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 action is in reply to the amendments filed on March 11, 2026.
Claims 1–20 are pending in this application.
Claims 2–4, 6, 9–12, 14–16, and 18 are currently amended.
Claims 1, 5, 7–8, 13, 17, 19, and 20 are in original form.
Claims 1-20 are currently pending and have been examined.
The current application is now being handled by Examiner Michael Anderson.
Response to Arguments
Applicant's arguments filed March 11, 2026 have been fully considered but they are not persuasive.
I. Double Patenting
Applicant’s request to hold the double patenting rejections in abeyance is acknowledged. The nonstatutory double patenting rejections of claims 1–20 are maintained. A terminal disclaimer cannot be held in abeyance; the rejections will be maintained until a terminal disclaimer is filed and approved, or the claims are amended to be patentably distinct.
II. Rejection under 35 U.S.C. § 101
Applicant’s arguments filed March 11, 2026 have been fully considered but are not persuasive. Applicant argues that the claims integrate the alleged abstract idea into a practical application because they address a specific technical problem—namely, distinguishing primary movement from secondary movement when processing accelerometer data from a device that is free to move within a vehicle and that the recited spectral power analysis based upon a kernel smoothed estimate constitutes a specific signal processing technique that provides a technological improvement over existing techniques. The Examiner respectfully disagrees. The Examiner does not dispute that the specification describes a technical problem and a purported solution. However, the relevant inquiry under Step 2A, Prong 2 is whether the claims themselves recite additional elements that integrate the judicial exception into a practical application. Here, the claims recite: (1) receiving data via a computer network; (2) selecting data points; (3) performing a mathematical computation (determining total spectral power based on a kernel smoothed estimate); and (4) classifying data based on the mathematical result (identifying a primary movement window based on a threshold comparison). These steps describe the mathematical algorithm itself and its inputs and outputs they do not recite a particular technological improvement to a computer or other technology. The fact that the mathematical technique produces a useful result (classification of movement data) does not transform the abstract mathematical concept into a practical application. The claims do not recite any downstream technological action performed as a result of identifying the primary movement window. No control signal is generated, no device is adjusted, no accelerometer is recalibrated, no data is retransmitted, and no vehicle system is modified. The output is a data label—a classification of data points as “primary movement”—which remains an abstract data characterization regardless of its utility. Applicant’s reliance on specification paragraphs describing the technical problem and solution is noted. However, the specification’s description of technical advantages does not cure the deficiency in the claims themselves. The claims must recite the alleged improvement; it is insufficient for the improvement to exist only in the specification. See Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1340 (Fed. Cir. 2017) (claims must capture the alleged improvement in their limitations). Applicant further argues that the claims do not preempt all uses of spectral analysis or mathematical calculations. The Examiner notes that while preemption is the concern underlying § 101, the absence of complete preemption does not demonstrate patent eligibility. See Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015) (“While preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility.”). A narrow abstract idea is still an abstract idea. Applicant also argues that the claims recite “a specific application” of spectral analysis to a particular problem. Specificity of the mathematical technique does not remove it from the realm of abstract ideas. A particular mathematical formula applied to a particular type of data remains a mathematical concept under the MPEP. See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163 (Fed. Cir. 2018) (“We may assume that the techniques claimed are ‘ichgroundbreaking, innovative, or even brilliant,’ but that is not enough for eligibility.”). Regarding Applicant’s characterization of the claims as reciting a “specific signal processing technique,” the Examiner notes that the claims recite what is computed (total spectral power via kernel smoothed estimate) and what decision is made based on the computation (threshold comparison to classify data), but do not recite how this computation improves the functioning of the computer itself or effects a transformation of an article. The improvement described by Applicant is an improvement in the accuracy of data classification—i.e., better mathematical analysis—not an improvement to computer functionality or another technology. Mathematical techniques that improve the accuracy or quality of data analysis remain abstract. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327 (Fed. Cir. 2017). The claims’ recitation of the vehicle, accelerometer, and telematics context describes the field of use and source of data but does not integrate the mathematical analysis into a practical application. The independent claims do not require the computing device to be placed in a vehicle or free to move (claim 1 merely recites “receiving” data that includes multi-axis accelerometer data via a network). Claim 8, which does recite the device placement, adds only field-of-use limitations describing the data’s origin and the semantic meaning of the output label, which is insufficient for integration. Accordingly, the rejection of claims 1–20 under 35 U.S.C. § 101 is maintained.
