DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 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) without significantly more.
Step 1: Is the claimed invention a statutory category of invention?
Claims 1, 8 and 15 are directed to a method / system /computer program for improving driving skill based on telematics information (Step 1, Yes).
Step 2A, Prong 1: Does the claim recite an abstract idea?
The limitation of steps: … receiving telematics data from one or more sensors of an electronic device of a user; determining a respective skill level for each of a plurality of driving skills of the user based on the telematics data; determining, from among the plurality of driving skills, a driving skill for the user to improve; generating personalized feedback for the user, wherein the personalized feedback is for improving the driving skill; and transmitting the personalized feedback to a mobile device of the user for display to the user as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components and sensor devices. The claimed method akin to mental process of observations, evaluations, and judgements of a driving instructor that providing personalized feedback based on driving data. The mere nominal recitation of a non-transitory computer readable storage medium, a processor, one or more sensors of an electronic device and a mobile device performing these steps does not take the claim limitation outside of the mental processes grouping. Thus, the claim recites a mental process (Step 2A, Prong 1: yes).
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Per the 2019 Revised Patent Subject Matter Eligibility Guidance, if a claim as a whole integrates the recited judicial exception into a practical application of that exception, a claim is not "directed to" a judicial exception. Alternatively, a claim that does not integrate a recited judicial exception into a practical application is directed to the exception. Evaluating whether a claim integrates an abstract idea into a practical application is performed by a) identifying whether there are any additional elements recited in the claim beyond the abstract idea, and b) evaluating those additional elements individual and in combination to determine whether they integrate the abstract idea into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit. Exemplary considerations indicative that an additional element (or combination of elements) may have or has not been integrated into a practical application are set forth in the 2019 PEG
With respect to the instant claims, claims 1, 8 and 15 recite the additional elements of: a non-transitory computer readable storage medium, a processor, one or more sensors of an electronic device and a mobile device. It is particularly noted that the use of processor "as a tool" to perform an abstract method and steps/device for receiving telematic data from one or more sensor of an entertain device that only amount to extra solution activity (i.e., data acquisition) are indicated in the 2019 PEG as examples that an additional element has not been integrated into a practical application. Even in combination, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits, such as an improvement to a computing system, on practicing the abstract idea (STEP 2A, Prong 2: NO).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claims 1, 8 and 15 recite the additional elements of: a non-transitory computer readable storage medium, a processor, one or more sensors of an electronic device and a mobile device set forth above for Step 2A, Prong 2. Regarding these limitations: Applicant's specification describes these features in generic manner "each of user device(s) 350 can include one or more input devices (e.g., input device(s) 3510), one or more output devices (e.g., output device(s) 3520), one or more processors (e.g., processor(s) 3530), and/or one or more memory storage devices (e.g., memory storage device(s) 3540) … input device(s) 3510 can include one or more GPS (Global Positioning System) sensor(s) (e.g., GPS sensor(s) 35110), one or more accelerometers (e.g., accelerometer(s) 35120), and/or one or more gyroscopes (e.g., gyroscope(s) 35130).” in the Applicant’s specification, para. [0035]). There is no indication in the Specification that Applicants have achieved an advancement or improvement in computer for providing driving feedback. Dependent claims 2 – 7, 9 – 14 and 16 – 20 inherit the deficiencies of their respective parent claims through their dependencies and do not recite additional limitations sufficient to direct the claims to more than the claimed abstract idea, and are thus rejected for the same reasons.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2,4-9,11-15,17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ricci (US 2016/0039426 A1).
