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 the Claims
2. This Office Action is in response to the Applicant’s filing on 05/05/2026. Claims 21 - 40 were previously pending, of which claims 21, 34 - 35 have been amended, no claims have been cancelled, and no new claims have been newly added. Accordingly, claims 21 - 40 are currently pending and are being examined below.
Response to Arguments
3. With respect to the Applicant’s remarks, see pages 9 - 13, filed on 05/05/2026; Applicant’s “Amendment and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
4. With respect to the rejection under 35 U.S.C. 103, applicant’s “Amendment and Remarks” have been fully considered and are persuasive. The prior art of record does not appear to disclose the newly added limitations as stated in claim 1. However, due to the nature of the applicant’s amendments, the scope of the applicant’s invention has changed and thus requires new analysis and new application of prior art and further search found that Onti, Qian, and Boehme did disclose these limitations as mapped in the final office action below.
Although, the argument that claim 1 is not taught by Balu is not persuasive. Balu does disclose both levels of aggregation. Balu expressly teaches first spatially partitioning vehicle data into data subsets associated with geographical areas [0061] – [0062] and then spatially aggregating vehicle data within the corresponding data subsets, while the subsequent spatial aggregation operation is performed on the vehicle data contained within the resulting data subsets [0063] – [0064], [0078], [0080]. Accordingly, Balu teaches both spatial partitioning and spatial aggregation within the same processing workflow.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 21, 24 - 25, 27, 29 – 37 are rejected under 35 U.S.C. 103 as being unpatentable over US20200019585A1 (hereinafter, “Balu”), and further in view of US20230171314A1 (hereinafter, “Onti”), and further in view of US20220108609A1 (hereinafter, “Qian”), and further in view of US20180293251A1 (hereinafter, “Boehme”).
7. Regarding claim 21, Balu teaches a method for dynamic spatial anonymization of vehicle data in a cloud environment, the method comprising [0043], [0180]: Balu teaches an anonymity controller (121) that modifies a set of trajectory data to provide anonymization to vehicles (124) [0043]. Cloud computing is done which implies that a cloud environment is utilized [0180].
collecting a plurality of vehicle data records [0043]; Vehicles (124) are connected to a server (125) to communicate may receive and send data from the vehicles (124). The server (125) is collecting data from the vehicles (124) constitutes as vehicle data records.
determining one or more geographical areas [0059], [0166]; Balu teaches a geographic database (123) that teaches determining one or more geographical areas by processing vehicle location information and associating collected vehicle data with specific geographic regions such as road segments, route sections, or predefined geographic areas.
8. Balu does not explicitly teach assigning the one or more geographical areas an associated unique identifier of multiple identifiers;
However, Onti in the same field of endeavor, teaches assigning the one or more geographical areas an associated unique identifier of multiple identifiers ([0081] Fig. 8); Onti teaches assigning geographic regions (802) identifiers (804). Onti is of the same field of endeavor because Onti uses specific identifiers for different geographic regions. When doing spatial anonymization, having regions with different identifiers can still provide useful data in those regions while maintaining privacy protection by not having an exact location identified but instead having a broader region identified.
One of ordinary skill in the art, before the effective filing date of the instant application with a reasonable expectation of success, would have been motivated to modify the disclosure of Balu with the teachings of Onti, to further uniquely identify each associated region.
9. Balu does not explicitly teach associating each of the plurality of vehicle data records with one of the multiple identifiers;
However, Qian teaches associating each of the plurality of vehicle data records with one of the multiple identifiers [0052], [0064], [0066]; Qian is analogous because Qian associates a plurality of vehicles with a geohash cell which is important for spatial anonymization in order to provide conditional data specific to that region. Qian teaches receiving respective operating status data from vehicles traveling within corresponding Geohash cells. Geohash cells are specific geographical regions [0052]. Qian further teaches that respective Geohash cells are associated with respective cell identifiers and that a cell ID representing the unique identifier of the cell within which the vehicle was traveling when the operating status data was sent may be included with the data [0064], [0066]. Accordingly, each vehicle data record is associated with the cell identifier corresponding to the geographical area in which the record was obtained, thereby teaching associating each of the plurality of vehicles data records with one of the multiple identifiers.
