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 .
Status of Claims
This Office Action is in response to the Applicant’s amendments and remarks filed on November 13, 2025.
Claims 1, 8, and 15 are currently amended.
Claims 2-3. 9-10, and 16-17 were previously canceled.
Claims 1, 4-8, 11-15, and 18-20 are pending and have been examined.
Response to Arguments
Regarding the outstanding 35 U.S.C. § 101 Rejections:
Applicant’s arguments filed on November 13, 2025 have been fully considered but they are not persuasive.
Regarding A. Claims 1, 8, and 15
In the Response, Applicant argues that, “claims 1, 8, and 15, and their respective dependent claims, are patent eligible under 35 USC 101 at least because the claims integrate any abstract ideas allegedly recited by the claims into a practical application by improving technology or a technical field”, on page 11 of Applicant’s Response dated November 13, 2025.
Applicant further argues that, “the claims recite a particular solution to a problem or a particular way to achieve a desired outcome, and thus improve an existing technology”, on page 12 of Applicant’s response dated November 13, 2025.
Applicant goes on to cite from the specification a description of vehicle(s) that are “located in a particular location (e.g., based upon GPS data from the vehicle) may utilize onboard sensors to directly or indirectly detect or measure weather conditions, such as an amount of rain or snow, an amount or size of hail, blood conditions, hurricane conditions, lightning conditions, tornado conditions, etc., or conditions related to other natural events such as wildfires, earthquakes, etc.”, on page 12 of Applicant’s response dated November 13, 2025.
Further, Applicant summarizes by stating that, “the present Application unconventionally provides a particular solution in which vehicles that were incidentally parked or travelling near a location of interest at a particular time can be used to capture hyper-localized sensor data associated with a weather event at the location of interest at that particular time”, on page 13 of Applicant’s response dated November 13, 2025.
The Examiner respectfully disagrees. The use of vehicle sensors to capture hyper-localized sensor data associated with a weather event at a location of interest at a particular time is well-understood, routine, conventional activity in the art. Primary reference, Kusama, et al. (Publication US 2020/0189527 A1) states, “[i]n the related art, a technique of providing weather information indicating the weather or the like is known. In general, weather information which is provided has a constant geographical resolution (for example, an area unit or a location unit) and a constant temporal resolution (for example, a time period unit or a time unit). Regarding such a technique, in order to improve a geographical or temporal resolution, for example, Japanese Patent Application Publication Nol. 2018-109820…discloses a technique of generating information on weather with a second resolution which is higher than a first resolution based on position information of a vehicle, information in which weather conditions of an area in which the vehicle is located are reflected and which is acquired by an onboard device, and information on weather acquired with the first resolution by a weather observation device. … [0004] As in the technique described in JP 2018-109820A, when data collected by a vehicle is used … there is a demand for improvement of a technique of providing weather information and curbing of an increase in a processing load.” [see Kusama [0003]-[0004] (Emphasis Added.)]
Therefore, under Step 2B of the 35 USC 101 analysis, the additional elements of “receiving, …, indications of location data captured by location sensors associated with each of a plurality of vehicles, the plurality of vehicles not including the damaged vehicle” and “receiving, …, environmental sensor data captured by environmental sensors associated with the one or more identified vehicles at the particular time” do not amount to significantly more than the judicial exception because the extra-solution activity of “receiving, …, indications of location data captured by location sensors associated with each of a plurality of vehicles, the plurality of vehicles not including the damaged vehicle” and “receiving, …, environmental sensor data captured by environmental sensors associated with the one or more identified vehicles at the particular time” are well-known, well-understood, routine, and conventional activities, as supported, at least, in the disclosure in the Primary reference, Kusama.
As a result, the Examiner respectfully disagrees that the additional elements amount to significantly more than the judicial exception.
Regarding B. Claims 5-7, 12-24, and 19-20
In the Response, Applicant argues, “claims 5-7, 12-14, and 19-20 are patent eligible under 35 USC 101 because these claims do not recite an abstract idea”, on page 14 of Applicant’s Response dated November 13, 2025.
Applicant further argues that these claims “each encompass AI in a way that cannot be practically performed in the human mind, and do not fall within the mental process grouping”, on page 15 of Applicant’s Response dated November 13, 2025.
The Examiner respectfully disagrees. Dependent claims 5 (representative of claims 12 and 19), 6 (representative of claims 13 and 20), and 7 (representative of claim 14), are all similar to claim 2 in Example 47 (Anomaly Detection) that is explained in the July 2024 Subject Matter Eligibility Examples.
Current claim 5 recites, inter alia, “applying a trained machine learning model to the environmental sensor data captured by the environmental sensors associated with the one or more identified vehicles.” This limitation is similar to Example 47, claim 2 (d), which recites, “(d) detecting one or more anomalies in a data set using the trained ANN”. The guidance explains, “[s]tep (d) recites detecting one or more anomalies in a data set using the trained ANN. The claim does not provide any details about how the trained ANN operates or how the detection is made, and the plain meaning of ‘detecting’ encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of an anomaly in a data set.” See page 6, July 2024 Subject Matter Eligibility Examples. Further, on page 8, the explanation continues, “[t]he limitations in (d) and (e) reciting ‘using the trained ANN’ provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP2106.05(f).” The explanation continues on page 9, “[t]he trained ANN is used to generally apply the abstract idea without placing any limits on how the trained ANN functions. Rather, these limitations only recite the outcome of ‘detecting one or more anomalies’ and ‘analyzing the one or more detected anomalies’ and do not include any details about how the ‘detecting’ and ‘analyzing’ are accomplished. See MPEP 2106.05(f).” Additionally, the explanation further continues on page 9, “[t]he recitation of ‘using a trained ANN’ in limitations (d) and (e) also merely indicates a field of use or technological environment in which the judicial exception is performed….this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).” Ultimately, the conclusion is that this limitation does not contribute to Example 47, claim 2 being patent eligible. The Examiner takes the position that the current claim language is recited at a very high level by stating, “applying a trained machine learning model to the environmental sensor data captured by the environmental sensors associated with the one or more identified vehicles” and does not discuss details about how the trained machine learning model operates, nor does it explain any output from the applied trained learning model after applying to the environmental sensor data captured by the environmental sensors associated with the one or more identified vehicles. Therefore, the Examiner concludes that claim 5 is not patent eligible under 35 USC 101.
