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 .
Information Disclosure Statement
The references listed on the information disclosure statement filed on 01/07/2025 have been considered by the Examiner.
Claim Objections
Claim(s) 1, 10 and 15 is/are objected to because of the following informalities:
Claim 1, line 3, recites “the operator of the vehicle:” but should recite – the operator of the vehicle; –; claim 15 is objected to for similar reasoning.
Claim 10, line 1, is missing the transitional language and recites “The method of claim 1, updating the training dataset with a label” but should recite – The method of claim 1, further comprising updating the training dataset with a label –
Appropriate correction 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 an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g., an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that the claims are directed toward non-statutory subject matter, as shown below:
STEP 1: Do the claims fall within one of the statutory categories? Yes. Claims 1-14 are directed towards a method, i.e., process. Claims 15-17 are directed towards a non-transitory computer-readable storage medium, i.e., machine. Claims 18-20 are directed towards a vehicle, i.e., machine.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claims are directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
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
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The method in claims 1-14 (and non-transitory computer-readable medium and vehicle in claims 15-17 and 18-20, respectively) is a mathematical concept that is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. With regard to independent claim 1, the method recites the steps of: (a) generating a data cluster corresponding to each destination in the trip history by extracting a plurality of input features from trip information for each trip in the trip history, wherein the plurality of input features characterize a relationship between the operator of the vehicle and the plurality of previous trips, (b) generating a training dataset based on collecting the data cluster corresponding to each of the destinations in the trip history, (c) utilizing the training dataset to develop a gradient boosted trees model and (d) predicting at least one destination for the operator of the vehicle with the gradient boosted trees model utilizing at least one of an origin location of the operator of the vehicle, a time of day, or a day of the week as input conditions for the gradient boosted trees model. These limitations, under their broadest reasonable interpretation, recites a mathematical concept or are merely limitations that are based on or involve a mathematical concept. The Examiner notes that under MPEP 2106.04(a)(2)(I), the Supreme Court has identified a number of concepts falling within this grouping as abstract ideas including: a procedure for converting binary-coded decimal numerals into pure binary form, Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972); a mathematical formula for calculating an alarm limit, Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ2d 193, 195 (1978); the Arrhenius equation, Diamond v. Diehr, 450 U.S. 175, 191, 209 USPQ 1, 15 (1981); and a mathematical formula for hedging, Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ 2d 1001, 1004 (2010). The Court’s rationale for identifying these "mathematical concepts" as judicial exceptions is that a ‘‘mathematical formula as such is not accorded the protection of our patent laws,’’ Diehr, 450 U.S. at 191, 209 USPQ at 15 (citing Benson, 409 U.S. 63, 175 USPQ 673), and thus ‘‘the discovery of [a mathematical formula] cannot support a patent unless there is some other inventive concept in its application.’’ Flook, 437 U.S. at 594, 198 USPQ at 199. In the past, the Supreme Court sometimes described mathematical concepts as laws of nature, and at other times described these concepts as judicial exceptions without specifying a particular type of exception. See, e.g., Benson, 409 U.S. at 65, 175 USPQ2d at 674; Flook, 437 U.S. at 589, 198 USPQ2d at 197; Mackay Radio & Telegraph Co. v. Radio Corp. of Am., 306 U.S. 86, 94, 40 USPQ 199, 202 (1939) (‘‘[A] scientific truth, or the mathematical expression of it, is not patentable invention[.]’’). More recent opinions of the Supreme Court, however, have affirmatively characterized mathematical relationships and formulas as abstract ideas. See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 218, 110 USPQ2d 1976, 1981 (2014) (describing Flook as holding "that a mathematical formula for computing ‘alarm limits’ in a catalytic conversion process was also a patent-ineligible abstract idea."); Bilski v. Kappos, 561 U.S. 593, 611-12, 95 USPQ2d 1001, 1010 (2010) (noting that the claimed "concept of hedging, described in claim 1 and reduced to a mathematical formula in claim 4, is an unpatentable abstract idea,"). For example, generating a data cluster corresponding to each destination in the trip history by extracting a plurality of input features from trip information for each trip in the trip history, wherein the plurality of input features characterize a relationship between the operator of the vehicle and the plurality of previous trips; generating a training dataset based on collecting the data cluster corresponding to each of the destinations in the trip history; utilizing the training dataset to develop a gradient boosted trees model; and predicting at least one destination for the operator of the vehicle with the gradient boosted trees model utilizing at least one of an origin location of the operator of the vehicle, a time of day, or a day of the week as input conditions for the gradient boosted trees model is a mathematical concept or recites merely limitations that are based on a mathematical concept for using for training a machine learning model and using the machine learning model to predict a destination. Thus, the limitations fall within a mathematical concept grouping, and therefore, is directed to an abstract idea.