One of ordinary skill in the art, before the effective filing date of the instant application with a reasonable expectation of success, would have been motivated to modify the disclosure of Balu with the teachings of Qian, to further increase privacy and anonymization.
10. Balu teaches spatial partitioning of the vehicle data into data subsets, dynamically associated with the one or more geographical areas [0058], [0061]: Balu teaches using spatial partitioning on vehicle data related to model trajectory data by creating partitions of a trajectory path into a sequence of points. This data is then grouped into segments based on the neighborhood statistics which is considered data being grouped into subsets [0058]. Figure 5 shows the geographic spacing for a clustering technique that is partition based for trajectory data and how each vehicle data subset is associated with one or more geographical areas [0061].
11. Balu does not explicitly teach determining for each data subset, whether the data subset complies with a maximal number of records within each data subset, wherein the maximal number of records is predefined based on a computational capacity constraint of a performing device,
However, Boehme in the same field of endeavor, teaches determining for each data subset, whether the data subset complies with a maximal number of records within each data subset, wherein the maximal number of records is predefined based on a computational capacity constraint of a performing device [0058] – [0059], Boehme is analogous because data that consists of location trajectories needs to be stored somewhere and also not have that dataset overfill when being stored. Boehme solves that by dividing the datasets. Boehme teaches dividing a dataset into multiple sub-datasets and for each sub-dataset, determines whether the sub-dataset fits within a data block by comparing the number of entries of the sub-dataset with a predefined maximum number of entries assigned to the data block. Boehme further teaches assigning to each data block a predefined maximum number of entries or a predefined maximum amount of storage and recursively subdividing sub-datasets that exceed the predefined limit.
in case a first data subset of the data subsets exceeds the maximal number of records, selectively splitting, using an iterative splitting, the first data subset into multiple data subsets [0058], [0083]; Boehme teaches determining that a first sub-dataset does not fit within a predefined maximum number of entries associated with a data block, repeating the dataset dividing operation on the first sub-dataset itself. Boehme further teaches recursively partitioning datasets using divide and conquer sorting algorithms such as quicksort or radix sort until resulting sub-datasets fit within the predefined capacity constraints. Therefore, Boehme teaches selectively splitting a first data subset that exceeds a maximal number of records into multiple data subsets using iterative (recursive) splitting.
One of ordinary skill in the art, before the effective filing date of the instant application with a reasonable expectation of success, would have been motivated to modify the disclosure of Balu with the teachings of Boehme, to fit subsets into the available memory and improve processing efficiency.
12. Balu teaches spatial aggregation of the vehicle data within the data subsets, providing two level aggregation:
aggregation of vehicle data, coming from a single vehicle, to a corresponding data point; and ([0061] – [0063] Fig. 5) Balu teaches aggregating vehicle data from a single vehicle into a corresponding sampled point (51) (data point). A vehicle collects multiple sensor measurements and location data over a short time interval while the vehicle is moving along its path. Instead of keeping every measurement. The system will combine these multiple measurements from the same vehicle into one representative sampled point (51) that summarizes the vehicle’s data for that time period.
aggregation of data points, coming from a group of vehicles comprising a particular number of vehicles, to a spatial aggregated data set ([0061] – [0064] Fig. 5). Balu also teaches aggregation of vehicle data coming from multiple vehicles into a spatial aggregated dataset. After vehicle data from individual vehicles is first aggregated into corresponding sampled points (51), these sampled points (51) are then grouped based on geographic proximity of the vehicles. The grouped vehicle sampled points (51) from multiple vehicles are subsequently aggregated together to form a spatial aggregated dataset called partition-based clustering or K-means clustering. This further indicates that these spatial aggregated datasets represent combined information from a plurality of vehicles within the same area.
