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 .
Formal Matters
Applicant's response, filed 03 December 2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1, 3-10, 12-31, 33-39, 41-46, and 48-49 are currently pending and have been examined.
Claims 1, 3-6, 31, 33-35, 49 have been amended.
Claims 2, 11, 32, 40, 47, and 50 have been canceled.
Claims 1, 3-10, 12-31, 33-39, 41-46, and 48-49 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. GB2009486.8, filed on 12/22/2022.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 22 June 2020 claiming benefit to GB2009486.8.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 1, 31, 49, 8, and 37 are rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Independent claims 1, 31, and 49 recite wherein the user’s operation of the delivery device comprises activation of a user interface to trigger generation of an aerosol by the delivery device and/or an inhalation action of the delivery device to trigger generation to trigger generation of an aerosol by the delivery device [emphasis added] while dependent claims 8 and 37 recite in which the operation of the delivery device comprises one selected from the list consisting of: i. activation of a user interface to trigger generation of an aerosol by the delivery device; and ii. an inhalation action on the delivery device to trigger generation of an aerosol by the delivery device [emphasis added]. The dependent claim, by requiring just one selection from a consisting/closed list, fails to include all the limitations of the independent claim wherein both may be selected from a comprising/open list. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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, 3-10, 12-31, 33-39, 41-46, and 48-49 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.
Step 1 – Statutory Categories of Invention:
Claims 1, 3-10, 12-31, 33-39, 41-46, and 48-49 are drawn to a system, method, device, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a system for user feedback for a user of a delivery device within a delivery ecosystem in part performing the steps of obtain two or more user factors indicative of a state of the user, wherein a first obtained user factor of the two or more user factors is based upon an aspect of the user’s operation of the delivery device; and wherein a second obtained user factor of the two or more user factors is based upon an at aspect of the user’s situation separate from the user’s operation of the delivery device, the aspect of the user’s situation occurring during one or more selected from the list consisting of: i. a predetermined period preceding the user’s operation of the delivery device, ii. a predetermined period following the user’s operation of the delivery device, and iii. a period during the user’s operation of the delivery device; and identify a corresponding feedback action expected to alter a state of the user as indicated at least in part by the first and second obtained user factors; and a circular data buffer configured to record data descriptive of the second user factor.
Independent claim 31 recites a method for user feedback for a user of a delivery device within a delivery ecosystem in part performing the steps of obtaining two or more user factors indicative of a state of the user, wherein a first obtained user factor of the two or more user factors is based upon an aspect of the user’s operation of the delivery device; and wherein a second obtained user factor of the two or more user factors is based upon an at aspect of the user’s situation separate from the user’s operation of the delivery device, the aspect of the user’s situation occurring during one or more selected from the list consisting of: i. a predetermined period preceding the user’s operation of the delivery device, ii. a predetermined period following the user’s operation of the delivery device, and iii. a period during the user’s operation of the delivery device; recording data descriptive of the second user factor in a circular data buffer; and identifying a corresponding feedback action expected to alter a state of the user as indicated at least in part by the first and second obtained user factors.
Independent claim 49 recites a system for user feedback for a user of a delivery device within a delivery ecosystem in part performing the steps of obtaining two or more user factors indicative of a state of the user, wherein a first obtained user factor of the two or more user factors is based upon an aspect of the user’s operation of the delivery device; and wherein a second obtained user factor of the two or more user factors is based upon an aspect of the user’s situation separate from the user’s operation of the delivery device, the aspect of the user’s situation occurring during one or more selected from the list consisting of: i. a predetermined period preceding the user’s operation of the delivery device, ii. a predetermined period following the user’s operation of the delivery device, and iii. a period during the user’s operation of the delivery device, recording data descriptive of the second user factor in a circular data buffer; and identifying a corresponding feedback action expected to alter a state of the user as indicated at least in part by the first and second obtained user factors.
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to ii. considering historical usage information while inputting data, BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018) – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping). Examiner notes that the “delivery device” in the instant claims is not actively claimed, but instead utilized as descriptive material to the abstract idea. Therefore “of the delivery device” is considered under Step 2A Prong 1 when used as a descriptive clause in a prepositional phrase.
Dependent claim 3 recites, in part, when the user operates the delivery device, for the second obtained user factor which occurs during the predetermined period preceding the user’s operation of the delivery device, the circular data buffer is configured to output, for association with data of the first obtained user factor, recorded data descriptive of the second obtained user factor for the predetermined period preceding the user’s operation of the delivery device.
Dependent claim 4 recites, in part, when the user operates the delivery device, for the second obtained user factor which occurs during the predetermined period following the user’s operation of the delivery device, the circular data buffer is configured to record data that is descriptive of the second obtained user factor following the user’s operation of the delivery device for the predetermined period following the user’s operation of the delivery device, and is configured to subsequently output the recorded data for association with data of the first obtained user factor.
Dependent claim 5 recites, in part, when the user operates the delivery device, for the second obtained user factor which occurs during the period during the user’s operation of the delivery device, the circular data buffer is configured to record data that is descriptive of the second obtained user factor during the user’s operation of the delivery device, and is configured to subsequently output the recorded data for association with data of the first obtained user factor.
Dependent claim 6 recites, in part, the circular buffer is configured to record one selected from the list consisting of: i. 5 seconds’ worth of the data; ii. 10 seconds’ worth of the data; iii. 15 seconds’ worth of the data; iv. 30 seconds’ worth of the data; v. 45 seconds’ worth of the data; and vi. 60 seconds’ worth of the data.
Dependent claim 7 recites, in part, data of the second obtained user factor is used to provide contextual data for data of the first obtained user factor.
Dependent claim 9 recites, in part, select the corresponding feedback action identified for the delivery device within the delivery ecosystem.
Dependent claim 10 recites, in part, cause a modification of one or more operations of at the delivery device within the delivery ecosystem according to the selected corresponding feedback action.
Dependent claim 12 recites, in part, in which data relating to the second obtained user factor is obtained from a textual analysis of content that the user has interacted with.
