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 is in response to Applicant’s case, no. 18/837,803, with an effective filing date of 8/12/2024. Claims 1-9, 11-13, 15-16, 18, 21, and 23-26 are currently pending. Claims 10, 14, 17, 19-20, 22, and 27 have been previously canceled.
Response to Arguments
Examiner acknowledges that the necessary changes were made regarding the Drawings, Specification and Claim Objection sections in Applicant’s arguments, see page 11-12, and subsequently withdraws the previous objections to said sections. However, upon further consideration, new objections to the Applicant’s Drawings (Figs. 1-4, 6, 9-12 and 14) are hereby made as further detailed below.
Examiner acknowledges that the necessary changes were made regarding the rejection of claim(s) 1-9, 11-13, 15-16, 18, 21, and 23-26 under 35 USC § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter regarded as the invention due to containing relative terminology in Applicant’s amendments, see pp. 4-6 and 9-10, even though the Applicant did not address the rejections officially in their remarks. Subsequently, the Examiner withdraws the previous 35 USC § 112(b) rejection to said claims.
Examiner acknowledges the changes made regarding the rejection under 35 USC § 101 to claims 1 and 26 found in Applicant’s arguments, see pp. 12-13 (under the “Rejections under 35 USC § 112(b)” section). The Examiner has considered the amended claim limitations and arguments that the limitations cannot be performed within the human mind and is an improvement to the functioning of the computer. The Examiner reminds the Applicant that merely stating the use of processors is not a sufficient means to overcome an abstract idea argument. Furthermore, the “creating” and “predicting” are construed as mathematical concepts which are also abstract ideas.
Therefore this argument is unpersuasive.
However, further regarding the rejection under 35 USC § 101, the combination of the clustering algorithm and the selection of a future movement of the vessel are considered to be an improvement of a functioning computer process properly integrates the judicial exception into a practical application.
Therefore, this argument is persuasive and the rejection based on35 USC § 101 is hereby withdrawn.
Anderson et al. (US Pat. Pub. No. 2021/0109235 A1), hereinafter referred to as Anderson, in view of Fridman et al. (US Pat. Pub. No. 2020/0018844 A1), hereinafter referred to as Fridman
Regarding the 35 USC § 103 rejection of claims 1-2, 4, 6, 9, 11-13, 15-16, 21, and 24-26 as being unpatentable over Anderson et al. (US Pat. Pub. No. 2021/0109235 A1) [hereinafter referred to as Anderson] in view of Fridman et al. (US Pat. Pub. No. 2020/0018844 A1) [hereinafter referred to as Fridman], the Applicant has elected to amend the aforementioned claims. Therefore, the Examiner’s rejection in the previous Office Action based on 35 USC § 103 is rendered moot. However, due to said amendments, new reference Liang et al. (CN Pat. Pub. No. 112164247 A) [hereinafter referred to as Liang] has been necessitated. Therefore, a new rejection based on 35 USC § 103 has been made and is discussed in detail below.
Regarding claim 1, Applicant argues on pp. 13-14 that Anderson does not disclose multiple representative sequences. However, as stated in the previous action, Anderson discloses in [0013] a forecasting algorithm utilizes location (e.g., current geographical location) and direction information for the vessel, and estimates one or more possible headings based on previous paths taken by other vessels from that location, and heading in substantially the same direction. This is construed by the examiner as multiple representative sequences. Further in [0027] the forecasting algorithm estimates one or more possible headings based on previous paths taken by vessels from that location, and heading in that direction.
Therefore, this argument is unpersuasive.
Applicant further argues on pg.14 that Anderson does not disclose the amended limitation clusters of sequences between stop points. However, after further consideration it was discovered that, also in [0013] sentence 2 of Anderson, that a body of water can be divided into “bins” of location and direction information, and a spatial index can be built based on the previous paths taken by other vessels after passing through that bin. The “path” as stated above is construed as a sequence between stop points and the respective “bins” may be interpreted as clusters of sequences between said stop points of a particular path.
Therefore, this argument is unpersuasive.
