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 Arguments
Applicant's arguments filed 10/24/2025 have been fully considered but they are not persuasive. The amended claims attempt to hinge patentability on a user making an input that instructs a computer to update statistics using a data model. Recent guidance provided by the courts in RECENTIVE ANALYTICS, INC. v. FOX CORP., FOX BROADCASTING CO., FOX SPORTS PRODUCTIONS, LLC (2023-2437, 04/18/2025) establishes that claims directed to employing generic machine learning techniques to train a learning model in a particular environment or for a new conceived field of use are not patent eligible.
The pending claims recite providing gathered vehicle data to some unspecified type of “data model” and using the data model through some unspecified “processing” operations to update unspecified “one or more statistics” and to output user-context specific maps – an activity already held to be abstract in the courts. The pending claims do not recite any particulars of how computers are programmed to perform any model training or processing functions or what statistics are analyzed or what machine learning model(s) are used. The claims are essentially a list of functionally-recited desired end results for a conceived field of use of some generic machine learning model. The claims do not delineate steps through which any particular data model achieves an improvement to computers themselves or in the field of machine learning (outputting gathered and analyzed data to inform human consumers is not an example of such an improvement).
The RECENTIVE ANALYTICS, INC. v. FOX CORP., FOX BROADCASTING CO., FOX SPORTS PRODUCTIONS, LLC (2023-2437, 04/18/2025) Federal Circuit decision is instructive for conducting the two part Alice analysis of the pending claims. In RECENTIVE, the two patents at issue, the ‘367 and ‘960 patents are “Machine Learning Training” patents that are used to learn from the scheduling of live events by iteratively collecting, training, outputting, and updating a model. The outputs of the model in RECENTIVE were, in one patent, used for recommending updates to optimize a live event schedule.
The pending claims parallel RECENTIVE in that they too concern collecting data for use by some learning model, and wherein the outputs have an intended use (disclosed but not claimed) of recommending possible changes to driving behaviors.
The Federal Circuit noted that in RECENTIVE, the specification teaches that the machine learning model “employs any suitable machine learning technique” such as a “random forest, a regression, a neural network, a decision tree” or “a Bayesian network [or] other type of technique”.
The pending application is not even this specific. [0044] in the pending specification merely admits that “the processor 205 may generate the data model according to various data analysis techniques, calculations, algorithms, and/or the like. Generally, the analysis techniques process and analyze the raw sensor data and generate a set of information.” Nowhere in the specification is any particular type of data model identified or is any description provided of how a data model is trained to accomplish the intended functions. Even more so than in RECENTIVE, the pending application amounts to expressions of a conceived field of use for generic existing data models.
In step 1 of the Alice test, the Federal Circuit found that RECENTIVE claimed an activity that pre-dated the existence of machine learning – event planners considering prior ticket sales, weather forecasts, etc. to determine when and where to schedule event(s). The Federal Circuit explained that applying machine learning to such a field of use was not patent eligible, citing that “[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.” Intell. Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015). The court also held that “the application of existing technology to a novel database does not create patent eligibility.” and cited SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018); Elec. Power, 830 F.3d at 1353 (“[W]e have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas.”
The pending claims to observing a driver operating a vehicle, some unspecified data being analyzed or updated, and the post-solution activity of mapmaking all pre-date the existence of machine learning and were traditionally conducted by human beings. Limiting the use of some unclaimed type of data model that is admittedly sourced from the prior art to the field of use of analyzing a driver’s vehicle operation is not patent eligible and furthermore collecting driver data in a database and updating the data does not create patent eligibility. There is no evidence in the instant specification that any driving data (“particular content”) that is claimed as being “update[d] … using a data model” has its character changed.
In Alice step two, the Federal circuit court in RECENTIVE agreed with the district court’s finding that the RECENTIVE patents were not directed to an “inventive concept” that would “amount[] to significantly more than a patent upon the [ineligible concept] itself,” id. at 456 (quoting Alice, 573 U.S. at 217–18), because the machine learning limitations were no more than “broad, functionally described, well-known techniques” and claimed “only generic and conventional computing devices,” id. at 457.
The pending claims merely recite the use of data model in broad, functionally described, well-known techniques and fail to provide an inventive concept for the same reason.
The Federal Circuit held in RECENTIVE that patents that merely claim applying existing machine learning models to a new field of use are not patent eligible. The Federal Circuit agreed with the District Court that claims to machine learning training were directed to abstract ideas when they “do not delineate steps through which the machine learning technology achieves an improvement.” The Federal circuit explained that patents to machine learning training were not eligible when “they appl[ied] machine learning to [a] new field of use.” The court held “that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
The same conclusion is reached in the pending claims. The description in the claims of “a data model” is limited to mere mention thereof and as mentioned, [0044] in the specification admits that the data model comprises “various data analysis techniques, calculations, algorithms and/or the like”. There is nothing about the type of model, any programming of computers using it, or any of the computer hardware claimed that reveals anything more than generic application of the model to a conceived new field of use. The claims are drafted using result-oriented language that lists desired end results of operating generic computers without describing in any detail how any of the desired end results are accomplished. As such a broadest reasonable reading of the claims is that prior art existing data models are being trained, at best, in a new field of use. It is maintained the claims 1-21 are ineligible under 35 U.S.C. § 101 as being directed to abstract ideas without significantly more.
