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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 submission filed on 01/30/2026 has been entered.
Claims 1-20 are pending.
Claims 1 and 17 are amended.
Response to Arguments
Applicant's arguments filed 01/30/2026 have been fully considered but they are not persuasive.
In response to Applicant’s arguments that Mason in view of Garcia and Sur does not explicitly disclose the case wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model. First of all, Examiner is using the broadest reasonable interpretation regarding the rejection of the claims. Mason in view of Garcia and Sur clearly teaches the same concept that is disclosed in the claims by the Applicant, wherein Mason discloses an aerosol generation device 100 comprises a body 102 technically equivalent to the case of the aerosol device, housing various components of the aerosol generation device 100, wherein the body 102 can be any shape so long as it is sized to fit the components described in the aerosol generation device 100, and the body 102 can be formed of any suitable material, or indeed layers of material (par[0112]). Examiner clarifies that the body and case of a device are often used interchangeably to describe the outer shell, casing, or housing that protects internal components. While "body" can sometimes imply the structural main part and "case" a removable cover, they both refer to the outer metal or plastic shell of the device. Further, Mason discloses the aerosol generation device 100 comprises a closure 106 technically equivalent to a lid arranged so as to be moveable between at least a closed position, in which the closure obstructs the aperture 104 so that materials cannot enter the heating chamber 108, and an open position, in which the aperture 104 is uncovered to allow access to the heating chamber 108 (par[0119]). Further, Mason discloses the movement of the closure 106 is determined using an accelerometer, whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position (par[0224]).
Furthermore, Sur is introducing the feature of using a machine learning model with the motion detected from the accelerometer, wherein Sur discloses the second sensor(s) 916b include an accelerometer, the accelerometer may be configured to produce measurements of acceleration of the aerosol delivery device 900, and the target variable may be a logical activity of a user of the aerosol delivery device. In some of these examples, the machine learning model may be or include an activity detection model to predict the logical activity of the user. And the processing circuitry 906 may be configured to build the activity detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration (par[0168]). Furthermore, Sur discloses the second sensor(s) 916b include an accelerometer, the target variable may be a logical carry position of the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a carry position detection model to predict the logical carry position of the aerosol delivery device. And the processing circuitry 906 may be configured to build the carry position detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration (par[0169]). Further, Sur discloses the second sensor(s) 916b include an accelerometer, the target variable may be a gesture performed using the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a gesture recognition model to predict the gesture. And the processing circuitry 906 may be configured to build the gesture recognition model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration (par[0170]) technically equivalent to the case wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model.
Therefore, Examiner maintains his rejection.
In response to Applicant’s arguments regarding the objection to the drawing in page 1. Examiner respectfully disagrees. The drawings are objected to under 37 CFR 1.83(a) because figures 7-9 fail to have proper labels for all the rectangular boxes such as assigning word titles to the rectangular boxes as required by 37 CFR 1.83(a), and as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d).
Drawings
The drawings are objected to under 37 CFR 1.83(a) because figures 7-9 fail to have proper labels for all the rectangular boxes as required by 37 CFR 1.83(a), and as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
1. Claims 12-17 are objected to because of the following informalities:
Claim 12 recites the limitations “a closed position to an open position” in line 9. In order to expedite the prosecution, Examiner suggests to add “the” in order to be consistent with the steps of claim 12, and change it to “the closed position to the open position”.
Claims 13-16 are objected as stated above because due to their dependency from claim 12.
Claim 17 recites the limitations “a closed position to an open position” in line 10. In order to expedite the prosecution, Examiner suggests to the Applicant to add “the” in order to be consistent with the steps of claim 17, and change it to “the closed position to the open position”.
Appropriate correction is required.
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.
1. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1:
Claim 1 is rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without significantly more for the following reason(s):
Step 1: Claim 1 recites series of steps for receiving collected data, determining based on the collected data and activating a battery status indicator based on the collected data. Thus, the claim is directed to a method, which is one of the statutory categories of the invention.
Step 2A — Prong One (Judicial Exception)
1. Claim 1 is directed to the abstract idea of analyzing observed data to determine a correspondence between events (movement of a lid from open to close positions) and then inferring a relationship (that a battery status indicator is activated). The claim recites performing movement detection and use of a sensor of a machine learning model, which constitutes a mathematical concept and mental process—i.e., data analysis, pattern recognition, and making determinations/inferences based on a model. See Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344 (Fed. Cir. 2014) (mathematical/algorithms); SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018) (statistical/analytical concepts); MPEP § 2106.04.
2. The claim limitations reflecting the judicial exception include, inter alia: “receiving movement data,” “determining whether the movement data are indicative of a lid position changing from a closed position to an open position using a machine learning model to process data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position,” “activating a battery status indicator of the case if the movement data are determined to be indicative of the lid position changing from a closed position to an open position” These steps recite evaluation and correlation of data—activities that can be characterized as mental processes or mathematical analysis of data.
Step 2A — Prong Two (Integration into a Practical Application)
3. The claim does not recite a specific improvement to the functioning of a computer, the controller, the aerosol delivery device hardware, the sensor, or any other technology. The claim language merely recites detecting data, processing data and performing determinations based on a model, without specifying particular signal processing techniques, sensor arrangements, sampling/processing parameters, hardware configuration, or other limitations that would meaningfully integrate the abstract idea into a technical application. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) (eligible where claims recite specific improvement to computer functionality).
4. The only physical elements recited (accelerometer, controller, battery, lid) are recited at a high level of generality and serve as the field of use for the claimed abstract idea. The “detecting” of the movement is data gathering; the remainder of the claim results in an informational determination (that the battery is activated based on the gathering of data). There is no recited transformation of an article or specific machine integral to the claimed steps beyond generic computer/monitoring implementation. See Diamond v. Diehr, 450 U.S. 175 (1981) (claims eligible when directed to a process that effects a physical transformation); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) (claims that merely collect, analyze, and display information held abstract).
Step 2B — “Significantly More” Analysis
5. The additional elements of the claim—“receiving movement data,” “determining whether the movement data are indicative of a lid position changing from a closed position to an open position using a machine learning model to process data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position,” “activating a battery status indicator of the case if the movement data are determined to be indicative of the lid position changing from a closed position to an open position” —are well-understood, routine, and conventional activities for monitoring systems and signal-analysis implementations absent specific non-conventional detail. As recited, these elements amount to using generic data acquisition and data analysis techniques implemented by conventional processors and do not supply an inventive concept. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); Electric Power Group, 830 F.3d at 1353–56.