Furthermore, the Applicant argues, see pg. 14, that Anderson does not disclose predicting future movement of the tracked vessel as proceeding along the selected one of the multiple representative sequences between the stop points. Although, as stated in the previous action, that [0014] does build a probability of future positions which is construed as predicting future movement of the vessel, it does not explicitly disclose the future movement proceeding along the selected sequences between the stop points. However, Liang teaches in pg.2 ¶1 a method for predicting the route of ships in a controlled river section based on the clustering of ship trajectories, which can effectively improve the accuracy of ship route prediction in the controlled river section. Further in steps 2-5 of pg.2 ¶3, Liang teaches a ship detected heading into a “matching area” according to historical data. The historical data is filtered through trajectory data points for clustering. The automatic clustering extracts a potential characteristic trajectory matching the historical data and uses that to achieve route prediction of the targeted ship. Further in pg.2 ¶10, step 5 includes the matching between the current trajectory of the ship and the characteristic trajectory in the matching area, and measures the similarity of the trajectory of the ship, so as to select the route that the ship may choose through the control section of the river.
Therefore, this argument is moot.
Regarding to the allowable subject matter found in claim 8, the necessitation of Liang to address claim 1 further necessitated the reconsideration of the allowable subject matter of claim 8. Based on this reconsideration, the Examiner submits that findings of allowable subject matter are rendered moot and a new reference, Sharif et al. article “Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method” [hereinafter referred to as Sharif], has been necessitated and teaches the limitations of wherein the clustering comprises determining a similarity by warping the historical locations of a first sequence to determine an optimal match with the historical locations of a second sequence.
Therefore, a new rejection based on 35 USC § 103 has been made to claim 8 and is discussed in detail below.
In regards to independent claim 26, Applicant argues, while differing in scope, these claim recites similar features to claim 1 and its rejections should likewise be withdrawn.
However, this argument is unpersuasive for the same reasons as given above.
Applicant argues the dependent claims are patentable by virtue of their dependency.
This argument is unpersuasive as each independent claim has been fully rejected for the reasons as given above.
Drawings
Drawings 1-4, 6, 9-12 and 14 are objected to under 37 CFR 1.83(a) because they fail to show: (a) with respect to Figs. 1-4, 6, and 9-11 details regarding the identifications (i.e.,Fig.3 items 301-303 and how they differ from the other lines), as described in the specification, (b) with respect to Fig. 12 details regarding the identifications (e.g.,Fig.12 items 1200-1208, 1211-1215, and 1218), as described in the specification and (c) with respect to Fig. 14 details regarding the identifications (e.g.,Fig.14 items 1400-1406), as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d). If the changes are not accepted by the Examiner, the applicant will be notified an informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-2, 4, 6, 9, 11-13, 15-16, 21, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al. (US Pat. Pub. No. 2021/0109235 A1), hereinafter referred to as Anderson, in view of Liang et al. (CN Pat. Pub. No. 112164247 A), hereinafter referred to as Liang.
Regarding claim 1, Anderson discloses:
A computer-implemented method for predicting vessel movement, the method comprising by one or more processors of a computer system:
creating by the one or more processors historical trip data by:
receiving historical location data indicative of historical locations of multiple vessels ([0013] sentence (s.) 1, utilizes location and direction information from previous paths from other vessels, construed as historical location data),
identifying stop points in the historical locations indicative of ports where the multiple vessels stopped ([0046] s.5, “point databases” can be maintained for ships such as stated destination of the ship, construed as a stop point and necessarily indicative of a port),
splitting the historical location data for each of the multiple vessels into multiple sequences of historical locations between the stop points ([0013] as discussed above, s.1, estimates one or more possible headings, construed as multiple sequences, and s.2 where a body of water can be divided into “bins” of location and direction information and [0027] the forecasting algorithm estimates one or more possible headings based on previous paths taken by vessels from that location, and heading in that direction), each of the multiple sequences representing one of multiple historical trips of one of the multiple vessel ([0013] and [0027] as discussed above and [0046] s.2, separate process periodically analyzes all available ship track histories to create a database of the typical behavior of ships in a given state (e.g. position and direction) for each small geospatial region of the world), and
predicting by the one or more processors future vessel movement by:
receiving a current geographical location for a current trip of a tracked vessel ([0015] s.1, the system is adapted to generate a dynamic probability cloud starting at the time of receipt of the reported position message),
selecting one of the multiple representative sequences between the stop points that is close to the current geographical location (spatial indexing as disclosed in [0013] forecasting algorithm utilizes location and direction information for the vessel, and estimates one or more possible headings based on previous paths taken by other vessels from that location, and heading in substantially the same direction, [0013] s.2, a body of water can be divided into “bins” of location and direction information, and a spatial index can be built based on the previous paths taken by other vessels after passing through that bin and the “path” as stated above is construed as a sequence between stop points and the respective “bins” may be interpreted as clusters of sequences between said stop points of a particular path, and [0047] where geospatial region of probability is generated for a target’s location for a specified point in time (i.e., future)), and
predicting future movement of the tracked vessel as proceeding along the selected one of the multiple representative sequences between the stop points ([0014] system can quickly build a probability cloud to represent the current and future position of any vessel from a recent S-AIS message).
