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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submissions filed on 11/10th/2022 (amendment) and 01/12th/2023 (RCE) have been entered.
Examiner's Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Response to Arguments
The arguments on pages 9-19 dated 01/28th/2026 with respect to the 35 U.S.C. 101 rejections set forth have been fully considered but are not persuasive.
Applicant Argument #1
Step 2A - Prong One. (Pages 11-13) Which list arguments that the claim does not recite mental processes that could be performed in the human mind or with the aid of pen and paper.
Examiner Response #1
The examiner respectfully disagrees. The applicant is kindly asked to review the detailed 101 rejection below. In the detailed rejection below, the element of transmitting data is analyzed as an insignificant extra solution activity of outputting data, it is not analyzed as a mental process. Furthermore, the use of an electronic device or a machine learning model to perform a process is mere instructions to implement the exception using generic computer components which is also not a mental process.
Applicant Argument #2
Step 2A - Prong Two, and Step 2B. (Pages 13-19) Which list arguments that the claim recites an improvement to a technology.
Examiner Response #2
The examiner further notes that while the specification [0027] recites, as improvement, “the performance and/or a state of health of an electronic device ... as compared to conventional electronic devices”, and while [0029] states, as another improvement, “the device abuse detection system 102 can provide an improvement to one or more technologies such as electronic device technologies, device drop detection technologies, device abuse technologies, digital technologies and/or other technologies. In an implementation, the device abuse detection system 102 can improve performance of an electronic device. For example, the device abuse detection system 102 can improve performance of an electronic device and/or a state of health of an electronic device, as compared to conventional electronic devices”, there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of comparing data and generating predictions, rather than to an improvement on the functioning of a computer or to any other technology. See MPEP 2106.05(a). Thus, even when considering the elements in combination, the claim as a whole does not integrate the recited exception into a practical application nor does it amount to significantly more than the exception itself.
The arguments on pages 20-24 dated 01/28th/2026 with respect to the 35 U.S.C. 103 rejections set forth have been fully considered but they are moot in light of the new grounds of rejection necessitated by the amendment.
Claim Rejections - 35 USC § 101
101 Rejection
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 1-9, and 18-20 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter
Step 1 analysis for all claims:
Claims 1-9 are directed to a system which is a machine. Claims 18-20 are directed to a method which is a process. Therefore, claims 1-9, and 18-20 are directed to one of the statutory categories of invention (process, machine, manufacture or composition of matter).
Regarding Claim 1:
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1 Analysis:
Claim 1 recites in part process steps which, under the broadest reasonable interpretation, are a series of mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process or a mathematical concept but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. The claim recites in part:
compare accelerometer data of an electronic device with a plurality of defined accelerometer threshold values to identify a primary abuse event category associated with the electronic device under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as comparing the change in height of a device to certain values and determining based on that change if a given device has fallen from the user or not). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
in response to identification of the primary abuse event category, generate, by a first machine learning model executed locally on the electronic device, a first prediction for a secondary abuse event category associated with the electronic device based on inertial data of the electronic device, image data generated by the electronic device, and audio data captured by the electronic device, wherein the secondary abuse event category corresponds to a subclass of the primary abuse event category under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as predicting if a device has been thrown or has been dropped by the user based on the rate of change in height of the device). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
compare the first prediction for the secondary abuse event category and the second prediction for the secondary abuse event category to verify the first prediction for the secondary abuse event category under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as comparing a first prediction and second prediction to determine if the related events are similar). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2 Analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of:
abuse event category This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
accelerometer data This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
by a first machine learning model executed locally on the electronic device is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)
transmit the inertial data, the image data and the audio data to a network server device associated with a machine learning service which amounts to mere data outputting and transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g).
to facilitate generation of a second prediction for the secondary abuse event category by a second machine learning model executed at the network server device, wherein the second machine learning model is different from the first machine learning model and generates the second prediction based on the inertial data, the image data, and the audio data is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
in response to a determination that the second prediction for the secondary abuse event category corresponds to the first prediction for the secondary abuse event category, alter one or more functionalities of the electronic device by initiating an abuse-response action associated with the secondary abuse event category, including generating or storing abuse event information associated with the electronic device which amounts to the insignificant extra-solution activity of data transmitting. The claim further recites the additional element of storing abuse event information associated with the electronic device which amounts to the insignificant extra-solution activity of data storage. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
After considering all claim elements, both individually and in combination, it has been determined that the claim does not integrate the abstract idea into a practical application. Therefore, claim 1 is directed to a judicial exception.
