DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-22 are pending and are subject to this Office Action. This is the first Office Action on the merits of the claims. Claims 20-22 are withdrawn. Claims 1 and 5 are amended.
Response to Amendments
The amendments to the claims filed on January 29, 2026 are acknowledged. The 112(b) rejections of Claims 5-6 have been withdrawn due to the amendments.
Response to Arguments
Applicant' s arguments, see pgs 6-11, filed January 29, 2026, with respect to the rejection(s) of claims 1-19 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
On pg. 6-7, Applicant argues that contrary to the rule stated in MPEP 2106.04 that Examiners should consider the claim as a whole when performing a step 2A analysis under the Alice/Mayo test for subject matter eligibility, the Examiner is considering only a portion of the claim in determining that the claim is directed to an abstract idea without significantly more. Applicant further argues that the recitations of the claimed invention are so explicitly tied to actual physical components (i.e. the motion sensor) of an actual physical device, that the instantiation of the activities performed are unmistakably integrated into a practical application. While Examiner acknowledges that a human mind cannot receive data from a motion sensor, quantify the movement of the system both laterally and vertically, and comparing the quantified movements to values to discriminate a character in the alphabet, the 101 evaluation of Claim 1 does not require the human mind to perform each function performed by the AI model. Specifically, the 101 analysis of Claim 1 centers on the limitation: “based on the received data, to discriminate a particular character from the alphabet of multiple characters as user input to the electronic aerosol provision system when the spatial motion of the electronic aerosol provision system matches the movement pattern of the particular character. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” enumerated grouping of abstract ideas. But for the recitation of an AI model, the limitation of discriminating a particular character when the spatial motion matches the movement pattern based on received data can be performed by the human mind by manually referring to a look-up table to determine that a spatial motion of the electronic aerosol provision system matches the movement pattern of the particular character. Looking at the claim as a whole, the system Claim 1 requires a motion sensor, a computing device, and an AI model configured to run on the device. However, because the other elements of the claim are the electronic aerosol provision system and the computing device which are well understood, routine and conventional within the prior art, the claim does not recite any elements which are significantly more than the abstract ideas/law of nature/natural phenomenon. With respect to whether the judicial exception is integrated into a practical application, AI model is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitation of discriminating a particular character when the spatial motion matches the movement pattern adds an insignificant extra solution activity to the judicial exception, as the limitation does not impose meaningful limits on the claim and essentially amounts to data gathering and outputting. As such, the arguments are unpersuasive.
On pg. 8, Applicant argues that Henry and Valafar, either alone or in combination, does not disclose the limitation: “wherein the AI model has been trained on an external computing system, and then loaded into the at least one computing device prior to use of the aerosol provision system by the user”. Examiner respectfully disagrees with the argument because the limitation is directed to a method of forming the system. The patentability of a product or apparatus does not depend on its method of production or formation. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even if the prior product was made by a different process. In this instance, Henry in view of Valafar discloses a structurally and compositionally equivalent electronic aerosol provision system wherein said alphabet comprises characters used to input one or more operations relating to aerosol provision by the electronic aerosol provision system (Henry, [0047]-[0056]), as required by amended Claim 1. Training of the AI model on an external computing system has no impact on whether the AI model can perform the functions of Claim 1. As such, the arguments are unpersuasive.
The following is a modified rejection based on amendments made to the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recite(s) the limitation: “wherein the AI model is further configured to receive data… and based on the received data, to discriminate a particular character from the alphabet of multiple characters as user input to the electronic aerosol provision system when the spatial motion of the electronic aerosol provision system matches the movement pattern of the particular character”.
The limitation of discriminating a particular character when the spatial motion matches the movement pattern based on received data, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of an AI model running on a computing device. That is, other than reciting that the discrimination step of performed by an AI model, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “AI model” language, “discriminating” in the context of this claim encompasses the user manually referring to a look-up table to determine that a spatial motion of the electronic aerosol provision system matches the movement pattern of the particular character. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” enumerated grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim
only recites one additional element – using an AI model to perform the discriminating step. The AI model is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitation of discriminating a particular character when the spatial motion matches the movement pattern adds an insignificant extra solution activity to the judicial exception, as the limitation does not impose meaningful limits on the claim and essentially amounts to data gathering and outputting. Further, the limitation generally links the use of the judicial exception to the field of endeavor, and the particularity of the character discrimination step is not further described in the claims or specification. 2106.05(g) or 2106.05(h). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the AI model, the other elements of the claim are the electronic aerosol provision system and the computing device which are well understood, routine and conventional within the prior art. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using an AI model to perform discriminating step amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-19, which depend from Claim 1, do not remedy the issues raised by Claim 1 as described above. Therefore, claims 2-19 are similarly rejected by dependency.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5, 7, and 9-19 are rejected under 35 U.S.C. 103 as being unpatentable over Henry (US 2016/0158782 A1, cited on the IDS dated 9/19/2022) in view of Valafar (US 2018/0292910 A1, cited on the IDS dated 9/19/2022).