6. The ordered combination of steps—obtain movement data → process data using a model→ activate the battery indicator—reflects a conventional information-processing workflow (collect/analyze/activate) and does not recite an unconventional arrangement that effects a technological improvement. Absent claim limitations or evidence demonstrating that the recited “machine learning model” or processing data is unconventional or yields a concrete technical improvement, the claim does not provide “significantly more.” See BASCOM Global Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016); Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018) (factual showing required to rebut a finding of well understood, routine, conventional).
Conclusion
7. For the reasons stated above, Claim 1 is directed to an abstract idea (mathematical/mental process of event correlation and attribution) and the additional recited elements, individually and as an ordered combination, do not add significantly more. Therefore, Claim 1 is rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Regarding dependent claims 2-11.
Dependent claims 2-11, The Judicial exception is not integrated into a practical application and said claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claims are not patent eligible.
Regarding claim 12:
Claim 12 is rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without significantly more for the following reason(s):
Step 1: Claim 12 recites series of steps for receiving collected data, determining based on the collected data and activating a battery status indicator based on the collected data. Thus, the claim is directed to a method, which is one of the statutory categories of the invention.
Step 2A — Prong One (Judicial Exception)
1. Claim 12 is directed to the abstract idea of analyzing observed data to determine a correspondence between events (movement of a lid from open to close positions) and then inferring a relationship (that a battery status indicator is activated). The claim recites performing movement detection and use of a sensor of a machine learning model, which constitutes a mathematical concept and mental process—i.e., data analysis, pattern recognition, and making determinations/inferences based on a model. See Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344 (Fed. Cir. 2014) (mathematical/algorithms); SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018) (statistical/analytical concepts); MPEP § 2106.04.
2. The claim limitations reflecting the judicial exception include, inter alia: “receiving movement data,” “determining whether the movement data are indicative of a lid position changing from a closed position to an open position using a machine learning model to process data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position,” “activating a battery status indicator of the case if the movement data are determined to be indicative of the lid position changing from a closed position to an open position” These steps recite evaluation and correlation of data—activities that can be characterized as mental processes or mathematical analysis of data.
Step 2A — Prong Two (Integration into a Practical Application)
3. The claim does not recite a specific improvement to the functioning of a computer, the controller, the aerosol delivery device hardware, the sensor, or any other technology. The claim language merely recites detecting data, processing data and performing determinations based on a model, without specifying particular signal processing techniques, sensor arrangements, sampling/processing parameters, hardware configuration, or other limitations that would meaningfully integrate the abstract idea into a technical application. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) (eligible where claims recite specific improvement to computer functionality).
4. The only physical elements recited (accelerometer, controller, battery, lid) are recited at a high level of generality and serve as the field of use for the claimed abstract idea. The “detecting” of the movement is data gathering; the remainder of the claim results in an informational determination (that the battery is activated based on the gathering of data). There is no recited transformation of an article or specific machine integral to the claimed steps beyond generic computer/monitoring implementation. See Diamond v. Diehr, 450 U.S. 175 (1981) (claims eligible when directed to a process that effects a physical transformation); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) (claims that merely collect, analyze, and display information held abstract).
Step 2B — “Significantly More” Analysis
5. The additional elements of the claim—“receiving movement data,” “determining whether the movement data are indicative of a lid position changing from a closed position to an open position using a machine learning model to process data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position,” “activating a battery status indicator of the case if the movement data are determined to be indicative of the lid position changing from a closed position to an open position” —are well-understood, routine, and conventional activities for monitoring systems and signal-analysis implementations absent specific non-conventional detail. As recited, these elements amount to using generic data acquisition and data analysis techniques implemented by conventional processors and do not supply an inventive concept. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); Electric Power Group, 830 F.3d at 1353–56.
6. The ordered combination of steps—obtain movement data → process data using a model→ activate the battery indicator—reflects a conventional information-processing workflow (collect/analyze/activate) and does not recite an unconventional arrangement that effects a technological improvement. Absent claim limitations or evidence demonstrating that the recited “machine learning model” or processing data is unconventional or yields a concrete technical improvement, the claim does not provide “significantly more.” See BASCOM Global Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016); Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018) (factual showing required to rebut a finding of well understood, routine, conventional).
Conclusion
7. For the reasons stated above, Claim 12 is directed to an abstract idea (mathematical/mental process of event correlation and attribution) and the additional recited elements, individually and as an ordered combination, do not add significantly more. Therefore, Claim 1 is rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Regarding dependent claims 13-16.
Dependent claims 13-16, The Judicial exception is not integrated into a practical application and said claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claims are not patent eligible.
Regarding claim 17:
Claim 17 is rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without significantly more for the following reason(s):
Step 1: Claim 17 recites series of steps for receiving collected data, determining based on the collected data and activating a battery status indicator based on the collected data. Thus, the claim is directed to a method, which is one of the statutory categories of the invention.
Step 2A — Prong One (Judicial Exception)
1. Claim 17 is directed to the abstract idea of analyzing observed data to determine a correspondence between events (movement of a lid from open to close positions) and then inferring a relationship (that a battery status indicator is activated). The claim recites performing movement detection and use of a sensor of a machine learning model, which constitutes a mathematical concept and mental process—i.e., data analysis, pattern recognition, and making determinations/inferences based on a model. See Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344 (Fed. Cir. 2014) (mathematical/algorithms); SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018) (statistical/analytical concepts); MPEP § 2106.04.
2. The claim limitations reflecting the judicial exception include, inter alia: “receiving movement data,” “determining whether the movement data are indicative of a lid position changing from a closed position to an open position using a machine learning model to process data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position,” “activating a battery status indicator of the case if the movement data are determined to be indicative of the lid position changing from a closed position to an open position” These steps recite evaluation and correlation of data—activities that can be characterized as mental processes or mathematical analysis of data.
Step 2A — Prong Two (Integration into a Practical Application)
3. The claim does not recite a specific improvement to the functioning of a computer, the controller, the aerosol delivery device hardware, the sensor, or any other technology. The claim language merely recites detecting data, processing data and performing determinations based on a model, without specifying particular signal processing techniques, sensor arrangements, sampling/processing parameters, hardware configuration, or other limitations that would meaningfully integrate the abstract idea into a technical application. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) (eligible where claims recite specific improvement to computer functionality).
