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 .
Claim Status
Claims 1-20 are currently pending and are presented for examination on the merits. Independent Claim 1 differs from allowed parent Claim 1 primarily as follows:
Deleting “over a period of at least 6 months”
Deleting “generating scores based on said observation data”
Change “least likely to remain independent and/or at home” to “unlikely to remain independent”
Deleting “according to research data at least about improving quality of life, at least one of said interventions being non-medical”
Deleting “wherein said determining comprises:
running said model . . . .
if said starting . . . .
running said changed model . . . .
if said subsequent risk level . . . .”
Priority
Applicant's claim for the benefit of U.S. patent application 17/406131 filed 8/19/2021 under 35 U.S.C. 120 is acknowledged.
Claim objections
Please use either 1.5 or double line spacing consistently throughout claims.
Claim 1 “portion of policy holders” and “portion of block of policy holders” need to be consistent.
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 1-20 are rejected under 35 U.S.C. § 101, because they recite non-patentable subject matter under the 2019 PEG. The claimed invention is directed to a judicial exception (e.g., an abstract idea, etc.) without practical application or significantly more.
More particularly, when considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Broad categories of abstract ideas include fundamental economic practices, certain methods of organizing human activities, an idea itself, and mathematical relationships/formulas. See, generally Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. __ (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc.,132 S. Ct. 1289, 1294, 1297-98 (2012)); Federal Register notice titled 2014 Interim Guidance on Patent Subject Matter Eligibility (79 FR 74618), which is found at: http:// www. gpo.gov/fdsys/pkg/FR-2014-12-16/pdf/2014-29414.pdf; 2015 Update to the Interim Guidance; the 2019 Revised Patent Subject Matter Eligibility Guidance, Fed. Reg., Vol. 84, No. 4, January 7, 2019; and associated Office memoranda.
Under the 2019 PEG, step 2a-prong 1, Claims 1-20 recite a judicial exception(s), including a method of organizing human activity (e.g. fundamental economic principle). More particularly, the entirety of the method steps is directed towards predicting those who will soon (in the near term) be in need of long-term healthcare (file a claim); and interventions for reducing the risk or potential need for such long-term healthcare for said individuals. These are long standing commercial practices previously performed by humans (e.g., claims adjusters, lenders, insurance companies, employers, etc.) manually and via mental steps. For example, long term care insurance (LTCI) carriers, as noted in the Background of the specification, typically employ humans to determine the likelihood that a policy holder will file a claim in the near term. Consideration of historical data (including the data recited in the Claims) as well as research and development in science (our understanding of what is healthy for the human body) have long governed decision making. As such, the inventions include an abstract idea under the 2019 PEG, and Alice Corporation.
For example, the abstract idea(s) composing Claim 1 is highlighted below, wherein the extraneous limitations are in bold:
1. A method for helping a particular policy holder of a long-term care insurance policy stay independent during a next year, the method being implemented on a computing device, the method comprising:
receiving claim data about when previous long term care insurance policy holders made a claim for long term care and for what type of care;
for a block of policy holders, selecting a portion of said block which meets a set of intervention criteria;
generating observation data about an ability of said portion of policy holders to handle problems when they arrive, said observation data comprising medical data and self-sufficiency data at least about home environment, financial sustainability, life engagement, and functionality;
using said computing device, providing scores to said at least said observation data and generating features as a function of said scores;
using said computing device, building a probability based, predictive model from features based on a combination of said claim data and said observation data, at least two of said features being related to said self-sufficiency data, to identify which of said portion of policy holders are unlikely to remain independent during said next year;
using said computing device, determining a reduceable risk feature for an identified policy holder, identified by said model, from among said features, wherein said reduceable risk feature is a changed feature of said model most likely to enable said identified policy holder to remain independent during said next year;
having an intervention table listing a set of interventions, each intervention having an associated reduceable risk feature, wherein said set of interventions comprises: a home optimization or modification, organizing transportation services, managing loss of a caregiver, preventing caregiver burnout, educating said particular senior about how to handle his/her diseases, encouraging said particular senior to engage in social activities, and encouraging said particular senior to be physically active; and
using said computing device, identifying at least one intervention from said intervention table associated with said reduceable risk feature for said identified policy holder to utilize as an aid to enable said identified policy holder to remain independent for said next year.
