DETAILED ACTION
Status of the Application
In response filed on November 12, 2025, the Applicant amended claims 1, 11-16, 19, and 20; added claim 23; and cancelled claim 17. Claims 8 and 18 were previously cancelled. Claims 1-7, 9-16, and 19-23 are pending and currently under consideration for patentability.
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 Amendments and Arguments
v Applicant’s arguments, with respect to the rejection of claims 1-7, 9-16, and 19-23 under 35 U.S.C. 101 have been fully considered and are not persuasive. The rejections of claims 1-7, 9-16, and 19-23 under 35 U.S.C. 101 have been maintained accordingly.
Applicant specifically argues that
“Applicant respectfully submits that the amendments to claim 1 establish a specific structural arrangement that integrates any alleged abstract idea into a practical application and provides significantly more than any alleged judicial exception…. This distributed architecture creates a concrete technological arrangement wherein a remote server performs computationally intensive machine learning operations to generate action values, while an in-vehicle display presents the results to the user at an appropriate time (i.e., when the vehicle is at rest).This structural configuration addresses the technical problem of coordinating data processing and user interaction across physically separated components in a car sharing platform, and represents a specific implementation that improves upon generic computer systems. The claimed arrangement is analogous to the distributed network architecture found patent-eligible in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014), where the court held that claims directed to a specific way of automating the creation of composite web pages were patent-eligible because they were "necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks." Here, the claimed distributed architecture between the remote server and the in- vehicle display is necessarily rooted in the car sharing platform technology and addresses the problem of how to efficiently process telematics data and coordinate action messaging across a fleet of vehicles while ensuring safe presentation of information to users.
Examiner respectfully disagrees with Applicant’s first argument.
The required combination of “one or more processors of a server” and “a display within the vehicle” is not very specific (e.g., merely requires use of any server anywhere to process data, and any sort of display device that happens to be within a vehicle to display information). These high-level recitations amount to “apply it” using computers and/or mere field of use limitations (e.g., do it on the internet with a distributed computers). Applicant’s suggestion that this “specific structural arrangement” addresses a technical problem is not persuasive. Applicant’s specification does not suggest there is a “problem of coordinating data processing and user interaction across physically separate components in a car sharing platform”. The specification does not appear to suggest there is any technical problem related to where certain aspects of the process are implemented, or that a certain inventive configuration addresses any technical problem. Instead, Applicant’s disclosure explains that the specific computing configuration is flexible (i.e., multiple equivalent configurations able to be chosen by the implementer of the process), and that any number of configurations may be used to implement the claimed steps/features. (see published disclosure at [0081]-[0082] “In general, the techniques herein (e.g., the method 600) may be implemented using a combination of one or more clients and/or one or more server devices. For example, depending on the hardware and software implementation chosen by an implementer, the method 600 may be entirely implemented using a single computing device (e.g., the telematics system 104)… may be entirely contained within a module of the telematics system 104 of FIG. 1. For example, the user interface framework may be included in a mobile application (e.g., Android or iPhone application) downloadable from an application store which is stored in the memory 122 and is executed by the processor 120.”, see also [0022] “referred to herein as a "server," the server 108 may, in some implementations, include multiple servers and/or other computing devices. Moreover, the server 108 may include multiple servers and/or other computing devices distributed over a large geographic area (e.g., including devices at one or more data centers), and any of the operations, computations, etc., described below may be performed in by remote computing devices in a distributed manner.” & [0023] “some or all of the components of the telematics system 104 may be built into the dash of the vehicle 102 or affixed elsewhere within the vehicle 102 ( e.g., via an onboard diagnostics port of the vehicle 102). In an embodiment, a portion of the telematics system 104 may be implemented using a mobile computing device (e.g., a smart phone of the user)” & [0026] “some embodiments, the telematics system 104 may include multiple different implementations of the display 124 (e.g., a first display 124 associated with the vehicle 102 and a second display 124 associated with a mobile computing device of the user).” & [0030] “The sensor 134 may include one or more sensors associated with the vehicle 102 (e.g., a speedometer sensor) and/or a mobile device of the user (e.g., an accelerometer in a smartphone)” & [0031] “The database 136 may be any suitable database” & [0052] “computer-implemented methods discussed herein may include additional, fewer, or alternate actions, including those discussed elsewhere herein. The methods may be implemented via one or more local or remote processors, transceivers, servers, and/or sensors (such as processors, transceivers, servers, and/or sensors mounted on drones, vehicles or mobile devices, or associated with smart infrastructure or remote servers), and/or via computer-executable instructions stored on non-transitory computer readable media or medium” & [0064] “The device 202 may correspond to a mobile computing device of the user, such as a smart phone, tablet, etc. In some embodiments, the device 202 may be a laptop or other suitable connected computing device. In still further embodiments, the device 202 may be a device of the CSP that is integral to the vehicle”
Applicant’s suggestion that the instant claims are similar to DDR Holdings is not persuasive. As discussed above, Applicant’s disclosure does not suggest to a PHOSITA that the invention relates to a particular inventive configuration used to address any technical problem. Applicant has instead appeared to merely select one potential embodiment involving use of remote servers and argued ex post facto that it addresses a technical problem.
Examiner maintains that being safer for the user if the messages are displayed when the vehicle is at rest is a non-technical and subjective problem/advantage. An improvement to the level of distraction or possible safety of the human is not an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem.
Applicant specifically argues that
“In addition, new claim 23, supported in the specification at least at paragraph 0071, recites: ...automatically verifying, by the one or more processors of the server, completion of each respective action by analyzing vehicle telematics data received from one or more vehicle sensors to determine a location of the vehicle, and releasing the respective action value only in response to the vehicle being relocated to a predetermined location. This limitation reflects a specific technical process for verifying action completion using sensor data analysis, rather than relying on manual verification. The specification explains that the system may “periodically check additional data (e.g., the location of the vehicle 102, data received from the sensor 134, vehicle telematics data, etc.) to determine whether a change to the vehicle imperative corresponding to the accepted action has occurred.” See specification, para. 0078. This automatic verification process using sensor data analysis is a technical solution that improves the efficiency and reliability of the car sharing platform by eliminating the need for manual verification of completed actions. Consideration of new claim 23 is respectfully requested.”
Examiner respectfully disagrees with Applicant’s second argument.
The method involves analyzing data to determine whether something has occurred. This is part of the abstract idea. That the telematics data is described as having been received from one or more vehicle sensors merely describes elements (the sensors themselves) external to the scope of the claimed invention, similar to the claims in Electric Power Group (e.g., where the claims describe the data being analyzed as having been collected from various locations in the power grid). The receiving of the data, which is also recited at a high level of generality (and arguably not as part of the claimed method) amounts to data gathering. Any efficiency or convenience realized via use of computers rather than relying on manual techniques is merely a result of “applying it” using general purpose computers. This is not a technical solution to a technical problem.
v Applicant’s arguments, with respect to the rejection of amended independent claims 1, 11, and 20 (as well as each of the respective dependent claims) under 35 U.S.C. §103 have been considered, but are not persuasive.
Applicant argues “Applicant respectfully submits that even if the individual elements were present in the prior art, a person of ordinary skill in the art would not have been motivated to combine the teachings of Tanuma, Shah, and Uenohara in the manner claimed.”