4. The only physical elements recited (accelerometer, controller, battery, lid) are recited at a high level of generality and serve as the field of use for the claimed abstract idea. The “detecting” of the movement is data gathering; the remainder of the claim results in an informational determination (that the battery is activated based on the gathering of data). There is no recited transformation of an article or specific machine integral to the claimed steps beyond generic computer/monitoring implementation. See Diamond v. Diehr, 450 U.S. 175 (1981) (claims eligible when directed to a process that effects a physical transformation); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) (claims that merely collect, analyze, and display information held abstract).
Step 2B — “Significantly More” Analysis
5. The additional elements of the claim—“receiving movement data,” “determining whether the movement data are indicative of a lid position changing from a closed position to an open position using a machine learning model to process data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position,” “activating a battery status indicator of the case if the movement data are determined to be indicative of the lid position changing from a closed position to an open position” —are well-understood, routine, and conventional activities for monitoring systems and signal-analysis implementations absent specific non-conventional detail. As recited, these elements amount to using generic data acquisition and data analysis techniques implemented by conventional processors and do not supply an inventive concept. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); Electric Power Group, 830 F.3d at 1353–56.
6. The ordered combination of steps—obtain movement data → process data using a model→ activate the battery indicator—reflects a conventional information-processing workflow (collect/analyze/activate) and does not recite an unconventional arrangement that effects a technological improvement. Absent claim limitations or evidence demonstrating that the recited “machine learning model” or processing data is unconventional or yields a concrete technical improvement, the claim does not provide “significantly more.” See BASCOM Global Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016); Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018) (factual showing required to rebut a finding of well understood, routine, conventional).
Conclusion
7. For the reasons stated above, Claim 17 is directed to an abstract idea (mathematical/mental process of event correlation and attribution) and the additional recited elements, individually and as an ordered combination, do not add significantly more. Therefore, Claim 1 is rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Regarding claim 18:
Claim 18 is rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without significantly more for the following reason(s):
Step 1: Claim 18 recites series of steps for receiving collected data, determining based on the collected data and activating a battery status indicator based on the collected data. Thus, the claim is directed to a method, which is one of the statutory categories of the invention.
Step 2A — Prong One (Judicial Exception)
1. Claim 18 is directed to the abstract idea of analyzing observed data to determine a correspondence between events (movement of a lid from open to close positions) and then inferring a relationship (that a battery status indicator is activated). The claim recites performing movement detection and use of a sensor of a machine learning model, which constitutes a mathematical concept and mental process—i.e., data analysis, pattern recognition, and making determinations/inferences based on a model. See Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344 (Fed. Cir. 2014) (mathematical/algorithms); SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018) (statistical/analytical concepts); MPEP § 2106.04.
2. The claim limitations reflecting the judicial exception include, inter alia: “receiving movement data,” “determining whether the movement data are indicative of a lid position changing from a closed position to an open position using a machine learning model to process data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position,” “activating a battery status indicator of the case if the movement data are determined to be indicative of the lid position changing from a closed position to an open position” These steps recite evaluation and correlation of data—activities that can be characterized as mental processes or mathematical analysis of data.
Step 2A — Prong Two (Integration into a Practical Application)
3. The claim does not recite a specific improvement to the functioning of a computer, the controller, the aerosol delivery device hardware, the sensor, or any other technology. The claim language merely recites detecting data, processing data and performing determinations based on a model, without specifying particular signal processing techniques, sensor arrangements, sampling/processing parameters, hardware configuration, or other limitations that would meaningfully integrate the abstract idea into a technical application. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) (eligible where claims recite specific improvement to computer functionality).
4. The only physical elements recited (accelerometer, controller, battery, lid) are recited at a high level of generality and serve as the field of use for the claimed abstract idea. The “detecting” of the movement is data gathering; the remainder of the claim results in an informational determination (that the battery is activated based on the gathering of data). There is no recited transformation of an article or specific machine integral to the claimed steps beyond generic computer/monitoring implementation. See Diamond v. Diehr, 450 U.S. 175 (1981) (claims eligible when directed to a process that effects a physical transformation); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) (claims that merely collect, analyze, and display information held abstract).
Step 2B — “Significantly More” Analysis
5. The additional elements of the claim—“receiving movement data,” “determining whether the movement data are indicative of a lid position changing from a closed position to an open position using a machine learning model to process data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position,” “activating a battery status indicator of the case if the movement data are determined to be indicative of the lid position changing from a closed position to an open position” —are well-understood, routine, and conventional activities for monitoring systems and signal-analysis implementations absent specific non-conventional detail. As recited, these elements amount to using generic data acquisition and data analysis techniques implemented by conventional processors and do not supply an inventive concept. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); Electric Power Group, 830 F.3d at 1353–56.
6. The ordered combination of steps—obtain movement data → process data using a model→ activate the battery indicator—reflects a conventional information-processing workflow (collect/analyze/activate) and does not recite an unconventional arrangement that effects a technological improvement. Absent claim limitations or evidence demonstrating that the recited “machine learning model” or processing data is unconventional or yields a concrete technical improvement, the claim does not provide “significantly more.” See BASCOM Global Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016); Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018) (factual showing required to rebut a finding of well understood, routine, conventional).
Conclusion
7. For the reasons stated above, Claim 18 is directed to an abstract idea (mathematical/mental process of event correlation and attribution) and the additional recited elements, individually and as an ordered combination, do not add significantly more. Therefore, Claim 1 is rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Regarding dependent claims 19-20.
Dependent claims 19-20, The Judicial exception is not integrated into a practical application and said claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The 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.
1. Claim(s) 1-2, 4-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mason et al. (US2022/0142247A1) hereafter Mason in view of Garcia et al. (US2023/0148677A1) hereafter Garcia, and further in view of Sur (US2020/0337382A1).