Under step 2a-prong 2, the claims fail to recite a practical application of the exception, because the extraneous limitations (e.g., the structure including the model, and computer, the specific set of interventions, etc.) merely add insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g), generally link the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)), and/or instruct the user to apply it (the method) across generic computing technology. In particular, the model is generic computing technology being used to automate the recited method. A claim does not cease to be abstract for section 101 purposes simply because the claim confines the abstract idea to a particular technological environment in order to effectuate a real-world benefit. See Alice, 573 U.S. at 222; BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1287 (Fed. Cir. 2018); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1353 (Fed. Cir. 2014). “[I]t is not enough, however, to merely improve a fundamental practice or abstract process by invoking a computer merely as a tool.” Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020) (citations omitted). More particularly, the claims fail to recite an improvement to the functioning of a computer or technology (under MPEP § 2106.05(a)), the use of a particular machine (under § 2106.05(b)), effect a transformation or reduction of a particular article (§ 2106.05(c)), or apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (§ 2106.05(e)).
Under Part 2b, the additional elements offered by the dependent claims (e.g., the model, using scoring, etc.) either further delineate the abstract idea, recite insignificant extra-solution activity, or instruct the artisan to apply it (the abstract idea) across generic computing technology. The claims as a whole, do not amount to significantly more than the abstract idea itself. This is because no one claim effects an improvement to another technology or technical field, an improvement to the functioning of a computer itself, or move beyond a general link of the use of the abstract idea to a particular technological environment. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Under Alice, merely applying or executing the abstract idea on one or more generic computer system (e.g., a computer system comprising a generic database; a generic element (NIC) for providing website access, etc.; a generic element for receiving user input; and a generic display on the computer, in any of their forms) to carry out the abstract idea more efficiently fails to cure patent ineligibility. See, e.g., Content Extraction, 776 F.3d at 1347 (claims reciting a “scanner” are nevertheless directed to an abstract idea); Mortg. Grader, Inc. v. First Choice Loan Serv. Inc., 811 F.3d 1314, 1324–25 (Fed. Cir. 2016) (claims reciting an “interface,” “network,” and a “database” are nevertheless directed to an abstract idea).
Courts have recognized the following computer functions to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations, receiving, processing, and storing data, electronically scanning or extracting data from a physical document, electronic recordkeeping, automating mental tasks, and receiving or transmitting data over a network, e.g., using the Internet to gather data. MPEP 2106.05(d), wherein the italicized tasks are particularly germane to the instant invention.
Claim Rejections - 35 USC § 112
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 1-20 is 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. Independent Claims 1, 9, and 17 are hard to follow regarding the changed features. The phrases “said features” is ambiguous because the term “features” was introduced twice. Moreover, it is not clear why the term “block” is introduced. It is requested that the claims be written to facilitate the public’s understanding of the captured scope. The remaining claims depend from the independent claims, and are therefore likewise rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
a. Determining the scope and contents of the prior art.
b. Ascertaining the differences between the prior art and the claims at issue.
c. Resolving the level of ordinary skill in the pertinent art.
d. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-6, 8-14, and 16-20 are rejected under 35 U.S.C. §103 as being unpatentable over U.S. 2019/0214113 to Abramowitz (hereinafter “Abram”), in view of US 10,740,437 to Shannon et al.