Dependent claim 13 recites, in part, in which the content comprises one or more selected from the list consisting of: i. one or more social media posts that the user is reading or sending; ii. one or more news articles that the user is reading; iii. one or more websites that the user is visiting; iv. one or more searches that the user is making online; v. one or more text messages that the user is reading or sending; vi. content of one or more calls that the user is participating in; and vii. content of one or more conversations that the user is having.
Dependent claim 14 recites, in part, in which the content comprises calendar information for the user.
Dependent claim 15 recites, in part, in which the calendar information indicates one or more selected from the list consisting of: i. a person that the user is currently meeting or about to meet; ii. an event that the user is currently attending or about to attend; iii. a location that the user is currently visiting or about to visit; and iv. a task that the user is currently performing or about to perform.
Dependent claim 16 recites, in part, in which the content comprises one or more user responses to a questionnaire relating to the state of the user.
Dependent claim 17 recites, in part, in which data relating to the second obtained user factor is obtained from the user’s interaction with devices within the delivery ecosystem other than the delivery device.
Dependent claim 18 recites, in part, in which the user’s interaction comprises one or more selected from the list consisting of: i. a choice of app used by the user; ii. a choice of device within the delivery ecosystem by the user to interact with; iii. a duration of interaction by the user with a chosen app or a chosen device within the delivery ecosystem; iv. a type of interaction by the user with a chosen app or a chosen device within the delivery ecosystem; and v. a type of media consumed with a chosen app or a chosen device within the delivery ecosystem.
Dependent claim 19 recites, in part, in which data relating the second obtained user factor is based is obtained from data indicative of the user’s environment.
Dependent claim 20 recites, in part, in which the data indicative of the user’s environment is based upon one or more selected from the list consisting of: i. the user’s current or next location; ii. whether the user is in an open or enclosed space; iii. whether the user is in daylight or artificial light; iv. the user’s current mode of transport; and iv. weather at the user’s location.
Dependent claim 21 recites, in part, in which data relating to the second user factor is obtained from data indicative of the user’s social situation.
Dependent claim 22 recites, in part, in which the data indicative of the user’s social situation is based upon one or more selected from the list consisting of: i. whether the user is alone or with one or more others; ii. whether the user is with one or more work colleagues; iii. whether the user is with one or more friends; iv. whether the user is with one or more family members; and v. whether the user is with one or more children.
Dependent claim 23 recites, in part, in which data relating to the second user factor is obtained from data indicative of a past history of the user.
Dependent claim 24 recites, in part, in which the data indicative of the past history of the user is based on one or more selected from the list consisting of: i. the user’s place of birth; ii. a cultural background of the user; iii. a religion of the user; iv. a purchase history of the user; and v. settings preferences of the user for the delivery device or one or more other devices of the delivery ecosystem.
Dependent claim 25 recites, in part, use a plurality of the two or more user factors as input when identifying the corresponding feedback action.
Dependent claim 26 recites, in part, in which the estimation… does not generate an explicit estimation of user state as an interim procedure in the identification of the corresponding feedback action.
Dependent claim 27 recites, in part, in which the estimation … uses different respective [models] responsive to a composition of the two or more user factors provided as input.
Dependent claim 28 recites, in part, generate the corresponding feedback action relating to one or more selected from the list consisting of: i. a behavioral feedback action for affecting at least a first behavior of the user; ii. a pharmaceutical feedback action for affecting consumption of an active ingredient by the user; and i. a non-consumption feedback action for affecting one or more non-consumption operations of the delivery ecosystem.
Dependent claim 33 recites, in part, when the user operates the delivery device, for the second obtained user factor which occurs during the predetermined period preceding the user’s operation of the delivery device, the method comprises: outputting, for association with data of the first obtained user factor, recorded data descriptive of the second obtained user factor for the predetermined period preceding the user’s operation of the delivery device.
Dependent claim 34 recites, in part, when the user operates the delivery device, for the second obtained user factor which occurs during the predetermined period following the user’s operation of the delivery device, the method comprises: recording in the circular data buffer data that is descriptive of the second obtained user factor following the user’s operation of the delivery device for the predetermined period following the user’s operation of the delivery device; and subsequently outputting the recorded data for association with data of the first obtained user factor.
Dependent claim 35 recites, in part, when the user operates the delivery device, for the second obtained user factor which occurs during the period during the user’s operation of the delivery device, the method comprises : recording in the circular data buffer data that is descriptive of the second obtained user factor during the user’s operation of the delivery device; and subsequently outputting the recorded data for association with data of the first obtained user factor.
Dependent claim 36 recites, in part, data of the second obtained user factor is used to provide contextual data for data of the first obtained user factor.
Dependent claim 38 recites, in part, selecting the corresponding feedback action identified by the estimation step for the delivery device within the delivery ecosystem.
Dependent claim 39 recites, in part, causing a modification of one or more operations of the delivery device within the delivery ecosystem according to the selected corresponding feedback action.
Dependent claim 42 recites, in part, in which data relating to the second obtained user factor is obtained from a textual analysis of content that the user has interacted with.
Dependent claim 43 recites, in part, in which data relating to the second obtained user factor is obtained from the user’s interaction with devices within the delivery ecosystem other than the delivery device.
Dependent claim 44 recites, in part, in which data relating to the second obtained user factor is obtained from data indicative of the user’s environment.
Dependent claim 45 recites, in part, in which data relating to the second obtained user factor is obtained from data indicative of the user’s social situation.
Dependent claim 46 recites, in part, in which data relating to the second obtained user factor is obtained from data indicative of a past history of the user.
