DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/17/2025 has been entered.
Status of the Claims
Claims 1-2, 4-13, and 15-22 are pending. Claims 5, 12-13, and 15-20 are withdrawn. Claim 1 has been amended.
Response to Arguments
Applicant’s arguments with respect to claims 1-2, 4, 6-11, and 21-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4, 6-11, and 21-22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wagner et al. (US 2021/0346617 A1).
Regarding claim 1, Wagner discloses a vaporizer profile management system (“vaporizer system”, Fig. 1, ¶ 0084) comprising:
a vaporizer device (“vaporizer device 100”, Fig. 1, ¶ 0084) comprising:
an electronic memory (“memory”, ¶ 0086) configured to store vapor production data (combination of “product name (or other identifiers of a product)”, ¶ 0088 and “input parameters” including “a container temperature indicating a temperature being used to vaporize the product” ¶ 0086) from a vape session (“inhalation event”, ¶ 0105) for a sample material having an active ingredient (“vaporize the product and release an active compound from the product into the vapor”, ¶ 0086), the vapor production data including a sample identifier (“product name (or other identifiers of a product)”, ¶ 0088) and vapor production parameters representing in data a detected property that affects vapor production from the sample material (“a container temperature indicating a temperature being used to vaporize the product” ¶ 0086); and
a communications interface (“a vaporizer device includes a communication unit that effectuates communication,” ¶ 0026) configured to transmit the stored vapor production data (“may transmit the dosing model 164 to the vaporizer device 100 via the communication link between the user device 140 and the vaporizer device 100”, ¶ 0088);
a vapor analyzing device (combination of “puff simulation system 190” ¶ 0100, 0143, and testing equipment for analysis, ¶ 0090) configured to generate vapor content data (“measure the amount of an active compound that is released into the vapor”, ¶ 0143) derived from vapor collected during the vape session from an exhaust port of the vaporizer device (“vapor created by the simulated inhalation events”, ¶ 0100), the vapor content data further including the sample identifier (“product identifier that identifies the product”, ¶ 0090) and data including amount of the active ingredient collected from the vapor (“concentration of the product”, ¶ 0090, “the amount of an active compound that is released into the vapor”, ¶ 0143); and
a computing platform (“dosing platform 160”, Fig. 1, ¶ 0089, 0092, which includes “model creation system 170”, ¶ 0089, 0092) comprising a processor (“one or more processors”, ¶ 0092) configured to:
receive the stored vapor production data transmitted from the communications interface (“relevant factors taken into account by the model creation system 170 may include a container temperature (or coil temperature), a container cooldown rate or heating rate, the environmental temperature (or “ambient temperature”), the inhalation pressure, an ambient pressure, an amount of time between inhalation events, the dosage amount during the previous inhalation event”, ¶ 0093);
receive the vapor content data (“data relating to the generation and/or updating of the dosing model 164, including data obtained from the puff simulation system 190”, ¶ 0089); and
correlate, using a mathematical model (“dosing models 164”, ¶ 0093), the vapor content data with at least one sensor-measured (“temperature related features may be measured by temperature sensors”, ¶ 0094) vapor production parameter including receptacle temperature (“container temperature”, ¶ 0093) to generate a temperature-dependent correlation relationship between the vapor content data and the vapor production data (“container temperature is correlated to the dosage”, ¶ 0094); and
store the correlation relationship as a polynomial equation or lookup table for retrieval during future dosing requests (“dosing model 164 may be implemented . . . a linear model,” ¶ 0086, a linear model being a type of polynomial equation).
Regarding claim 2, Wagner discloses the vaporizer profile management system of claim 1 as discussed above. Wagner further discloses wherein the processor of the computing platform is further configured to analyze and correlate the vapor content data with the vapor production parameters (“the model creation system 170 generates dosing models based on the output of a puff simulation system 190 . . . . that simulates inhalation events, captures the vapor created by the simulated inhalation events, and measures the dosages in the resultant vapor”, ¶ 0100, and “the puff simulation system 190 may record one or more features of the inhalation event . . . . the container temperature during the inhalation event”, ¶ 0105).
Regarding claim 4, Wagner discloses the vaporizer profile management system of claim 1 as discussed above. Wagner further discloses wherein the temperature-dependent correlation relationship of the vapor content data with the vapor production data is represented by a polynomial equation (“dosing model 164 may be implemented . . . a linear model,” ¶ 0086, a linear model being a type of polynomial equation).
Regarding claim 6, Wagner discloses the vaporizer profile management system of claim 1 as discussed above. Wagner further discloses wherein the computing platform comprises a machine-learning module configured to analyze relationships between the vapor production data and the vapor content data (“The model creation system 170 may then execute a machine-learning algorithm (e.g., neural network, regression-based learning, decision trees, or the like) to determine a transfer function of the dosing model”, ¶ 0106), and further configured to generate a vaping profile for the vaporizer device (“the dosing models 164 are generated by the model creation system 170”, ¶ 0089, “the vaporizer device 100 loads a dosing model 164 that is specific to the product being vaporized into its memory”, ¶ 0086).
Regarding claim 7, Wagner discloses the vaporizer profile management system of claim 1 as discussed above. Wagner further discloses wherein the vaping profile comprises data including the sample identifier (¶ 0108) and a dosing relationship between the active ingredient and at least one of crucible temperature, air flow rate, pressure differential, and duration of flow (“the model creation system 170 may include a computing device that creates the dosing models 164 . . . . relevant factors taken into account by the model creation system 170 may include a container temperature (or coil temperature), a container cool down rate or heating rate, the environmental temperature (or "ambient temperature"), the inhalation pressure, an ambient pressure, an amount of time between inhalation events, the dosage amount during the previous inhalation event”, ¶ 0093).