With respect to Claims 1, 9, and 17, Abram teaches a method for helping a particular policy holder of a long-term care insurance policy ([0028];[0070], long-term care insurance assessments) stay independent during a next year (FIG. 9, ability to remain in a trial), the method being implemented on a computing device, the method comprising: receiving claim data about when previous long term care insurance policy holders made a claim for long term care and for what type of care ([0051]); for a block of policy holders, selecting a portion of said block (“group of individuals”) which meets a set of intervention criteria ([0051], drug intervention); generating observation data about an ability of said portion of policy holders to handle problems when they arrive, said observation data comprising medical data and self-sufficiency data at least about home environment, financial sustainability, life engagement, and functionality (FIG. 5; [0028];[0034];[0070)); using said computing device, providing scores to said at least said observation data and generating features as a function of said scores ([0064], “weighted average insurance risk metric e.g., in the range 1 to 100”;[0083], suitability index); using said computing device, building a probability based, predictive model (FIG. 5, Decision Engine 532) from features based on a combination of said claim data and said observation data, at least two of said features being related to said self-sufficiency data, to identify which of said portion of policy holders are unlikely to remain independent during said next year ([0064], “indicative of the likelihood of the person needing medical care . . . in the next year”); using said computing device, determining a reduceable risk feature ([0064], “insurability risk metric) for an identified policy holder, identified by said model (FIG. 5, Decision Engine 532, 614), from among said features, wherein said reduceable risk feature is a changed feature (FIG. 8, filtered information) of said model most likely to enable said identified policy holder to remain independent during said next year (FIG. 8, health assessment); having an intervention table listing a set of interventions ([0031], “recommended actions”), each intervention having an associated reduceable risk feature, and using said computing device, identifying at least one intervention from said intervention table associated with said reduceable risk feature for said identified policy holder to utilize as an aid to enable said identified policy holder to remain independent for said next year ([0031], list . . . habits . . . intervention . . . assessments, etc.).
Abram fails to expressly teach the list of interventions recited in Claim 1: “wherein said set of interventions comprises: a home optimization or modification, organizing transportation services, managing loss of a caregiver, preventing caregiver burnout, educating said particular senior about how to handle his/her diseases, encouraging said particular senior to engage in social activities, and encouraging said particular senior to be physically active.” However, the recitation of a precise list of interventions or recommended actions is a matter of design choice. Inasmuch as the references disclose these elements as art recognized equivalents, it would have been obvious to one of ordinary skill in the exercise art to substitute one for the other. In re Fout, 675 F.2d 297, 301, 213 USPQ 532, 536 (CCPA 1982). See Uber Techs., Inc. v. X One, Inc., 957 F.3d 1334, 1339-40 (Fed. Cir. 2020)(when the record shows “‘a finite number of identified, predictable solutions’ to a design need that existed at the relevant time, which a person of ordinary skill in the art ‘ha[d] a good reason to pursue,’” “common sense” can supply a motivation to combine)(quoting KSR, 550 U.S. at 421). As such, it would have been obvious to one of ordinary skill in the art to modify Abram to include the specific listing of interventions recited in the claim.
Although it teaches at [0041];[0064], the use of risk assessment models and a decision engine, Abram fails to expressly teach the use of probability based predictive models. Shannon, however, teaches use of such model (col 8, ln 7-20). Shannon discusses the limitations of using aggregated historical data in conventional data analytics in areas such as health care (col 1, background). It would have been obvious to one of ordinary skill in the art to modify Abram to include the use of probability based predictive model, so as to address the limitations in using aggregated historical data.
With respect to Claims 2 and 10, Abram teaches wherein said probability based, predictive model is based on at least one feature which is a function of age. ([0064];[0070-71])
With respect to Claims 3, 11, and 18, Abram teaches wherein said building a model comprises building an initial model from said research and updating said initial model with data from said policy holders. ([0007];[0064])
With respect to Claims 4, 12, and 19, Abram teaches wherein said observation data further comprises data about cognitive function, physical function, and social engagement of said portion of policy holders. ([0070-71])
With respect to Claims 5, 13, and 20, Abram teaches wherein said intervention table is dynamically updated based on effectiveness data of previously implemented interventions. ([0031], real-time assessments; [0075], updated health or drug-related data)
With respect to Claims 6 and 14, Abram teaches generating a personalized intervention plan for said identified policy holder based on said at least one identified intervention and specific characteristics of said identified policy holder ([0071]).
With respect to Claims 8 and 16, Abram teaches monitoring the effectiveness of said at least one identified intervention over time; and adjusting said probability based, predictive model based on said monitored effectiveness. ([0018];[0031];[0045];[0050];[0061])
Claims 7 and 15 are rejected under § 103 as being unpatentable over Abramowitz, in view of Shannon, and further in view of US 2020/0098463 to Arunachalam et al.
