DETAILED ACTION
This action is responsive to papers filed on 12/23/2025.
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 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 21-27, 29-34, 36-40, 42, and 43 are rejected under 35 U.S.C. 101 because, while the claims herein are directed to a method and/or system, which could be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes), 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.
Regarding claims 21, 29, and 36, the claims recite, in part, receiving driving data associated with a driver; iteratively training a model based at least in part upon the driving data associated with the driver using out of fold prediction techniques, wherein the out of fold prediction techniques comprise shuffling the driving data, dividing the driving data into a plurality of groups, and training the first model based at least in part upon a subset of the plurality of groups, and wherein the first machine learning model is trained to predict a driving score for the driver based at least in part upon braking, acceleration, or speed data; calculating a predicted driving score for the driver based at least in part upon the first machine learning model, wherein the predicted driving score defines a prediction mean; iteratively train a second model to predict a confidence value of the predicted driving score, wherein the second machine learning model is trained to calculate a squared error variance of the predicted driving score to predict an error of the first machine learning model to improve prediction accuracy of the driving score; calculating the confidence value of the predicted driving score based at least in part upon the squared error variance of the predicted driving score from the second model; generating a data distribution based at least in part upon the prediction mean from the first model and the squared error variance of the predicted driving score from the second model; determining a probability that the driver will achieve the predicted driving score based at least in part upon the data distribution; generate at least one individualized driving challenge for the driver based at least in part upon the predicted driving score, the confidence value, and the probability, wherein the at least one individualized driving challenge is generated based at least in part upon the driving data associated with the driver, wherein the at least one individualized driving challenge is unique for the driver, and wherein the at least one individualized driving challenge is configured to have an equivalent difficulty level for the driver relative to a plurality of other drivers based on respective probabilities of the plurality of other drivers achieving their respective predicted driving scores; transmit the at least one individualized driving challenge to the driver; and provide a reward to the driver based at least in part upon the probability of the driver achieving the predicted driving score.
The limitations, as drafted and detailed above, recite predicting a driving score determining a probability that the driver will achieve the predicted driving score, which falls within the “Mental Processes” grouping of abstract ideas, and more specifically concepts performed in the human mind including evaluation and judgment. The limitations, as drafted and detailed above, also recite determining a driving challenge, sending it to a driver, and providing a reward to a driver, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and more specifically commercial interactions. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of computer system (claim 21), processor (claims 21, 29, 36), memory device (claim 21), non-transitory computer-readable storage media (claim 36), device associated with the driver (claims 21, 29, 36, not actively part of the claimed invention, but rather data is merely transmitted to the device) one or more sensors (claims 21, 29, 36, not actively part of the claimed invention, but rather data is merely received from the sensors), and machine learning (claims 21, 29, 36, merely using machine learning in an “apply it” manner). The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of receiving, iteratively training, calculating, generating, determining, transmitting, and providing) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. There are no additional functional limitations to be considered under prong two.
Accordingly, the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the
judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes).
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using computer system (claim 21), processor (claims 21, 29, 36), memory device (claim 21), non-transitory computer-readable storage media (claim 36), device associated with the driver (claims 21, 29, 36, not actively part of the claimed invention, but rather data is merely transmitted to the device) one or more sensors (claims 21, 29, 36, not actively part of the claimed invention, but rather data is merely received from the sensors), and machine learning (claims 21, 29, 36, merely using machine learning in an “apply it” manner) to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). 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 the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent- eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat' l Ass' n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computer (see Applicant specification Paragraphs 0059-0064, Figures 4, 5); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
The dependent claims 22-27, 30-34, 37-40, 42, and 43 appear to merely limit a broad recitation of a machine learning model being supervised or unsupervised, generation of driving score goals, generate an actual driving score based on specific types of telematics data and provide a reward based on a driving score goal being met, specification of insurance and a discount on insurance, storing of the models and specifics on training the models, and specifics of the driving data and the confidence value based on the squared error variance, and therefore only limit the application of the idea, and not add significantly more than the idea (i.e. “PEG” Step 2B=No).
The computer system (claim 21), processor (claims 21, 29, 36), memory device (claim 21), non-transitory computer-readable storage media (claim 36), device associated with the driver (claims 21, 29, 36, not actively part of the claimed invention, but rather data is merely transmitted to the device) one or more sensors (claims 21, 29, 36, not actively part of the claimed invention, but rather data is merely received from the sensors), and machine learning (claims 21, 29, 36, merely using machine learning in an “apply it” manner) are each functional generic computer components that perform the generic functions of receiving, iteratively training, calculating, generating, determining, transmitting, and providing, all common to electronics and computer systems.
Applicant's specification does not provide any indication that the computer system (claim 21), processor (claims 21, 29, 36), memory device (claim 21), non-transitory computer-readable storage media (claim 36), device associated with the driver (claims 21, 29, 36, not actively part of the claimed invention, but rather data is merely transmitted to the device) one or more sensors (claims 21, 29, 36, not actively part of the claimed invention, but rather data is merely received from the sensors), and machine learning (claims 21, 29, 36, merely using machine learning in an “apply it” manner) are anything other than generic, off-the-shelf computer components. Therefore, the claims do not amount to significantly more than the abstract idea (i.e. “PEG” Step 2B=No).
Thus, based on the detailed analysis above, claims 21-27, 29-34, 36-40, 42, and 43 are not patent eligible.
Novel/Non-Obvious Subject Matter
Claims 21-27, 29-34, 36-40, 42, and 43 as currently written are novel/non-obvious over prior art. However, the rejection under 35 U.S.C. 101 is currently pending and represents a barrier to allowability. Examiner notes that any amendments made to the claims in an attempt to correct pending rejections could drastically alter the claim scope and could open up the possibility of prior art being applied in a future action.
Response to Arguments and Examiner Notes
Applicant has amended the claims and argues “the claims recite an improvement to other technology or technical field, and also recite use of the ideas in a meaningful way beyond generally linking to a particular technological environment”. However, the added limitations are either applied mathematical or computational techniques applied to the abstract idea, or further narrowing of the abstract idea. Any improvement provided by these new limitations is merely an improvement to the abstract idea. In the SAP decision (See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018)), the courts found that an improvement made to the abstract idea is not patent eligible. SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non- abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
Applicant states that the claims are directly analogous to BASCOM and argues “the ordered combination of limitations of amended independent claims 21, 29, and 36 add specific limitations that are improvements beyond what is well-understood, routine, or conventional in the field. Specifically, as quoted above, amended independent claims 21, 29, and 36 recite a non-conventional and non-routine arrangement of systems and ordered combination of limitations above and beyond any alleged abstract idea”. However, the fact pattern in BASCOM was entirely different. There is nothing specifically in the claim language of the instant specification that shows an unconventional arrangement of elements. In BASCOM, the courts pointed to the specification to show how the unconventional arrangement of the invention improved filtering. The current claims merely obtain data from a source (in this case, data-gathering sensors), then perform numerous calculations applied using iteratively trained machine learning models on a general purpose computer system, and finally transmit data to a destination (in this case, a device associated with a driver). Applicant has not explained how the data source, general purpose computer, and destination device of the present claims is an unconventional arrangement, and the specification does not appear to provide any insight on this matter either. Further, the added “out of fold techniques” and “individualized driving challenges” does not add anything to the claims outside of the abstract idea. Therefore, this argument is not persuasive.
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.
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/MICHAEL BEKERMAN/Primary Examiner, Art Unit 3682