Regarding Claim 1, Henry, directed to gesture detection for smoking applications ([0001], [0004]) and motion sensors ([0053]), teaches an electronic aerosol provision system ([0026], Fig. 1; Aerosol delivery device 100 is an electronic device forming a system comprising control body 102 and cartridge 104), said system including
a motion sensor ([0053], Fig. 1; Aerosol delivery device 100 includes a motion sensor 146),
at least one computing device ([0055]-[0056], Fig. 1; Aerosol delivery device 100 includes a control component 108 (e.g., microprocessor). A microprocessor is a computing device),
the at least one computing device defining an alphabet of multiple characters ([0055]-[0056], Fig. 1; The motion sensor 146 may be configured to detect a defined motion of the aerosol delivery device caused by user interaction with the housing to perform a gesture. The control component 108 (e.g., microprocessor) of the aerosol delivery device 100 may be configured to receive the electrical signal from the motion sensor, recognize the gesture and an operation associated with the gesture based on the electrical signal, and control at least one functional element of the aerosol delivery device to perform the operation. [0047]-[0048], Examples of suitable gestures include tracing one character within alphabet of multiple character (e.g. an “l” or a “b”) with the housing. [0051], The gestures (characters) may be preset or user-defined. If multiple different alphabetical characters can be traced as gestures, and control component 108 can recognize the gesture based on receive the electrical signal from the motion sensor 146, the control component 108 (computing device) must define an alphabet of multiple characters),
each character corresponding to a movement pattern ([0047]-[0048], [0066], Fig. 8);
wherein the computing device is further configured to receive data from the motion sensor representing spatial motion of the electronic aerosol provision system ([0055]-[0056], Fig. 1; The motion sensor 146 may be configured to detect a defined spatial motion of the aerosol delivery device caused by user interaction with the housing to perform a gesture. The control component 108 (computing device) of the aerosol delivery device 100 may be configured to receive the electrical signal (data) from the motion sensor), and
based on the received data, to discriminate a particular character from the alphabet of multiple characters as user input to the electronic aerosol provision system when the spatial motion of the electronic aerosol provision system matches the movement pattern of the particular character ([0055]-[0056], Fig. 1; The control component 108 (computing device) of the aerosol delivery device 100 may be configured to receive the electrical signal (data) from the motion sensor, recognize the gesture and an operation associated with the gesture based on the electrical signal, and control at least one functional element of the aerosol delivery device to perform the operation. [0047]-[0048], As the gesture may be one of alphabet of multiple characters defined by the computing device, the step of “recogniz[ing] the gesture” must comprise discriminating from the alphabet of multiple characters as user input to the electronic aerosol provision system when the spatial motion of the electronic aerosol provision system matches the movement pattern of the particular character),
wherein said alphabet comprises characters used to input one or more operations relating to aerosol provision by the electronic aerosol provision system ([0047]-[0056]),
but does not teach the system including an artificial intelligence (AI) model configured to run on the at least one computing device, the model defining the alphabet of multiple characters, wherein the AI model is further configured to receive data from the motion sensor representing spatial motion of the electronic aerosol provision system, and based on the received data, to discriminate a particular character from the alphabet of multiple characters as user input to the electronic aerosol provision system when the spatial motion of the electronic aerosol provision system matches the movement pattern of the particular character, wherein the AI model has been trained on an external computing system, and then loaded into the at least one computing device prior to use of the aerosol provision system by the user.