With respect to the prior art, the amended limitations now requiring a user to choose a virtual operator for use in association with a data model is well-known in the prior art of gamified driving. The teaching reference of US 9,691,298 B1 to Hsu-Hoffman et al. has been supplied along with rationale for combination with the analogous prior art references of Sadiq, Uhlir and Kuramura.
Claim Rejections - 35 USC § 112
Claims 1-21 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 claims contain 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.
The amended limitation of “update one or more statistics using a data model” fails to meet the 35 U.S.C. 112(a) requirement for written description support because the specification fails to describe how this functionally-claimed result is achieved. Especially in the cases of applications using artificial intelligence (“AI”) and machine learning (“ML”), mere mention in a specification of a field of use of these technologies is insufficient to prove the inventor had possession of the invention including these technologies. The specification must particularly point out, for example, what data model is being used and how.
MPEP 2163.03(v) cites Ariad Pharms., Inc. v. Eli Lilly & Co. as evidence that, “An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved” (emphasis added). This section also cites Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002) as evidence that, “The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement."
In consideration of the fact that although [0043] identifies uses for a data model but [0044] in the specification merely admits that the data model comprises “various data analysis techniques, calculations, algorithms and/or the like,” one having ordinary skill in the art would be unable to conclude that Applicant had possession of the invention including a functional data model configured to update statistics as of the effective filing date.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite displaying a virtual vehicle on a map that is animated to operate like a corresponding vehicle in the real world. This sort of “collect[ing] information on a user’s movements and location history”, “electronically recording” it, “creating a digital travel log” and “tailoring content based on a user’s location” are all activities the courts have determined to fall into the enumerated grouping of “certain methods of organizing human activity” as will be discussed in further detail in the analysis that follows. And as held by the Federal Circuit in RECENTIVE ANALYTICS, INC. v. FOX CORP., FOX BROADCASTING CO., FOX SPORTS PRODUCTIONS, LLC (2023-2437, 04/18/2025), claim limitations directed to using a generic “data model” to collect and store new data and/or in a newly conceived field of use does not create patent eligibility. The court held “that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” It is noted that the pending specification in [0044] does not identify any particular data model being used in the invention and as such it would be impossible to find an improvement to a particular data model resulting from the functions recited in the pending claims.
MPEP 2106.04(a)(2) describes that the grouping of “certain methods of organizing human activity” covers “managing personal behavior” including “certain activity between a person and a computer”. Federal circuit court rulings cited in this section of the MPEP that prove how subject matter similar to that of the pending claims is directed to covered abstract ideas include:
The court’s finding in Intell. Ventures I v. Cap. One, 792 F.3d at 1369. that “tailoring content based on a user’s location” is covered activity.
Weisner v. Google LLC, 51 F.4th 1073, 1082 (Fed. Cir. 2022), in which it was determined that “Claims to “collect[ing] information on a user's movements and location history [and] electronically record[ing] that data” (i.e., “creating a digital travel log”) is provided as an example of “managing personal behavior or relationships or interactions between people.”
The court concluded in TecSec, 978 F.3d at 1293 that “providing information based on a location on a map is an abstract idea because it is directed to filtering or picking information or materials relevant to a location or context, which is a human problem, not any specific improvement to a computing technology.”
And in Location Based Servs., LLC v. Niantic, Inc., 295 F. Supp. 3d 1031, 1045–49 (N.D. Cal. 2017), aff’d, 742 F. App’x 506 (Fed. Cir. 2018)”, it was determined that “providing map-related data based on a user’s “status” (meaning, any information about location) is just analyzing information about a location, which is data analysis and an abstract idea.”
The instant-claimed gathering of vehicle operation data through unknown sensors by a computer of unknown technical specifications or programming, storing the gathered data in a data model, and displaying content tailored to a relevant location-based scene on a map is activity in line with what the courts found to be directed to covered abstract ideas for organizing human activity through interactions between people and computers.
The steps directed to abstract ideas in each of independent claims 1, 11 and 21 are:
generating, in a virtual environment, a virtual trip of a virtual vehicle operated by a virtual operator, the virtual trip corresponding to the real-world geographic area, wherein the virtual trip comprises a virtual route simulating one or more elevation changes corresponding to the real-world geographic area; a virtual map in a user interface, visual data indicative of the virtual operation of the virtual vehicle in association with the virtual trip.
This is the sort of “tailoring content based on a user’s location” found to be abstract in Intell. Ventures I v. Cap. One, and is also similar to “collect[ing] information on a user's movements and location history [and] electronically record[ing] that data” (i.e., “creating a digital travel log”). Furthermore, “Providing map-related data based on a user’s “status” (meaning, any information about location) is just analyzing information about a location, which is data analysis and an abstract idea” Location Based Servs., LLC v. Niantic, Inc.