III. Rejection under 35 U.S.C. § 103
Applicant’s arguments filed March 11, 2026 have been fully considered but are not persuasive. Applicant argues that Girod’s spectrogram analysis is applied to audio data and not accelerometer data, that Girod’s technique for detecting device movement relies on time-domain analysis of changes in the direction of gravity rather than spectral analysis, and that Doudet’s inter-spectral matrix processing involves fixed-position transducers and does not include a kernel smoothed estimate or distinguish between primary and secondary movement. Applicant concludes that neither reference alone or in combination teaches “determining whether a total spectral power of the one or more data points meets a threshold value based upon a kernel smoothed estimate using the multi-axis accelerometer data.” The Examiner respectfully disagrees. The rejection is based on the combination of Girod and Doudet, not on either reference individually. In response to Applicant’s arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 426 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 1097 (Fed. Cir. 1986). Regarding Applicant’s argument that Girod applies spectral analysis only to audio data: The Examiner acknowledges this distinction in the rejection and relies on Doudet for teaching spectral power density analysis applied to accelerometer data. The combination proposes applying spectral domain analysis—which Girod demonstrates is useful for extracting motion-related information from time-series sensor data ([0071])—to the accelerometer data in a vehicle telematics context, using the inter-spectral matrix framework taught by Doudet. A person of ordinary skill in the art would recognize that spectral analysis techniques are applicable across sensor modalities (audio, accelerometer, vibration) and would have been motivated to apply spectral power analysis to accelerometer data, particularly given that Doudet explicitly does so with accelerometers ([0060]). Regarding Applicant’s argument that Girod relies on time-domain gravity vector analysis: Girod is not relied upon solely for teaching spectral analysis of accelerometer data. Girod is relied upon for teaching: (1) the overall framework of classifying periods of time when a mobile device is static versus moving relative to a vehicle ([0009]); (2) threshold-based classification of accelerometer data ([0051]–[0052]); and (3) spectral analysis techniques including windowed (kernel-weighted) transforms ([0071], Hann window). The Examiner does not assert that Girod alone teaches all elements of the claim. Rather, the combination proposes replacing or augmenting Girod’s time-domain movement detection technique with the spectral power density analysis of Doudet applied to accelerometer data, motivated by the desire to more comprehensively analyze temporal data from accelerometers as discussed in Doudet [0008] and improve the robustness of movement classification. Regarding Applicant’s argument that Doudet’s transducers are in fixed positions: The position of the transducers in Doudet does not negate the applicability of the spectral power density matrix technique to other contexts. Doudet teaches the mathematical and signal processing framework of computing an inter-spectral matrix representing spectral power density from accelerometer data. One of ordinary skill would understand that this spectral analysis technique is not limited to fixed transducers and could be applied to multi-axis accelerometer data from a single device to characterize motion states, as motivated by the goal of extracting meaningful information from accelerometer signals. The test for obviousness is not whether a secondary reference must be bodily incorporated into the primary reference or applied in the identical context; rather, the question is whether the teachings of the references can be combined by one of ordinary skill. See In re Keller, 642 F.2d at 425. Regarding Applicant’s argument that Doudet does not teach a kernel smoothed estimate: The Examiner notes that Doudet’s inter-spectral matrix processing inherently involves spectral power density estimation, which is a smoothed spectral representation (as opposed to a raw periodogram). Furthermore, Girod explicitly teaches the use of a Hann window in spectral analysis ([0071]), which is a kernel weighting function applied during spectral estimation. The combination of Girod’s kernel-windowed spectral analysis approach with Doudet’s spectral power density matrix framework applied to accelerometer data yields the claimed “total spectral power… based upon a kernel smoothed estimate using the multi-axis accelerometer data.” A person of ordinary skill in the art would have found it obvious to apply kernel smoothing (a well-known technique in spectral estimation for reducing variance of periodogram estimates) when computing spectral power density from accelerometer data, as it is a predictable application of known signal processing principles. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (combining known techniques to yield predictable results supports obviousness). Regarding Applicant’s argument that Doudet does not distinguish between primary and secondary movement: Girod is relied upon for this teaching, not Doudet. Girod explicitly teaches classifying periods when a mobile device is not moving relative to the vehicle versus when it is moving relative to the vehicle ([0009]). Doudet is relied upon for the spectral power density matrix analysis technique applied to accelerometer data. The combination proposes using Doudet’s spectral power analysis technique within Girod’s vehicle telematics framework to improve the classification of primary versus secondary movement—a straightforward combination of known elements yielding predictable results. Accordingly, the rejection of claims 1–20 under 35 U.S.C. § 103 as unpatentable over Girod in view of Doudet is maintained.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,002,105. Although the claims at issue are not identical, they are not patentably distinct from each other because the claimed subject matter is nearly identical albeit arranged differently.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,416,946. Although the claims at issue are not identical, they are not patentably distinct from each other because the claimed subject matter is nearly identical albeit arranged differently.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-15 of U.S. Patent No. 10,832,346. Although the claims at issue are not identical, they are not patentably distinct from each other because the claimed subject matter is nearly identical albeit arranged differently.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6 of U.S. Patent No. 10,304,138. Although the claims at issue are not identical, they are not patentably distinct from each other because the claimed subject matter is nearly identical albeit arranged differently.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1–20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 1 – Statutory Category:
Claim 1 recites a computer-implemented method, which falls within the statutory category of a process.