The Examiner notes that these limitations can also be interpreted to be a mental process capable of being performed in the human mind. In other words, a person would be capable of generating (i.e., determining) a data cluster corresponding to each destination in the trip history by extracting a plurality of input features from trip information for each trip in the trip history, wherein the plurality of input features characterize a relationship between the operator of the vehicle and the plurality of previous trips; generating (i.e., determining) a training dataset based on collecting the data cluster corresponding to each of the destinations in the trip history; utilizing (i.e., generating) the training dataset to develop a gradient boosted trees model; and predicting at least one destination for the operator of the vehicle with the gradient boosted trees model utilizing at least one of an origin location of the operator of the vehicle, a time of day, or a day of the week as input conditions for the gradient boosted trees model either mentally, or with pen and paper. The mere nominal recitation that the method is being performed by processor (e.g., computer) does not take the limitation out of the mental process grouping. Thus, the claim recites a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element 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.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element 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;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
With regard to claim 1, data gathering is a form of insignificant extra-solution activity. See MPEP 2106.05(g). Receiving a trip history for an operator of the vehicle, wherein the trip history includes trip information regarding a plurality of previous trips by the operator of the vehicle, is mere data gathering. Therefore, receiving a trip history for an operator of the vehicle, wherein the trip history includes trip information regarding a plurality of previous trips by the operator of the vehicle is insignificant extra-solution activity. Therefore, claim 1 does not recite additional elements that integrate the judicial exception into a practical application.
Claim 15 recites the additional limitations of a “non-transitory computer-readable storage medium embodying programmed instructions which, when executed by a processor, are operable for performing a method”. These non-transitory computer-readable storage medium embodying programmed instructions executed by a processor is simply a computer recited at a high level of generality. The generic computer is used to perform the abstract idea. Using a computer as a tool to perform the abstract idea does not integrate the exception into a practical application. Data gathering is a form of insignificant extra-solution activity. See MPEP 2106.05(g). Receiving a trip history for an operator of a vehicle, wherein the trip history includes trip information regarding a plurality of previous trips by an operator of the vehicle, is mere data gathering. Therefore, receiving a trip history for an operator of a vehicle, wherein the trip history includes trip information regarding a plurality of previous trips by an operator of the vehicle is insignificant extra-solution activity. Therefore, claim 15 does not recite additional elements that integrate the judicial exception into a practical application.
Claim 18 recites the additional limitations of “a vehicle body supported by a plurality of road wheels”, “a vehicle navigation system” and “a controller in communication with the navigation system”. These limitations/elements are recited at a high level of generality and are merely used as tools to perform the abstract ideas and generally link the use of a judicial exception to a particular technological environment or field of use. Data gathering is a form of insignificant extra-solution activity. See MPEP 2106.05(g). Receive a trip history for an operator of the vehicle, wherein the trip history includes trip information regarding a plurality of previous trips by the operator of the vehicle, is mere data gathering. Therefore, receive a trip history for an operator of the vehicle, wherein the trip history includes trip information regarding a plurality of previous trips by the operator of the vehicle is insignificant extra-solution activity. Therefore, claim 18 does not recite additional elements that integrate the judicial exception into a practical application.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
The following computer functions have been recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality): receiving or transmitting data over a network. See MPEP 2106.05(d)(II). Receiving a trip history for an operator of a vehicle is receiving or transmitting data over a network (i.e., from one computing device networked to another computing device). Therefore, the limitation “receive a trip history for an operator of the vehicle, wherein the trip history includes trip information regarding a plurality of previous trips by the operator of the vehicle” is well-understood, routine, conventional activity in the field and does not recite additional elements that amount to significantly more than the judicial exception.
CONCLUSION
Thus, since claims 1, 15 and 18 are: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claims 1, 15 and 18 are directed towards non-statutory subject matter.
Further, dependent claims 2-14, 16-17 and 19-20 further limit the abstract idea without integrating the abstract idea into practical application or adding significantly more. Each of the claimed limitations either expand upon or add either 1) new mental process, 2) a new additional element, 3) previously presented mental process, and/or 4) a previously presented additional element. As such, claims 2-14, 16-17 and 19-20 are similarly rejected as being directed towards non-statutory subject matter.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 13-17 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hershey et al. (US 20150134244 A1) in view of Poddar et al. (US 20180276571 A1).