Regarding claim 1, Mason discloses a case for an aerosol delivery device, the case (fig 1:102; par[0112]: FIG. 1, according to a first embodiment of the disclosure, an aerosol generation device 100 comprises a body 102 housing various components of the aerosol generation device 100.), the case (fig 1:102; par[0112]: the body 102 is technically equivalent to an outer shell to the aerosol delivery device. FIG. 1, according to a first embodiment of the disclosure, an aerosol generation device 100 comprises a body 102 housing various components of the aerosol generation device 100. The body 102 can be any shape so long as it is sized to fit the components described in the aerosol generation device 100. The body 102 can be formed of any suitable material, or indeed layers of material.) comprising:
a lid having an open position and a closed position (fig 1:106; par[0119]: The aerosol generation device 100 comprises a closure 106 arranged so as to be moveable between at least a closed position, in which the closure obstructs the aperture 104 so that materials cannot enter the heating chamber 108, and an open position, in which the aperture 104 is uncovered to allow access to the heating chamber 108.);
an accelerometer configured to detect movement indicative of a lid position changing from the closed position to the open position (par[0224]: Accelerometer; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position.);
a battery status indicator (par[0227]: Specifically, the controller typically operates a component of the aerosol generation device 100 in dependence on a signal received indicating a position of the closure 106. Typical components that are operated include: a heater; a status indicator; a battery indicator; and a display.); and
a controller configured to activate when a signal from the accelerometer indicates that the lid position has changed from the closed position to the open position (par[0227]: Specifically, the controller typically operates a component of the aerosol generation device 100 in dependence on a signal received indicating a position of the closure 106. Typical components that are operated include: a heater; a status indicator; a battery indicator; and a display.).
Mason does not explicitly disclose the case comprising: a controller configured to activate the battery status indicator when said signal indicates that the lid position has changed from the closed position to the open position.
Garcia discloses the case comprising: a controller configured to activate the battery status indicator when said signal indicates that the lid position has changed from the closed position to the open position (par[0014], [0085]: the aerosol generation device further comprises a closable opening through which the aerosol generating material is received, and wherein the controller is configured to: measure the charge level of the battery when determining the closable opening is moved to a closed position; and/or measure the charge level of the battery when determining the closable opening is moved to an opened position. When determining that the closable opening is moved to the opened position, for example when the controller determines that the movable lid has moved to the open position, the controller can initiate a standby mode during which the charge level of the battery is measured. The controller can switch the heater on, and exit the standby mode, in response to determining that the charge level of the battery is sufficient for at least one full aerosolisation session).
One of ordinary skill in the art would be aware of both the Mason and the Garcia references since both pertain to the field of aerosol systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the case of Mason to implement the activation feature as disclosed by Garcia to gain the functionality of providing a battery monitoring for aerosol generation devices and determining when only enough power remains in the battery to fully aerosolize N aerosol generating material consumables.
Mason in view of Garcia does not explicitly disclose the case wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model.
Sur discloses the case wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model (par[0169]: In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a logical carry position of the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a carry position detection model to predict the logical carry position of the aerosol delivery device. And the processing circuitry 906 may be configured to build the carry position detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration. In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a gesture performed using the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a gesture recognition model to predict the gesture. And the processing circuitry 906 may be configured to build the gesture recognition model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration.).
One of ordinary skill in the art would be aware of the Mason, Garcia and Sur references since all pertain to the field of aerosol systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the case of Mason to implement the machine learning feature as disclosed by Sur to gain the functionality of providing the ability to automate tasks, improve accuracy and efficiency in data analysis, identify patterns and trends, and make predictions.
Regarding claim 2, Mason in view of Garcia and Sur discloses the case as claimed in claim 1, wherein the controller is configured to receive a signal from the accelerometer indicative of the lid position changing from the closed position to the open position (Mason par[0224]: Accelerometer; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position.).
Regarding claim 4, Mason in view of Garcia and Sur discloses the case as claimed in claim 1,wherein the accelerometer is provided within a main body of the case (Mason fig 16a; par[0224]: fig 16a discloses an accelerometer inside the main body of the case; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position).
Regarding claim 5, Mason in view of Garcia and Sur discloses the case wherein the battery status indicator comprises one or more light emitting diodes (Garcia fig 1A:104; par[0023], [0051], [0055]: the controller is further configured to: determine whether the measured charge level of the battery is lower than that required for one full aerosolisation session by the heater; and indicate, using the indicator in a second manner, that the measured charge level is insufficient for an aerosolisation session by the heater in response to determining that the charge level of the battery is lower than that required for one full aerosolisation session. One or more indicator(s) 104, such as light emitting indicators (for example, one or more light emitting diodes, LEDs) are arranged in the outer casing 110 of the aerosol generation device 100. The indicator(s) 104 can be an LED bar comprising a plurality of LEDs. The indicator(s) 104 are used to provide operational state information to a user of the aerosol generation device 100. For example, the indicator(s) 104 can indicate information relating to the charge state of the battery 106 as described subsequently.).
Regarding claim 6, Mason in view of Garcia and Sur discloses the case as claimed in claim 1,wherein the battery status indicator indicates a charge level of the case (Mason par[0238]: The first open activation signal and the second open activation signal may each initiate other operations, such as checking a battery level, checking a heater temperature, or monitoring a use time.).
Regarding claim 7, Mason in view of Garcia and Sur discloses the case wherein the battery status indicator indicates a charge level of an aerosol delivery device received in the case (Garcia fig 1A:104; par[0023], [0051], [0055]: the controller is further configured to: determine whether the measured charge level of the battery is lower than that required for one full aerosolisation session by the heater; and indicate, using the indicator in a second manner, that the measured charge level is insufficient for an aerosolisation session by the heater in response to determining that the charge level of the battery is lower than that required for one full aerosolisation session. One or more indicator(s) 104, such as light emitting indicators (for example, one or more light emitting diodes, LEDs) are arranged in the outer casing 110 of the aerosol generation device 100. The indicator(s) 104 can be an LED bar comprising a plurality of LEDs. The indicator(s) 104 are used to provide operational state information to a user of the aerosol generation device 100. For example, the indicator(s) 104 can indicate information relating to the charge state of the battery 106 as described subsequently.).
Regarding claim 8, Mason in view of Garcia and Sur discloses the case as claimed in claim 1,wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer (Mason fig 16a; par[0224]: fig 16a discloses an accelerometer inside the main body of the case; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position).
Regarding claim 9, Mason in view of Garcia and Sur discloses the case wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model (Sur par[0169], [0170]: In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a logical carry position of the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a carry position detection model to predict the logical carry position of the aerosol delivery device. And the processing circuitry 906 may be configured to build the carry position detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration. In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a gesture performed using the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a gesture recognition model to predict the gesture. And the processing circuitry 906 may be configured to build the gesture recognition model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration.).