Step 2A, Prong 1 – Abstract Idea:
Claim 1 recites:
“determining, by the one or more processors, whether a total spectral power of the one or more data points meets a threshold value based upon a kernel smoothed estimate using the multi-axis accelerometer data”
“identifying, by the one or more processors, a primary movement window including the one or more data points based upon determining that the total spectral power of the one or more data points is less than the threshold value”
These limitations, under their broadest reasonable interpretation, recite mathematical concepts. Specifically, determining a “total spectral power” based upon a “kernel smoothed estimate” is a mathematical calculation involving Fourier decomposition of time-series data, computation of periodograms, and application of kernel smoothing weights to produce a spectral power value. Comparing that spectral power value to a threshold is a mathematical relationship/comparison. Identifying a “primary movement window” based on whether the computed value is less than a threshold is a mathematical classification of data based on the result of the mathematical calculation.
The specification confirms that these steps are mathematical operations. Paragraphs describing FIG. 5A–5B detail the Fourier expansion of accelerometer signals, the amplitude periodogram Jkx=n2((Akx)2+(Bkx)2), the kernel smoothed estimate Skxx=∑j=−ppWpJk+jx, and the total spectral power matrix Tlij=∑kSkij. These are purely mathematical formulas applied to data.
Additionally, the limitation “selecting, by one or more processors, one or more data points from the plurality of telematics data” recites a mental process (observation/selection of data) or, at best, an insignificant data selection step that could be performed mentally or with pen and paper for a small dataset.
Accordingly, the claim recites an abstract idea in the form of mathematical concepts (mathematical relationships, mathematical formulas/equations, and mathematical calculations).
Step 2A, Prong 2 – Practical Application:
The claim recites the following additional elements beyond the abstract idea:
“receiving, via a computer network, a plurality of telematics data including multi-axis accelerometer data” — This is a data-gathering step that amounts to insignificant extra-solution activity. Receiving data over a network is mere data transmission necessary to feed the mathematical algorithm and does not impose a meaningful limit on the abstract idea.
“by one or more processors” — This is a generic recitation of computer hardware that amounts to mere instructions to “apply” the abstract idea using generic computing components. There is no specific machine or specialized processor architecture recited.
“a computer network” — This is a generic technological environment through which data is received. It serves only as a generic data conduit and does not integrate the abstract idea into a practical application.
The claim does not recite any improvement to computer functionality or to another technology. The claim does not apply the judicial exception with or to a particular machine (beyond a generic processor). The claim does not effect a transformation of a particular article to a different state or thing. The output of the claim—a “primary movement window”—is a data classification label; no downstream technological action, control signal, device adjustment, or tangible effect is recited. The vehicle and accelerometer context serves as a field-of-use limitation describing the source of the data, not a meaningful integration of the mathematical analysis into a practical application.
Accordingly, the additional elements do not integrate the abstract idea into a practical application.
Step 2B – Significantly More:
Considering the additional elements individually and in combination:
“receiving, via a computer network, a plurality of telematics data including multi-axis accelerometer data” — Receiving data over a computer network is well-understood, routine, and conventional activity. Transmitting sensor data from a device over a network is ubiquitous in the telematics and mobile computing arts.
“by one or more processors” — Performing mathematical calculations on a generic processor is well-understood, routine, and conventional.
“selecting … one or more data points from the plurality of telematics data” — Selecting or extracting data points from a dataset is well-understood, routine, and conventional data manipulation.
The combination of these additional elements amounts to nothing more than receiving data on a generic computer and performing mathematical operations on that data using a generic processor—activities that are well-understood, routine, and conventional in the art. The additional elements, individually and in ordered combination, do not provide an inventive concept sufficient to amount to significantly more than the abstract idea.
The additional element of receiving telematics data including multi-axis accelerometer data via a computer network amounts to insignificant extra-solution activity that is well-understood, routine, and conventional. The courts have recognized “receiving or transmitting data over a network” as well-understood, routine, and conventional. See Symantec, 838 F.3d 1307, 1321 (Fed. Cir. 2016); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); MPEP § 2106.05(d)(II).
