DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/29/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because: Fig. 11, “Display 1108” should be corrected to “Display 1106” as recited in paragraphs 174 and 179. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to because: Fig. 11, “DSP 1106” should be corrected to “DSP 1108” as recited in paragraphs 174 and 176-179. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: the Specification recites “sensor data 108A” should be corrected to “sensor data 106A” in paragraphs 128-131, 134, 150, 153, 159, 161, 163-164-165, 167, 171, 173 and 179.
Appropriate correction is required.
The disclosure is objected to because of the following informalities: the Specification recites “battery interface and power control unit 054” should be corrected to “battery interface and power control unit 1154” in paragraph 175.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claims 1-20 are directed toward a method, system and non-transitory computer-readable medium. Therefore, it can be seen that they fall within one of the four statutory categories of invention. However, the claims clearly do not meet the three-prong test for patentability.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and/or
Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method comprising:
determining, from at least one sensor, first smoking event data associated with a first smoking event on a first road link, wherein the first smoking event is associated with usage of at least one smoking product by a first user on the first road link;
retrieving a first set of features comprising: i) road link properties of the first road link, and ii) context information associated with the first smoking event on the first road link;
training a machine learning (ML) model, using the retrieved first set of features to determine an association between the retrieved first set of features and the first smoking event; and
storing the trained ML model.
The examiner submits that the foregoing bold limitation(s) constitute a “mental process” and/or “certain methods of organizing human activity” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining first smoking event data associated with a first smoking event on a first road link, wherein the first smoking event is associated with usage of at least one smoking product by a first user on the first road link” in the context of this claim encompasses the user mentally analyzing the data and determining a first smoking event data on a first road link. Similarly, the limitations of “retrieving a first set of features comprising: i) road link properties, and ii) context information”, “training a machine learning (ML) model” and “storing the trained ML model” in the context of this claim encompass the user mentally retrieving a first set of features, training and storing a machine learning model using the retrieved first set of features. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrated the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea , adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method comprising:
determining, from at least one sensor, first smoking event data associated with a first smoking event on a first road link, wherein the first smoking event is associated with usage of at least one smoking product by a first user on the first road link;
retrieving a first set of features comprising: i) road link properties of the first road link, and ii) context information associated with the first smoking event on the first road link;
training a machine learning (ML) model, using the retrieved first set of features to determine an association between the retrieved first set of features and the first smoking event; and
storing the trained ML model.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “from at least one sensor” and “using the retrieved first set of features to determine an association between the retrieved first set of features and the first smoking event”, the examiner submits that these limitations are mere data gathering in conjunction with a law of nature or abstract ideal (MPEP § 2106.05).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using one or more processors to perform the determining ... amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “using the retrieved first set of features to determine an association between the retrieved first set of features and the first smoking event”, the examiner submits that these limitations are insignificant extra-solution activities as previously discussed.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well- understood, routine, conventional activity in the field. The additional limitations of “using the retrieved first set of features to determine an association between the retrieved first set of features and the first smoking event” are well-understood, routine, and conventional activities because the specification does not provide any indication that the user device is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Dependent claims 2-11, 13-17 and 19-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application (i.e., further characterizing the receipt of data and the mental processes). Therefore, dependent claims 2-11, 13-17 and 19-20 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Srivastava et al. (US 2022/0013006 A1) (Srivastava hereinafter).
Regarding claim 1, Srivastava discloses a method comprising:
obtaining, from at least one sensor, first smoking event data associated with a first smoking event on a first road link, wherein the first smoking event is associated with usage of at least one smoking product by a first user on the first road link ([0022], alerts may be generated from Driver Monitoring System (DMS) that detects driver behavioral events, such as distracted-driver events, impaired-driver events, phone usage, smoking, and drowsiness);
retrieving a first set of features comprising: i) road link properties of the first road link, and ii) context information associated with the first smoking event on the first road link ([0055], the map database 204 may include any suitable type of database storing(digital) map data for the vehicle 100, for the safety system 200. The map database 204 may include data relating to the position, in a reference coordinate system, of various items, including roads, water features, geographic features, businesses, points of interest, restaurants, gas stations, etc., as well as parameters of such items, such as road width, grade, slope, elevation, or the like);
training a machine learning (ML) model, using the retrieved first set of features to determine an association between the retrieved first set of features and the first smoking event ([0200], the systems and methods of the disclosure may utilize one or more machine learning models to perform corresponding functions of the vehicle. The term “model” as, for example, used herein may be understood as any kind of algorithm, which provides output data from input data. A machine learning model may be executed by a computing system to progressively improve performance of a specific task. In some aspects, parameters of a machine learning model may be adjusted during a training phase based on training data. A trained machine learning model may then be used during an inference phase to make predictions or decisions based on input data); and
storing the trained ML model ([0060], the memory 325 may store map data, road condition data, alert/event data, driver score data, sensor data, and/or other data as would be understood by one or ordinary skill in the art).
Regarding claim 2, Srivastava discloses the method of claim 1, as stated above, wherein the ML model is trained to provide a first probability score associated with the usage of the at least one smoking product by the first user based at least on the determined association between the first set of features and the first smoking event ([0021] – [0022]).
Regarding claim 3, Srivastava discloses the method of claim 1, as stated above, wherein the first user is traveling on the first road link on a vehicle associated with a user device ([0038]).
Regarding claim 4, Srivastava discloses the method of claim 1, as stated above, wherein the first road link is determined by map matching a location of the first smoking event data associated with the first smoking event, and wherein the road link properties of the first road link are retrieved from a geographic map database (Fig. 2, map database 204).
Regarding claim 5, Srivastava discloses the method of claim 1, as stated above, wherein the context information of the first set of features comprises at least one of: emotional state information associated with the first user, a first user profile associated with the first user, traffic information, weather information , visibility information, occupancy information, air quality information, route information, and waiting event information ([0103]).
Regarding claim 6, the examiner takes official notice that it is well known in the art for the smoking product to include at least one of: a cigarette, a cigar, a pipe tobacco, an electronic cigarette, a vape, a pod, an herbal cigarette, or a water pipe
Regarding claim 7, Srivastava discloses the method of claim 1, as stated above, wherein the at least one sensor comprises at least one of: a smoke detector, an image capture device, an audio capture device, an infrared sensor, or a combination thereof ([0023]).
Regarding claim 8, Srivastava discloses the method of claim 7, as stated above, wherein obtaining the first smoking event data further comprises: detecting a behavior pattern based on sensor data collected from the at least one sensor, wherein the behavior pattern is associated with a smoking activity ([0022]).
Regarding claim 9, Srivastava discloses the method of claim 7, as stated above, further comprising: determining a smoking product ignition pattern associated with the first user based on the first smoking event data; and training the ML model based on the determined smoking product ignition pattern ([0203]).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached form PTO-892.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Luke Huynh whose telephone number is 571-270-5746. The examiner can normally be reached Mon 8-5, Tues 8-12, Thurs & Fri 8-2.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hitesh Patel can be reached at 571-270-5442. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LUKE HUYNH/Primary Examiner, Art Unit 3667
03/02/2026