Valafar, directed to gesture detection for smoking applications ([0001], [0004]) and machine learning ([0007]), teaches a system ([0059], Fig. 6; Computing system 100 includes a wearable computing device 102 and a machine learning computing system 130 that are communicatively coupled over a network 180) including
a motion sensor ([0023], [0059], Fig. 6; Wearable computing device 102 comprises one or more sensors 104. The one or more sensors 104 can include one or more accelerometers or a gyroscope to provide spatial motion data);
at least one computing device ([0059]-[0061], Fig. 6; Wearable computing device 102 includes one or more processors 112. A microprocessor is a computing device);
an artificial intelligence (AI) model configured to run on the at least one computing device ([0065]-[0066], Fig. 6; Wearable computing device 102 can store or include one or more machine-learned classifier models 110. The wearable computing device 102 can then use or otherwise implement the one or more machine-learned models 110 (e.g., by processor(s) 112). [0027], The machine-learned classifier models 110 can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models),
wherein the AI model is further configured to receive data from the motion sensor representing spatial motion of the system ([0023]-[0026], Fig. 1; The one or more sensors 104 output sensor (accelerometer) data 106 representing spatial motion of the wearable computing device 102 (system). Smoking gesture detection model 110 (AI model) can receive the sensor data 106), and
based on the received data, to discriminate a particular gesture as user input to the system when the spatial motion of the system matches the movement pattern of the particular gesture ([0023]-[0026], Fig. 1; The one or more sensors 104 output sensor (accelerometer) data 106 representing spatial motion of the wearable computing device 102 (system). The smoking gesture detection model 110 can receive the sensor data 106 (e.g., the raw and/or pre-processed sensor data) and, in response, output a gesture classification 112. In particular, the gesture classification 112 can indicate whether the sensor data 106 was indicative of a smoking gesture (particular gesture). If the gesture classification 112 is a system output, the gesture is a user input).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the system of Henry comprising an artificial intelligence (AI) model configured to run on the at least one computing device as taught by Valafar, wherein the model defines the alphabet of multiple characters, wherein the AI model is further configured to receive data from the motion sensor representing spatial motion of the electronic aerosol provision system, and based on the received data, to discriminate a particular character from the alphabet of multiple characters as user input to the electronic aerosol provision system when the spatial motion of the electronic aerosol provision system matches the movement pattern of the particular character, as performed by the computing device of Henry, because Henry and Valafar are directed to gesture detection for smoking applications, Valafar demonstrates that AI models are capable of receiving data from a motion sensor representing spatial motion of the system and discriminate a particular gesture as user input to the system when the spatial motion of the system matches the movement pattern of the particular gesture (Valafar, [0023]-[0026], Fig. 1), and that the AI model can be trained to detect a gesture regardless of the device orientation (Valafar, [0019]), and this involves combining prior art elements according to known methods to yield predictable results.
The limitation: “wherein the AI model has been trained on an external computing system, and then loaded into the at least one computing device prior to use of the aerosol provision system by the user” is a product-by-process limitation. The patentability of a product or apparatus does not depend on its method of production or formation. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even if the prior product was made by a different process. In this instance, Henry in view of Valafar discloses a structurally and compositionally equivalent electronic aerosol provision system.
Regarding Claim 2, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1. Henry further teaches the system wherein the motion sensor is configured to output 3-axis linear motion or acceleration data ([0053]-[0054], Fig. 1; Motion sensor 146 may be implemented as a multi-axis accelerometer. [0058]-[0059], Fig. 2; Multi-axis accelerometer 200 is configured to output 3-axis linear motion or acceleration data).
Regarding Claim 5, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1. Henry further teaches the system wherein the motion sensor is configured to collect samples of motion data at a rate of at least 10 Hz ([0014], [0058], Figs. 6-7; The motion sensor 146/accelerometer 200 is configured to collect samples of motion data at a rate of 62.5 Hz).
Regarding Claim 7, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1, but does not teach the system wherein the motion sensor is configured to assemble multiple samples of motion data occurring during a time window, and for each time window, to send together to the AI model the multiple samples of motion data occurring during that time window.