Regarding claim 10, Mason in view of Garcia and Sur discloses the case as claimed in claim 9, wherein the machine learning model is provided as part of the accelerometer (Sur par[0169]: In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a logical carry position of the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a carry position detection model to predict the logical carry position of the aerosol delivery device. And the processing circuitry 906 may be configured to build the carry position detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration. In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a gesture performed using the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a gesture recognition model to predict the gesture. And the processing circuitry 906 may be configured to build the gesture recognition model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration.).
Regarding claim 11, Mason in view of Garcia and Sur discloses the case as claimed in claim 9, wherein the machine learning model is provided as part of the controller (Sur par[0143]: the aerosol delivery device 900 may be equipped with other machine learning functionality. In accordance with some example implementations, the processing circuitry 906 may be configured to record data for a plurality of uses of the aerosol delivery device. For each use, the data may include measurements of properties from the sensors 916, including the first sensor 916a and/or second sensor(s) 916b. The processing circuitry may then be configured to build a machine learning model to predict a target variable, and deploy the machine learning model to predict the target variable, and control at least one functional element of the aerosol delivery device based thereon. In this regard, the machine learning model may be built using a machine learning algorithm, at least one feature selected from the properties, and a training set produced from the measurements of the properties.).
Regarding claim 12, Mason discloses a method comprising:
receiving movement data from an accelerometer (par[0224]: Accelerometer; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position.) comprised in a case for an aerosol delivery device (fig 1:100; par[0112]: FIG. 1, according to a first embodiment of the disclosure, an aerosol generation device 100 comprises a body 102 housing various components of the aerosol generation device 100.);
determining whether the movement data are indicative of a lid position changing from a closed position to an open position (par[0224]: Accelerometer; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position. fig 1:106; par[0119]: The aerosol generation device 100 comprises a closure 106 arranged so as to be moveable between at least a closed position, in which the closure obstructs the aperture 104 so that materials cannot enter the heating chamber 108, and an open position, in which the aperture 104 is uncovered to allow access to the heating chamber 108.); and
a battery status indicator of the case in the event that the movement data are determined to be indicative of the lid position changing from a closed position to an open position (par[0227]: Specifically, the controller typically operates a component of the aerosol generation device 100 in dependence on a signal received indicating a position of the closure 106. Typical components that are operated include: a heater; a status indicator; a battery indicator; and a display.).
Mason does not explicitly disclose the method comprising: activating a battery status indicator of the case if the movement data are determined to be indicative of the lid position changing from a closed position to an open position.
Garcia discloses the method comprising: activating a battery status indicator of the case in the event that the movement data are determined to be indicative of the lid position changing from a closed position to an open position (par[0014], [0085]: the aerosol generation device further comprises a closable opening through which the aerosol generating material is received, and wherein the controller is configured to: measure the charge level of the battery when determining the closable opening is moved to a closed position; and/or measure the charge level of the battery when determining the closable opening is moved to an opened position. When determining that the closable opening is moved to the opened position, for example when the controller determines that the movable lid has moved to the open position, the controller can initiate a standby mode during which the charge level of the battery is measured. The controller can switch the heater on, and exit the standby mode, in response to determining that the charge level of the battery is sufficient for at least one full aerosolisation session).
One of ordinary skill in the art would be aware of both the Mason and the Garcia references since both pertain to the field of aerosol systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the case of Mason to implement the activation feature as disclosed by Garcia to gain the functionality of providing a battery monitoring for aerosol generation devices and determining when only enough power remains in the battery to fully aerosolize N aerosol generating material consumables.
Mason in view of Garcia does not explicitly disclose the method further comprising using a machine learning model to process data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position.
Sur discloses the case wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model (par[0169]: In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a logical carry position of the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a carry position detection model to predict the logical carry position of the aerosol delivery device. And the processing circuitry 906 may be configured to build the carry position detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration. In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a gesture performed using the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a gesture recognition model to predict the gesture. And the processing circuitry 906 may be configured to build the gesture recognition model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration.).
One of ordinary skill in the art would be aware of the Mason, Garcia and Sur references since all pertain to the field of aerosol systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the case of Mason to implement the machine learning feature as disclosed by Sur to gain the functionality of providing the ability to automate tasks, improve accuracy and efficiency in data analysis, identify patterns and trends, and make predictions.
Regarding claim 13, Mason in view of Garcia and Sur discloses the method as claimed in claim 12, further comprising receiving or generating a signal from the accelerometer indicative of the lid position changing from the closed position to the open position (Mason fig 16a; par[0224]: fig 16a discloses an accelerometer inside the main body of the case; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position).
Regarding claim 14, Mason in view of Garcia and Sur discloses the method as claimed in claim 12, wherein the battery status indicator indicates a charge level of the case and/or a charge level of an aerosol delivery device received in the case (Mason par[0238]: The first open activation signal and the second open activation signal may each initiate other operations, such as checking a battery level, checking a heater temperature, or monitoring a use time.).
Regarding claim 15, Mason in view of Garcia and Sur discloses the method as claimed in claim 12, further comprising processing data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position (Mason fig 16a; par[0224]: fig 16a discloses an accelerometer inside the main body of the case; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position).
Regarding claim 16, Mason in view of Garcia and Sur discloses the case wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model (Sur par[0169]: In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a logical carry position of the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a carry position detection model to predict the logical carry position of the aerosol delivery device. And the processing circuitry 906 may be configured to build the carry position detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration. In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a gesture performed using the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a gesture recognition model to predict the gesture. And the processing circuitry 906 may be configured to build the gesture recognition model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration.).
Regarding claim 17, Mason discloses a method comprising:
receiving movement data from an accelerometer (par[0224]: Accelerometer; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position.) comprised in a case for an aerosol delivery device (fig 1:100; par[0112]: FIG. 1, according to a first embodiment of the disclosure, an aerosol generation device 100 comprises a body 102 housing various components of the aerosol generation device 100.); and
based on the received movement data, movement indicative of a lid position of the case changing from a closed position to an open position (par[0224]: Accelerometer; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position.),
wherein the case comprises the lid having the open position and the closed position (fig 1:100; par[0112]: FIG. 1, according to a first embodiment of the disclosure, an aerosol generation device 100 comprises a body 102 housing various components of the aerosol generation device 100.), the accelerometer (par[0224]: Accelerometer; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position.), a battery status indicator (par[0227]: Specifically, the controller typically operates a component of the aerosol generation device 100 in dependence on a signal received indicating a position of the closure 106. Typical components that are operated include: a heater; a status indicator; a battery indicator; and a display.), and a controller in the event that the movement data are determined to be indicative of the lid position changing from a closed position to an open position (par[0227]: Specifically, the controller typically operates a component of the aerosol generation device 100 in dependence on a signal received indicating a position of the closure 106. Typical components that are operated include: a heater; a status indicator; a battery indicator; and a display.).
