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 .
Response to Amendments
This office action responds to the amendments filed on October 6, 2025 for application 18/286,837. Claims 3, 4, and 7 are amended, and claims 1-9 remain pending in the application.
Response to Arguments
The Examiner has fully considered the Applicant’s arguments filed on October 6, 2025, and the Examiner responds as provided below.
Regarding the objections to the claims (which the Applicant failed to address in the Remarks), the amendments to the claims are sufficient to address the deficiencies and the objections are withdrawn.
At pages 5-9 of the Remarks, the Applicant presents numerous arguments that Bennati in view of Farmer does not teach or suggest the claimed invention. The Examiner respectfully disagrees with the Applicant’s conclusion, and the § 103 rejection is sustained.
As an initial matter, the Examiner notes that the Applicant used broad limitations to claim the invention, with the claimed subject matter seemingly being illustrated in Figures 2 and 3. While Figures 2 and 3 suggest a method for anonymization based upon mathematical operations, the use of broad limitations in the claims allows for prior art to teach or suggest the claimed invention despite the applied references seemingly not being directly related to the mathematical relationships illustrated in Figures 2 and 3. While broadly claimed inventions are useful for enforcement, broadly claimed subject matter is easily rejected during prosecution.
Generally, Applicant’s arguments take the form of discussing the teachings of Bennati and Farmer, reiterating the limitations of the claims, and finally making a conclusory statement that the cited references do not teach or suggest the claimed limitation. This generic form of argument does not provide sufficient detail to effectively allege examiner error, i.e., it fails to cite specifically to the rejection and explain where the examiner erred.
Notwithstanding the Applicant’s general failure to cite specific instances of examiner error, the Examiner reviewed the mapping of Bennati and Farmer to the claim limitations, as presented below, and maintains no error exists. The generic mapping of the claim limitations is as follows (i.e., claim limitation => reference teaching):
data acquisition => collect position data (Bennati)
data acquisition unit => vehicle sensor (Bennati)
processing unit => processor (Bennati, with further elaboration below)
vehicle data record => probe trajectory (Bennati)
protected data record => equipment identification code (Farmer)
predetermined degree of anonymity => user defined privacy preferences (Bennati)
method => algorithm (Bennati)
secured vehicle data record => cropped probe trajectory (Bennati)
indirect conclusion => complete privacy that prevents “indirect conclusion” (Bennati)
While the Applicant attempts to narrow the teachings of Bennati and Farmer and argue these narrow interpretations do not map to Bennati and Farmer, the Examiner maintains that Bennati and Farmer have been interpreted correctly and fairly teach or suggest the claimed invention.
Again, while the teachings of Bennati and Farmer may not map directly to Applicant’s Figures 2 and 3—which probably goes to the core of Applicant’s intended claimed subject matter—Applicant’s choice to employ broad claim language leaves the claims susceptible to rejection under a wide variety of references. In hindsight, if the Examiner were to adopt a different approach to formulate a rejection that more closely mirrored Figures 2 and 3, the Examiner might employ references that explicitly teach the use of mathematical operations, such as those involving Advanced Encryption Standard. (The Applicant is encouraged see the plethora of mathematical operations of AES that teach or suggest the broadly illustrated mathematical concepts of Figures 2 and 3.)
Although much of Applicant’s argument rests upon conclusory allegations of examiner error, the Applicant provided one specific and meaningful argument of Examiner error. The Applicant states, “In the embodiments as claimed, both the data acquisition unit and the processing unit are arranged in or on the vehicle. The Examiner appears to interpret the sensor units 107 of Bennati as a data acquisition unit. The Examiner further appears to interpret the mapping platform 103, which is not arranged in or on the vehicle, as the processing unit.” The Examiner notes that claim 1 recites “data acquisition in a vehicle” and a “data acquisition unit,” which can be interpreted to occur on the vehicle. However, no limitation exists in claim 1 that requires the “processing unit” to be on the vehicle. Thus, any processor cited by Bennati or Farmer need not be on the vehicle. Additionally, although Bennati does not literally teach that the “sensor units 107” possess a “processing unit,” one skilled in the art would readily recognize any sensor unit would possess a “processing unit” or microprocessor to implement its functionality as a computerized unit.