Regarding claim 8, Wagner discloses the vaporizer profile management system of claim 1 as discussed above. Wagner further discloses wherein the computing platform comprises a machine-learning module configured to analyze relationships between the vapor production data and the vapor content data (“The model creation system 170 may then execute a machine-learning algorithm (e.g., neural network, regression-based learning, decision trees, or the like) to determine a transfer function of the dosing model”, ¶ 0106), and further configured to generate a vaping profile for the sample material (“the dosing models 164 are generated by the model creation system 170”, ¶ 0089, “the vaporizer device 100 loads a dosing model 164 that is specific to the product being vaporized into its memory”, ¶ 0086).
Regarding claim 9, Wagner discloses the vaporizer profile management system of claim 1 as discussed above. Wagner further discloses wherein the computing platform comprises a machine-learning module configured to analyze relationships between the vapor production data and the vapor content data (“The model creation system 170 may then execute a machine-learning algorithm (e.g., neural network, regression-based learning, decision trees, or the like) to determine a transfer function of the dosing model”, ¶ 0106), and further configured to generate a vaping profile for sample material and the vaporizer device (“the dosing models 164 are generated by the model creation system 170”, ¶ 0089, “the vaporizer device 100 loads a dosing model 164 that is specific to the product being vaporized into its memory”, ¶ 0086).
Regarding claim 10, Wagner discloses the vaporizer profile management system of claim 8 as discussed above. Wagner further discloses wherein the computing platform is further configured to:
receive a request from a vaporizer user's vaporizer device for vaporizer parameters to provide a desired dose of active ingredient (“the model creation system 170 may include a computing device that creates the dosing models 164. In some of these embodiments, the computing device may receive input from one or more sensors that monitor conditions of a vaporizer device 100, the product, the container, and/or an environment of the vaporizer device 100. Many factors may be taken into consideration when determining the predicted dose using a model. Thus, when creating dosing models 164, the model creation system 170 may monitor and/or take into consideration one or more of these factors during model creation. In some cases, these factors are implicit and do not need to be measured. In embodiments, relevant factors taken into account by the model creation system 170 may include a container temperature (or coil temperature), a container cooldown rate or heating rate, the environmental temperature (or “ambient temperature”), the inhalation pressure, an ambient pressure, an amount of time between inhalation events, the dosage amount during the previous inhalation event, the type of container (e.g., the type of cartridge or receptacle), the cartridge volume, an airflow temperature, airflow curves, an amount of product in the container, vapor density, particle size in the vapor, a viscosity of the product (if a liquid), an opacity of the product (if a liquid), the density of the product (if a solid), and/or any other suitable factors”, ¶ 0093);
query a database containing the vaping profile (“retrieving the product record of the product to be vaporized from the product database based on the product identifier”, ¶ 0037); and
determine the vaporizer parameters to provide the desired dose of active ingredient (“The transfer function of a dosing model 164 translates the input parameters surrounding an inhalation event to a predicted dosage at a given time during the inhalation event”, ¶ 0106; and provide the vaporizer parameters to the vaporizer device of the vaporizer user (“The vaporizer device 100 may receive the target dosage amount and the dosage model 164”, ¶ 0110).
Regarding claim 11, Wagner discloses the vaporizer profile management system of claim 1 as discussed above. Wagner further discloses wherein the computing platform is a remote cloud computing platform (“the processor may be part of a . . . cloud server”, ¶ 0154).
Regarding claim 21, Wagner discloses the vaporizer profile management system of claim 1 as discussed above. Wagner further discloses wherein the vapor production parameters include an identification of the active ingredient, a crucible temperature, an air flow rate, a pressure differential, or a duration of flow (¶ 0086, 0093).
Regarding claim 22, Wagner discloses the vaporizer profile management system of claim 1 as discussed above. Wagner further discloses wherein the vaporizer device comprises:
a receptacle adapted to contain the sample material including the active ingredient from which vapor is produced in the vape session (“a container may be a permanent or removable cartridge 134 (e.g., a 510 thread cartridge as shown in FIG. 6, a disposable pod, a refillable pod, a refillable tank, and the like), whereby the cartridge contains an “eliquid” (also referred to as a “concentrate” or “oil” or “juice”). Examples of eliquids that may be vaporized include, but are not limited to, nicotine juices, cannabis oils, CBD oils, herbal oils, and the like”, ¶ 0085); and
a receptacle temperature sensor configured to detect a temperature of the receptacle during a vape session (“the vaporizer device 100 may include a temperature sensor that is placed in proximity to the container, such that the container (or coil) temperature can be approximated from the temperature reading output by the temperature sensor”, ¶ 0094), wherein the vapor production parameters include the temperature of the receptacle and the temperature-dependent correlation relationship correlates the temperature of the receptacle with the vapor content data (“the model creation system 170 generates dosing models 164”, ¶ 0092, “when creating dosing models 164, the model creation system 170 may monitor and/or take into consideration one or more of these factors during model creation”, ¶ 0093).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COURTNEY G CULBERT whose telephone number is (571)270-0874. The examiner can normally be reached Monday-Friday 9am-4pm.
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/C.G.C./Examiner, Art Unit 1747
/Michael H. Wilson/Supervisory Patent Examiner, Art Unit 1747