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 . 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 is a divisional (“DIV”) application of U.S. application 16940173 filed on 07/27/2020. See MPEP §201.06. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
DETAILED ACTION
The following NON-FINAL Office action is in response to application 19026051 filed 01/16/2025
Status of Claims
Claims 1-20 are currently pending and have been rejected as follows.
Priority
Examiner noted Applicants claiming Priority from Divisional Application 16940173 filled 07/27/2020, which at its turn, claims priority from Provisional 62878703 filled 07/25/2019.
IDS
The information disclosure statement filed on 01/16/2025 and 12/17/2025 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Making the record clear - Claim Interpretation under MPEP 2181
Claim 1-11 are “apparatus” claims with independent Claim 1 reciting: “An apparatus for route-based vehicle sensor evaluation, the apparatus comprising:
a sensor interface that receives sensor data from a plurality of sensors of a vehicle, wherein the sensor data includes measurements indicative of respective statuses of a plurality of components of the vehicle as captured by the plurality of sensors;
a positioning receiver that detects positions of the vehicle at different times;
an input device within the vehicle, wherein the input device receives input data that is associated with an interaction with the input device;
an output device within the vehicle, wherein the output device outputs a response”;
As per apparatus Claim 1, Examiner notes that the “sensor interface”, “positioning receiver”,
“input device” and “output device” appear to correspond to hardware or software when read in light of Original Specification ¶ [0018] -¶ [0019], and thus it is believed that recitation of the
“sensor interface”, “positioning receiver”, “input device” and “output device” should not be interpreted as generic placeholders and thus should not invoke 112(f) as failing prone one of the 3-prong test described by MPEP 2181. Same rationale applies to the memory and processor.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 3,14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 3,14 are dependent and recite among others “the estimate is based on the change in the environment” detect[ed] from analyze[d] “Light Detection and Ranging (LiDAR) data”.
Original Specification ¶ [0023] last sentence and ¶ [0038] merely provides support for the estimate of the identified number of miles that can be traveled, based on fuel level and average gas mileage. At not point does the Original Specification provide any clear, deliberate and sufficient disclosure to show that the Applicant had possession for “the estimate is based on the change in the environment” detect[ed] from analyze[d] “Light Detection and Ranging (LiDAR) data” as recited at each of dependent Claims 3,14. Examiner points to Slide 17 of Examining Claims for Compliance with 35 U.S.C. 112(a): Overview & Part I Written Description of July 2015 available at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials to remind Applicant that “written description applies to all claims, including original claims”. Examiner also reminds Applicant that “One shows possession of the invention by describing the invention, with all its claimed limitations, not that which makes it obvious. Lockwood v. American Airlines, Inc., 41 USPQ2d 1961, No. 96-1168, 107 F3d 1565, p.1961 ¶3, p.1966 ¶2.
Clarification and/or correction is/are required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claim(s) recite(s) or at least describe or set forth the abstract Organizing Human Activities of MPEP 2106.04(a)(2) II. Specifically, the claims recite, “generating an estimate” (independent Claims 1,12,20) is read in light of Original Specification ¶ [0023] last sentence and ¶ [0038] to include, but not limited to, an estimate of the number of miles that can be traveled, which is not meaningfully different than determining optimal visit by a business representative to a client, found abstract in In re Maucorps, 609 F.2d 481, 485, 203 USPQ 812, 816 (CCPA 1979) cited by MPEP 2106.04(a)(2) II B. Further, the features of:
i. “receiving input data that is associated with an interaction with the input device”, ii. “interpreting the input data to identify a query”, iii. “identifying, based on the query” actions “to perform to support generation of a response to the query” and iv “generating the response to the query” “the response indicative of the estimate” (Claims 1,12,20) are not meaningfully different than:
i. acquiring content from an information source, ii. controlling the timing of the display of acquired content, iii. displaying the content, and iv. acquiring an updated version of the previously-acquired content when the information source updates its content, without interfering with the person’s primary activity, as was the case in Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018), as cited by MPEP 2106.04(a)(2) C.
It is also noted that the generat[ed] “estimate”, as found abstract above, is based on “the positions” “of the vehicle at different times” (independent Claims 1,12,20) and “network data” “associated with a route to be driven by the vehicle” (independent Claims 1,12,20), “wherein the network data identifies at least one of traffic conditions along the route, weather conditions along the route, or road conditions along the route” (dependent Claims 8,19) , which is not meaningfully different than considering historical usage information while inputting data as was the case in BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018) cited as abstract by MPEP 2106.04(a)(2) II C.
Further still the recitations of “the estimate is at least one of a recommended departure time for the route or an estimated arrival time for the route” (dependent Claims 7,18), “wherein the network data identifies a location of a merchant along the route, and wherein the estimate is associated with a stop along the route at the location of the merchant” (dependent Claim 9) set forth or describe fundamental economic practices of principles of MPEP 2106.04(a)(2) II A, under the broad umbrella of Organizing Human Activities of MPEP 2106.04(a)(2) II.
It can perhaps also be argued that “generating an estimate” “to perform the evaluation action” could be argued1 as an evaluation based on observation of vehicle components and sensors followed by a subsequent judgment. Such observation, evaluation and judgment are listed by MPEP 2106.04(a)(2) III ¶2 as integral to the abstract exception with MPEP 2106.04(a)(2) III C2 #1,#2,#3 corroborating that performing abstract cognitive processes on a computer, in a computer environment or by using a computer as a tool, not precluding the claims from reciting an abstract idea. In fact MPEP 2106.04(a)(2) III A ¶8, cited Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), which found that in a power grid network environment, the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, still set forth the abstract exception. This rationale was later echoed in TDE Petroleum Data Sols., Inc v. AKM Enter., Inc 657 Fed. Appx. 991 (Fed. Cir. 2016), where the Court found determining operation state of a well as an abstract idea: “As we discussed at greater length in Electric Power, the claims of the '812 patent recite the what of the invention, but none of the how that is necessary to turn the abstract idea into a patent-eligible application. Electric Power [2016 BL 247416] 2016 U.S. App. LEXIS 13861 [2016 BL 247416], 2016 WL 4073318 at *4-5. Therefore, we find that claim 1 is patent-ineligible under § 101”. Following such legal precedents as articulated by MPEP 2106.04(a)(2), the Examiner finds that determining operation state, be it for a well, as in TDE Petroleum, for a power grid as in Electric Power Group, or for a “vehicle” as currently claimed, would all follow a similar ineligibility path. In fact, here, the current claims can be argued as less technologically involved than in
Electric Power Group or TDE Petroleum, because here, an intelligent co-pilot, co-driver, first officer, experienced, skilled mechanic, vehicle engineer, cartographer, spouse, or friend would be perfectly capable to observe or “captur[e]” [with his or her eyes and ears] “statuses of a plurality of components of the vehicle” (independent Claims 1,12,20) and “positions of the vehicle at different times” (independent Claims 1,12,20) [cognitively deduce, imply, infer or] “interpret” [through ordinary lexical, syntactic and semantic knowledge, the driver’s vocal] “input” “to identify a query” [akin to pace-notes provided by a co-driver or do-confirm and read-do checklists for a vehicle i.e. aviation], with the intelligent co-pilot, co-driver, first officer, experienced, skilled mechanic, vehicle engineer, cartographer, spouse, or friend, which at his/her turn would read to visually a knowledgebase of do-confirm and read-do checklists for a vehicle i.e. aviation] [based on the received verbal] query to [verbalize or] “generate” and “output” “the response to the query, wherein the response is indicative of the estimate” as recited at Claims 1,12,20.
Also, the Examiner does note that here, the abstract query[ing], and associated “response to the query” “indicative of the estimate” are aided by what appear to be computer aids such as “input device receives input data that is associated with an interaction with the input device” and “output device outputs a response”; as well as “a positioning receiver that detects positions of the vehicle at different times” and “sensor interface that receives sensor data from a plurality of sensors of a vehicle, wherein the sensor data includes measurements indicative of respective statuses of a plurality of components of the vehicle as captured by the plurality of sensors” as recited at independent Claim 1 and similarly at independent Claims 12,20. Yet, Examiner also recognizes that per MPEP 2106.04(a) (2) III C performing abstract processes: #1 on a generic computer, #2 in a computer environment, or #3 using a computer as tool to perform the abstract processes do not preclude the claims to recite the abstract exception. Thus, the Examiner reasons that here, use of such “input” and “output” device(s), “positioning receiver”, and “sensor interface” at independent Claim 1 and similarly recited at independent Claims 12,20, and possibly the “vision system” of dependent Claims 2,13, Light Detection and Ranging (LiDAR) of dependent Claims 3,14, “touchscreen” of dependent Claims 4,15, and “microphone” voice recognition system” and “speaker” of dependent Claims 5,16 can analogously be argued as such use of generic computer, computer environment, or tools to perform the processes identified above.
Last but certainly not least, while Examiner notes “using an artificial intelligence network” [to] “generate an estimate based on processing of the sensor data and the network data and the positions” “to perform the evaluation action” is recited at independent Claims 1,12,20, Examiner also finds that according to MPEP 2106.04(a)(2) III #2 performing a cognitive process in a computer environment, does not preclude the claims to recite the abstract idea. Additionally, MPEP 2106.04(a)(2) III ¶2 made it clear that observations, evaluations, judgments, represent such abstract mental processes. Based on the legal findings of MPEP 2106.04(a)(2) III #2 and MPEP 2106.04(a)(2) III ¶2, it then follows that here, “using an artificial intelligence network” [to] “generate an estimate, as generally recited at independent Claims 1,12,20, could be possibly argued to represent such an example of a computer [or artificial] environment in which to perform the abstract evaluation based on equally abstract judgments [akin here to estimation] and prior observations [here from “sensor data”]. In an abundance of caution, the Examiner will more granularly test the computer elements, including use of “artificial intelligence network” at subsequent steps below. For now, it is clear that given the preponderance of legal evidence above the claims recite or at a minimum describe or set forth the abstract exception. Step 2A prong one.
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This judicial exception is not integrated into a practical application because the additional computer-based elements, merely apply the aforementioned abstract exception [MPEP 2106.05(f)] and/or narrow it to a field of use or technological environment [MPEP 2106.05(h)].
Here, if not already computer aids as argued as part of the abstract exception supra, the “input” and “output” device(s), “positioning receiver”, and “sensor interface” of independent Claim 1, and similarly recited at Claims 12,20, the “vision system” of dependent Claims 2,13 the, “Light Detection and Ranging (LiDAR)” of dependent Claims 3,14, and the “touchscreen” of dependent Claims 4,15, and “microphone” voice recognition system” and “speaker” of dependent Claims 5,16, would certainly constitute along with the memory instructed or executable processor of Claims 1-3,6,10,20, additional computer-based elements that would at most represent mere invocation of commuter components or machinery to apply the above processes, which according to MPEP 2106.05(f) do(es) not integrate the abstract exception into a practical application.
For example, recitation of “using an artificial intelligence network to perform the evaluation action” [to] “generate an estimate based on processing of the sensor data and the network data and the positions” at independent Claims 1,12,20 can be argued as a mathematical algorithm applied on a computer, which according to MPEP 2106.05(f)(i) is an example of invoking [akin here to “using”] a computers or other machinery [akin here to “artificial intelligence network”] as
a tool that does not integrate the abstract exception into a practical application.
Similarly, MPEP 2106.05(f)(2) ¶1 is clear that the combined capabilities of the additional elements to: perform tasks to receive, store, and transmit data2 are also examples of applying the abstract idea. Here such received data refers to “positions of the vehicle at different time” and “input data that is associated with an interaction with the input device;” as well as “network data is associated with a route to be driven by the vehicle” at independent Claims 1,12,20. Also here, the transmitted data refers to “the response through” output[ted] “through an output device within the vehicle” at independent Claims 12,20 and similarly at Claim1.
Also, MPEP 2106.05(f)(2) ¶23 found that a combination between a server and a portable imaging unit to receive data, extract classification information from the received data, and stores the digital images based on the extracted information is also example of invocation of computers or other machinery merely as a tool to perform an existing process which again, does not integrate the abstract exception into a practical application. Here, such an example is represented by “using a vision system” [to] “analyze visual data from the sensor data” “to detect a change in an environment that the vehicle is in, wherein the plurality of sensors of the vehicle include a camera that captures the visual data, and wherein the estimate is based on the change in the environment” at dependent Claims 2,13, and “analyze Light Detection and Ranging (LiDAR) data from the sensor data to detect a change in an environment that the vehicle is in, wherein the plurality of sensors of the vehicle include a LiDAR sensor that captures the LiDAR data, and wherein the estimate is based on the change in the environment” at dependent Claims 3,14.