Although Anderson discloses the multiple sequences to cluster the multiple sequences of historical locations into multiple clusters of sequences between the stop points to determine multiple representative sequences between the stop points, each of the multiple representative sequences between the stop points representing one of the multiple clusters of sequences between the stop points in [0013] as discussed above and further where a spatial index can be built based on the previous paths (which necessarily comprise of historical locations) taken by other vessels after passing through that bin, which is construed as clustering sequences to determine multiple representative sequences between stop points, but it does not explicitly disclose:
applying a clustering algorithm to cluster the multiple sequences to cluster the multiple sequences to cluster the multiple sequences of historical locations into multiple clusters of sequences between the stop points to determine multiple representative sequences between the stop points.
However, Liang teaches in pg.2 ¶1 a method for predicting the route of ships in a controlled river section based on the clustering of ship trajectories, which can effectively improve the accuracy of ship route prediction in the controlled river section. Further in steps 2-5 of pg.2 ¶3, Liang teaches a ship detected heading into a “matching area” according to historical data. The historical data is filtered through trajectory data points for clustering. The automatic clustering extracts a potential characteristic trajectory matching the historical data and uses that to achieve route prediction of the targeted ship. Further in pg.2 ¶10, step 5 includes the matching between the current trajectory of the ship and the characteristic trajectory in the matching area, and measures the similarity of the trajectory of the ship, so as to select the route that the ship may choose through the control section of the river.
Therefore it would have been obvious to one of ordinary skill in the art of movement forecasting and vehicle controls before the effective filing date of the current invention to modify the vehicle control system and method of Anderson, by incorporating the clustering teachings of Liang, such that the combination would provide for the predictable result of improving an end-to-end route trajectory.
Regarding claim 2, Anderson, as modified by Liang, discloses:
The method of claim 1, wherein the method comprises, during creating the historical trip data:
storing each of the multiple sequences as a separate record on a database ([0046] s.2, periodically analyzes all available ship track histories to create a database of the typical behavior of ships in a given state (e.g. position and direction) for each small geospatial region of the world);
performing the clustering by performing a query on the database for one or more stop points to retrieve records holding sequences comprising the one or more stop points of the query and clustering the sequences retrieved from the database (see claim 1 regarding bins and geospatial indexing and [0046] that different point bases are maintained and can be queried to receive state variables including that of the position of the ship and the stated destination, which is construed as performing a query of the database which comprise stop points and clustering sequences based on that information).
Regarding claim 4, Anderson, as modified by Liang, discloses:
The method of claim 1, wherein the method further comprises, during creating the historical trip data, filtering the historical location data by:
determining an information content in a time difference between the historical locations in each of the multiple sequences ([0003] AIS provides information such as the identification of the vessel, its speed, heading, and position at a given point in time as well as static information about the vessel and dynamic information about the current voyage and [0046] s.2, periodically analyzes all available ship track histories to create a database of the typical behavior of ships in a given state (e.g. position and direction) for each small geospatial region of the world ); and
selecting one or more of the multiple sequences that have a high information content for the clustering ([0030] dynamic probability cloud that identifies regions of probability in which a vessel is located based on the aggregation of information and can be color-coded to display higher and lower levels of probability based on aggregated information).