Dependent claim 48 recites, in part, in which identifying comprises generating the corresponding feedback action relating to one or more selected from the list consisting of: i. a behavioral feedback action for affecting at least a first behavior of the user; ii. a pharmaceutical feedback action for affecting consumption of an active ingredient by the user; and iii. a non-consumption feedback action for affecting one or more non-consumption operations of the delivery ecosystem.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 31 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 1 recites an obtaining processor and an estimation processor. Claim 49 recites a computer program stored on a non-transitory machine-readable medium comprising computer executable instructions. Claims 9-10 recite a feedback processor. Claims 30 recite in which functionality of one or more of the obtaining processor, estimation processor, and feedback processor is provided at least in part by one or more processors located within the delivery device or one or more other devices of the delivery ecosystem, or a remote server. Claim 29 recites a one or more of: i. one or more mobile terminals; ii one or more wearable devices; and one or more docking units for the delivery device. The specification defines the hardware as either integrated into an e-cigarette or a remote processing device such as a mobile smartphone (see the instant specification on p 21 lines 13-21) specifically noting that the hardware is conventional hardware suitably adapted by software instruction on p. 105 lines 3-6. The use of these processors and corresponding hardware devices are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Furthermore, the specification defines the “delivery device” as any device capable of delivering any consumable product to a user (see the instant specification on p. 5 line 18- p. 6 line 4). The delivery device therefore amounts to a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself (MPEP § 2106.05(h) similar to example vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) wherein the additional elements do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use). Furthermore, the delivery device generally attempts to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Claim 27 recites machine learning systems. The specification does not define a specific model or algorithm for the machine learning systems (see the instant specification on p. 62 lines 9-11). The use of a machine learning systems, in this case to identify a corresponding feedback action, only recites the machine learning systems as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claims 1, 31, 49, 8, and 37 recite wherein the user’s operation of the delivery device comprises activation of a user interface to trigger generation of an aerosol by the delivery device and/or an inhalation action of the delivery device to trigger generation to trigger generation of an aerosol by the delivery device OR in which the operation of the delivery device comprises one selected from the list consisting of: i. activation of a user interface to trigger generation of an aerosol by the delivery device; and ii. an inhalation action on the delivery device to trigger generation of an aerosol by the delivery device. The limitations are only recited as a tool which only serves as display/output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
Claim 41 recites a first physical sensor. The specification defines the sensor for obtaining a second factor based on an aspect of the user's situation separate from the user’s operation of the delivery device to be any sensor for determining contextual information such as a microphone or camera (instant specification on p. 36 lines 13-14). The use of a first physical sensor, in this case to obtain data relating to the second obtained user factor, only recites the first physical sensor as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre-solution activity) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer. Claim 27 recites machine learning systems.
Claim 1 recites an obtaining processor and an estimation processor. Claim 49 recites a computer program stored on a non-transitory machine-readable medium comprising computer executable instructions. Claims 9-10 recite a feedback processor. Claim 27 recites machine learning systems. Claim 29 recites a one or more of: i. one or more mobile terminals; ii one or more wearable devices; and one or more docking units for the delivery device. Claims 30 recite in which functionality of one or more of the obtaining processor, estimation processor, and feedback processor is provided at least in part by one or more processors located within the delivery device or one or more other devices of the delivery ecosystem, or a remote server.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claims 1, 31, 49, 8, and 37 recite in which the operation of the delivery device comprises one selected from the list consisting of: i. activation of a user interface to trigger generation of an aerosol by the delivery device; and ii. an inhalation action on the delivery device to trigger generation of an aerosol by the delivery device. The broadest reasonable interpretation of this claim only requires the output of the feedback on a display device. Therefore, the courts have decided that presenting generated data as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example iv. presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
Claim 41 recites a first physical sensor for obtaining data relating to the second obtained user factor. The courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)). Furthermore, the use of a sensor to collect biometric information for a user is well understood, routine, and conventional. This position is supported by Majumder et al., Wearable Sensors for Remote Health Monitoring, 17(1) SENSORS (BASEL) 1-45 (Jan 12, 2017) teaching on wearable sensors collecting live biometric health data from users as known in the art and commercially available in the § 1. Introduction and Table 1 on p. 2 (treated as a review under MPEP § 2106.07(a)(III)(C) that describes the state of the art and discusses what is well-known and in common use in the relevant industry). Therefore, the use of a first physical sensor is not sufficient to amount to significantly more than the recited judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1, 3-10, 12-31, 33-39, 41-46, and 48-49 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Claims 1, 3-10, 12-31, 33-39, 41-46, and 48-49 are rejected under 35 U.S.C. 103 as being unpatentable over Dagnello et al. (US Patent App No 2019/0167927)[hereinafter Dagnello] in view of Martinez, Heart, and Ong, Sensor Network Applications, Sensor Network 50-56 (August 2004)[hereinafter Martinez].