The additional elements of independent claims 1, 11 and 21 beyond the abstract ideas include:
to receive, from one or more sensors; receiving, from an operator, a selection of a virtual operator;
The receiving of data by a computer having unknown technical specifications or programming represents insignificant pre-solution data gathering. “The receiving of input and storing steps represent the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) […] does not integrate a judicial exception into a practical application or provide significantly more.” Affinity Labs v. DirectTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016 attributing particular generic computer functions for computer hardware to perform from well-known, routine, conventional functions performed by such hardware has been held to be insufficient to show an improvement to technology, Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1264, 120 USPQ2d 1201, 1208 (Fed. Cir. 2016).
a data model indicative of operation of a vehicle by an operator in a real-world geographic area; and a frequency of the operation of the vehicle by the operator in a real-life route; update one or more statistics using a data model;
The Federal Circuit in RECENTIVE ANALYTICS, INC. v. FOX CORP., FOX BROADCASTING CO., FOX SPORTS PRODUCTIONS, LLC (2023-2437, 04/18/2025) held that claims directed to employing generic machine learning techniques to train a learning model in a particular environment or for a new conceived field of use are not patent eligible, especially when the claims do not delineate steps through which the technology achieves an improvement. RECENTIVE also reiterates that what particular content of data is collected and stored is within the realm of abstract ideas.
In RECENTIVE, The Federal circuit explained that patents to machine learning training were not eligible when “they appl[ied] machine learning to [a] new field of use.” citing that “[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.” Intell. Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015). The court also held that “the application of existing technology to a novel database does not create patent eligibility.” and cited SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018); Elec. Power, 830 F.3d at 1353 (“[W]e have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas.” And the Federal Circuit agreed with the District Court that claims to machine learning training were directed to abstract ideas when they “do not delineate steps through which the machine learning technology achieves an improvement.” The court held “that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
transmitting for displaying on a virtual map;
processing, based at least in part on the data model, virtual operation of the virtual vehicle in association with the virtual trip.
The additional elements of “transmitting for displaying” and “processing” are not attributed to any hardware or software or any particular instructions executed from memory. This amounts to “the use of a computer or other machinery in its ordinary capacity … or simply adding a general-purpose computer or computer components after the fact to an abstract idea” which “does not integrate a judicial exception into a practical application or provide significantly more.” See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016). A claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Also, a claim that generically recites an effect of the judicial exception or claims every mode of accomplishing that effect amounts to a claim that is merely adding the words "apply it" to the judicial exception. See Internet Patents Corporation v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (The recitation of maintaining the state of data in an online form without restriction on how the state is maintained and with no description of the mechanism for maintaining the state describes "the effect or result dissociated from any method by which maintaining the state is accomplished" and does not provide a meaningful limitation because it merely states that the abstract idea should be applied to achieve a desired result).
Because there is no definition of what computer hardware and software are used to perform the displaying or processing step, or what data model is used or how it is used to update statistics, the scope of the claim encompasses every mode of accomplishing it – any data model, any hardware and any software and any programming instructions. “Displaying” and “processing” steps recite effects dissociated from any method of performing it and is equivalent to merely stating that the abstract ideas for organizing human activity (data analysis of human driving and displaying a digital travel log using a computer) are applied.
a system, a user interface, memory, a processor; a non-transitory computer-readable medium;
All of these additional elements are recited in the claims at a high level of generality and merely outline a generic technological field in which to apply the abstract ideas of the claims through routine and conventional use of generic hardware. Applicant’s disclosure, which the claims are read in light of, does not attribute any novel or unobvious hardware specifications or configuration to the claimed apparatus which serves as evidence that the computing devices of the instant claims are equivalent to prior art computing devices merely being used for their inherent purposes. “[T]he invocation of ‘already-available computers that are not themselves plausibly asserted to be an advance … amounts to a recitation of what is well-understood, routine, and conventional.’” Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1366 (Fed. Cir. 2020). And “The receiving of input and storing steps represent the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea […] does not integrate a judicial exception into a practical application or provide significantly more.” See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)
The dependent claims are summarized as follows:
Claims 2-4 and 14 recite the desired high-level operations of receiving, accessing and updating data on some unspecified hardware device(s) using some unspecified software instructions. This, again, is the use of a computer in its ordinary capacity for economic or other tasks to receive, store or transmit data, which Affinity Labs indicated failed to integrate a judicial exception into a practical application or provide significantly more.
These claims also contain further specifics on certain attributes of data, such as data indicative of a route or current vehicle operation data. As explained in MPEP § 2106.03, data per se, when claimed as a product without any structural recitations, is not directed to any of the statutory categories of invention. When evaluated as additional elements during an Alice analysis, data per se cannot impart eligibility to judicial exceptions. Restricting data in a claim to a particular type or content is also seen as filtering gathered data, which has been held in Bascom Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349, 119 USPQ2d 1236, to represent an abstract idea of the grouping of “certain methods of organizing human activity” because interacting with a database and selecting certain data by generic computers is not fundamentally different from human beings interacting with printed content. “[f]iltering software, apparently composed of filtering schemes and filtering elements, was well-known in the prior art” and “using ISP servers to filter content was well-known to practitioners.”