Mason does not explicitly disclose the method comprising: training a machine learning model to detect, based on the received movement data; and a controller to activate the battery status indicator if the movement data are determined to be indicative of the lid position changing from a closed position to an open position.
Garcia discloses the method comprising: a controller to activate the battery status indicator if the movement data are determined to be indicative of the lid position changing from a closed position to an open position (par[0014], [0085]: the aerosol generation device further comprises a closable opening through which the aerosol generating material is received, and wherein the controller is configured to: measure the charge level of the battery when determining the closable opening is moved to a closed position; and/or measure the charge level of the battery when determining the closable opening is moved to an opened position. When determining that the closable opening is moved to the opened position, for example when the controller determines that the movable lid has moved to the open position, the controller can initiate a standby mode during which the charge level of the battery is measured. The controller can switch the heater on, and exit the standby mode, in response to determining that the charge level of the battery is sufficient for at least one full aerosolisation session).
One of ordinary skill in the art would be aware of both the Mason and the Garcia references since both pertain to the field of aerosol systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the case of Addis to implement the activation feature as disclosed by Garcia to gain the functionality of providing a battery monitoring for aerosol generation devices and determining when only enough power remains in the battery to fully aerosolize N aerosol generating material consumables.
Mason in view of Garcia does not explicitly disclose the method comprising: training a machine learning model to detect, based on the received movement data.
Sur discloses the method comprising: training a machine learning model to detect, based on the received movement data (Sur par[0143]: the aerosol delivery device 900 may be equipped with other machine learning functionality. In accordance with some example implementations, the processing circuitry 906 may be configured to record data for a plurality of uses of the aerosol delivery device. For each use, the data may include measurements of properties from the sensors 916, including the first sensor 916a and/or second sensor(s) 916b. The processing circuitry may then be configured to build a machine learning model to predict a target variable, and deploy the machine learning model to predict the target variable, and control at least one functional element of the aerosol delivery device based thereon. In this regard, the machine learning model may be built using a machine learning algorithm, at least one feature selected from the properties, and a training set produced from the measurements of the properties.).
One of ordinary skill in the art would be aware of the Mason, Garcia and Sur references since all pertain to the field of aerosol systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the case of Mason to implement the machine learning feature as disclosed by Sur to gain the functionality of providing the ability to automate tasks, improve accuracy and efficiency in data analysis, identify patterns and trends, and make predictions.
Regarding claim 18, Mason in view of Garcia and Sur discloses the kit of parts comprising the case as claimed in claim 1, an aerosol delivery device and an article for use in the aerosol delivery device (Mason fig 1:100; par[0112], [0172]: FIG. 1, according to a first embodiment of the disclosure, an aerosol generation device 100 comprises a body 102 housing various components of the aerosol generation device 100. With the closure 106 in the open position, the user inserts an aerosol substrate 148 into the heating chamber 108 via the aperture 104. More specifically, a first end of the aerosol substrate 148 is inserted in an insertion direction (B) into the heating chamber 108 while a second end of the aerosol substrate 148 remains external to the aerosol generation device 100 and is thereby accessible to the user).
Regarding claim 20, Mason in view of Garcia and Sur discloses the kit of parts as claimed in claim 18, wherein the article is a removable article comprising an aerosol generating material (Mason fig 1:100; par[0112], [0172], [0178]: FIG. 1, according to a first embodiment of the disclosure, an aerosol generation device 100 comprises a body 102 housing various components of the aerosol generation device 100. With the closure 106 in the open position, the user inserts an aerosol substrate 148 into the heating chamber 108 via the aperture 104. More specifically, a first end of the aerosol substrate 148 is inserted in an insertion direction (B) into the heating chamber 108 while a second end of the aerosol substrate 148 remains external to the aerosol generation device 100 and is thereby accessible to the user. When the user has exhausted the aerosol substrate 148, the user removes the aerosol substrate 148 from the heating chamber 108 and disposes of the aerosol substrate 148. The user then applies a closing force on the external cover 112 of the closure 106 in the direction of the closed position from the open position (e.g. to the left in FIGS. 5a-c). The closing force is initially resisted by the resilient element 114, as shown in FIG. 5c, so that if the user releases the closure 106 before it has moved substantially, the closure 106 returns to the open position.).
2. Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mason in view of Garcia and Sur, and further in view of Long et al. (CN213879307U) hereafter Long.
Regarding claim 3, Mason in view of Garcia and Sur discloses the case as claimed in claim 1,wherein the accelerometer is provided within a main body of the case (Mason fig 16a; par[0224]: fig 16a discloses an accelerometer inside the main body of the case; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position).
Mason in view of Garcia and Sur does not explicitly disclose the case wherein the accelerometer is provided within the lid of the case.
Long discloses the case wherein the accelerometer is provided within the lid of the case (page 8 ln 24-30: The vaporization device 200b includes an airflow sensor 205 and an airflow detection port 206. The airflow detection port 256 is connected to the smoking passage 213 in the vaporization box 200a from one side of the airflow sensor 205, and the airflow detection port 206 is connected Outside, when a user smokes, a negative pressure is generated in the smoking passage 213, and the airflow sensor 205 senses the pressure difference between the two sides through the airflow detection port 206 and the airflow detection port 256, thereby generating a trigger signal to activate the vaporization device).
One of ordinary skill in the art would be aware of the Mason, Garcia, Sur and Long references since both pertain to the field of aerosol systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the case of Mason to implement the accelerometer feature as disclosed by Long to gain the functionality of providing the ability to detect and measure vibrations and accelerations accurately, making them suitable for various applications like condition monitoring, structural aerosol monitoring, and safety systems.
3. Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mason in view of Garcia and Sur, and further in view of Flora et al. (US2020/0229507A1) hereafter Flora.