Valafar, directed to gesture detection for smoking applications ([0001], [0004]), teaches a system ([0059], Fig. 6; Computing system 100 includes a wearable computing device 102 and a machine learning computing system 130 that are communicatively coupled over a network 180) including
a motion sensor ([0023], [0059], Fig. 6; Wearable computing device 102 comprises one or more sensors 104. The one or more sensors 104 can include one or more accelerometers or a gyroscope to provide spatial motion data);
at least one computing device ([0059]-[0061], Fig. 6; Wearable computing device 102 includes one or more processors 112. A microprocessor is a computing device);
an artificial intelligence (Al) model configured to run on the at least one computing device ([0065]-[0066], Fig. 6; Wearable computing device 102 can store or include one or more machine-learned classifier models 110. The wearable computing device 102 can then use or otherwise implement the one or more machine-learned models 110 (e.g., by processor(s) 112). [0027], The machine-learned classifier models 110 can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models),
wherein the motion sensor is configured to assemble multiple samples of motion data occurring during a time window, and for each time window, to send together to the AI model the multiple samples of motion data occurring during that time window ([0023]-[0026], Fig. 1; The one or more sensors 104 output sensor (accelerometer) data 106 representing spatial motion of the wearable computing device 102 (system). Smoking gesture detection model 110 (AI model) can receive the sensor data 106. [0028]-[0029], The smoking gesture detection model 110 can receive a plurality of sets of sensor data 106 over a plurality of time periods (time windows) and can output, for each time period, a gesture classification 112).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the system of Henry in view of Valafar wherein the motion sensor is configured to assemble multiple samples of motion data occurring during a time window, and for each time window, to send together to the AI model the multiple samples of motion data occurring during that time window as disclosed by Valafar because Valafar demonstrates that this configuration provides an additional layer of intelligence beyond the basic detection of a smoking gesture by the gesture detection model 110, thereby reducing false positives (Valafar, [0028]-[0029], Fig. 1), and this involves combining prior art elements according to known methods to yield predictable results.
Regarding Claim 9-10, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1. Henry further teaches the system wherein said alphabet contains at least 4 characters ([0048], [0058], lowercase “l”, lowercase “b”, uppercase “L”, and uppercase “U” as sample characters), but does not teach the system wherein said alphabet contains 5 or more characters, 10 or more characters, 20 or more characters, 40 or more characters, wherein said alphabet contains between 20 and 60 characters.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the system wherein said alphabet contains between 20 and 60 characters because Henry states that the character may be any letter, number or other shape (Henry, [0048]), that the character may correspond any number of operations that may be performed by at least one functional element of the aerosol delivery device (Henry, [0062]), one of ordinary skill in the art would know that there are 26 letters in the alphabet, and providing additional characters to the alphabet would increase the number of operations which can be performed through motion sensor detection.
Regarding Claim 11, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1. Henry further teaches the system wherein said characters represent abstract, symbolic user input for the electronic aerosol provision system ([0048], the aerosol delivery device may be configured with an “unlock code” to lock/unlock the device, such as by the user tracing a particular character (e.g., lowercase “l”) with the device. The trace of a distinct particular character (e.g., lowercase “b”) may cause the aerosol delivery device to indicate a charge-level of its battery 112).
Regarding Claim 12, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1. Henry further teaches the system wherein said characters are used to input one or more of the following operations: sending the electronic aerosol provision system into a low power mode; setting a power level for the electronic aerosol provision system ([0062], [0069], recognizable gestures may be associated with any of a number of operations that may be performed by at least one functional element of the aerosol delivery device 100. For example, the operation may include altering a power state of the aerosol delivery device (e.g., turn on/off, enter standby/low-power mode, enter operational mode)).
Regarding Claim 13, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1. Henry further teaches the system wherein said characters are used to lock and/or unlock the electronic aerosol provision system, wherein the electronic aerosol provision system must be unlocked to produce vapor ([0048], [0052], on first use, the aerosol delivery device may be configured with an “unlock code” to lock/unlock the device, such as by the user tracing a particular character (e.g., lowercase “l”) with the device. It is reasonably understood that the electronic aerosol provision system must be unlocked to produce vapor).
Regarding Claim 14, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1. Valafar further teaches the system wherein the AI model is configured to determine a string of one or more characters from the motion sensor data ([0023]-[0026], Fig. 1; The one or more sensors 104 output sensor (accelerometer) data 106 representing spatial motion of the wearable computing device 102 (system). The smoking gesture detection model 110 can receive the sensor data 106 (e.g., the raw and/or pre-processed sensor data) and, in response, output a gesture classification 112. In particular, the gesture classification 112 can indicate whether the sensor data 106 was indicative of a smoking gesture (particular gesture). If the gesture classification 112 is a system output, the gesture is a user input. As applied to Claim 1, the gestures may be characters. Therefore, a string of at least one character can be determined from the motion sensor data 106).