Claim 6 recites, inter alia, “the trained machine learning model is trained using training data including historical environmental sensor data captured by environmental sensors at locations of historical weather events, to identify weather events at a given location based upon environmental sensor data captured within a proximity of the given location.” This limitation is similar to Example 47, claim 2 (c), which recites “training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” and 2 (d), which recites “detecting one or more anomalies in a data set using the trained ANN”. The guidance explains “[s]teps (a), (b), and (c) are all recited as being performed by a computer. The recited computer is recited at a high level of generality, i.e., as a generic computer performing generic computer functions.” See page 6, July 2024 Subject Matter Eligibility Examples. The evaluation states on page 7, “[s]tep (c) requires specific mathematical calculations (a backpropagation algorithm and a gradient descent algorithm) to perform the training of the ANN and therefore encompasses mathematical concepts.” Further, on page 8, “[i]n limitations (b) and (c), the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).” The Examiner concludes that the first part of claim 6, from the current application, “wherein the trained machine learning model is trained using training data including historical environmental sensor data captured by environmental sensors at locations of historical weather events” is similar to step 2 (c) in Example 47 and is performed by a generic computer that is recited at a high level of generality and is used to perform an abstract idea, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The remaining claim language, “to identify weather events at a given location based upon environmental sensor data captured within a proximity of the given location” is similar to step 2(d) in Example 47 and, therefore, the use of the trained learning model provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) which “the following considerations for determining whether a claim simply recites a judicial exception with the words ‘apply it’ (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.” See pages 8-9, July 2024 Subject Matter Eligibility Examples. Similar to limitations (c) and (d), the judicial exception of “identify weather events at a given location based upon environmental sensor data captured within a proximity of the given location” is performed by “the trained machine learning model”. The “trained machine learning model” is used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions. Rather, the claim only recites the outcome of “identify weather events at a given location based upon environmental sensor data captured within a proximity of the given location” and does not include any details about how the identifying is accomplished. Therefore, the Examiner concludes that claim 6 is not patent eligible under 35 USC 101.
Claim 7 recites, inter alia, “the trained machine learning model is further trained using times at which the historical environmental sensor data is captured and times of the historical weather events, to identify weather events occurring at the given location at a given time or range of times based upon environmental sensor data captured within the proximity of the given location at the given time or range of times.” The Examiner takes the position that the 35 USC 101 analysis for claim 7 is similar to that for claim 6, above. The Examiner concludes that the first part of claim 7, from the current application, “wherein the trained machine learning model is further trained using times at which the historical environmental sensor data is captured and times of the historical weather events” is similar to step 2 (c) in Example 47 and is performed by a generic computer that is recited at a high level of generality and is used to perform an abstract idea, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The remaining claim language, “to identify weather events occurring at the given location at a given time or range of times based upon environmental sensor data captured within the proximity of the given location at the given time or range of times” is similar to step 2(d) in Example 47 and, therefore, the use of the trained learning model provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) which “the following considerations for determining whether a claim simply recites a judicial exception with the words ‘apply it’ (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.” See pages 8-9, July 2024 Subject Matter Eligibility Examples. Similar to limitations (c) and (d), the judicial exception of “identify weather events occurring at the given location at a given time or range of times based upon environmental sensor data captured within the proximity of the given location at the given time or range of times” is performed by “the trained machine learning model”. The “trained machine learning model” is used to generally apply the abstract idea without placing any limits on how the trained machine learning model functions. Rather, the claim only recites the outcome of “identify weather events occurring at the given location at a given time or range of times based upon environmental sensor data captured within the proximity of the given location at the given time or range of times” and does not include any details about how the identifying is accomplished. Therefore, the Examiner concludes that claim 7 is not patent eligible under 35 USC 101.
Regarding the outstanding 35 U.S.C. § 103 Rejections:
Applicant’s arguments filed on November 13, 2025 have been fully considered but they are not persuasive.
Applicant argues that, “the cited references, even in combination, would fail to disclose ‘retrieving, by one or more processors, from a weather event database, an indication of a weather event associated with one or more of: a damaged home, a damaged business, or a damaged vehicle, at a particular time,’ as recited by claim 1.”