Re claims 1, 8 and 15:
Ricci teaches 1. A computer-implemented method (Ricci, Abstract; figs. 1 – 4) comprising:
receiving telematics data from one or more sensors of an electronic device of a user (Ricci, [0578]; [0179]; [0276], “GPS”; [0337]);
determining a respective skill level for each of a plurality of driving skills of the user based on the telematics data (Ricci, [0337]; [0344]; fig. 21, 2120 - “Scoring Module”; [0068]; [0545], “best practices, proficiency levels, and the like”; [0553]);
determining, from among the plurality of driving skills, a driving skill for the user to improve (Ricci, fig. 21, 2124 – “Suggestion / Feedback / Education Module”; [0103]; [0118]; [0545]; [0549]);
generating personalized feedback for the user, wherein the personalized feedback is for improving the driving skill (Ricci, [0545], “include feedback from other vehicles reporting on how a particular driver is performing, and/or data from one or more internal and external vehicle sensors, ratings, and the like. Based on the feedback from any one or more of these systems”; [0547], “The games module 2112, cooperating with the rules set 2116, and the suggestion/feedback/education module 2124, can also provide feedback to a user based on their driving behavior/performance”; [0551], “the education module 2104 is capable of providing users with driving education via the head unit/dashboard of the vehicle in a game-style format”); and
transmitting the personalized feedback to a mobile device of the user for display to the user (Ricci, [0551], “these educational tips and feedback can be provided to one or more other devices, such as a smartphone, tablet, personal computer, or the like”; [0570]).
8. A system (Ricci, Abstract; figs. 1 – 4) comprising:
one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations (Ricci, Abstract; figs. 1 – 4) comprising:
receiving telematics data from one or more sensors of an electronic device of a user (Ricci, [0578]; [0179]; [0276], “GPS”; [0337]);
determining a respective skill level for each of plurality of a driving skills of the user based on the telematics data (Ricci, [0337]; [0344]; fig. 21, 2120 - “Scoring Module”; [0068]; [0545], “best practices, proficiency levels, and the like”; [0553]);
determining, from among the plurality of driving skills, a driving skill for the user to improve (Ricci, fig. 21, 2124 – “Suggestion / Feedback / Education Module”; [0103]; [0118]; [0545]; [0549]);
generating personalized feedback for the user, wherein the personalized feedback is for improving the driving skill (Ricci, [0545], “include feedback from other vehicles reporting on how a particular driver is performing, and/or data from one or more internal and external vehicle sensors, ratings, and the like. Based on the feedback from any one or more of these systems”; [0547], “The games module 2112, cooperating with the rules set 2116, and the suggestion/feedback/education module 2124, can also provide feedback to a user based on their driving behavior/performance”; [0551], “the education module 2104 is capable of providing users with driving education via the head unit/dashboard of the vehicle in a game-style format”); and
transmitting the personalized feedback to a mobile device of the user for display to the user (Ricci, [0551], “these educational tips and feedback can be provided to one or more other devices, such as a smartphone, tablet, personal computer, or the like”; [0570]).
15. A non-transitory computer readable storage medium storing computing instructions that, when run on a processor, cause the processor to perform operations (Ricci, Abstract; figs. 1 – 4) comprising:
receiving telematics data from one or more sensors of an electronic device of a user (Ricci, [0578]; [0179]; [0276], “GPS”; [0337]);
determining a respective skill level for each of a plurality of driving skills of the user based on the telematics data (Ricci, [0337]; [0344]; fig. 21, 2120 - “Scoring Module”; [0068]; [0545], “best practices, proficiency levels, and the like”; [0553]);
determining, from among the plurality of driving skills, a driving skill for the user to improve (Ricci, fig. 21, 2124 – “Suggestion / Feedback / Education Module”; [0103]; [0118]; [0545]; [0549]);
generating personalized feedback for the user, wherein the personalized feedback is for improving the driving skill (Ricci, [0545], “include feedback from other vehicles reporting on how a particular driver is performing, and/or data from one or more internal and external vehicle sensors, ratings, and the like. Based on the feedback from any one or more of these systems”; [0547], “The games module 2112, cooperating with the rules set 2116, and the suggestion/feedback/education module 2124, can also provide feedback to a user based on their driving behavior/performance”; [0551], “the education module 2104 is capable of providing users with driving education via the head unit/dashboard of the vehicle in a game-style format”); and
transmitting the personalized feedback to a mobile device of the user for display to the user (Ricci, [0551], “these educational tips and feedback can be provided to one or more other devices, such as a smartphone, tablet, personal computer, or the like”; [0570]).