More specifically, Applicant argues that “Shah is directed to a ride-sharing system (not a car-sharing system) that uses machine learning to optimize driver incentives for vehicle relocation. Uenohara is directed to a vehicle safety system that controls the display of text messages based on vehicle state. These references address different problems in different contexts.” And that with respect to the combination of Tanuma and Shah “this reasoning is conclusory and does not explain why a person of ordinary skill would have looked to Shah's ride-sharing system when designing Tanuma's car-sharing maintenance system. The two systems operate in fundamentally different contexts: Shah's system involves drivers who own their vehicles and are incentivized to relocate them, while Tanuma's system involves a fleet of vehicles owned by the car sharing company where users are customers, not vehicle owners…. statement is conclusory and does not account for the specific technical challenges of the claimed invention. That is, the Office Action does not explain what results would be predictable or how the combination would address the specific problem solved by the claimed invention… These technical challenges are not addressed by the cited references, and the Office Action has not explained how a person of ordinary skill would have predictably overcome them by combining the references”. Examiner respectfully disagrees with Applicant’s argument. First, Applicant’s assertion that “Shah's system involves drivers who own their vehicles “or that “Tanuma's system involves a fleet of vehicles owned by the car sharing company where users are customers, not vehicle owners” is conclusory and not necessarily true. There is no basis for this assertion. Furthermore, Examiner disagrees that ride-sharing and car-sharing are very different from one another
Generally arguing that the prior art references involve “different contexts” or do not address the “problem solved by the claimed invention” is not persuasive. MPEP 2141 explicitly states that “In determining obviousness, neither the particular motivation to make the claimed invention nor the problem the inventor is solving controls”. In fact, the Supreme Court in KSR stressed that the Federal Circuit had errored by holding that Examiner’s should be looking only at the problem the patentee was trying to solve. Applicant’s repeated argument regarding the references “different contexts” or different “problems” is therefore not persuasive. These arguments appear to be loosely related to whether or not these references qualify as analogous art. So does Applicant’s argument that the Examiner’s rejection does not explain why a person of ordinary skill would have looked to Shah when designing Tanuma’s car-sharing maintenance system. However, Applicant has not explicitly argued that neither reference qualifies as analogous art. As such, the Examiner need not address this hypothetical argument. Examiner notes, however, that both prior art references are in the field of the inventor’s endeavor and/or are reasonably pertinent to the particular problem with which the inventor was concerned (managing vehicle operation/actions and/or motivating drivers to perform certain actions). Examiner further notes that the Examiner’s rejection need to “explain why a person of ordinary skill would have looked to Shah”. This is not a requirement for making a rejection using prior art. Applicant’s suggestion that the rejection does not explains what results would be predictable is not true or persuasive. The Examiner indicated that it would have been recognized that “applying the action value optimization technique of Shah to the method/system of Tanuma would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more optimal incentivization. In other words, Shah demonstrates that one skilled in the art at the effective filing date of the invention would have been able to train a plurality of different machine learning models to output action values given sets of inputs, that different models could be used for different contexts (e.g., users, vehicle requirements), and that one skilled in the art would have recognized that it would be advantageous to do so because then the incentivization would be optimized based on expense to the operator and probability of acceptance by the user.” Applicant’s argument is therefore not accurate or persuasive. Examiner further notes that presenting a message while the vehicle is at rest is merely one aspect of the claimed invention, and is not the sole “problem” being addressed by the claimed invention.
Applicant’s argument against combining Uenohara with Tanuma in view of Shah is similarity not persuasive. Applicant argues that “The Office Actions reasoning for combining…is that ‘doing so can ensure the safety of the driver and provide safeguards preventing the user's interaction/distraction with information while the vehicle is in motion." See Office Action, p. 26. However, this reasoning does not explain why a person of ordinary skill would have been motivated to integrate Uenohara's text message safety system into a car sharing maintenance platform.”. The Examiner is not arguing that it would have been obvious to integrate all of Uenohara's system with Tanuma’s system. The Examiner merely argues that it would have been obvious to modify the system of Tanuma to determine that the user is operating the vehicle at rest, and displaying the information on a display within the vehicle in response to determining that the user is operating the vehicle at rest. The Examiner’s reasoning does explain why a person of ordinary skill would have been motivated to do this. Specifically, the Examiner stated that a PHOSITA would have been motivation to do so because doing so can ensure the safety of the driver and provide safeguards preventing the user's interaction/distraction with information while the vehicle is in motion. Again, Uenohara need not be concerned specifically with displaying maintenance action messages in a car sharing context to qualify as prior art.
Applicant further argues that the “applied references do not teach the claimed databinding”. Examiner respectfully disagrees. Applicant’s suggestion that the claimed feature includes “where the framework automatically updates graphical representations when data with matching identifiers is received. The specification explains that "Components may include an identifier that allows them to be bound, styled, and unambiguously referenced by the user interface framework" and that "whenever data with the label 'destination-name' is received, the value of the text area depicting the destination name is always updated." Specification, para. 0086.” is incommensurate with what is actually claimed. The instant claims do not require that the framework automatically updates graphical representations when data with matching identifiers is received. The claims merely suggest that each data element has a data type and is associated with an identifier that enables automatic updating (of a graphical representation of a corresponding data element) upon receipt of update data. Suggesting an identifier enables something to happen is different from actually claiming that thing happening. The claimed high-level suggestion also hardly defines the claimed “user interface framework”, other than suggesting it generally comprises logically programming (“binding”) certain corresponding location of the interface (e.g., a button, an overlay, an element of the interface – i.e., a component of the interface corresponding to the particular information element) in a data structure to correspond to a particular information element (e.g., an information element indicative of a respective action), where each element has a data type and associated identifier. Applicant’s assertion that HTML scripting or combinations of Javascript and XML do not read on this is conclusory and not persuasive. Applicant’s own published disclosure at [0090] explains that the “framework” and “data binding” may comprise rendering information as “one or more an HTML output file” and “may be performed by calling a render function of the user interface framework which outputs complete or partial HTML/Javascript/CSS files corresponding to the mobile application”. Applicant is attempting to manufacture a difference between the “HTML/Javascript” framework of Applicant’s own specification and the “HTML scripting or combinations of Javascript and XML” of Shah based on the fact that Applicant’s specification describes details of how these “frameworks” operation. Not only does Shah disclose use HTML/Javascript framework, a PHOSITA would understand that HTML/Javascript implementations of the application GUI described as displaying certain information at certain locations and also that describes certain data elements of the application GUI as being dynamically updated in real involves programming language that involves data element tagging (i.e., logically programming (“binding”) certain corresponding locations of the interface (e.g., a button, an overlay, an element of the interface) to a particular information elements and where each element has a data type and associated identifier.
For example, Shah Fig 12 tags 110 & 1210 & 1214 & Figs 13E and 13F show a user interface with a current location element (tag 1314) and a current action value element (tag 1318) and various zone elements (tags 1310 and 1312) that are each “components” on the display that are dynamically updated in real time (6:49-60 “The accumulation interface portion includes an incremental bonus metric (i.e., accumulation metric) that increases as the provider approaches the target area and/or based on the time that the provider stays in each of the zones…as the provider moves, the mapping interface portions update…”). The current location element (tag 1314) and/or a current action value element (tag 1318) and or the target area zone to which the user should relocate are all “components indicative of the one or more respective actions” (e.g., because they relate to and/or are indicative of a relocation action). Shah further discloses that these component are binded to corresponding data element of a data structure that has a data type and is associated with an identifier (e.g., 47:25-60 “the transportation matching system 102 inputs the selected providers 912 and the offered incentives 914 to the interface generator 110. In some embodiments, the selected providers 912 include the identifier of each selected provider (e.g., provider_id), the current location of the provider, and an identifier of the target area to which the provider is being attracted (e.g., target_area_id)…. the interface generator 110 generate a separate map for each provider that shows the provider and 45 the target area. In some embodiments, the target area is the epicenter of the map 1208…interface generator 110 generates a single zone matching at least the boundaries of the target area identified from the 60selected providers 912.” & 48:54-62 “the interface generator 110 creates a zone by identifying the area identifier (e.g., area_id) that belongs in a zone. For example, the interface generator 110 creates an inner zone table that includes the identifier of the target area. In addition, the interface generator 110 creates an outer zone table that includes identifiers of areas surrounding the target zone. Then, using the tables, the interface generator 110 creates an overlay for the map 1208.” & 50:1-14 “can continually update a provider's customized interface…as the provider travels toward and lingers in the target area, the interface generator 110 updates the provider's current location on the map as well as increases the incremental accumulation metric (until the maximum incentive is reached)…”). As such, Shah discloses “binding” user interface components indicative of one or more respective actions (e.g., a target zone component corresponding to a relocation action) to corresponding data element (e.g., target zone data element) that has a data type and associated identifier (e.g., “target_area_id” data element) via a user interface “framework” (e.g., a programming language – such as HTML scripting or combinations of Javascript and XML) and that enables the system to dynamically update the graphical representation of the corresponding data element as data is updated.