13. Regarding claims 24, Balu discloses the method of claim 21, wherein the vehicle data will be collected periodically with a time interval,
wherein the spatial aggregation is used for anonymization of the vehicle data within an associated geographical area ([0039], [0059] Fig. 6). As mentioned above in claim 21, Balu teaches that spatial aggregation is used for anonymization of vehicle data within a corresponding data subset or associated geographical area [0059]. Balu describes spatially partitioning collected vehicle data into sampled points (51) associated with different geographical areas as shown in figure 5. Spatial aggregation such as partition-based clustering or K-means clustering is then performed on the vehicle data within those sampled points (51). Balu further explains that the spatial aggregation combines vehicle data from multiple vehicles within the same geographic area to produce spatial aggregated data subsets called centroids (Fig. 6). All this is done to prevent identification of vehicle trajectories [0039]. Thus, Balu teaches using spatial aggregation within geographic areas to anonymize vehicle data.
14. Regarding claim 25, Balu teaches the method of claim 21, wherein the spatial aggregation is provided using the k-anonymity methodology, and/or ([0039], [0061] – [0063] Fig. 5) Balu teaches aggregating vehicle data from a single vehicle into a corresponding sampled point (51) (data point). A vehicle collects multiple sensor measurements and location data over a short time interval while the vehicle is moving along its path. Instead of keeping every measurement. The system will combine these multiple measurements from the same vehicle into one representative sampled point (51) that summarizes the vehicle’s data for that time period [0061] – [0063]. Once these sampled points (51) are within a geographic area, a clustering technique such as K-means clustering is used so that each aggregated region contains at least K contributing vehicles. The aggregation ensures that any individual vehicle’s data cannot be distinguished from at least K-1 other vehicles within the same group [0039]. Therefore, Balu satisfies the K-anonymity requirement.
15. Regarding claim 27, Balu discloses the method of claim 21, wherein the vehicle data comprise sensor data and spatial data, and/or
wherein a spatial aggregated data set coming from a group of vehicles comprises aggregated sensor data and aggregated spatial data for the group of vehicles ([0061] – [0063] Fig. 5). Balu also teaches aggregation of vehicle data coming from multiple vehicles into a spatial aggregated dataset. After vehicle data from individual vehicles is first aggregated into corresponding sampled points (51), these sampled points (51) are then grouped based on geographic proximity of the vehicles. The grouped vehicle sampled points (51) from multiple vehicles are subsequently aggregated together to form a spatial aggregated dataset called partition-based clustering or K-means clustering. This further indicates that these spatial aggregated datasets represent combined information from a plurality of vehicles within the same area.
16. Regarding claim 29, Balu discloses the method of claim 21, wherein vehicle data within a data point will be highlighted in order to detect environmental effects in restricted areas within a particular geographical area [0044] – [0045], [0140]. Balu specifically teaches receiving sensor data from vehicles, where the sensor data includes environmental condition information surrounding the vehicle in which the vehicle could be in a particular geographic location. The system will process the collected sensor data and determine environmental conditions at corresponding locations based on the vehicle sensor readings from the probe on the vehicle [0044] – [0045]. Due to each sensor reading being associated with a location and time, the system is able to evaluate environmental conditions within a particular geographic region using the collected vehicle data.
17. Regarding claim 30, Balu discloses the method of claim 21, wherein the spatial aggregation is performed using different aggregation methodologies for different vehicle services, sensor types, signal sources and/or data types, and/or [0040] The group size of the vehicles is considered a data type. When it is a single vehicle, they are considered a sample point (51) which requires a different methodology of aggregation. When all these sample points (51) are bundled together, Balu performs a methodology called partition-based clustering or K-means clustering. Therefore, Balu teaches performing different aggregation methodologies for single vehicles and a group of vehicles.
wherein a particular aggregation methodology is chosen depending on vehicle service, sensor type, signal source and/or data type.
18. Regarding claim 31, Balu discloses the method of claim 21, wherein the spatial partitioning is provided using a method of geospatial indexing [0039] – [0040]. Balu doesn’t explicitly recite “geospatial indexing”, but it does disclose dividing a map into regions/tiles by incorporating boundaries (52). According to the specification of the present application, this constitutes a geospatial indexing style of spatial partitioning. The present application defines Geospatial indexing as “the process of partitioning areas of the earth into identifiable grid cells” on page 4 right below where the specification says “I Spatial partitioning”.