The Examiner respectfully disagrees with this reasoning. Primary reference Kusama is used for it’s disclosure, “retrieving, by one or more processors, from a weather event database, an indication of a weather event associated with … {a location} … at a particular time”, as disclosed in [0020] that states, “the server 20 stores weather information indicating weather” and in [0039] that states, “the server control unit 23 stores weather information indicating weather in the server storage unit 22. The weather information may have a predetermined geographical resolution such as an area unit or a location unit. The weather information may have a predetermined temporal resolution such as a time period unit or a time unit. The weather information may be provided from an organization such as a meteorological agency via the network 30 or may be generated based on observation data which is received from a weather observation device via the network 30.” Kusama further discloses that the observation data which is received from a weather observation device via the network 30 can be weather information acquired from a vehicle 10 through image recognition, as disclosed in [0021], “[t]he server 20 detects weather from the image acquired by the vehicle 10 through image recognition. The server updates weather information stored therein based on a result of comparison between the weather detected from the image and the weather indicated by the weather information stored therein.” Therefore, Kusama discloses that the weather event data stored in the database is weather event data received from multiple sources, including an organization such as a meteorological agency via the network 30 or may be generated based on observation data which is received from a weather observation device, like a vehicle, via the network 30. In summary, Kusama discloses that information is transmitted and received back and forth between the server holding the database and the vehicle(s). The database is updated based on information that is received from multiple sources, including vehicle(s).
Secondary reference, Ghannam, is used for its disclosure of, “an indication of a weather event associated with one or more of: a damaged home, a damaged business, or a damaged vehicle”, as disclosed in [0057], which states, “the sensor 115 data can be image and/or video data. In such an example, one or more ECUs 126 can analyze the image and/or video data to identify damage to the vehicle from, e.g. a collision, a flood, a fire, etc.” and in [0060], which states, “the respective ECU 126 can determine an event code based on the received sensor 115 data, e.g., a detected damage type.”
Therefore, the Examiner takes the position that it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kusama to include the sensor data from Ghannam that includes image and/or video data that identifies damage to a vehicle from either a flood or a fire, and update the weather information in the database with that type of information from a vehicle. Therefore, the Examiner respectfully disagrees with Applicant’s first argument and maintains the outstanding 35 USC 103 rejection for claim 1.
Applicant further argues, “that the cited references, even in combination, would fail to disclose ‘identifying, by the one or more processors, one or more vehicles, of the plurality of vehicles, that were within a proximity of the location of interest associated with the one or more of the damaged home, the damaged business, or the damaged vehicle at the particular time,’ as recited by claim 1.”
The Examiner respectfully disagrees. Applicant amended this limitation in claim 1, which necessitated a new search that revealed a new reference, Tay, et al. (Publication US 2019/0339709) (hereinafter referred to as “Tay”.) The modified 35 USC 103 rejection, based on Applicant’s amendments is now explained.
The Gaetje reference discloses, “identifying, by the one or more processors, one or more vehicles, of the plurality of vehicles, … within a proximity of the location of interest”, as disclosed in [0018] “…[a]fter determining the location of the user equipment, the weather application identifies weather information sources that are within a threshold distance of the user’s location” and in [0020] “…the weather application selects the second area 106 as the threshold distance and identifies … two weather information sources 112A and 112B in the second area 106.” This disclosure reflects identifying one or more vehicles within a proximity of a location of interest.
Tay discloses “one or more vehicles … that were within a proximity of the location of interest associated with the one or more of the damaged home, the damaged business, or the damaged vehicle at the particular time”, as disclosed in [0014], “while recording scan data during a mapping period, the autonomous vehicle can also passively record data that is relevant or valuable to other entities…the autonomous vehicle can: implement computer vision…and/or other perception techniques to detect a fire nearby while traversing a road segment during a mapping period; capture a 2D color image or video clip of this detected fire; and then automatically transmit the 2D color image or video clip, a location of the autonomous vehicle or the detected fire, a time of the detected fire, … In another example, during a mapping period, the autonomous vehicle can: … implement computer vision, … to identify such deviations as downed trees, downed power telephone lines, or damaged or missing road signs; and then automatically transmit types, locations, and 2D color images of these detected deviations … .” The Examiner takes the position that this discloses an autonomous vehicle that perceives environmental conditions, including fires and weather related events, including downed trees, downed power lines, damaged or missing road signs, and records data related to these environmental conditions, including location and time data for the autonomous vehicle and/or the detected environmental condition. Tay’s disclosure in the following paragraphs is interpreted by the Examiner to disclose that not only is the information detected by the autonomous vehicles done in real time, but that it is also scanned, saved and used later for various purposes. Tay discloses in [0015] that the “autonomous vehicle can then transition into a mapping period ...in order to: ... collect scan data for internal localization map updates..."; [0017] "The autonomous vehicle can therefore execute ... a mapping period; to collect scan data for road segments within its assigned geographic region--which the computer system can then access to update a localization map for the geographic region--during a mapping period; and to opportunistically collect data--during the mapping period--that may be useful or relevant to one or more external entities."; [0041] "...the computer system can aggregate ... data recently recorded by the same or other autonomous vehicles traversing road segments within the geographic region... ." Finally, Tay discloses the ability to detect and characterize static objects, including building facades, as disclosed in [0022] the “autonomous vehicle can also implement one or more local neural networks to process LIDAR feeds…, video feeds…, to localize the autonomous vehicle to a known location and orientation in real space, to interpret (or ‘perceive’) its surroundings … the controller can: pass LIDAR and video feeds into a localization/perception neural network to detect and characterize static objects—such as ...road signs, telephone poles, and building facades—near the autonomous vehicle substantially in real-time; and then compare types and relative locations of these static objects to a localization map to determine the autonomous vehicle's position in real space." Therefore, Tay discloses the ability to identify a fire, and its time and location, and the ability to identify a building façade, and its time and location. Therefore, one of ordinary skill would conclude that Tay discloses the capacity to identify a building and a fire that are identified at the same time and same location.
