DETAILED ACTION
1. 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 .
2. Status of Application and Claims
Claims 1-20 are pending.
This office action is being issued in response to the Applicant's filing(s) on 7/30/2025.
3. Claim Objections
Claims 1, 19 and 20 are objected to because of the following informalities: lack of antecedent basis.
Claim 1 recites “the retrained machine learning model.” However, there is no earlier recitation of a retrained machine learning model nor a retraining of the trained machine learning model
Claims 19 and 20 have similar issues.
4. Claim Interpretation
The subject matter of a properly construed claim is defined by the terms that limit its scope when given their broadest reasonable interpretation. see MPEP §2013(I)(C). Specifically, the “broadest reasonable construction ‘in light of the specification as it would be interpreted by one of ordinary skill in the art.’” See MPEP §2111, citing Phillips v. AWH Corp., 75 USPQ2d 1321, 1329 (Fed. Cir. 2005). However, “[t]hough understanding the claim language may be aided by explanations contained in the written description, it is important not to import into claim limitations that are not part of the claim.” See MPEP §2111.01, citing Superguide Corp. v. DirecTV Enterprises, Inc., 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). Construing claims broadly during prosecution is not unfair to the applicant, because the applicant has the opportunity to amend the claims to obtain more precise claim coverage. See MPEP §2111, citing In re Yamamoto, 222 USPQ 934, 936 (Fed. Cir. 1984).
As a general matter, grammar and the plain meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. See MPEP §2013(I)(C). Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. See MPEP §2013(I)(C).
As such, claim limitations that contain statement(s) such as “if,” “may,” “might,” “can,” and “could” are treated as containing optional language. See MPEP §2013(I)(C). As matter of linguistic precision, optional claim elements do not narrow claim limitations, since they can always be omitted. See MPEP §2013(I)(C).
Similarly, a method step exercised or triggered upon the satisfaction of a condition, where there remains the possibility that the condition was not satisfied under the broadest reasonable interpretation, is an optional claim limitation. See MPEP §2111.04(II). As the Applicant does not address what happens should the optional claim limitations fail, Examiner assumes that nothing happens (i.e., the method stops). An alternate interpretation is that merely the claim limitations based upon the condition are not triggered or performed.
In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See MPEP §2143.03, citing Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298 (Fed. Cir. 2009);
Language in a method or system claim that states only the intended use or intended result, but does not result in a manipulative difference in the steps of the method claim nor a structural difference between the system claim and the prior art, fails to distinguish the claims from the prior art.
The following types of claim language may raise a question as to its limiting effect (this list is not exhaustive):
Statements of intended use or field of use, including statements of purpose or intended use in the preamble. See MPEP §2111.02;
Clauses such as “adapted to”, “adapted for”, “wherein”, and “whereby.” See MPEP §2111.04;
Contingent limitations. See MPEP §2111.04(II);
Printed matter. See MPEP §2111.05; and
Functional language associated with a claim term. See MPEP §2181.
As such, while all claim limitations have been considered and all words in the claims have been considered in judging the patentability of the claimed invention, the following italicized, underlined and/or boldened language is interpreted as not further limiting the scope of the claimed invention.
Additionally, the following italicized, underlined and emboldened language is not necessarily an exhaustive list of claim language that is interpreted as not further limiting the scope of the claimed invention. Applicant should review all claims for additional claim interpretation issues.
Claim 19 recites a method comprising:
outputting a predicted travel profile for the candidate user for the trial period by inputting the trial travel data into a trained machine learning model for predicting a travel profile, wherein the trained model is configured to predict a travel profile of a new candidate user by inputting new candidate travel data including new telematics data of the new candidate user into the trained model, wherein the new candidate user is different from a plurality of historical users of a learning dataset, and wherein the predicted travel profile includes one or more predicted travel aspects including predicted modes of transportation and one or more predicted travel routes.