Regarding claim 19, Mason in view of Garcia and Sur does not explicitly disclose the kit of parts as claimed in claim 18, wherein the aerosol delivery device is a non-combustible aerosol generating device.
Flora discloses the kit of parts as claimed in claim 18, wherein the aerosol delivery device is a non-combustible aerosol generating device (par[0045], [0046]: FIG. 1 is a side view of non-combustible aerosol system according to at least one example embodiment. As shown in FIG. 1, a non-combustible aerosol system 10 includes a non-combustible aerosol device 100 and a pre-aerosol formulation housing 200. The pre-aerosol formulation housing 200 may include pre-aerosol formulation that is a solid substrate (in example embodiments with tobacco, referred to as a tobacco housing). The non-combustible aerosol device 100 may include a power section 105 and a heating section 110. In FIG. 1, the non-combustible aerosol device 100 includes a housing 115. In at least one example embodiment, the housing 115 may have a generally square cross-section. In other example embodiments, the housing 115 may have a generally triangular or circular cross-section ).
One of ordinary skill in the art would be aware of the Mason, Garcia, Sur and Flora references since all pertain to the field of aerosol systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the case of Mason to implement the non-combusting feature as disclosed by Flora to gain the functionality of providing rapid Fire Suppression, suitability for Confined Spaces, minimal water damage, environmentally friendly, low Maintenance, cost-Effective, versatility, source Protection and long Service Life.
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.
1. Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable of copending Application No. 18257356 hereafter Co-1 in view of Mason et al. (US2022/0142247) hereafter Mason, and further in view of Sur (US2020/0337382A1).
This is a provisional nonstatutory double patenting rejection.
Instant Application # 18837584
Co-pending Application # Co-1
1. (Currently Amended) A case for an aerosol delivery device, the case comprising: a lid having an open position and a closed position; an accelerometer configured to detect movement indicative of a lid position changing from the closed position to the open position; a battery status indicator; and a controller configured to activate the battery status indicator when a signal from the accelerometer indicates that the lid position has changed from the closed position to the open position-, wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model.
1. (Currently Amended) A case for an aerosol delivery device, the case comprising: a lid having an open position and a closed position; a magnet and a hall sensor pair configured to provide a signal indicative of whether the lid is in the open position or the closed position; and a controller configured to receive a signal from the hall sensor indicative of whether the lid is in the open position or the closed position; and a battery status indicator, the controller is configured to activate the battery status indicator upon detection of the lid changing from a closed position to an open position.
2. (Previously Presented) The case as claimed in claim 1, wherein the controller is configured to receive a signal from the accelerometer indicative of the lid position changing from the closed position to the open position.
1. (Currently Amended) A case for an aerosol delivery device, the case comprising: a lid having an open position and a closed position; a magnet and a hall sensor pair configured to provide a signal indicative of whether the lid is in the open position or the closed position; and a controller configured to receive a signal from the hall sensor indicative of whether the lid is in the open position or the closed position; and a battery status indicator, the controller is configured to activate the battery status indicator upon detection of the lid changing from a closed position to an open position.
3. (Previously Presented) The case as claimed in claim 1, wherein the accelerometer is provided within the lid of the case.
2. (Previously Presented) The case as claimed in claim 1, wherein the magnet is provided within the lid of the case.
4. (Previously Presented) The case as claimed in claim 1, wherein the accelerometer is provided within a main body of the case.
3. (Previously Presented) The case as claimed in claim 1, wherein the hall sensor is provided within a main body of the case.
5. (Previously Presented) The case as claimed in claim 1, wherein the battery status indicator comprises one or more light emitting diodes.
5. (Previously Presented) The case as claimed in claim 4, wherein the battery status indicator comprises one or more light emitting diodes.
6. (Previously Presented) The case as claimed in claim 1, wherein the battery status indicator indicates a charge level of the case.
6. (Previously Presented) The case as claimed claim 4, wherein the battery status indicator is configured to indicate a status of a battery of an aerosol delivery device mounted within the case.
7. (Previously Presented) The case as claimed in claim 1, wherein the battery status indicator indicates a charge level of an aerosol delivery device received in the case.
6. (Previously Presented) The case as claimed claim 4, wherein the battery status indicator is configured to indicate a status of a battery of an aerosol delivery device mounted within the case.
12. (Previously Presented) A method comprising: receiving movement data from an accelerometer comprised in a case for an aerosol delivery device; determining whether the movement data are indicative of a lid position changing from a closed position to an open position using a machine learning model to process data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position; and activating a battery status indicator of the case if the movement data are determined to be indicative of the lid position changing from a closed position to an open position.
1. (Currently Amended) A case for an aerosol delivery device, the case comprising: a lid having an open position and a closed position; a magnet and a hall sensor pair configured to provide a signal indicative of whether the lid is in the open position or the closed position; and a controller configured to receive a signal from the hall sensor indicative of whether the lid is in the open position or the closed position; and a battery status indicator, wherein the controller is configured to activate the battery status indicator upon detection of the lid changing from a closed position to an open position.
13. (Previously Presented) The method as claimed in claim 12, further comprising receiving or generating a signal from the accelerometer indicative of the lid position changing from the closed position to the open position.
1. (Currently Amended) A case for an aerosol delivery device, the case comprising: a lid having an open position and a closed position; a magnet and a hall sensor pair configured to provide a signal indicative of whether the lid is in the open position or the closed position; and a controller configured to receive a signal from the hall sensor indicative of whether the lid is in the open position or the closed position; and a battery status indicator, wherein the controller is configured to activate the battery status indicator upon detection of the lid changing from a closed position to an open position.
14. (Previously Presented) The method as claimed in claim 12, wherein the battery status indicator indicates a charge level of the case and/or a charge level of an aerosol delivery device received in the case.
6. (Previously Presented) The case as claimed claim 4, wherein the battery status indicator is configured to indicate a status of a battery of an aerosol delivery device mounted within the case.
17. (Currently Amended) A method comprising: receiving movement data from an accelerometer comprised in a case for an aerosol delivery device; and training a machine learning model to detect, based on the received movement data, movement indicative of a lid position of the case changing from a closed position to an open position, wherein the case comprises the lid having the open position and the closed position, the accelerometer, a battery status indicator, and a controller to activate the battery status indicator if the movement data are determined to be indicative of the lid position changing from a closed position to an open position.
