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 .
DETAILED ACTION
This is a final rejection. Claims 1-20 is pending.
Status of Claims
Applicant’s amendment date 11/10/2025, amending claims 1-6, 8-13, and 15-19.
IDS
The information disclosure statement filed on 04/10/2023 comply with the provisions of 37 CFR 1.97, 1.98 and MPEP 609. Accordingly, the information disclosure statement(s) is being considered by the examiner.
Continuation
This application is a continuation of U.S. application 15/650,080 (filed 07/14/20217, now U.S. Patent No. 11,651,316). See MPEP §201.08. 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 Applications. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Applications 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).
Response to Amendment
The previously pending rejection under 35 USC 101, will be maintained. The 101 rejection is updated in light of the amendments.
The previously pending double patenting will be maintained.
The previously pending rejection under 35 USC 103, will be maintained. The 103 rejection is updated in light of the amendments.
Response to Arguments
Applicant's argument received on date 11/10/2025 have been fully considered, but they are not persuasive, moreover, any new grounds of rejection have been necessitated by applicant’s amendments to the claims.
Response to Argument under 35 USC 101:
Applicant argues (pages 8-9) of the remark:
Applicant submits that, to the extent the claims recite an abstract idea, the claims are not "directed to" an alleged abstract idea because claims recite elements that enhance of risk assessment systems. For example, claim 1, as amended, recites in part "after detecting" one or more voice prints that do not correspond to a voice print previously determined to be associated with the first driver, determining the one or more first periods to be shared mobility service periods" and "after determining the shared mobility service periods, generating" based on driving information received during the shared mobility service periods, a safety score indicating a level of safety of the first driver during"
In this regard, Applicant notes that Kislovskiy discloses a risk assessment system that "receive[s] driver state data 482 from the driver devices of the IDV's 487. The driver state data 482""indicate[s] whether the driver is on-trip (i.e., transporting a passenger), awaiting a transport invitation, or off-duty"[0096]. Kislovskiy continues that "an aggregate trip risk" is calculated "for each vehicle in the candidate set (1330)" and that "[i] t is contemplated that this risk calculation can [sic] highly individual based on the driver state data (1332)"[0166]. In other words, Kislovskiy's aggregation approach across diverse driving states creates high variability in the risk assessment data, making it difficult to provide accurate safety scores for specific mobility contexts. The claimed method improves risk assessment systems by specifically identifying and focusing computational resources on shared mobility service periods, eliminating the variability problem inherent in Kislovskiy's general aggregation approach. This targeted approach provides enhanced accuracy for shared mobility risk assessment and enables more reliable vehicle guidance data generation.
Therefore, for at least the reasons noted above, Applicant submits that the claimed subject matter is integrated into a practical application and, as such, that the claims are not "directed to" a judicial exception. Accordingly, Applicant respectfully requests withdrawal of the rejections under 35 U.S.C. § 101.
Examiner respectfully disagrees:
The Applicant's Specification titled " Controlling Vehicles Using Contextual Driver And/Or Rider Data Based On Automatic Passenger Detection and Mobility Status". "In summary, the present disclosure relates to methods and systems for sending a notification of a ride opportunity to the first driver responsive to determining that the first score for the first driver is higher than a second score for a second driver. In example aspects, based on different user data " (Spec. [0003-0004]).
As the bolded claim limitations above demonstrate, independent claims 1, 10, and 16 recites the abstract idea of generate based on the driving information, a safety score indicating a level of safety of the first driver to generate a recommendation for improving the safety score . which is “commercial or legal interaction “including agreements in the form of contract; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people” expressly categorized under certain methods of organizing human activity.
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional element, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use exception, such that it is more than a drafting effort designed to monopolize the exception.
The claims recites the additional limitation platform, Platform, a non-transitory, at a shared mobility service management computing platform comprising at least one processor, memory, and speaker, a communication interface: sensor a recorded audio signal and a wireless signal are recited in a high level of generality and recited as performing generic computer functions routinely used in computer applications. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp. 134 S. Ct, at 2360,110 USPQ2d at 1984 (see MPEP 2106.05(f). The additional elements of a “machine learning model” in claims 6, and 13. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general purpose computer.
The use of generic computer component does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (step 2A-prong two: NO).
Further, with regard to mining (i.e., searching over a network), receiving, processing, storing data, and parsing (i.e. extract, transform data), the courts have recognized the following computer functions as well-understood, routing, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (i.e. “receiving, processing, transmitting, storing data”, etc.) are well-understood, routine, etc. (MPEP 2106.05(d))
The Alice framework, step 2B (Part 2 of Mayo) determine if the claim is sufficient to ensure that the claim amounts to “significantly more” than the abstract idea itself. These additional elements recite conventional computer components and conventional functions of:
Claims 1, 8, and 15 does not include my limitations amounting to significantly more than the abstract idea, along. Claims 1, 8, and 15 includes various elements that are not directed to the abstract idea. These elements include “Platform, a non-transitory, at a shared mobility service management computing platform comprising at least one processor, speaker, memory, and a communication interface: sensor a recorded audio signal and a wireless signal.