Examiner further notes that Applicant appears to suggest that two different mobile application frameworks (e.g., React.js, Angular) were known at the effective filing date, and that these frameworks are not invented by Applicant. This appears to be an admission of prior art that discloses the claimed frameworks as well.
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.
v Claim(s) 1-7, 9-16, and 19-23 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claim(s) 1-7, 9, 10, 22, and 23 is/are drawn to methods (i.e., a process), while claim(s) 11-16 and 19-21 is/are drawn to systems (i.e., a machine/manufacture). As such, claims 1-7, 9-16, and 19-23 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 1 (representative of independent claim(s) 11 and 20) recites/describes the following steps;
analyzing… a first data set to determine one or more vehicle imperatives, each comprising one or more respective actions;
generating…a respective action value for each of the one or more respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the one or more respective actions using a respective one of a plurality of trained models according to a vehicle imperative type associated with each of the one or more respective actions, each model having been trained to generate respective action values for actions corresponding to respective vehicle imperative types, at least one of the plurality of models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type;
analyzing…a second data set to identify changes to the one or more determined vehicle imperatives indicating one or more completed actions;
releasing…based upon the analyzing of the second data set, the respective action value to the user for each of the one or more completed actions
determining that the user is operating the vehicle at rest; and in response to determining that the user is operating the vehicle at rest, displaying the one or more respective actions
These steps, under its broadest reasonable interpretation, describe or set-forth analyzing data associated with a vehicle to determine required task(s) (e.g., vehicle maintenance/service requirements) and using respective models to determine respective incentive(s) (i.e., “action values”) to offer a user to perform the required task(s) based on the type of task, analyzing subsequent data to determine whether or not the task(s) was/were completed, providing the user with the inventive(s) for each completed task, and displaying the one or more respective actions to the user in response to determining that the user is operating the vehicle at rest, which amounts to a commercial or legal interactions (specifically, an advertising, marketing or sales activity or behavior; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
Each of these steps, additionally and/or alternatively encompass a human manually (e.g., in their mind, or using paper and pen) analyzing data associated with a vehicle to determine required task(s) (e.g., maintenance/service requirements) and using respective models to determine respective incentive(s) (i.e., “action values”) to offer a user to perform the required task(s) based on the type of task, analyzing subsequent data to determine whether or not the task(s) was/were completed, providing the user with the inventive(s) for each completed task, and displaying the one or more respective actions to the user in response to determining that the user is operating the vehicle at rest (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the “mental processes” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Independent claim(s) 11 and 20 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
Each of the depending claims likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis. Any element(s) recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim. The same reasoning is similarly applicable to the limitations in the remaining dependent claims, and their respective limitations are not reproduced here for the sake of brevity.
Step 2A - Prong Two:
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The claim(s) recite the additional elements/limitations of
“computer-implemented…by one or more processors of a server remote from the vehicle…by the one or more processors of the server……by the one or more processors of the server…by the one or more processors of the server…by the one or more processors of the server” (claim 1)
“a system…including, a non-transitory computer-readable medium containing program instructions that when executed, cause a server remote from the vehicle to…” (claim 11)
“a computing system…comprising: one or more processors of a server remote from the vehicle configured to” (claim 20)
“on a display within the vehicle, wherein the displaying comprises binding, via a user interface framework, components indicative of the one or more respective actions to corresponding data elements of a data structure, wherein each data element has a data type and is associated with an identifier that enables automatic updating of a graphical representation of the corresponding data element upon receipt of updated data” (claims 1 and 20)
“machine learning models” (claims 1, 11, and 20)
“transmitting the one or more actions to a mobile computing device of the user via a push message” (claims 9 and 19)
“by the one or more processors” (claim 22)
“by the one or more processors of the server” (claim 23)
“updating… a structured query language (SQL) database to indicate…wherein the SQL database stores” (claim 22)
The requirement to execute the claimed steps/functions via a method that is “computer-implemented…by one or more processors of a server remote from the vehicle…by the one or more processors of the server……by the one or more processors of the server…by the one or more processors of the server…by the one or more processors of the server” (claim 1) or “a system…including, a non-transitory computer-readable medium containing program instructions that when executed, cause a server remote from the vehicle to…” (claim 11) or “a computing system…comprising: one or more processors of a server remote from the vehicle configured to” (claim 20) and/or “on a display within the vehicle” (claims 1, 11, and 20) and/or “by the one or more processors” (claim 22) and/or “by the one or more processors of the server” (claim 23) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s own disclosure explains that the above-identified components may be embodied as a general-purpose computer (e.g., paragraphs [0022] & [0032]-[0033] & [0052] & [0093] of the published specification). Examiner notes that the recitation of using “machine learning models” (claims 1, 11, and 20) provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The machine learning models are used to generally apply the abstract idea without placing any limits on how the machine learning models functions. Rather, these limitations only recite the outcome of “generating…a respective action value…by analyzing each of the respective actions” and do not include any details about how the “generating” is accomplished. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. Furthermore, the recited “on a display” (claims 1 and 20) are conventional computers or other machinery that are invoked merely as a tool to perform an existing process (i.e., display information) and that are being used in their ordinary capacity. In other words, the claims invoke the display merely as tools to execute the abstract idea. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The recited additional element(s) of “on a display within the vehicle, wherein the displaying comprises binding, via a user interface framework, components indicative of the one or more respective actions to corresponding data elements of a data structure, wherein each data element has a data type and is associated with an identifier that enables automatic updating of a graphical representation of the corresponding data element upon receipt of updated data” (claims 1 and 20) additionally/alternatively serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Applicant’s disclosure suggests in paragraph [0081] of the published disclosure that “Increasingly, software developers are using user interface frameworks to build applications that allow application data, structure, state, behavior, and styling to be independently designed and managed. React.js and Angular are two popular application platforms for creating mobile applications, for example.” More generally, Applicant’s claims refer broadly to a “user interface framework” and “binding” and “data type” and “identifiers”. Even apart from mobile application user interface frameworks (e.g., React.js, Angular), these high-level requirements could be interpreted as describing how web-pages function to load certain data elements, where html data elements/tags are used to retrieve/update and embed different information into certain scripted portions of a web-pages. The requirement of “wherein the displaying comprises binding, via a user interface framework, components indicative of the one or more respective actions to corresponding data elements of a data structure, wherein each data element has a data type and is associated with an identifier that enables automatic updating of a graphical representation of the corresponding data element upon receipt of updated data” does not amount to an inventive “solution” to which the claims are directed, and instead serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computing environments, such as the internet and or web-based interfaces (or, at most, an interface using a particular known and available mobile application platform). This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The recited additional element(s) of “transmitting the one or more actions to a mobile computing device of the user via a push message” (claims 9 and 19) serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computing environments, such as distributed computing environments and/or the internet, where information is represented digitally, exchanged between computers over a network (e.g., via a push message), and presented using graphical user interfaces. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The recited additional element(s) of “updating… a structured query language (SQL) database to indicate…wherein the SQL database stores” (claim 22) serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. The SQL is non-inventive (Applicant did not invent an SQL database), and Applicant’s disclosure suggests this is merely one of multiple types of databases that may be used. Applicant’s published disclosure at paragraph [0031] explicitly states that “the database 136 may be any suitable database” and mentions SQL as merely one exemplary type of database. Requiring use of an SQL databased in the claims does not mean the claimed invention is directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and/or a technology-based solution to a technology-based problem. The SQL database is recited at an “apply it” level, any advantages associated with an SQL database are purely resulting from use of this conventional type of database (i.e., it is non-inventive), and the requirement to use an SQL database serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computing environments where information is stored in an SQL database. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The recitation of using “machine learning models” (claims 1, 11, and 20) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “machine learning models” (claims 1, 11, and 20) limits the identified judicial exceptions “generating…using a respective one of a plurality of trained machine learning models” (claims 1, 11, and 20), this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
Furthermore, although the claims recite a specific sequence of computer-implemented functions, and although the specification suggests certain functions may be advantageous for various reasons (e.g., business reasons), the Examiner has determined that the ordered combination of claim elements (i.e., the claims as a whole) are not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. For example, Applicant’s as-filed specification suggests that it is advantageous for advertisers/business to perform the abstract idea identified above because it can help ensure a fleet of vehicles are properly maintained by incentivizing customers/users to facilitate vehicle servicing without requiring dedicated staff/technicians to facilitate the vehicle servicing (subjective business purpose) (see, for example, paragraphs [0006]-[0008] & [0066] & [0079]-[0080] of Applicant’s published disclosure). These are non-technical business advantages/improvements. At most, the ordered combination of claim elements is directed to a non-technical improvement to an abstract idea itself (e.g., a method of incentivizing vehicle maintenance).