Regarding claim 6, Anderson, as modified by Liang, discloses:
The method of claim 4, wherein the method further comprises selecting one or
more of the multiple historical trips that have a small variation in time difference between the historical locations ([0045] s.3, IPA uses the most recent two ship position reports and forecast a new position based on the elapsed time since the last report and the prior positions and speed data, which is construed as selected one or more historical trips that have small variation in time difference between the historical locations).
Regarding claim 9, Anderson, as modified by Liang, discloses:
The method of claim 1, wherein the method further comprises a comparison between the prediction and an updated geographical location and determining an anomaly in the vessel movement based on the comparison ([0098] In another embodiment, the system is further adapted to compare each new position report with the statistical forecasting accuracy for that vessel and determines if the new position is sufficiently different from the expected position that it exceeds a predefined threshold constituting an anomalous position).
Regarding claims 10, 14, 17, 19-20, 22, and 27, the Applicant has elected to cancel the claims prior to examination and are therefore not currently under consideration.
Regarding claim 11, Anderson, as modified by Liang, discloses:
The method of claim 1, wherein the prediction of vessel movement comprises a prediction that the tracked vessel will follow the selected one of the multiple representative sequences ([0013] forecasting algorithm utilizes location and direction information for the vessel, and estimates one or more possible headings based on previous paths taken by other vessels from that location, and heading in substantially the same direction).
Regarding claim 12, Anderson, as modified by Liang, discloses:
The method of claim 1, wherein the stop points are one or more of vessel origin and vessel destination ([0046] s.5, “point databases” can be maintained for ships such as stated destination of the ship, construed as a stop point and necessarily indicative of a port).
Regarding claim 13, Anderson, as modified by Liang, discloses:
The method of claim 1, wherein the method further comprises:
filtering the historical location data by one or more of vessel origin and vessel destination to select historical locations related to each of the multiple historical trips ([0013] a body of water can be divided into “bins” of location and direction information, and a spatial index can be built based on the previous paths taken by other vessels after passing through that bin, which is construed as filtering location data from a bin to a spatial index to point databases pertaining to stop points or destinations which necessarily may comprise of vessel origins or destinations); and
using respective stop points for the one or more of vessel origin and vessel destination (see claim 1 regarding [0046]).
Regarding claim 15, Anderson, as modified by Liang, discloses:
The method of claim 1, further comprising triggering, based on the prediction, events in a logistics application ([0003] Modern marine vessels including ships with gross tonnage exceeding 300GT, which is construed as vessels being used in a logistic application).
Regarding claim 16, Anderson, as modified by Liang, discloses:
The method of claim 1, wherein the vessel movement is predicted by a first software system and the method further comprises generating, by the first software system, an event to notify a second software system of the predicted vessel movement and the method further comprises triggering, by the event, an action performed by the second software system (see claim 1 regarding prediction software and [0051] s.3, anomaly tags can be used by display or alerting software that is downstream of the forecasting algorithms).
Regarding claim 21, Anderson, as modified by Liang, discloses:
The method of claim 1, further comprising, storing the received historical location data on a relational database (see claim 3 regarding relational databases) as multiple data records comprising one record for each of the historical locations (see claim 1 regarding point databases) wherein identifying the multiple historical trips comprises creating a field value for each of the records in the relational database indicative of a trip identifier to indicate an association between a data record and one of the multiple historical trips (see claim 1 regarding the relationship between point databases, bins, and spatial index and [0023] disambiguating Automatic Identification System (AIS) transmissions from different vessels using the same Maritime Mobile Service Identity (MMSI) identifier and in the table following [0050] an IMO (International Maritime Organization number - a unique identifier assigned to vessels)).
Regarding claim 24, Anderson, as modified by Liang, discloses:
The method of claim 1, wherein performing the method comprises running a first service and a second service,
the first service is configured to perform the steps of receiving the historical location data, identifying stop points, and splitting the historical location data into the multiple sequences, the first service further stores the multiple sequences on a database (see claim 1 regarding the method and point databases and the organization of historical data); and
the second service is configured to retrieve the sequences from the database for the clustering of the sequences (see claim 2 regarding querying the databased).