As per claim 1, Dagnello teaches on the following limitations of the claim:
a user feedback system for a user of a delivery device within a delivery ecosystem, comprising is taught in the Detailed Description in ¶ 0077, ¶ 0082, ¶ 0092, and in the Figures at fig. 5 (teaching on user experience and feedback model for a personal vapor inhalation device (PVID) device (treated as synonymous to a delivery device) connected to a central processing server (treated as synonymous to a delivery ecosystem))
an obtaining processor adapted to obtain two or more user factors indicative of a state of the user is taught in the Detailed Description in ¶ 0068 and ¶ 0077 (teaching on collecting both the user's descriptive inhalation data and other user data)
wherein a first obtained user factor of the two or more user factors is based upon an aspect of the user’s operation of the delivery device; and is taught in the Detailed Description in ¶ 0068-69 (teaching on collecting descriptive inhalation data related to the user's operation of the inhalation device)
wherein the user's operation of the delivery device comprises activation of a user interface to trigger generation of an aerosol by the delivery device and/or inhalation action on the delivery device to trigger generation of an aerosol by the delivery device is taught in the Detailed Description in ¶ 0068-69 (teaching on collecting descriptive inhalation data related to the user's operation of the inhalation device via triggering the vaporizing element)
wherein a second obtained user factor of the two or more user factors is based upon an at aspect of the user’s situation separate from the user’s operation of the delivery device, the aspect of the user’s situation occurring during one or more selected from the list consisting of is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation)
i. a predetermined period preceding the user’s operation of the delivery device, ii. a predetermined period following the user’s operation of the delivery device, and iii. a period during the user’s operation of the delivery device; and is taught in the Detailed Description in ¶ 0082 and ¶ 0104 (teaching on collecting the user data before, during, and after the user of the PVID device in predetermined time frames)
an estimation processor adapted to identify a corresponding feedback action expected to alter a state of the user as indicated at least in part by the first and second obtained user factors; and is taught in the Detailed Description in ¶ 0067 and ¶ 0103 (teaching on a machine learning algorithm for analyzing the user data to generate specific recommendations (treated as synonymous to a feedback action) related to the user's state and changes)
record data descriptive of the second user factor is taught in the Detailed Description in ¶ 0060, ¶ 0082, and ¶ 0104 (teaching on collecting and recording the user histological, environmental, and experience data before, during, and after the user of the PVID device in predetermined time frames (treated as synonymous to a circular data buffer) such as over days, hours, minutes, and seconds)
Dagnello does not explicitly teach the following limitation of claim 1. Martinez, however, does teach the following:
a circular data buffer configured to record data descriptive of the second user factor is taught in the § Sensor nodes on p. 53 col 1 (teaching on collecting environmental data from a remote sensor and storing in a ring buffer data structure)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the ring buffer structure of Martinez for the time structured record system of the Dangello. Thus, the simple substitution of one known element for another producing a predictable result of renders the claim obvious. Examiner also notes that one of ordinary skill in the art would recognize that a circular buffer is the industry standard for small electronic device digital signal processors as further evidenced by Smith, the Scientist and Engineer's Guide to Digital Signal Processing, Ch. 8 Digital Signal Processors 503-534 (1997) in § Circular Buffering on p. 506-509.
As per claim 3, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which: when the user operates the delivery device, for the second obtained user factor which occurs during the predetermined period preceding the user’s operation of the delivery device, the circular data buffer is configured to output, for association with data of the first obtained user factor, recorded data descriptive of the second obtained user factor for the predetermined period preceding the user’s operation of the delivery device is taught in the Summary in ¶ 0013, in the Detailed Description in ¶ 0082, and ¶ 0104 (teaching on collecting the user histological, environmental, and experience data before, during, and after the user of the PVID device in predetermined time frames (treated as synonymous to a circular data buffer)such as over days , hours , minutes , and seconds and associating the user's personal data and collecting descriptive inhalation data together with associated timestamps)
As per claim 4, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which: when the user operates the delivery device, for the second obtained user factor which occurs during the predetermined period following the user’s operation of the delivery device, the circular data buffer is configured to record data that is descriptive of the second obtained user factor following the user’s operation of the delivery device for the predetermined period following the user’s operation of the delivery device, and is configured to subsequently output the recorded data for association with data of the first obtained user factor is taught in the Summary in ¶ 0013, in the Detailed Description in ¶ 0082, and ¶ 0104 (teaching on collecting the user histological, environmental, and experience data before, during, and after the user of the PVID device in predetermined time frames (treated as synonymous to a circular data buffer) such as over days, hours, minutes, and seconds and associating the user's personal data and collecting descriptive inhalation data together with associated timestamps)
As per claim 5, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which: when the user operates the delivery device, for the second obtained user factor which occurs during the period during the user’s operation of the delivery device, the circular data buffer is configured to record data that is descriptive of the second obtained user factor during the user’s operation of the delivery device, and is configured to subsequently output the recorded data for association with data of the first obtained user factor is taught in the Summary in ¶ 0013, in the Detailed Description in ¶ 0082, and ¶ 0104 (teaching on collecting the user histological, environmental, and experience data before, during, and after the user of the PVID device in predetermined time frames (treated as synonymous to a circular data buffer) such as over days, hours, minutes, and seconds and associating the user's personal data and collecting descriptive inhalation data together with associated timestamps)
As per claim 6, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which: the circular buffer is configured to record one selected from the list consisting of: i. 5 seconds’ worth of the data; ii. 10 seconds’ worth of the data; iii. 15 seconds’ worth of the data; iv. 30 seconds’ worth of the data; v. 45 seconds’ worth of the data; and vi. 60 seconds’ worth of the data is taught in the Summary in ¶ 0013, in the Detailed Description in ¶ 0082, and ¶ 0104 (teaching on collecting the user histological, environmental, and experience data before, during, and after the user of the PVID device in predetermined time frames (treated as synonymous to a circular data buffer) such as over days, hours, minutes, and seconds and associating the user's personal data and collecting descriptive inhalation data together with associated timestamps - Examiner notes that "seconds" includes the rage of 0-60 seconds wherein said range is overlapping with the options of the instant claims and there is no showing of criticality of the claimed range within the instant disclosure (see MPEP § 2144.05(I)))
As per claim 7, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which data of the second obtained user factor is used to provide contextual data for data of the first obtained user factor is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation but related to effects of the use)
As per claim 8, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which the operation of the delivery device comprises one selected from the list consisting of: i. activation of a user interface to trigger generation of an aerosol by the delivery device; and ii. an inhalation action on the delivery device to trigger generation of an aerosol by the delivery device is taught in the Detailed Description in ¶ 0068-69 (teaching on collecting descriptive inhalation data related to the user's operation of the inhalation device via triggering the vaporizing element)