Regarding Claim 15, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1. Valafar further teaches the system wherein the AI model runs on the at least one computing device to discriminate characters from the motion sensor data ([0023]-[0026], Fig. 1; The one or more sensors 104 output sensor (accelerometer) data 106 representing spatial motion of the wearable computing device 102 (system). The smoking gesture detection model 110 can receive the sensor data 106 (e.g., the raw and/or pre-processed sensor data) and, in response, output a gesture classification 112. In particular, the gesture classification 112 can indicate whether the sensor data 106 was indicative of a smoking gesture (particular gesture). If the gesture classification 112 is a system output, the gesture is a user input. Henry has been modified in view of Valafar such that the AI model of Valafar runs on the computing device of Henry).
Regarding Claim 16, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1, wherein the AI model offloads at least some of the processing onto an external system to discriminate characters from the motion sensor data.
Valafar, directed to gesture detection for smoking applications ([0001], [0004]), teaches a system ([0059], Fig. 6; Computing system 100 includes a wearable computing device 102 and a machine learning computing system 130 that are communicatively coupled over a network 180) including
a motion sensor ([0023], [0059], Fig. 6; Wearable computing device 102 comprises one or more sensors 104. The one or more sensors 104 can include one or more accelerometers or a gyroscope to provide spatial motion data);
at least one computing device ([0059]-[0061], Fig. 6; Wearable computing device 102 includes one or more processors 112. A microprocessor is a computing device);
an artificial intelligence (Al) model configured to run on the at least one computing device ([0065]-[0066], Fig. 6; Wearable computing device 102 can store or include one or more machine-learned classifier models 110. The wearable computing device 102 can then use or otherwise implement the one or more machine-learned models 110 (e.g., by processor(s) 112). [0027], The machine-learned classifier models 110 can be a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models),
wherein the AI model is further configured to receive data from the motion sensor representing spatial motion of the system and based on the received data, to discriminate a particular gesture as user input to the system when the spatial motion of the system matches the movement pattern of the particular gesture ([0023]-[0026], Fig. 1; The one or more sensors 104 output sensor (accelerometer) data 106 representing spatial motion of the wearable computing device 102 (system). The smoking gesture detection model 110 can receive the sensor data 106 (e.g., the raw and/or pre-processed sensor data) and, in response, output a gesture classification 112. In particular, the gesture classification 112 can indicate whether the sensor data 106 was indicative of a smoking gesture (particular gesture). If the gesture classification 112 is a system output, the gesture is a user input),
wherein the AI model offloads at least some of the processing onto an external system to discriminate characters from the motion sensor data ([0059], Fig. 6; Computing system 100 includes a wearable computing device 102 and a machine learning computing system 130 (external system) that are communicatively coupled over a network 180. [0072], In addition or alternatively to the model(s) 110 at the wearable computing device 102, the machine learning computing system 130 can include one or more machine-learned models 140. machine-learned models 110 can located and used at the wearable computing device 102 and/or machine-learned models 140 can be located and used at the machine learning computing system 130).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the system of Henry in view of Valafar wherein the Al model offloads at least some of the processing onto an external system to discriminate characters from the motion sensor data as taught by Valafar because Valafar demonstrates that the external system provides the additional function of training and storing the AI models before their application with the aerosol generating system (Valafar, [0066]-]0068]), and this involves combining prior art elements according to known methods to yield predictable results.
Regarding Claim 17, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1. Henry further teaches the system wherein one or more characters in the alphabet may be enabled or disabled for discrimination by the AI model based on the type of cartridge currently included in the electronic aerosol provision system ([0055]-[0056], Fig. 1; As the claim states that one or more characters in the alphabet may be enabled or disabled, the language “may be” appears to introduce optionality into the claim language. As Henry in view of Valafar discloses the electronic aerosol provision system of claim 1, the system reads on the limitations of Claim 17, and is necessarily capable of being configured to perform the claimed function).
Regarding Claims 18-19, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1. Henry further teaches the system wherein the AI model includes a facility to change the alphabet by removing or amending existing and/or by adding new characters, wherein changing the alphabet is performed by interacting with an external system ([0051], The gestures may be preset or user-defined. In some examples, the aerosol delivery device may enable the user to define gestures for various operations. This may be accomplished in a number of different manners, such as through direct interaction with the aerosol delivery device, or interaction with the aforementioned or another software application. [0049] refers to a software application operating on an external system. As the computing device of Henry has been modified such that an AI model performs the functions of the computing device, the AI model of Henry in view of Valafar would necessarily include a facility to change the alphabet by adding new gestures (characters)).