Method claims are defined by the method steps being actively performed, not method steps that may or may not be performed. Reciting a system element in a method claim is configured to perform a method step (i.e., configured to predict a travel profile of a new candidate user by inputting new candidate travel data including new telematics data of the new candidate user into the trained model) does not mean that the method step is actually performed (i.e., inputting new candidate travel data including new telematics data of the new candidate user into the trained model).
Claim 1 has similar claim interpretation issues, as Claim 1 does not recite that the system is configured to train the model or is executing instructions to train the model.
Claim 19 recites a method comprising:
outputting a predicted travel profile for the candidate user for the trial period by inputting the trial travel data into a trained machine learning model for predicting a travel profile, wherein the trained model is configured to predict a travel profile of a new candidate user by inputting new candidate travel data including new telematics data of the new candidate user into the trained model, wherein the new candidate user is different from a plurality of historical users of a learning dataset, and wherein the predicted travel profile includes one or more predicted travel aspects including predicted modes of transportation and one or more predicted travel routes.
A new candidate user, by default, is different from historical users, as the candidate user is new. If the candidate was part of the historical users, the candidate would not be “new.”
Additionally, the claim elements (i.e., the learning dataset) pertain to nonfunctional descriptive material and are not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See MPEP §2111.05 (III).
Claims 1 and 20, due to similar claim language, result in a similar claim interpretation.
Claim 19 recites a method comprising:
inputting the trial travel data and the validation travel data into the retrained model to output an updated user travel profile for the candidate user, wherein the retrained model is trained using the determined model updates for the candidate user;
Method claims are defined by the method steps being actively performed (i.e., inputting data into the retrained data), not the motivation for performance of the method steps (i.e., to output an updated user travel profile for the candidate user).
Additionally, method claims are defined by the method steps being actively performed, not method steps performed in the past (i.e., trained model or determined model updates). Claiming method steps in the past tense can be interpreted as the method steps performed in the past are outside the scope of the claimed method. Alternatively stated, the scope of the claimed method are the active method steps which are building off a pre-existing state. The method steps performed for creation of the pre-existing state are outside the scope of the claimed invention.
Claim 1 has similar claim interpretation issues, as Claim 1 does not recite that the system is configured to retrain the model or determine updates to the model, or is executing instructions to retrain the model or determine updates to the model.
Claim 20 has similar claim interpretation issues, as Claim 20 does not recite that the computer-readable media causes the processor to retrain the model or determine updates to the model.
Claim 2 recites a system wherein the at least one processor is further configured to:
retrieve, from the at least one memory, a plurality of data records associated with a plurality of different historical users, wherein each of the plurality of data records includes historical user data including (i) demographic data associated with each different historical user of the plurality of different historical users, (ii) travel data including historical travel habits, historical telematics data, historical modes of transportation associated with the historical telematics data, and (iii) historical accident data associated with one or more modes of transportation; and
determine a historical travel risk score for each of the plurality of historical users based on the travel data of each respective historical user indicating a likelihood of loss associated with the travel.
The claim elements (i.e., the demographic data and the historical accident data) pertain to nonfunctional descriptive material and are not functionally involved in the steps recited. The determination is based on the travel data, not the demographic data or the historical accident data. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See MPEP §2111.05 (III).
Claim 5 recites a system wherein the notification message includes a prompt presented to the user via the computing device for the candidate user to confirm one or more of the predicted travel aspects of the user updated travel profile.
The claim elements (i.e., the contents of the message) pertain to nonfunctional descriptive material and are not functionally involved in the steps recited. The determination is based on the travel data, not the demographic data or the historical accident data. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See MPEP §2111.05 (III).
Claim 18, due to similar claim language, result in a similar claim interpretation.
Claim 7 recites a system wherein the processor is further configured to:
when the validation process is successful, transmit a validation message to the user computing device.
Claim 8 recites a system wherein the processor is further configured to:
in response to the validation process being unsuccessful, retrain the machine learning model using the validation travel data collected during the validation period;
apply the retrained model to the trial travel data to determine an updated travel profile; and
transmit a notification message to the user computing device.