Claims 5 and 7 and 15, 17 recite processing, by some unspecified computing device and using unspecified software instructions, additional data and transmitting this data to some unspecified destination for an intended use of displaying it.
As no structural components are recited in any of claims 2-5 and 7, these claims do not contain evidence of a particular machine, and as no particular computer hardware, software, or instructions are claimed, there is no evidence of any improvements to a computer or a field of technology. And as discussed prior, “the use of a computer or other machinery in its ordinary capacity … (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea […] does not integrate a judicial exception into a practical application or provide significantly more.” See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016). And regarding displaying transmitted data in claims 5 and 7, this amounts to insignificant post-solution activity. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). In Flook, the Court reasoned that “[t]he notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance.”
Claims 6, 12-13 and 16 describe particular contents of data. This amounts to a recitation of data per se or nonfunctional descriptive material, which does not belong to a statutory category of invention and cannot impart eligibility to otherwise ineligible claims. Restricting data in a claim to a particular type or content is also akin to filtering gathered data. Filtering content was found to be an abstract idea under step 2A because it represented an activity rooted in fundamental human behavior, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016).
Claims 8 and 18 recite identifying social media contacts (filtering content, an abstract fundamental human behavior as explained in BASCOM Global Internet v. AT&T Mobility), a duplication of parts of a virtual trip (“creating a digital travel log,” an abstract idea as explained in Intell. Ventures I v. Cap. One), and a post-solution transmitting and displaying step that does not amount to an inventive concept as explained in Flook.
Claims 9-10 and 19-20 recite insignificant post-solution activity, adding an award to a user account pursuant to achieving a goal, which does not amount to a practical application of the abstract ideas of the independent claims or an inventive concept. Rewarding a human user based on their performance also represents fundamental human activity long predating the digital era, which is itself an example of an abstract idea. Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."); Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)), “the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.”
The judicial exception of claims 1-20, per the guidance outlined in MPEP 2106.04(d), are not integrated into a practical application because the claims as presented merely express observation and recordation of data and “providing map-related data based on […] any information about location” which “is just analyzing information about a location, which is data analysis and an abstract idea” Location Based Servs., LLC v. Niantic, Inc, without any evidence of the existence of a particular machine or evidence of providing any improvements to a computer or to a field of technology. In the field of the instant invention -- recording data, analyzing it in the form of a data model, and displaying it using graphics that merely convey the information to a human observer using existing computer hardware, an improvement would have to be found to an inherently technical problem existing in computers and would have to reveal how the computer(s) themselves are tangibly improved as a direct result of the claimed invention. And the details of the improvement to computers cannot be found in the details of the abstract ideas themselves. Genetic Techs v Merial, an inventive concept "cannot be furnished by the unpatentable law of nature" itself. A hypothetical improvement in how vehicle event and location data is gathered or graphically conveyed to human users is not an improvement to computers themselves or to computer technology, but rather an improvement to humans. TecSec, 978 F.3d at 1293, “providing information based on a location on a map is an abstract idea because it is directed to filtering or picking information or materials relevant to a location or context, which is a human problem, not any specific improvement to a computing technology.”
With respect to a conclusion that the pending claims lack defining a particular machine,
MPEP 2106.05(b) instructs that, "When determining whether a machine recited in a claim provides significantly more, the following factors are relevant." including: "The particularity or generality of the elements of the machine or apparatus, i.e., the degree to which the machine in the claim can be specifically identified" wherein "It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions)." And, "Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)"
Reviewing pending claims 1-20 with the above in mind, the only hardware recited is, in claim 1, “one or more sensors” and in claim 11, “one or more processors,” “one or more non-transitory computer-readable media,” “and the “one or more sensors”. There is no manner by which any particular machine can be specifically identified from these claim limitations. And there is no particular software that can be identified from the claim limitations. The pending claims are drafted using result-focused functional language that lists desired outcomes for receiving data from a vehicle that are attributed to a generic computing framework. There are no detailed descriptions in the claims of how any of the functional outcomes, including receiving vehicle frequency of operation, are achieved by any particular software programming and/or particular hardware.