Claim 1 is therefore rejected under 35 U.S.C. § 101 as being directed to a judicial exception without significantly more.
Claim 2
Claim 2 depends from claim 1 and further recites:
“converting, by the one or more processors, the plurality of telematics data from a time domain to a spectral domain”
“identifying, by the one or more processors, a diagonal and an off-diagonal data point of a total spectral power matrix from the plurality of telematics data”
These limitations further define the mathematical operations. Converting data from a time domain to a spectral domain is a mathematical transformation (e.g., Fourier transform). Identifying diagonal and off-diagonal data points of a total spectral power matrix is identifying elements of a mathematically computed matrix. These are additional mathematical concepts that do not alter the abstract nature of the claim nor provide integration into a practical application or significantly more.
Claim 2 is therefore rejected under 35 U.S.C. § 101.
Claim 3
Claim 3 depends from claim 2 and further recites:
“comparing, by the one or more processors, the diagonal and the off-diagonal data point with the threshold value”
“determining, by the one or more processors, whether at least one of the diagonal or the off-diagonal data point is above the threshold value”
These limitations recite mathematical comparisons—comparing computed matrix elements to a threshold and determining whether those values exceed the threshold. These are further mathematical relationships/calculations that do not alter the abstract nature of the claim nor provide integration into a practical application or significantly more.
Claim 3 is therefore rejected under 35 U.S.C. § 101.
Claim 4
Claim 4 depends from claim 3 and further recites:
“determining, by the one or more processors, whether at least one of the diagonal or the off-diagonal data point is below the threshold value”
This limitation recites an additional mathematical comparison (determining whether computed values are below a threshold). This is a further mathematical relationship that does not alter the abstract nature of the claim nor provide integration into a practical application or significantly more.
Claim 4 is therefore rejected under 35 U.S.C. § 101.
Claim 5
Claim 5 depends from claim 1 and further recites:
“determining, by the one or more processors, a vehicle insurance risk using at least the primary movement window”
This limitation recites a certain method of organizing human activity—specifically, a fundamental economic practice (insurance risk assessment/underwriting). Using a mathematically derived data classification to determine an insurance risk is an abstract commercial/financial activity. This additional abstract idea does not provide integration into a practical application or significantly more.
Claim 5 is therefore rejected under 35 U.S.C. § 101.
Claim 6
Claim 6 depends from claim 1 and further recites:
“summarizing, by the one or more processors, the plurality of telematics data at a specified sample rate”
“splitting, by the one or more processors, the plurality of telematics data into one or more temporal segments”
These limitations recite data manipulation steps—summarizing (e.g., averaging or down-sampling) data and splitting data into segments. These are mathematical data pre-processing operations or, alternatively, mental processes that could be performed with pen and paper. They do not alter the abstract nature of the claim nor provide integration into a practical application or significantly more.
Claim 6 is therefore rejected under 35 U.S.C. § 101.
Claim 7
Claim 7 depends from claim 6 and further recites:
“shifting down one or more temporal segments by at least one data point”
This limitation recites a data manipulation step (shifting a temporal window by one data point) which is a mathematical/data pre-processing operation. It does not alter the abstract nature of the claim nor provide integration into a practical application or significantly more.
Claim 7 is therefore rejected under 35 U.S.C. § 101.
Claim 8
Claim 8 depends from claim 1 and further recites:
“wherein the plurality of telematics data originates from a client computing device placed in a vehicle, wherein the client computing device includes an accelerometer and is free to move with respect to movement of the vehicle, wherein the multi-axis accelerometer data includes data corresponding to each axis measured by the accelerometer, and wherein the primary movement window is indicative of the accelerometer being static with respect to the vehicle”
These limitations describe the source and nature of the data (a freely-placed accelerometer in a vehicle) and the meaning of the output classification (primary movement = device static relative to vehicle). These are field-of-use limitations that describe the technological environment and the type of data being processed. They do not impose meaningful structural or functional limitations on the claim beyond describing the origin of the data and the semantic label of the output. They do not alter the abstract nature of the claim nor provide integration into a practical application or significantly more.
Claim 8 is therefore rejected under 35 U.S.C. § 101.
Claim 9
Step 1 – Statutory Category:
Claim 9 recites a computer device comprising one or more processors and one or more memories, which falls within the statutory category of a machine.