This latter detection can also be argued as an instance of monitoring of audit log data executed on a computer which according to MPEP 2106.05(f)(2)(iii)4 remains an example of invoking computers or other machinery as a mere tool to perform an existing process, which does not integrate the abstract exception into a practical application. The same is true about the capabilities of the additional, computer-based elements to: remotely access user-specific information5, and require use of software to tailor information and provide it to the user on a computer6. Thus here, when tested per MPEP 2106.05(f)(2), the “sensor data from a plurality of sensors of a vehicle” [that] “includes measurements indicative of respective statuses of a plurality of components of the vehicle as captured by the plurality of sensors” and “a positioning receiver that detects positions of the vehicle at different times” at independent Claim 1 and similarly at independent Claims 12,20 is comparable to the capabilities of computer components to monitor audit log data [here “sensor” and “position” data] executed on a computer7 [here “sensor”, “positioning receiver”], remotely accessing user-specific information8 [here “retrieve the sensor data through the sensor interface to perform the sensor action”; “retrieve network data from a network data store to perform the network action, wherein the network data is associated with a route to be driven by the vehicle”] and to require use of software or analogous computer components [here “interface” at Claims 1,4,15, “Light Detection and Ranging (LiDAR) data” at dependent Claims 3,14, “touch interface of a touchscreen” at dependent Claims 4,15, “wherein the input data includes a voice input recorded using the microphone, wherein interpreting the input data is based on a voice recognition system, wherein the output device includes a speaker, and wherein the output device outputs the response by playing the response through the speaker” at dependent Claims 5,16] to tailor [her “output”] information and provide it to the user on a generic computer9 represent mere invocation of computers or machinery as a tool that do not integrate the abstract exception into a practical appclaition when tested per MPEP 2106.05(f)(2)(i),(iii),(v).
Similarly, according to MPEP 2106.05(h), merely specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment10 does not integrate it to a practical application, since this requirement merely limits the claims to a computer field. MPEP 2106.05(h) also states that language specifying that the abstract idea was to be implemented using a communication medium and networks11 [akin here to “voice input recorded using the microphone” at dependent Claims 5,16] also does not integrate the abstract idea into a practical application, because such limitation merely limits the use of the exception to a particular [computerized] technological environment. MPEP 2106.05(h)(vi) also cites “Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350,1354, 119 USPQ2d 1739,1742 (Fed. Cir. 2016)” to submit that limiting the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to a particular technological environment, also does not integrate abstract idea into practical application. It then follows that here, limiting the “input data” [to] include[…] “a voice input recorded using the microphone, wherein interpreting the input data is based on a voice recognition system, wherein the output device includes a speaker, and wherein the output device outputs the response by playing the response through the speaker” at dependent Claims 5,16, and further limiting combination of collecting [here captured statuses at Claim 1] and analysis [here “identify, based on the query, a sensor action, a network action, and an evaluation action to perform to support generation of a response to the query”, “generate an estimate based on processing of the sensor data and the network data and the positions using an artificial intelligence network to perform the evaluation action” at Claims 1,12,20] and then limiting the certain results of collection and analysis to “output device” at independent Claims 12,20 and similarly at Claim 1, and further narrowed as “touchscreen” at dependent Claims 4,15 or “speaker” at dependent Claims 5,16 would also represent a narrowing of the abstract idea to a field of use or technological environment characterized by sensing and input/output interface, which would also not integrate the abstract idea into a practical application, as tested per MPEP 2106.05(h).
As per the limitations of “cause the vehicle to perform an action associated with the response” at dependent Claims 6,17, the Examiner applies the MPEP 2106.05(f)(3) test, to note the high level of generality of such broadly limitation as it relates to the preciously identified abstract processes, which according to MPEP 2106.05(f)(3) does not integrate such abstract processes into a practical application.
Based on all the legal evidence above, the additional elements, as contested, argued and tested above, would represent mere tools to apply the abstract exception [MPEP 2106.05(f)] and/or narrow it to a field of use or technological environment [MPEP 2106.05(h)], none of which would integrate the abstract exception into a practical application. Step 2A prong two.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the additional computer-based elements merely apply the already recited abstract idea, [MPEP 2106.05(f)], and/or narrow it to a field of use or technological environment [MPEP 2106.05(h)] which also do not provide significantly more than the abstract idea itself, in light of as option for evidence, without having to rely on the conventionality test of MPEP 2106.05(d). Yet assuming arguendo, that further evidence would be required to demonstrate conventionality of the additional, computer-based elements, the Examiner would also point to MPEP 2106.05(d) I 2.b and MPEP 2106.05(d)(II), to demonstrate that the following functions are well-understood, routine and conventional: receiving or transmitting data over a network12 including utilizing an intermediary computer to forward information13, electronic recordkeeping14, updating an activity log15, gathering statistics16, arranging a hierarchy of groups, sorting information17, storing and retrieving information in memory18. Here such data and information is represented by “sensor data” of [that] “includes measurements indicative of respective statuses of a plurality of components of the vehicle as captured by the plurality of sensors” and “positions of the vehicle at different times” detect[ed] by “a positioning receiver” and “network data” “associated with a route to be driven by the vehicle” “from a network data store to perform the network action” at Claims 1,12,20 etc.
If necessary, Examiner would also point as evidence to Applicant’s own Specification [MPEP 2106.05(d) I 2. a] as well as several publications [MPEP 2106.05(d) I 2. c] to demonstrate conventionality of the additional, computer-based elements:
Original Specification ¶ [0025] “One skilled in the art will appreciate that, for this and other embodiments disclosed herein, the elements associated with the system for the Vehicle Intelligent Assistant are exemplary in nature. Some of the elements may be combined into fewer elements, or expanded into additional elements without detracting from the disclosed embodiments. Furthermore, some of the elements of the methods and apparatus consistent with the present disclosure may be optional and others may be added also without detracting from how the vehicle intelligent assistant functions”.
In fact, the Original Disclosure provides a preponderance of evidence on the high level of generality of the additional, computer-based elements, as follows:
Original Specification ¶ [0020] last sentence: “User interface 170 may be a collection of devices and software instructions that allow a user to interact with an electronic device, for example, a voice recognition system or a graphical user interface”
Original Specification ¶ [0019] 6th-13th sentences reciting at high level of generality: “Sensors 115 may include tire pressure sensors, temperature sensors, fluid level sensors (that sense levels of fuel, oil, hydraulic fluid, windshield wiper fluid, coolant, etc.), oxygen sensors, ultrasonic sensors, Lidar sensors, speed sensors, cameras, optical sensors, and/or other sensors. Fig.1 illustrates a communication interface or device (vehicle COMM) 120 located inside or on the vehicle 105. This communication device 120 may allow vehicle computer 110 to send and/or receive information from vehicle 105 to other devices. Vehicle COMM 120 may take the form of a physical connection, such as a port to connect to the vehicle on-board diagnostic (OBD) system. Alternatively, the vehicle COMM 120 may send and receive information via electromagnetic waves, such as a radio transmitter, a WIFI connection, a cellular communication system (3G, 4G, 5G, or other), a global positioning system, or a Bluetooth connection. Cloud or Internet 135 may allow computer 110 at vehicle 105 to communicate with third party network computer 125A via COMM 120 of vehicle 105 and communication interface 130 of third party network 125. Third party network 125 may be a digital communication network that sends, receives, and stores information related to the user activity and preferences. Third party network 125 may store or track vehicle specifications, external conditions, or other information that may relate to, for example, vehicle manufacturing, part manufacturing, accident records, insurance, social media preferences, retail shopping history, traffic conditions, weather conditions, fuel prices and customer loyalty program”.
Original Specification ¶ [0019] 6th sentence reciting at high level: “Sensors 115 may include tire pressure sensors, temperature sensors, fluid level sensors (that sense levels of fuel, oil, hydraulic fluid, windshield wiper fluid, coolant, etc.), oxygen sensors, ultrasonic sensors, Lidar sensors, speed sensors, cameras, optical sensors, and/or other sensors”.
Original Specification ¶ [0019] 14th-17th sentences reciting at high level of generality: “Vehicle artificial intelligence (AI) agent network 180 may be a digital communication network that sends, receives, and stores information related to the activities of a vehicle AI agent. Vehicle AI agent network may include a computer (not illustrated) that executes instructions of vehicle network module 185, these instructions may allow the computer at vehicle AIagent network 180 to receive user requests from a use case software module. Instructions of this use case software module may retrieve appropriate data from the network use case database 190 of FIG. 1. The network use case database 190 is an organized collection of data pertaining to possible user requests made to the vehicle AI agent and the appropriate data and actions that correspond to such requests”.
Original Specification ¶ [0020] last sentence, reciting at high level of generality: “User interface 170 may be a collection of devices and software instructions that allow a user to interact with an electronic device, for example, a voice recognition system or a graphical user interface”.
Original Specification ¶ [0022] last sentence disclosing combination: Operation of these software module instructions may allow CPU 145 to retrieve appropriate data from various sources, to perform required actions, and to return appropriate responses - similar to other context-based search systems well known in the art.
Original Specification ¶ [0027] last two sentences reciting at high level of generality:
“One skilled in the art will appreciate that, for this and other processes and methods disclosed herein, the functions performed in the processes and methods may be implemented in differing order. Furthermore, the outlined steps and operations are only provided as examples, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments”
Original Specification ¶ [0055]: “components contained in the computer system 800 of Fig.8 are those typically found in computer systems that may be suitable for use with embodiments of the present invention and are intended to represent a broad category of such computer components that are well known in the art. Thus, the computer system 800 of Fig.8 can be a personal computer, a hand held computing device, a telephone (smart or otherwise), a mobile computing device, a workstation, a server (on a server rack or otherwise), a minicomputer, a mainframe computer, a tablet computing device, a wearable device (such as a watch, a ring, a pair of glasses, or another type of jewelry/clothing/accessory ), a video game console (portable or otherwise), an e-book reader, a media player device (portable or otherwise), a vehicle-based computer, some combination thereof, or any other computing device. computer can also include different bus configurations, networked platforms, multi-processor platforms, etc. computer system 800 may in some cases be a virtual computer system executed by another computer system. Various operating systems can be used including Unix, Linux, Windows, Macintosh OS, Palm OS, Android, iOS, and other suitable operating systems”.
Original Specification ¶ [0056]: “The present invention may be implemented in an application that may be operable using a variety of devices. Non-transitory computer-readable storage media refer to any medium or media that participate in providing instructions to a central processing unit (CPU) for execution. Such media can take many forms, including, but not limited to, non-volatile and volatile media such as optical or magnetic disks and dynamic memory, respectively. Common forms of non-transitory computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM disk, digital video disk (DVD), any other optical medium, RAM, PROM, EPROM, a FLASH EPROM, and any other memory chip or cartridge”.
Also, assuming arguendo conventionality of “voice input recorded using the microphone, wherein interpreting the input data is based on a voice recognition system, wherein the output device includes a speaker, and wherein the output device outputs the response by playing the response through the speaker”, would be necessary to be tested under MPEP 2106.05(d) I 2.c), such conventionality would be demonstrated by at least the following publications:
* US 20190084420 A1 ¶ [0005] To use smart devices as connected to vehicle head units that support phone projection such as Google's Android Auto, Apple's CarPlay and Nokia's Mirrorlink by a wired or wireless method, conventionally, users could use them after setting up (select Android Auto icon, select Mirrorlink connection, etc.) via an input device provided in the vehicle head unit or an input device provided in the smart device and executing phone projection.
* US 20190189132 A1 ¶ [0084] With technological advances in artificial intelligence (AI), the voice recognition is more commonplace as are voice-based devices, such as Amazon's Echo and Apple's Siri. Such voice-based devices include application programmable interfaces (APIs) so that voice-based devices can be integrated with and control any device, such as gateways, televisions (TVs), set top boxes, washing machines, dryers, refrigerators, lighting, window shades, microwaves, ranges, dishwashers, security systems, computers, laptops, tablets (tablet computers), PDAs, pagers, etc. Such voice-based devices may also be a separate device or may be integrated with the gateway itself. Irrespective of the deployment scenario, the voice-based commands enhance user's experience.
* US 20180300337 A1 ¶ [0004] last sentence: in some conventional methods and systems, the system generates the score for each answers based on historical results obtained by a machine learning of the plurality of virtual assistants.
* US 10629191 B1 column 1 lines 59-63: Conventional virtual assistant platforms implement natural language conversations with end users in one of several ways, either via decision trees or finite state machines, menu-driven approaches, frame-slot approaches, or machine learning on existing conversation datasets.
* US 20210173377 A1 ¶ [0002] 1st sentence: Conventional machine learning technologies can allow intelligent systems such as robots and personal assistants to acquire knowledge and solve difficult problems by learning from examples or instruction.