Regarding claims 1, 15 and 18, Hershey teaches a method of operating a vehicle, the method comprising: receiving a trip history for an operator of the vehicle (abstract “predicting travel destinations according to a history of destinations” and ¶[0015] “acquires navigation data 101, (vehicle) system bus data 102, weather data 103, and derived data”), wherein the trip history includes trip information regarding a plurality of previous trips by the operator of the vehicle (¶[0024] “input features derived from the current and past trajectories, such as the previous destinations”): generating a data cluster corresponding to each destination in the trip history by extracting a plurality of input features from trip information for each trip in the trip history (¶[0017] “maintains a destination database 150 containing the locations, address, names, identifiers, categories associated with specific destinations, such as businesses, government facilities, residences, landmarks, and other geographically located entities” ¶[0024] “input features”), wherein the plurality of input features characterize a relationship between the operator of the vehicle and the plurality of previous trips (abstract “favorite, i.e., most probable, destinations for a user” and ¶[0024] “input features”); generating a training dataset based on collecting the data cluster corresponding to each of the destinations in the trip history (¶[0017] and [0024] “maintaining a destination database”, i.e., during training phase); utilizing the training dataset to develop a model (¶[0016]-[0018] “constructing a predictive model”); and predicting at least one destination for the operator of the vehicle with the model utilizing at least one of an origin location of the operator of the vehicle, a time of day, or a day of the week as input conditions for the model (abstract and ¶[0024] “input features derived from the current and past trajectories, such as the previous destinations, destination categories, as well as the time of day, day of week, status of the trip, direction of travel, and so-on”).
Hershey does not explicitly teach a gradient boosted trees model. However, Poddar discloses providing travel related content by predicting travel intent and teaches a gradient boosted trees model (¶[0047] “machine learning” “boosted trees” “gradient boosting”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the method for predicting travel destinations based on historical data of Hershey to provide, with a reasonable expectation of success, a gradient boosted trees model, as taught by Poddar, to training one or more models for providing travel related content that is appropriate for the user. (Poddar at ¶[0046])
Regarding the further limitations of claim 18, Hershey teaches a vehicle body supported by a plurality of road wheels (¶[0015] “vehicle”, e.g., automobile); a vehicle navigation system configured to provide directions to a destination (¶[0015] “vehicle navigation system” and ¶[0020] “routing information generated”); and a controller in communication with the navigation system (¶[0013] “methods can be performed in a processor”).
Regarding claims 2, 16 and 19, Hershey teaches the method of claim 1, wherein the trip history includes at least one of a starting location, an ending location, and a start time for each of the plurality of previous trips (¶[0024] “input features” “previous destinations”).
Regarding claim 3, 17 and 20, Hershey teaches the method of claim 2, wherein at least one of the input features characterizes a current location for a newly initiated trip with respect to a destination for each of the plurality of previous trips (abstract and ¶[0008] “model also uses a context that can include features such as… current location”).
Regarding claim 13, Hershey teaches the method of claim 1, wherein the at least one destination includes two possible destinations (¶[0017] “specific destinations”).
Regarding claim 14, Hershey teaches the method of claim 1, including displaying the at least one destination on a display in the vehicle along with a confidence level in the at least one destination (¶[0020] “number of selections with highest probabilities displayed”).
Claim(s) 4-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hershey et al. (US 20150134244 A1) in view of Poddar et al. (US 20180276571 A1), as applied to claim 3 above, and in further view of Li et al. (US 20190145779 A1).