Dependent claims 2-7, 10, 12-17, and 21 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 2-7, 10, 12-17, and 21 is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). For example, claim 2 recites “wherein the first data set is a vehicle telematics data set”. This merely describes the type of data being analyzed. This is an abstract limitation which further sets forth the abstract idea encompassed by claim 2. This limitation is not an “additional element”, and therefore it is not subject to further analysis under Step 2A- Prong Two or Step 2B. The same logic applies to each of the other dependent claims, whose limitations are not being repeated here for the sake of brevity and clarity.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B:
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966)
As discussed above in “Step 2A – Prong 2”, the requirement to execute the claimed steps/functions via a method that is “computer-implemented…by one or more processors of a server remote from the vehicle…by the one or more processors of the server……by the one or more processors of the server…by the one or more processors of the server…by the one or more processors of the server” (claim 1) or “a system…including, a non-transitory computer-readable medium containing program instructions that when executed, cause a server remote from the vehicle to…” (claim 11) or “a computing system…comprising: one or more processors of a server remote from the vehicle configured to” (claim 20) and/or using “machine learning models” (claims 1, 11, and 20) and/or “by the one or more processors” (claim 22) and/or “on a display” (claims 1 and 20) or “by the one or more processors of the server” (claim 23) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recited additional element(s) of “transmitting the one or more actions to a mobile computing device of the user via a push message” (claims 9 and 19) and/or using “machine learning models” (claims 1, 11, and 20) and/or “on a display, wherein the displaying comprises binding, via a user interface framework, components indicative of the one or more respective actions to corresponding data elements of a data structure, wherein each data element has a data type and is associated with an identifier that enables automatic updating of a graphical representation of the corresponding data element upon receipt of updated data” (claims 1 and 20) and/or “updating… a structured query language (SQL) database to indicate…wherein the SQL database stores” (claim 22) serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g)).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer, and generally link the abstract idea to a particular technological environment or field of use.
Dependent claims 2-7, 10, 12-17, and 21 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 2-7, 10, 12-17, and 21 is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea identified by the Examiner to which each respective claim is directed).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
v Claims 1-7, 9, 11-16, 19-21, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Tanuma et al. (U.S. PG Pub No. 2020/0126320 April 23, 2020 - hereinafter "Tanuma”) in view of in view of Shah et al. (U.S. Patent No. 10,552,773 February 4, 2020 - hereinafter "Shah”) in view of in view of Uenohara et al. (U.S. PG Pub No. 2020/0174261 June 4, 2020 - hereinafter "Uenohara”)
With respect to claim 1, 11, and 20, Tanuma teaches a computer-implemented method of causing a user of a car sharing platform to perform service or relocation actions with respect to a vehicle; a non-transitory computer readable medium containing program instructions that when executed, cause a computer system to perform the method; and a computing system for causing a user of a car sharing platform to perform service or relocation actions with respect to a vehicle, comprising:
one or more processors of a server remote from a vehicle (Fig 1 tag 120 & [0044]] & [0065] & [0078] & [0114]-[0115])
and one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to (claim 20) ([0044] & [0065] & [0078] & [0114]-[0115])
analyzing, by one or more processors of the server, a first data set to determine one or more vehicle imperatives, each comprising one or more respective actions; ([0029]-[0031] “sensor data of a vehicle is monitored…detected whether the work related to maintenance (such as confirmation of current condition, vehicle washing, clearing of vehicle interior, check, inspection, maintenance, and repair) is required…is selectively specified out of the work…confirmation work on wearing out of a tire…cleaning of a vehicle, and supply of gasoline” – therefore the system analyzes a first data set (e.g., vehicle sensor data) to determine one or more vehicle imperatives (e.g., car interior/exterior needs washing, needs an inspection, needs maintenance, needs gas), each comprising one or more respective actions (e.g., wash car interior/exterior, fill up with gas, take to get inspection, take to mechanic to get maintenance/repair, etc.), [0038]-[0039] “monitors or accumulates data such as sensor data of a vehicle…and maintenance history data up to now…arithmetic and threshold value processing…detects whether work…is required and specifies the word that requires maintenance…identifies selectively, out of the work requiring maintenance to the vehicle as the management object, general work…data collection work…work to measure data…arranging for a user to perform the selectively specified work”, [0042] “collected data of the vehicle…determines the necessity for the work related to the maintenance”, see also [0086]-[0087] “detection based on image processing technology and machine learning…determines whether the work requiring the maintenance is detected…”, [0052]-[0053] & [0062]-[0065] & [0069]-[0070] & [0084]-[0087] – analyzes data set to determine plurality of maintenance requirements which require respective actions (e.g., associated task(s) that need to be completed to address the requirement) and each of the actions corresponding to a respective action value (e.g., associated reward value for completing the action))
generating, by the one or more processors of the server, a respective action value for each of the one or more respective actions corresponding to the determined one or more vehicle imperatives (Fig 4 tag 404 shows respect reward values (i.e., “action values”) generated for each of the respective actions corresponding to the determined one or more vehicle imperatives (e.g., fill with gas, check tire wear, drive the vehicle to the maintenance shop), [0065] “reward that a user obtains when performing the work…value of the reward includes money to receive and a point allotted to the fee of a vehicle”, [0065] reward mapping table, [0099] “reward of the multiple records extracted…”)
analyzing, by the one or more processors of the server, a second data set to identify changes to the one or more determined vehicle imperatives indicating one or more completed actions; and ([0042] “receives a notice that the work has been performed from the client terminal and/or the external system”, [0048] “processing to transmit the information on completion to the vehicle management system…collect the data using sensors, such as a camera, and to transmit the data to the vehicle management system”, [0055] “completion processing unit receives the information …sending file…indicative of the completion of the work…” and per [0068] “the sending file is data acquired from sensors, such as a camera…photograph of a tire shot…and measurement data measured by a sensor of a vehicle” – therefore the system analyzes second data (e.g., second sensor/user data) to identify changes to determine indication of the completion of the work, [0110]-[0115] & [0068])
releasing, by the one or more processors of the server and based upon the analyzing of the second data set, the respective action value to the user for each of the one or more completed actions ([0076] “payment procedure of a reward in return for the work by the user of a vehicle, directed for the reward provision system”, [0111]-[0115] – “notifying…that the work has been performed…that the maintenance to the vehicle has been performed…transmits reward instructions to the client terminal of the user who performed the work…”)
displaying, on a display within the vehicle, the one or more respective actions on a display, ((Fig 1 tag 160 and [0042] “presents…the work to the client terminal” and [0047]-[0048] “work support software…for the client terminal…”, [0049] “the in-vehicle terminal 170 may have the same configuration as the client terminal 160 (for example, a car-navigation system may perform the function of the client terminal 160)”. [0054] “outputs the information…that indicates the work…at which the user performs selection whether to perform the work” - displaying the one or more actions to the user via their mobile device, [0101] in vehicle terminal display)
components indicative of the one or more respective actions (Figs 4 -9 , Fig 11 tags 1101-1104 - Tanuma clearly discloses wherein a user interface is updated to display one or more respective actions, and wherein different data elements (e.g., actions) are different data types and are associated with identifiers that enable storage/retrieval of specific types of data for presentation on the user GUI)
Tanuma does not appear to disclose that the size of the reward values (i.e., “action values”) are determined/optimized using trained machine learning models that learned from historical acceptance rates (e.g., in order to identify optimal reward values to encourage a user to complete actions corresponding to the determined imperative). Tanuma does not appear to disclose,
generating, by the one or more processors, a respective action value for each of the one or more respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the respective actions using a respective one of a plurality of trained machine learning models according to a vehicle imperative type associated with each of the one or more respective actions, each model having been trained to generate respective action values for actions corresponding to respective vehicle imperative types,
at least one of the plurality of machine learning models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type;
determining, by the one or more processors, that the user is operating the vehicle at rest; (claims 1 and 20)
in response to determining that the user is operating the vehicle at rest, displaying…