* US 20210217409 A1 ¶ [0068] 2nd sentence: The artificial intelligence agent, as a dedicated program for providing an artificial intelligence based service (e.g., voice recognition service, personal assistant service, translation service, search service, etc) may be executed by a conventional generic-purpose processor (e.g CPU)
* US 20190318035 A1 ¶ [0135] “electronic digital assistants may computationally consider and provide assistance within multiple party conversations digitally captured and processed by the electronic digital assistant, allowing electronic digital assistant to provide more substantive responses that consider additional context and inter-party and role-based information compared to traditional single-person inquiries and responses processed by conventional electronic digital assistants, and without requiring large memory spaces and processing power required to store every possible situation and response, and without requiring large datasets and time-consuming training periods required by deep-learning and other machine learning mechanisms. Other features and advantages are possible”.
* US 20110307100 A1 ¶ [0089] 5th sentence: “These generic macros can be used repeatedly across various OEMs and across various product families as well as schematic and layout configurations”
* Macro, computer science, wikipedia, archives org Feb 11, 2020, emphasis p.1 below
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* US 20030041317 A1 claim 8. A Java macro instruction as recited in claim 7, wherein said predetermined criteria is whether said conventional sequence has been repeated more than a predetermined number of times.
* US 20180165610 A1 ¶ [0008]: “Describing complex data topologies may rapidly become very difficult and lead to large and intricate expressions. It may therefore be desirable to provide systems and methods to facilitate the use of business intelligence language macro expressions in an intuitive and flexible manner. These macro expressions may play the same role as functions in conventional programming languages, making it possible to reuse repeated computations. As a result, the definition and maintenance of complex expressions may be made easier”.
With respect to the “Light Detection and Ranging (LiDAR)” at dependent Claims 3,14, if necessary, in additional to the factual evidence of its level of generality enumerated by,
Original Specification ¶ [0019] 6th sentence, if necessary the Examiner would further point:
* US 20210160306 A1 ¶ [0059] 4th sentence: Purely by way of example, detection component 340 may achieve this by monitoring (e.g. using conventional LiDAR)…
* US 20200133271 A1 ¶ [0024] 3rd sentence: the ranging unit 62 is a LIDAR unit working in a conventional manner known to those skilled in the art.
* US 20260010168 A1 ¶ [0035] 2nd sentence: assist human workers (e.g., human worker 106) via conventional radio mapping (such as LiDAR) for the case of a single object to be detected and tracked.
In conclusion, although Claims 1-20 directed to statutory categories (“apparatus” or machine at Claims 1-11, “method” or process at Claims 12-19, “non-transitory” “medium” or article of manufacture at Claim 20) they recite, or at least set forth the abstract idea (Step 2A prong one), with their additional, computer-based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B). Thus, the Claims 1-20 are ineligible.
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Rejections under 35 § U.S.C. 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.
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.
The factual inquiries 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.
Claims 1,4-12 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Krishnan et al, US 20200356339 A1 by Applicant Google hereinafter Krishnan,
Penilla et al, US 20170061965 A1 hereinafter Penilla 2. As per,
Claims 1,12,20 Krishnan teaches: “An apparatus for route-based vehicle sensor evaluation, the apparatus comprising: a sensor interface that receives sensor data from a plurality of sensors of a vehicle, wherein the sensor data includes measurements indicative of respective statuses of a plurality of components of the vehicle as captured by the plurality of sensors” (Krishnan ¶ [0039] 4th sentence: OEM vehicle computing device 318 include camera and other sensors, exemplified at ¶ [0052] 5th sentence: as fluid sensor of the vehicle notifying about an alarm state) “a positioning receiver that detects positions of the vehicle at different times” (Krishnan Figs.1-2 below, ¶ [0022] 1st, 5th sentences: vehicle computing device 104 include OEM vehicle application 124 such as navigation application provide data characterizing upcoming navigation instructions, rendered as first graphical element 112 in first area 110 of user interface 106. For example ¶ [0007] the OEM vehicle application provide the automated assistant with various data over time, such as estimated time of arrival at destination per ¶ [0026]) “an input device within the vehicle, wherein the input device receives input data that is associated with an interaction with the input device”; (Krishnan ¶ [0059] teaches several examples of user interface input devices 522 such as… touchpad, graphics tablet, touchscreen incorporated into the display, audio input devices such as voice recognition systems, microphones, and/or other types of input devices. In general, use of the term input device is intended to include all possible types of devices and ways to input information into computer system 510 or onto a communication network. For example, ¶ [0039] 1st sentence: a user can initialize the automated assistant 304 by providing a verbal, textual, and/or a graphical input to an assistant interface (e.g., an assistant interface of the OEM vehicle computing device 318 and/or an assistant interface of any other client device) to cause the automated assistant 304 to perform a function (e.g., provide data, control a peripheral device, access an agent, generate an input and/or an output, etc. Another example at ¶ [0039] 2nd sentence: OEM vehicle computing device 318 can include a display device, which can be a display panel that includes a touch interface for receiving touch inputs and/or gestures for allowing a user to control applications of the OEM vehicle computing device 318 via the touch interface. Another example at ¶ [0039] 4th sentence: Further, the OEM vehicle computing device 318 can provide a user interface, such as a microphone(s), for receiving spoken natural language inputs from a user. ¶ [0006] 5th-6th sentence noting a spoken input that includes at least a portion of the spoken utterance rendered at the graphical user interface) “an output device within the vehicle, wherein the output device outputs a response” (Krishnan Figs. 2A-C element 104/204. ¶ [0039] 1st sentence: automated assistant 304 to perform a function e.g. generate output. ¶ [0060] 4th sentence: In general, use of the term output device is intended to include all possible types of devices and ways to output information from computer system 510 to the user or to another machine or computer system) “a memory storing instructions” (Krishnan ¶ [0062] 2nd sentence: memory 525 used in the storage subsystem 524 can include a number of memories including a main random access memory 530 for storage of instructions and data during program execution and a read only memory 532 in which fixed instructions are stored); “and a processor that executes the instructions, wherein execution of the instructions causes the processor to” (Krishnan ¶ [0058] 2nd-3rd sentences: computer system 510 includes processor 514 which communicates with peripheral devices via bus subsystem 512. Such as storage subsystem 524, including, for example, a memory 525 and a file storage subsystem 526, user interface output devices 520, user interface input devices 522, and a network interface subsystem 516) (Claim 1)
“A method for route-based vehicle sensor evaluation, the method comprising” (Claim 12)
“A non-transitory computer readable storage medium having embodied thereon a program, wherein the program is executable by a processor to perform a method of route-based vehicle sensor evaluation, the method comprising” (Krishnan ¶ [0014] 1st sentence: non-transitory computer readable storage medium storing instructions executable by processors) (Claim 20)
- “interpret the input data to identify a query”;
(Krishnan ¶ [0043] converting audio data to text include a speech recognition algorithm, which employ neural networks, and/or statistical models for identifying groups of audio data corresponding to words or phrases. The text converted from the audio data can be parsed by a data parsing engine 310 and made available to the automated assistant as textual data used to generate and/or identify command phrase(s), intent(s), action(s), slot value(s), and/or any other content specified by user. In some implementations, output data provided by data parsing engine 310 can be provided to a parameter module 312 to determine whether the user provided input that corresponds to a particular intent, action, and/or routine capable of being performed by the automated assistant 304 and/or an application or agent that is capable of being accessed via the automated assistant 304. For example, assistant data 316 can be stored at server device 302 and/or OEM vehicle computing device 318 as vehicle data 324, and include data that defines one or more actions capable of being performed by the automated assistant 304 and/or client automated assistant 322, as well as parameters necessary to perform the actions)
- “identify, based on the query, a sensor action, a network action, and an evaluation action to perform to support generation of a response to the query” (Krishnan teaches many examples:
Krishnan ¶ [0043] 2nd-3rd sentences: The text converted from the audio data can be parsed by data parsing engine 310 and made available to the automated assistant as textual data used to generate and/or identify command phrase(s), intent(s), action(s), slot value(s), and/or any other content specified by the user. In some implementations, output data provided by data parsing engine 310 can be provided to a parameter module 312 to determine whether the user provided input that corresponds to a particular intent, action, and/or routine capable of being performed by automated assistant 304 and/or an application or agent capable of being accessed via automated assistant 304. For example, assistant data 316 can be stored at server device 302 and/or OEM vehicle computing device 318 as vehicle data 324, and can include data that defines one or more actions capable of being performed by the automated assistant 304 and/or client automated assistant 322, as well as parameters necessary to perform the actions.
Krishnan ¶ [0011] 2nd -5th sentences: For example, when the third party application corresponds to a vehicle maintenance application, and the vehicle maintenance application provides a notification indicating that a part of the vehicle needs attention, the automated assistant can be informed of this notification via the OEM vehicle application. For instance, when the vehicle maintenance application indicates that the vehicle needs gas and/or charge, the vehicle maintenance application can provide notifications about nearby places to refuel the vehicle. The OEM vehicle application can generate data based on the notifications and/or content being rendered at the graphical user interface of the third-party application and provide the generated data to the automated assistant to use the data to generate suggestions, which can be ranked and/or filtered according to what has already been presented to the use.
Krishnan ¶ [0012] noting when a generated suggestion corresponds to a nearby place to refuel the vehicle, the generated suggestion can be ranked (i.e., prioritized) lower than a separate suggestion that does not correspond to the nearby place to refuel the vehicle. As a result, other suggestions regarding, for example, spoken utterances for obtaining other information about the vehicle (e.g., “Assistant, what is my highway miles per gallon?”), can be prioritized higher than suggestions related to nearby places to refuel. Alternatively, or additionally, other suggestions can be generated based on a comparison between the data that is based on the content from the third-party application, and assistant data, where assistant data is data that is associated with interactions between the user and the automated assistant. For example, the assistant data can comprise content of previous interactions between the user and the automated assistant, contact information or calendar information linked to, or associated with, an account of the user and interacted with by the user through the automated assistant, and/or the assistant data can comprise time and/or location information associated with a time and/or location of user interaction with the automated assistant. In one example, the data from the OEM vehicle application can be compared to the assistant data to determine that the notification from the third-party application is associated with an operating feature of the vehicle. Based on this determination, the automated assistant can determine when the user has previously participated in a dialogue session with the automated assistant regarding operating features of the vehicle.
Krishnan ¶ [0013] For instance, the user may have queried the automated assistant to find out what the appropriate tire pressure is for their vehicle. Therefore, in response to receiving the data from the OEM vehicle application, the automated assistant can generate a suggestion characterizing a spoken utterance such as, “Assistant, what is the tire pressure of my vehicle?” Suggestion data corresponding to this suggestion can be transmitted from the automated assistant to the OEM vehicle application and/or third-party application, and the third-party application can then present the suggestion at the graphical user interface with the content characterizing the fuel status of the vehicle. In this way, while the user is being notified about refueling their vehicle, the user can learn to conduct similar dialogue sessions to check on other matters related to the vehicle, in order that such matters can be addressed sooner, thereby promoting a healthy routine of vehicle maintenance.
Krishnan ¶ [0035] 4th sentence: Furthermore, the content can characterize a location of a nearby grocery store, details regarding the route through which the user is currently taking, and details about when the user 202 will arrive at the destination.
Krishnan ¶ [0048] 1st-2nd sentences: suggestions can be ranked and/or presented according to the contextual data, which include details of a route of the user. For instance, the contextual data indicate whether the user is currently being driven on a portion of the route that lasts for X minutes and/or Y miles. In some implementations, when the portion of the route satisfies a time and/or distance threshold, suggestion engine 328 cause suggestion elements to be presented at a graphical user interface of the OEM vehicle computing device 318.
Krishnan ¶ [0052] method 400 include optional operation 406 of determining whether the (vehicle) application data characterizes a past action(s). A past action can be providing a notification, sending data, accessing data, generating data, etc. That that has been executed within a period of time before the vehicle application data was received. In some implementations, the period of time can be a threshold period of time that is static or dynamic according to properties of interactions between the user and of the vehicle computing device, and/or the user and the automated assistant. As an example, a third-party application available at the vehicle computing device can provide a notification indicating that a fluid sensor of the vehicle is in an alarm state. This notification can be considered a past action because the notification is being provided at the graphical user interface rendered at the display panel within a threshold period of time of receiving the vehicle application data. In some implementations, a time at which the notification was presented can be determined based on time information that is characterized by the vehicle application data. The automated assistant can have access to an application for tracking certain properties of the vehicle, and therefore can also be privy to the information characterized by the notification about the fluid sensor. However, depending on when the notification was presented at the display panel of the vehicle computing device, the automated assistant may or may not bypass causing a notification about the fluid sensor to appear at the display panel
Krishnan ¶ [0053] when the application data characterizes past action at the operation 406, the method 400 can proceed to the operation 408, which can include generating other suggestion data that avoids suggesting the past action. As an example, when the third-party application provides a notification related to the fluid sensor, the other suggestion data can avoid suggesting obtaining information about the status of the fluid sensor by providing one or more suggestions that do not include requesting the information detailing the status of the fluid sensor. Rather, the automated assistant can generate suggestion data characterizing a spoken utterance for requesting information about purchasing the fluid corresponding to the fluid sensor, sending a message that includes the information detailing the status of the fluid sensor, placing a phone call to a business that performs maintenance associated with the fluid sensor, and/or any other request that is different from requesting the information detailing the status of the fluid sensor.