None of the instant-claimed descriptions of data per se or display of certain graphics meant to inform human users in the form of a content-rich digital map or virtual travel-log serves to solve any stated problem that is inherently technical in nature or provide any meaningful limitations beyond generally linking the abstract idea to the technological environments of the general-purpose computing devices. The implicit utility of the instant claims is for providing more data to a human consumer and/or improving the user’s experience while using the computer application. The court ruled in International Business Machines Corporation v. Zillow Group, Inc., (CAFC, 17 October, 2022) that "improving a user's experience while using a computer application is not, without more, sufficient to render the claims" patent-eligible at step one. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020). Identifying, analyzing, and presenting certain data to a user is not an improvement specific to computing. "Merely requiring the selection and manipulation of information---to provide a 'humanly comprehensible' amount of information useful for users ... ---by itself does not transform the otherwise-abstract processes of information collection and analysis." Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016). We have repeatedly held claims "directed to collection of information, comprehending the meaning of that collected information, and indication of the results, all on a generic computer network operating in its normal, expected manner" to be abstract. In re Killian, 45 F.4th 1373, 1380 (Fed. Cir. 2022); see also Intell. Ventures I LLC v. Cap. One Fin. Corp., 850 F.3d 1332, 1340 (Fed. Cir. 2017)
“A patent-eligible technical improvement requires solving an actual problem.” McRO, 837 F.3d at 1314. Whereas McRO v. Bandai Namco Games Am. involved a specified, automated, rules-based process for facial animation that was different from manual approaches performed by animators and that solved, in contrast, the instant claims merely involve generic steps. And whereas Data Engine Techs provided a solution to an actual problem existing in prior art spreadsheets, the instant claims do not solve any such problem existing in computers themselves or in the relevant computing art. The instant claims do not focus on any asserted improvements to computers themselves or computer technology, and none of the examples provided by the courts, see MPEP 2106.05(a), for improving computer functionality are found in the instant claims.
Furthermore, none of the additional elements identified above are sufficient to amount to an inventive concept(s) because none of the additional elements reveal any specific improvements to the function of any computer(s) or to a field of technology, no particular machine or manufacture is claimed that is used to implement the abstract idea or that is/are integral to the claim, there is no recited effectuation of a transformation or reduction of a particular article to a different state or thing, and there is no application of the abstract ideas in any other meaningful way beyond generally linking the use of the abstract idea to a technological environment such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Additional evidence against eligibility: Result-oriented claim language
The pending claims are drafted as a list of desired results obtainable from operating generic computers, without any description in the claims as to how the desired results are achieved. This is the very scenario described in Beteiro LLC v. DraftKings Inc., (Fed. Cir 2024) wherein "the claims are drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves these results. Claims of this nature are almost always found to be ineligible for patenting under Section 101."
Examples of result-oriented claim language in the instant application includes “receiving, from an operator, a selection of a virtual operator”, “receiving the data model,” “generating, in a virtual environment, a virtual trip…”, “processing, based at least in part on the data model, virtual operation of the virtual vehicle…”, “transmitting for displaying…”. The claims lack any technical specifications of hardware or software attributed to the "computer” that are necessary to accomplish the claimed results. The claims also lack specifying by what method or process ("how") these results are achieved. Courts have repeatedly emphasized that claims must describe the method or process used to accomplish the claimed results rather than simply claiming outcomes themselves.
Several Federal Circuit court cases are supportive of a conclusion that claims directed to results without a concrete machine or concrete method are ineligible. These include:
Interval Licensing LLC v. AOL Inc. (896 F.3d 1335): The court found that claims to a computer software "attention manager" that displays content on unused portions of a screen were result-oriented and invalid under 35 U.S.C. § 101 because they did not recite a specific technological method for achieving the claimed result.
Contour IP Holding LLC v. GoPro, Inc., 2024 U.S. App. LEXIS 22825 (Fed. Cir. 2024): The court held that claims must not only describe desired outcomes but also include a specific process or machinery for achieving that result.
In re Killian, 45 F.4th 1373 (Fed. Cir. 2022): The court reaffirmed that claims simply reciting a desired result without specifying how to achieve it are directed to an abstract idea and are ineligible under 35 U.S.C. § 101. The claims at issue were directed to analyzing data from two databases. In the Step Two of the Alice test, the court determined that there was no inventive concept because the additional elements merely involved generic and routine data gathering and analysis steps that could have been performed with or without a computer.
Broadband iTV, Inc. v. Amazon.com, Inc., No. 2023-1107, 2024 WL 4018253, *4 (Fed. Cir. Sept. 3, 2024). In the first step of the Alice test, the Federal Circuit affirmed that in the '026 patent family, the District Court, “correctly determined that receiving metadata and organizing the display of video content based on that metadata is abstract.”, likening the claimed subject matter to receiving and displaying information, and organizing it based on classifications. The Federal Circuit observed that claiming a user interface does not “automatically” provide patentable subject matter.
In the second step of the Alice test, the Federal Circuit affirmed that the District Court in the '026 patent family correctly “determined nothing transforms the claims into something other than the abstract idea because ... the claims ‘recite only generic and conventional components, arranged in a conventional manner, and provide only conventional functionalities.’” The Federal Circuit applied the same test to the '825 patent and held that “of the ’825 patent is directed to the abstract idea of collecting and using viewing history data to recommend categories of video content.” and wherein it was concluded that “the ’825 patent claims do not claim a technological solution to a technological problem.”
In Alice Step 2, the Federal Circuit agreed with the District Court's determination that generating and arranging displays based on relevance was merely a "feature of the abstract idea of recommending categories" and that "the idea of creating categories is a longstanding human practice that does not transform the claims," considering that the claim "does not describe how the desired end result is achieved."