wherein the displaying comprises binding, via a user interface framework,…to corresponding data elements of a data structure, wherein each data element has a data type and is associated with an identifier that enables automatic updating of a graphical representation of the corresponding data element upon receipt of updated data
However, Shah discloses
generating, by the one or more processors, a respective action value for each of the one or more respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the respective actions using a respective one of a plurality of trained machine learning models according to a vehicle imperative type associated with each of the one or more respective actions, each model having been trained to generate respective action values for actions corresponding to respective vehicle imperative types (37:1-35 & 38:1-34 & 39:30-44 & 40:1-21 system trains a plurality of models (may be neural network models per 11:5-23) that output respective incentive values (i.e., “action values”) for actions corresponding to respective vehicle imperative type associated with each of the one or more respective actions (e.g., a particular user relocating the vehicle to a certain zone/location for density balance, each model personalized for each user/driver based on their history of acceptance/rejection and incentive value and the task to be performed) and uses one or more respective models to generate respective action value for each of the one or more actions (e.g., an action of relocating a vehicle to a certain zone/location)
wherein the displaying comprises binding, via a user interface framework,…to corresponding data elements of a data structure, wherein each data element has a data type and is associated with an identifier that enables automatic updating of a graphical representation of the corresponding data element upon receipt of updated data (Shah suggests that the user interface presented to the users using data retrieved from the server may comprise a web interface such as a web browser/page or mobile application interface (see 1:9-11 & 8:58-64 & 9:15-39). Shah also discloses dynamically/automatically iteratively updating this interface with data from the database (see 8:106 & 18:1-11, 50:3-24, 52:21-29), and that the GUI (e.g., webpage or web application) utilizes an interface framework such as HTML scripting or XML file execution/loading or JavaScript and/or combinations of JavaScript and XML (see 61:52-67 & 62:1-14) , all of which constitute a user interface framework that “binds” data components to corresponding data elements of a data structure (e.g., via element/component tagging/types) to enable automatic updating of information on the GUI and loading of certain data types/elements to respective locations on the GUI per the layout instructions/scripting. Applicant’s own published disclosure at [0090] also explains that the “framework” and “data binding” may comprise rendering information using HTML/Javascript/CCS files – also see Shah Fig 12 tags 110 & 1210 & 1214 & Figs 13E and 13F show a user interface with a current location element (tag 1314) and a current action value element (tag 1318) and various zone elements (tags 1310 and 1312) that are each “components” on the display that are dynamically updated in real time (6:49-60 “The accumulation interface portion includes an incremental bonus metric (i.e., accumulation metric) that increases as the provider approaches the target area and/or based on the time that the provider stays in each of the zones…as the provider moves, the mapping interface portions update…”). The current location element (tag 1314) and/or a current action value element (tag 1318) and or the target area zone to which the user should relocate are all “components indicative of the one or more respective actions” (e.g., because they relate to and/or are indicative of a relocation action). Shah further discloses that these component are binded to corresponding data element of a data structure that has a data type and is associated with an identifier (e.g., 47:25-60 “the transportation matching system 102 inputs the selected providers 912 and the offered incentives 914 to the interface generator 110. In some embodiments, the selected providers 912 include the identifier of each selected provider (e.g., provider_id), the current location of the provider, and an identifier of the target area to which the provider is being attracted (e.g., target_area_id)…. the interface generator 110 generate a separate map for each provider that shows the provider and 45 the target area. In some embodiments, the target area is the epicenter of the map 1208…interface generator 110 generates a single zone matching at least the boundaries of the target area identified from the 60selected providers 912.” & 48:54-62 “the interface generator 110 creates a zone by identifying the area identifier (e.g., area_id) that belongs in a zone. For example, the interface generator 110 creates an inner zone table that includes the identifier of the target area. In addition, the interface generator 110 creates an outer zone table that includes identifiers of areas surrounding the target zone. Then, using the tables, the interface generator 110 creates an overlay for the map 1208.” & 50:1-14 “can continually update a provider's customized interface…as the provider travels toward and lingers in the target area, the interface generator 110 updates the provider's current location on the map as well as increases the incremental accumulation metric (until the maximum incentive is reached)…”). As such, Shah discloses “binding” user interface components indicative of one or more respective actions (e.g., a target zone component corresponding to a relocation action) to corresponding data element (e.g., target zone data element) that has a data type and associated identifier (e.g., “target_area_id” data element) via a user interface “framework” (e.g., a programming language – such as HTML scripting or combinations of Javascript and XML) and that enables the system to dynamically update the graphical representation of the corresponding data element as data is updated.)
Examiner further notes that Applicant appears to suggest that two different mobile application frameworks (e.g., React.js, Angular) were known at the effective filing date, and that these frameworks are not invented by Applicant. This appears to be an admission of prior art that discloses the claimed frameworks as well.
Shah suggests it is advantageous to include generating, by the one or more processors, a respective action value for each of the one or more respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the respective actions using a respective one of a plurality of trained machine learning models according to a vehicle imperative type associated with each of the one or more respective actions, each model having been trained to generate respective action values for actions corresponding to respective vehicle imperative types, and wherein the displaying comprises binding, via a user interface framework,…to corresponding data elements of a data structure, wherein each data element has a data type and is associated with an identifier that enables automatic updating of a graphical representation of the corresponding data element upon receipt of updated data, because doing so can enable the system to optimally generate the respective action values in an economically efficient way based on the personal preferences of each person, and because doing so can enable efficient and automated generation and updating of information on a user’s GUI (40:1-21 & 8:106 & 18:1-11, 50:3-24, 52:21-29 & 61:52-67 & 62:1-14 ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, medium, and system of Tanuma to include generating, by the one or more processors, a respective action value for each of the one or more respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the respective actions using a respective one of a plurality of trained machine learning models according to a vehicle imperative type associated with each of the one or more respective actions, each model having been trained to generate respective action values for actions corresponding to respective vehicle imperative types and wherein the displaying comprises binding, via a user interface framework,…to corresponding data elements of a data structure, wherein each data element has a data type and is associated with an identifier that enables automatic updating of a graphical representation of the corresponding data element upon receipt of updated data, as taught by Shah, because doing so can enable the system to optimally generate the respective action values in an economically efficient way based on the personal preferences of each person, and because doing so can enable efficient and automated generation and updating of information on a user’s GUI. Furthermore, one of ordinary skill in the art would have recognized that applying the known technique of Shah to Tanuma would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Shah to the teaching of Tanuma would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to train a plurality of machine learning models such that each model is used to generate incentive/reward values (i.e., “action values”) for a specific context (e.g., specific user, specific task) such that the reward/incentive values are dynamic and optimized for a particular context (i.e., “generating, by the one or more processors, a respective action value for each of the respective actions corresponding to the determined one or more vehicle imperatives by analyzing each of the one or more respective actions using a respective one of a plurality of trained machine learning models according to a vehicle imperative type associated with each of the one or more respective actions, each model having been trained to generate respective action values for actions corresponding to respective vehicle imperative types”). Further, applying the action value optimization technique of Shah to the method/system of Tanuma would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more optimal incentivization. In other words, Shah demonstrates that one skilled in the art at the effective filing date of the invention would have been able to train a plurality of different machine learning models to output action values given sets of inputs, that different models could be used for different contexts (e.g., users, vehicle requirements), and that one skilled in the art would have recognized that it would be advantageous to do so because then the incentivization would be optimized based on expense to the operator and probability of acceptance by the user.