Step 2A, Prong 1 – Abstract Idea:
Claim 9 recites substantially similar limitations to claim 1 in apparatus form:
“determine whether a total spectral power of the one or more data points meets a threshold value based upon a kernel smoothed estimate using the multi-axis accelerometer data”
“identify a primary movement window including the one or more data points based upon determining that the total spectral power of the one or more data points is less than the threshold value”
For the same reasons as discussed with respect to claim 1, these limitations recite mathematical concepts (mathematical formulas, mathematical calculations, and mathematical relationships). The claim additionally recites “select one or more data points from the plurality of telematics data,” which is a mental process or trivial data manipulation step.
Step 2A, Prong 2 – Practical Application:
The claim recites the following additional elements beyond the abstract idea:
“a computer device … placed in a vehicle and free to move with respect to movement of the vehicle” — This describes the physical placement of a generic computing device. While it provides context for the data, it does not impose a meaningful structural limitation that integrates the abstract idea into a practical application. The device is a generic computing device (smartphone, tablet, etc.) placed loosely in a vehicle.
“one or more processors” — Generic computer hardware amounting to mere instructions to “apply” the abstract idea.
“one or more memories coupled to the one or more processors, the one or more memories including non-transitory computer-executable instructions stored therein” — Generic memory storing instructions is a conventional computer component.
“receive, via a computer network, a plurality of telematics data including multi-axis accelerometer data” — Insignificant extra-solution data-gathering activity over a generic network.
The claim does not recite an improvement to computer functionality or another technology. The output remains a data classification (primary movement window) with no downstream technological action. The recitation of a device “placed in a vehicle and free to move” describes a field-of-use/technological environment but does not transform the abstract mathematical analysis into a practical application.
Accordingly, the additional elements do not integrate the abstract idea into a practical application.
Step 2B – Significantly More:
“a computer device … placed in a vehicle and free to move with respect to movement of the vehicle” — A generic computing device (e.g., smartphone) loosely placed in a vehicle is well-understood, routine, and conventional in the telematics arts. The specification acknowledges that mobile phones placed in vehicles are known.
“one or more processors” and “one or more memories … including non-transitory computer-executable instructions” — Generic computer components that are well-understood, routine, and conventional.
“receive, via a computer network, a plurality of telematics data including multi-axis accelerometer data” — Receiving sensor data over a network is well-understood, routine, and conventional.
The additional elements, individually and in ordered combination, do not provide an inventive concept.
Claim 9 is therefore rejected under 35 U.S.C. § 101 as being directed to a judicial exception without significantly more.
Claims 10–15
Claims 10–15 depend from claim 9 and recite substantially similar additional limitations as claims 2–4, 6–7, and 8, respectively, in apparatus form.
Claim 10 (parallel to claim 2) further recites converting data from time domain to spectral domain and identifying diagonal/off-diagonal data points of a spectral power matrix—additional mathematical operations.
Claim 11 (parallel to claim 3) further recites comparing diagonal and off-diagonal data points with a threshold and determining if at least one is above the threshold—additional mathematical comparisons.
Claim 12 (parallel to claim 4) further recites determining whether at least one diagonal or off-diagonal data point is below the threshold—an additional mathematical comparison.
Claim 13 (parallel to claim 8) further recites field-of-use limitations describing the data source (accelerometer in a freely-placed client computing device) and the semantic meaning of the output.
Claim 14 (parallel to claim 6) further recites summarizing data at a sample rate and splitting data into temporal segments—data pre-processing/mathematical operations.
Claim 15 (parallel to claim 7) further recites shifting temporal segments by at least one data point—a data manipulation step.
For the same reasons discussed with respect to their method counterparts, these additional limitations recite further abstract ideas (mathematical concepts) or field-of-use limitations and do not provide integration into a practical application or significantly more.
Claims 10–15 are therefore rejected under 35 U.S.C. § 101.
Claim 16
Step 1 – Statutory Category:
Claim 16 recites a computer-readable storage medium comprising non-transitory computer-readable instructions, which falls within the statutory category of a manufacture.
Step 2A, Prong 1 – Abstract Idea:
Claim 16 recites substantially similar limitations to claim 1 in computer-readable medium form:
“determine whether a total spectral power of the one or more data points meets a threshold value based upon a kernel smoothed estimate using the multi-axis accelerometer data”
“identify a primary movement window including the one or more data points based upon determining that the total spectral power of the one or more data points is less than the threshold value”
For the same reasons as discussed with respect to claim 1, these limitations recite mathematical concepts.
Step 2A, Prong 2 – Practical Application:
The claim recites the following additional elements:
“A computer-readable storage medium comprising non-transitory computer-readable instructions stored thereon … when executed on one or more processors” — A generic non-transitory storage medium storing instructions for execution on a generic processor amounts to mere instructions to “apply” the abstract idea on a generic computer.