Examiner asserts that Platform, a non-transitory, at a shared mobility service management computing platform comprising at least one processor, speaker, memory, and a communication interface: sensor a recorded audio signal and a wireless signal are a generic computing element performing generic computing functions. (See MPEP 2106.05(f))
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices.
In addition, figure 1, of the specifications detail any combination of a generic computer system program to perform the method. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well-understood routing, conventional activity in the industry for many years.
Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F .3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
Response to Argument under 35 USC 103:
Applicant argues (page 10) of the remark:
Applicant submits that this aggregated risk calculation approach across all driver states is fundamental to Kislovskiy's vehicle matching methodology, as evidenced by repeated disclosure throughout the reference, including at [0096], [0162], [0166], and [0169], where Kislovskiy consistently teaches that effective risk assessment requires aggregating data across on-trip, awaiting transport, and off-duty states for vehicle matching purposes. As such, Kislovskiy teaches away from "after detecting" during one or more first periods one or more voice prints that do not correspond to a voice print previously determined to be associated with the first driver, determining the one or more first periods to be shared mobility service periods" and "after determining the shared mobility service periods, generating" based on driving information received during the shared mobility service periods, a safety score indicating a level of safety of the first driver," in combination with the other claim limitations.
Accordingly, for at least the reasons presented above, Applicant submits that claim 1 is
patentable over the art of record. Applicant, therefore, respectfully requests withdrawal of the rejection under 35 U.S.C. § 103 against claim 1 and those claims that depend from claim 1.
Examiner respectfully disagrees:
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Also, with regard to “after detecting" during one or more first periods one or more voice prints that do not correspond to a voice print previously determined to be associated with the first driver, determining the one or more first periods to be shared mobility service periods. Referring back to applicant specification for support, in [0058-0059], “speech of a media program may be more uniform in volume than a spoken conversation between passengers at different locations in a cabin of the vehicle. Accordingly, shared mobility service application 153 may detect a media program in a recorded audio signal based on measuring a volume property of the recorded audio signal over time. Additionally or alternatively, speech of a media program may include musical interludes, commercial advertisements, and the like that may be detected by the presence”. The specification does not specifically disclose do not correspond to a voice print previously determined to be associated with the first driver. Grokop disclose Grokop [0344], “audio data can be collected and analyzed to determine if speech is present in the audio stream, such as the residual audio stream after background audio stream cancellation. One embodiment of the passenger detection technique is summarized in flowchart 400 of FIG. 31, detecting if passengers are present in the automobile via audio. If speech is observed in the audio stream during multiple wake-ups, the system may infer that passengers are in the vehicle (431 of FIG. 31)-for example, if the fraction of wake-ups, or the total number of wake-ups, for which speech is detected is greater than a certain threshold at step 430. Here the assumption is that the user will not speak for prolonged periods of time unless passengers are in the vehicle. The exception is if the user is speaking on the phone, such as at step 410. As these events are also detected (as described in a section above) the system can eliminate these false alarms. The other alternative is if the user is listening to talk radio, an audio book, or any other forms of speech emanating from the radio/stereo at step 415. As this can be ascertained using the techniques described above, the system can also eliminate these false alarms by reporting that no speech/ passenger is present if speech is detected at step 417 from the radio/stereo. Furthermore, if the radio/stereo is detected from the radio/stereo, it can be cancelled from the audio stream at step 416 using the technique described above. It then becomes easier to detect if speech from passengers is present, in step 419, in the residual audio stream. This technique is particularly helpful if the radio/stereo is turned up to a high volume such that it partially or fully masks the speech from passengers. It can also help deal with the case where speech is both emanating from the car speakers and from a passenger by removing the speech from the car's speakers to reveal the presence of the speech from the car's passengers”. Also, see fig. 31 [0349]. Examiner assert that the references disclose the limitation in question.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5 of U.S. Patent No. 11,651,316. Although the claims at issue are not identical, they are not patentably distinct from each other because it would be obvious to broaden the claim to leave out based at least in part on the information regarding the at least one location, causing, by the electronic apparatus, an automated device to move the target item to the at least one location. Claim 5 in the referenced patent is more narrow. The breadth of claim 1 of the instant application would read on the more narrow claim of the reference patent.