Krishnan ¶ [0054] 3rd-5th sentences: when a graphical user interface at the third-party application includes content characterizing a route to a particular destination, and the automated assistant has not provided a notification related to the fluid sensor within a threshold period of time, the automated assistant can provide the suggestion data corresponding to a notification for the fluid sensor. The suggestion data can characterize natural language content, such as a spoken utterance that, when spoken by the user, causes the automated assistant to provide information related to a status of the fluid sensor. For example, the spoken utterance can be, “Assistant, what is the status of the fluid sensor?”
Krishnan ¶ [0057] 1st -4th sentences: When they rendered suggestion element is selected within a particular period of time, the method can proceed from the operation 416 to the optional operation 418. The optional operation 418 can include assigning priority for the suggestion data according to the selection of the suggestion. However, if the user does not select the suggestion within a particular period of time, the method can proceed from the operation 416 to the operation 420. The operation 420 can include assigning priority for the suggestion data according to the non-selection of the suggestion element.
Krishnan ¶ [0068] 2nd-3rd sentences: the method can further include determining an estimated time of arrival of the user to a destination location, wherein determining the priority of the suggestion data relative to the priority data that is associated with other suggestion data is at least partially based on the estimated time of arrival of the user to the destination location. In some implementations, the method include, when the priority of the suggestion data relative to the priority data indicates that the suggestion data is prioritized over the other suggestion data: determining, subsequent to causing the particular suggestion element to be rendered at the graphical user interface of the vehicle application, that the particular suggestion element was selected, and causing, in response to determining that the particular suggestion element was selected, the priority of the suggestion data to be modified.
Krishnan ¶ [0073] In some implementations, subsequent to the natural language content being rendered at the graphical user interface: determining that the user and/or another user has provided a spoken utterance that includes at least some of the natural language content rendered at the graphical user interface, and causing, in response to determining that the user and/or the other user has provided the spoken utterance, the automated assistant to initialize performance of an action by the third-party application. In some implementations, the method can further include determining, in response to receiving the suggestion data, an amount of time and/or distance remaining for the vehicle to navigate the user to the destination; and determining whether the amount of time and/or the distance remaining for the vehicle to navigate the user to the destination satisfies a threshold, wherein causing the third-party application to render the natural language content at the graphical user interface of the third-party application is performed when the amount of time and/or the distance remaining satisfies the threshold. In some implementations, the method can further include providing updated content data characterizing additional data that is being presented at the graphical user interface),
- “retrieve the sensor data through the sensor interface to perform the sensor action”;
(Krishnan ¶ [0011] 2nd-5th sentences: when a third-party application corresponds to vehicle maintenance application, and the vehicle maintenance application provides notification indicating that a part of the vehicle needs attention, the automated assistant is informed of this notification via the OEM vehicle application. For instance, when the vehicle maintenance application indicates that the vehicle needs gas and/or charge, the vehicle maintenance application can provide notifications about nearby places to refuel the vehicle. The OEM vehicle application can generate data based on the notifications and/or content being rendered at the graphical user interface of the third-party application, and provide the generated data to the automated assistant. The automated assistant can use the data to generate suggestions, which can be ranked and/or filtered according to what has already been presented to the user.
Krishnan ¶ [0013] the user may have queried the automated assistant to find out what the appropriate tire pressure is for their vehicle. Therefore, in response to receiving the data from the OEM vehicle application, the automated assistant can generate a suggestion characterizing a spoken utterance such as, “Assistant, what is the tire pressure of my vehicle?” Suggestion data corresponding to this suggestion can be transmitted from the automated assistant to the OEM vehicle application and/or third-party application, and the third-party application then present the suggestion at the graphical user interface with the content characterizing the fuel status of the vehicle. In this way, while user is being notified about refueling their vehicle, the user can learn to conduct similar dialogue sessions to check on other matters related to the vehicle, in order that such matters can be addressed sooner to promote healthy routine of vehicle maintenance.
Krishnan ¶ [0052] a third-party application available at the vehicle computing device can provide a notification indicating that a fluid sensor of the vehicle is in an alarm state. This notification can be considered a past action because the notification is being provided at the graphical user interface rendered at the display panel within a threshold period of time of receiving the vehicle application data. In some implementations, a time at which the notification was presented can be determined based on time information that is characterized by the vehicle application data. The automated assistant can have access to an application for tracking certain properties of the vehicle, and therefore can also be privy to the information characterized by the notification about the fluid sensor. However, depending on when the notification was presented at the display panel of the vehicle computing device, the automated assistant may or may not bypass causing a notification about the fluid sensor to appear at the display panel
Krishnan ¶ [0053] when the application data characterizes past action at the operation 406, the method 400 can proceed to the operation 408, which can include generating other suggestion data that avoids suggesting the past action. As an example, when the third-party application provides a notification related to the fluid sensor, the other suggestion data can avoid suggesting obtaining information about the status of the fluid sensor by providing one or more suggestions that do not include requesting the information detailing the status of the fluid sensor. Rather, the automated assistant can generate suggestion data characterizing a spoken utterance for requesting information about purchasing the fluid corresponding to the fluid sensor, sending a message that includes the information detailing the status of the fluid sensor, placing a phone call to a business that performs maintenance associated with the fluid sensor, and/or any other request that is different from requesting the information detailing the status of the fluid sensor.
Krishnan ¶ [0054] 3rd sentence: when graphical user interface at third-party application includes content characterizing a route to a particular destination, and the automated assistant has not provided a notification related to the fluid sensor within a threshold time period, the automated assistant provide the suggestion corresponding to a notification for the fluid sensor)
- “retrieve network data from a network data store to perform the network action, wherein the network data is associated with a route to be driven by the vehicle”;
(Krishnan ¶ [0035] 4th sentence: Furthermore, the content can characterize a location of a nearby grocery store, details regarding the route through which the user is currently taking, and details about when the user 202 will arrive at the destination. A similar example at ¶ [0033] 3rd-5th sentences: the suggestion data can characterize a spoken utterance such as, “Assistant, where is the nearest grocery store?” This is spoken utterance can be suggested based on the navigation application conducting ongoing action of directing the user to a particular destination, and user 202 receiving a message regarding picking up coffee during their drive in vehicle 208. The suggestion data can be provided to the launcher OEM vehicle application, which can process the suggestion data and cause a third graphical element 214 to be presented within a third area 222 of graphical user interface 206. ¶ [0047] 2nd sentence: suggestion data characterizing the suggestions can be stored as assistant data 316 and processed by a suggestion ranking engine 336. Then at ¶ [0048] 1st-2nd sentences: suggestions can be ranked and/or presented according to the contextual data, which include details of a route of the user. For instance, the contextual data indicate whether the user is currently being driven on a portion of the route that lasts for X minutes and/or Y miles. In some implementations, when the portion of the route satisfies a time and/or distance threshold, suggestion engine 328 cause suggestion elements to be presented at a graphical user interface of the OEM vehicle computing device 318
Krishnan ¶ [0054] 3rd sentence: when graphical user interface at the third-party application includes content characterizing a route to a particular destination, and the automated assistant has not provided a notification related to fluid sensor within threshold time period, the automated assistant can provide the suggestion data corresponding to a notification for the fluid sensor).
- “generate an estimate based on processing of the sensor data and the network data and the positions using
where the estimate is associated with the route”;
(Krishnan ¶ [0006] 1st sentence, ¶ [0068] 2nd-3rd sentences: determining estimated time of arrival of user to a destination location, wherein determining the priority of the suggestion data relative to the priority data that is associated with other suggestion data is based on the estimated time of arrival of the user to the destination location. In some implementations, the method include, when the priority of the suggestion data relative to the priority data indicates that the suggestion data is prioritized over the other suggestion data: determining, subsequent to causing the particular suggestion element to be rendered at the graphical user interface of the vehicle application, that the particular suggestion element was selected, and causing, in response to determining that the particular suggestion element was selected, the priority of the suggestion data to be modified. ¶ [0011] 2nd-3rd sentences: For example, when a third-party application corresponds to a vehicle maintenance application, and the vehicle maintenance application provides a notification indicating that a part of the vehicle needs attention, the automated assistant can be informed of this notification via the OEM vehicle application. For instance, when the vehicle maintenance application indicates that the vehicle needs gas and/or charge, the vehicle maintenance application can provide notifications about nearby places to refuel the vehicle. ¶ [0012] 1st sentence: For example, when a generated suggestion corresponds to a nearby place to refuel the vehicle, the generated suggestion can be ranked (i.e., prioritized) lower than a separate suggestion that does not correspond to the nearby place to refuel. Indeed
Krishnan ¶ [0048] 1st -2nd sentences: suggestions can be ranked and/or presented according to the contextual data, which include details of a route of the user. For instance, the contextual data indicate whether the user is currently being driven on a portion of the route that lasts for X minutes and/or Y miles. In some implementations, when the portion of the route satisfies a time and/or distance threshold, suggestion engine 328 cause suggestion elements to be presented at a graphical user interface of the OEM vehicle computing device 318. Similarly,
Krishnan ¶[0073] 2nd-3rd sentences: determining, in response to receiving the suggestion data, an amount of time and/or distance remaining for the vehicle to navigate the user to the destination; and determining whether the amount of time and/or the distance remaining for the vehicle to navigate the user to the destination satisfies a threshold, wherein causing the third-party application to render the natural language content at the graphical user interface of the third-party application is performed when the amount of time and/or the distance remaining satisfies the threshold. In some implementations, the method include providing updated content data characterizing additional data that is being presented at the graphical user interface) “and”
- “generate the response to the query, wherein the response is indicative of the estimate”
(Krishnan ¶ [0026] 1st sentence: In response to user providing a spoken input that includes some amount of content of suggestion element 132, the automated assistant communicate an estimated time of arrival at destination. For example, ¶ [0035] 3rd-4th sentences where: app 226 provide content to launcher OEM vehicle application to provide details regarding the route through which the user is currently taking, and details about when user 202 will arrive at destination. For example, at Fig.2B below ETA [estimated time of arrival]: 6:02. Fig.2C new ETA: 6:16);
- “outputting the response through an output device within the vehicle”
(Krishnan Figs.2A-C element 104/204. ¶ [0039] 1st sentence: automated assistant 304 to perform a function e.g. generate output. ¶ [0043] 2nd-3rd sentences: The text converted from audio data parsed by data parsing engine 310 and made available to the automated assistant as textual data used to identify command phrases, actions, etc. output data provided by data parsing engine 310 provided to parameter module 312 to determine whether the user provided input that corresponds to intent, action, routine capable of being performed automated assistant 304 and/or an app or agent capable of being accessed via the automated assistant 304),
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Krishnan Figs. 1B-C and 2A-C in support of rejection arguments
Krishnan ¶ [0043] employs neural networks but does not explicitly recite to clearly anticipate:
- “an artificial intelligence network” at the generate an estimate limitation. However,
Penilla 2 in analogous art of intelligent vehicle assistance teaches or suggests:
- “an artificial intelligence network”
(Penilla 2 ¶ [0369] 1st sentence: Overtime, machine learning [interpreted as artificial intelligence] can be used to reinforce learned behavior, which provide weighting to certain inputs. For example at ¶ [0231] traffic data is obtained when the system determines that the user would likely be checking traffic info triggered when, user appears to be taking longer to drive home after work than normal, or the driver is driving slower than a current speed limit of a road, or a traffic accident is identified ahead, or based on learned use (e.g. user typically checks traffic at 5 pm on workday, etc. ¶ [0280] 4th-5th sentences: “you look like you are having trouble finding your destination, would you like a route to your destination?” Indeed per ¶ [0281] voice interchanges occur between user and vehicle which is context aware of online calendars of the user, status of local charging or fueling stations, etc., and geo-location of the vehicle, geo-location paths taken, preferred paths and navigation routes. Additionally, the vehicle and/or cloud system will maintain or have access to user preferences associated or maintained for a user account of the user. The preferences include preferred settings of the vehicle, settings preferred for specific periods of time or conditions, preferred goods and/or services of the user, preferred routes taken in certain geo-locations, historical selections made for vehicle settings, tendencies of the user, voice profiles for different tones of voice of user, etc. Another example at ¶ [0146] where Low Fuel or battery level/range prediction can be provided. This can be determined in numerous ways. For example, a fuel or battery measurement is taken, a calculation of range vs. destination, a calculation of range vs. next available fueling or battery/charging station. If a threshold percentage of conditions exist, e.g., greater than 50%, or greater than 80%, etc. If the threshold is met, as preset, the systems of the vehicle can provide a visual and/or auditory alert. A verbal query may be provided, e.g., “Your fuel is low, would you like directions to your favorite Shell or Chevron or the closer?”, “Your driving range is 60 miles, however no fueling stations or charging stations are available after the next fueling station located 5 miles from your location”, “Highly advisable, stop at “kwick-E Gas” located at 123 main street. Map this fueling station?”).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Krishnan’s apparatus/ method /non-transitory medium to have included Penilla 2’s teachings in order to have provided more intelligent data used in a more efficient manner via the connected vehicle (Penilla 2 ¶ [0115] 2nd sentence & MPEP 2143 G), with less distracted driving and more efficient usage of user’s time (Penilla 2 ¶ [0228] 3rd sentence & MPEP 2143 G) while taking advantage of a distributed cloud computing system that would have enabled users of various vehicles, structures and objects to have accessed the Internet, and be presented with more flexible processing power that have provided the requested services in a more effective manner (Penilla 2 ¶ [0127] last sentence and MPEP 2143 G). The predictability of such modification would have been corroborated by the broad level of skill of one or ordinary skills in the art as articulated by Krishnan ¶ [0066] 3rd sentence in view of Penilla 2 ¶ [0367] 1st sentence.