Step 2B Analysis:
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
abuse event category This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
accelerometer data This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
by a first machine learning model executed locally on the electronic device is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)
transmit the inertial data, the image data and the audio data to a network server device associated with a machine learning service which amounts to mere data outputting and transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g). Lastly, The courts have found limitations directed to outputting/transmitting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”). Therefore, claim 1 is not patent eligible.
to facilitate generation of a second prediction for the secondary abuse event category by a second machine learning model executed at the network server device, wherein the second machine learning model is different from the first machine learning model and generates the second prediction based on the inertial data, the image data, and the audio data is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
in response to a determination that the second prediction for the secondary abuse event category corresponds to the first prediction for the secondary abuse event category, alter one or more functionalities of the electronic device by initiating an abuse-response action associated with the secondary abuse event category, including generating or storing abuse event information associated with the electronic device which amounts to mere data transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g). Lastly, The courts have found limitations directed to outputting/transmitting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”). The claim further recites the additional element of storing abuse event information associated with the electronic device which amounts to the insignificant extra-solution activity of data storage. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The courts have found that limitations regarding data storage and data display, when recited at a high level of generality, denote mere data storage and data output indicative of an insignificant extra-solution activity (see MPEP 2106.05(g)). Wherein “receiving or transmitting data over a network” or “storing and retrieving information in memory” are known to be well-understood, routine, and conventional activities when recited at a high level of generality (see MPEP 2106.05(d)(II)).
Therefore, claim 1 is not patent eligible.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101.
The additional limitations of the dependent claims contain no additional elements that provide a practical application or amount to significantly more than the abstract idea and are addressed briefly below
Dependent claim 2 recites:
Step 2A Prong 1 Analysis: The claim is directed to the same abstract idea identified above.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
receive the accelerometer data from an accelerometer sensor of the electronic device which amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receive the accelerometer data from an accelerometer sensor of the electronic device As discussed above, the additional elements of data gathering which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Dependent claim 3 recites:
Step 2A Prong 1 Analysis:
identify the primary abuse event category associated with the electronic device based on a first comparison between a first defined accelerometer threshold value and first accelerometer data associated with an x coordinate of an accelerometer sensor of the electronic device a second comparison between a second defined accelerometer threshold value and second accelerometer data associated with a y-coordinate of the accelerometer sensor, and a third comparison between a third defined accelerometer threshold value and third accelerometer data associated with a z-coordinate of the accelerometer sensor under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as comparing the acceleration of a device along three axis to predefined values and determining based on that acceleration if a given device has fallen from the user or not). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include any other limitations to analyze.
Step 2B: The claim does not include any other limitations to analyze.
Dependent claim 4 recites:
Step 2A Prong 1 Analysis:
identify the primary abuse event category as a potential hit event associated with the electronic device in response to a determination that the accelerometer data satisfies a defined sensor value under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as identifying if a device has been hit by something based on a quick and abrupt change in speed of the device along three axis). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include any other limitations to analyze.
Step 2B: The claim does not include any other limitations to analyze.
Dependent claim 5 recites:
Step 2A Prong 1 Analysis:
identify the primary abuse event category as a potential throw event associated with the electronic device in response to a determination that the accelerometer data is above a defined sensor value for a certain interval of time under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as identifying if a device has been thrown based on a quick movement of the device along three axis). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include any other limitations to analyze.
Step 2B: The claim does not include any other limitations to analyze.
Dependent claim 6 recites:
Step 2A Prong 1 Analysis:
identify a particular type of abuse event associated with the electronic device based on the first machine learning model under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as identifying a type of movement that happened to a device based on the acceleration rate of the device, audio clip and image or video recording by the device). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include any other limitations to analyze.
Step 2B: The claim does not include any other limitations to analyze.
Dependent claim 7 recites:
Step 2A Prong 1 Analysis:
identify a particular type of throw event associated with the electronic device based on the first machine learning model under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as identifying a device has been thrown based on the acceleration rate of the device, audio clip and image or video recording by the device). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include any other limitations to analyze.
Step 2B: The claim does not include any other limitations to analyze.