Examiner notes that, regardless of whether the validation is successful or unsuccessful, the system transmits a validation message/notification. The conditional language has no impact on transmission of a validation message/notification.
Claim 16 recites a system wherein the at least one processor is further configured to:
automatically populate, using the user data, a form for registering for an insurance policy; and
provide the populated form in the notification message to the candidate user, wherein the form includes a one-click option for the candidate user to verify the populated form and approve registering for the insurance policy and associated premium.
The claim elements (i.e., the contents of the form or policy) pertain to nonfunctional descriptive material and are not functionally involved in the steps recited. The determination is based on the travel data, not the demographic data or the historical accident data. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See MPEP §2111.05 (III).
Claim 17, due to similar claim language, result in a similar claim interpretation.
5. 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.
STEP 1
The claimed invention falls within one of the four statutory categories of invention (i.e., process, machine, manufacture and composition of matter). See MPEP §2106.03.
STEP 2A – PRONG ONE
The claim(s) recite(s) a method, a system to perform a method and/or computer-readable medium containing instructions, when executed, causes a computer to perform a method comprising:
retrieving, from a … candidate user, trial travel data including … data for the candidate user for a first interval of time comprising a trial period;
outputting a predicted travel profile for the candidate user for the trial period by inputting the trial travel data into a … model for predicting a travel profile, wherein the … model is configured to predict a travel profile of a new candidate user by inputting new candidate travel data including new telematics data of the new candidate user into the … model, wherein the new candidate user is different from a plurality of historical users of a learning dataset, and wherein the predicted travel profile includes one or more predicted travel aspects including predicted modes of transportation and one or more predicted travel routes;
retrieving, from … the candidate user, validation travel data for a second interval of time comprising a validation period, the validation travel data forming an actual travel profile for the candidate user for the validation period;
inputting the trial travel data and the validation travel data into the … model to output an updated user travel profile for the candidate user, … using the determined model updates for the candidate user; and
causing to be presented to the user … a notification message including one or more predicted aspects of the user updated travel profile.
These limitations, as drafted, under its broadest interpretation, covers a series of steps that can be practically performed in the human mind (e.g., observations, evaluations, judgments and opinions) which are mental process, a second enumerated grouping of abstract ideas. See MPEP §2106.04(a)(2)(III).
Examiner notes that “’collecting information, analyzing it, and displaying certain results of the collection and analysis,’ where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind” is a court-provided example of a mental process. See MPEP §2106.04(a)(2)(III)(A) citing Electric Power Group v. Alstom, SA. (Fed. Cir. 2016).
Accordingly, the claimed invention recites an abstract idea.
STEP 2A – PRONG TWO
The claimed invention recites additional elements (i.e., computer elements) of a user computing device (Claim(s) 1, 19 and 20), a telematics data (Claim(s) 1, 19 and 20) and a trained/retrained machine learning model (Claim(s) 1, 19 and 20).
Examine notes that the preamble(s) also recite a computing system (Claim(s) 1, 19 and 20), a processor (Claim(s) 1, 19 and 20) and a memory (Claim(s) 1, 19 and 20). However, a preamble is generally not accorded any patentable weight where it merely recites the purpose of a process or the intended use of a structure, and where the body of the claim does not depend on the preamble for completeness but, instead, the process steps or structural limitations are able to stand alone. see In re Hirao, 190 USPQ 15 (CCPA 1976); Kropa v. Robie, 88 USPQ 478, 481 (CCPA 1951).
The claimed invention does not include additional elements that integrate the judicial exception into a practical application of the exception because the claims do not provide improvements to another technology or technical field; improvements to the functioning of the computer itself; are not applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; are not applying the judicial exception with or by use of a particular machine; are not effecting a transformation or reduction of a particular article to a different state or thing; and are not applying the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP §2106.04(d).
The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. See MPEP §2106.05(f). Alternately, the additional elements amount to no more than generally linking the exception to a particular technological environment or field of use. See MPEP §2106.05(h). Accordingly, these additional element(s), when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claimed invention is directed to an abstract idea without a practical application.