Re claims 2, 9:
2. The computer-implemented method of claim 1, further comprising: determining a driving score based at least in part on the telematics data; and providing a reward to the user when the driving score reaches a predetermined driving score threshold. 9. The system of claim 8, wherein the operations further comprise: determining a driving score based at least in part on the telematics data; and providing a reward to the user when the driving score reaches a predetermined driving score threshold (Ricci, [0543]; [0545], “user may be scored based on predetermined driver behavior characteristics, and in accordance with one or more rules in the rules set 2116, points awarded (or deducted) for certain driving behavior”; [0083], “ rewards are provided to a user based on the user's score”).
Re claims 4, 11, 17:
4. The computer-implemented method of claim 1, wherein (a) the telematics data or (b) the
respective skill level for at least one of the plurality of driving skills of the user is transmitted
to a third-party comprising one or more of: a parent; a driving instructor; or an automobile insurance company. 11. The system of claim 8, wherein (a) the telematics data or (b) the respective skill level for at least one of the plurality of driving skills of the user is transmitted to a third-party comprising one or more of: a parent; a driving instructor; or an automobile insurance company. 17. The non-transitory computer readable storage medium of claim 15, wherein (a) the telematics data or (b) the respective skill level for at least one of the plurality of driving skills of the user is transmitted to a third-party comprising one or more of: a parent; a driving instructor; or an automobile insurance company (Ricci, fig. 22, S2228; [0016]; [0089]).
Re claims 5, 12, 18:
5. The computer-implemented method of claim 1, wherein the plurality of driving skills comprise at least one of: a steering skill; a braking skill; a speeding skill; or a focus skill. 12. The system of claim 8, wherein the plurality of driving skills comprise at least one of: a steering skill; a braking skill; a speeding skill; or a focus skill. 18. The non-transitory computer readable storage medium of claim 15, wherein the plurality of driving skills comprise at least one of: a steering skill; a braking skill; a speeding skill; or a focus skill (Ricci, [0523]; [0475]).
Re claims 6, 13, 19:
6. The computer-implemented method of claim 1, wherein generating the personalized feedback for the user comprises:
determining a tone and a sentiment of the user, using natural language processing (Ricci, fig. 24, S2404 – “Assess Mood Modification Variable – Audible Input Visual Data Emotions Biometric Information”; [0530]; [0542], “the gesture recognition module 2024 may determine that a user-provided gesture matches a vulgar, angry, emotional, or frustrated gesture. In response, and in cooperation with the mood module 2008, the system may interpret the mood of the user and respond as discussed above. Similar to detecting or identifying, an important gesture, the gesture recognition module 2024 may detect sarcasm of the user's spoken word”; [0562] – [0563]); and
generating the personalized feedback in a compatible tone and compatible sentiment using a machine learning model (Ricci, [0531]; [0541]; [0546]; [0540], “incorporate artificial intelligence, fuzzy logic”; [0534] - [0539]), wherein:
the machine learning model is calibrated by the user (Ricci, [0540]; [0186]); and
the machine learning model is further calibrated through interactions with the user (Ricci, [0540]; [0186]).
13. The system of claim 8, wherein generating the personalized feedback for the user comprises:
determining a tone and a sentiment of the user, using natural language processing (Ricci, fig. 24, S2404 – “Assess Mood Modification Variable – Audible Input Visual Data Emotions Biometric Information”; [0530]; [0542], “the gesture recognition module 2024 may determine that a user-provided gesture matches a vulgar, angry, emotional, or frustrated gesture. In response, and in cooperation with the mood module 2008, the system may interpret the mood of the user and respond as discussed above. Similar to detecting or identifying, an important gesture, the gesture recognition module 2024 may detect sarcasm of the user's spoken word”; [0562] – [0563]); and
generating the personalized feedback in a compatible tone and compatible sentiment using a machine learning model (Ricci, [0531]; [0541]; [0546]; [0540], “incorporate artificial intelligence, fuzzy logic”; [0534] - [0539]),
wherein:
the machine learning model is calibrated by the user (Ricci, [0540]; [0186]); and
the machine learning model is further calibrated through interactions with the user (Ricci, [0540]; [0186]).