19. Regarding claim 33, Balu discloses the method of claim 21, wherein the maximal amount of records will be chosen according to computational capacity of a performing device [0055] – [0056], [0068]. Balu teaches spatially partitioning collected vehicle data into sampled points (51) associated with geographic areas while limiting each subset to a threshold [0068] (maximal amount of records). Balu further explains that the spatial partitioning may continue until each subset difference is greater than a threshold to address computational constraints of the performing device [0055] – [0056].
20. Regarding claim 34 specifically, Balu discloses a non-transitory storage medium comprising instructions which, when the instructions are executed by a computer, cause the computer to conduct [0044], [0143], [0147] – [0148]: Balu teaches multiple different types of processors that help the system process different types of information and provide instructions to the components within the system.
21. Regarding claim 35 specifically, Balu discloses a device, comprising:
memory in which program code is stored, and
a processor configured to execute the program code, wherein executing the program code causes the processor to conduct [0048], [0057], [0088]: Balu teaches multiple different types of memories that help the system process and keep data stored. Therefore, memories that comprise of instructions are present in Balu.
22. Regarding claim 36, Balu discloses the device claim 35, wherein the memory comprises a database for spatial aggregated data sets [0043], [0061]. A geographic database (123) is present which contains the trajectory data from vehicles that is spatially aggregated.
23. Regarding claim 37, Balu discloses the device claim 35, wherein the processor is configured to provide vehicle services, comprising navigation services [0041], [0041], [0092] - [0093] Data from the vehicle sensors can be used in applications such as navigational services or mapping services. Balu teaches that after collecting, spatially partitioning, and spatially aggregating vehicle data into anonymized aggregated data sets, the computing device may be configured to provide vehicle services such as navigation services and map services [0092] - [0093].
Claim(s) 22 – 23, 38 - 40 are rejected under 35 U.S.C. 103 as being unpatentable over US20200019585A1 (hereinafter, “Balu”), and further in view of US20230171314A1 (hereinafter, “Onti”), and further in view of US20220108609A1 (hereinafter, “Qian”), and further in view of US20180293251A1 (hereinafter, “Boehme”), and further in view of NPL - Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking (hereinafter, “Gruteser”).
25. Regarding claim 22, Balu does not explicitly teach the method of claim 21, wherein the method additionally comprises modifying the spatial aggregated data sets in order to reduce overlapping of the spatial aggregated data sets.
However, Gruteser in the same field of endeavor, teaches the method of claim 21, wherein the method additionally comprises modifying the spatial aggregated data sets in order to reduce overlapping of the spatial aggregated data sets (8.3 Para. 6). Gruteser is in the same field of endeavor because it relates the organization of k-anonymity by preventing overlapping from occurring. Gruteser uses a cloaking algorithm in order to prevent overlapping requests from happening. The tuples mentioned could be the spatial aggregated data sets because these tuples are data sets related to location of the vehicles which is in line with what spatial information is. The BRI of reduce or reduction also entails prevention. Preventing is a much more narrower implementation due to the prevention of overlapping requests being a more extreme reduction.
One of ordinary skill in the art, before the effective filing date of the instant application with a reasonable expectation of success, would have been motivated to modify the disclosure of the modified Balu reference with the teachings of Gruteser, to reduce overlapping of spatial data in order to further prevent inaccurate calculations.
26. Regarding claim 23, Balu discloses the method of claim 22, wherein modification of the spatial aggregated data sets will be performed randomly, and/or
wherein modification of the spatial aggregated data sets will be performed through changing the size [0063], the shape and/or the location of the corresponding groups of vehicles. Balu teaches that the clustering technique may also include modifications. These modifications may be spatial properties or other considerations. The modification of the spatial properties implies changing the size, shape, or location of the groups of the K members in that cluster in order to improve anonymization.