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar intelligent vehicle assistance field of endeavor. In such combination each element merely would have performed same function as it did separately, and one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Krishnan in view of Penilla 2 above, the to be combined elements would have fitted together, like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claims 4,15. Krishnan / Penilla 2 teaches all the limitations in claims 1,12 above. Furthermore,
Krishnan teaches or suggests: “wherein the input device includes a touch interface of a touchscreen, wherein the input data includes a touch input detected using the touch interface of the touchscreen” (Krishnan ¶ [0058] 1st-3rd sentences: Fig.5 is a block diagram of an example computer system 510 that typically includes at least one processor 514 which communicates with a number of peripheral devices including, user interface input devices 522. ¶ [0059] 2nd sentence:
In general, use of the term input device is intended to include all possible types of devices and ways to input information into computer system 510 or onto a communication network. For example, at ¶ [0038] 3rd sentence: user interact automated assistant 304 via assistant interface, which can be a touch screen display, capable of providing an interface between a user and an application. ¶ [0039] 1st-2nd sentences: a user initialize the automated assistant 304 by providing input to an assistant interface (e.g., assistant interface of the OEM vehicle computing device 318 and/or an assistant interface of any other client device) to cause the automated assistant 304 to perform a function (e.g. to generate an input etc.). The OEM vehicle computing device 318 can include a display device, which can be a display panel that includes a touch interface for receiving touch inputs and/or gestures for allowing a user to control applications of the OEM vehicle computing device 318 via the touch interface), “wherein the output device includes a screen of the touchscreen, and wherein the output device outputs the response by displaying the response on the screen of the touchscreen” (Krishnan ¶ [0058] 1st-3rd sentences: Fig.5 is a block diagram of an example computer system 510 that typically includes at least one processor 514 which communicates with a number of peripheral devices including, user interface output devices 520. ¶ [0060] 4th sentence: In general, use of the term output device is intended to include all possible types of devices and ways to output information from computer system 510 to the user or to another machine or computer system. For example, at ¶ [0039] 1st-2nd sentences: automated assistant 304 to perform a function to generate an output, etc.. The OEM vehicle computing device 318 can include a display device, which can be a display panel that includes a touch interface for receiving touch inputs and/or gestures for allowing a user to control applications of the OEM vehicle computing device 318 via the touch interface).
Claims 5,16 Krishnan / Penilla 2 teaches all the limitations in claims 1,12 above.
Krishnan teaches or suggests: “wherein the input device includes a microphone,
wherein the input data includes a voice input recorded using the microphone, wherein interpreting the input data is based on a voice recognition system” (Krishnan ¶ [0038] 3rd sentence: a user interact with the automated assistant 304 via an assistant interface, which can be a microphone, capable of providing an interface between a user and an application. ¶ [0039] 3rd sentence: the OEM vehicle computing device 318 can provide a user interface, such as a microphone(s), for receiving spoken natural language inputs from a user. ¶ [0042] 2nd-3rd sentences: input processing engine 306 include a speech processing engine 308, which can process audio data received at an assistant interface to identify the text embodied in the audio data. The audio data can be transmitted from OEM vehicle computing device 318 to server device 302 to preserve computational resources at the OEM vehicle computing device 318. ¶ [0043] speech recognition algorithm, employ neural networks, and/or statistical models to identify groups of audio data corresponding to words or phrases. Similarly, ¶ [0059] 1st sentence) “wherein the output device includes a speaker, and wherein the output device outputs the response by playing the response through the speaker” (Krishnan ¶ [0060] 1st, 3rd sentences: user interface output devices 520 include audio output devices. In general, the term output device is intended to include all possible types of devices and ways to output information from computer system 510 to the user or to another machine or computer system).
Penilla 2 also teaches or suggests: “wherein the input device includes a microphone,
wherein the input data includes a voice input recorded using the microphone, wherein interpreting the input data is based on a voice recognition system” (Penilla 2 ¶ [0135] 2nd sentence: using machine learning techniques that process different modalities, such as speech recognition, speech waveforms, natural language processing, ¶ [0142] 1st-4th sentences: Fig.2 illustrates an example of processing of voice input, in one embodiment. For example, the vehicle receives voice input 130. The voice input is examined in operation 131 against user data graph 108′ of learned behavior and/or tones of voice. Based on this processing, in operation 132, a voice profile is identified for the voice input. In operation 133, the voice response is identified for the voice profile. Similar mid-¶[0300] voice response is moderated for the tone identified in voice input 14) “wherein the output device includes a speaker, and wherein the output device outputs the response by playing the response through the speaker” (Penilla 2 ¶ [0145] 1st sentence: if threshold percentage of conditions exist, the electronics/systems of the vehicle provide verbal feedback/output to the user (e.g., via speakers of the vehicle or speakers of a portable device, or combinations thereof).
Rationales to have modified/combined Krishnan / Penilla 2 are above and reincorporated.
Claims 6,17 Krishnan / Penilla 2 teaches all the limitations in claims 1,12 above. Further,
Krishnan teaches or suggests:
- “cause the vehicle to perform an action associated with the response”
(Krishnan ¶ [0021] 4th sentence: While user 102 is riding in vehicle 108, one or more applications accessible via [original equipment] OEM vehicle computing device 104 communicate with a particular OEM vehicle application 124 to cause certain content to be rendered at the graphical user interface 106. Specifically, per ¶ [0065] 1st sentence: the systems collects user preferences. For example, at ¶ [0024] 3rd sentence vehicle app data correspond to content of previous interaction or dialog session between user and automated assistant, relate to a previous action performed by automated assistant, indicate action associated with location etc.
Krishnan ¶ [0026] 1st sentence: In response to user providing spoken input that includes some amount of content of suggestion element 132, the automated assistant communicate an estimated time of arrival at the destination to user. For example, ¶ [0035] 3rd-4th sentences where: app 226 provide content to launcher OEM vehicle application for providing details regarding the route through which the user is currently taking, and details about when user 202 will arrive at destination. For example, at Fig.2B below ETA: 6:02. Fig.2C new ETA: 6:16. Krishnan ¶ [0043] 2nd-3rd sentences: The text converted from audio data parsed by data parsing engine 310 and made available to the automated assistant as textual data used to identify command phrases, actions, etc. output data provided by data parsing engine 310 can be provided to a parameter module 312 to determine whether the user provided an input that corresponds to particular intent, action, and/or routine capable of being performed automated assistant 304 and/or an app or agent that is capable of being accessed via the automated assistant 304.
Krishnan ¶ [0048] 2nd-3rd sentences: contextual data indicate whether the user is currently being driven on a portion of the route that lasts for X minutes and/or Y miles. when the portion of route satisfies a time and/or distance threshold the suggestion engine 328 can cause suggestion elements to be presented at a graphical user interface of the OEM vehicle computing device 318)
Penilla 2 also teaches or suggest:
- “cause the vehicle to perform an action associated with the response”
(Penilla 2 ¶ [0148] 4th sentence: the vehicle can react in various ways, for example, the vehicle queries are clear, the vehicle queries are brief, the GUI on all screens change to limit distractions, routes are changed to quickest based on traffic, and not most direct, accident information is automatically displayed, etc. ¶ [0149] 5th-8th sentences: vehicle react by scaling back the number of questions posed to the user while in the vehicle. In another embodiment, the vehicle suggests turning off queries visually instead of verbally. In another embodiment, the vehicle GUI becomes more standard and easy to read. In still another embodiment, the vehicle changes ambient lighting to a calming hue. ¶ [0154] In still other embodiments, mood sensor and vehicle reactions are refined over time with more use. For instance, if a user is angry, yet still wants more interaction and more questions from the vehicle, the vehicle learns that the user does not mind queries and will adjust the number of queries based on how the user reacts over time.
Penilla 2 ¶ [0199] 3rd sentence: decision and action engine 1218 also has the ability to change vehicle controls automatically on behalf of a user).
Rationales to have modified/combined Krishnan / Penilla 2 were presented above.
Claims 7,18 Krishnan / Penilla 2 teaches all the limitations in claims 1,12 above. Further,
Krishnan teaches/suggests: “wherein the estimate is at least one of a recommended departure time for the route or an estimated arrival time for the route”.
(Krishnan ¶ [0006] 1st sentence: For example, the automated assistant can provide a suggestion element at the graphical user interface for invoking a messaging application to provide an estimated s-time of arrival. Similarly, ¶ [0026] 1st sentence. For example, ¶ [0035] 3rd-4th sentences where: app 226 provide content to launcher OEM vehicle application to provide details regarding the route through which the user is currently taking, and details about when user 202 will arrive at destination. For example, at Fig.2B below ETA [estimated time of arrival]: 6:02. Fig.2C new ETA: 6:16).
Claims 8,19 Krishnan / Penilla 2 teaches all the limitations in claims 1,12 above. Further,
Krishnan teaches or suggest: “wherein the network data identifies at least one of traffic conditions along the route” (Krishnan ¶ [0048] 2nd sentence: contextual data indicate whether the user is currently being driven on a portion of the route that lasts for X minutes and/or Y miles), “weather conditions along the route, or road conditions along the route” (Krishnan ¶ [0048] 2nd-3rd sentences: contextual data indicate whether the user is currently being driven on a portion of the route that lasts for X minutes and/or Y miles. In some implementations, when the portion of the route satisfies a time and/or distance threshold, suggestion engine 328 cause suggestion elements to be presented at a graphical user interface of the OEM vehicle computing device 318. ¶[0073] 2nd-3rd sentences: determining, in response to receiving the suggestion data, an amount of time and/or distance remaining for the vehicle to navigate the user to the destination; and determining whether the amount of time and/or the distance remaining for the vehicle to navigate the user to the destination satisfies a threshold, wherein causing the third-party application to render the natural language content at the graphical user interface of the third-party application is performed when the amount of time and/or the distance remaining satisfies the threshold. In some implementations, the method include providing updated content data characterizing additional data that is being presented at the graphical user interface)
Penilla 2 also teaches or suggest: “wherein the network data identifies at least one of traffic conditions along the route, weather conditions along the route, or road conditions along the route” (Penilla 2 ¶ [0147] 4th sentence noting heavy traffic ¶ [0148] 5th sentence, ¶ [0176] 3rd sentence noting traffic data is provided. ¶ [0212] contextual information that may be viewed may include historical travel times during the time of day, traffic information associated to the current geo-location of the vehicle, the current weather. ¶ [0231] traffic data is obtained when the system determines that the user would likely be checking traffic information. This may be triggered when, for example, the user appears to be taking longer to drive home after work than normal, or the driver is driving slower than a current speed limit of a road, or a traffic accident is identified ahead, or based on learned use (e.g., the user typically checks traffic at 5 pm on a workday, etc. Similarly, ¶ [0226], ¶ [0280] 3rd - 5th sentences: noting environment conditions, geo-location, time of day, time of week, etc. In one example, the vehicle can determine if you are lost by examining GPS travel patterns of your travels, and other factors (e.g., searching for an address on the mapping function, making calls, etc.). In one embodiment, the vehicle can verbalize: “you look like you are having trouble finding your destination, would you like a route to your destination?” In other examples, the vehicle can identify that the driver is having trouble staying within the lines of a lane. If the time of day is late at night, and if the user is far from home, and/or the vehicle is traveling to a mapped destination, and/or the vehicle has been traveling for an extended period of time, e.g., 6-12 hours, the vehicle system can deduct that the user is getting tired, and may need to rest or find a hotel. ¶ [0281] 1st sentence. ¶ [0285] 3rd-4th sentences: the system can also determine the driving conditions, environmental conditions, geo-location, and mood of the user. If the vehicle is traversing a curvy road, in heavy traffic, in bad weather, etc., the system may refrain or not ask questions to the user that may distract the user).