18/132,569
11,651,316
Claim 1
Claim 5
A computer-implemented method comprising:
A method comprising: at a shared mobility service management computing platform comprising at least one processor, memory, and a communication interface:
receiving, by at least one processor, via a communication interface, driving information associated with a first driver of a shared mobility service, wherein the driving information indicates a driving performance of the first driver during one or more periods and is based on processed sensor data captured by one or more sensors of a vehicle, which is associated with the first driver, during the one or more periods;
receiving, by the at least one processor, via the communication interface, driving information associated with a first driver of a shared mobility service, the driving information based on processed sensor data received from one or more sensors in proximity of a vehicle associated with the first driver during driving;
receiving audio data associated with audio content captured within the vehicle during the one or more periods,
the audio content comprising one or more voice prints and media content;
receiving a digital output stream associated with media content before the media content is output by one or more speakers in the vehicle during the one or more periods;
determining, by the at least one processor, one or more first time periods when the first driver was transporting at least one passenger by detecting at least one indicator of the at least one passenger, the at least one indicator being a recorded voice of the at least one passenger, a recorded video of the at least one passenger, or a recorded passive wireless signal of a passenger device associated with the at least one passenger, wherein the one or more first time periods are associated with a passenger mobility status;
determining at least one of one or more second time periods when the first driver is not transporting at least one passenger and waiting for a ride associated with a waiting mobility status, one or more third time periods when the first driver is heading towards a ride associated with a ride-bound mobility status, or one or more fourth time periods when the first driver is driving on personal time associated with a personal mobility status;
receiving a digital output stream associated with media content before the media content is output by one or more speakers in the vehicle during the one or more periods;
subtracting the media content from the audio content to isolate the one or more voice prints;
after detecting, within the isolated one or more voice prints and during one or more first periods one or more voice prints that do not correspond to a voice print previously determined to be associated with the first driver, determining the one or more first periods to be shared mobility service periods during which the first driver is providing a shared mobility service to a passenger,
Claim 5 wherein the analyzing of the recorded audio signal to detect the voice of the passenger associated with the shared mobility service comprises: generating, by the at least one processor, from the recorded audio signal, a first voice print; comparing, by the at least one processor, the first voice print to a second voice print associated with the first driver; and determining, by the at least one processor, based on the comparison, that the first voice print does not match the second voice print.
filtering, by the at least one processor and based on at least one of the at least one indicator of the at least one passenger or trip data, the driving information to obtain a first subset of the driving information associated with the one or more first time periods and at least one of a second subset of the driving information associated with the one or more second time periods, a third subset of the driving information associated with the one or more third time periods, or a fourth subset of the driving information associated with the one or more fourth time periods;
after determining the shared mobility service periods, generating, by the at least one processor, based on driving information received during the shared mobility service periods, a safety score indicating a level of safety of the first driver during the shared mobility service periods; and
using a first machine learning model to generate, by the at least one processor, based on the first subset of the driving information, a first safety score indicating a level of safety of the first driver during the one or more first time periods based on at least the processed sensor data, wherein the first machine learning model is trained by trip data that correlates to the passenger mobility status;
using at least one of a second machine learning model, a third machine learning model, or a fourth machine learning model to generate, by the at least one processor, a second safety score indicating a level of safety during the one or more second time periods, a third safety score indicating a level of safety during the one or more third time periods, a fourth safety score indicating a level of safety during the one or more fourth time periods, respectively, based on at least the processed sensor data, wherein the second machine learning model is trained by trip data that correlates to the waiting mobility status, wherein the third machine learning model is trained by trip data that correlates to the ride-bound mobility status, and wherein the second machine learning model is trained by trip data that correlates to the personal mobility status;.
sending, by the at least one processor, vehicle guidance data to the vehicle that controls operation of the vehicle, wherein the vehicle guidance data is based on the generated safety score.
setting different insurance costs per mile for the first driver between when the first driver is transporting at least one passenger and at least one of waiting for a ride, heading toward a ride, or driving on personal time, based on the first safety score and at least one of the second safety score, the third safety score, or the fourth safety score, respectively; and determining, by the at least one processor, based on the first safety score, a percentage of a fare to award to the first driver.
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 non-statutory subject matter, specifically an abstract idea without a practical application or significantly more than the abstract idea.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1
Claims 1-7 are directed toward a method (process). Claims 8-14 are directed to a computer platform (machine). Claims 15-20 are directed toward a non-transitory computer-readable media. Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-20 are directed toward the judicial exception of an abstract idea. Independent claims 8 and 15 recites essentially the same abstract features as claim 1, thus are abstract for the same reasons as claim 1.
Regarding independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1. (Currently amended) A computer-implemented method comprising:
receiving, by at least one processor, via a communication interface, driving information associated with a first driver of a shared mobility service, wherein the driving information indicates a driving performance of the first driver during one or more periods and is based on processed sensor data captured by one or more sensors of a vehicle, which is associated with the first driver, during the one or more periods;
receiving audio data associated with audio content captured within the vehicle during the one or more periods,
the audio content comprising one or more voice prints and media content;
receiving a digital output stream associated with media content before the media content is output by one or more speakers in the vehicle during the one or more periods;
subtracting the media content from the audio content to isolate the one or more voice prints;
after detecting, within the isolated one or more voice prints and during one or more first periods one or more voice prints that do not correspond to a voice print previously determined to be associated with the first driver, determining the one or more first periods to be shared mobility service periods during which the first driver is providing a shared mobility service to a passenger,
after determining the shared mobility service periods, generating, by the at least one processor, based on driving information received during the shared mobility service periods, a safety score indicating a level of safety of the first driver during the shared mobility service periods; and
sending, by the at least one processor, vehicle guidance data to the vehicle that controls operation of the vehicle, wherein the vehicle guidance data is based on the generated safety score.