27. Regarding claim 38, Balu discloses wherein the vehicle data will be collected periodically with a time interval,
wherein the time interval is chosen depending on vehicle service, sensor type, signal source and/or data type, and/or
wherein spatial partitioning is used for reducing the amount of records intended to be anonymized in one execution, and/or
wherein the spatial aggregation is used for anonymization of the vehicle data within a corresponding data subset and/or within an associated geographical area ([0039], [0059] Fig. 6). As mentioned above in claim 21, Balu teaches that spatial aggregation is used for anonymization of vehicle data within a corresponding data subset or associated geographical area [0059]. Balu describes spatially partitioning collected vehicle data into sampled points (51) associated with different geographical areas as shown in figure 5. Spatial aggregation such as partition-based clustering or K-means clustering is then performed on the vehicle data within those sampled points (51). Balu further explains that the spatial aggregation combines vehicle data from multiple vehicles within the same geographic area to produce spatial aggregated data subsets called centroids (Fig. 6). All this is done to prevent identification of vehicle trajectories [0039]. Thus, Balu teaches using spatial aggregation within geographic areas to anonymize vehicle data.
28. Regarding claim 39, Balu discloses wherein the vehicle data will be collected periodically with a time interval,
wherein the time interval is chosen depending on vehicle service, sensor type, signal source and/or data type, and/or
wherein spatial partitioning is used for reducing the amount of records intended to be anonymized in one execution, and/or
wherein the spatial aggregation is used for anonymization of the vehicle data within a corresponding data subset and/or within an associated geographical area ([0039], [0059] Fig. 6). As mentioned above in claim 21, Balu teaches that spatial aggregation is used for anonymization of vehicle data within a corresponding data subset or associated geographical area [0059]. Balu describes spatially partitioning collected vehicle data into sampled points (51) associated with different geographical areas as shown in figure 5. Spatial aggregation such as partition-based clustering or K-means clustering is then performed on the vehicle data within those sampled points (51). Balu further explains that the spatial aggregation combines vehicle data from multiple vehicles within the same geographic area to produce spatial aggregated data subsets called centroids (Fig. 6). All this is done to prevent identification of vehicle trajectories [0039]. Thus, Balu teaches using spatial aggregation within geographic areas to anonymize vehicle data.
29. Regarding claim 40, Balu teaches the method of claim 21, wherein the spatial aggregation is provided using the k-anonymity methodology, and/or ([0039], [0061] – [0063] Fig. 5) Balu teaches aggregating vehicle data from a single vehicle into a corresponding sampled point (51) (data point). A vehicle collects multiple sensor measurements and location data over a short time interval while the vehicle is moving along its path. Instead of keeping every measurement. The system will combine these multiple measurements from the same vehicle into one representative sampled point (51) that summarizes the vehicle’s data for that time period [0061] – [0063]. Once these sampled points (51) are within a geographic area, a clustering technique such as K-means clustering is used so that each aggregated region contains at least K contributing vehicles. The aggregation ensures that any individual vehicle’s data cannot be distinguished from at least K-1 other vehicles within the same group [0039]. Therefore, Balu satisfies the K-anonymity requirement.
Claim(s) 26 is rejected under 35 U.S.C. 103 as being unpatentable over US20200019585A1 (hereinafter, “Balu”), and further in view of US20230171314A1 (hereinafter, “Onti”), and further in view of US20220108609A1 (hereinafter, “Qian”), and further in view of US20180293251A1 (hereinafter, “Boehme”), and further in view of US20190325350A1 (hereinafter, “Achilles”).
31. Regarding claim 26, Balu teaches the method of claim 21, wherein a spatial aggregated data set multiply aggregates data points coming from different vehicles,… ([0061] – [0063] Fig. 5). Balu also teaches aggregation of vehicle data coming from multiple vehicles into a spatial aggregated dataset. After vehicle data from individual vehicles is first aggregated into corresponding sampled points (51), these sampled points (51) are then grouped based on geographic proximity of the vehicles. The grouped vehicle sampled points (51) from multiple vehicles are subsequently aggregated together to form a spatial aggregated dataset called partition-based clustering or K-means clustering. This further indicates that these spatial aggregated datasets represent combined information from a plurality of vehicles within the same area.