Regarding claim 4, the combination of Hershey and Poddar does not explicitly teach the method of claim 3, wherein the plurality of input features for each data cluster include at least one of a distance from the origin location to a corresponding destination for each data cluster or an elapsed time since the operator of the vehicle visited the corresponding destination for each data cluster. However, Li discloses methods and apparatuses for predicting a destination of a user’s current travel path and teaches the method of claim 3, wherein the plurality of input features for each data cluster include at least one of a distance from the origin location to a corresponding destination for each data cluster or an elapsed time since the operator of the vehicle visited the corresponding destination for each data cluster (¶[0052]-[0054] “start of trip”, “distance between the user's current location and each predicted destination can be tracked to determine to which location the user is actually going to” and “distance change”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the method for predicting travel destinations based on historical data of Hershey as modified by Poddar to provide, with a reasonable expectation of success, wherein the plurality of input features for each data cluster include at least one of a distance from the origin location to a corresponding destination for each data cluster or an elapsed time since the operator of the vehicle visited the corresponding destination for each data cluster, as taught by Li, to provide predicting a user's destination and can adaptively and automatically provide better estimates with more driving records collected. (Li at ¶[0053])
Regarding claim 5, the combination of Hershey and Poddar does not explicitly teach the method of claim 4, wherein the plurality of input features for each data cluster includes at least one of a number of visits to each destination corresponding to the data cluster or a number of visits to each destination corresponding to the data cluster with a starting location matching with the origin location. However, Li discloses methods and apparatuses for predicting a destination of a user’s current travel path and teaches the method of claim 4, wherein the plurality of input features for each data cluster includes at least one of a number of visits to each destination corresponding to the data cluster or a number of visits to each destination corresponding to the data cluster with a starting location matching with the origin location (¶[0015] “predicting the at least one destination” “further based on a number of past visits of the respective potential destination indicated by the historic travel data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the method for predicting travel destinations based on historical data of Hershey as modified by Poddar to provide, with a reasonable expectation of success, wherein the plurality of input features for each data cluster includes at least one of a number of visits to each destination corresponding to the data cluster or a number of visits to each destination corresponding to the data cluster with a starting location matching with the origin location, as taught by Li, to provide predicting a user's destination and can adaptively and automatically provide better estimates with more driving records collected. (Li at ¶[0053])
Regarding claim 6, the combination of Hershey and Poddar does not explicitly teach the method of claim 2, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster where a current location matches with the origin location and a current day of the week matches a day of the week of a corresponding one of the plurality of previous trips. However, Li discloses methods and apparatuses for predicting a destination of a user’s current travel path and teaches the method of claim 2, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster where a current location matches with the origin location and a current day of the week matches a day of the week of a corresponding one of the plurality of previous trips (¶[0015] “predicting the at least one destination” “further based on a number of past visits of the respective potential destination indicated by the historic travel data” as well as ¶[0056] “user's visit number to each destination, time of day of the visit, day of week of the visit, and etc.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the method for predicting travel destinations based on historical data of Hershey as modified by Poddar to provide, with a reasonable expectation of success, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster where a current location matches with the origin location and a current day of the week matches a day of the week of a corresponding one of the plurality of previous trips, as taught by Li, to provide predicting the at least one destination using a user’s preference to each candidate or potential destination. (Li at ¶[0056])
Regarding claim 7, the combination of Hershey and Poddar does not explicitly teach the method of claim 2, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster where a current part of day matches part of day of a corresponding one of the plurality of previous trips. However, Li discloses methods and apparatuses for predicting a destination of a user’s current travel path and teaches the method of claim 2, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster where a current part of day matches part of day of a corresponding one of the plurality of previous trips (¶[0015] “predicting the at least one destination” “further based on a number of past visits of the respective potential destination indicated by the historic travel data” as well as ¶[0056] “user's visit number to each destination, time of day of the visit, day of week of the visit, and etc.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the method for predicting travel destinations based on historical data of Hershey as modified by Poddar to provide, with a reasonable expectation of success, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster where a current part of day matches part of day of a corresponding one of the plurality of previous trips, as taught by Li, to provide predicting the at least one destination using a user’s preference to each candidate or potential destination. (Li at ¶[0056])