Therefore, the Examiner takes the position that it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gaetje to include data utilizing information from Tay pertaining to one or more vehicles that were within a proximity of the location of interest associated with the one or more of a time and location associated with a fire/downed tree/downed powerline/downed sign and the time and location associated with a building façade. Therefore, the Examiner respectfully disagrees with Applicant’s second argument and maintains the outstanding 35 USC 103 rejection for claim 1.
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, 4-8, 11-15, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
A claim that recites an abstract idea, a law of nature, or a natural phenomenon is directed to a judicial exception. Abstract ideas include the following groupings of subject matter, when recited as such in a claim limitation: (a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; (b) Certain methods of organizing human activity – 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); and (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See the 2019 Revised Patent Subject Matter Eligibility Guidance.
Even when a judicial element is recited in the claim, an additional claim element(s) that integrates the judicial exception into a practical application of that exception renders the claim eligible under §101. A claim that integrates a judicial exception into a practical application will 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 judicial exception. The following examples are indicative that an additional element or combination of elements may integrate the judicial exception into a practical application:
the additional element(s) reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
the additional element(s) that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
the additional element(s) implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
the additional element(s) effects a transformation or reduction of a particular article to a different state or thing; and
the additional element(s) applies or uses 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.
Examples in which the judicial exception has not been integrated into a practical application include:
the additional element(s) merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
the additional element(s) adds insignificant extra-solution activity to the judicial exception; and
the additional element does no more than generally
link the use of a judicial exception to a particular technological environment or field of use.
See the 2019 Revised Patent Subject Matter Eligibility Guidance and the 2024 Patent Subject Matter Eligibility Guidance Update Including on Artificial Intelligence.
Claims 1, 8, and 15 recite the abstract idea of using autonomous and connected vehicles as secondary data sources to confirm weather data and other triggering events, as drafted, is a process and system that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer elements. The claims are practically able to be performed in the mind. For example (claim 1 is discussed and is representative of claims 8 and 15), but for the “by the one or more processors”, “comparing … the location data captured by the location sensors associated with each of the plurality of vehicles to a location of interest associated with the one or more of the damaged home, the damaged business, or the damaged vehicle”, “identifying … one or more vehicles, of the plurality of vehicles, that were within a proximity of the location of interest associated with the one or more of the damaged home, the damaged business, or the damaged vehicle at the particular time”, and “determining … based upon the environmental sensor data captured by the environmental sensors associated with the one or more of the identified vehicles at the particular time, whether the indication of the weather event associated with the one or more of: the damaged home, the damaged business, or the damaged vehicle, at the particular time, as retrieved from the weather event database, exceeds a threshold level of accuracy” in the context of this claim encompasses the user discerning a weather event at a location of interest, comparing the location data captured by the location sensors from the vehicles to a location of interest, and determining whether the indication of the weather event exceeds a threshold level of accuracy.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims only recite additional elements – “by the one or more processors”, “retrieving … from a weather event database, an indication of a weather event associated with one or more of: a damaged home, a damaged business, or a damaged vehicle, at a particular time”, “receiving … indications of location data captured by location sensors associated with each of a plurality of vehicles, the plurality of vehicles not including the damaged vehicle”, “receiving … environmental sensor data captured by environmental sensors associated with the one or more identified vehicles at the particular time.”
The limitation of “by the one or more processors” is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
The limitations of “retrieving … from a weather event database, an indication of a weather event associated with one or more of: a damaged home, a damaged business, or a damaged vehicle, at a particular time”, “receiving … indications of location data captured by location sensors associated with each of a plurality of vehicles, the plurality of vehicles not including the damaged vehicle”, and “receiving … environmental sensor data captured by environmental sensors associated with the one or more identified vehicles at the particular time” are additional elements that are recited at a high level of generality, and amount to mere data gathering, which is a form of insignificant extra-solution activity.
Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, of the “by the one or more processors”, “retrieving … from a weather event database, an indication of a weather event associated with one or more of: a damaged home, a damaged business, or a damaged vehicle, at a particular time”, “receiving … indications of location data captured by location sensors associated with each of a plurality of vehicles, the plurality of vehicles not including the damaged vehicle”, “receiving … environmental sensor data captured by environmental sensors associated with the one or more identified vehicles at the particular time” do not amount to significantly more than the judicial exception.
The “by one or more processors” merely describes how to generally “apply” the abstract ideas in a generic or general purpose computing environment. The “by one or more processors” is recited at a high level of generality and merely automates the abstract idea limitations.
The limitations of “retrieving … from a weather event database, an indication of a weather event associated with one or more of: a damaged home, a damaged business, or a damaged vehicle, at a particular time”, “receiving … indications of location data captured by location sensors associated with each of a plurality of vehicles, the plurality of vehicles not including the damaged vehicle”, and “receiving … environmental sensor data captured by environmental sensors associated with the one or more identified vehicles at the particular time” are recited at a high level of generality, and amount to mere data gathering, which is a form of insignificant extra-solution activity, well understood, routine, and conventional, and does not amount to significantly more than the judicial exception.
Accordingly, these additional elements, even in combination, do not amount to significantly more than the judicial exception. The claims are not patent eligible.