As per claim 9, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, comprising: a feedback processor adapted to select the corresponding feedback action identified by the estimation processor for the delivery device within the delivery ecosystem is taught in the Detailed Description in ¶ 0067 and ¶ 0103 (teaching on a machine learning algorithm for analyzing the user data to generate (treated as synonymous to selecting)specific recommendations (treated as synonymous to a feedback action) related to the user's state and changes)
As per claim 10, the combination of Dagnello and Martinez discloses the limitations of claim 9. Dagnello also discloses the following:
the user feedback system according to claim 9, in which the feedback processor is adapted to cause a modification of one or more operations of at the delivery device within the delivery ecosystem according to the selected corresponding feedback action is taught in the Detailed Description in ¶ 0104 (teaching on the machine learning algorithm determining a user state and automatically delivering a particular substances to the user (treated as synonymous to a modification to the operations) via the PVID device)
As per claim 12, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which data relating to the second obtained user factor is obtained from a textual analysis of content that the user has interacted with is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, and ¶ 0090 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation wherein the data is collected from social media engagement of the user)
As per claim 13, the combination of Dagnello and Martinez discloses the limitations of claim 12. Dagnello also discloses the following:
the user feedback system according to claim 12, in which the content comprises one or more selected from the list consisting of: i. one or more social media posts that the user is reading or sending; ii. one or more news articles that the user is reading; iii. one or more websites that the user is visiting; iv. one or more searches that the user is making online; v. one or more text messages that the user is reading or sending; vi. content of one or more calls that the user is participating in; and vii. content of one or more conversations that the user is having is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, and ¶ 0090 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation wherein the data is collected from social media engagement of the user)
As per claim 14, the combination of Dagnello and Martinez discloses the limitations of claim 12. Dagnello also discloses the following:
the user feedback system according to claim 12, in which the content comprises calendar information for the user is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, and ¶ 0090 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation wherein the data is collected from working and personal activity schedules)
As per claim 15, the combination of Dagnello and Martinez discloses the limitations of claim 14. Dagnello also discloses the following:
the user feedback system according to claim 14, in which the calendar information indicates one or more selected from the list consisting of: i. a person that the user is currently meeting or about to meet; ii. an event that the user is currently attending or about to attend; iii. a location that the user is currently visiting or about to visit; and iv. a task that the user is currently performing or about to perform is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, ¶ 0090, and in the Claims in claim 109 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation wherein the data is collected from working and personal activity schedules (treated as synonymous to a location that the user is about to or currently visiting) and geographical location)
As per claim 16, the combination of Dagnello and Martinez discloses the limitations of claim 12. Dagnello also discloses the following:
the user feedback system according to claim 12, in which the content comprises one or more user responses to a questionnaire relating to the state of the user is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data via a questionnaire)
As per claim 17, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which data relating to the second obtained user factor is obtained from the user’s interaction with devices within the delivery ecosystem other than the delivery device is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data via a questionnaire via a mobile phone)
As per claim 18, the combination of Dagnello and Martinez discloses the limitations of claim 17. Dagnello also discloses the following:
the user feedback system according to claim 17, in which the user’s interaction comprises one or more selected from the list consisting of: i. a choice of app used by the user; ii. a choice of device within the delivery ecosystem by the user to interact with; iii. a duration of interaction by the user with a chosen app or a chosen device within the delivery ecosystem; iv. a type of interaction by the user with a chosen app or a chosen device within the delivery ecosystem; and v. a type of media consumed with a chosen app or a chosen device within the delivery ecosystem is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data via a questionnaire via a mobile phone application (treated as synonymous to a choice of a device and a choice of an app within the delivery ecosystem))
As per claim 19, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which data relating the second obtained user factor is based is obtained from data indicative of the user’s environment is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation)
As per claim 20, the combination of Dagnello and Martinez discloses the limitations of claim 19. Dagnello also discloses the following:
the user feedback system according to claim 19, in which the data indicative of the user’s environment is based upon one or more selected from the list consisting of: i. the user’s current or next location; ii. whether the user is in an open or enclosed space; iii. whether the user is in daylight or artificial light; iv. the user’s current mode of transport; and iv. weather at the user’s location is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, ¶ 0090, and in the Claims in claim 109 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation wherein the data is collected from working and personal activity schedules (treated as synonymous to a location that the user is next to visit or currently visiting) and geographical location)
As per claim 21, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which data relating to the second user factor is obtained from data indicative of the user’s social situation is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, ¶ 0087, ¶ 0090, and in the Summary in ¶ 0010 (teaching on collecting user's personal data including physiological, environmental, and experience data wherein the environmental data includes event/occasion data (treated as synonymous to social data))
As per claim 22, the combination of Dagnello and Martinez discloses the limitations of claim 21. Dagnello also discloses the following:
the user feedback system according to claim 21, in which the data indicative of the user’s social situation is based upon one or more selected from the list consisting of: i. whether the user is alone or with one or more others; ii. whether the user is with one or more work colleagues; iii. whether the user is with one or more friends; iv. whether the user is with one or more family members; and v. whether the user is with one or more children is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, ¶ 0087, ¶ 0090, and in the Summary in ¶ 0010 (teaching on collecting user's personal data including physiological, environmental, and experience data wherein the environmental data includes event/occasion data (treated as synonymous to the user being alone or with others))
As per claim 23, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which data relating to the second user factor is obtained from data indicative of a past history of the user is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, ¶ 0087, and ¶ 0090 (teaching on collecting user's personal data including physiological, environmental, and experience data wherein the personal data includes social history/description data)
As per claim 24, the combination of Dagnello and Martinez discloses the limitations of claim 23. Dagnello also discloses the following:
the user feedback system according to claim 23, in which the data indicative of the past history of the user is based on one or more selected from the list consisting of: i. the user’s place of birth; ii. a cultural background of the user; iii. a religion of the user; iv. a purchase history of the user; and v. settings preferences of the user for the delivery device or one or more other devices of the delivery ecosystem is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, ¶ 0087, and ¶ 0090 (teaching on collecting user's personal data including physiological, environmental, and experience data wherein the personal data includes social history/description data including demographic data, religious data, race, etc.)