STEP 2B
Upon reconsideration of the indicia noted under Step 2A in concert with the Step 2B considerations, the additional claim element(s) amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §2106.07(a)(II). The same analysis applies in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim does not provide an inventive concept significantly more than the abstract idea.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
DEPENDENT CLAIMS
Dependent Claim(s) 2-18 recite claim limitations that further define the abstract idea recited in respective independent Claim(s) 1. As such, the dependent claims are also grouped an abstract idea utilizing the same rationale as previously asserted against the independent claims.
No additional computer components other than those found in the respective independent claims is recited, thus it is presumed that the claim is further utilizing the same generically recited computer.
As such, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application of the judicial exception or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Accordingly, the dependent claim(s) are also not patent eligible.
Appropriate correction is requested.
6. Claim Rejections - 35 USC § 112 (b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites the system of Claim 1 wherein the travel profile includes at least one of: age range of the user, residence of the user, user occupational information, a user routine travel, a user periodic travel, distance traveled using the one or more modes of transportation, an amount of time traveled using the one or more modes of transportation, a time of day of travel, and frequency of travel.
Claim 1 does not recite “a travel profile.” Claim 1 does recite a predicted travel profile, an actual travel profile and an updated travel profile. Which travel profile is Claim 9 referencing?
Claim 10 recites the system of Claim 1 wherein the candidate user data and the data records associated with the plurality of users includes at least one of (i) personal data including demographics data, (ii) sensor data retrieved from one or more sensors of a user computing device of the user, the sensor data including the telematics data, and (iii) third- party data retrieved from computing devices associated with one or more third parties, the third-party data including a transaction history of transactions carried out at the third parties by the user, the transaction history including ride sharing transactions, bike rentals, public transportation data, and e-scooter rentals.
Claim 1 does not recite “a candidate user data.” Claim 1 does recite candidate travel data. Is Claim 10 referencing candidate travel data?
Claim 1 does not recite “data records associated with the plurality of users.” Claim 1 does recite a learning dataset pertaining to a plurality of historical users. Is Claim 10 referencing the learning dataset?
Claims 11 and 12, due to similar claim language (i.e., the candidate user data), result in a similar claim rejection.
Claim 13, due to similar claim language (i.e., the data records of plurality of users), results in a similar claim rejection.
Claim 15, due to similar claim language (i.e., the travel profile for each cluster of users), results in a similar claim rejection.
Claim 16, due to similar claim language (i.e., the user data), results in a similar claim rejection.
Claims 14, 17 and 18 are rejected based upon their dependency to previously rejected claims.
Appropriate correction is requested.
7. Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4-12 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez ‘706 (US PG Pub. 2023/0349706) in view of Allen (US PG Pub. 2024/0078481).
Regarding Claim 19, Sanchez ‘706 discloses a computer-implemented method for analyzing telematics data of a user to output a travel profile of the user, the method implemented using a computing device including at least one processor in communication with at least one memory, the method comprising:
retrieving, from a user computing device (mobile device) of a candidate user (particular driver), trial travel data including telematics data (vehicle telematics data) for the candidate user (particular driver) for a first interval of time comprising a trial period (the present). (see para. 23, 33, 44 and 48);
outputting a predicted travel profile (personalized driver profile) for the candidate user (particular driver) for the trial period (the present) by inputting the trial travel data (telematics data) into a trained machine learning model for predicting a travel profile (personalized driver profile), wherein the trained model is configured to predict a travel profile of a new candidate user by inputting new candidate travel data including new telematics data of the new candidate user into the trained model, wherein the new candidate drive is different from a plurality of historical users (other similar drivers) of a learning dataset (sample containing historical telematic data), and wherein the predicted travel profile includes one or more predicted travel aspects including predicted modes of transportation (vehicle type) and one or more predicted travel routes (driving route). (see para. 21-25 and 69);
retrieving, from the user computing device of the candidate user, validation travel data (new telematics data) for a second interval of time (either in real-time, live or the future) comprising a validation period, the validation travel data forming an actual travel profile for the candidate user for the validation period. (see para. 35 and 43-48); and
causing to be presented to the user via the computing device a notification message including one or more predicted aspects of the user travel profile (personalized risk value). (see para. 5).