Dependent claim 8 recites:
Step 2A Prong 1 Analysis:
generate the first prediction for the secondary abuse event category under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as identifying a device has been thrown or dropped based on the acceleration rate of the device, audio clip and image or video recording by the device). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
based on a machine learning model received from the network server device associated with the machine learning service amounts to mere instructions for applying the judicial exception on a generic computing device (see MPEP 2106.05(f)).
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
based on a machine learning model received from the network server device associated with the machine learning service amounts to mere instructions for applying the judicial exception on a generic computing device (see MPEP 2106.05(f)).
Dependent claim 9 recites:
Step 2A Prong 1 Analysis: The claim is directed to the same abstract idea identified above.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
receive, from the network server device, a notification that is generated based on the first prediction for the secondary abuse event category and the second prediction for the secondary abuse event category which amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receive, from the network server device, a notification that is generated based on the first prediction for the secondary abuse event category and the second prediction for the secondary abuse event category As discussed above, the additional elements of data gathering which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding Claim 18:
Claim 18 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites similar steps to claim 1 (see above for analysis), with the additional element of a computer-implemented method.
Step 2A Prong 2, Step 2B:
The additional element of a computer-implemented method is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Implementing an abstract idea on generic computer components does not integrate the abstract idea into a practical application, nor does it add significantly more to the exception. Thus, the claim is not patent eligible.
Dependent claim 19 recites:
Claim 19 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a computer-implemented method with similar steps to claim 8, and thus is not patent eligible for the same reasons (see above).
Dependent claim 20 recites:
Claim 20 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a computer-implemented method with similar steps to claim 9, and thus is not patent eligible for the same reasons (see above).
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.
Claims 1-5, 8-9, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour (US20190164020A1), in view of Klusmann (US20170188946A1), further in view of Bulling (A tutorial on human activity recognition using body-worn inertial sensors), further in view of Hu (US20150164430A1), further in view of SCHOEDL (US20210366618A1), and further in view of Brown (US5267345).
Regarding claim 1 Sabripour teaches compare accelerometer data of an electronic device with a plurality of defined accelerometer threshold values to identify a primary abuse event category associated with the electronic device ([0030] In some circumstances, a sudden movement may not be indicative of an incident (for example, the portable electronic device 100 is intentionally removed or accidently dropped by the user). The electronic processor 102 may be configured to differentiate between an incident and an accidental triggering or detection of an incident. The electronic processor 102 may do this, for example, by analyzing and processing one or more signals from one or more of the plurality of sensors 114 and the cameras 110, 112. For example, the electronic processor 102 may analyze a signal from the inertial gravitational sensor 116 to determine a particular pattern in acceleration and/or jerk and determines an incident when the signal exceeds a predetermined threshold).
Based on inertial data of the electronic device, image data generated by the electronic device, and audio data captured by the electronic device ([0019] In the example illustrated, the portable electronic device 100 includes an electronic processor 102, a memory 104, an input and output (I/O) interface 106, a transceiver 108, a first camera 110, a second camera 112, and a plurality of sensors 114. In some embodiments, the plurality of sensors 114 includes an inertial gravitational sensor 116 and/or a microphone 118).
On the other hand, Sabripour is not relied upon to explicitly teach in response to identification of the primary abuse event category, generate, by a first machine learning model executed locally on the electronic device, a first prediction for a secondary abuse event category associated with the electronic device based on a machine learning technique. Sabripour is also not relied upon to explicitly teach wherein the secondary abuse event category corresponds to a subclass of the primary abuse event category. Sabripour is also not relied upon to explicitly teach transmit the inertial data, the image data and the audio data to a network server device associated with a machine learning service to facilitate generation of a second prediction for the secondary abuse event category by a second machine learning model executed at the network server device, wherein the second machine learning model is different from the first machine learning model and generates the second prediction based on the inertial data, the image data, and the audio data. Sabripour is also not relied upon to explicitly teach by using a different machine learning process, Sabripour is also not relied upon to explicitly teach compare the first prediction for the secondary abuse event category and the second prediction for the secondary abuse event category to verify the first prediction for the secondary abuse event category, Sabripour is also not relied upon to explicitly teach in response to a determination that the second prediction for the secondary abuse event category corresponds to the first prediction for the secondary abuse event category, alter one or more functionalities of the electronic device.