As per claim 25, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which the estimation processor is operable to use a plurality of the two or more user factors as input when identifying the corresponding feedback action is taught in the Detailed Description in ¶ 0067 and ¶ 0103 (teaching on a machine learning algorithm for analyzing all of the user data to generate specific recommendations (treated as synonymous to a feedback action) related to the user's state and changes)
As per claim 26, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which the estimation processor does not generate an explicit estimation of user state as an interim procedure in the identification of the corresponding feedback action is taught in the Detailed Description in ¶ 0067 and ¶ 0103 (teaching on a machine learning algorithm for analyzing all of the user data to generate specific recommendations (treated as synonymous to a feedback action) related to the user's changes - Examiner notes that the reference teaches on outcomes the machine learning algorithm "can" generate not required outcomes)
As per claim 27, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which the estimation processor uses different respective machine learning systems responsive to a composition of the two or more user factors provided as input to the estimation processor is taught in the Detailed Description in ¶ 0067, ¶ 0092, and ¶ 0103 (teaching on a plurality of predictive machine learning algorithms for analyzing all of the user data to generate specific recommendations (treated as synonymous to a feedback action) related to the user's changes or state)
As per claim 28, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which the estimation processor is operable to generate the corresponding feedback action relating to one or more selected from the list consisting of: i. a behavioral feedback action for affecting at least a first behavior of the user; ii. a pharmaceutical feedback action for affecting consumption of an active ingredient by the user; and i. a non-consumption feedback action for affecting one or more non-consumption operations of the delivery ecosystem is taught in the Detailed Description in ¶ 0067 and ¶ 0103 (teaching on a machine learning algorithm for analyzing the user data to generate specific recommendations (treated as synonymous to a feedback action) related to the user's state and changes, such as a change posture while inhaling (treated as synonymous to a behavioral feedback action))
As per claim 29, the combination of Dagnello and Martinez discloses the limitations of claim 1. Dagnello also discloses the following:
the user feedback system according to claim 1, in which the delivery ecosystem comprises one or more selected from the list consisting of: i one or more mobile terminals; ii one or more wearable devices; and iii one or more docking units for the delivery device is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data via a questionnaire via a mobile phone application)
As per claim 30, the combination of Dagnello and Martinez discloses the limitations of claim 9. Dagnello also discloses the following:
the user feedback system according to claim 9, in which functionality of one or more of the obtaining processor, estimation processor, and feedback processor is provided at least in part by one or more processors located within the delivery device or one or more other devices of the delivery ecosystem, or a remote server is taught in the Detailed Description in ¶ 0077, ¶ 0082, ¶ 0092, and in the Figures at fig. 5 (teaching on user experience and feedback model for a personal vapor inhalation device (PVID) device (treated as synonymous to a delivery device) connected to a central processing server (treated as synonymous to a delivery ecosystem))
As per claim 31, Dagnello teaches on the following limitations of the claim:
a user feedback method for a user of a delivery device within a delivery ecosystem comprising is taught in the Detailed Description in ¶ 0077, ¶ 0082, ¶ 0092, and in the Figures at fig. 5 (teaching on user experience and feedback model for a personal vapor inhalation device (PVID) device (treated as synonymous to a delivery device) connected to a central processing server (treated as synonymous to a delivery ecosystem))
obtaining two or more user factors indicative of a state of the user is taught in the Detailed Description in ¶ 0068 and ¶ 0077 (teaching on collecting both the user's descriptive inhalation data and other user data)
wherein a first obtained user factor of the two or more user factors is based upon an aspect of the user’s operation of the delivery device is taught in the Detailed Description in ¶ 0068-69 (teaching on collecting descriptive inhalation data related to the user's operation of the inhalation device)
wherein the user's operation of the delivery device comprises activation of a user interface to trigger generation of an aerosol by the delivery device and/or an inhalation action on the delivery device to trigger generation of an aerosol by the delivery device; and is taught in the Detailed Description in ¶ 0068-69 (teaching on collecting descriptive inhalation data related to the user's operation of the inhalation device via triggering the vaporizing element)
wherein a second obtained user factor of the two or more user factors is based upon an at aspect of the user’s situation separate from the user’s operation of the delivery device, the aspect of the user’s situation occurring during one or more selected from the list consisting of is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation)
i. a predetermined period preceding the user’s operation of the delivery device, ii. a predetermined period following the user’s operation of the delivery device, and iii. a period during the user’s operation of the delivery device is taught in the Detailed Description in ¶ 0082 and ¶ 0104 (teaching on collecting the user data before, during, and after the user of the PVID device in predetermined time frames)
recording data descriptive of the second user factor is taught in the Detailed Description in ¶ 0082 and ¶ 0104 (teaching on collecting the user histological, environmental, and experience data before, during, and after the user of the PVID device in predetermined time frames (treated as synonymous to a circular data buffer) such as over days , hours , minutes , and seconds)
Dagnello does not explicitly teach the following limitation of claim 31. Martinez, however, does teach the following:
recording data descriptive of the second user factor in a circular data buffer; and is taught in the § Sensor nodes on p. 53 col 1 (teaching on collecting environmental data from a remote sensor and storing in a ring buffer data structure)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the ring buffer structure of Martinez for the time structured record system of the Dangello. Thus, the simple substitution of one known element for another producing a predictable result of renders the claim obvious. Examiner also notes that one of ordinary skill in the art would recognize that a circular buffer is the industry standard for small electronic device digital signal processors as further evidenced by Smith, the Scientist and Engineer's Guide to Digital Signal Processing, Ch. 8 Digital Signal Processors 503-534 (1997) in § Circular Buffering on p. 506-509.