Sanchez ‘706 does not explicitly teach a method comprising inputting the trial travel data and the validation travel data into the retrained model to output an updated user travel profile for the candidate user, wherein the retrained model is trained using the determined model updates for the candidate user.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have modified Sanchez ‘706 by duplicating claim elements contained in Sanchez ‘706 (e.g., inputting a first data into a model to output a user travel profile or utilizing a trained model) to create additional claim elements (e.g., inputting new data into a model to output an updated user travel profile or utilizing a retrained model) wherein each additional claim element would serve the same function as the original claim element. In the combination each element, original element and additional element, would merely have performed the same function as it did previously, and one of ordinary skill in the art at the effective filing date of the invention would have recognized that the results of the combination were predictable. see MPEP §2144.04 (VI)(B).
Regardless, Allen discloses a method comprising inputting the trial travel data and the validation travel data (additional driving data, trips data, passenger data, conditions data) into the retrained model to output an updated user travel profile (updated driver profile) for the candidate user, wherein the retrained model is trained using the determined model updates (model updates) for the candidate user. (see para. 71, 80 and 147; Claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sanchez ‘706 to incorporate the ability to update a profile, as disclosed by Allen, thereby ensuring that the user travel profile is kept accurate and up-to-date.
Regarding Claim(s) 1 and 20, such claim(s) recite substantially similar limitations as claimed in previously rejected claim(s) and, therefore, would have been obvious based upon previously rejected claim(s).
Regarding Claim 4, Sanchez ‘706 discloses a system wherein the processor is configured to compare the predicted travel profile to the actual travel profile for the candidate user to determine one or more model updates (whether the driver took the safest route). (see par. 83).
Sanchez ‘706 does not explicitly teach a system configured to, using machine learning and/or artificial intelligence techniques, retrain the model using the determined one or more model updates, although Sanchez ‘706 does teach a system configured to, using machine learning and/or artificial intelligence techniques, train the model using the determined one or more model data. (see para. 5, 21-25, 35, 45-48 and 69);
However, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have modified Sanchez ‘706 and Allen by duplicating claim elements contained in Sanchez ‘706 (e.g., training the model using model data) to create additional claim elements (e.g., re-training the model using updated model data) wherein each additional claim element would serve the same function as the original claim element. In the combination each element, original element and additional element, would merely have performed the same function as it did previously, and one of ordinary skill in the art at the effective filing date of the invention would have recognized that the results of the combination were predictable. see MPEP §2144.04 (VI)(B).
Regardless, Allen discloses a system wherein the at least one processor is further configured to:
retrain the machine learning model using the validation travel data (additional driving data, trips data, passenger data, conditions data) collected during the validation period. (see para. 71, 80 and 147; Claim 1); and
apply the retrained model to the trial travel data to determine an updated travel profile (updated driver profile). (see para. 71, 80 and 147; Claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sanchez ‘706 and Allen to incorporate the ability to determine a model update and retrain the model based upon the update, as disclosed by Allen, thereby ensuring that the user travel profile and the model are kept accurate and up-to-date
Regarding Claim 5, Sanchez ‘706 discloses a system wherein the notification message includes a prompt presented to the user via the computing device for the candidate user to confirm one or more of the predicted travel aspects of the user updated travel profile. (see para. 5).
Regarding Claim 6, Sanchez ‘706 discloses a system wherein the at least one processor is further configured to compare the validation travel data to the trial travel data to validate the trial travel data and complete a validation process, wherein when the validation travel data matches the trial travel data, the travel profile is validated. (see para. 83).
Sanchez ‘706 does not teach a system wherein the data has to be within a certain threshold.
Allen discloses a system wherein the data has to be within a certain threshold. (see para. 50, 60 and 102).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sanchez ‘706 and Allen to incorporate a threshold, to account for variations and variability during the comparison process.