Beteiro LLC v. DraftKings Inc., (Fed. Cir 2024). The court stated that "the claims are drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves these results. Claims of this nature are almost always found to be ineligible for patenting under Section 101. See, e.g., Elec. Power Grp., 830 F.3d at 1356 (“[T]he essentially result-focused, functional character of claim language has been a frequent feature of claims held ineligible under § 101, especially in the area of using generic computer and network technology to carry out economic transactions.”); Two-Way Media Ltd. v. Comcast Cable Commc’ns, LLC, 874 F.3d 1329, 1337 (Fed. Cir. 2017) (“The claim requires the functional results of ‘converting,’ ‘routing,’ ‘controlling,’ ‘monitoring,’ and ‘accumulating records,’ but does not sufficiently describe how to achieve these results in a non-abstract way.”).
The preceding court cases serve as additional evidence that the instant claims, that are drafted using result-oriented language devoid of any specificity as to what particular machine or method are used to accomplish the claimed results, are directed to ineligible subject matter.
A thorough analysis of each and every limitation of each and every claim, both individually and as part of an ordered combination shows that the claims 1-21 are not patent-eligible under 35 USC § 101.
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, 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.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over US 2012/0195272 to Sadiq et al. in view of US 9,691,298 B1 to Hsu-Hoffman et al., 2005/0203922 to Uhlir et al., and US 2014/0129130 to Kuramura et al.
Background of the prior art: Sadiq comprises a gamification platform 200 that uses a game engine to apply game mechanics to solve the problem of risky driving behaviors such as excessive speed, hard braking and sudden acceleration, [0041]. A game application process 500 includes game-based interactions with drivers, who can personalize their avatars and vehicles, [0042]. The game engine creates a rich, contextualized environment incorporating traffic, weather, landmarks, and road conditions, [0107]. Although the game world can be updated in near real-time as a driver drives, the interaction between the driver and the game is intended to occur at a time other than when they are driving, [0109].
Re claim 1, Sadiq discloses: a computer-implemented method
[0016]-[0024] describes that the invention of Sadiq is enabled by computing devices executing instructions from computer readable medium by circuits such as microprocessors or other logic devices.
comprising: receiving a data model, wherein the data model is indicative of operation of a vehicle by an operator in a real-world geographic area
To establish a claim interpretation of “data model”, Applicant's disclosure is considered, which describes this term as comprising various performance characteristics and metrics that are representative of real-life operation of a real-life vehicle of a real-life operator. Applicant’s data model may indicate real-life routes, roadways on which the vehicle has operated, and a frequency of operation. See [0015], [0045], [0049]-[0050] of the pre-grant publication of the instant specification, US 2024/0320396 A1.
[0042]-[0045] of Sadiq describes establishing and updating a driver profile wherein “Driving event history can be created, for example, through data capture from a device, such as, for example, a dongle or GPS device … All vehicles 102 connected via a device 104 can collect data on the behavior of the driver … Each event can be associated with or tagged to the vehicle and the driver associated with that vehicle at the time of the event … the driver profile 504 may be a function of data derived from 1) event and trip data (e.g., GPS and OBD data on location, speed, cornering, braking, accelerating etc. plus geospatial data); and 2) historical driver information from the carrier database 506 (e.g., age, gender, marital status, number of years licensed, accident, claim and violation history, etc.). … “
[0046] of Sadiq describes that “the driver profile 504 … can provide visual representation of one trip or multiple trips … for example: similar trips that are overlaid on a map… trips may be grouped for continuous learning algorithm”
[0047] describes that a driver profile may aggregate trip data using a rule base.
[0146] describes that, “Referring back to FIG. 5, the driver profile update module 528 can transform simple demographic variables into a complex view of behaviors, including learned behaviors from the game-based interactions, allowing carriers to refine their pricing and product offerings, as well as the games offered in future interactions. See FIG. 6 for a depiction of exemplary elements associated with the driver profile update module 528.”
A driver profile 504 that aggregates real-world trip data using a rule base, updates a record of events that describe a driver’s behavior, performs continuous learning for discovering trips, and admittedly provides a “complex view of behaviors” as it is continuously updated teaches a data model indicative of real-world operation of a vehicle by an operator in a real-world geographic area as per the claim.
generating, in a virtual environment, a virtual trip of a virtual vehicle operated by a virtual operator,
[0039] describes that a gamification platform 200 and process 400 compares a user’s profile, which as discussed in [0042]-[0047] comprises continuous learning-determined real-world trips and learned driver behaviors, to conditional rules and provides virtual games to the users that lead to rewards and recognition.
the virtual trip corresponding to the real-world geographic area;
[0107] describes that the “HIMEX Virtual World” used to generate the gamified environment shown in Figs. 7-16 is based on spatially available data and live real-time data combined to create a contextualized environment that reflects traffic, weather, landmarks, road conditions.