Rationales to have modified/combined Krishnan / Penilla 2 were presented above.
Claim 9 Krishnan / Penilla 2 teaches all the limitations in claims 1 above. Further,
Krishnan teaches or suggests: “wherein the network data identifies a location of a merchant along the route, and wherein the estimate is associated with a stop along the route at the location of the merchant” (Krishnan ¶ [0011] 3rd-5th sentences: when the vehicle maintenance application indicates that the vehicle needs gas and/or charge, the vehicle maintenance application provide notifications about nearby places to refuel the vehicle. The OEM vehicle application generate data based on the notifications and/or content being rendered at the graphical user interface of the third-party application, and provide the generated data to the automated assistant. The automated assistant can use the data to generate suggestions, which can be ranked and/or filtered according to what has already been presented to the user. ¶ [0012] For example, when a generated suggestion corresponds to a nearby place to refuel the vehicle, the generated suggestion can be ranked (i.e. prioritized) lower than a separate suggestion that does not correspond to nearby place to refuel the vehicle. Similarly, ¶ [0033] 3rd-4th sentences: the suggestion data can characterize a spoken utterance such as, “Assistant, where is the nearest grocery store?” This is spoken utterance can be suggested based on the navigation application conducting an ongoing action of directing user to a particular destination, and user 202 receiving a message regarding picking up coffee during their drive in the vehicle 208. ¶ [0035] 4th sentence: the content can characterize a location of a nearby grocery store, details regarding the route through which user is currently taking, and details about when user 202 will arrive at destination).
Penilla 2 also teaches or suggests “wherein the network data identifies a location of a merchant along the route, and wherein the estimate is associated with a stop along the route at the location of the merchant” (Penilla 2 ¶ [0206] 3rd-4th sentences: The fuel gauge in this example is shown to have appeared on the dashboard display because the vehicle's state is that the fuel is low and requires refueling. In one embodiment, the vehicle computer can be communicating with cloud services 120, which will automatically identify information regarding available fueling stations nearby. ¶ [0207] For example, the displays of the vehicle shown in Fig.13 illustrates that contextual information can be provided as a recommendation, which identifies that a gas station is within 0.25 miles of the current location of the vehicle. In addition, a mapping service or map program of the vehicle can be automatically displayed on one of the displays of the vehicle showing the location of the gas station (e.g., Ted's Gas). Accordingly, the information being displayed on the vehicle is contextually related to the state of the vehicle, the location of the vehicle, and applications are automatically loaded and provided for generating information relevant to the vehicle and its state
Rationales to have modified/combined Krishnan / Penilla 2 were presented above.
Claim 10 Krishnan / Penilla 2 teaches all the limitations in claims 1 above. Further,
Krishnan does not “identify the route based on the sensor data and the network data, wherein the estimate is associated with a time during which the vehicle is to be driving the route”. Penilla 2 in analogous art of intelligent vehicle assistance teaches or suggests:
- “identify the route based on the sensor data and the network data, wherein the estimate is associated with a time during which the vehicle is to be driving the route”. (Penilla 2 ¶ [0212] contextual information that may be viewed may include them on a fuel that remains in the vehicle at the particular time (which is a state of the vehicle, among many different types of states of the vehicle), the day of the week, whether the day of the week of the holiday, information from the personal calendar, historical travel times during the time of day, the time of day, …, traffic information associated to the current geo-location of the vehicle, the current weather, learned past behavior (when the user likes to stop for coffee), nearby coffee shops (coffee shops being a learned type of good liked by the user), discounts located nearby, discounts located nearby other services that are needed at a particular point in time, and other factors. Similarly, ¶ [0026]. A different example at example at ¶ [0233] If the user's shows that the user does not have appointments or does not urgently need to arrive at the destination, the system may not provide a re-route option if the extra distance is more than the user likes to drive. Other contextual information can be mined, including a learned profile of the user, which shows what the user likes, does, prefers, has done over time as a pattern, etc. ¶ [0250] Further, the culling of supplemental content can also changes over time as the driver moves around and the geo-location changes. For example, if the user's favorite fueling station is nearby, at 8:15 am, and the vehicle needs fuel (but still has enough to driver another 90 miles), but the user needs to take a conference call from 8:15-8:30 am, the system will not surface (e.g., cull so this supplemental content is no provided to a display or audio output) information regarding the nearby fueling station. Instead, the vehicle will surface and notify the user of the conference call and/or show the option for another fueling station that is along the path or near the destination.
Penilla 2 ¶ [0369] Overtime, machine learning can be used to reinforce learned behavior, which can provide weighting to certain inputs. For instance, the more times a user turns on the windshield wipers when it is raining, and within two minutes of turning on the car, may signal that this patterns is likely to happen again. In another example, if a user stops to charge his vehicle at a particular charge station, which is 20 miles from his home, repeatedly on Tuesdays, at 6 pm, when nobody is a passenger in the vehicle, and the vehicle had less than 5% charge, may be used as a strong pattern that this may occur again in the future. This data, combined with other data, may be used to recommend data regarding the charge station in advance, so that the user need no look up the charge station to reserve a spot, or the like. It should be understood that these are just simplified examples to convey examples of recommendations which may be based on some learning, preferences or pattern analysis, or likelihoods).
Rationales to have modified/combined Krishnan / Penilla 2 are above and reincorporated.
Claim 11. Krishnan / Penilla 2 teaches all the limitations in claims 1 above. Further,
Krishnan ¶ [0011] 3rd sentence takes into consideration whether the vehicle is gas or and/or charge to provide notifications about nearby places to refuel the vehicle
Krishnan however does not explicitly recite to clearly anticipate: “wherein the network data includes vehicle specifications associated with the vehicle, and wherein the estimate is based on the sensor data and the vehicle specifications” as claimed.
Penilla 2 in analogous art of intelligent vehicle assistance teaches or suggests:
- “wherein the network data includes vehicle specifications associated with the vehicle, and wherein the estimate is based on the sensor data and the vehicle specifications”
(Penilla 2 ¶ [0146] Low Fuel or battery [specification for] level/range prediction can be provided. This can be determined in numerous ways. For example, a fuel or battery measurement is taken, a calculation of range v. destination, a calculation of range v. next available fueling or battery/ charging station. If a threshold percentage of conditions exist, e.g. > 50%, 80%, etc. If the threshold is met, as preset, the systems of the vehicle can provide a visual and/or auditory alert).
Rationales to have modified/combined Krishnan / Penilla 2 are above and reincorporated.
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Claims 2,13 are rejected under 35 U.S.C. 103 as being unpatentable over:
Krishnan / Penilla 2 as applied to claims 1,12, in view of
Suzuki et al, US 20210114618 A1 hereinafter Suzuki. As per,
Claims 2,13 Krishnan / Penilla 2 teaches all the limitations in claims 1,12 above.
Krishnan ¶ [0039] last sentence teaches an OEM [original equipment manufacturer] vehicle computing device 318 that includes a camera,
Penilla 2 ¶ [0152] last sentence also teaches sensors be used to detect a driver's affect, such as a camera.
Krishnan / Penilla 2 however as a combination does not explicitly recite:
- “analyze visual data from the sensor data using a vision system to detect a change in an environment that the vehicle is in, wherein the plurality of sensors of the vehicle include a camera that captures the visual data, and wherein the estimate is based on the change in the environment” as claimed.
Suzuki however in analogous art of vehicle sensor evaluation teaches or suggests:
- “analyze visual data from the sensor data using a vision system to detect a change in an environment that the vehicle is in, wherein the plurality of sensors of the vehicle include a camera that captures the visual data, and wherein the estimate is based on the change in the environment” (Suzuki ¶ [0027] 1st,3rd,5th sentence: detection device 50 detects the situation around the target vehicle. Although not particularly limited, the detection device 50 according to one or more embodiments of the present invention includes a camera 51. Camera 51 is, for example, an imaging device including an imaging element such as a CCD or a CMOS. ¶ [0111] Figs. 7A-C illustrate display examples of the cause information. As illustrated in Fig.7A, the cause of a change in the driving plan with the route change (rerouting) is presented by the cause information including the text “Vehicle will reroute because it cannot enter right-turn lane.” The cause information may include a captured image G1 indicating the state in which “the vehicle cannot enter the right-turn lane.” As illustrated in Fig.7B, the cause of a change in the driving plan with the delay in the estimated time of arrival is presented by the cause information including the text “Time of arrival will be delayed by 5 minutes due to congestion.” The cause information may include the captured image G1 indicating the state of “congestion.” As illustrated in Fig.7C, the cause of a change in the driving plan with the change of the vehicle to be allocated is presented by the cause information including the text “Designated vehicle has some trouble and another vehicle will be allocated.” The cause information may include an image G3 indicating the “trouble in the vehicle”).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Krishnan / Penilla 2’ “apparatus” / “method” to have included Suzuki’s teachings in order to have periodically checked and updated the driving paths to have determined a better course of action (Suzuki ¶ [0108]-¶ [0112] in view of MPEP 2143 G)
Further, the claimed invention can also be viewed as a mere combination of old elements in a similar field of endeavor dealing with vehicle sensor evaluation. In such combination each element merely would have performed the same analytical and detection function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Krishnan / Penilla 2 in view of Suzuki, the to be combined elements would have fitted together like puzzle pieces in complementary, logical, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
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Claims 3,14 are rejected under 35 U.S.C. 103 as being unpatentable over:
Krishnan / Penilla 2 as applied to claims 1,12, in view of
Pedersen et al, US 20200064831 A1 hereinafter Pedersen. As per,
Claims 3,14. Krishnan / Penilla 2 teaches all the limitations in claims 1,12 above.
Krishnan / Penilla 2 does not explicitly recite:
- “analyze Light Detection and Ranging (LiDAR) data from the sensor data to detect a change in an environment that the vehicle is in, wherein the plurality of sensors of the vehicle include a LiDAR sensor that captures the LiDAR data, and wherein the estimate is based on the change in the environment” as claimed.
Pedersen however in analogous vehicle sensor evaluation teaches or suggests:
- “analyze Light Detection and Ranging (LiDAR) data from the sensor data to detect a change in an environment that the vehicle is in, wherein the plurality of sensors of the vehicle include a LiDAR sensor that captures the LiDAR data, and wherein the estimate is based on the change in the environment”
(Pedersen ¶ [0088] maintenance data associated with ongoing maintenance of the vehicle including scheduled tune-ups; vehicle energy state data, including an amount of fuel remaining or an amount of battery charge remaining; sensor data including vehicle sensor data based on outputs from sensors including, optical sensors (e.g., light detection and ranging sensors), … can be used to generate a representation of the physical environment in and around the vehicle;…; current task data associated with a current task (e.g., drop off a passenger) of the vehicle including an estimated time for completion of the current task; destination data associated with a destination of the vehicle, including one or more routes that the vehicle can traverse to arrive at the destination; vehicle stoppage data associated with the stoppage of other vehicles (e.g., other vehicles stopped in a roadway); construction activity data associated with construction activity and the location of construction zones; lane direction data including data associated with the regulated traffic flow direction in the lane of a roadway; lane closure data associated with data relating to the closure of lanes including closure of roads; road surface condition data associated with the type (e.g., paved road, unpaved road) and condition (e.g., slippery due to ice) of road surfaces; and pedestrian activity data.