“receive, via a computer network, a plurality of telematics data including multi-axis accelerometer data” — Insignificant extra-solution data-gathering activity.
“select one or more data points from the plurality of telematics data” — Trivial data selection that is either a mental process or insignificant extra-solution activity.
The additional elements do not integrate the abstract idea into a practical application for the same reasons as discussed with respect to claim 1.
Step 2B – Significantly More:
The additional elements (generic storage medium, generic processor, receiving data via a network, selecting data) are well-understood, routine, and conventional and do not provide an inventive concept for the same reasons as discussed with respect to claim 1.
Claim 16 is therefore rejected under 35 U.S.C. § 101 as being directed to a judicial exception without significantly more.
Claims 17–20
Claims 17–20 depend from claim 16 and recite substantially similar additional limitations as claims 2–4 and 6–7, respectively, in computer-readable medium form.
Claim 17 (parallel to claim 2) further recites converting data from time domain to spectral domain and identifying diagonal/off-diagonal data points of a spectral power matrix—additional mathematical operations.
Claim 18 (parallel to claim 3) further recites comparing diagonal and off-diagonal data points with a threshold and determining if at least one is above the threshold—additional mathematical comparisons.
Claim 19 further recites comparing three unique off-diagonal or diagonal data points with the threshold value and determining whether all three are above the threshold—additional mathematical comparisons applied to additional matrix elements. This does not alter the abstract nature of the claim.
Claim 20 (parallel to claims 6–7) further recites summarizing data at a sample rate, splitting data into temporal segments, and shifting segments by at least one data point—data pre-processing/mathematical operations.
For the same reasons discussed with respect to their method and apparatus counterparts, these additional limitations recite further abstract ideas (mathematical concepts) and do not provide integration into a practical application or significantly more.
Claims 17–20 are therefore rejected under 35 U.S.C. § 101.
Conclusion
Claims 1–20 are rejected under 35 U.S.C. § 101 as being directed to mathematical concepts (spectral power calculations, kernel smoothed estimates, threshold comparisons, and data classification) without integration into a practical application and without reciting additional elements that amount to significantly more than the judicial exception. The claims recite receiving data via generic computing hardware, performing mathematical computations on that data, and outputting a data classification label, without effecting any technological improvement, transformation of an article, or downstream technological action.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Girod (US 2014/0278206) in view of Doudet (US 2011/0119041).
Claim 1
Girod discloses a computer-implemented method for determining a primary movement window from a vehicle trip (Girod, [0009], a method for classifying periods of time into categories including periods of time when the mobile device is not moving in a reference frame of the vehicle and the vehicle is in motion), the computer-implemented method comprising:
receiving, via a computer network, a plurality of telematics data including multi-axis accelerometer data (Girod, Fig. 2, [0022], output of three-axis accelerometer; [0070], audio data; [0080], server computer in communication with smartphone);
selecting, by one or more processors, one or more data points from the plurality of telematics data (Girod, [0024], acceleration sample; [0071], sample audio);
determining, by the one or more processors, whether a total spectral power of the one or more data points meets a threshold value based upon a kernel smoothed estimate using the multi-axis accelerometer data (Girod, [0051]–[0052], threshold applied to accelerometer data; [0071], spectrogram generated via short-term Fourier transform (STFT) using a Hann window, which is a kernel weighting function applied to spectral estimation).
Girod discloses applying spectral analysis (STFT/spectrogram) to audio data ([0071]) and discloses threshold-based classification of accelerometer data ([0051]–[0052]), but does not explicitly disclose applying spectral power density analysis in matrix form to the accelerometer data.
Doudet teaches processing an inter-spectral matrix representing the spectral power density of transducers on its main diagonal and the inter-spectra in the off-diagonal terms (Doudet, [0074], [0029], [0124]), where the transducers are accelerometers (Doudet, [0060]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the spectral analysis techniques of Girod, including the kernel-windowed spectral estimation, to accelerometer data organized in an inter-spectral matrix representing spectral power density as taught by Doudet, in order to analyze temporal data from accelerometers and extract structural/motion properties as discussed in Doudet [0008] and Girod [0005]; and
identifying, by the one or more processors, a primary movement window including the one or more data points based upon determining that the total spectral power of the one or more data points is less than the threshold value (Girod, [0009], “classifying periods of time into three categories: (1) periods of time when the mobile device is not moving in a reference frame of the vehicle and the vehicle is in motion”—the acceleration data is classified or segregated hierarchically into these categories to produce accurate longitudinal and lateral acceleration and velocity estimates for periods of time corresponding to a particular category; [0051], threshold; [0054]–[0057], filter out degenerate values and identify principal component in the forward direction of travel; [0071], spectrogram. Modified by Doudet, [0074], spectral power density matrix analysis applied to accelerometer transducer data, wherein low spectral power indicates the device is static relative to the vehicle).
