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 Status
Claims 1-20 are pending and stand rejected.
Response to Arguments
I. Applicant’s arguments made with respect to the rejection under 35 USC 101 have been fully considered but are not persuasive.
Applicant argues that the claimed invention recites “a specific computer-implemented machine learning workflow for generating service recommendations, which includes identifying service stations based on vehicle location or route, determining similarity using user latent factors and service- station latent factors estimated by a machine learning algorithm, training the machine learning algorithm, generating user latent factor vectors and service-station latent factor vectors, and combining those vectors to produce the predicted recommendation.”. Applicant then argues that “the claims define a specific data-processing architecture and trained latent-factor modeling technique. They are not commercial interactions, advertising, sales activity, or interpersonal management”. The Examiner disagrees.
Initially, the Examiner reminds Applicant that Step 2A is a two-prong inquiry, in which examiners determine in Prong One whether a claim recites a judicial exception, and if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
As written, the claims clearly recite an abstract idea because they set forth or describe analyzing date to generate recommended service stations. Applicant’s own description of the claims underscores that the claims recite an abstract idea. The recommendation of a service station is a commercial interaction because it relates specifically to recommended a place of business, such as a service station whereby a user can recharge or refuel a vehicle (see also: Specification: 0001, 0036). This is a marketing or advertising behavior, and thus a commercial interaction.
Furthermore, like In re Meyer, the procedure for analyzing data to determine appropriate service station recommendations is at least similar to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions. This is because each involves a series of steps performable by a human mentally. For claim 1, this includes at least:
determine at least one of a location and a route of a vehicle; and
identifying a plurality of service stations based on the at least one of the location and the route of the vehicle;
comparing a preference of a first user of the vehicle to preference data related to a second user of another vehicle wherein the first user and the second user are part of a plurality of users, and the second user is selected from the plurality of users based on a similarity between the second user and the first user,
wherein the similarity is determined based on a first set of latent factors for each user of the plurality of users, and a second set of latent factors for the plurality of service stations
predicting a preferred service station of the plurality of service stations based on the comparing.
Additionally, claim 1 recites generating a user latent factor vector for each user of the plurality of users, generating a service station latent factor vector for each service station of the plurality of service stations, and combining the user latent factor vectors and the service station latent factor vectors. This limitation expressly recites at least a mathematical relationship or calculation (i.e., generating and combining vectors). Note that the vectors are discussed in e.g., 0086 and depictured in Fig. 5 (116, 124) as specific mathematical quantities. The function of combining vectors is a mathematical operation/calculation, the operation/calculation underscored expressly in view of Specification paragraph 0086 and claim 3.
While the Examiner acknowledges that the claims set forth a “computer-implemented machine learning workflow” for achieving the service station recommendations, this not more than the mere instructions to implement an abstract idea or other exception on a computer. This remains true even in light of the use of a “trained machine learning model”, which at best operates only as computer algorithm executing on a generic computer in order to expedite the analysis for recommending service stations based on user preferences and service station factors. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Applicant further argues that “the claims also provide improvements in the service station recommendation technology described in the specification, which explains that existing systems recommend public charging locations along a driving route based solely on distance, whereas the disclosed embodiments customize recommendations for specific users based on preferences and behaviors and can infer user preferences and scores without directly querying the user.”. At best, this is an improvement to the abstract idea itself – not to the functioning of the computer or another technology or technical field.
Lastly, the Examiner draws Applicant’s attention to Recentive Analytics, Inc v. Fox Corp (Fed Cir, 2023-2437, 4/18/2025), which held claims to the use of machine learning for generation of network maps and schedules for television broadcasts and live events to be ineligible. The court affirmed the district court in upholding the determination the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment. Similar to Recentive, the current claims seek to use machine learning concepts in a particular field of use in an effort to improve the abstract idea of recommending service stations. Notably, the circuit court maintained their finding despite the recitation of an “iterative” training process.
Furthermore, the claims, specification and Applicant’s remarks purport to solve problems existing specifically to recommending service stations. The claims merely apply these machine learning techniques to a new environment sans any improvement to the underlying machine learning technology itself, or otherwise to a computer or another technology or technical field.
With respect to Step 2B, the Examiner perceives nothing in the claims, whether considered individually or in their ordered combination, that would transform the claims into something “significantly more” than the abstract idea of generating service station recommendations. Accordingly the rejection under 35 USC 101 is maintained.
II. Applicant’s arguments made with respect to the rejection under 35 USC 103 have been fully considered and are persuasive. For further details, see below under Subject Matter Allowable Over the Prior Art.
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 a judicial exception (abstract idea) without significantly more.