Regarding the Applicant’s response at page 9 of the Remarks that concerns the § 103 rejection of the dependent claim 2, the arguments for patentability rest upon the patentability of independent claim 1. Because claim 1 is not patentable over the prior art as detailed below, the argument for the patentability of the dependent claim is moot and claim 2 is similarly not allowed.
For the above reasons, the § 103 rejection is sustained.
Claim Rejections - 35 USC § 103
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 following conventions apply to the mapping of the prior art to the claims:
Italicized text – claim language.
Parenthetical plain text – Examiner’s citation and explanation.
Citation without an explanation – an explanation has been previously provided for the respective limitation(s).
Quotation marks – language quoted from a prior art reference.
Underlining – language quoted from a claim.
Brackets – material altered from either a prior art reference or a claim, which includes the Examiner’s explanation that relates a claim limitation to the quoted material of a reference.
Braces – a limitation taught by another reference, but the limitation is presented with the mapping of the instant reference for context.
Numbered superscript – a first phrase to be moved upwards to the primary reference analysis.
Lettered superscript – a second phrase to be moved after the movement of the first phrase from which it was lifted, or more succinctly, move numbered material first, lettered material last.
A. Claims 1, 3-6, and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Bennati et al. (US 11,317,247, “Bennati”) in view of Farmer (US 2003/0130893, “Farmer”).
Regarding Claim 1
Bennati discloses
A method (abstract) for data acquisition in a vehicle (Col. 6:25-43, “In one embodiment, the vehicles 101 include one or more vehicle sensors 107 a-107 n (also collectively referred to as vehicle sensors 107) (e.g., global positioning system (GPS) sensors, positioning sensors, etc.) that can enable the system 100 to collect and/or receive [data acquisition] information or data regarding a vehicle 101's behavior in the form of trajectory data (e.g., probe data).”), comprising:
at least one data acquisition unit and at least one processing unit (Col. 6:25-43, “In one embodiment, the vehicles 101 include one or more vehicle sensors [data acquisition unit] 107 a-107 n (also collectively referred to as vehicle sensors 107) (e.g., global positioning system (GPS) sensors, positioning sensors, etc.) that can enable the system 100 to collect and/or receive information or data regarding a vehicle 101's behavior in the form of trajectory data (e.g., probe data).”; and Fig. 3, Col. 10:1-17, “FIG. 3 is a flowchart of a process for evaluating heuristics for trajectory cropping, according to example embodiment(s). In various embodiments, the mapping platform 103, the machine learning system 109, and/or any of the modules 201-211 may perform one or more portions of the process 300 and may be implemented in, for instance, a chip set including a processor [processing unit] and a memory as shown in FIG. 7.”),
wherein the at least one data acquisition unit records at least one vehicle data record…1 (Col. 6:25-43, “In one embodiment, the vehicles 101 include one or more vehicle sensors 107 a-107 n (also collectively referred to as vehicle sensors [data acquisition unit] 107) (e.g., global positioning system (GPS) sensors, positioning sensors, etc.) that can enable the system 100 to collect [record] and/or receive information or data regarding a vehicle 101's behavior in the form of trajectory data [as vehicle data records] (e.g., probe data).”)
