event based on the normalized data;
4) generating an episode based on a segmentation of the normalized data and the trigger event;
5) identifying at least one set of observations associated with the episode based on the normalized data and one or more relevancy metrics, whereas the one or more relevancy metrics comprises a similarity metric based on the trigger event;
6) iteratively performing operations until a threshold value has been reached, wherein the operations comprise:
6a) generating a hypothesis based on a subset of observational features using a) machine learning,
6b) generating a measure of one or more outcomes based on one or more outcome metrics, wherein the one or more outcomes metrics comprise a provider match, a concierge referral, monetary savings, reduction in expenditure, or other product metrics, and
6c) validating the hypothesis based on the generated measure;
7) identifying one or more driving factors for the one or more outcomes using an optimal hypothesis;
8) using the at least one set of observations and the one or more driving factors, generating b) a machine learning model for outputting individualized healthcare recommendations for a user; and
9) adjusting the machine learning model based on the one or more outcomes metrics.
Claim 21 recites, in part, performing the steps of 1) receiving multi-domain data from a plurality of data sources, 2) normalizing the multi-domain data, 3) identifying a trigger event based on the normalized data, 4) generating an episode based on a segmentation of the normalized data and the trigger event, 5) identifying at least one set of observations associated with the episode based on the normalized data and one or more relevancy metrics, whereas the one or more relevancy metrics comprises a similarity metric based on the trigger event, 6) iteratively performing operations until a threshold value has been reached, wherein the operations comprise: 6a) generating a hypothesis based on a subset of observational features, 6b) generating a measure of one or more outcomes based on one or more outcome metrics, wherein the one or more outcomes metrics comprise a provider match, a concierge referral, monetary savings, reduction in expenditure, or other product metrics, and 6c) validating the hypothesis based on the generated measure, 7) identifying one or more driving factors for the one or more outcomes using an optimal hypothesis, and 8) using the at least one set of observations and the one or more driving factors, generating a model for outputting individualized healthcare recommendations for a user. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes how one could use hypothesis to generate a model.
Independent claims 30-31 and 40 recite similar limitations and are also directed to an abstract idea under the same analysis.
Claim 30 however adds the additional limitations of:
10) generating a set of recommendations based on the identified set of driving factors;
11) generating b) a machine learning model for outputting individualized healthcare recommendations for a user; and
12) outputting the recommendations through a set of outflow channels comprising customers, product and service staff, user interfaces, or digital agents.
Claim 40 adds the additional steps of:
13) using the at least one set of observations and the one or more driving factors, generating b) a machine learning model for outputting individualized healthcare recommendations for a user;
14) generating a set of recommendations using b) the machine learning model;
12) outputting the recommendations through a set of outflow channels comprising customers, product and service staff, user interfaces, or digital agents.
Depending claims 22-29 and 32-39 include all of the limitations of claims 21 and 31, and therefore likewise incorporate the above described abstract idea. Depending claims 26 and 36 add the additional step of “determining an optimal hypothesis based on the generated hypothesis upon reaching a threshold value” and claims 27 and 37 add the additional step of “generates labels to determine insights”. Additionally, the limitations of 23-25, 28-29, 33-35, and 38-39 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 22-29 and 32-39 are nonetheless directed towards fundamentally the same abstract idea as independent claims 21 and 31 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) machine learning and b) a machine learning model to perform the claimed steps.
The claims also include the additional element step of 9) “adjusting the machine learning model based on the one or more outcomes metrics”.
The recitation of a) machine learning and the b) machine learning model in these steps and the additional element step of 9) “adjusting the machine learning model based on the one or more outcomes metrics” are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, paragraph [0080] – [0082] where there is a generic recitation of machine learning which is used, see MPEP 2106.05(f)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a) machine learning and b) a machine learning model to perform the claimed steps and the additional element step of 9) “adjusting the machine learning model based on the one or more outcomes metrics” amounts to no more than mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain mental steps, certain method steps of organizing human activity, or certain mathematical steps. Specifically, MPEP 2106.05(f) recites that the following limitations are not significantly more:
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The current invention generates a model using a) machine learning and b) a machine learning model and the step of 9) “adjusting the machine learning model based on the one or more outcomes metrics”, thus these elements of machine learning are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 21-40 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this 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 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 Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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