Regarding claim 25, Anderson, as modified by Liang, discloses:
A non-transitory, computer readable medium with program code stored thereon
that, when executed by a computer, causes the computer to perform the method of claim 1 ([0092] random access memory which is a form of non-transitory computer readable medium that contains program code).
Claim 26 recites a system having substantially the same features of claims 1 and 25 above, therefore claim 26 is rejected for the same reasons as claims 1 and 25.
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Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al. (US Pat. Pub. No. 2021/0109235 A1), hereinafter referred to as Anderson, in view of Liang et al. (CN Pat. Pub. No. 112164247 A), hereinafter referred to as Liang, and Estrada et al. (US Pat. No. 11,861,894 B1), hereinafter referred to as Estrada
Regarding claim 3, Anderson, as modified by Liang, which discloses the use of tables and databases, but does not explicitly disclose:
wherein the database is a relational database.
However, Estrada teaches in column (col) 5 lines (ln) 15-30, in a system for tracking sailing vessels the use of relational databases.
Therefore it would have been obvious to one of ordinary skill in the art of movement forecasting and vehicle controls before the effective filing date of the current invention to modify the vehicle control system and method of Anderson, as already modified by Fridman, by incorporating the clustering relational database teachings of Estrade, as this is construed as a simple substitution that would provide for a predictable result of storing correlating information.
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Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al. (US Pat. Pub. No. 2021/0109235 A1), hereinafter referred to as Anderson, in view of Liang et al. (CN Pat. Pub. No. 112164247 A), hereinafter referred to as Liang, and Franklin et al. (WIPO Pub. No. 2020/142850 A1), hereinafter referred to as Franklin
Regarding claim 5, Anderson, as modified by Liang, which discloses determining information content, but does not explicitly disclose:
wherein determining the information content comprises determining an entropy of the historical locations.
However, Franklin teaches in [0121] how the probability cloud which expresses relative levels of quality yet is effected by increasing uncertainty (i.e., entropy, as understood in information theory) with time thereby making the cloud gradually uninformative due to widening probability.
Therefore it would have been obvious to one of ordinary skill in the art of movement forecasting and vehicle controls before the effective filing date of the current invention to modify the vehicle control system and method of Anderson, as already modified by Fridman, by incorporating the entropy teachings of Franklin, as taught in [0052], that this allows improved the safety of vessels in and around a particular port.
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Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al. (US Pat. Pub. No. 2021/0109235 A1), hereinafter referred to as Anderson, in view of Liang et al. (CN Pat. Pub. No. 112164247 A), hereinafter referred to as Liang, and Rasmus-Vorrath et al. (CA Pat. App. No. 3,109,581 A1), hereinafter referred to as Rasmus-Vorrath.
Regarding claim 7, Anderson, as modified by Liang, discloses:
The method of claim 1, wherein the method further comprises determining each representative sequence (see claim 1),
but they do not explicitly disclose:
calculating a barycentre average of the multiple sequences of historical locations in each of the multiple clusters.
However, Rasmus-Vorrath teaches in [0006] the one or more forecasting modules are configured to calculate Barycenter Averages. Further, in [0250] it teaches perform weighted Barycenter averaging can be applied to a historical sequence of sensor data obtained in the past to provide a distribution of sensor values that can be used as a substitute for current readings. These are utilized to predict weather conditions but one of ordinary skill in the art of data science would understand this time of averaging can be used on various forms of data to forecast based on historical occurrences.
Therefore it would have been obvious to one of ordinary skill in the art of movement forecasting and vehicle controls before the effective filing date of the current invention to modify the vehicle control system and method of Anderson, as already modified by Fridman, by incorporating the barycenter average teachings of Rasmus-Vorrath, as taught in [0249], that this allows reinforced and improved predictions.
Regarding claim 18, Anderson, as modified by Liang and Rasmus-Vorrath, discloses:
The method of claim 1, wherein predicting the future movement of the vessel comprises calculating a probability of the future movement and the probability is indicative of a weight associated with the selected one of the multiple representative trips and the weight is indicative of a number of sequences in the cluster represented by the selected one of the multiple representative trips.