As per claim 33, the combination of Dagnello and Martinez discloses the limitations of claim 32. Dagnello also discloses the following:
the user feedback method of claim 32 in which: when the user operates the delivery device, for the second obtained user factor which occurs during the predetermined period preceding the user’s operation of the delivery device, the method comprises: outputting, for association with data of the first obtained user factor, recorded data descriptive of the second obtained user factor for the predetermined period preceding the user’s operation of the delivery device is taught in the Summary in ¶ 0013, in the Detailed Description in ¶ 0082, and ¶ 0104 (teaching on collecting the user histological, environmental, and experience data before, during, and after the user of the PVID device in predetermined time frames (treated as synonymous to a circular data buffer)such as over days , hours , minutes , and seconds and associating the user's personal data and collecting descriptive inhalation data together with associated timestamps)
As per claim 34, the combination of Dagnello and Martinez discloses the limitations of claim 32. Dagnello also discloses the following:
the user feedback method of claim 32, in which: when the user operates the delivery device, for the second obtained user factor which occurs during the predetermined period following the user’s operation of the delivery device, the method comprises: recording in the circular data buffer data that is descriptive of the second obtained user factor following the user’s operation of the delivery device for the predetermined period following the user’s operation of the delivery device; and subsequently outputting the recorded data for association with data of the first obtained user factor is taught in the Summary in ¶ 0013, in the Detailed Description in ¶ 0082, and ¶ 0104 (teaching on collecting the user histological, environmental, and experience data before, during, and after the user of the PVID device in predetermined time frames (treated as synonymous to a circular data buffer) such as over days, hours, minutes, and seconds and associating the user's personal data and collecting descriptive inhalation data together with associated timestamps)
As per claim 35, the combination of Dagnello and Martinez discloses the limitations of claim 32. Dagnello also discloses the following:
the user feedback method of claim 32, in which: when the user operates the delivery device, for the second obtained user factor which occurs during the period during the user’s operation of the delivery device, the method comprises: recording in the circular data buffer data that is descriptive of the second obtained user factor during the user’s operation of the delivery device; and subsequently outputting the recorded data for association with data of the first obtained user factor is taught in the Summary in ¶ 0013, in the Detailed Description in ¶ 0082, and ¶ 0104 (teaching on collecting the user histological, environmental, and experience data before, during, and after the user of the PVID device in predetermined time frames (treated as synonymous to a circular data buffer) such as over days, hours, minutes, and seconds and associating the user's personal data and collecting descriptive inhalation data together with associated timestamps)
As per claim 36, the combination of Dagnello and Martinez discloses the limitations of claim 31. Dagnello also discloses the following:
the user feedback method of claim 31, in which data of the second obtained user factor is used to provide contextual data for data of the first obtained user factor is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation but related to effects of the use)
As per claim 37, the combination of Dagnello and Martinez discloses the limitations of claim 31. Dagnello also discloses the following:
the user feedback method of claim 31, in which the operation of the delivery device comprises one selected from the list consisting of: i. activation of a user interface to trigger generation of an aerosol by the delivery device; and ii. an inhalation action on the delivery device to trigger generation of an aerosol by the delivery device is taught in the Detailed Description in ¶ 0068-69 (teaching on collecting descriptive inhalation data related to the user's operation of the inhalation device via triggering the vaporizing element)
As per claim 38, the combination of Dagnello and Martinez discloses the limitations of claim 31. Dagnello also discloses the following:
the user feedback method according to claim 31, comprising a step of: selecting the corresponding feedback action identified by the estimation step for the delivery device within the delivery ecosystem is taught in the Detailed Description in ¶ 0067 and ¶ 0103 (teaching on a machine learning algorithm for analyzing the user data to generate (treated as synonymous to selecting)specific recommendations (treated as synonymous to a feedback action) related to the user's state and changes)
As per claim 39, the combination of Dagnello and Martinez discloses the limitations of claim 38. Dagnello also discloses the following:
the user feedback method according to claim 38, comprising: causing a modification of one or more operations of the delivery device within the delivery ecosystem according to the selected corresponding feedback action is taught in the Detailed Description in ¶ 0104 (teaching on the machine learning algorithm determining a user state and automatically delivering a particular substances to the user (treated as synonymous to a modification to the operations) via the PVID device)
As per claim 41, the combination of Dagnello and Martinez discloses the limitations of claim 31. Dagnello also discloses the following:
the user feedback method according to claim 31, in which data relating to the second obtained user factor is obtained from at least a first physical sensor is taught in the Detailed Description in ¶ 0046-48, ¶ 0077-80, and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data from physical sensors distinct from the user of vaporization device operation)
As per claim 42, the combination of Dagnello and Martinez discloses the limitations of claim 31. Dagnello also discloses the following:
the user feedback method according to claim 31, in which data relating to the second obtained user factor is obtained from a textual analysis of content that the user has interacted with is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, and ¶ 0090 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation wherein the data is collected from social media engagement of the user)
As per claim 43, the combination of Dagnello and Martinez discloses the limitations of claim 31. Dagnello also discloses the following:
the user feedback method according to claim 31, in which data relating to the second obtained user factor is obtained from the user’s interaction with devices within the delivery ecosystem other than the delivery device is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data via a questionnaire via a mobile phone)
As per claim 44, the combination of Dagnello and Martinez discloses the limitations of claim 31. Dagnello also discloses the following:
the user feedback method according to claim 31, in which data relating to the second obtained user factor is obtained from data indicative of the user’s environment is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation)
As per claim 45, the combination of Dagnello and Martinez discloses the limitations of claim 31. Dagnello also discloses the following:
the user feedback method according to claim 31, in which data relating to the second obtained user factor is obtained from data indicative of the user’s social situation is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, ¶ 0087, ¶ 0090, and in the Summary in ¶ 0010 (teaching on collecting user's personal data including physiological, environmental, and experience data wherein the environmental data includes event/occasion data (treated as synonymous to social data))
As per claim 46, the combination of Dagnello and Martinez discloses the limitations of claim 31. Dagnello also discloses the following:
the user feedback method according to claim 31, in which data relating to the second obtained user factor is obtained from data indicative of a past history of the user is taught in the Detailed Description in ¶ 0077-80, ¶ 0082, ¶ 0087, and ¶ 0090 (teaching on collecting user's personal data including physiological, environmental, and experience data wherein the personal data includes social history/description data)