The Applicant's Specification titled " Controlling Vehicles Using Contextual Driver And/Or Rider Data Based On Automatic Passenger Detection and Mobility Status". "In summary, the present disclosure relates to methods and systems for sending a notification of a ride opportunity to the first driver responsive to determining that the first score for the first driver is higher than a second score for a second driver. In example aspects, based on different user data " (Spec. [0003-0004]).
As the bolded claim limitations above demonstrate, independent claims 1, 10, and 15 recites the abstract idea of generate based on the driving information, a safety score indicating a level of safety of the first driver to generate a recommendation for improving the safety score. which is “commercial or legal interaction “including agreements in the form of contract; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people” expressly categorized under certain methods of organizing human activity. See MPEP §2106.04(a)(2)(II). See MPEP §2106.04(a)(2)(II).
Dependent claims 2-7, 9-14, and 16-20 further reiterate the same abstract ideas with further embellishments, such as
claims 2 (Similarly Claims 9 and 16) determining at least one of one or more second periods when the first driver is not transporting at least one passenger and waiting for a ride associated with a waiting mobility status.
claims 3 (Similarly Claims 10 and 17) determining one or more third periods when the first driver is heading towards a ride associated with a ride-bound mobility status
claims 4 (Similarly Claims 11 and 18) determining or one or more fourth periods when the first driver is driving on personal time associated with a personal mobility status.
claims 5 (Similarly Claims 12 and 19) filtering, by the at least one processor and based on determining the shared mobility service periods, the driving information to obtain a first subset of the driving information associated with the one or more first periods and at least one of a second subset of the driving information associated with one or more second periods.
claims 6 (Similarly Claim 13) wherein the safety score is generated using a first machine learning model, wherein the first machine learning model is trained by trip data that correlates to a passenger mobility status.
claims 7 (Similarly Claims 14 and 20) transmitting, by the at least one processor, via the communication interface, to a mobile system associated with the first driver, an indication of the safety score and a recommendation for improving the safety score.
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claims 1, 8, and 15.
Regarding Step 2A [prong 2]
Claims 1-20 fail to integrate the abstract idea into a practical application.
Independent claims 1, 8, and 15 include the following additional elements which do not amount to a practical application:
Claims 1, 8, and 15 Platform, a non-transitory, at a shared mobility service management computing platform comprising at least one processor, memory, speaker and a communication interface: sensor a recorded audio signal and a wireless signal
The bolded limitations recited above in independent claims pertain to additional elements which merely provide an abstract-idea-based-solution implemented with computer hardware and software components, including the additional elements of Platform, a non-transitory, at a shared mobility service management computing platform comprising at least one processor, speaker, memory, and a communication interface: sensor a recorded audio signal and a wireless signal. which fail to integrate the abstract idea into a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, (4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04(d)(1) and §2106.05 (a-c & e-h), (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2). The Specification provides a high level of generality regarding the additional elements claimed without sufficient detail or specific implementation structure so as to limit the abstract idea, for instance, (figure 1). Nothing in the Specification describes the specific operations recited in claim 1 as particularly invoking any inventive programming, or requiring any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is somehow implemented using any specialized element other than all-purpose computer components to perform recited computer functions. The claimed invention is merely directed to utilizing computer technology as a tool for solving a business problem of data analytics. Nowhere in the Specification does the Applicant emphasize additional hardware and/or software elements which provide an actual improvement in computer functionality, or to a technology or technical field, other than using these elements as a computational tool to automate and perform the abstract idea. See MPEP §2106.05(a & e).
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant's claimed invention which merely pertains to steps for sending a notification of a ride opportunity to the first driver responsive to determining that the first score for the first driver is higher than a second score for a second driver and the additional computer elements a tool to perform the abstract idea, and merely linking the use of the abstract idea to a particular technological environment. See MPEP §2106.04 and §21062106.05(f-h). Alternatively, the Office has long considered data gathering, analysis and data output to be insignificant extra-solution activity, and these additional elements do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.04 and §2106.05(g). Thus, the additional elements recited above fail to provide an actual improvement in computer functionality, or to a technology or technical field. See MPEP §2106.04(d)(1) and §2106§2106.05 (a & e).
Instead, the recited additional elements above, merely limit the invention to a technological environment in which the abstract concept identified above is implemented utilizing the computational tools provided by the additional elements to automate and perform the abstract idea, which is insufficient to provide a practical application since the additional elements do no more than generally link the use of the abstract idea to a particular technological environment. See MPEP §2106.04. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Alternatively, the Office has long considered data gathering and data processing as well as data output recruitment information on a social network to be insignificant extra-solution activity, and these additional elements used to gather and output recruitment information on a social network are insignificant extra-solution limitations that do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(g). The current invention sending a notification of a ride opportunity to the first driver responsive to determining that the first score for the first driver is higher than a second score for a second driver. When considered in combination, the claims do not amount to improvements of the functioning of a computer, or to any technology or technical field. Applicant's limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits. furthermore, merely using/applying in a computer environment such as merely using the computer as a tool to apply instructions of the abstract idea do nothing more than provide insignificant extra-solution activity since they amount to data gathering, analysis and outputting. Furthermore, they do not pertain to a technological problem being solved in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and/or the limitations fail to achieve an actual improvement in computer functionality or improvement in specific technology other than using the computer as a tool to perform the abstract idea.