However, Klusmann teaches in response to identification of the primary abuse event category, generate, by a first machine learning model executed locally on the electronic device, a first prediction for a secondary abuse event category associated with the electronic device ([0053] iii) Executing a classification algorithm, which allows to discriminate a first impact sensor data set, said first impact data set being correlated to a first impact event which is detrimental for the device, from a second impact sensor data set, said second impact data set being correlated to a second impact event which is not detrimental for the device. Said first impact event is a member of the first class of impacts. Said second impact event is a member of the second class of impacts. The examiner notes that Sabripour and Klusmann are both directed towards portable electronic device monitoring and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sabripour’s event detection to incorporate in response to identification of the primary abuse event category, generate, by a first machine learning model executed locally on the electronic device, a first prediction for a secondary abuse event category associated with the electronic device as taught by Klusmann [0053] to classify impact events [0045]).
Furthermore, Brown teaches wherein the secondary abuse event category corresponds to a subclass of the primary abuse event category ([Col. 5, Line 52-56] The apparatus may further comprise means for classifying an event from the first class of events in a second sub-class if the predictor feature value of the event is not a member of the set of predictor feature values having the best secondary prediction score. The examiner notes that Sabripour and Brown are both directed towards portable data modeling and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sabripour’s event detection to incorporate wherein the secondary abuse event category corresponds to a subclass of the primary abuse event category as taught by Brown [Col. 5, Line 52-56] to estimate how well the set of predictor feature values predicts the occurrence of one set of category feature values for all events in the first class of observed events [Col. 5, Line 25-28]).
Furthermore, Bulling teaches transmit the inertial data, the image data and the audio data to a network server device associated with a machine learning service to facilitate generation of a second prediction for the secondary abuse event category by a second machine learning model executed at the network server device, wherein the second machine learning model is different from the first machine learning model and generates the second prediction based on the inertial data, the image data, and the audio data ([Page 33:8, and Page 33:13]
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Table III
Fig. 1. Typical Activity Recognition Chain (ARC) to recognize activities from wearable sensors. An ARC comprises stages for data acquisition, signal preprocessing and segmentation, feature extraction and selection, training, and classification. Raw signals (D) are first processed (D’) and split into m segments (Wi) from which feature vectors (Xi) are extracted. Given features (Xi), a model with parameters θ scores c activity
classes Yi = {y1, . . . , yc} with a confidence vector pi. The examiner notes that Bulling teaches sending sensor data to a networked device to perform a classification of an abuse event. The examiner further notes that Bulling [Table III] teaches that the sensor data includes inertial data, audio data, and image data. The examiner also notes that Sabripour and Bulling are both directed towards portable electronic device monitoring and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sabripour’s event detection to incorporate transmit the inertial data, the image data and the audio data to a network server device associated with a machine learning service to facilitate generation of a second prediction for the secondary abuse event category by a second machine learning model executed at the network server device, wherein the second machine learning model is different from the first machine learning model and generates the second prediction based on the inertial data, the image data, and the audio data as taught by Bulling [Page 33:8] to recognize activities from wearable sensors [Fig. 1]).
Furthermore, Hu teaches compare the first prediction for the secondary abuse event category and the second prediction for the secondary abuse event category to verify the first prediction for the secondary abuse event category ([0071] In this example, Block S350 can then confirm the (first) action prediction based on a comparison of the (first) action prediction with the second action prediction. The examiner notes that Sabripour and Hu are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sabripour’s machine learning process to incorporate compare the first prediction for the secondary abuse event category and the second prediction for the secondary abuse event category to verify the first prediction for the secondary abuse event category as taught by Hu [0071] in order to output primary and secondary feature sets [0071]).
Furthermore, Schoedl teaches in response to a determination that the second prediction for the secondary abuse event category corresponds to the first prediction for the secondary abuse event category, alter one or more functionalities of the electronic device by initiating an abuse-response action associated with the secondary abuse event category, including generating or storing abuse event information associated with the electronic device ([0054] According to some embodiments, the backend program compares the first prediction result and the second prediction result computed for each prediction task. The sending of the second prediction results or the sending of the notification of their computation is performed selectively for those prediction tasks for which a first prediction result and a second prediction result were computed. The examiner notes that Schoedl teaches comparing two prediction results and based on the result of such comparison, sending a notification of the result to a user devices, and altering the functionality of the devices by displaying the results on a screen. The examiner also notes that Sabripour and Schoedl are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sabripour’s machine learning process to incorporate in response to a determination that the second prediction for the secondary abuse event category corresponds to the first prediction for the secondary abuse event category, alter one or more functionalities of the electronic device as taught by Schoedl [0054] in order to ensure that a plurality of users are always provided with the latest available prediction results for an arbitrary number of different prediction tasks [0053]).