As per claim 48, the combination of Dagnello and Martinez discloses the limitations of claim 31. Dagnello also discloses the following:
the user feedback method according to claim 31, in which identifying comprises generating the corresponding feedback action relating to one or more selected from the list consisting of: i. a behavioral feedback action for affecting at least a first behavior of the user; ii. a pharmaceutical feedback action for affecting consumption of an active ingredient by the user; and iii. a non-consumption feedback action for affecting one or more non-consumption operations of the delivery ecosystem is taught in the Detailed Description in ¶ 0067 and ¶ 0103 (teaching on a machine learning algorithm for analyzing the user data to generate specific recommendations (treated as synonymous to a feedback action) related to the user's state and changes, such as a change posture while inhaling (treated as synonymous to a behavioral feedback action))
As per claim 49, Dagnello teaches on the following limitations of the claim:
computer program stored on a non-transitory machine-readable medium comprising computer executable instructions adapted to cause a computer system to perform; is taught in the Detailed Description in ¶ 0077, ¶ 0082, ¶ 0092, and in the Figures at fig. 5 (teaching on user experience and feedback model for a personal vapor inhalation device (PVID) device (treated as synonymous to a delivery device) connected to a central processing server with cooresponding hardware (treated as synonymous to a delivery ecosystem))
obtaining two or more user factors indicative of a state of the user is taught in the Detailed Description in ¶ 0068 and ¶ 0077 (teaching on collecting both the user's descriptive inhalation data and other user data)
wherein a first obtained user factor of the two or more user factors is based upon an aspect of the user’s operation of the delivery device; and is taught in the Detailed Description in ¶ 0068-69 (teaching on collecting descriptive inhalation data related to the user's operation of the inhalation device)
wherein the user's operation of the delivery device comprises activation of a user interface to trigger generation of an aerosol by the delivery device and/or an inhalation action on the delivery device to trigger generation of an aerosol by the delivery device is taught in the Detailed Description in ¶ 0068-69 (teaching on collecting descriptive inhalation data related to the user's operation of the inhalation device via triggering the vaporizing element)
wherein a second obtained user factor of the two or more user factors is based upon an aspect of the user’s situation separate from the user’s operation of the delivery device, the aspect of the user’s situation occurring during one or more selected from the list consisting of is taught in the Detailed Description in ¶ 0077-80 and ¶ 0082 (teaching on collecting user's personal data including physiological, environmental, and experience data distinct from the user of vaporization device operation)
i. a predetermined period preceding the user’s operation of the delivery device, ii. a predetermined period following the user’s operation of the delivery device, and iii. a period during the user’s operation of the delivery device, and is taught in the Detailed Description in ¶ 0082 and ¶ 0104 (teaching on collecting the user data before, during, and after the user of the PVID device in predetermined time frames)
recording data descriptive of the second user factor is taught in the Detailed Description in ¶ 0082 and ¶ 0104 (teaching on collecting the user histological, environmental, and experience data before, during, and after the user of the PVID device in predetermined time frames (treated as synonymous to a circular data buffer) such as over days , hours , minutes , and seconds)
identifying a corresponding feedback action expected to alter a state of the user as indicated at least in part by the first and second obtained user factors is taught in the Detailed Description in ¶ 0067 and ¶ 0103 (teaching on a machine learning algorithm for analyzing the user data to generate specific recommendations (treated as synonymous to a feedback action) related to the user's state and changes)
Dagnello does not explicitly teach the following limitation of claim 49. Martinez, however, does teach the following:
recording data descriptive of the second user factor in a circular data buffer; and is taught in the § Sensor nodes on p. 53 col 1 (teaching on collecting environmental data from a remote sensor and storing in a ring buffer data structure)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the ring buffer structure of Martinez for the time structured record system of the Dangello. Thus, the simple substitution of one known element for another producing a predictable result of renders the claim obvious. Examiner also notes that one of ordinary skill in the art would recognize that a circular buffer is the industry standard for small electronic device digital signal processors as further evidenced by Smith, the Scientist and Engineer's Guide to Digital Signal Processing, Ch. 8 Digital Signal Processors 503-534 (1997) in § Circular Buffering on p. 506-509.
Response to Arguments
Applicant's arguments filed 03 December 2025 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that the triggering of the collection of data via either an interface activation or an inhalation event is significantly more than a mere abstract idea and is not pre/post solution activity. Applicant asserts that this “real-world” control of the operation of the device is more than mere collection and/or management of data. Examiner disagrees. The data collection triggering events are pre-solution activity that merely indicate the start event for data collection. Examiner notes that a claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (see MPEP § 2106.04(d) - Integration of a Judicial Exception Into A Practical Application). The court has provided limitations that are indicative that an additional element (or combination of elements) may have integrated the exception into a practical application and limitations that did not integrate a judicial exception into a practical application (see MPEP § 2106.04(d)(I) – Relevant Considerations for Evaluating Whether Additional Elements integrate a Judicial Exception into a Practical Application) wherein the claims may amount to (1) improvements to the functioning of a computer, (2) improvements to a technological field, (3) applying the judicial exception to a particular machine (as evaluated above in ¶ ), (4) transforming or reducing a particular article ot a different state or thing, (5) unconventional activity or steps that confine the claim to a particular useful application, or (6) other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Here the instant claims seem more analogous to "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Applicant's arguments filed 03 December 2025 with respect to 35 USC § 102 have been fully considered. First, Applicant asserts that Dagnello, by the use of a machine learning algorithm, fails to compare a first direct user factor specifically with a second but instead inputs all of the data together into the machine learning system. Examiner is not persuaded. The instant claims says identify a corresponding feedback action expected to alter a state of the user as indicated at least in part by the first and second obtained user factors. There is no requirement in the claim that the feedback is limited to the analysis of just two data points. Next, Applicant asserts that Examiner reliance the knowledge of one of ordinary skill in the art that a time series data stream being stored locally on a device is analogous to a circular buffer is incorrect. While Examiner has provided evidentiary support for the position citing Smith, the Scientist and Engineer's Guide to Digital Signal Processing, Ch. 8 Digital Signal Processors 503-534 (1997), Examiner has none the less withdrawn the rejection in view of the additional amendments made to the independent claims. A new clarifying grounds of rejection is made in view of Martinez, as per the rejection above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857