Claims 3-4, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Henry (US 2016/0158782 A1, cited on the IDS dated 9/19/2022) in view of Valafar (US 2018/0292910 A1, cited on the IDS dated 9/19/2022) as applied to Claims 1 and 5, and further in view of Veneshetty (US 2020/0256038 A1).
Regarding Claim 3-4, and 6, Henry in view of Valafar does not teach the system wherein the motion sensor is configured to output 3-axis angular motion or acceleration data, wherein the motion sensor is configured to output 3-axis absolute spatial orientation data, wherein the motion sensor is configured to collect samples of motion data at an interval in the range 0.001 ~ 0.01 seconds.
Veneshetty, directed to motion sensors ([0026]), teaches a motion sensor ([0026], Fig. 2; Movement characteristic sensor 32 may embody or comprise a six-axis or a nine-axis inertial motion sensor),
wherein the motion sensor is configured to output 3-axis linear motion or acceleration data, wherein the motion sensor is configured to output 3-axis angular motion or acceleration data, wherein the motion sensor is configured to output 3-axis absolute spatial orientation data ([0026], Fig. 2; Movement characteristic sensor 32 may embody or comprise a six-axis or a nine-axis inertial motion sensor. A nine-axis inertial motion sensor may combine a three-axis accelerometer with a three-axis gyroscope and a three-axis compass or magnetometer. It is reasonably understood that the three-axis accelerometer is configured to output 3-axis linear motion or acceleration data, the three-axis gyroscope is configured to output 3-axis angular motion or acceleration data, and the three-axis compass or magnetometer is configured to output 3-axis absolute spatial orientation data),
wherein the motion sensor is configured to collect samples of motion data at an interval in the range 0.001 ~ 0.01 seconds ([0042]-[0043], the measurement intervals may be two milliseconds in length (0.002 seconds)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to replace the motion sensor of Henry with the nine-axis inertial motion sensor of Veneshetty such that the motion sensor is configured to output 3-axis angular motion or acceleration data and 3-axis absolute spatial orientation data and to collect samples of motion data at an interval in the range 0.001 ~ 0.01 seconds, because Henry and Veneshetty are directed to motion sensors, Henry demonstrates that the motion sensor is only configured to output 3-axis linear motion or acceleration data (Henry, [0053]-[0059], Fig. 1-2; Motion sensor 146 may be a multi-axis accelerometer 200 configured to output 3-axis linear motion or acceleration data), outputting 3-axis angular motion or acceleration data and 3-axis absolute spatial orientation data would increase the accuracy of the motion sensor, and thereby increase the efficacy of the character discrimination performed by the AI model.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Henry (US 2016/0158782 A1, cited on the IDS dated 9/19/2022) in view of Valafar (US 2018/0292910 A1, cited on the IDS dated 9/19/2022), and further in view of Chai (US 2022/0091837 A1).
Regarding Claim 8, Henry in view of Valafar teaches the electronic aerosol provision system of claim 1, wherein the AI model is provided as a flat buffer file.
Chai, directed to machine learning ([0002]), teaches an AI model ([0034]-[0038], Machine-learned models),
wherein the AI model is provided as a flat buffer file ([0037]-[0038], The platform can use a conversion tool known as TensorFlow Lite Optimizing Converter (“TOCO”) to convert a standard TensorFlow graph of a model into a TensorFlow Lite graph, where TensorFlow Lite is a lightweight machine learning library designed for mobile applications. Converting an AI model using the TensorFlow Light Converter program generates a flat buffer file; see instant specification, pg 17, ln 21-23).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the AI model as a flat butter file because Valafar and Chai are directed to machine learning, Chai demonstrates that a mobile optimized version of an AI model can be generated by converting an AI model to a flat buffer file using the TensorFlow Lite Converter (Chai, [0038]), Henry demonstrates that the AI model is implemented on a mobile aerosol provision system (Henry, [0055]-[0056], Fig. 1; The control component 108 (computing device) of the aerosol delivery device 100 has been modified in view of Valafar such that the AI model of Valafar runs on the control component 108 of Henry), and this involves combining prior art elements according to known methods to yield predictable results.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.M.M./
Examiner, Art Unit 1755
/PHILIP Y LOUIE/Supervisory Patent Examiner, Art Unit 1755
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