For claims 4, 11, and 18, “wherein determining the indication of the weather event includes determining a time or a range of times associated with the weather event, and wherein identifying the one or more vehicles, of the plurality of vehicles, within a proximity of the location of interest includes identifying the one or more vehicles, of the plurality of vehicles, that were within the proximity of the location of interest at the time or range of times associated with the weather event”, as drafted, under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “the computer-implemented method”, nothing in the claim precludes the limitation from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
For claims 5, 12, and 19, “wherein determining, based upon the environmental sensor data captured by the environmental sensors associated with the one or ore identified vehicles at the particular time, whether the indication of the accuracy of the indication of the weather event, as retrieved from the weather event database, exceeds the threshold level of accuracy includes applying a trained machine learning model to the environmental sensor data captured by the environmental sensors associated with the one or more identified vehicles” recites applying a trained machine learning model to data. The claim does not limit how the application is performed, and there is nothing about how any results are utilized.
The limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer. The recitation of “applying a trained machine learning model” merely indicates a field of use or technological environment in which the judicial exception is performed, and confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
For claims 6, 13, and 20, “wherein the trained machine learning model is trained using training data including historical environmental sensor data captured by environmental sensors at locations of historical weather events, to identify weather events at a given location based upon environmental sensor data captured within a proximity of the given location”, is recited at a high level of generality (i.e. as a general means of gathering data for use in the “machine learning model”), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
For claims 7 and 14, “wherein the trained machine learning model is further trained using times at which the historical environmental sensor data is captured and times of the historical weather events, to identify weather events occurring at the given location at a given time or range of times based upon environmental sensor data captured within the proximity of the given location at the given time or range of times”, is recited at a high level of generality (i.e. as a general means of gathering data for use in the “machine learning model”), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Accordingly, these additional elements, even in combination, do not amount to significantly more than the judicial exception. The claims are not patent eligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The 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, 4-5, 8, 11-12, 15, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kusama, et al. (Publication US 2020/0189527 A1), in view of Ghannam, et al. (Publication US 2021/0078512 A1), Gaetje (Publication US 2022/0159085 A1), and Tay, et al. (Publication US 2019/0339709 A1) (hereinafter referred to as “Kusama”, “Ghannam”, “Gaetje”, and “Tay”.)
As per claim 1 (representative of claims 8 and 15), Kusama discloses a computer-implemented method for using autonomous and connected vehicles as secondary sources to confirm weather data and other triggering events, comprising:
retrieving, by one or more processors, from a weather event database, an indication of a weather event associated with {a location} …, at a particular time [see at least Kusama [0020] "…The server 20 stores weather information indicating weather."; [0039] "...the server control unit 23 stores weather information indicating weather in the server storage unit 22. The weather information may have a predetermined geographical resolution such as an area unit or a location unit. The weather information may have a predetermined temporal resolution such as a time period unit or a time unit. The weather information may be provided from an organization such as a meteorological agency via the network 30 or may be generated based on observation data which is received from a weather observation device via the network 30."];
receiving, by the one or more processors, indications of location data captured by location sensors associated with each of a plurality of vehicles, the plurality of vehicles not including the damaged vehicle [see at least Kusama FIG. 2; [0033] "…the control unit 15 acquires an image in which a scene outside the vehicle is captured using the imaging unit 13. Here the control unit 15 may acquire an image by causing the imaging unit 13 to image a scene outside the vehicle … The control unit 15 acquires a time (an imaging time) at which the acquired image has been captured and a position (an imaging position) of the vehicle 10 at that time."; [0034] "The control unit 15 transmits the acquired image, the imaging time, and the imaging position to the server 20 via the communication unit 11."];
comparing, by the one or more processors, the location data captured by the location sensors associated with each of the plurality of vehicles to a location of interest … [see at least Kusama [0041] "The server control unit 23 updates weather information stored in the server storage unit 22 based on a result of comparison between the weather detected from the image and the weather indicated by the weather information."; (info from vehicles has associated location data)[0034] "The control unit 15 transmits the acquired image, the imaging time, and the imaging position to the server 20 via the communication unit 11."; (info from server has associated location data) [0039] "...the server control unit 23 stores weather information indicating weather in the server storage unit 22. The weather information may have a predetermined geographical resolution such as an area unit or a location unit. The weather information may have a predetermined temporal resolution such as a time period unit or a time unit."];
… receiving, by the one or more processors, environmental sensor data captured by environmental sensors associated with the one or more identified vehicles at the particular time [see at least Kusama [0033] "…the control unit 15 acquires an image in which a scene outside the vehicle is captured using the imaging unit 13. Here the control unit 15 may acquire an image by causing the imaging unit 13 to image a scene outside the vehicle … The control unit 15 acquires a time (an imaging time) at which the acquired image has been captured and a position (an imaging position) of the vehicle 10 at that time."]; and
determining, by the one or more processors, based upon the environmental sensor data captured by the environmental sensors associated with the one or more identified vehicles at the particular time, whether the indication of the weather event associated with {a location} …, as retrieved from the weather event database, exceeds a threshold level of accuracy [see at least Kusama [0041] "The server control unit 23 updates weather information stored in the server storage unit 22 based on a result of comparison between the weather detected from the image and the weather indicated by the weather information."; {Examiner note: the threshold includes comparing the image data with vehicle function data (WIPER OR FOG LIGHTS ON)}; FIGS. 6, 7 [0032] "The control unit 15 identifies an onboard device and a prescribed operating state corresponding to the weather indicated by the weather information based on the above-mentioned correspondence information. The control unit 15 acquires the operating state of the identified onboard device, for example, from the onboard network. The control unit 15 determines whether the operating state of the onboard device is the prescribed operating state corresponding to the weather indicated by the weather information. In this embodiment, when the weather information indicates “rainy,” the control unit 15 determines whether the operating state of the “wiper” is “ON” based on the correspondence information illustrated in FIG. 3."; FIG.6, S101 "Receive Weather Information From Server" -> FIG. 6 "NO" -> FIG 6, S103 "Acquire Image in Which Scene Outside Vehicle is Captured, Imaging Time, and Imaging Position" -> FIG> 6, S104 "Transmit Image, Imaging Time, and Imaging Position to Server" -> FIG. 7, S201 "Receive Image, Imaging Time, and Imaging Position From Vehicle" -> FIG. 7, S202 "Detect Weather from Image" -> FIG. 7, S203 "Update Stored Weather Information Based on Result of Comparison Between Weather Detected from Image and Weather Indicated by Weather Information"; [0041] "...when the weather detected from the image is different from the weather indicated by the weather information, the server control unit 23 updates the weather information stored in the server storage unit 22 to indicate the weather detected from the image."]