wherein a preprocessing operation of the at least one vehicle data record is carried out in the at least one processing unit (Fig. 3, Col. 10:1-17, “FIG. 3 is a flowchart of a process for evaluating heuristics for trajectory cropping [that incorporates a preprocessing operation], according to example embodiment(s). In various embodiments, the mapping platform 103, the machine learning system 109, and/or any of the modules 201-211 may perform one or more portions of the process 300 and may be implemented in, for instance, a chip set including a processor [processing unit] and a memory as shown in FIG. 7.”; and Fig. 1, Col. 5:50-6:24, “In one embodiment, the system 100 can construct a framework to compare different heuristics for trajectory cropping and to choose one heuristic over another based on multiple characteristics like computational cost, utility of input data (e.g., accuracy), alignment with user-defined privacy preferences, etc.”; and “…user-defined privacy [via anonymization] preferences which describe the mental heuristic used to define [pre-determine] privacy risk. There preferences can either be in a raw form (e.g., answers to a questionnaire [as part of a preprocessing operation]) or in a form that makes them directly applicable to an anonymization heuristic (e.g., parameter values).”),
wherein the preprocessing operation comprises the following steps, taking into account a predetermined degree of anonymity (Fig. 1, Col. 5:50-6:24, “…user-defined privacy [via anonymization] preferences which describe the mental heuristic used to define [pre-determine] privacy risk. There preferences can either be in a raw form (e.g., answers to a questionnaire [as part of a preprocessing operation]) or in a form that makes them directly applicable to an anonymization heuristic (e.g., parameter values).”):
loading the at least one vehicle data record into the preprocessing operation (Fig. 3, Col. 10:54-11:5, “In step 303, the data processing module 205 can process [upon loading] the probe trajectory [vehicle data record] using the cropping heuristic to generate [with part of the preprocessing operation] a cropped probe trajectory.”),
applying at least one method to the at least one vehicle data record to modify the at least one vehicle data record (Col. 10:18-32, “In step 301, the selection module 201 can determine a cropping heuristic [to modify the vehicle data record], wherein the cropping heuristic comprises an algorithm [one method] for cropping [applied to] a probe trajectory [vehicle data record] collected from one or more sensors of a mobile device (e.g., a UE 111) to anonymize the probe trajectory data.”),
analyzing whether the degree of anonymity is met by the modified vehicle data record (Figs 4A-C, Col. 21:13-51, “In one instance, the chart 465 may include a y-axis representing a degree of privacy from 0 to 100, for example, where ‘0’ represents no privacy protection and ‘100’ represents complete privacy. In this example, the cropping heuristic ‘A’ appears to be the cropping heuristic of the set that is the most aligned with the user's privacy expectations [meeting the degree of anonymity for the modified vehicle data] as determined [via analyzing] by the system 100 via the UI 405 in FIGS. 4A-4C.”),
2 …which meets the degree of anonymity as an at least one secured vehicle data record to prevent indirect conclusion from the secured vehicle data record on the at least one protected data record (Figs 4A-C, Col. 21:13-51, “In one instance, the chart 465 may include a y-axis representing a degree of privacy from 0 to 100, for example, where ‘0’ represents no privacy protection and ‘100’ represents complete privacy [that meets the degree of anonymity to prevent indirect conclusion from the secured vehicle data record].).
Bennati doesn’t disclose
1 …that is marked by at least one protected data record,
2 storing the at least one modified vehicle data record…
Farmer, however, discloses
1 …that is marked by at least one protected data record (¶¶ [0018]-[0019], “Normally, a data-transmitting device attaches [marks] an equipment identification code [as a protected data record] to the transmitted data so that the transmission system can authenticate that the user of the transmission service is a valid registered user. However, by using an independent anonymizing system that replaces the equipment identification code with a randomly generated identification code the privacy of the collected raw [protected] data and identity of the motorist is increased.”; and “As an example, in association with the data, there may be identification information, such as an equipment identification code which may be assigned by the data transmission equipment such as a cellular phone, modem or other data transmission system to identify the user that is transmitting data via the data transmission system 14.”),
2 storing the at least one modified vehicle data record…(¶ [0021], “As location, speed, time, or other data are initially collected at the beginning of the vehicle's trip, the acquired data is stored in the buffer's memory. After the buffer is filled, the data contents of the buffer is deleted from the buffer's memory and the data is never transmitted to the anonymizing system or to the anonymous database.”)