Regarding Claim 7, Sanchez ‘706 does not explicitly teach a system, wherein the at least one processor is further configured to when the validation process is successful, transmit a validation message (concerning an insurance discount or reward) to the user computing device (graphic user interface). (see para. 82-84).
Regarding Claim 8, Sanchez ‘706 does not explicitly teach a system wherein the at least one processor is further configured to, in response to the validation process being unsuccessful, retrain the machine learning model using the validation travel data collected during the validation period; apply the retrained model to the trial travel data to determine an updated travel profile; and transmit a notification message to the user computing device.
However, Sanchez ‘706 does disclose a system wherein the at least one processor is further configured to train the machine learning model using the validation travel data collected during the validation period; apply the trained model to the trial travel data to determine a travel profile; and transmit a notification message to the user computing device. (see para. 5, 21-25, 35, 45-48 and 69);
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have modified Sanchez ‘706 by duplicating claim elements contained in Sanchez ‘706 (e.g., using the first training data to train a model to generate a user profile) to create additional claim elements (e.g., using new training data to retrain a model to generate an updated user profile) wherein each additional claim element would serve the same function as the original claim element. In the combination each element, original element and additional element, would merely have performed the same function as it did previously, and one of ordinary skill in the art at the effective filing date of the invention would have recognized that the results of the combination were predictable. see MPEP §2144.04 (VI)(B).
Regardless, Allen discloses a system wherein the at least one processor is further configured to:
retrain the machine learning model using the validation travel data (additional driving data, trips data, passenger data, conditions data) collected during the validation period. (see para. 71, 80 and 147; Claim 1); and
apply the retrained model to the trial travel data to determine an updated travel profile (updated driver profile). (see para. 71, 80 and 147; Claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sanchez ‘706 and Allen to incorporate the ability to determine a model update and retrain the model based upon the update, as disclosed by Allen, thereby ensuring that the user travel profile and the model are kept accurate and up-to-date
Examiner notes that if the prior art always performs the functions and the claimed invention only performs the functions in a limited subset of situations (i.e. when the validation process is unsuccessful), the claimed invention is reciting a range (i.e., when the validation process is unsuccessful) that lies inside a range (i.e., always) disclosed by the prior art. In the case where the claimed ranges “overlap or lie inside ranges disclosed by the prior art” a prima facie case of obviousness exists. See §2144.05(I), citing In re Wertheim, 191 USPQ 90 (CCPA 1976).
Regarding Claim 9, Sanchez ‘706 discloses a system wherein the travel profile includes at least one of:
age range of the user, residence of the user, user occupational information, a user routine travel, a user periodic travel, distance traveled using the one or more modes of transportation, an amount of time traveled using the one or more modes of transportation (travel time and distance), a time of day of travel, and frequency of travel. (see para. 26); and
wherein the trial travel data is collected using an app (software application) executed by a candidate user mobile computing device and one or more sensors integrated within the candidate user mobile computing device, wherein the one or more sensors include a location sensor (positioning unit, such as a GPS unit), an accelerometer, and a gyroscope for collecting telematic data, wherein the candidate user mobile computing device is configure to automatically transmit the telematic data to the at least one processor for further analysis. (see para. 42 and 47).
Regarding Claim 10, Sanchez ‘706 discloses a system wherein the candidate user data and the data records associated with the plurality of users includes at least one of (i) personal data including demographics data, (ii) sensor data retrieved from one or more sensors (GPS, accelerometer and gyroscope) of a user computing device of the user, the sensor data including the telematics data and (iii) third- party data retrieved from computing devices associated with one or more third parties, the third-party data including a transaction history of transactions carried out at the third parties by the user, the transaction history including ride sharing transactions, bike rentals, public transportation data, and e-scooter rentals. (see para. 47).