Kusama fails to disclose … an indication of a weather event associated with one or more of: a damaged home, a damaged business, or a damaged vehicle … . However, Ghannam teaches this limitation [see at least Ghannam [0057] "…the sensor 115 data can be image and/or video data. In such an example, one or more ECUs 126 can analyze the image and/or video data to identify damage to the vehicle from, e.g, a collision, a flood, a fire, etc."; [0060] "…the respective ECU 126 can determine an event code based on the received sensor 115 data, e.g., a detected damage type... ."]
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 as disclosed in Kusama to incorporate the teaching … an indication of a weather event associated with one or more of: a damaged home, a damaged business, or a damaged vehicle … of Ghannam with a reasonable expectation of success for the benefit of improved collection and storage of sensor data [see at least Ghannam para [0037]-[0038].]
The combination of Kusama and Ghannam fails to disclose … comparing, by the one or more processors, the location data captured by the location sensors associated with … a location of interest … ; and identifying, by the one or more processors, one or more vehicles, of the plurality of vehicles, … within a proximity of the location of interest... . However, Gaetje teaches these limitations [see at least Gaetje [0018] "...After determining the location of the user equipment device, the weather application identifies weather information sources that are within a threshold distance of the user's location."; [0020] "...the weather application selects the second area 106 as the threshold distance and identifies ... two weather information sources 112A and 112B in the second area 106."]
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 as disclosed in the combination of Kusama and Ghannam to incorporate the teaching … comparing, by the one or more processors, the location data captured by the location sensors associated with … a location of interest … ; and identifying, by the one or more processors, one or more vehicles, of the plurality of vehicles, … within a proximity of the location of interest... of Gaetje with a reasonable expectation of success for the benefit of improved systems and methods for supplying localized weather information to a user that is more accurate [see at least Gaetje [0002]-[0003].]
The combination of Kusama, Ghannam, and Gaetje fails to disclose … location data captured by the location sensors associated with … {a vehicle at} … a location of interest associated with the one or more of the damaged home, the damaged business, or the damaged vehicle … ; …one or more vehicles … that were within a proximity of the location of interest associated with the one or more of the damaged home, the damaged business, or the damaged vehicle at the particular time; and … environmental sensor data captured by the environmental sensors associated with the one or more identified vehicles at the particular time, … the indication of the weather event associated with the one or more of: the damaged home, the damaged business, or the damaged vehicle, at the particular time ..... However, Tay teaches these limitations:
… location data captured by the location sensors associated with … {a vehicle at} … a location of interest associated with the one or more of the damaged home, the damaged business, or the damaged vehicle … [see at least Tay [0014] "…while recording scan data during a mapping period, the autonomous vehicle can also passively record data that is relevant or valuable to other entities... . For example, the autonomous vehicle can: implement computer vision, ... and/or other perception techniques to detect a fire nearby while traversing a road segment during a mapping period; capture a 2D color image or video clip of this detected fire; and then automatically transmit the 2D color image or video clip, a location of the autonomous vehicle or the detected fire, a time of the detected fire,... .In another example, during a mapping period, the autonomous vehicle can: ... implement computer vision, ... to identify such deviations as downed trees, downed power of telephone lines, or damaged or missing road signs; and then automatically transmit types, locations, and 2D color images of these detected deviations... .";];
…one or more vehicles … that were within a proximity of the location of interest associated with the one or more of the damaged home, the damaged business, or the damaged vehicle at the particular time [see at least Tay [0014] "…while recording scan data during a mapping period, the autonomous vehicle can also passively record data that is relevant or valuable to other entities... . For example, the autonomous vehicle can: implement computer vision, ... and/or other perception techniques to detect a fire nearby while traversing a road segment during a mapping period; capture a 2D color image or video clip of this detected fire; and then automatically transmit the 2D color image or video clip, a location of the autonomous vehicle or the detected fire, a time of the detected fire,... .In another example, during a mapping period, the autonomous vehicle can: ... implement computer vision, ... to identify such deviations as downed trees, downed power of telephone lines, or damaged or missing road signs; and then automatically transmit types, locations, and 2D color images of these detected deviations... ."; [0015] "...The autonomous vehicle can then transition into a mapping period ...in order to: ... collect scan data for internal localization map updates..."; [0017] "The autonomous vehicle can therefore execute ... a mapping period; to collect scan data for road segments within its assigned geographic region--which the computer system can then access to update a localization map for the geographic region--during a mapping period; and to opportunistically collect data--during the mapping period--that may be useful or relevant to one or more external entities."; [0041] "...the computer system can aggregate ... data recently recorded by the same or other autonomous vehicles traversing road segments within the geographic region..."; [0022] "Autonomous vehicle can also implement one or more local neural networks to process LIDAR feeds…, video feeds…, to localize the autonomous vehicle to a known location and orientation in real space, to interpret (or ‘perceive’) its surroundings… the controller can: pass LIDAR and video feeds into a localization/perception neural network to detect and characterize static objects—such as ...road signs, telephone poles, and building facades—near the autonomous vehicle substantially in real-time; and then compare types and relative locations of these static objects to a localization map to determine the autonomous vehicle's position in real space."]; and