Regarding the combination of Bennati and Farmer, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle data system of Bennati to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the vehicle data system of Bennati, upon which the claimed invention can be seen as an “improvement” through the use of a data marking feature;
2) the prior art contained a “comparable” system, namely the data privacy system of Farmer, that has been improved in the same way as the claimed invention through the data marking feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the data marking feature to the base vehicle data system of Bennati, and the results would have been predictable to one of ordinary skill in the art.
Regarding Claim 3
Bennati in view of Farmer (“Bennati-Farmer”) discloses the method according to claim 1, and Bennati further discloses
wherein the preprocessing operation (Fig. 3, Col. 10:1-17, Fig. 1, Col. 5:50-6:24) applies at least one method permanently to the vehicle data record, and thereby the at least one secured vehicle data record (Col. 4:51-5:20, “For example, privacy-enhancing algorithms typically work by deleting [permanently removing] parts of the data (e.g., cropping the trajectory). In one instance, cropping the trajectory means removing the initial and final sections of the trajectory to introduce uncertainty [to secure the vehicle data record] about the actual origin and destination of the trajectory.”).
Regarding Claim 4
Bennati-Farmer discloses the method according to claim 1, and Bennati further discloses
wherein the preprocessing operation (Fig. 3, Col. 10:1-17, Fig. 1, Col. 5:50-6:24), depending upon at least one control variable, applies at least one method to the at least one vehicle data record, and thereby the at least one secured vehicle data record (Col. 5:59-6:15, “In one embodiment, the system 100 can construct the framework based on one or more of the following inputs provided by way of illustration and not limitation: a set of heuristics [associated with the method/”algorithm”] to choose from (e.g., crop [to create a secured vehicle data record] the trajectory [vehicle data record] after the speed of a vehicle [control variable] goes above 30 kilometers per hour (km/h), crop the trajectory after 500 meters (m) from the start [control variable], crop the trajectory after 3 similar map features (e.g., junctions, POIs, traffic signals, etc.) from the start, etc.);…”).
Regarding Claim 5
Bennati-Farmer discloses the method according to claim 1, and Bennati further discloses
wherein the preprocessing operation (Fig. 3, Col. 10:1-17, Fig. 1, Col. 5:50-6:24) depends upon at least one correlation function and upon a context (Col. 5:59-6:15, “In one embodiment, the system 100 can construct the framework based on one or more of the following inputs provided by way of illustration and not limitation: a set of heuristics to choose from (e.g., crop the trajectory after the speed [as a context] of a vehicle goes above 30 kilometers per hour [the specific speed implemented via an equation/correlation function] (km/h), crop the trajectory after 500 meters (m) from the start, crop the trajectory after 3 similar map features (e.g., junctions, POIs, traffic signals, etc.) from the start, etc.);…”).
Regarding Claim 6
Bennati-Farmer discloses the method according to claim 1, and Bennati further discloses
wherein the preprocessing operation (Fig. 3, Col. 10:1-17, Fig. 1, Col. 5:50-6:24) applies at least two methods sequentially or in parallel (Col. 5:59-6:15, “In one embodiment, the system 100 can construct the framework based on one or more of the following inputs provided by way of illustration and not limitation: a set of heuristics to choose from (e.g., crop the trajectory after the speed of a vehicle goes above 30 kilometers per hour (km/h), crop the trajectory after 500 meters (m) from the start, crop the trajectory after 3 similar map features (e.g., junctions, POIs, traffic signals, etc.) from the start, etc.);…”, i.e., the three crop methods may be performed sequentially or in parallel, as performing the crop methods at the same time or one after the other represent two of very limited number of ways to perform the cropping, and thus would be obvious to one skilled in the art).
Regarding Claim 8
Bennati-Farmer discloses the method according to claim 1, and Bennati further discloses
wherein the degree of anonymity is bound to a cost factor and receives a categorization (Col. 5:50-58, “In one embodiment, the system 100 can construct a framework to compare different heuristics for trajectory cropping [to provide anonymity] and to choose one heuristic over another based on multiple characteristics [that are categorized] like computational cost, utility of input data (e.g., accuracy), alignment with user-defined privacy preferences, etc.”).