Pedersen ¶ 0090] The determination that the state data satisfies the state criterion can be based on whether the value of one of the properties or attributes of the state data that equals a state threshold value, exceeds a state threshold value, or is less than a state threshold value. Further, satisfaction of the state criterion can be based on the evaluation of more than one of the properties or attributes of the state data. For example, satisfying the state criterion include a state criterion for travel of the vehicle to a destination within an estimated amount of time (e.g., vehicle is scheduled to arrive at an airport within thirty minutes). The sensor data of the state data can indicate that the main roadway on the shortest path to the airport is obstructed due to a fallen tree and that waiting for the fallen tree to be removed would result in the estimated arrival time at the airport exceeding the estimated amount of time by two hours. The state criterion could have a tardiness threshold of ten percent of the estimated amount of time of thirty minutes (three minutes) which is exceeded by the estimated time of two hours before the obstruction is removed from the roadway. Accordingly, the state criterion would be satisfied by exceeding the tardiness threshold).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Krishnan / Penilla 2’s “apparatus” / “method” to have included Pedersen’s teachings in order to have provided a more effective way to have generated solutions associated with the monitoring and operation of vehicles, as well as to have effectively processed or generated state data associated with the state of a vehicle and a vehicle environment (e.g., the surrounding environment around the vehicle) to generate solution data that can assist any of the vehicle, a driver in the vehicle, or a remote operator of the vehicle (e.g., a human or an automated remote operator). Pedersen would have also more efficiently assigned solution data to operators, including vehicle managers, in different ways based on data including the operator's familiarity with the location of the autonomous vehicle, the complexity of the solution, and the proficiency of vehicle managers. Further, Pedersen would have further efficiently saved solution data for future retrieval, based at least on completion time by respective vehicle managers (Pedersen ¶ [0019] in view of MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with vehicle related evaluation. In such combination each element merely would have performed the same analytical and detection function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Krishnan / Penilla 2 in further view of Pedersen, the to be combined elements would have fitted together like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable
Conclusion
Following art is made of record and considered pertinent to Applicant’s disclosure:
* WO 2020091806 A1 teaching context aware navigation voice assistant
* AirWire Technologies and Reliance Jio Partner to Bring Connected Car Apps and Services to India, Business Wire, Feb 26, 2017
* US 20210404833 A1 by Google teaching at its Abstract: To provide context-aware audio navigation instructions, the server obtains sets of audio navigation instructions previously provided to users along with sensor data descriptive of a context in which the audio navigation instructions were provided and an indication of whether a driver correctly responded to the audio navigation instructions. The server trains the machine learning model using this data, to identify audio navigation instruction parameters for a particular context. In response to a request for navigation directions, the server device receives sensor data from the client computing device generating the request that is indicative of the environment surrounding the client computing device. The server device then applies the sensor data and navigation instructions to the machine learning model to generate a set of audio navigation instructions responsive to the request. The server device provides the set of audio navigation instructions to the client computing device. For additional details see ¶ [0019], ¶ [0060] - ¶ [0064], Fig. 5 step 504: obtain context signals while audio navigation instructions were provided -> step 506: obtain instructions of whether drivers correctly followed instructions -> step 508: generate machine learning based on whether drivers
* US 20140136187 A1 ¶ [0108] 6th sentence: if user requests the vehicle personal assistant 112 to "Remind me when I need to change my transmission fluid," the vehicle personal assistant 112 may retrieve data indicating the preferred fluid change schedule for the vehicle 104 (e.g. every 30,000 miles) from the knowledge base 140. ¶ [0109] 1st-2nd sentences: method 400 then stores the interpreted conditional instruction in a manner that allows it to be checked as a condition against the current context. For example, method 400 periodically (e.g., each time the odometer reading changes) check whether current odometer reading is a multiple of 30,000, and notify the user when such an event occurs)
* US 20190178661 A1 teaching Navigation apparatus, system and image display method
- ¶ [0095] last sentence: the information acquisition unit 13 acquires use history data including the numbers of times of use of gas stations that the user used in the past on a brand-by-brand basis and use history data including the numbers of times of use of convenience stores the user used in the past on a chain-by-chain basis from the storage unit 19 as the use history information of the facilities related to the suggested candidates. Sakaida ¶ [0095] 2nd sentence: the facilities related to the suggested candidates include, e.g., gas stations of brands that are the same as brands of the gas stations that are the suggested candidates and convenience stores often attached to gas stations
- ¶ [0114] 1st, 9th sentences: Gas stations A to I, indicated in Fig.9, are gas stations located in vicinity of the planned running route. in step S86, the priority provision unit 16 determines gas station E closest to the destination from among gas stations of a brand agreeing with brand A (see Fig.10) of gas stations that user uses most frequently, the reaching probability for the gas stations being level 1. Figs.7,12, ¶ [0111] 2nd, 4th sentences: vehicle mounted apparatus 1 displays such refueling point in highlighted manner to suggest a refueling point the user desires. Therefore, providing a high priority to the gas station the user often used in the past and displaying such gas station in a highlighted manner enable suggesting a gas station user wishes to choose preferentially. Also, vehicle-mounted apparatus 1 displays info of a brand of a gas station that is a suggested candidate together with an icon of the suggested candidate, enabling provision of relevant info for determining whether or not to choose the gas station to the user. Also ¶ [0114]
* US 20090006194 A1 reciting at ¶ [0025] User X continues along her route home and the time is now 5:15 pm, and close to dinner time. Knowing that she and her family have preference for Chinese food, the navigation system outputs a custom advertisement for a local Chinese food restaurant near her house, along her route. If she presses the custom icon, a dialog box appears asking if she would like to send them her favorite order, (or possibly offering the option to see a menu). If she accepts, the navigation system adds the restaurant as a stop along her route, where she can stop and pick up her dinner order.
* US 20150195678 A1 teaching situation sensitive push of information to a vehicle with emphasis on Fig.2A step 126 identifying nearby restaurants, rest stops, rest rooms, hotels, coffee shops and other potential stops
* US 20160349075 A1 teaching route scheduling and presenting route-based fuel information with emphasis on Figs. 3-10 and associated text for recommending specific products and ¶ [0045] reciting: For example, if a current vehicle position is not far from home, a house icon 304 indicating a distance from the current vehicle position and the location of the home can be placed on the second segment 302 based on a distance between the current vehicle position and the location of the house. When the driver enters a restaurant as a point of interest (POI) which is estimated to consume a fuel amount to reach from the current vehicle position less than the current fuel amount, the screen 300 also displays a restaurant icon 305 indicating the restaurant on the second segment 302 based on the drivable distance and the estimated fuel amount to be consumed to reach the restaurant. In this way, any POI search result may be included in consideration of fuel planning. Alternatively, a mere address entry or a possible entry from a list in the driver's address book, either in the navigation system, in the external device connected to the navigation system or in a cloud resource, may be used as the point.
* US 20170256106 A1 teaching at ¶ [0128] Fig. 4 [below] is a diagram showing an example of info provided by the informing device G1. ¶ [0129] Upon receiving the store visit promotion signal from store visit promotion signal output unit 153, the informing device G1 displays a figure for promoting a store visit, as shown in Fig. 4. The figure in Fig. 4 expresses a vehicle going to a store. The figure in Fig.4 prompts the user to visit a store. ¶ [0135] Fig. 6 [below] is a diagram showing an example of information provided by the informing device G2 shown in Fig. 5.
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* US 20100106401 A1 Traveling guidance system, traveling guidance method, and computer program reciting at ¶ [0003] “Conventionally, when the driver of a vehicle looks at the fuel gauge when traveling and notices that the remaining fuel is low, the driver drives to a gas station he or she remembers, to fuel the vehicle, Further, when using a navigation system, it is possible to detect the vehicle location with the navigation system even in an area unfamiliar to the driver, and then nearby fueling facilities are displayed on the navigation screen, so that the driver can find the location of the nearest gas station by referring to the displayed navigation screen”.
* US 20200250898 A1 with emphasis on
- ¶ [0021] navigation module 108 may be configured, to determine an expected travel time from current location of vehicle 10 to intended destination. An intended destination may be a location where scheduled appointments, meetings, or other events are scheduled to take place as determined by user input, a scheduling application, a learned pattern-of-life of the user, or the like. The expected travel time may be based on, expected speed from one location to other (e.g. current location of vehicle 10 to appointment location) and distance between the two locations. The expected travel time may be calculated for multiple routes between the two or more locations (e.g. through number of waypoints). The expected travel time may be used in conjunction with other data (e.g. appointment time, a user punctuality preference or user punctuality preferences or the like) to determine a recommended departure time. The recommended departure time may be a suggested time for leaving one location in order to make it to a second location at a particular time. The recommended departure time may be calculated and communicated to a user using one or more of the systems described herein. In embodiments, the system 100 may send a notification to a user at a predetermined interval before the recommended departure time, informing the user of weather and other characteristics at vehicle's location or, along a travel route. The system 100 may request a user input in response to the estimated departure time and/or the recommended departure time.
- ¶ [0021] 4th-7th sentences: The expected travel time may be calculated for multiple routes between the two or more locations (e.g. through a number of waypoints). The expected travel time may be used in conjunction with other data (e.g., the appointment time, a user punctuality preference or user punctuality preferences, or the like) to determine a recommended departure time. The recommended departure time may be a suggested time for leaving one location in order to make it to a second location at a particular time. The recommended departure time may be calculated and communicated to a user using one or more of the systems described herein. In embodiments, the system 100 may send a notification to a user at a predetermined interval before the recommended departure time, informing the user of weather and other characteristics at the vehicle's location or, in some embodiments, along a travel route.
- ¶ [0034] 1st-5th sentences: At 302 system 100 determine expected departure time that the user of the vehicle is expected to depart in the vehicle in order to go to a second location. The expected departure time may be based on data generated or processed by the system 100. For example, data may be processed by navigation module 108 and pattern-of-life module 118 to determine the expected departure time. The expected departure time may be based on info contained in an electronic communication or appointment reminder accessible through one or more email servers, text message servers, or other apps.
- ¶ [0027]: determine an anticipated departure time based on an appointment location, an appointment time, a distance to the appointment, an expected travel time to the appointment location (based on traffic congestion, refueling/recharging, and the like), and other user preference as described in greater detail herein.
- ¶ [0028] 3rd sentence: if a user is habitually 15 minutes early for scheduled appointments, the pattern-of-life module 118 may utilize the machine learning algorithms to determine that the user prefers to be early for appointments and may adjust the recommended departure time accordingly.
- ¶ [0028] 5th sentence: if a user prefers being early to appointments scheduled with a particular person or group or groups of persons, the pattern-of-life module 118 may apply a punctuality buffer, for example, and inform the user 5 minutes earlier than normal that he or she should leave for their appointment and request a user input as to cabin climate described herein.
- ¶ [0028] 7th sentence: For example, a user may prefer to be early to doctor's appointments, job interviews, work shifts, or the like
* US 20100082246 A1 Navigation Features for Obtaining Fuel Before Returning A Rental Vehicle ¶ [0052]: “selection of the gasoline station may be based on one or more factors. The gasoline station to be identified or determined may be the gasoline station that is nearest or closest to the rental service return location 508 (such as the gasoline station 528), the gasoline station that has the cheapest or lowest gasoline price, the gasoline station of a preferred name brand or type (e.g., a user may want to use a discount or preferred card at a specific type of gasoline station or a user may prefer one type of gasoline over another type), and/or the gasoline station that is located conveniently or relatively the most conveniently with respect to driving to the rental return service return location. More, fewer, or different factors may be considered”.
* US 20030065427 A1 Method and device for interfacing a driver information system using a voice portal server reciting at ¶ [0026]: “Find a gas station enroute") to determine the best gas station on the route. This gas station request may also be qualified in any number of ways (e.g. "Service station brand A," "Cheapest gas," "gas station closest to the highway," "gas station closest to the destination," etc.)”
* US 20200211553 A1 Two-way in-vehicle virtual personal assistant
* US 20200193549 A1 Autonomous Vehicle Monitoring Using Generated Interfaces reciting at
- ¶ [0039]: “if the vehicle is running low on fuel (vehicle state) and the traffic is congested (external state), then the issue type determined could be a fuel issue and the system could then navigate the vehicle to the nearest gas station based on knowing that the vehicle could not reach the current task's destination”.
* US 20200092689 A1 Outputting an entry point to a target service reciting at
- ¶ [0092]: For example, an event field included in Detectable Event A is "enters Gas Station A." As such, the condition associated with determining that Detectable Event A has occurred is: the current GPS data associated with a vehicle matches the location data corresponding to the area within Gas Station A. An event field included in Detectable event F is "arrives in vicinity of Gas Station A." As such, the condition associated with determining that Detectable Event F has occurred is: the current GPS data associated with a vehicle matches to the area around Gas Station A. An event field included in Detectable event B is "vehicle tire pressure is too high or too low." As such, the condition associated with determining that Detectable Event B has occurred is: the vehicle tire pressure is less than a first threshold tire pressure value, or vehicle tire pressure is greater than a second threshold tire pressure value, where the first threshold tire pressure value is less than the second threshold tire pressure value. An event field included in Detectable event C is the "distance between vehicle and third party object is too short."