Regarding claim 8, the combination of Hershey and Poddar does not explicitly teach the method of claim 7, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster where a current day of the week matches at least one of a weekday type for the plurality of previous trips or a workday type for the plurality of previous trips. However, Li discloses methods and apparatuses for predicting a destination of a user’s current travel path and teaches the method of claim 7, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster where a current day of the week matches at least one of a weekday type for the plurality of previous trips or a workday type for the plurality of previous trips (¶[0015] “predicting the at least one destination” “further based on a number of past visits of the respective potential destination indicated by the historic travel data” as well as ¶[0056] “user's visit number to each destination, time of day of the visit, day of week of the visit, and etc.”, e.g., in the morning of a weekday, the most likely initial prediction may be the location of the user's work since this is the user's most common location in the morning of a weekday).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the method for predicting travel destinations based on historical data of Hershey as modified by Poddar to provide, with a reasonable expectation of success, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster where a current day of the week matches at least one of a weekday type for the plurality of previous trips or a workday type for the plurality of previous trips, as taught by Li, to provide predicting the at least one destination using a user’s preference to each candidate or potential destination. (Li at ¶[0056])
Regarding claim 9, the combination of Hershey and Poddar does not explicitly teach the method of claim 8, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster with a starting location matching with a current location. However, Li discloses methods and apparatuses for predicting a destination of a user’s current travel path and teaches the method of claim 8, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster with a starting location matching with a current location (¶[0052]-[0054] “start of trip”, “distance between the user's current location and each predicted destination can be tracked to determine to which location the user is actually going to” and “distance change”; ¶[0015] “predicting the at least one destination” “further based on a number of past visits of the respective potential destination indicated by the historic travel data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the method for predicting travel destinations based on historical data of Hershey as modified by Poddar to provide, with a reasonable expectation of success, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster with a starting location matching with a current location, as taught by Li, to provide predicting a user's destination and can adaptively and automatically provide better estimates with more driving records collected. (Li at ¶[0053])
Regarding claim 10, the combination of Hershey and Poddar does not explicitly teach the method of claim 1, updating the training dataset with a label indicating if a destination in the trip history was visited at a conclusion of a newly initiated trip. However, Li discloses methods and apparatuses for predicting a destination of a user’s current travel path and teaches the method of claim 1, updating the training dataset with a label indicating if a destination in the trip history was visited at a conclusion of a newly initiated trip (¶[0048] “each trip is collected and stored, such that travel data of a current trip becomes historic travel data for future trips”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the method for predicting travel destinations based on historical data of Hershey as modified by Poddar to provide, with a reasonable expectation of success, wherein the plurality of input features for each data cluster includes a number of visits to each destination corresponding to the data cluster with a starting location matching with a current location, as taught by Li, to provide predicting a user's destination and can adaptively and automatically provide better estimates with more driving records collected. (Li at ¶[0053])
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hershey et al. (US 20150134244 A1) in view of Poddar et al. (US 20180276571 A1), as applied to claim 3 above, and in further view of Wang et al. (US 20250190817 A1).
Regarding claim 11, the combination of Hershey and Poddar does not explicitly teach the method of claim 1, including applying hyperparameter tuning to the gradient boosted trees model. However, Wang discloses methods and systems for accelerated tree learning and teaches the method of claim 1, including applying hyperparameter tuning to the gradient boosted trees model (abstract “gradient boosted trees” and ¶[0161] “hyperparameter tuning”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the method for predicting travel destinations based on historical data of Hershey as modified by Poddar to provide, with a reasonable expectation of success, applying hyperparameter tuning to the gradient boosted trees model, as taught by Wang, to provide assessing each algorithm’s performance. (Wang at ¶[0161])
Regarding claim 12, the combination of Hershey and Poddar does not explicitly teach the method of claim 11, wherein the hyperparameter tuning includes applying at least one of class weights to the plurality of input features, Laplace smoothing, or exponential decay. However, Wang discloses methods and systems for accelerated tree learning and teaches the method of claim 11, wherein the hyperparameter tuning includes applying at least one of class weights to the plurality of input features, Laplace smoothing, or exponential decay (¶[0006] “weighted sum of an output of each tree” or “exponential smoothing”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the method for predicting travel destinations based on historical data of Hershey as modified by Poddar to provide, with a reasonable expectation of success, applying hyperparameter tuning to the gradient boosted trees model, as taught by Wang, to provide accelerated tree learning compared to benchmark models. (Wang at ¶[0151])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Holder et al. (US 20240247939 A1) is pertinent because it is a method for providing a predicted current destination to a user of a vehicle.
Xie et al. (CN 111324824 A) is pertinent because it is a destination recommendation method.
Cox et al. (US 20190186939 A1) is pertinent because it relates to intelligent trip prediction in autonomous vehicles.
Radosavljevic et al. (US 20190094858 A1) is pertinent because it is a method for predicting one or more parking locations.
Chehreghani et al. (EP 3270334 A1) is pertinent because it is a method of trip prediction.
Jotanovic (US 20130166096 A1) is pertinent because it is a predictive destination entry for a navigation system.
Krumm et al. (US 20110282571 A1) is pertinent because it relates to methods for predicting destinations from partial trajectories.
Lehmann et al. (US 20110238289 A1) is pertinent because it is a navigation device and method for predicting the destination of a trip.
Ohler (US 20050283311 A1) is pertinent because it is a method for identifying regularly traveled routes.
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/C.L.K/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666