1. (Currently Amended) A case for an aerosol delivery device, the case comprising: a lid having an open position and a closed position; a magnet and a hall sensor pair configured to provide a signal indicative of whether the lid is in the open position or the closed position; and a controller configured to receive a signal from the hall sensor indicative of whether the lid is in the open position or the closed position; and a battery status indicator, wherein the controller is configured to activate the battery status indicator upon detection of the lid changing from a closed position to an open position.
18. (Previously Presented) A kit of parts comprising the case as claimed in claim 1, the aerosol delivery device and an article for use in the aerosol delivery device.
1. (Currently Amended) A case for an aerosol delivery device, the case comprising: a lid having an open position and a closed position; a magnet and a hall sensor pair configured to provide a signal indicative of whether the lid is in the open position or the closed position; and a controller configured to receive a signal from the hall sensor indicative of whether the lid is in the open position or the closed position; and a battery status indicator, wherein the controller is configured to activate the battery status indicator upon detection of the lid changing from a closed position to an open position.
9. (Previously Presented) The case as claimed in claim 8, wherein the aerosol delivery device is configured to receive a removable article comprising an aerosolizable material.
19. (Previously Presented) The kit of parts as claimed in claim 18, wherein the aerosol delivery device is a non-combustible aerosol generating device.
7. (Currently Amended) The case as claimed in claim 8, wherein the aerosol delivery device is a non-combustible aerosol provision device.
20. (Previously Presented) The kit of parts as claimed in claim 18, wherein the article is a removable article comprising an aerosol generating material.
9. (Previously Presented) The case as claimed in claim 8, wherein the aerosol delivery device is configured to receive a removable article comprising an aerosolizable material.
Regarding claims 1, 12 and 17, Co-1 does not explicitly disclose the case comprising: an accelerometer configured to detect movement indicative of a lid position changing from the closed position to the open position, wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model.
Mason discloses a case comprising: an accelerometer configured to detect movement indicative of a lid position changing from the closed position to the open position (par[0224]: Accelerometer; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position.).
One of ordinary skill in the art would be aware of both the Co-1 and Mason references since both pertain to the field of aerosol systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the case of Co-1 to implement the acceleration feature as disclosed by Mason to gain the functionality of measuring acceleration, vibration, tilt, and shock, providing precise real-time, objective data for motion, orientation, and structural monitoring.
Co-1 in view of Mason does not explicitly disclose wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model.
Sur discloses the case wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model (par[0169]: In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a logical carry position of the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a carry position detection model to predict the logical carry position of the aerosol delivery device. And the processing circuitry 906 may be configured to build the carry position detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration. In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a gesture performed using the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a gesture recognition model to predict the gesture. And the processing circuitry 906 may be configured to build the gesture recognition model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration.).
One of ordinary skill in the art would be aware of the Co-1, Mason, and Sur references since all pertain to the field of aerosol systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the case of Co-1 to implement the machine learning feature as disclosed by Sur to gain the functionality of providing the ability to automate tasks, improve accuracy and efficiency in data analysis, identify patterns and trends, and make predictions.
Regarding claim 8, Co-1 in view of Mason and Sur discloses the case as claimed in claim 1,wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer (Mason fig 16a; par[0224]: fig 16a discloses an accelerometer inside the main body of the case; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position).
Regarding claim 9, Co-1 in view of Mason and Sur discloses the case wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model (Sur par[0169], [0170]: In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a logical carry position of the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a carry position detection model to predict the logical carry position of the aerosol delivery device. And the processing circuitry 906 may be configured to build the carry position detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration. In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a gesture performed using the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a gesture recognition model to predict the gesture. And the processing circuitry 906 may be configured to build the gesture recognition model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration.).
Regarding claim 10, Mason in view of Garcia and Sur discloses the case as claimed in claim 9, wherein the machine learning model is provided as part of the accelerometer (Sur par[0169]: In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a logical carry position of the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a carry position detection model to predict the logical carry position of the aerosol delivery device. And the processing circuitry 906 may be configured to build the carry position detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration. In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a gesture performed using the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a gesture recognition model to predict the gesture. And the processing circuitry 906 may be configured to build the gesture recognition model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration.).
Regarding claim 11, Mason in view of Garcia and Sur discloses the case as claimed in claim 9, wherein the machine learning model is provided as part of the controller (Sur par[0143]: the aerosol delivery device 900 may be equipped with other machine learning functionality. In accordance with some example implementations, the processing circuitry 906 may be configured to record data for a plurality of uses of the aerosol delivery device. For each use, the data may include measurements of properties from the sensors 916, including the first sensor 916a and/or second sensor(s) 916b. The processing circuitry may then be configured to build a machine learning model to predict a target variable, and deploy the machine learning model to predict the target variable, and control at least one functional element of the aerosol delivery device based thereon. In this regard, the machine learning model may be built using a machine learning algorithm, at least one feature selected from the properties, and a training set produced from the measurements of the properties.).
Regarding claim 15, Co-1 in view of Mason and Sur discloses the method as claimed in claim 12, further comprising processing data from the accelerometer to generate a signal indicative of the lid position changing from the closed position to the open position (Mason fig 16a; par[0224]: fig 16a discloses an accelerometer inside the main body of the case; the movement of the closure 106 is determined using an accelerometer; whether the movement is due to the closure 106 opening, closing, or moved to the activation position is determinable by features of the acceleration, e.g. the biasing causes the lid to accelerate towards the open or closed position, but not towards the activation position).
Regarding claim 16, Co-1 in view of Mason and Sur discloses the case wherein the movement indicative of the lid position changing from the closed position to the open position is detected by processing data from the accelerometer using a machine learning model (Sur par[0169]: In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a logical carry position of the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a carry position detection model to predict the logical carry position of the aerosol delivery device. And the processing circuitry 906 may be configured to build the carry position detection model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration. In other examples in which the second sensor(s) 916b include an accelerometer, the target variable may be a gesture performed using the aerosol delivery device 900. In some of these examples, the machine learning model may be or include a gesture recognition model to predict the gesture. And the processing circuitry 906 may be configured to build the gesture recognition model using the machine learning algorithm, the at least one feature that includes the acceleration, and the training set produced from the measurements of acceleration.).
Conclusion
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/AMINE BENLAGSIR/Primary Examiner, Art Unit 2688