With respect to Claims 7 and 15, the combination of Abram and Shannon fails to expressly teach, but Arunachalam teaches wherein said determining a reduceable risk feature comprises: ranking said features based on their impact on the likelihood of said identified policy holder remaining independent and/or at home during said next year; and selecting the highest-ranked feature that can be modified through an available intervention. ([0044]) Arun discusses a desire to reduce manual errors and increase effectiveness, in the treatment of patients. [0004] As such, it would have been obvious to one of ordinary skill in the art to modify Abram to including ranking interventions to determine the most efficacious treatment.
Moreover, as entered into the patent family afore, please note the additional pinpoint citations directed to the teachings of Abram with respect to the present invention:
Abram teaches a system for and method of population health management (Abstract; [0070-71]) implemented on a computing device (FIGS.), the method comprising: receiving claim data about when previous long term care insurance policy holders made a claim for long term care and for what type of care, research data about health issues and improving quality of life, and observation data about a plurality of policy holders (FIG. 5; [0028];[0076-77]) said observation data including data about at least living and home environment ([0028]); building a likelihood model (see, “predict” “model” throughout) from features based on at least said claim data and said observation data ([0028]) to determine which policy holders are likely to claim for long term care within a predefined period of time ([0028];[0030]; [0064];[0070-71]); and for a particular policy holder, determining at least one scientifically based intervention to improve a probability that said particular policy holder remain at home for another year ([0018];[0022];[0070-71]; [0082];[0028]).
Abram teaches rerunning said model after changing features to determine which change in at least one of said features is most likely to produce a reduced risk level for said identified policy holder to make a claim for long term care within said predefined period of time ([0028]:
Such a system can provide vastly improved performances that would have been unsatisfactorily conducted in-part by the insurance companies, the operations that would have been performed unsatisfactorily by big data providers, while providing many unique features that cannot be provided by conventional systems. Most of the big medical data that are provided by the existing services can be irrelevant, or be of little relevance, to the tasks of personalized health, life or a long-term care insurance risk assessment. Furthermore, the disclosed technology provides various filters that can reduce large amounts of collected or received data and identify data that relevant to such determinations and/or predictions, even when the collected data includes rapidly changing (e.g., real-time or semi-real-time) health data, environmental factors, personalized habits, and other factors.
Id; see also, [0029]; [0007], real time updating teaches this as well). Thus, ¶ [0028] teaches various “filters” that “identify data that relevant to such determinations and/or predictions,” which teaches turning off/on various features and/or changing them to discern risk assessment. [0075] the filters can be used to produce “a customized data set based on at least the identity of the individual and the type of insurance policy. The customized data set is changeable in response to real-time changes in the information obtained from the plurality of data sources. . . . the customized data set is used to produce an insurability risk metric comprising information indicative of the individual's estimated a health assessment that is relevant to the particular type of insurance policy.” See also [0079]
Although it teaches at [0041];[0064], the use of risk assessment models, Abram fails to expressly teach the use of probability based predictive models. Shannon, however, teaches use of such model (col 8, ln 7-20). Shannon discusses the limitations of using aggregated historical data in conventional data analytics in areas such as health care (col 1, background) it would have been obvious to one of ordinary skill in the art to modify Abram to include the use of probability based predictive model, so as to address the limitations in using aggregated historical data.
Abram teaches wherein said receiving comprises providing scores to said data and generating features as a function of said scores. ([0064], values, parameters, weighted)
Abram teaches wherein said receiving comprises selecting said plurality of policy holders from among a block of policy holders according to their scores on said features. [0063-64], filtered data)
Abram teaches wherein said building a model comprises generating impact coefficients for each said feature of said features. ([0064], weights)
Abram teaches wherein said building a model comprising building an initial model from said research and updating said initial model with data from said policy holders. ([0007];[0064])
Abram teaches wherein the observation data includes one of financial sustainability, life engagement, mental issues, and functionality. ([0028];[0052], behavior data)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM J JACOB whose telephone number is (571)270-3082. The examiner can normally be reached on M-F 8:00-5:00, alternating Fri. off.
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/WILLIAM J JACOB/Examiner, Art Unit 3696
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