Regarding claim 2 Sabripour teaches receive the accelerometer data from an accelerometer sensor of the electronic device ([0027] The plurality of sensors 114 include one or more additional sensors included within the portable electronic device 100. As mentioned previously, in some embodiments the plurality of sensors 114 includes the inertial gravitational sensor 116. The inertial gravitational sensor 116 is a sensor configured to detect/measure a movement of the portable electronic device 100. The inertial gravitational sensor 116 may be a one or a combination of an accelerometer, gyro scope, magnetometer, and the like. As explained in more detail below, the inertial gravitational sensor 116 detects an incident based on particular measurements of movement with respect to the portable electronic device 100. In some embodiments, the plurality of sensors 114 also includes an audio sensor or microphone 118).
Regarding claim 3 Sabripour teaches The system of claim 1, wherein the executable instructions further cause the processor to. However, Sabripour is not relied upon to explicitly teach identify the primary abuse event category associated with the electronic device based on a first comparison between a first defined accelerometer threshold value and first accelerometer data associated with an x-coordinate of an accelerometer sensor of the electronic device a second comparison between a second defined accelerometer threshold value and second accelerometer data associated with a y-coordinate of the accelerometer sensor, and a third comparison between a third defined accelerometer threshold value and third accelerometer data associated with a z-coordinate of the accelerometer sensor. On the other hand, Klusmann teaches identify the primary abuse event category associated with the electronic device based on a first comparison between a first defined accelerometer threshold value and first accelerometer data associated with an x-coordinate of an accelerometer sensor of the electronic device a second comparison between a second defined accelerometer threshold value and second accelerometer data associated with a y-coordinate of the accelerometer sensor, and a third comparison between a third defined accelerometer threshold value and third accelerometer data associated with a z-coordinate of the accelerometer sensor ([0050] at least one multi-channel impact sensor unit, wherein the impact sensor unit is adapted to detect linear acceleration ( ax; ay; az) along the three space axes (x; y; z) and angular rates ( wx; wy; wz) around the three space axes (x; y; z), wherein the impact sensor unit generates impact sensor data sets, and wherein the data sets generated by the sensor are electronically transferred to the controller via an interface. The examiner notes that Sabripour and Klusmann are both directed towards portable electronic device monitoring and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sabripour’s event detection to incorporate identify the primary abuse event category associated with the electronic device based on a first comparison between a first defined accelerometer threshold value and first accelerometer data associated with an x-coordinate of an accelerometer sensor of the electronic device a second comparison between a second defined accelerometer threshold value and second accelerometer data associated with a y-coordinate of the accelerometer sensor, and a third comparison between a third defined accelerometer threshold value and third accelerometer data associated with a z-coordinate of the accelerometer sensor as taught by Klusmann [0050] to classify impact events [0045] ).
Regarding claim 4 Sabripour teaches The system of claim 1, wherein the executable instructions further cause the processor to. However, Sabripour is not relied upon to explicitly teach identify the primary abuse event category as a potential hit event associated with the electronic device in response to a determination that the accelerometer data satisfies a defined sensor value. On the other hand, Klusmann teaches identify the primary abuse event category as a potential hit event associated with the electronic device in response to a determination that the accelerometer data satisfies a defined sensor value ([0086] A device according to this preferred embodiment is adapted to record an impact, if the impact and/or rotation values measured by at least one of the six sensors channels of the multi-channel impact sensor unit are above a predetermined threshold level. The examiner notes that Sabripour and Klusmann are both directed towards portable electronic device monitoring and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sabripour’s event detection to incorporate identify the primary abuse event category as a potential hit event associated with the electronic device in response to a determination that the accelerometer data satisfies a defined sensor value as taught by Klusmann [0086] to further save memory space [0086] ).