Claims 9 and 16
Claims 9 and 16 recite a computer device and a computer-readable storage medium, respectively, for determining a primary movement window from a vehicle trip, with substantially similar limitations as claim 1.
Regarding device claim 9, Girod discloses a computer device placed in a vehicle and free to move with respect to movement of the vehicle (Girod, [0021], smartphone not necessarily rigidly attached to the vehicle), comprising one or more processors and one or more memories coupled to the one or more processors, the one or more memories including non-transitory computer-executable instructions stored therein (Girod, [0080], instructions stored on non-transitory computer-readable media, processor, server computer in communication with smartphone).
Regarding computer-readable storage medium claim 16, Girod discloses a computer-readable storage medium comprising non-transitory computer-readable instructions stored thereon (Girod, [0080], “instructions stored on non-transitory computer-readable media”).
Claims 9 and 16 are rejected on the same grounds and for the same reasons as claim 1.
Claim 2
Girod in view of Doudet teaches the computer-implemented method of claim 1, further comprising:
converting, by the one or more processors, the plurality of telematics data from a time domain to a spectral domain (Girod, [0071], applying a short-term Fourier transform using a Hann window with a step of 50 samples to produce 10 spectra per second—converting time domain data to spectral domain); and
identifying, by the one or more processors, a diagonal and an off-diagonal data point of a total spectral power matrix from the plurality of telematics data (Girod, [0071], spectrogram. Girod does not specifically disclose identifying a diagonal and an off-diagonal data point of a total spectral power matrix. Doudet, [0074], [0124], discloses a symmetric inter-spectral matrix representing the spectral power density of each of the transducers on its main diagonal and the inter-spectra in the off-diagonal terms. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the spectrogram analysis of Girod to identify diagonal and off-diagonal elements of a spectral power matrix as taught by Doudet in order to analyze temporal data from multiple accelerometer axes and extract motion properties as discussed in Doudet [0008] and Girod [0005]).
Claims 10 and 17
Claims 10 and 17 recite limitations corresponding to claim 2 in device and computer-readable storage medium form, respectively, and are rejected on the same grounds.
Claim 3
Girod in view of Doudet teaches the computer-implemented method of claim 2, further comprising:
comparing, by the one or more processors, the diagonal and the off-diagonal data point with the threshold value (Girod, [0051], vector distance exceeds threshold. Girod does not specifically disclose comparing the diagonal and the off-diagonal data point with the threshold value. Doudet, [0074], [0124], discloses a symmetric inter-spectral matrix having spectral power density on its main diagonal and inter-spectra in the off-diagonal terms. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the threshold comparison of Girod to the diagonal and off-diagonal data point of the spectral power matrix as taught by Doudet in order to analyze and classify data as discussed in Doudet [0008] and Girod [0005]); and
determining, by the one or more processors, whether at least one of the diagonal or the off-diagonal data point is above the threshold value (Girod, [0051], determining when a vector distance exceeds a threshold, as modified by Doudet, [0074], [0124], to apply to diagonal and off-diagonal elements of the inter-spectral matrix).
Claims 11 and 18
Claims 11 and 18 recite limitations corresponding to claim 3 in device and computer-readable storage medium form, respectively, and are rejected on the same grounds.
Claim 4
Girod in view of Doudet teaches the computer-implemented method of claim 3, further comprising:
determining, by the one or more processors, whether at least one of the diagonal or the off-diagonal data point is below the threshold value (Girod, [0052], vector angle is below threshold. Girod does not specifically disclose whether at least one of the diagonal or the off-diagonal data point is below the threshold value. Doudet, [0074], [0124], discloses a symmetric inter-spectral matrix having spectral power density on its main diagonal and inter-spectra in the off-diagonal terms. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the below-threshold comparison of Girod to the diagonal and off-diagonal data point of the spectral power matrix as taught by Doudet in order to classify data segments as discussed in Doudet [0008] and Girod [0005]).
Claim 12
Claim 12 recites limitations corresponding to claim 4 in device form and is rejected on the same grounds.
Claim 5
Girod in view of Doudet teaches the computer-implemented method of claim 1, further comprising:
determining, by the one or more processors, a vehicle insurance risk using at least the primary movement window (Girod, [0005], insurance telematics, driver behavior and risk assessment; [0079], usage-based or behavior-based insurance policies, where the terms of the contract depend on the driving characteristics determined using the disclosed techniques).
