DETAILED ACTION
This rejection is in response to application filed 03/05/2025.
Claims 1-20 are currently pending and have been examined.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. IN202511002489, filed on 01/10/2025.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1, 9, and 17 recite: A method/computing device/ non-transitory computer readable storage medium for providing a recommendation for selecting a vehicle,… receiving, by the at least one processor, diagnostic data associated with a vehicle, rendering said claims indefinite because it is unclear whether the first recitation of a vehicle in the preamble is the same or different from the subsequent recitation of a vehicle. Appropriate correction or clarification is 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 (an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test, it must be considered whether the claims are directed to one of the four statutory classes of invention. See MPEP § 2106. In the instant case, claims 1-8 are directed to a method, claims 9-16 are directed to a computing device, and claims 17-20 are directed to a non-transitory computer storage medium which falls within one of the four statutory categories of invention(process/apparatus). Accordingly, the claims will be further analyzed under revised step 2:
Under step 2A (prong 1) of the Subject Matter Eligibility Test, it must be considered whether the claims recite a judicial exception if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. If the claim recites a judicial exception (i.e., an abstract idea), the claim requires further analysis in Prong Two. One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
Regarding representative independent claim 1, recites the abstract idea of:
A method for providing a recommendation for selecting a vehicle,… , the method comprising:
receiving,… , diagnostic data associated with a vehicle;
deriving, …, a first set of parameters based on an analysis of the diagnostic data;
processing, …, the diagnostic data and the first set of parameters to generate a user profile and a plurality of scores associated with the user profile;
comparing, …, each score within the plurality of scores to corresponding predetermined threshold levels for determining a vehicle recommendation for the user profile; and
providing, …, the vehicle recommendation to a user.
The above-recited limitations amounts to certain methods of organizing human activity associated with sales activities and commercial interaction by reciting providing vehicle recommendations by receiving vehicle diagnostic data, deriving parameters from the analysis of the vehicle diagnostic data, processing the vehicle diagnostic data and parameters to generate a user profile and scores, and comparing each score based on a threshold to determine the vehicle recommendation. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts. See MPEP § 2106.
The Step 2A (prong 2) of the Subject Matter Eligibility Test, is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.
In this instance, the claims recite the additional elements such as:
Claim 1:… the method being implemented by at least one processor;… by the at least one processor…; … by the at least one processor…;… by the at least one processor using a trained model…;… by the at least one processor…; … by the at least one processor…
Claims 4, 12, and 20: an on-boarding diagnostic (OBD) module
Claims 5 and 8: …by the at least one processor....
Claims 7 and 15: wherein the trained model is trained...
Claims 8 and 16: …at least one external application that is associated with a platform.
Claim 9: A computing device configured to provide a recommendation for selecting a vehicle, the computing device comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to:...
Claims 13 and 16: the processor is further configured to...
Claim 17: A non-transitory computer readable storage medium storing instructions for providing a recommendation for selecting a vehicle, the storage medium comprising executable code which, when executed by a processor, causes the processor to:..
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Independent claims and dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same. See MPEP § 2106.
In Step 2A, several additional elements were identified as additional limitations:
Claim 1:… the method being implemented by at least one processor;… by the at least one processor…; … by the at least one processor…;… by the at least one processor using a trained model…;… by the at least one processor…; … by the at least one processor…
Claims 4, 12, and 20: an on-boarding diagnostic (OBD) module
Claims 5 and 8: …by the at least one processor....
Claims 7 and 15: wherein the trained model is trained...
Claims 8 and 16: …at least one external application that is associated with a platform.
Claim 9: A computing device configured to provide a recommendation for selecting a vehicle, the computing device comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to:...
Claims 13 and 16: the processor is further configured to...
Claim 17: A non-transitory computer readable storage medium storing instructions for providing a recommendation for selecting a vehicle, the storage medium comprising executable code which, when executed by a processor, causes the processor to:..
These additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because the recitations above do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101.
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.
Claim(s) 1-5, 7-13, and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Beaurepaire et al. (US Pub. No. 20220005140 A1, hereinafter “Beaurepaire”).