Regarding Claim 11, Sanchez ‘706 discloses a system wherein the process is configured to:
apply the model to the candidate user data to predict a preferred travel routine (safest route) for the candidate user. (see para. 4); and
transmit a notification message to the user computing device of the candidate user for display on the user computing device, the notification message including the preferred travel routine formatted for display to the candidate user on the user computing device based on a current location of the candidate user and a current time. (see fig. 3A-3B).
Regarding Claim 12, Sanchez ‘706 discloses a system wherein the at least one processor is configured to:
analyze the sensor data of the candidate user data during a real-time travel event to determine when the candidate user engages in at least one of the preferred travel routine (followed the safest route). (see para. 83); and
transmit a reward (insurance discount) to the candidate user. (see para. 83).
Claim(s) 2 and 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez ‘706 and Allen, as applied to Claim 1 above, and further in view of Dahl (US PG Pub. 2020/0357075).
Regarding Claim 2, Sanchez ‘706 discloses a system wherein the processor is configured to:
retrieve, from the at least one memory, a plurality of data records associated with a plurality of different historical users (other similar driver), wherein each of the plurality of data records includes historical user data including (ii) travel data including historical travel habits, historical telematics data, historical modes of transportation associated with the historical telematics data, and (iii) historical accident data (vehicle collisions) associated with one or more modes of transportation. (see para. 21).
Sanchez ‘706 does not explicitly reach a system wherein the processor is configured to determine a historical travel risk score for each of the plurality of historical users based on the travel data of each respective historical user indicating a likelihood of loss associated with the travel, although Sanchez ‘706 discloses a processor configured to determine a travel risk score for a user based on the travel data of the user indicating a likelihood of loss associated with the travel. (see para. 69).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have modified Sanchez ‘706 and Allen by duplicating claim elements contained in Sanchez ‘706 (e.g., the first risk score for the first user) to create additional claim elements (e.g., a second risk score for the second user) wherein each additional claim element would serve the same function as the original claim element. In the combination each element, original element and additional element, would merely have performed the same function as it did previously, and one of ordinary skill in the art at the effective filing date of the invention would have recognized that the results of the combination were predictable. see MPEP §2144.04 (VI)(B).
Sanchez ‘706 does not teach a system wherein the data records include (i) demographic data associated with each different historical user of the plurality of different historical users.
Dahl discloses a system wherein the (i) demographic data (ages and gender) associated with each different historical user of the plurality of different historical users. (see para. 54 and 150).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sanchez ‘706 and Allen to incorporate the demographic data, as disclosed by Dahl, thereby enabling the machine learning model to discern similarities and differences between the candidate user and historical users.
Regarding Claim 3, Sanchez ‘706 discloses a system wherein the processor is configured to:
create a learning dataset (sample for training) including the plurality of data records and the historical travel risk scores associated with the plurality of different historical users. (see para. 21 and 23); and
using machine learning and/or artificial intelligence techniques, train the model using the learning dataset, wherein the trained model is configured to predict a travel profile (personalized travel profile) of a new candidate user (particular driver) by inputting new candidate travel data including new telematics data of the new candidate user into the trained model, wherein the new candidate user is different from the plurality of historical users of the learning dataset, and wherein the predicted travel profile includes one or more predicted travel features including predicted modes of transportation (vehicle type), one or more predicted travel routes (driving route), one or more predicted accidents, and a risk score associated with the travel profile. (see para. 21-25 and 69).
Claim(s) 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez ‘706 and Allen, as applied to Claim 1 above, and further in view of Sanchez ‘250 (US PG Pub. 2024/0420250).
Regarding Claim 13, Sanchez ‘706 does not teach a system wherein the model divides the plurality of users into clusters based upon locations of the plurality of users, and wherein the at least one processor is further configured to apply the model to the plurality of data records associated with a plurality of users to determine a most frequent travel profile for each cluster of users.
Sanchez ‘250 discloses a system wherein the model divides the plurality of users into clusters based upon locations of the plurality of users, and wherein the at least one processor is further configured to apply the model to the plurality of data records associated with a plurality of users to determine a most frequent travel profile (frequency of their visits) for each cluster of users. (see para. 179).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sanchez ‘706 and Allen to incorporate the clustering, as disclosed by Sanchez ‘250, as clustering in machine learning is an unsupervised learning technique that groups datapoints based upon similarity.