Dependent claims 2-7, 9-14, and 16-20 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims 1, 8, and 15 respectively, for example,
Claims 6-7, 13-14, and 20, a first machine learning model a mobile system but these features only serve to further limit the abstract idea of independent claims 1, 8, and 15, The additional elements of a “machine learning model”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. furthermore, merely using/applying in a computer environment such as merely using the computer as a tool to apply instructions of the abstract idea do nothing more than provide insignificant extra-solution activity since they amount to data gathering, analysis and outputting. Furthermore, they do not pertain to a technological problem being solved in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and/or the limitations fail to achieve an actual improvement in computer functionality or improvement in specific technology other than using the computer as a tool to perform the abstract idea.
Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element(s) as described above with respect to Step 2A Prong 2, the additional element of claim 1 include Platform, a non-transitory, at a shared mobility service management computing platform comprising at least one processor, speaker, memory, and a communication interface: sensor a recorded audio signal and a wireless signal. Claims 6-7, 13-14, and 20, a first machine learning model a mobile system The displaying interface and storing data merely amount to a general purpose computer used to apply the abstract idea(s) (MPEP 2106.05(f)) and/or performs insignificant extra-solution activity, e.g. data retrieval and storage, as described above (MPEP 2106.05(g)) which are further merely well-understood, routine, and conventional activit(ies) as evidenced by MPEP 2106.06(05)(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser’s back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to sending a notification of a ride opportunity to the first driver responsive to determining that the first score for the first driver is higher than a second score for a second driver.
Claims 1-20 are accordingly rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
REJECTIONS BASED ON PRIOR ART
Examiner Note: Some rejections will be followed/begin by an “EN” that will denote an examiner note. This will be place to further explain a rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kislovskiy et al US 2018/0342033 A1 (hereinafter Kislovskiy) in view of Grokop et al. US 2015/0120336 (hereinafter Grokop) in view of Zhang C, Rui Y, Crawford J, He LW. An automated end-to-end lecture capture and broadcasting system. ACM Transactions on multimedia computing, communications, and applications (TOMM). 2008 Feb 11;4(1):1-23. (hereinafter Zhang).
Regarding Claim 1:
(Currently amended) A computer-implemented method comprising:
receiving, by at least one processor, via a communication interface, driving information associated with a first driver of a shared mobility service, wherein the driving information indicates a driving performance of the first driver during one or more periods and is based on processed sensor data captured by one or more sensors of a vehicle, which is associated with the first driver, during the one or more periods; (see Kislovskiy Figs. 3-4 & 15 [0032], “an example risk regressor may compute an individualized fractional risk quantity for each path segment on a per vehicle or per driver basis given the vehicle’s or driver’s attributes, ….. driver’s safety history, current state, and driving characteristics”. Also, see Kislovskiy [0082], “the database 340 can store live driver data 347 that indicates per driver … driver’s profile information, which can indicate preferred driving areas, driver rating, an incident log and/or the driving habits or characteristics of the driver. Also, see Kislovkiy [0083]-[0085], “determining the current or historical driving characteristics of the driver …. Can receiver accelerometer data or inertial measurement unit (IMU) data from the driver’s vehicle or the driver’s computing device”. Also, see Kislovskiy [0096], “the vehicle monitor 460 can also receive driver state data 482 from the driver devices of the HDVs 487. Also, see Kislovskiy [0172], “receiver IMU data (1596), image or video data (1597), and/or audio data (1598) from the driver’s computing device or vehicle sensors to determine the driving characteristics of the driver”.)
receiving audio data associated with audio content captured within the vehicle during the one or more periods, (see Kislovskiy Figs. 13 & 15 [0084], “determine the individual risk value 333 for the driver based on, for example, how long the driver has been on-duty and the current and/or historical driving characteristics of the driver …… transport management system can further receiver GPS data, image or video data, and/or audio data from a microphone of the driver device 385 or vehicle hardware to determine the individual risk value 333”. Kislovskiy [0093], “if a passenger is being transported, the on-trip monitoring system 400 can transmit a notification to the o-demand transport system 401”. Kislovskiy [0095-0096], “triggered based on the AV’s location, the current conditions, or the AV’s state (e.g., on-trip with a passenger versus without a passenger”.) … the driver state data 482 can indicate whether the driver is on-trip (i.e., transporting a passenger)”. Also, see Kislovskiy [0172], image or video data (1597), and/or audio data (1598) from the driver’s computing device or vehicle sensors”.)