… environmental sensor data captured by the environmental sensors associated with the one or more identified vehicles at the particular time, … the indication of the weather event associated with the one or more of: the damaged home, the damaged business, or the damaged vehicle, at the particular time .... [see at least Tay [0014] "…while recording scan data during a mapping period, the autonomous vehicle can also passively record data that is relevant or valuable to other entities... . For example, the autonomous vehicle can: implement computer vision, ... and/or other perception techniques to detect a fire nearby while traversing a road segment during a mapping period; capture a 2D color image or video clip of this detected fire; and then automatically transmit the 2D color image or video clip, a location of the autonomous vehicle or the detected fire, a time of the detected fire,... .In another example, during a mapping period, the autonomous vehicle can: ... implement computer vision, ... to identify such deviations as downed trees, downed power of telephone lines, or damaged or missing road signs; and then automatically transmit types, locations, and 2D color images of these detected deviations... ."; [0015] "...The autonomous vehicle can then transition into a mapping period ...in order to: ... collect scan data for internal localization map updates..."; [0017] "The autonomous vehicle can therefore execute ... a mapping period; to collect scan data for road segments within its assigned geographic region--which the computer system can then access to update a localization map for the geographic region--during a mapping period; and to opportunistically collect data--during the mapping period--that may be useful or relevant to one or more external entities."; [0041] "...the computer system can aggregate ... data recently recorded by the same or other autonomous vehicles traversing road segments within the geographic region..."; [0022] "Autonomous vehicle can also implement one or more local neural networks to process LIDAR feeds…, video feeds…, to localize the autonomous vehicle to a known location and orientation in real space, to interpret (or ‘perceive’) its surroundings… the controller can: pass LIDAR and video feeds into a localization/perception neural network to detect and characterize static objects—such as ...road signs, telephone poles, and building facades—near the autonomous vehicle substantially in real-time; and then compare types and relative locations of these static objects to a localization map to determine the autonomous vehicle's position in real space."].
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 as disclosed in the combination of Kusama, Ghannam, and Gaetje to incorporate the teaching … location data captured by the location sensors associated with … {a vehicle at} … a location of interest associated with the one or more of the damaged home, the damaged business, or the damaged vehicle … ; …one or more vehicles … that were within a proximity of the location of interest associated with the one or more of the damaged home, the damaged business, or the damaged vehicle at the particular time; and … environmental sensor data captured by the environmental sensors associated with the one or more identified vehicles at the particular time, … the indication of the weather event associated with the one or more of: the damaged home, the damaged business, or the damaged vehicle, at the particular time ... of Tay with a reasonable expectation of success for the benefit of improving geospatial location accuracy. [See at least Tay [0058].]
As per claim 4 (representative of claims 11 and 18), the combination of Kusama, Ghannam, Gaetje, and Tay, as shown above, discloses all of the limitations of claim 1 above.
Kusama discloses … wherein determining the indication of the weather event includes determining a time or a range of times associated with the weather event … [see at least Kusama [0029] "...an electronic control unit (ECU) which is mounted in the vehicle 10 may serve as the control unit 15. The control unit 15 has a clocking function of acquiring a current time. "; [0031] "...the control unit 15 receives weather information indicating weather corresponding to a current time or a time period to which the current time belongs and weather corresponding to a current position of the vehicle 10 or a geographical area to which the current position belongs."; [0033] "...The control unit 15 acquires a time (an imaging time) at which the acquired image has been captured and a position (an imaging position) of the vehicle 10 at that time."; [0039] "...the server control unit 23 stores weather information indicating weather in the server storage unit 22. ... The weather information may have a predetermined temporal resolution such as a time period unit or a time unit."; [0040] "...as illustrated in FIG. 5, the server control unit 23 stores the imaging time, the imaging position, and the weather detected from the image in the server storage unit 22 in correlation with each other."; [0043] "When the temporal resolution of the weather information stored in the server storage unit 22 is a time unit, weather at the imaging time is updated."]
The combination of Kusama and Ghannam fails to disclose … wherein identifying the one or more vehicles, of the plurality of vehicles, within a proximity of the location of interest includes identifying the one or more vehicles, of the plurality of vehicles, … within the proximity of the location of interest at the time or range of times associated with the weather event. However, Gaetje teaches this limitation [see at least Gaetje [0030] "…the weather application can require data points to expire after a certain amount of time (e.g., one minute, five minutes, etc.) to ensure accurate data…"; [0023] "...In some embodiments, temperature data submitted by a newer weather information source is weighted differently than temperature data submitted by an older weather information source."]
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 as disclosed in the combination of Kusama and Ghannam to incorporate the teaching … wherein identifying the one or more vehicles, of the plurality of vehicles, within a proximity of the location of interest includes identifying the one or more vehicles, of the plurality of vehicles, … within the proximity of the location of interest at the time or range of times associated with the weather event of Gaetje with a reasonable expectation of success for the benefit of improved systems and methods for supplying localized weather information to a user that is more accurate [see at least Gaetje [0002]-[0003].]