Regarding Claim 14, Sanchez ‘706 discloses a system wherein the processor is configured to determine a cluster (location) associated with the candidate user based upon sensor (GPS) data of the candidate user data. (see para. 47).
Regarding Claim 15, Sanchez ‘706 does not explicitly teach system wherein the at least one processor is further configured to determine a cluster risk score associated with the travel profile for each cluster of users, although Sanchez ‘706 discloses a system wherein the at least one processor is further configured to determine a risk score (likelihood of loss) for a user associated with a travel profile for each user.
Examiner notes that, under the broadest reasonable interpretation, a cluster consists of just one user.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have modified Sanchez ‘706, Bahvsar and Sanchez ‘250 by duplicating claim elements contained in Sanchez ‘706 (e.g., the first risk score for the first user) to create additional claim elements (e.g., a second risk score for the second user) wherein each additional claim element would serve the same function as the original claim element. In the combination each element, original element and additional element, would merely have performed the same function as it did previously, and one of ordinary skill in the art at the effective filing date of the invention would have recognized that the results of the combination were predictable. see MPEP §2144.04 (VI)(B).
Claim(s) 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez ‘706 and Allen, as applied to Claim 1 above, and further in view of Roberts (US PG Pub. 2014/0330594)
Regarding Claim 16, Sanchez ‘706 does not teach a system wherein the processor is configured to automatically populate, using the user data, a form for registering for an insurance policy; or provide the populated form in the notification message to the candidate user, wherein the form includes a one-click option for the candidate user to verify the populated form and approve registering for the insurance policy and associated premium.
Roberts discloses a system wherein the processor is configured to:
automatically populate, using the user data, a form for registering for an insurance policy. (see para. 251); and
provide the populated form in the notification message to the candidate user, wherein the form includes a one-click option (confirm all) for the candidate user to verify the populated form and approve registering for the insurance policy and associated premium. (see para. 261).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sanchez ‘706 and Allen to incorporate registration of an insurance policy, as disclosed by Roberts, thereby addressing the risk score (likelihood of loss) calculated in Sanchez ‘706.
Regarding Claim 17, Sanchez ‘706 does not teach a system wherein the insurance policy includes a personal mobility insurance policy, and wherein the personal mobility insurance policy includes coverage of one or more modes of transportation including walking, public transportation, ride sharing services, driving a rental vehicle, riding a bike, and riding an electric scooter.
Roberts discloses a system wherein the insurance policy includes a personal mobility insurance policy (automotive insurance), and wherein the personal mobility insurance policy includes coverage of one or more modes of transportation including walking, public transportation, ride sharing services, driving a rental vehicle, riding a bike, and riding an electric scooter. (see para. 211).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sanchez ‘706, Allen and Roberts to incorporate a personal mobility insurance policy, as disclosed by Roberts, as travel and transportation are standard and conventional items to be covered via an insurance policy.
Regarding Claim 18, Sanchez ‘706 discloses a system wherein the processor is configured to:
predict a current mode of transportation (vehicle type) using current sensor data (telematics data). (see abstract; para. 69); and
transmit a notification message to the user computing device (graphical user interface). (see para. 5).
Sanchez ‘706 does not explicitly teach a system wherein the notification message including a prompt for the candidate user to confirm the current mode of transportation.
Roberts discloses a system wherein the notification message including a prompt for the candidate user to confirm (confirm all) the current mode of transportation (vehicle identification numbers). (see para. 261-262).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sanchez ‘706, Allen and Roberts to incorporate a confirmation of information, as disclosed by Roberts, thereby ensuring that the information is accurate.
8. Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON M. BORLINGHAUS whose telephone number is (571)272-6924. The examiner can normally be reached M-F 9-5.
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/Jason M. Borlinghaus/Primary Examiner, Art Unit 3692 June 27, 2026
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