Balu does not explicitly teach …wherein some vehicles will be arranged to more than one group.
However, Achilles teaches …wherein some vehicles will be arranged to more than one group [0043]. Achilles teaches on aggregated data known as clusters which is vehicle related data. Nodes get formed into clusters and some nodes may belong to more than one cluster. Some vehicles, may be a node, may get arranged to more than one group.
Balu and Achilles are analogous art because Balu teaches on having spatial aggregated data that can come from different vehicles while Achilles teaches on having nodes that are vehicle related nodes of information. These nodes get arranged into clusters and some nodes may get arranged into more than one cluster.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Balu and Achilles, to modify the teachings of the modified Balu reference to include the teachings of Achilles because this will create more accurate data instead of limiting information to only one group.
Claim(s) 28 is rejected under 35 U.S.C. 103 as being unpatentable over US20200019585A1 (hereinafter, “Balu”), and further in view of US20230171314A1 (hereinafter, “Onti”), and further in view of US20220108609A1 (hereinafter, “Qian”), and further in view of US20180293251A1 (hereinafter, “Boehme”), and further in view of US20180165586A1 (hereinafter, “Saxena”).
33. Regarding claim 28, Balu does not explicitly teach the method of claim 21, wherein vehicle data within a data point will be filtered in order to exclude unusual measurements.
However, Saxena in the same field of endeavor, teaches the method of claim 21, wherein vehicle data within a data point will be filtered in order to exclude unusual measurements [0039], [0044]. Unusual measurements can be interpreted broadly. The filtering of data that isn’t within the norm can be considered unusual. Saxena implements a collaborative filtering (206) filter. This filter can be used to filter large types of data sets, can also be known as a data point, which can include data that is unusual. That unusual data will be excluded by the filter once this collaborative filtering (206) is configured to do so.
One of ordinary skill in the art, before the effective filing date of the instant application with a reasonable expectation of success, would have been motivated to modify the disclosure of the modified Balu reference with the teachings of Saxena, to have more accurate data and exclude data that isn’t pertinent to the anonymization.
34. Claim(s) 32 is rejected under 35 U.S.C. 103 as being unpatentable over US20200019585A1 (hereinafter, “Balu”), and further in view of US20230171314A1 (hereinafter, “Onti”), and further in view of US20220108609A1 (hereinafter, “Qian”), and further in view of US20180293251A1 (hereinafter, “Boehme”), and further in view of US20200314642A1 (hereinafter, “Shirani-Mehr”)
35. Regarding claim 32, Balu does not explicitly teach the method of claim 21, wherein the spatial partitioning is provided until a data subset comprise an amount of records lower than the maximal amount of records.
However, Shirani-Mehr in the same field of endeavor, teaches the method of claim 21, wherein the spatial partitioning is provided until a data subset comprise an amount of records lower than the maximal amount of records ([0025], [0028] – [0030] Fig. 4). Shirani-Mehr is analogous because it teaches the algorithm used in vehicles for k-anonymization. Shirani teaches that the recursive partitioning continues until a stopping condition is satisfied, namely, when the number of addresses contained within a quadrant is less than a threshold value (n). Thus, Shirani-Mehr repeatedly performs spatial partitioning, evaluates the number of records within each resulting geographic partition and continues subdividing partitions that exceed the threshold until each partition contains fewer than the predetermined maximum number of records. A person of ordinary skill in the art would recognize that the addresses stored within each geographic quadrant constitute records associated with that spatial partition and that the threshold value (n) represents the claimed maximal amount of records.
One of ordinary skill in the art, before the effective filing date of the instant application with a reasonable expectation of success, would have been motivated to modify the disclosure of the modified Balu reference with the teachings of Shirani-Mehr, to further improve anonymization quality by working on smaller geographic subsets instead of very large datasets.
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 DAVID MESQUITI OVALLE JR. whose telephone number is (571)272-6229. The examiner can normally be reached Monday - Friday 7:30am - 5pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Piateski can be reached on (571) 270-7429. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
/DAVID MESQUITI OVALLE/ Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
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