However, Rasmus-Vorrath in [0250] it teaches perform weighted Barycenter averaging can be applied to a historical sequence of sensor data obtained in the past to provide a distribution of sensor values that can be used as a substitute for current readings.
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Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al. (US Pat. Pub. No. 2021/0109235 A1), hereinafter referred to as Anderson, in view of Liang et al. (CN Pat. Pub. No. 112164247 A), hereinafter referred to as Liang, and Sharif et al. article “Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method”, hereinafter referred to as Sharif.
Regarding claim 8, Anderson, as modified by Liang, discloses:
The method of claim 1 (see claim 1),
but Anderson, as modified by Liang, does not explicitly disclose:
wherein the clustering comprises determining a similarity by warping the historical locations of a first sequence to determine an optimal match with the historical locations of a second sequence.
However, Sharif teaches on pg. 428 2 the method of dynamic time warping (DTW). The DTW method relies on pair-wise comparisons of trajectory sampling points and warps the time to measure the similarities of trajectories including those trajectories of different lengths. It measures the similarity of the trajectories based on contextual information and weights each context with respect to its importance. This is construed as determining a similarity by warping the historical locations of one sequence with a second sequence to determine an optimal match to the historical locations of the second. This is further illustrated on pg. 429 section 2.1 lines 25-26 where it teaches DTW warps the first trajectory temporally with the aim of minimizing the distance to the second trajectory.
Therefore it would have been obvious to one of ordinary skill in the art of movement forecasting and vehicle controls before the effective filing date of the current invention to modify the vehicle control system and method of Anderson as modified by the clustering teachings of Liang, by incorporating the dynamic time warping teachings of Sharif, such that the combination would provide for the predictable result of improving an end-to-end route trajectory according to historical contexts.
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Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al. (US Pat. Pub. No. 2021/0109235 A1), hereinafter referred to as Anderson, in view of Liang et al. (CN Pat. Pub. No. 112164247 A), hereinafter referred to as Liang, Rasmus-Vorrath et al. (CA Pat. App. No. 3,109,581 A1), hereinafter referred to as Rasmus-Vorrath, and Estrada et al. (US Pat. No. 11,861,894 B1), hereinafter referred to as Estrada.
Regarding claim 23, Anderson, as modified by Liang and Rasmus-Vorrath, discloses:
The method of claim 1, wherein
clustering comprises creating a field value indicative of an association between trip identifiers and cluster identifiers to indicate which trip belongs to which cluster (see claims 1 and 21), and
determining the multiple representative sequences comprises:
querying, based on one cluster identifier, the relational database for historical locations associated with a trip identifier that is associated with the one cluster identifier (see claim 3 regarding relational databases and claim 2 regarding querying the database), and
averaging the returned sequences, as indicated by trip identifiers, to determine one of the multiple representative sequences (see claim 7 regarding forecasting based on averages).
Therefore it would have been obvious to one of ordinary skill in the art of movement forecasting and vehicle controls before the effective filing date of the current invention to modify the vehicle control system and method of Anderson, as already modified by Fridman, by incorporating the entropy teachings of Rasmus-Vorrath and Estrada, as acknowledged by Rasmus-Vorrath, in regards to claim 7, and Estrada above.
Prior Art
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Please see:
Fridman et al. (US Pat. Pub. No. 2020/0018844 A1) is directed towards use of machine learning tools such as clustering to predict an optimal leg segment from a plurality of leg segments of a route;
Denyse article “Time series clustering - deriving trends and archetypes from sequential data” is directed toward time series analysis, dynamic time warping (DTW) is one of the algorithms for measuring similarity between two temporal sequences that do not align exactly in time, speed, or length; and
Dow et al. (US Pat. No. 10,168,449) is directed toward dynamic time warping (DTW) of the historical weather forecast and the historical weather observations; and modifying the new weather forecast based on the historical weather observations at the shifted point in time; and outputting the updated new weather forecast to a marine vessel pilot .
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.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEITH ALLEN VON VOLKENBURG whose telephone number is (703)756-5886. The Examiner can normally be reached Monday-Friday 8:30 am-5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin D. Bishop can be reached at (571) 270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Keith A von Volkenburg/Examiner, Art Unit 3665 /Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
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