Claim 6
Girod in view of Doudet teaches the computer-implemented method of claim 1, further comprising:
summarizing, by the one or more processors, the plurality of telematics data at a specified sample rate (Girod, [0050], each sample is tagged with a timestamp at an implied rate; [0071], sampling rate, downsample to f₂ Hz); and
splitting, by the one or more processors, the plurality of telematics data into one or more temporal segments (Girod, [0024]–[0026], time series sequence; [0037], segmentation stage producing non-overlapping segments).
Claim 14
Claim 14 recites limitations corresponding to claim 6 in device form and is rejected on the same grounds.
Claim 7
Girod in view of Doudet teaches the computer-implemented method of claim 6, further comprising:
shifting down one or more temporal segments by at least one data point (Girod, [0071], downsample; [0050], resampled at a defined rate—shifting the temporal analysis window).
Claim 15
Claim 15 recites limitations corresponding to claim 7 in device form and is rejected on the same grounds.
Claim 8
Girod in view of Doudet teaches the computer-implemented method of claim 1,
wherein the plurality of telematics data originates from a client computing device placed in a vehicle (Girod, Fig. 1, [0021], telematics system, mobile device 110 traveling in vehicle 120),
wherein the client computing device includes an accelerometer and is free to move with respect to movement of the vehicle (Girod, Fig. 2, [0022], three-axis accelerometer; [0021], smartphone is not necessarily rigidly attached to the vehicle frame of reference, e.g., the smartphone may be in the pocket of a driver or passenger and may be moved from time to time),
wherein the multi-axis accelerometer data includes data corresponding to each axis measured by the accelerometer (Girod, Fig. 2, [0022], three-axis accelerometer; [0024], x, y, z components of each acceleration sample), and
wherein the primary movement window is indicative of the accelerometer being static with respect to the vehicle (Girod, [0009], “classifying periods of time into three categories: (1) periods of time when the mobile device is not moving in a reference frame of the vehicle and the vehicle is in motion”—i.e., the device/accelerometer is static with respect to the vehicle during primary movement).
Claim 13
Claim 13 recites limitations corresponding to claim 8 in device form and is rejected on the same grounds.
Claim 19
Girod in view of Doudet teaches the computer-readable storage medium of claim 18, comprising further instructions stored thereon that cause the one or more processors to:
compare three unique off-diagonal or diagonal data points with the threshold value; and determine whether all of the three unique off-diagonal or diagonal data points is above the threshold value (Girod, [0051]–[0052], comparing vector distances/angles against a threshold. Girod does not specifically disclose comparing three unique off-diagonal or diagonal data points with the threshold value and determining whether all three are above the threshold value. Doudet, [0074], [0124], discloses a symmetric inter-spectral matrix representing the spectral power density of each transducer on its main diagonal and the inter-spectra in the off-diagonal terms—for a three-axis accelerometer (Doudet, [0060]), the matrix yields three unique diagonal elements and three unique off-diagonal elements. Doudet, [0130], further discloses the interleaving of three recursions for processing multi-transducer modeling. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the threshold comparison of Girod to compare three unique diagonal or off-diagonal data points of the inter-spectral matrix as taught by Doudet and determine whether all three exceed the threshold, in order to comprehensively analyze the spectral power across all axes/cross-axes of the accelerometer data as discussed in Doudet [0008] and Girod [0005]).
Claim 20
Girod in view of Doudet teaches the computer-readable storage medium of claim 16, comprising further instructions stored thereon that cause the one or more processors to:
summarize the plurality of telematics data at a specified sample rate (Girod, [0050], each sample is tagged with a timestamp at an implied rate; [0071], sampling rate, downsample to f₂ Hz);
split the plurality of telematics data into one or more temporal segments (Girod, [0024]–[0026], time series sequence; [0037], segmentation stage producing non-overlapping segments); and
shift down one or more temporal segments by at least one data point (Girod, [0071], downsample; [0050], resampled at a defined rate—shifting the temporal analysis window).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure includes: US 12008653; US 10902521; US 10768013; US 9228836; US 20130182002; US 20120296175; US 20110307079; US 20110119041; US 20080219336; US 7136794; US 20130046559; US 20100030582; and US 6847917.
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 MICHAEL W ANDERSON whose telephone number is (571)270-0508. The examiner can normally be reached Monday - Thursday 9am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tariq Hafiz can be reached at (571) 272-5350. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Mike Anderson
Supervisor Patent Examiner
Art Unit 3693
/Mike Anderson/Supervisory Patent Examiner, Art Unit 3693
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