Regarding Claim 9
With respect to independent claim 9, a corresponding reasoning as given earlier for independent claim 1 applies, mutatis mutandis, to the subject matter of claim 9 (noting the additional element of a “memory unit” would be obvious to one skilled in the art). Therefore, claim 9 is rejected, for similar reasons, under the grounds set forth for claim 1.
B. Claims 2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Bennati and Farmer, and further in view of Bernau et al. (US 2018/0307854, “Bernau”).
Regarding Claim 2
Bennati-Farmer discloses the method according to claim 1, and Bennati further discloses
wherein characterized in that the preprocessing operation (Fig. 3, Col. 10:1-17, Fig. 1, Col. 5:50-6:24) can access at least one method from a method…1 (Col. 4:51-5:20, “For example, privacy-enhancing algorithms [methods] typically work by deleting parts of the data (e.g., cropping the trajectory). In one instance, cropping the trajectory means removing the initial and final sections of the trajectory to introduce uncertainty about the actual origin and destination of the trajectory.”; and Col. 10:18-32, “In step 301, the selection module 201 can determine a cropping heuristic, wherein the cropping heuristic comprises an algorithm [one method] for cropping a probe trajectory [vehicle data record] collected from one or more sensors of a mobile device (e.g., a UE 111) to anonymize the probe trajectory data.”).
Bennati-Farmer doesn’t disclose
1 …{method} library.
Bernau, however, discloses
1 …{method} library (¶ [0045], “In one example embodiment, anonymization of master data (e.g., from SAP systems instances (e.g., ERP, CRM, SCM) or other systems) may be provided by a library component (e.g., an SAP HANA library component, etc.) referred to herein as a privacy component. The privacy component is similar to a computer program library and contains anonymization functionalities [methods], such as differential privacy algorithms.”).
Regarding the combination of Bennati-Farmer and Bernau, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle data system of Bennati-Farmer to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the vehicle data system of Bennati-Farmer, upon which the claimed invention can be seen as an “improvement” through the use of a library feature;
2) the prior art contained a “comparable” system, namely the data privacy system of Bernau, that has been improved in the same way as the claimed invention through the library feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the library feature to the base vehicle data system of Bennati-Farmer, and the results would have been predictable to one of ordinary skill in the art.
Regarding Claim 7
Bennati in view of Farmer, and further in view of Bernau (“Bennati-Farmer-Bernau”) discloses the method according to claim 2, and Bennati further discloses
wherein the method library functions on a subscription basis (Col. 9:42-67, “In another embodiment, the mapping platform 103, the machine learning system [that implements new methods via training] 109, and/or the modules 201-211 may be implemented as a cloud-based service [subscription], local service, native application, or combination thereof.”), and
new methods are added to the method library (Col. 14:51-15:9, “In one embodiment, the training module 211 and the machine learning system 109 can select and/or tune respective weights or weighting schemes used by the data processing system 205 and/or the selection module 201 to rank and/or to select the cropping heuristic from among a plurality of cropping heuristics. In one instance, the training module 211 can continuously provide and/or update [or add new methods to the library of methods/“heuristics”] a machine learning module (e.g., a support vector machine (SVM), neural network, decision tree, etc.) of the machine learning system 109 during training using, for instance, supervised deep convolution network or equivalents. By way of example, the training module 211 can train the machine learning module using the respective weights or weighting schemes of the one or more heuristic-based features, the one or more privacy-based features, or a combination thereof to tune the selected cropping heuristic to reflect the respective privacy preferences of the users providing the probe trajectory data.”).
Conclusion
THIS ACTION IS MADE FINAL. 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 D'ARCY WINSTON STRAUB whose telephone number is (303)297-4405. The examiner can normally be reached Monday-Friday 9:00-5:00 Mountain Time.
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/D'Arcy Winston Straub/Primary Examiner, Art Unit 2491