Regarding claims 1-20, under Step 2A claims 1-20 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites:
determine at least one of a location and a route of a vehicle; and
identifying a plurality of service stations based on the at least one of the location and the route of the vehicle;
comparing a preference of a first user of the vehicle to preference data related to a second user of another vehicle wherein the first user and the second user are part of a plurality of users, and the second user is selected from the plurality of users based on a similarity between the second user and the first user,
wherein the similarity is determined based on a first set of latent factors for each user of the plurality of users, and a second set of latent factors for the plurality of service stations, the first set of latent factors and the second set of latent factors estimated;
predicting a preferred service station of the plurality of service stations based on the comparing, wherein the predicting includes generating a user latent factor vector for each user of the plurality of users, generating a service station latent factor vector for each service station of the plurality of service stations, and combining the user latent factor vectors and the service station latent factor vectors; and
presenting a recommendation to the first user based on the combined user latent factor vectors and the service station latent factor vectors, the recommendation indicating the preferred service station.
These limitations recite ‘certain methods of organizing human activity’, such as by performing commercial interactions and/or managing personal behavior or relationships or interactions between people (see: MPEP 2106.04(a)(2)(II)). This is because claim 1 sets forth or describes personalization service station recommendations. Accordingly, under step 2A (prong 1) claim 1 recites an abstract idea because claim 1 recites limitations that fall within the “Certain methods of organizing human activity” grouping of abstract ideas.
In addition to ‘certain methods of organizing human activity’, claim 1 is also understood as reciting mathematical concepts (see MPEP 2106.04(a)(2)(I)). This is because claim 1 recites generating a user latent factor vector for each user of the plurality of users, generating a service station latent factor vector for each service station of the plurality of service stations, and combining the user latent factor vectors and the service station latent factor vectors. This limitation expressly recites at least a mathematical relationship or calculation (i.e., generating and combining vectors). Note that the vectors are discussed in e.g., 0086 and depictured in Fig. 5 (116, 124). Accordingly, claim 1 is also understood as reciting mathematical concepts.
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements, including a system comprising a monitoring module including a first processor, a recommendation module including a second processor, where the latent factors are estimated based on a machine learning algorithm, and training the machine learning algorithm. Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea).
Secondly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, taken alone or in combination these elements are not sufficient to provide “significantly more”. This is again because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea).
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding dependent claims 2-9, dependent claims 2-9 recite more complexities descriptive of the abstract idea itself, and at least inherit the abstract idea of claim 1. Furthermore, at least certain claims expressly recite mathematical concepts (e.g., claim 3: the set of predicted scores estimated based on combining the user latent factor vectors into a first dense layer, combining the service station latent factor vectors into a second dense layer, and calculating a cross product of the first dense layer and the second dense layer; claim 7: generates the user latent factor vector based on inputting a user score to an embedding space; claim 8: generates the service station latent factor vector based on inputting a service station score to an embedding space). As such, claims 2-9 are understood to recite an abstract idea under step 2A (prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-9 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. Claims 2-9 rely upon at least similar additional elements as recited in claim 1. Further additional elements such as a dense layer (claim 3) are also recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea).
Lastly, under step 2B, claims 2-9 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely apply the exception on generic computing hardware such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claims 2-9 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Regarding claims 10-17 (method), claims 10-17 recite at least substantially similar concepts and elements as recited in claims 1-9 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. Notably, claim 10 is devoid of additional elements. Claims 10-17 are rejected under at least similar rationale as discussed above.
Regarding claims 10-17 (method), claims 10-17 recite at least substantially similar concepts and elements as recited in claims 1-9 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. Notably, claim 10 is devoid of additional elements. Claims 10-17 are rejected under at least similar rationale as discussed above.
Regarding claims 18-20 (vehicle system), claims 18-20 recite at least substantially similar concepts and elements as recited in claims 1-9 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. Notably, claims 18 recites additionally is devoid of additional elements. Claims 18-20 are rejected under at least similar rationale as discussed above.
Subject Matter Allowable Over the Prior Art
Claim 1 and parallel claims 10 and 18 have been amended to include subject matter from claims 7-8 and 15-16 that was previously indicated as allowable over the prior art. Though rejected on other grounds, claims 1-20 are now allowable over the prior art by virtue of their inclusion of the previously indicated subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
PTO form 892-U discusses utilizing mathematical models to analyze EV user preferences in order to make charging station recommendation (see: Fig. 4, Section III(C), Section IV, Fig. 7).
Discloses a method for recommending a point of interest (POI) including generating a user explicit feature based on a user profile of a user and generating a POI explicit feature based on a POI profile of each candidate POI (see: 0007, 0023, 0042).
Applicant's amendment necessitated any changes to the ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM J ALLEN whose telephone number is (571)272-1443. The examiner can normally be reached Monday-Friday, 8:00-4:00.
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WILLIAM J. ALLEN
Primary Examiner
Art Unit 3625
/WILLIAM J ALLEN/Primary Examiner, Art Unit 3619
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