[0109] describes that the “HIMEX” virtual game world seen in Figs. 7-16 comprises a virtual vehicle is depicted on virtual roads. Trip and performance data in the virtual game world can be mapped to the HIMEX Virtual World 3D Live Map 800 on a real-time or near-real-time basis.
Fig. 13 is a screen shot of the HIMEX Virtual World 3D map depicting a real-world route “from Work to Home”
processing, based at least in part on the data model, virtual operation of the virtual vehicle in association with the virtual trip; and transmitting for displaying, on a virtual map in a user interface, visual data indicative of the virtual operation of the virtual vehicle in association with the virtual trip
[0109] describes that “Figs 7-16 show exemplary screenshots … a user (driver) would see while “playing a game”. Sadiq notes that this game does not occur while driving.
Figs. 7-11, 13 and 15 which illustrate screen shots of the game user interface comprising a virtual map and visual data indicative of virtual operation of a virtual vehicle in association with some virtual trips. Fig. 8 shows a virtual vehicle operating on a virtual map and Fig. 10 shows some trips such as from work to home, from home to work. Fig. 11 shows a virtual vehicle that is “standard saloon” that is associated with User “Daytripper”.
Although Sadiq, see Fig. 11, is directed to gamified driving wherein a virtual vehicle is associated with an illustrative user “Daytripper”, Sadiq is silent as to whether operators of his invention can select virtual operators used to update one or more statistics using a data model.
Hsu-Hoffman is an analogous gamified driving reference that teaches, see 15:29-46, “FIG. 5 illustrates a flow diagram for an example process … a client-side computing device 100b may perform to provide a video game that takes into account a user's driving performance. … the process may begin with step 501 in which a user enters information about themselves. At step 502, the user may choose a character from a listing of characters and/or images of characters. In some embodiments, the user may design his/her own character. The user may choose a name for his/her character at step 502 as well. This character name may be used throughout the safe driver application 300 to give the user a unique/custom user experience.”
It would have been obvious to one having ordinary skill in the art before the effective filing date of the instant invention that Sadiq could have enabled operators to select their virtual operators associated with their gamified driving accounts as taught by Hsu-Hoffman without causing any unexpected results. It is a notoriously old and well-known video game feature to allow players to select and customize their characters so that they feel more connected to the video game.
And although Sadiq teaches in [0057] the gamification algorithm 514 can factor in real conditions that were involved in a real driver’s events, such as “snowy conditions on a freeway in rush hour,” and “Road conditions” in [0083], “Type of road” in [0086], Sadiq is silent as to whether elevation changes that correspond to the real world geographic area are generated in the game.
Kuramura is an analogous gamified driving system and method that teaches it was known for a game to reflect “terrain” such as a “mountain” that were encountered during real life operation of a vehicle by a real-life operator, see [0029] and [0038] of Kuramura.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention that Sadiq could have additionally generated elevation changes such as mountain terrain in a game based on real-world geographic areas encountered in a real-life trip as taught by Kuramura without causing any unexpected results. Sadiq provides motivation for this improvement – in [0057] he explains the importance that “gamification algorithm 514 can consider factors and prior situations that were involved in the driver’s events in order to generate a customized game – to educate the driver” on certain conditions encountered on the road in real-life.
Also, although Sadiq teaches, see [0107][, [0109], that the HIMEX Virtual World engine generates game maps, see Figs. 8, 13, using real-world trip, event, traffic, landmarks, and road condition data, which is suggestive of the virtual world having common appearance traits with the real world it is based on, Sadiq doesn’t explicitly state that the depicted virtual area and virtual map are corresponding to the real-world geographic area – in other words that the virtual game area and map look like the real-world area it is based on.
Uhlir is an analogous reference in the field of “computer games that depict or represent actual, real world geographic areas as part of the scenarios of the games … geographic features in a region including roads in the region” that are sourced from a database (see Abstract.) Uhlir states in [0004] that as of the 2004 filing date of his application, "Computer game developers have recognized the need to provide realistic depictions of actual real world places in computer games.
[0005] teaches that it was known to use collected real world geographic data "to depict a particular real world locale (such as a city) with the richness and detail expected by game players"
[0019] indicates that the "geographic database 100 includes data about a road network 120 located in the coverage area 104" that "include various kinds of information, such as the geographic coordinates of positions of the roads, street names of the roads, address ranges along the roads, turn restrictions at intersections of roads, and so on." [0020] indicates that real-world driving data is collected by real-world vehicles being driven by drivers "to observe features and record information" and are then stored in database 100.
[0024] describes that computer games could be developed to "include representations of actual geographic features, such as roads, in a geographic area." [0031] states that "computer games 132 created using the data from the geographic database 118 provide for representing geographic features in play scenarios of the computer games. The geographic features represented by the computer games include features located in some or all the coverage area of the geographic database 118, including some or all the road network represented by the geographic database 118."
[0077] describes that it was known in computer game applications for data that represents a road network in the real world to be used in combination with traffic feeds and models in order to simulate a real city with its existing road networks and traffic patterns.