after determining the shared mobility service periods, (see Kislovskiy Figs. 2-5 [0096], “the vehicle monitor 460 can also receive driver state data 482 from the driver devices of the HDV’s 487. The driver state data 482 can indicate whether the driver is on-trip (i.e., transporting a passenger), awaiting a transport invitation, or off-duty”. Also, see Kislovskiy [0162], “system can monitor driver states for on-duty drivers of various on-demand transportation services (1300). In doing so, the transport management system can track the time that the drivers are online or on-duty (1302)”. Examiner asset that Kislovskiy filter and obtain a subset of the driving information during a driver was transporting a passenger [0166])
generating, by the at least one processor, based on driving information received during the shared mobility service periods, a safety score indicating a level of safety of the first driver during the shared mobility service; and (see Kislovskiy Figs. 2-5 [0096], “the vehicle monitor 460 can also receive driver state data 482 from the driver devices of the HDV’s 487. The driver state data 482 can indicate whether the driver is on-trip (i.e., transporting a passenger), awaiting a transport invitation, or off-duty”. Also, see Kislovskiy [0162], “system can monitor driver states for on-duty drivers of various on-demand transportation services (1300). In doing so, the transport management system can track the time that the drivers are online or on-duty (1302)”. Examiner asset that Kislovskiy filter and obtain a subset of the driving information during a driver was transporting a passenger [0166])
sending, by the at least one processor, vehicle guidance data to the vehicle that controls operation of the vehicle, wherein the vehicle guidance data is based on the generated safety score. (see Kislovskiy [0028-0030], “fully autonomous self-driving … remotely operated autonomous … based on a number of criteria described herein, including risk, estimated time of arrival, expected earning or profit per candidate vehicle ….. selecting a most optimal vehicle to service the transport request. Also, see Kislovskiy [0133],[0166], & [0170], “select a most optimal driver (e.g., a least risky driver/route combination) to service the transport request. one a most optimal driver is selected, the transport management system can transmit a transport invitation and route data to the selected driver to facilitate the trip over the least risk route”.)
Kislovskiy disclose the above limitations but, specifically fails to disclose the audio content comprising one or more voice prints and media content; receiving a digital output stream associated with media content before the media content is output by one or more speakers in the vehicle during the one or more periods; subtracting the media content from the audio content to isolate the one or more voice prints; after, detecting within the isolated one or more voice prints and during one or more first periods one or more voice prints that do not correspond to a voice print previously determined to be associated with the first driver, determining the one or more first periods to be shared mobility service periods during which the first driver is providing a shared mobility service to a passenger,
However, Grokop teaches the following limitation:
EN: with regard to the limitation below, the limitation is interpreted in view of applicant specification ¶[0058], “The shared mobility service application 153 may then compare the digital stream to a recorded audio signals in order to determine a difference signal. The media program may be "cancelled out" of the difference signal by virtue of appearing 10 in both the digital stream and the recorded audio. However, passenger voices may remain in the difference signal because they are only included in the recorded audio. Therefore, the shared mobility service application 153 may analyze the difference signal to detect passenger presence”.
the audio content comprising one or more voice prints and media content; receiving a digital output stream associated with media content before the media content is output by one or more speakers in the vehicle during the one or more periods; subtracting the media content from the audio content to isolate the one or more voice prints; after, detecting within the isolated one or more voice prints and during one or more first periods one or more voice prints that do not correspond to a voice print previously determined to be associated with the first driver, determining the one or more first periods to be shared mobility service periods during which the first driver is providing a shared mobility service to a passenger, (Grokop [0344], “audio data can be collected and analyzed to determine if speech is present in the audio stream, such as the residual audio stream after background audio stream cancellation. One embodiment of the passenger detection technique is summarized in flowchart 400 of FIG. 31, detecting if passengers are present in the automobile via audio. If speech is observed in the audio stream during multiple wake-ups, the system may infer that passengers are in the vehicle (431 of FIG. 31)-for example, if the fraction of wake-ups, or the total number of wake-ups, for which speech is detected is greater than a certain threshold at step 430. Here the assumption is that the user will not speak for prolonged periods of time unless passengers are in the vehicle. The exception is if the user is speaking on the phone, such as at step 410. As these events are also detected (as described in a section above) the system can eliminate these false alarms. The other alternative is if the user is listening to talk radio, an audio book, or any other forms of speech emanating from the radio/stereo at step 415. As this can be ascertained using the techniques described above, the system can also eliminate these false alarms by reporting that no speech/ passenger is present if speech is detected at step 417 from the radio/stereo. Furthermore, if the radio/stereo is detected from the radio/stereo, it can be cancelled from the audio stream at step 416 using the technique described above. It then becomes easier to detect if speech from passengers is present, in step 419, in the residual audio stream. This technique is particularly helpful if the radio/stereo is turned up to a high volume such that it partially or fully masks the speech from passengers. It can also help deal with the case where speech is both emanating from the car speakers and from a passenger by removing the speech from the car's speakers to reveal the presence of the speech from the car's passengers”. Also, see fig. 31 [0349])
It would have been obvious to one having an ordinary skill in the art before the effective time of the invention was made to use Grokop teaching of cancelling a media content in Kislovskiy system enables for the advantage of subtracting/cancelling media content from one or more speakers of the vehicle. The system will subtracting/reveal/determine the presence of the speech from the car’s passenger by subtracting/cancelling/removing the media content from the car speaker (Grokop [0344]).