Regarding claims 1, 9, and 17
Beaurepaire discloses a method for providing a recommendation for selecting a vehicle, the method being implemented by at least one processor, the method comprising (Beaurepaire, [0004]: method for selecting recommended vehicle; [0005]: processor):
receiving, by the at least one processor, diagnostic data associated with a vehicle; deriving, by the at least one processor, a first set of parameters based on an analysis of the diagnostic data (Beaurepaire, FIG. 1, [0051]: collect contextually relevant information or data related to the vehicles from one or more sensors that includes attributes of all available vehicles; [0115]: attributes can also be any attribute normally collected by an on-board diagnostic (OBD); [0046]: use contextual information or data to determine index and a measure of efficient vehicle usage);
processing, by the at least one processor using a trained model, the diagnostic data and the first set of parameters to generate a user profile and a plurality of scores associated with the user profile (Beaurepaire, [0053]: can compute an index/score value per candidate vehicle 113 for a given trip or route based on all the available contextually relevant information (e.g., using the machine learning system 123) and computes the score as a ratio between energy related factors and user preference parameters and factors and attributes, contextually relevant information related to the vehicles 113, trip information or data, user preferences, respective weights or weighting schemes, etc. in the geographic database 109 as labeled or marked features for training of the machine learning system; [0089]: the training module 311 trains a machine learning model using the respective weights of the contextual data, the respective convenience factors, the balance of the respective energy-efficiency scores and the respective convenience factors, or a combination thereof to enable the recommendation module 305 to most efficiently select the recommended vehicle; FIG. 5D, [0103]: use machine learning system to update user preferences stored in database; [0140]: database includes user preferences);
comparing, by the at least one processor, each score within the plurality of scores to corresponding predetermined threshold levels for determining a vehicle recommendation for the user profile; and providing, by the at least one processor, the vehicle recommendation to a user (Beaurepaire, [0060]: the system 100 presents the recommendation via an application 107 as a comparative view of options with the respective rankings and their relative scores to support decision making and present those vehicles 113 with at least a certain threshold rank or score with users review such rankings and scores via a UE; [0085]: the comparative view may include a representation of the respective energy-efficiency scores of the recommended vehicle 113, the recommended mode of vehicle operation, the plurality of candidate vehicles 113, the plurality of candidate modes of vehicle operation; FIGs. 5A-5D, [0091]: generates UI for user to easily compare multiple vehicles; FIG. 5D, [0103]: display max score; [0099]: automatically select or reserve the highest scored vehicle 113 or automatically select or reserve a vehicle 113 that meets or exceeds a certain threshold score (e.g., <300)).
Regarding claims 2, 10, 18
The method as claimed in claim 1, wherein the diagnostic data comprises at least one from among a throttle position, an engine revolutions per minute (RPM), a fuel consumption rate, an engine load, a steering angle, a brake pedal pressure, a transmission gear position, vehicle stability control data, a driving global positioning system (GPS) location, a driving time, a driving duration, and a coolant temperature (Beaurepaire, [0115]: collect attributes of vehicle from on-board diagnostic (OBD) and from vehicle sensors. Collect other attributes of vehicle (e.g. altitude, tilt, steering angle, wiper activation, speed, time; [0051]: vehicle sensors include GPS, thermal, camera and other vehicle attributes include engine type and transmission type; [0109]: GPS determines times based on live location; [0117]: velocity sensors mounted on a steering wheel of the vehicles 113, switch sensors for determining whether one or more vehicle switches are engaged; [0118] Other examples of sensors 115 of a vehicle 113 may include light sensors, orientation sensors augmented with height sensors and acceleration sensor (e.g., an accelerometer can measure acceleration and can be used to determine orientation of the vehicle), tilt sensors to detect the degree of incline or decline of a vehicle 113 along a path of travel, moisture sensors, pressure sensors, etc; [0050]: GPS sensors) to determine a trip origin, a trip completion).
Regarding claims 3, 11, and 19
Beaurepaire discloses the method as claimed in claim 1, wherein the first set of parameters comprises at least one from among a time of day, a trip duration, frequency of stops, route types, a driving smoothness, an acceleration pattern, a deceleration pattern, and an ideal time (Beaurepaire, [0032]: determine the trip request based on a user's input of an origin, a destination, a desired time of travel, one or more user preferences; [0078]: trip request includes single leg or direction of travel, a trip to a destination and a return trip from the destination (e.g., a round trip the candidate modes of vehicle operation may include various modes of locomotion (e.g., such as gas and/or electric powered in a hybrid vehicle); various modes of control (e.g., driver control versus autonomous or semi-autonomous control), various temporal or environmental modes (e.g., certain days of the week, time of day, weather, etc.) [0147]: route calculation, route guidance, map display, speed calculation, distance and travel time functions; [0115]: attributes of vehicle (e.g. altitude, tilt, steering angle, wiper activation, speed, time); [0051]: user mobility pattern and vehicle sensors include GPS, thermal, camera and other vehicle attributes include engine type and transmission type; [0109]: GPS determines times based on live location; [0117]: velocity sensors mounted on a steering wheel of the vehicles 113, switch sensors for determining whether one or more vehicle switches are engaged; [0118] Other examples of sensors 115 of a vehicle 113 may include light sensors, orientation sensors augmented with height sensors and acceleration sensor (e.g., an accelerometer can measure acceleration and can be used to determine orientation of the vehicle), tilt sensors to detect the degree of incline or decline of a vehicle 113 along a path of travel, moisture sensors, pressure sensors, etc; [0050]: GPS sensors to determine a trip origin, a trip completion [0065] determine contextual data associated with a trip request includes: [0066] attribute data of the plurality of candidate vehicles, the plurality of candidate modes of vehicle operation, or a combination thereof; [0068] a trip length; [0073] weather data; [0074] traffic data; [0075] parking data; [0076] nearby event data; or [0077] road attribute data).