* US 20150210292 A1 Gaze driven interaction for a vehicle reciting at ¶ [0061]: “As a further embodiment, the message could be linked to a further action, for example display message "Low on fuel," and upon gaze, new options could appear "Navigate to nearest fuel station" and "Ignore," these options could be selected by gaze, spoken word, and/or other input such as button/control input”.
* US 20140200038 A1 Location Assisted Service Capability Monitoring reciting at ¶ [0071]: “In response to receiving a notification that the mobile device will be entering such a region within five minutes and is predicted to remain within that region for thirty minutes, the vehicular navigation system can measure the amount of fuel that the vehicle currently has in its tank, and can proactively search for gas stations along the route or in the regions through which the vehicle is predicted to travel during those thirty minutes. The vehicular navigation system can determine, based on all of this information, whether the vehicle will need to stop at one of these gas stations in order to refuel before the tank becomes empty. The vehicular navigation system can then notify its user of such a need, and of the location of nearby gas stations, in advance of the time that the mobile device and the vehicle carrying it enter into the region of poor quality communication services. Thus, by registering for such notifications from the mobile device, the vehicular navigation system can avoid being caught in a predicament in which the system failed to alert the user of the need to refuel because the system was unable to use a wireless communication network to search for the closest gas stations at the time that the system detected a low fuel level in the vehicle's gas tank”.
* US 20130308470 A1 reciting at ¶ [0049]: “Local office 103 may use the current fuel consumption information and current amount of fuel remaining to determine the distance remaining until the vehicle's fuel container will be empty or near empty and the distance to the nearest fuel station”.
* US 20140359499 A1 Systems and methods for dynamic user interface generation and presentation teaching at Fig.3F and ¶ [0029] last sentence: “In this example, the system determines that the vehicle is low on fuel and provides options to re-route to the nearest gas station”.
* US 20140278056 A1 System and method for route-specific searching
* US 20100063717 A1 Methods and systems for monitoring fuel status of vehicles
reciting at ¶ [0030]: “For example, the vehicle occupant may desire a first low fuel indication when refueling will be necessary relatively soon (e.g., when the fuel tank has sufficient fuel for the vehicle to travel to a nearest service station plus an additional distance, for example twenty miles in one exemplary embodiment), as well as a second low fuel indication when refueling is more immediately necessary (for example, when the fuel tank has sufficient fuel for the vehicle to travel to a nearest service station plus a shorter additional distance, for example five miles in one exemplary embodiment), among various other possible preferences of the vehicle occupant”.
* US 20070233363 A1 Navigation system for a motor vehicle reciting at ¶ [0081] If, for example, information that the tank is almost empty is transmitted, then navigation to the nearest gas station is offered and/or provided.
* US 20070061057 A1 Vehicle network advertising system reciting at ¶ [0028]: “One example of a working context is "fuel is low (from a sensor in the fuel gage), and a service station is in the vicinity (from a navigation location map)". A working context is a key trigger component for coupon presentation”.
* US 20020111822 A1 “Information mediating system and method for mediating
Information” reciting at ¶ [0305]: “The gas remaining sensor is installed in a gas tank of each automobile. When the sensor detects the remaining gas is low, then the mediating server searches the nearest gas station through the internet, and displays the direction to the nearest gas station”.
* US 9996878 B1 “In-vehicle infotainment insurance applications” reciting at column 17 lines 21-25: “At block 7124, the method presents, on the in-vehicle display, that the fuel level is low, the availability of the closest service station, and a "get gas" button. The method then continues to terminal G3”.
* US 20140210604 A1 Control system for controlling display device teaching at ¶ [0073] “After the display device 10 displays the information image G11 shown in the lower part of FIG. 3 as the first information image, the CPU 20A controls the display device 10 at step S140 to display the information image G12 as the second information image in a whole of the display region R1, the information image G12 is an notice image for notifying the handling strategy with respect to the low fuel warning and includes a route guide image to a nearest gas station”.
* US 20090018770 A1 Mobile notification system reciting at mid-¶ [0038]: If the vehicle is low on gasoline, the device may provide a list of gas stations in the vicinity.
* US 20140199980 A1 Location- Assisted Service Capability Monitoring reciting at ¶ [0072] last sentence: “Thus, by registering for such notifications from the mobile device, the vehicular navigation system can avoid being caught in a predicament in which the system failed to alert the user of the need to refuel because the system was unable to use a wireless communication network to search for the closest gas stations at the time that the system detected a low fuel level in the vehicle's gas tank”.
* “US 6339745 B1 System and method for fleet tracking”: reciting at column 145 lines 50-54: “For a "low fuel" indication, the user can send the driver information on the closest gas station. The transmission would be sent by the user seamlessly by simply highlighting the information and clicking on the send message feature”.
* US 20140188388 A1 System and method for vehicle navigation with multiple abstraction layers reciting at mid-¶ [0033]: “The abstraction layering processor 161 can also be configured to determine when the level of fuel is low and automatically initiate a search for near fuel stations using the map data 134 and generate a route to the nearest fuel station. The routing and the indication of the nearest fuel station can be displayed for the driver”.
* US 20120136865 A1 Method and apparatus for determining contextually relevant geographical locations reciting at ¶ [0074]: “By of example, in one use case, the recommendation module 511 recommends to the user the closest gasoline station when the user's gas tank is very low, or considering the user's gas usage profile”.
* US 20070005233 A1 Navigation device and method for displaying alternative routes reciting at ¶ [0043]: “The list is initialized to generally useful POI types (for car drivers) like petrol stations, restaurants, parking spots etc. Hence, a user can very readily ask the program to calculate a new route that will navigate him to the nearest petrol station etc. This can happen during the course of a drive--i.e. the user realises that he is low on fuel and will need the route to be re-calculated to pass by a petrol station, whilst still maintaining the original destination”.
* US 20100217482 A1 “Vehicle-based system interface for personal navigation device” reciting at ¶ [0060] “According to this illustrative embodiment, once the PND receives the low gas signal, it will display a low gas message, and ask the user if they would like to map to the nearest gas station”.
* US 20100198508 A1 Navigation system having route customization mechanism and method of operation thereof reciting at ¶ [0057]: For example, if the vehicle's fuel tank is at a low level, the recommend refuel module 220 will recommend the closest and cheapest fuel station within that range.
* US 20100145609 A1 Energy and emission responsive routing for vehicles ¶ [0025] “Route selection may also be a function of vehicle system and component status and requirements. In one embodiment, storage battery power levels or other status is monitored (e.g. at 110, Fig.1), automatically triggering a reroute when battery power level is low as compared to a low condition threshold; rerouting the hybrid vehicle to another route may include selecting another route having a lower total travel time to the destination, a proximity to a storage battery recharging station, and a proximity to a renewable fuel filling station (e.g. hydrogen fuel cell fuel, bio-diesel, etc.)”. * US 20100138146 A1 Routing method, routing arrangement, corresponding computer program, and processor-readable storage medium reciting at ¶ [0023] “These destinations are situated for example either along a route (for example points of interest, gas stations) or in the vicinity of a location (e.g., parking facilities, hotels), or in an area (e.g., a region or nature preserve), or are determined by the navigation system itself based on the current situation (e.g., gas stations, if the fuel level is low)”.
* US 20120179347 A1 reciting at mid-¶ [0039]: “Additionally, it is also contemplated that the vehicle trip may correspond to other durations (e.g., the time between vehicle recharging or refueling events, manual trip resets, etc.)”.
* US 20130274972 A1 System and method for a one-time departure schedule setup for charging battery-electric vehicles
* US 20180143029 A1 Intelligent system and method for route planning emphasis on Figs. 2A-2D and associated text
* US 20150158393 A1 teaching with a Charging management system with emphasis on the time estimations of Figs. 5-9, 13, 14, 16-21 and associated text
* US 10124682 B2 Charge control system, estimating departure time at Fgi.7 step S120
* US 20100127847 A1 teaching virtual dashboard with location based capabilities as per Fig.7 and associated text
* US 9536197 B1 teaching the processing data streams from data producing objects of vehicle and home entities and generating recommendations and settings with emphasis on the has recommendations at Fig. 13B
* US 10470025 B1 Generating and sending automated support communications using location detection and keyword identification, teaching a personal roadside assistant at Fig. 13
* US 9230379 B2 teaching Communication of automatically generated shopping list to vehicles and associated devices
* US 20200250696 A1 system and method for prioritizing content based on automobile-usage patterns with emphasis on Fugs. 2A-2C and associated text
* US 20100082246 A1 ¶ [0023] “The navigation device 108 and/or software thereof may provide a user an option to obtain gasoline before returning the rental vehicle 102 to a rental service return location or office, as described in more detail below. For example, the navigation device 108 provides an option to locate and route to a gasoline station around a rental service return location before returning the
rental vehicle 102 to the rental service return location. Such an option or feature may advantageously and conveniently assist a renter or customer in meeting his or her agreement of refueling the rental vehicle 102 before it is returned”.
¶ [0045] “Once the desired destination point is identified as a rental service return location or office, the application program 217 offers an option 405 via a display 409, such as the display 209. The option 405 is a graphical representation or icon that prompts or asks a user to decide if he or she would like to obtain gasoline before returning the rental vehicle. For example, the option 405 may include a phrase reciting "Would you like to obtain gasoline before returning the rental vehicle?" Other phrases or words may be used to convey an option or a query to obtain gasoline before returning a rental vehicle. The option 405 may also include software selection buttons or icons 413 for selecting or not selection the option 405. For example, a "Yes" icon 413 and "No" icon 413 may be provided for selection by the user. Alternatively, hardware buttons 417 may be used instead of or in addition to the software buttons or icons 413 for selection of the option 405”.
¶ [0046]: “if the option 405 to obtain gasoline is selected (e.g., the user selects the "Yes" icon 413), the navigation device 401 identifies or determines a gasoline station (such as to be an intermediate destination point) along a route to the rental service return location”.
* US 10791417 B1 teaches Low-fuel indicator enablement based on fuel station locations
* US 20140372221 A1 methods and systems for utilizing vehicle telematics teaching at Fig.6 & ¶ [0012] 2nd sentence: “For example, if a vehicle is low on gasoline or has a mechanical problem, it may be helpful for a user associated with the vehicle to be presented with information corresponding to the locations of gas stations or repair shops in close proximity to the vehicle”.
* US 10134042 B1 column 9 lines 34-46: “Referring back to FIG. 4, in one embodiment, at step 418a, the infrastructure platform 125 may be configured to automatically detect low fuel conditions by monitoring generated push notifications 312. In response to detecting a low fuel alert (decision block 418a, yes branch) the infrastructure platform 125 may automatically identify at least one closest fuel dispensing station based on the current location of the vehicle (at 418b) and based on the third party information provided by the cloud-based vehicle ownership support services 234a-n. Furthermore, the infrastructure platform 125 may automatically send map information corresponding to the identified fuel dispensing stations to the mobile application 202”.
* US 20130144471 A1 System and method for managing vehicle by using mobile terminal teaching the recommendation for servicing of vehicle at Figs. 7-8 extracted immediately below and associated text
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* US 20110224900 A1 teaching Route Planning Device and Route Planning System with emphasis on Figs. 14-15 and associated text
* US 10198877 B1 teaching providing communications channel between instances of automated assistants of automobile computer system, by causing automated assistant interface to provide output to user of automobile computer system
* US 20200027283 A1 Vehicle management system, vehicle management program, and vehicle management method
* US 20140207959 A1 emphasis on Figs. 11A-B and ¶ [0113]: displaying all gas stations in 20 mile/kilometer proximity, displaying the gas station locations, specials, and price of unleaded gas at that location
* US 20140032101 A1 ¶ [0034]: “In this example, the driver may be content to stop at any gas station that has a hamburger place in close proximity, regardless of the particular brands”.
* US 20120323664 A1 reciting at ¶ [0052]: a user with a coupon for a particular item such as brand name tortilla chips can be sent a reminder notification when the user is in close proximity to any store which sells brand name tortilla chips. The user can then, for example, be notified when he/she is in close proximity to a grocery store, convenience store, or gas station which sells the brand name tortilla chips. The proximity at which the user must be in to receive the notification can be a set distance or variable and in some embodiments is adjustable by the user.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/OCTAVIAN ROTARU/
Primary Examiner, Art Unit 3624 A
June 6th, 2026
1 MPEP 2106.04(a): “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible…”.
2 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
3 TLI Communications823 F.3d at 612, 118 USPQ2d at 1747-48
4 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
5 Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017)
6 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
7 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
8 Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017)
9 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
10 “FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016)”
11 “Intellectual Ventures I v. Capital One Bank, 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1640 (Fed. Cir. 2015)”
12 TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission);
OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network);
buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)
13 Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362;
14 Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014)
15 Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755
16 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;
17 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015)
18 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Prosecution Insights