19. The non-transitory computer readable storage medium of claim 15, wherein generating the personalized feedback for the user comprises:
determining a tone and a sentiment of the user, using natural language processing (Ricci, fig. 24, S2404 – “Assess Mood Modification Variable – Audible Input Visual Data Emotions Biometric Information”; [0530]; [0542], “the gesture recognition module 2024 may determine that a user-provided gesture matches a vulgar, angry, emotional, or frustrated gesture. In response, and in cooperation with the mood module 2008, the system may interpret the mood of the user and respond as discussed above. Similar to detecting or identifying, an important gesture, the gesture recognition module 2024 may detect sarcasm of the user's spoken word”; [0562] – [0563]); and
generating the personalized feedback in a compatible tone and compatible sentiment using a machine learning model (Ricci, [0531]; [0541]; [0546]; [0540], “incorporate artificial intelligence, fuzzy logic”; [0534] - [0539]),
wherein:
the machine learning model is calibrated by the user (Ricci, [0540]; [0186]); and
the machine learning model is further calibrated through interactions with the user (Ricci, [0540]; [0186]).
Re claims 7, 14, 20:
7. The computer-implemented method of claim 1, wherein the personalized feedback is based in part on a geographical region of the user. 14. The system of claim 8, wherein the personalized feedback is based in part on a geographical region of the user. 20. The non-transitory computer readable storage medium of claim 15, wherein the personalized feedback is based in part on a geographical region of the user (Ricci, [0264]; [0407]; [0405]).
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
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 3, 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ricci (US 2016/0039426 A1) in view of Bryer et al. (US 10445758 B1).
Re claims 3, 10, 16:
Ricci teaches 16. The non-transitory computer readable storage medium of claim 15, wherein: the operations further comprise: determining a driving score based at least in part on the telematics data; and providing a reward to the user when the driving score reaches a predetermined driving score threshold (Ricci, [0543]; [0545], “user may be scored based on predetermined driver behavior characteristics, and in accordance with one or more rules in the rules set 2116, points awarded (or deducted) for certain driving behavior”; [0083], “ rewards are provided to a user based on the user's score”).
Ricci does not explicitly disclose the driving score is reset at a predetermined interval of time. Bryer teaches System and methods for encouraging safe driving. Bryer teaches the driving score is reset at a predetermined interval of time (Bryer, col. 40, line 65 – col. 41, line 21, “the performance scoring module 938 may be configured to aggregate driving scores based on additional or alternative criteria including, for example: an aggregate driving score for a particular day, week, or month based on the individual driving scores for trips that occurred on that day, week, or month; an aggregate driving metric score for a particular driving performance metric such as braking, mileage, time of day, and speed based on individual driving metric scores of that driving performance metric for a set of trips; and combinations of such”). Therefore, in view of Bryer, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/system and computer program described in Ricci, by providing the periodic driving scores as taught by Bryer, since an aggregate driving metric score for a particular driving performance metric such as braking, mileage, time of day, and speed based on individual driving metric scores of that driving performance metric for a set of trips; and combinations of such. One example of a combination aggregate driving score may be, for example, an aggregate braking score for an insurance policy based on individual braking scores of each driver covered by the insurance policy. Another example of a combination aggregate driving score may be a weekly driving score for an insurance policy based on the individual trip scores for trips taken by each driver covered by the insurance policy during that week (Bryer, col. 40, line 65 – col. 41, line 21).
Conclusion
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/JACK YIP/ Primary Examiner, Art Unit 3715
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