Regarding claim 5 Sabripour teaches The system of claim 1, wherein the executable instructions further cause the processor to. However, Sabripour is not relied upon to explicitly teach identify the primary abuse event category as a potential throw event associated with the electronic device in response to a determination that the accelerometer data is above a defined sensor value for a certain interval of time. On the other hand, Klusmann teaches identify the primary abuse event category as a potential throw event associated with the electronic device in response to a determination that the accelerometer data is above a defined sensor value for a certain interval of time ([0227] For the estimation of the height of the fall the air resistance is disregarded. In the case shown in FIG. 8a the time span of the free fall (taken from the diagram) is about 0.33 s leading to an estimated drop height of 1.07 m [9.81 m/s2 x(0.33 s)2=1.07 m], which is a good approximation to the actual value of 1.00 m. The examiner notes that Klusmann teaches accounting for the time duration of the change in the inertial data to determine the type of event based on the distance the device traveled. The examiner also notes that Sabripour and Klusmann are both directed towards portable electronic device monitoring and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sabripour’s event detection to incorporate identify the primary abuse event category as a potential throw event associated with the electronic device in response to a determination that the accelerometer data is above a defined sensor value for a certain interval of time as taught by Klusmann [0227] to estimate the height of the fall [0227] ).
Regarding claim 8 Sabripour teaches generate the first prediction for the secondary abuse event category based on a machine learning model received from the network server device associated with the machine learning service ([0017] Another example embodiment provides an edge learning method for a portable electronic device. The method includes detecting, based on information obtained from one or more sensors, an incident and selecting, between a first camera and a second camera, a camera responsive to the incident. The method also includes capturing an image using the selected camera and determining a subject of interest within the image, wherein the subject of interest is at least one selected from the group consisting of a person, an object, and an entity. The method further includes initiating an edge learning process on the subject of interest to create a classifier for use in identifying the subject of interest and transmitting the classifier to a second portable electronic device within a predetermined distance from the portable electronic device).
Regarding claim 9 Sabripour teaches receive, from the network server device, a notification that is generated based on the first prediction for the secondary abuse event category and the second prediction for the secondary abuse event category ([0041] In some embodiments, the remote server 608 use the classifier received by the portable electronic device 100 to identify a particular known subject. Such information is then transmitted to the portable electronic device 100 as well as the portable electronic device(s) within the predetermined area (for example, area 606 of FIG. 4) of the portable electronic device 100. It should be understood that although only one remote server (remote server 608) is shown and described in the example embodiment, multiple remote servers could be implemented in alternative embodiments).
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour (US20190164020A1), in view of Klusmann (US20170188946A1), further in view of Bulling (A tutorial on human activity recognition using body-worn inertial sensors), further in view of Hu (US20150164430A1), further in view of SCHOEDL (US20210366618A1), and further in view of Brown (US5267345), and further in view of Sicconi (US20190213429A1).
Regarding claim 6 Sabripour teaches The system of claim 1, wherein the executable instructions further cause the processor to. However, Sabripour is not relied upon to explicitly teach identify a particular type of abuse event associated with the electronic device based on the first machine learning model. On the other hand, Sicconi teaches identify a particular type of abuse event associated with the electronic device based on the first machine learning model ([0062] FIG. 10 shows one possible architecture for the invention. It includes: Visible and NIR Camera pointed to driver face/eyes to analyze head pose; eye gaze tracking and record driver's face and back passenger seat in case of accident; Speech and Gesture Interface for driver to provide or request information via microphone, face or hand gestures; Biometric and Vital Signs (HRV, GSR) data provided via wearable bracelet, sensors on steering wheel or driver seat, wireless evaluation of Heart Beat and Breathing patterns; Forward-facing Camera to detect lane lines, distance from vehicles in front, scene analysis and recording; Rear Camera to view, analyze, record (in case of accident) back of car; 3D Accelerometer, Gyroscope, Compass, GPS (time, location, speed), plus VIN, Odometer, RPM, Engine Load via OBD II connection; Traffic, weather, day/night illumination, road conditions, in-cabin noise or voices; Feature extraction from visual clues (attention, distraction, drowsiness, drunkenness, face identification, problematic interactions between driver and passenger(s); Feature extraction of spoken words (Speech Recognition, Natural Language Processing (NLP)), detection of altered voice, detection of hand gestures; Feature extraction of fatigue, stress, reaction to fear/surprise, from biosensors; Feature extraction of objects (vehicles, walls, poles, signs, pedestrians, ... ) as well as relative distance and movements, position of car with respect to lane markings, detection of road signs; Feature extraction of vehicle position and speed behind car; Feature extraction of driving smoothness/aggressiveness; Feature extraction of ambient "harshness" and impact on driving stress; Machine Learning engine to continuously evaluate driver attention level; Machine Learning engine to continuously evaluate driving risk; Module to detect car crash conditions. The examiner notes that Sicconi teaches using machine learning to determine based on sensor data such as inertia, sound, and images when a destructive event takes place. The examiner also notes that Sabripour and Sicconi are both directed towards event monitoring and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sabripour’s event detection to incorporate identify a particular type of abuse event associated with the electronic device based on the first machine learning model as taught by Sicconi [0062] to by provide richer and more accurate services [0061] ).