While Kislovskiy and/or Grokop does not specifically teach “media content before the media content is output” is broad and does not necessarily further limit the detecting within the isolated one or more voice prints and during one or more first time periods one or more voice prints that do not correspond to a voice print previously determined to be associated with the first driver, wherein the one or more first time periods are associated with a passenger mobility status. Regardless, in order to advance the prosecution of this application, Zhang is introduced to teach receiving a digital output stream associated with media content before the media content is output by one or more speakers in the vehicle during the one or more periods; (Zhang fig. 2 page 6:9, also, see EN Fig. 5 “the audio capturing process” below)
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It would have been obvious to one having an ordinary skill in the art before the effective time of the invention was made to use Zhang teaching of media capturing process in Kislovskiy system enables for the advantage of receiving media content before outputting by speakers and subtracting/cancelling media content from one or more speakers of the vehicle. The system will subtracting/reveal/determine the presence of the speech from the car’s passenger by subtracting/cancelling/removing the media content from the car speaker.
Regarding Claim 2:
(Currently Amended) Kislovskiy in view Grokop in view of Zhang disclose the computer-implemented method of claim 1, further comprising:
Kislovskiy further teach determining at least one of one or more second periods when the first driver is not transporting at least one passenger and waiting for a ride associated with a waiting mobility status. (see Kislovskiy Figs. 3-4 & 11-13 [0096], “correlated to the current conditions 399 to indicate the performance and driving characteristics of the driver”. Also, Kislovskiy [0162], “method of individualized risk regression based on vehicle matching … and can therefore individualize risk assessment per vehicle and/or driver given the vehicle’s or the driver’s current state”. Also, see Kislovkiy [0166], “can calculate an aggregate trip for each vehicle in the candidate set (1330). It is contemplated that this risk calculation can highly individual based on the driver state data (1332). Also, see [0031]-[0033], [0096], “awaiting a transport invitation” and [0169]. The individual risk value is calculated based on the driver state based on data collected from different sensors as showing in Figs. 3-4 (i.e., elements 346-398 & live conditions monitor 420) and [0057]-[0059], [0085]-[0087], & [0172])
Examiner Note: the driver state (1332) is based if the driver is transporting a passenger or not transporting a passenger as disclose in [0096], [0151], [0162].
Regarding Claim 3:
(Currently Amended) Kislovskiy in view Grokop in view of Zhang disclose the computer-implemented method of claim 1, further comprising:
Kislovskiy further teach determining one or more third periods when the first driver is heading towards a ride associated with a ride-bound mobility status. (see Kislovskiy Figs. 3-4 & 11-13 [0096], “correlated to the current conditions 399 to indicate the performance and driving characteristics of the driver”. Also, Kislovskiy [0162], “method of individualized risk regression based on vehicle matching … and can therefore individualize risk assessment per vehicle and/or driver given the vehicle’s or the driver’s current state”. Also, see Kislovkiy [0117], “upon submitting an acceptance confirmation, the deiver application can place the driver device in en route state while the driver drives to the pick-up location”. Also, see [0031]-[0033], [0096], “awaiting a transport invitation” and [0169]. The individual risk value is calculated based on the driver state based on data collected from different sensors as showing in Figs. 3-4 (i.e., elements 346-398 & live conditions monitor 420) and [0057]-[0059], [0085]-[0087], & [0172])
Regarding Claim 4:
(Currently Amended) Kislovskiy in view Grokop in view of Zhang disclose the computer-implemented method of claim 1, further comprising:
Kislovskiy further teach determining one or more fourth periods when the first driver is driving on personal time associated with a personal mobility status. (see Kislovskiy Figs. 3-4 & 11-13 [0096], “correlated to the current conditions 399 to indicate the performance and driving characteristics of the driver”. Also, Kislovskiy [0162], “method of individualized risk regression based on vehicle matching … and can therefore individualize risk assessment per vehicle and/or driver given the vehicle’s or the driver’s current state”. Also, see Kislovkiy [0166], “can calculate an aggregate trip for each vehicle in the candidate set (1330). It is contemplated that this risk calculation can highly individual based on the driver state data (1332). Also, see [0031]-[0033], [0096], “off-duty ” and [0169]. The individual risk value is calculated based on the driver state based on data collected from different sensors as showing in Figs. 3-4 (i.e., elements 346-398 & live conditions monitor 420) and [0057]-[0059], [0085]-[0087], & [0172])
Examiner Note: the driver state (1332) is based if the driver is transporting
Regarding Claim 5:
(Currently Amended) Kislovskiy in view Grokop in view of Zhang disclose the computer-implemented method of claim 1, further comprising:
Kislovskiy further teach filtering, by the at least one processor and based on determining the shared mobility service periods, the driving information to obtain a first subset of the driving information associated with the one or more first periods and at least one of a second subset of the driving information associated with one or more second periods. (see Kislovskiy Figs. 2-5 [0096], “the vehicle monitor 460 can also receive driver state data 482 from the driver devices of the HDV’s 487. The driver state data 482 can indicate whether the driver is on-trip (i.e., transporting a passenger), awaiting a transport invitation, or off-duty”. Also, see Kislovskiy [0162], “system can monitor driver states for on-duty drivers of various on-demand transportation services (1300). In doing so, the transport management system can track the time that the drivers are online or on-duty (1302)”. Examiner asset that Kislovskiy filter and obtain a subset of the driving information during a driver was transporting a passenger [0166])
Regarding Claim 6:
(Currently Amended) Kislovskiy in view Grokop in view of Zhang disclose the computer-implemented method of claim 1,
Kislovskiy further teach wherein the safety score is generated using a first machine learning model, wherein the first machine learning model is trained by trip data that correlates to a passenger mobility status. (Kislovskiy [0031-0032], “machine learning techniques and/or algorithms to compute fractional risk quantities …. Risk quantities can be generalized for human driving”. Also, see [0036])
Regarding Claim 7:
(Previously presented) Kislovskiy in view Grokop in view of Zhang disclose the computer-implemented method of claim 1, further comprising:
Kislovskiy further teach transmitting, by the at least one processor, via the communication interface, to a mobile system associated with the first driver, an indication of the safety score and a recommendation for improving the safety score. (see Kislovskiy [0085], “the individualized risk assessment for drivers can enable the on-demand transport management system 300 to also provide notifications to the drivers, either praising the driver for excellent, low-risk driving, suggesting that the driver take a break, or cautioning the driver to driver less aggressively. Such notifications can be provided to the drivers via the driver app 386 executing on the driver’s computing device 385”. Also, see Kislovskiy [0097], with this individual risk value 432 for the driver, the on-trip monitoring system 400 can perform any number of functions, such as providing notifications corresponding to the driver’s risk value 432 to the drivers computing device”.)
Regarding Claim 8:
Claim 8 is the computing platform claim corresponding to the method claim 1 rejected above. Therefore, Claim 8 is rejected under the same rational as claim 1.
Regarding Claim 9:
Claim 9 is the computing platform claim corresponding to the method claim 2 rejected above. Therefore, Claim 9 is rejected under the same rational as claim 2.
Regarding Claim 10:
Claim 10 is the computing platform claim corresponding to the method claim 3 rejected above. Therefore, Claim 10 is rejected under the same rational as claim 3.
Regarding Claim 11:
Claim 11 is the computing platform claim corresponding to the method claim 4 rejected above. Therefore, Claim 11 is rejected under the same rational as claim 4.
Regarding Claim 12:
Claim 12 is the computing platform claim corresponding to the method claim 5 rejected above. Therefore, Claim 12 is rejected under the same rational as claim 5.
Regarding Claim 13:
Claim 13 is the computing platform claim corresponding to the method claim 6 rejected above. Therefore, Claim 13 is rejected under the same rational as claim 6.
Regarding Claim 14:
Claim 14 is the computing platform claim corresponding to the method claim 7 rejected above. Therefore, Claim 14 is rejected under the same rational as claim 7.
Regarding Claim 15:
Claim 15 is the non-transitory claim corresponding to the method claim 1 rejected above. Therefore, Claim 15 is rejected under the same rational as claim 1.
Regarding Claim 16:
Claim 16 is the non-transitory claim corresponding to the method claim 2 rejected above. Therefore, Claim 16 is rejected under the same rational as claim 2.
Regarding Claim 17:
Claim 17 is the non-transitory claim corresponding to the method claim 3 rejected above. Therefore, Claim 17 is rejected under the same rational as claim 3.
Regarding Claim 18:
Claim 18 is the non-transitory claim corresponding to the method claim 4 rejected above. Therefore, Claim 19 is rejected under the same rational as claim 4.
Regarding Claim 19:
Claim 19 is the non-transitory claim corresponding to the method claim 5 rejected above. Therefore, Claim 19 is rejected under the same rational as claim 5.
Regarding Claim 20:
Claim 20 is the non-transitory claim corresponding to the method claim 7 rejected above. Therefore, Claim 20 is rejected under the same rational as claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Feld, Michael, Tim Schwartz, and Christian Müller. "This is me: Using ambient voice patterns for in-car positioning." International Joint Conference on Ambient Intelligence. Springer, Berlin, Heidelberg, 2010.
Chu, Hon Lung, et al. "In-vehicle driver detection using mobile phone sensors." ACM MobiSys. 2011.
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/HAMZEH OBAID/Primary Examiner, Art Unit 3624