[0078] describes that Uhlir's invention can be used in computer games in which virtual vehicles operate on behalf of a player in an initially chosen city and an overlay of existing traffic conditions. The player’s virtual vehicles are managed to conduct pickups, deliveries, run fleet routes and the like.
[0117] of Uhlir teaches that data that represents actual, real world places can be used for a computer simulation application. The simulation would be based on a representation of the geographic database that included 3D renderings of buildings, signs, topographical features, and other related attributes.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the instant invention that the “HIMEX Virtual World” of Sadiq that is admittedly generated based on real-world data models of trips, events, driver behaviors, and road conditions could have represented “actual, real-world places” in the virtual world “to simulate a real city with its existing road networks” as taught by Uhlir without causing any unexpected results. As noted by Uhlir in [0004], it was known as early as 2004 that "Computer game developers have recognized the need to provide realistic depictions of actual real world places in computer games.” and in [0078] that a known field of application was computer games that depict virtual vehicles driving on a map.
Re claims 2, 12 [0030] of Sadiq teaches that “data, such as, for example, latitude/longitude of a vehicle 102” can be accessed from “the vehicle 102 OBD device” and wherein “data may be captured and/or transmitted directly from the vehicle 102” . And [0050] describes that, “A vehicle sensor input 510 … may provide real-time or near real-time information from the connected vehicle to the platform 200 via platform 100, including information associated with driving events, such as, for example, location, speed, accelerations, etc. The vehicle sensor 510 can update the status of the vehicle, which may include, for example, mileage, fuel and fluid levels, maintenance history, etc. The platform 200 may be designed to accept vehicle profile data from any type of vehicle sensor, including devices 104, such as, an original equipment manufacturer (OEM) or aftermarket OBD, a mobile device, or any other device capable of transmitting vehicle status and location data. The location of the vehicle for the driving event history may be determined using data from the vehicle sensor 510. The platform 200 can allow sensor data to be passed in real-time, near real-time, stored, and/or pre-cached via a pre-defined interface.” All of the above meets the limitation of telematics data indicative of the movement of the vehicle.
Regarding updating the data model:
[0042]-[0045] of Sadiq describes that all vehicles 102 connected via a device 104 can collect data on the behavior of the driver … Each event can be associated with or tagged to the vehicle and the driver associated with that vehicle at the time of the event … the driver profile 504 may be a function of data derived from 1) event and trip data (e.g., GPS and OBD data on location, speed, cornering, braking, accelerating etc. plus geospatial data); and 2) historical driver information from the carrier database 506 (e.g., age, gender, marital status, number of years licensed, accident, claim and violation history, etc.). … “
[0046] of Sadiq describes that “the driver profile 504 … can provide visual representation of one trip or multiple trips … for example: similar trips that are overlaid on a map… trips may be grouped for continuous learning algorithm”
[0047] describes that a driver profile may aggregate trip data using a rule base.
[0146] describes that, “Referring back to FIG. 5, the driver profile update module 528 can transform … variables into a complex view of behaviors, including learned behaviors” See FIG. 6 for a depiction of exemplary elements associated with the driver profile update module 528.”
Re claims 3, 13, this claim recites that the data model is, in some way, indicative of a route with an origin and a destination. As described with respect to [0046], work trips can be fed into the data model for continuous learning. Work trips, by definition, comprise an origin and a destination.
Re claims 4, 14, Fig. 11 of Sadiq shows that a virtual vehicle user interface depicts virtual trips included “Cornering” and that the user is “doing well” in those operations and [0042] indicates that data regarding cornering in the real-world was collected by device 104. Sadiq in [0058] and [0069] additionally states that “the following variables/factors are considered in designing a customized game … Cornering”. [0112] describes a scenario wherein a driver performs “careful cornering during a snowstorm” which affects the progress and graphics of the virtual game.
Re claims 5, 15 refer to Figs. 7-21 of Sadiq which show a plurality of user interfaces depicting the driving game each including different / additional visual data indicative of additional virtual operation of the virtual vehicle in association with a plurality of virtual trips.
Re claims 6, 16, [0027]-[0028] and [0151]-[0152] of Sadiq describe that the invention of Sadiq can be used by commercial fleet drivers and [0156] includes “businesses (truck fleets).
Re claims 7, 9-10, 17, 19-20, regarding statistics, rewards, and goals associated with a virtual vehicle in associated with a virtual trip, see [0039], [0046], [0110]-[0114].
Re claims 8 and 18, [0046] of Sadiq describes that the gamification platform “conducts a search via platform,” “uses a social media connection via the gamification platform 200 (e.g., Twitter, Facebook ...” and [0115] describes users linked via social graph can “invite friends to join future events” which teaches generating and transmitting an additional virtual trip comprising additional visual data.
Re claims 11 and 21, refer to the rejection of claim 1.
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 J HYLINSKI whose telephone number is (571)270-1995. The examiner can normally be reached Mon-Fri 10-530.
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/STEVEN J HYLINSKI/Primary Examiner, Art Unit 3715
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