Regarding claim 7 Sabripour teaches The system of claim 1, wherein the executable instructions further cause the processor to. However, Sabripour is not relied upon to explicitly teach identify a particular type of throw event associated with the electronic device based on the first machine learning model. On the other hand, Sicconi teaches identify a particular type of throw event associated with the electronic device based on the first machine learning model ([0062] FIG. 10 shows one possible architecture for the invention. It includes: Visible and NIR Camera pointed to driver face/eyes to analyze head pose; eye gaze tracking and record driver's face and back passenger seat in case of accident; Speech and Gesture Interface for driver to provide or request information via microphone, face or hand gestures; Biometric and Vital Signs (HRV, GSR) data provided via wearable bracelet, sensors on steering wheel or driver seat, wireless evaluation of Heart Beat and Breathing patterns; Forward-facing Camera to detect lane lines, distance from vehicles in front, scene analysis and recording; Rear Camera to view, analyze, record (in case of accident) back of car; 3D Accelerometer, Gyroscope, Compass, GPS (time, location, speed), plus VIN, Odometer, RPM, Engine Load via OBD II connection; Traffic, weather, day/night illumination, road conditions, in-cabin noise or voices; Feature extraction from visual clues (attention, distraction, drowsiness, drunkenness, face identification, problematic interactions between driver and passenger(s); Feature extraction of spoken words (Speech Recognition, Natural Language Processing (NLP)), detection of altered voice, detection of hand gestures; Feature extraction of fatigue, stress, reaction to fear/surprise, from biosensors; Feature extraction of objects (vehicles, walls, poles, signs, pedestrians, ... ) as well as relative distance and movements, position of car with respect to lane markings, detection of road signs; Feature extraction of vehicle position and speed behind car; Feature extraction of driving smoothness/aggressiveness; Feature extraction of ambient "harshness" and impact on driving stress; Machine Learning engine to continuously evaluate driver attention level; Machine Learning engine to continuously evaluate driving risk; Module to detect car crash conditions. The examiner notes that Sicconi teaches using machine learning to determine based on sensor data such as inertia, sound, and images when a destructive event takes place. The examiner also notes that Sabripour and Sicconi are both directed towards event monitoring and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sabripour’s event detection to incorporate identify a particular type of throw event associated with the electronic device based on the first machine learning model as taught by Sicconi [0062] to by provide richer and more accurate services [0061] ).
Claim 18 is rejected based upon the same rationale as the rejection of claim 1 since it’s the method claim corresponding to the system claim.
Claim 19 is rejected based upon the same rationale as the rejection of claim 8 since it’s the method claim corresponding to the system claim.
Claim 20 is rejected based upon the same rationale as the rejection of claim 9 since it’s the method claim corresponding to the system claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li (US20160019460A1)
“Li teaches a method for enabling event prediction as an on-device service for mobile interaction”
Ross (US 10,496,091 Bl)
“Ross teaches a method to predict the intent of an object including an action of a predetermined list of actions to be initiated by a road user and a point in time for initiation of the action”
Farooq (US20210182739A1)
“Farooq teaches an iterative training process to train an Ensemble Learning Model based on a plurality of observations associated with electronic devices so that the Ensemble Learning Model predicts conditions of the electronic devices”
Hosonuma (US20010021882A1)
“Hosonuma teaches a control method for a robot apparatus which is configured to select a transmission destination and transmit predetermined information to the selected transmission destination, thereby being capable of enhancing a probability of notification by selection of the transmission destination“
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAMCY ALGHAZZY whose telephone number is (571)272-8824. The examiner can normally be reached on M-F 7:30am-5:00pm EST.
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/SHAMCY ALGHAZZY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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