The combination of Kusama, Ghannam, and Gaetje fails to disclose … one or more vehicles…that were within the proximity of the location of interest at the time or range of times associated with the weather event. However, Tay teaches this limitation [see at least Tay [0014] "…while recording scan data during a mapping period, the autonomous vehicle can also passively record data that is relevant or valuable to other entities... . For example, the autonomous vehicle can: implement computer vision, ... and/or other perception techniques to detect a fire nearby while traversing a road segment during a mapping period; capture a 2D color image or video clip of this detected fire; and then automatically transmit the 2D color image or video clip, a location of the autonomous vehicle or the detected fire, a time of the detected fire,... .In another example, during a mapping period, the autonomous vehicle can: ... implement computer vision, ... to identify such deviations as downed trees, downed power of telephone lines, or damaged or missing road signs; and then automatically transmit types, locations, and 2D color images of these detected deviations... ."; [0015] "...The autonomous vehicle can then transition into a mapping period ...in order to: ... collect scan data for internal localization map updates..."; [0017] "The autonomous vehicle can therefore execute ... a mapping period; to collect scan data for road segments within its assigned geographic region--which the computer system can then access to update a localization map for the geographic region--during a mapping period; and to opportunistically collect data--during the mapping period--that may be useful or relevant to one or more external entities."; [0041] "...the computer system can aggregate ... data recently recorded by the same or other autonomous vehicles traversing road segments within the geographic region..."; [0022] "Autonomous vehicle can also implement one or more local neural networks to process LIDAR feeds…, video feeds…, to localize the autonomous vehicle to a known location and orientation in real space, to interpret (or ‘perceive’) its surroundings… the controller can: pass LIDAR and video feeds into a localization/perception neural network to detect and characterize static objects—such as ...road signs, telephone poles, and building facades—near the autonomous vehicle substantially in real-time; and then compare types and relative locations of these static objects to a localization map to determine the autonomous vehicle's position in real space."]
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 as disclosed in the combination of Kusama, Ghannam, and Gaetje to incorporate the teaching … one or more vehicles…that were within the proximity of the location of interest at the time or range of times associated with the weather event of Tay with a reasonable expectation of success for the benefit of improving geospatial location accuracy. [See at least Tay [0058].]
As per claim 5 (representative of claims 12 and 19), the combination of Kusama, Ghannam, Gaetje, and Tay, as shown above, discloses all of the limitations of claim 1 above.
Kusama discloses … wherein determining, based upon the environmental sensor data captured by the environmental sensors associated with the one or more identified vehicles at the particular time, whether the indication of the accuracy of the indication of the weather event, as retrieved from the weather event database, exceeds the threshold level of accuracy includes applying a trained machine learning model to the environmental sensor data captured by the environmental sensors associated with the one or more identified vehicles [see at least Kusama [0040] "The server control unit 23 receives an image in which a scene outside the vehicle is captured, an imaging time, and an imaging position from the vehicle 10 via the server communication unit 21. The server control unit 23 detects weather from the received image by image recognition. ... For example, a technique of detecting water droplets attached to a front windshield of the vehicle 10 by an image recognition algorithm such as pattern matching, feature point extraction, or machine learning can be employed to detect rainy weather... ."]
Claims 6-7, 13-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kusama, in view of Ghannam, Gaetje, Tay, and Raut, et al. (Publication US 2022/0207389 A1) (hereinafter referred to as “Raut”.)
As per claim 6 (representative of claims 13 and 20), the combination of Kusama, Ghannam, Gaetje, and Tay, as shown above, discloses all of the limitations of claim 5 above.
The combination of Kusama, Ghannam, Gaetje, and Tay fails to disclose … wherein the trained machine learning model is trained using training data including historical environmental sensor data captured by environmental sensors at locations of historical weather events, to identify weather events at a given location based upon environmental sensor data captured within a proximity of the given location. However, Raut teaches this limitation [see at least Raut [0061] "Table 1 indicates an example set of variable data for training the model."]
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 as disclosed in the combination of Kusama, Ghannam, Gaetje, and Tay to incorporate the teaching … wherein the trained machine learning model is trained using training data including historical environmental sensor data captured by environmental sensors at locations of historical weather events, to identify weather events at a given location based upon environmental sensor data captured within a proximity of the given location of Raut with a reasonable expectation of success for the benefit of improved data for driving to improve navigation accuracy. [See at least Raut [0020].]
As per claim 7 (representative of claim 14), the combination of Kusama, Ghannam, Gaetje, Tay, and Raut, as shown above, discloses all of the limitations of claim 6 above.
The combination of Kusama, Ghannam, Gaetje, and Tay fails to disclose … wherein the trained machine learning model is further trained using times at which the historical environmental sensor data is captured and times of the historical weather events, to identify weather events occurring at the given location at a given time or range of times based upon environmental sensor data captured within the proximity of the given location at the given time or range of times. However, Raut teaches this limitation [see at least Raut Table 1, "Timediff" "Latest Available Catalog Time."]
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 as disclosed in the combination of Kusama, Ghannam, Gaetje, and Tay to incorporate the teaching … wherein the trained machine learning model is further trained using times at which the historical environmental sensor data is captured and times of the historical weather events, to identify weather events occurring at the given location at a given time or range of times based upon environmental sensor data captured within the proximity of the given location at the given time or range of times of Raut with a reasonable expectation of success for the benefit of improved data for driving to improve navigation accuracy. [See at least Raut [0020].]
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/P.L.S/Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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