Tanuma and Shah do not appear to disclose,
at least one of the plurality of machine learning models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type;
determining, by the one or more processors, that the user is operating the vehicle at rest; (claims 1 and 20)
in response to determining that the user is operating the vehicle at rest, displaying on a display
However, Uenohara discloses
determining, by the one or more processors, that the user is operating the vehicle at rest; (claims 1 and 20) ([0019] “Vehicle safety state determining software 116 enables vehicle 102 to determine the safety state of vehicle 102 to determine whether to display a received communication for one or more occupants of vehicle…vehicle safety state determining software 116 can determine the current vehicle safety state (e.g., if vehicle 102 is moving, decelerating in response a yellow traffic signal at an intersection, or if vehicle 102 is currently stopped) . If the safety state of vehicle 102 is determined to be safe, vehicle safety state determining software 116 can then transmit the received text to content display managing software 118 to be displayed to the driver of vehicle 102…displays received content to an occupant of vehicle 102, based on the current safety state”, [0029] “where vehicle safety state determining software 116 calculates a high safety state (e.g., vehicle 102 is stopped at a red traffic signal), the entire body of the text message can be displayed to driver 306…sensor 304 can track the speed of vehicle 102, determine if the brake/accelerator pedal is being pressed, if the traction control system is enabled, if the anti-lock brake system (ABS) is activated, and/or if the cruise control system is activated. In general, sensor 304 can read and interpret information from any sensor contained in vehicle 102.”, [0046] “Upon determining vehicle 102 has stopped and determining a new safety degree by vehicle safety state determining unit 308, content display managing unit 310 can then display text message 418”, see also [0015] )
in response to determining that the user is operating the vehicle at rest, displaying on a display in the vehicle ([0019] “Vehicle safety state determining software 116 enables vehicle 102 to determine the safety state of vehicle 102 to determine whether to display a received communication for one or more occupants of vehicle…vehicle safety state determining software 116 can determine the current vehicle safety state (e.g., if vehicle 102 is moving, decelerating in response a yellow traffic signal at an intersection, or if vehicle 102 is currently stopped) . If the safety state of vehicle 102 is determined to be safe, vehicle safety state determining software 116 can then transmit the received text to content display managing software 118 to be displayed to the driver of vehicle 102…displays received content to an occupant of vehicle 102, based on the current safety state”, [0029] “where vehicle safety state determining software 116 calculates a high safety state (e.g., vehicle 102 is stopped at a red traffic signal), the entire body of the text message can be displayed to driver 306…sensor 304 can track the speed of vehicle 102, determine if the brake/accelerator pedal is being pressed, if the traction control system is enabled, if the anti-lock brake system (ABS) is activated, and/or if the cruise control system is activated. In general, sensor 304 can read and interpret information from any sensor contained in vehicle 102.”, [0046] “Upon determining vehicle 102 has stopped and determining a new safety degree by vehicle safety state determining unit 308, content display managing unit 310 can then display text message 418”, see also [0015] )
Uenohara suggests it is advantageous to include determining, by the one or more processors, that the user is operating the vehicle at rest; (claims 1 and 20) and in response to determining that the user is operating the vehicle at rest, displaying on a display within the vehicle, because doing so can ensure the safety of the driver and provide safeguards preventing the user’s interaction/distraction with information while the vehicle is in motion ([0015] & [0019] & [0029] & [0046]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, medium, and system of Tanuma in view of Shah to include determining, by the one or more processors, that the user is operating the vehicle at rest; (claims 1 and 20) and in response to determining that the user is operating the vehicle at rest, displaying on a display, as taught by Uenohara, because doing so can ensure the safety of the driver and provide safeguards preventing the user’s interaction/distraction with information while the vehicle is in motion. Furthermore, one of ordinary skill in the art would have recognized that applying the known technique of Uenohara to Tanuma in view of Shah would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Uenohara to the teaching of Tanuma in view of Shah would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to determine, by the one or more processors, that the user is operating the vehicle at rest; (claims 1 and 20) and in response to determining that the user is operating the vehicle at rest, displaying on a display. Further, applying this technique to the method/system of Tanuma in view of Shah would have been recognized by one of ordinary skill in the art as resulting in an improved system that would ensure the safety of the driver and provide safeguards preventing the user’s interaction/distraction with information while the vehicle is in motion. In other words, Shah demonstrates that one skilled in the art at the effective filing date of the invention would have been able to include determining, by the one or more processors, that the user is operating the vehicle at rest; (claims 1 and 20) and in response to determining that the user is operating the vehicle at rest, displaying on a display, and that one skilled in the art would have recognized that it would be advantageous to do so because doing so can ensure the safety of the driver and provide safeguards preventing the user’s interaction/distraction with information while the vehicle is in motion.
Tanuma, Shah, and Uenohara do not appear to disclose,
at least one of the plurality of machine learning models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type
However, this limitation is given no patentable weight by the Examiner. This language is descriptive of something that is external to the scope of the claimed invention. For example, the claims merely require determining one vehicle imperative comprising one action (see “analyzing… first data set to determine one or more vehicle imperatives, each comprising one or more respective actions”). This vehicle imperative may be a relocation service imperative (e.g., per claim 5 and paragraph [0050]-[0053] of Applicant’s published disclosure), exactly what is taught by Shah (Examiner notes Tanuma also teaches a broadest reasonable interpretation of “relocation vehicle imperative type, as discussed below). As such, the claims only require generating a single action value for the single action corresponding to the determine vehicle imperative using a single trained model according to the vehicle imperative type. Shah discloses this, as discussed above (Examiner notes Shah actually discloses a plurality of trained models, each trained for a respective imperative type -relocation- and user combination). As such, the suggestion that there exists at least one of the model trained to generate action values for actions corresponding to a non-relocation vehicle imperative type is irrelevant, as this model is not required to be used. Furthermore, the steps being performed would be performed in the same way regardless of whether or not there exists (somewhere) at least one model trained to generate action values for actions corresponding to a non-relocation vehicle imperative type. Examiner notes that the claimed system and medium are not required to even store the trained models, as the claims merely require using a model, which may be stored somewhere else. Therefore, the phrase “using a respective one of a plurality of trained machine learning models…at least one of the plurality of machine learning models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type” is given no patentable weight, and cannot distinguish the claimed invent over the prior art.
For the sake of expediting prosecution, Examiner notes that one of ordinary skill in the art would have recognized that applying the known technique of Shah to Tanuma would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Shah to the teaching of Tanuma would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to train and use a plurality of machine learning models such that each model is used to generate incentive/reward values (i.e., “action values”) for a specific context (e.g., specific user/task combination) such that the reward/incentive values are dynamic and optimized for a particular context. Further, applying the action value optimization technique of Shah to the method/system of Tanuma would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more optimal incentivization (e.g., optimally determined reward values) for a specific context. In other words, Shah demonstrates that one skilled in the art at the effective filing date of the invention would have been able to train a plurality of different machine learning models to output action values given sets of inputs, that different models could be used for different contexts (e.g., users, type of vehicle imperative/action), and that one skilled in the art would have recognized that it would be advantageous to do so because the action values would be optimized based on expense to the operator and probability of acceptance by the user. Application of this technique to the method and system of Tanuma, where there a plurality of different vehicle imperative/action types (at least one of which is a non-relocation vehicle requirement, such as clearing a vehicle interior or washing the vehicle or measure tire wear), would result in training a plurality of models by analyzing historical data to generate respective action values corresponding to respective vehicle imperative/action type, and at least one of the plurality of models would be trained to generate action values for non-relocation vehicle requirement types. As such, it would have been obvious to a PHOSITA to modify Tanuma, in view of Shah, to arrive at what is claimed (i.e., “at least one of the plurality of machine learning models trained to generate action values for actions corresponding to a non-relocation vehicle imperative type”) using this known technique to yield predictable results and an improved system.
Furthermore, Examiner notes that prior art reference Bankreira (cited at the end of this action and not relied on) discloses training and using respective machine learning models (e.g., neural network models) that output respective incentive values for respective tasks types ([0084] & [0091] & [0051]). A PHOSITA would understand that training a single model to output incentives for tasks or training a plurality of task-specific models to output incentives for tasks is within the capabilities of a PHOSITA at the effective filing date, and that the costs and benefits of training/using a single model or plurality of task-specific models was known and is merely a design choice.