Regarding claims 4, 12, and 20
Beaurepaire discloses the method as claimed in claim 1, wherein the diagnostic data is received from an on-boarding diagnostic (OBD) module (Beaurepaire, [0115]: collect attributes of vehicle from on-board diagnostic (OBD) and from vehicle sensors).
Regarding claims 5 and 13
Beaurepaire discloses the method as claimed in claim 1, wherein the method further comprises: receiving, by the at least one processor, feedback from the user in response to the vehicle recommendation (Beaurepaire, [0061]: determine whether the user acknowledged the proposal (e.g., via an input or gesture in connection with an application 107) or used the recommended vehicle 113 for the trip [0065]: collect contextual data such as: [0072] historical user experience data with the plurality of candidate vehicles 113; [0103]: automatically keep or save the user's selections for later processing).
Regarding claims 7 and 15
Beaurepaire discloses the method as claimed in claim 1, wherein the trained model is trained using a set of vehicle data associated with a plurality of vehicles and a set of user profile data associated with a plurality of users (Beaurepaire, [0053]: can compute an index/score value per candidate vehicle 113 for a given trip for a group of users or route based on all the available contextually relevant information for vehicles (e.g., using the machine learning system 123) and computes the score as a ratio between energy related factors and user preference parameters and factors and attributes, contextually relevant information related to the vehicles 113, trip information or data, user preferences, respective weights or weighting schemes, etc. in the geographic database 109 as labeled or marked features for training of the machine learning system; [0089]: the training module 311 trains a machine learning model using the respective weights of the contextual data, the respective convenience factors, the balance of the respective energy-efficiency scores and the respective convenience factors, or a combination thereof to enable the recommendation module 305 to most efficiently select the recommended vehicle; FIG. 5D, [0103]: use machine learning system to update user preferences stored in database; [0140]: database includes user preferences).
Regarding claims 8 and 16
Beaurepaire discloses the method as claimed in claim 1, wherein the method further comprises: providing, by the at least one processor, the vehicle recommendation to at least one external application that is associated with a platform (Beaurepaire, [0060]: the system 100 presents the recommendation via an application 107 as a comparative view of options with the respective rankings and their relative scores to support decision making and present those vehicles 113 with at least a certain threshold rank or score with users review such rankings and scores via a UE; [0085]: the comparative view may include a representation of the respective energy-efficiency scores of the recommended vehicle 113, the recommended mode of vehicle operation, the plurality of candidate vehicles 113, the plurality of candidate modes of vehicle operation; FIGs. 5A-5D, [0091]: generates UI for user to easily compare multiple vehicles; FIG. 5D, [0103]: display max score; [0099]: automatically select or reserve the highest scored vehicle 113 or automatically select or reserve a vehicle 113 that meets or exceeds a certain threshold score (e.g., <300); [0153]: providing a contextually relevant vehicle comparison for a given trip, is provided to the bus 810 for use by the processor from an external input device 812; [0155]: Communication interface 870 provides two-way communication coupling to a variety of external devices).
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.
Claim(s) 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire as applied to claim 1 above, and further in view of Katta et al. (US Pub. No. 20170161973 A1, hereinafter “Katta”).
Regarding claims 6 and 14
Beaurepaire discloses the method as claimed in claim 1, wherein the diagnostic data is received… (Beaurepaire, [0115]: collect attributes of vehicle from on-board diagnostic (OBD) and from vehicle sensors; [0051]: the vehicles 113 have connectivity to the mobility platform 103 via the communication network 105 and include one or more vehicle sensors 115 (also collectively referred to as vehicle sensors 115)).
Beaurepaire does not teach:
…received upon successful validation and transformation by the at least one processor (emphasis added).
However, Katta teaches:
…received upon successful validation and transformation by the at least one processor (emphasis added) (Katta, [0017]: if the third party application access is verified, granting the third party application access to the vehicle and/or data of the vehicle; [0024]: translate vehicle data requests from third party applications into resource queries tailored to the different data formats and different communications protocols; [0025]: verifying that third party applications have legitimate access to the requested vehicle parameters, before retrieving and providing values for the vehicle parameters; [0069]: processor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the received data of Beaurepaire with received upon successful validation and transformation by the at least one processor as taught by Katta because the results of such a modification would be predictable. Specifically, Beaurepaire would continue to teach the received data except that now received upon successful validation and transformation by the at least one processor is taught according to the teachings of Katta in order to improve security of vehicle data. This is a predictable result of the combination. (Katta, [0025]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is cited as Lewis et al. (US Pub. No. 20240046074 A1) related to data analysis for identifying, classifying, researching and analyzing vehicles, Gee et al. (US Pub. No. 20220284470 A1) related to machine learning algorithm for recommendations regarding vehicle, and non-patent literature, Car Recommendation System for Dealers in Different European Countries, related to car recommendation of car rating using collaborative filtering. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LATASHA DEVI RAMPHAL whose telephone number is (571)272-2644. The examiner can normally be reached 11 AM - 7:30 PM (EST).
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A. Smith can be reached at 5712726763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/KELLY S. CAMPEN/Primary Examiner, Art Unit 3691
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