With respect to claims 2 and 12, Tanuma teaches the method of claim 1 and the non-transitory medium of claim 11;
wherein the first data set is a vehicle telematics data set (([0029]-[0031] “sensor data of a vehicle is monitored…detected whether the work related to maintenance (such as confirmation of current condition, vehicle washing, clearing of vehicle interior, check, inspection, maintenance, and repair) is required”, [0038] “monitors or accumulates data such as sensor data of a vehicle…”, [0068] “the sending file is data acquired from sensors, such as… measurement data measured by a sensor of a vehicle” see also [0042] & [0062])
With respect to claims 3 and 13, Tanuma teaches the method of claim 1 and the non-transitory medium of claim 11;
wherein the first data set is a user data set ([0030] “confirmation work on wearing out of a tire, confirmation work on an interior/exterior system, confirmation work of the part where abnormalities have been detected” – user confirmation/input data is a “user data set” that is used to determine one or more vehicle imperatives (e.g., car interior/exterior needs washing, needs maintenance, needs gas, etc.), [0038] “monitors or accumulates data such as… maintenance history data up to now”, [0048] “…collect the data using sensors, such as a camera, and to transmit the data to the vehicle management system”, [0068] “the sending file is data acquired from…a camera mounted in the client terminal, at the time of the work by a user (for example, a photograph of a tire shot in order to check the degree of wear of the tire…”, see also [0085]-[0087] & [0029]-[0030] & [0042] & [0062])
With respect to claims 4 and 14, Tanuma teaches the method of claim 1 and the non-transitory medium of claim 11;
wherein analyzing the first data set to determine one or more vehicle imperatives includes determining a vehicle service imperative (([0029]-[0031] “detected whether the work related to maintenance (such as confirmation of current condition, vehicle washing, clearing of vehicle interior, check, inspection, maintenance, and repair) is required…is selectively specified out of the work…confirmation work on wearing out of a tire…cleaning of a vehicle, and supply of gasoline” – these are vehicle service imperatives each comprising one or more respective actions (e.g., wash car interior/exterior, fill up with gas, take to get inspection, take to mechanic to get maintenance/repair, etc.), [0038]-[0039] “monitors or accumulates data such as sensor data of a vehicle…and maintenance history data up to now…arithmetic and threshold value processing…detects whether work…is required and specifies the word that requires maintenance…identifies selectively, out of the work requiring maintenance to the vehicle as the management object, general work…data collection work…work to measure data…arranging for a user to perform the selectively specified work”, see also [0052]-[0053] & [0062]-[0065] & [0069]-[0070] & [0084]-[0087])
With respect to claims 5 and 15, Tanuma teaches the method of claim 1 and the non-transitory medium of claim 11;
wherein analyzing the first data set to determine one or more vehicle imperatives includes determining a relocation service imperative ([0065] “transportation for a periodic check” & [0112] relocation to a maintenance shop is a relocation service requirement)
Examiner notes that Shah also discloses relocation service requirements.
With respect to claims 6 and 16, Tanuma teaches the method of claim 1 and the non-transitory medium of claim 11;
further comprising: applying a respective set of rules to each of the one or more determined vehicle imperatives ([0065] “work ID stores a character string that specifies the work contents…outline of the work contents described in natural language…value of the work contents” – a rule table us used to map the vehicle imperatives determined from the sensor data from [0062]-[0064] to get the respective actions displayed to the user, see also [0085]-[0094] respective sets of processing rules/logic & [0038] & [0052]-[0054] & [0069]-[0070] )
With respect to claim 7, Tanuma, Shah, and Uenohara teach the method of claim 1. Although Tanuma discloses receiving action acceptances of the user ([0101]-[0102] at least), Tanuma does not appear to disclose,
analyzing the historical action acceptances of the user
However, Shah discloses
analyzing the historical action acceptances of the user (37:1-35 & 38:1-34 & 39:30-44 & 40:1-21 system trains a plurality of models (may be neural network models per 11:5-23) that determine personalized reward values for each user/driver based on their history of acceptance/rejection and incentive value and the task to be performed )
Shah suggests it is advantageous to include analyzing the historical action acceptances of the user, because doing so can enable the system to optimally generate the respective action values in an economically efficient way based on the personal preferences of each person (40:1-21).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and medium of Tanuma to include analyzing the historical action acceptances of the user, as taught by Shah, because doing so can enable the system to optimally generate the respective action values in an economically efficient way based on the personal preferences of each person. Furthermore, one of ordinary skill in the art would have recognized that applying the known technique of Shah to Tanuma would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Shah to the teaching of Tanuma would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to train a plurality of machine learning models by analyzing the historical action acceptances of the user such that each model is used to generate incentive/reward values (i.e., “action values”) for a specific context (e.g., specific user, specific task) such that the reward/incentive values are dynamic and optimized for a particular context. Further, applying the action value optimization technique of Shah to the method/system of Tanuma would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more optimal incentivization. In other words, Shah demonstrates that one skilled in the art at the effective filing date of the invention would have been able to train a plurality of different machine learning models by analyzing the historical action acceptances of the user to output action values given sets of inputs, that different models could be used for different contexts (e.g., users, vehicle requirements), and that one skilled in the art would have recognized that it would be advantageous to do so because then the incentivization would be optimized based on expense to the operator and probability of acceptance by the user. Application of this technique to the method and system of Tanuma, where there a plurality of different vehicle requirement types (at least one of which is a non-relocation vehicle requirement, such as clearing a vehicle interior or washing the vehicle or measure tire wear), would result in training a plurality of models by analyzing historical data to generate respective action values corresponding to respective vehicle requirement types, at least one of the plurality of models trained to generate action values for non-relocation vehicle requirement types. As such, it would have been obvious to a PHOSITA to modify Tanuma, in view of Shah, to arrive at what is claimed.
With respect to claims 9 and 19, Tanuma teaches the method of claim 1 and the non-transitory medium of claim 11
further comprising: transmitting the one or more actions to a mobile computing device of the user (Fig 1 tag 160 and [0042] “presents…the work to the client terminal” and [0047]-[0048] “work support software…for the client terminal…”, [0054] “outputs the information…provides the client terminal with the user interface…”)
Tanuma does not appear to disclose,
transmitting to a mobile computing device of the user via a push message
However, Shah discloses
transmitting to a mobile computing device of the user via a push message (63:50-67 “information may be pushed to a client device as notifications”, see also Fig 2 tag 210 & Fig 13C tag 1316 and 1318 &)
Shah suggests it is advantageous to include transmitting to a mobile computing device of the user via a push message, because doing so can provide an effective mechanism to notify the user about the actions and action values(Fig 2 tag 210 & Fig 13C tag 1316 and 1318 & 63:50-67).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and medium of Tanuma to include transmitting to a mobile computing device of the user via a push message, as taught by Shah, because doing so can provide an effective mechanism to notify the user about the actions.
Examiner notes that Grigg (cited at the end of this action) discloses push notifications of task/reward as well at [0010] & [0017])
With respect to claim 21, Tanuma teaches the system of claim 20
wherein the one or more respective include an oil change action, a wiper fluid addition action, or a car wash action ([0029]-[0030] “work related to maintenance…vehicle washing, cleaning of vehicle interior…cleaning of a vehicle” therefore the one or more respective include at least a car wash action”
With respect to claim 23, Tanuma teaches the system of claim 20
wherein releasing the respective action value includes: automatically verifying, by the one or more processors of the server, completion of each respective action by analyzing vehicle telematics data received from one or more vehicle sensors to determine the action is completed, and ([0048] “processing to transmit the information on completion to the vehicle management system…collect the data using sensors, such as a camera, and to transmit the data to the vehicle management system”, [0055] “completion processing unit receives the information …sending file…indicative of the completion of the work…” and per [0068] “the sending file is data acquired from sensors, such as a camera…photograph of a tire shot…and measurement data measured by a sensor of a vehicle” – therefore the system analyzes second data (e.g., second sensor/user data) to identify changes to determine indication of the completion of the work, [0110]-[0115] & [0068])
releasing the respective action value only in response to the action is completed ([0076] “payment procedure of a reward in return for the work by the user of a vehicle, directed for the reward provision system”, [0111]-[0115] – “notifying…that the work has been performed…that the maintenance to the vehicle has been performed…transmits reward instructions to the client terminal of the user who performed the work…”)
Tanuma does not appear to disclose wherein the action is a relocation action. Tanuma does not appear to disclose,
analyzing vehicle telematics data received from one or more vehicle sensors to determine a location of the vehicle, and
releasing the respective action value only in response to the vehicle being relocated to predetermined location
However, Shah discloses
analyzing vehicle telematics data received from one or more vehicle sensors to determine a location of the vehicle, and (Fig 13F & Fig 14 tag 1430 & 64:38-46 “the vehicle subsystem 1708 can include an on-board computing device communicatively linked to the network 1704 to transmit and receive data such as GPS location intonation, sensor-related information..”, 7:1-14 & 52:9-21 & 55:22-31 -determining relocated, 7:1-14 & 12:1-19 paying upon completion)
releasing the respective action value only in response to the vehicle being relocated to predetermined location (7:1-14 & 12:1-19 paying upon completion)
Shah suggests it is advantageous to include analyzing vehicle telematics data received from one or more vehicle sensors to determine a location of the vehicle, and releasing the respective action value only in response to the vehicle being relocated to predetermined location, because vehicle relocation imperatives may be helpful because use of the car may be more likely to be required in certain locations and therefore the system could/should incentivize relocation (7:1-14 & 52:9-21 & 55:22-31 & 12:1-19).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, medium, and system of Tanuma to include analyzing vehicle telematics data received from one or more vehicle sensors to determine a location of the vehicle, and releasing the respective action value only in response to the vehicle being relocated to predetermined location, as taught by Shah, because vehicle relocation imperatives may be helpful because use of the car may be more likely to be required in certain locations and therefore the system could/should incentivize relocation. Furthermore, one of ordinary skill in the art would have recognized that applying the known technique of Shah to Tanuma would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Shah to the teaching of Tanuma would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incentivize and use relocation imperatives (i.e., analyzing vehicle telematics data received from one or more vehicle sensors to determine a location of the vehicle, and releasing the respective action value only in response to the vehicle being relocated to predetermined location). Further, applying use of these imperative types of Shah to the method/system of Tanuma would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow for more types of useful imperatives. In other words, Shah demonstrates that one skilled in the art at the effective filing date of the invention would have been able to incentivize and use relocation imperatives (i.e., analyzing vehicle telematics data received from one or more vehicle sensors to determine a location of the vehicle, and releasing the respective action value only in response to the vehicle being relocated to predetermined location), and that one skilled in the art would have recognized that it would be advantageous to do so because vehicle relocation imperatives may be helpful because use of the car may be more likely to be required in certain locations and therefore the system could/should incentivize relocation.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Tanuma in view of Shah in view of Uenohara, as applied to claim 1 above, and further in view of Farrelly et al. (U.S. PG Pub No. 2015/0310379, October 29, 2015 - hereinafter "Farrelly”)
With respect to claim 10, Tanuma, Shah, and Uenohara teach the method of claim 1. Tanuma does not appear to disclose,
wherein the user is a first user and further comprising: receiving the second data set from the vehicle when the vehicle is being operated by a second user
However, Farrelly discloses
wherein the user is a first user and further comprising: receiving the second data set from the vehicle when the vehicle is being operated by a second user ([0013] “second user may be queried about the status of the vehicle…based on the second user’s input…”, see also [0043])
Farrelly suggests it is advantageous to include wherein the user is a first user and further comprising: receiving the second data set from the vehicle when the vehicle is being operated by a second user, because doing so can provide an effective mechanism to ensure the action was in fact completed by the first user ([0013] & [0043]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and medium of Tanuma in view of Shah in view of Uenohara to include wherein the user is a first user and further comprising: receiving the second data set from the vehicle when the vehicle is being operated by a second user, as taught by Farrelly, because doing so can provide an effective mechanism to ensure the action was in fact completed by the first user.
Claims 22 is rejected under 35 U.S.C. 103 as being unpatentable over Tanuma in view of Shah in view of Uenohara, as applied to claim 1 above, and further in view of Ryan et al. (U.S. PG Pub No. 2021/0366586 November 25, 2021 - hereinafter "Ryan”)
With respect to claim 22, Tanuma teaches the method of claim 1
updating, by the one or more processors, a relational database to indicate the one or more completed actions, wherein the relational database stores a global list of vehicles in the car sharing platform including the vehicle (Fig 7 tags 701 & 702 & 703 and Fig 12 tag S1201 as well as paragraphs [0112]-[0115] which show the work history table and/or required work table which are stored in the database and are updated to reflect one or more completed actions –the vehicle/work databases (tags 131 and 132 in Fig 1) are relational databases even though not specifically referred to as being relational databases because they comprise data organized into tables with rows and columns representing data records for interaction/retrieval of information (see Figs 2-9 which show the data tables and discussions throughout, including [0056]-[0076] and [0089]-[0115] illustrating data storing/querying using the relational database). Fig 2 tag 201 and Fig 3 tag 301 and Fig 4 tag 401 and Fig 5 tag 501 and [0060] which shows that the database stores a global list of vehicles in the car sharing platform including the vehicle)
Although Tanuma discloses use of a relational database(s) to store information, Tanuma does not appear to suggest wherein the system uses SQL to interact with the database, and therefore where the database is an SQL database. Tanuma does not appear to disclose,
a structured query language (SQL) database…the SQL database
However, Ryan discloses
a structured query language (SQL) database…the SQL database ([0693] “The data may be configured in any type of suitable database configuration, such as a relational database, a structured query language (SQL) database, a distributed database, an object database, etc. Suitable configurations and storage types will be apparent to persons having skill in the relevant art”, [0680] “the memory 147 may be comprised of or may otherwise include a relational database that utilizes structured query language for the storage, identification”, [0226])
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of use of an SQL database of Ryan for the generic relational database of Tanuma. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Furthermore, it would have been obvious to try, by one of ordinary skill in the art at the time of the invention, to modify the method of Tanuma to use an SQL database rather than a generic relational database, since there are a finite number of identified, predictable potential solutions (i.e., types of databases and/or ) to the recognized need (store data using a database) and one of ordinary skill in the art would have pursued the known potential solutions with a reasonable expectation of success (the costs and benefits of SQL databases were known before the effective filing date of the claimed invention).
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Oesterling et al. (U.S. PG Pub No. 2018/0197349, July 12, 2018 - hereinafter "Oesterling”) discloses analyzing telematics data to determine one or more service and/or relocation imperatives and corresponding actions and determining an action/reward value to incentives a user to perform the action and providing the action value/reward to a user for performing the action.
Bankreira et al. (U.S. PG Pub No. 2020/0334705 October 22, 2020 - hereinafter "Bankreira”) discloses training and using respective machine learning models (e.g., neural network models) that output respective incentive values for respective tasks types ([0084] & [0091] & [0051]).
Grigg et al. (U.S. PG Pub No. 2013/0046589 February 21, 2013 - hereinafter "Grigg”) discloses analyzing each of the one or more actions relating to the vehicle using a respective trained machine learning model to generate a respective action value and push notifications of task/reward [0010] & [0017])
VanderZanden et al. (U.S. PG Pub No. 2019/0311630 October 10, 2019) discloses crowdsourcing vehicle maintenance of a vehicle-sharing fleet.
Wright (U.S. Patent No. 9,311,271 April 12, 2016) discloses training and using different neural network models for different types of telematics data in order to detect the occurrence of driving events.
“Advanced Planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning” (You, Changxi et al., Publisehd on January 15, 2019 at www.elsevier.com/locate/robot) teaches analyzing one or more relocation actions using a respective neural network learning model to generate a respective action value.
Conclusion
No claim is allowed
THIS ACTION IS MADE FINAL. 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 extension fee 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 JAMES M DETWEILER whose telephone number is (571)272-4704. The examiner can normally be reached on Monday-Friday from 8 AM to 5 PM ET.
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/JAMES M DETWEILER/Primary Examiner, Art Unit 3621
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